From b37a1ae5d3a1d38ec94a08379d0a6681786ecc0a Mon Sep 17 00:00:00 2001
From: Andy Grigg <mail@andygrigg.com>
Date: Thu, 31 Oct 2024 14:50:18 -0400
Subject: [PATCH 1/3] First batch of files

---
 .../data_analytics.md                         | 25 +++++++
 .../docfx.json                                |  4 +-
 .../foundation_example.md                     | 18 +++++
 .../index.md                                  |  6 ++
 .../notebooks.md                              | 74 +++++++++++++++++++
 .../pyansys.md                                |  9 ++-
 .../release_notes.md                          |  5 +-
 .../stream_example.md                         | 35 +++++++++
 .../toc.yml                                   | 29 ++++----
 9 files changed, 181 insertions(+), 24 deletions(-)
 create mode 100644 2025R1_Test/grantami-scripting-toolkit-dev-portal/data_analytics.md
 create mode 100644 2025R1_Test/grantami-scripting-toolkit-dev-portal/foundation_example.md
 create mode 100644 2025R1_Test/grantami-scripting-toolkit-dev-portal/notebooks.md
 create mode 100644 2025R1_Test/grantami-scripting-toolkit-dev-portal/stream_example.md

diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/data_analytics.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/data_analytics.md
new file mode 100644
index 0000000000..57bd93adb3
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/data_analytics.md
@@ -0,0 +1,25 @@
+<a id="data-analytics-example-notebooks"></a>
+
+# Data Analytics Example Notebooks
+
+These examples show how to use the MI Scripting Toolkit to perform some common data analytics tasks, including
+statistical roll-ups and generation of plots. All examples use the MI Training database, available from the Ansys
+Download Center.
+
+Download all the notebooks and the
+example files [`here`](samples/data-analytics/example_files_data_analytics.zip).
+
+* [Create scatter plots](samples/data-analytics/00_Create_scatter_plots.md)
+* [Compare datasets graphically](samples/data-analytics/01_Compare_datasets_graphically.md)
+* [Plot data by category](samples/data-analytics/02_Plot_data_by_category.md)
+* [Fit series data and import fit results](samples/data-analytics/03_Fit_series_data_and_import.md)
+* [Process datasets and import results into Granta MI](samples/data-analytics/04_Process_datasets_and_import.md)
+* [Find the area under a curve](samples/data-analytics/05_Find_area_under_a_curve.md)
+* [Calculate statistics for a set of records](samples/data-analytics/06_Calculate_statistics_for_records.md)
+* [Calculate statistics for functional data attributes](samples/data-analytics/07_Calculate_statistics_for_functional_attributes.md)
+* [Fit a non-linear model to series data](samples/data-analytics/08_Fit_model_to_series_data.md)
+* [Compare test data to specification values](samples/data-analytics/09_Compare_test_data_to_specification_values.md)
+* [Generate a Material Datasheet Report using MI Scripting Toolkit and `python-docx`](samples/data-analytics/10_Material_datasheet_report.md)
+* [Generate a Material Summary Presentation using MI Scripting Toolkit and `python-pptx`](samples/data-analytics/11_Powerpoint_summary_example.md)
+* [Preparing data for Power BI](samples/data-analytics/12_Preparing_data_for_Power_BI.md)
+* [Data validation using Scripting Toolkit](samples/data-analytics/13_Data_validation.md)
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/docfx.json b/2025R1_Test/grantami-scripting-toolkit-dev-portal/docfx.json
index 938bd69978..515b752342 100644
--- a/2025R1_Test/grantami-scripting-toolkit-dev-portal/docfx.json
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/docfx.json
@@ -1,8 +1,8 @@
 {
     "build": {
       "globalMetadata": {
-        "title": "Granta scripting toolkit documentation 4.1",
-        "summary": "",
+        "title": "Granta scripting toolkit documentation 4.2",
+        "summary": "Granta scripting toolkit documentation",
         "version": "4.1",
         "product": "Granta",
         "programming language": "Python",
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/foundation_example.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/foundation_example.md
new file mode 100644
index 0000000000..3281c26c1f
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/foundation_example.md
@@ -0,0 +1,18 @@
+<a id="foundation-api-example-notebooks"></a>
+
+# Foundation API Example Notebooks
+
+These example scripts show you how to use the MI Scripting Toolkit to search for records, export data, and import data.
+All examples use the MI Training database, available from the Ansys Download Center.
+
+Download all the notebooks and example files
+[`here`](samples/foundation/example_files_foundation.zip).
+
+* [Text search](samples/foundation/Text_Search.md)
+* [Exporting data](samples/foundation/Exporting_data.md)
+* [Exporting data using the Engineering Data Service](samples/foundation/Exporting_data_using_EngineeringDataService.md)
+* [Functional data and importing data](samples/foundation/Functional_data_and_importing_data.md)
+* [Importing and exporting tabular data](samples/foundation/Importing_and_exporting_tabular_data.md)
+* [New Features in MI Scripting Toolkit v1.5](samples/foundation/Deleting_records_and_other_new_v1_5_features.md)
+* [Inspecting record link groups](samples/foundation/Inspecting_Record_Link_Groups.md)
+* [Modifying record link groups](samples/foundation/Modifying_Record_Link_Groups.md)
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/index.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/index.md
index 184f6b0748..3634f5afe5 100644
--- a/2025R1_Test/grantami-scripting-toolkit-dev-portal/index.md
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/index.md
@@ -16,6 +16,12 @@ Contents:
   * [Upgrading from MI Scripting Toolkit Version 4.0](upgrading.md#upgrading-from-mi-scripting-toolkit-version-4-0)
   * [Upgrading from MI Scripting Toolkit for Python Version 3.x](upgrading.md#upgrading-from-mi-scripting-toolkit-for-python-version-3-x)
   * [Upgrading from MI Scripting Toolkit for Python Version 2.X](upgrading.md#upgrading-from-mi-scripting-toolkit-for-python-version-2-x)
+* [Example Notebooks](notebooks.md)
+  * [Streamlined API Example Notebooks](stream_example.md)
+  * [Data Analytics Example Notebooks](data_analytics.md)
+  * [Foundation API Example Notebooks](foundation_example.md)
+  * [Package Dependencies](notebooks.md#package-dependencies)
+  * [Database Compatibility](notebooks.md#database-compatibility)
 * [MI Scripting Toolkit and PyAnsys](pyansys.md)
   * [New PyAnsys packages for Granta MI](pyansys.md#new-pyansys-packages-for-granta-mi)
   * [Interoperability between PyAnsys packages and the MI Scripting Toolkit](pyansys.md#interoperability-between-pyansys-packages-and-the-mi-scripting-toolkit)
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/notebooks.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/notebooks.md
new file mode 100644
index 0000000000..d8607a1b32
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/notebooks.md
@@ -0,0 +1,74 @@
+<a id="example-notebooks"></a>
+
+# Example Notebooks
+
+The MI Scripting Toolkit documentation includes example notebooks that cover key features of the Python interface. These
+are available as HTML reference documents, Jupyter Notebook files, and python scripts below:
+
+* [Streamlined API Example Notebooks](stream_example.md)
+* [Data Analytics Example Notebooks](data_analytics.md)
+* [Foundation API Example Notebooks](foundation_example.md)
+
+<a id="package-dependencies"></a>
+
+## Package Dependencies
+
+<a id="third-party-dependencies"></a>
+
+### Third-party Dependencies
+
+These notebooks depend on third-party Python packages in addition to the MI Scripting Toolkit. The list
+below defines the requirements for running the notebooks:
+
+- docx2pdf (>=0.1.8,<0.2.0)
+- jupyterlab (>=4.0.0,<5.0.0)
+- matplotlib (>=3.6.0,<4.0.0)
+- numpy (>=2.0.0,<3.0.0)
+- pandas (>=2.1,<3.0)
+- python-docx (>=1.1.0,<2.0.0)
+- python-pptx (>=1.0.0,<2.0.0)
+- scipy (>=1.11.0,<2.0.0)
+- seaborn (>=0.13,<0.14)
+- tqdm[notebook] (>=4.0,<5.0)
+
+Alternatively, if using pip, these can be installed automatically using the `[notebooks]` extra as follows:
+
+```default
+pip install GRANTA_MIScriptingToolkit[notebooks] --find-links=./path/to/whl/files
+```
+
+If you encounter dependency errors when installing the `[notebooks]` extra, you may need to first upgrade `pip`:
+
+```default
+python -m pip install --upgrade pip
+```
+
+<a id="pygranta-dependencies"></a>
+
+### PyGranta Dependencies
+
+The following notebooks also depend on additional PyGranta packages:
+
+> * [RecordLists and MI Scripting Toolkit](samples/streamlined/22_Record_Lists_Interoperability.md) requires `ansys-grantami-recordlists`.
+> * [JobQueue and MI Scripting Toolkit](samples/streamlined/23_Job_Queue_Interoperability.md)  requires `ansys-grantami-jobqueue`.
+
+Minor versions of these packages are typically compatible with a single version of Granta MI only,
+so they are not provided as part of the `[notebooks]` extra described above. To easily identify the
+correct package version for your version of Granta MI, the [PyGranta](https://pypi.org/project/pygranta/) meta-package can be used. For
+example, installing `pygranta` v2024.2 will also install versions of `ansys-grantami-recordlists`
+and `ansys-grantami-jobqueue` that are compatible with Granta MI 2024 R2.
+
+The [Package versions](https://grantami.docs.pyansys.com/version/dev/package_versions.html)
+section of the PyGranta documentation describes which versions of packages are installed with each version of the
+metapackage.
+
+<a id="database-compatibility"></a>
+
+## Database Compatibility
+
+These notebooks have been tested against the latest version of the MI Training database at the time of release.
+Every effort has been made to ensure the notebooks will execute without exceptions, even if they are run multiple times
+in succession by multiple users.
+
+However, it is possible that changes made as a result of other training activities could result in these notebooks
+exhibiting unexpected behavior. If this happens, please try restoring a fresh copy of the MI Training database.
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/pyansys.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/pyansys.md
index b2cb920a9e..762e337062 100644
--- a/2025R1_Test/grantami-scripting-toolkit-dev-portal/pyansys.md
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/pyansys.md
@@ -27,9 +27,12 @@ MI Scripting Toolkit objects. Instead, methods will be added to the MI Scripting
 interoperability with PyAnsys packages as required. Example notebooks will also be provided to
 demonstrate the use of these methods.
 
-Example notebooks are included in the MI Scripting Toolkit, this is
-included as part of the Granta MI Enterprise package on the Ansys Customer Portal. Contact
-your system administrator for more details.
+There are currently two notebooks that demonstrate this interoperability:
+
+* [RecordLists and MI Scripting Toolkit](samples/streamlined/22_Record_Lists_Interoperability.md)
+* [JobQueue and MI Scripting Toolkit](samples/streamlined/23_Job_Queue_Interoperability.md)
+
+More examples will be added as additional PyAnsys packages are released.
 
 <a id="scripting-toolkit-third-party-dependencies"></a>
 
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/release_notes.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/release_notes.md
index 3e534cb6f3..2241808038 100644
--- a/2025R1_Test/grantami-scripting-toolkit-dev-portal/release_notes.md
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/release_notes.md
@@ -208,9 +208,8 @@ modified according to the recommendations below. The features will be removed in
 
 ### Examples
 
-Example notebooks are included in the MI Scripting Toolkit, this is
-included as part of the Granta MI Enterprise package on the Ansys Customer Portal. Contact
-your system administrator for more details.
+* Added new [Data validation](samples/data-analytics/13_Data_validation.md) notebook that demonstrates how to use
+  Scripting Toolkit to validate data in a Granta MI database.
 
 <a id="foundation-api-enhancements-and-bug-fixes"></a>
 
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/stream_example.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/stream_example.md
new file mode 100644
index 0000000000..cc0da850e8
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/stream_example.md
@@ -0,0 +1,35 @@
+<a id="streamlined-api-example-notebooks"></a>
+
+# Streamlined API Example Notebooks
+
+The MI Scripting Toolkit provides you with ready-to-use sample code to help you get started, whether you’re a
+new user or upgrading from the Foundation API. All examples use the MI Training database, available from the Ansys
+Download Center.
+
+Download all the notebooks and the
+example files [`here`](samples/streamlined/example_files_streamlined.zip).
+
+* [Get Started](samples/streamlined/00_Get_Started.md)
+* [Get Attributes](samples/streamlined/01_Get_Attributes.md)
+* [Browse Granta MI](samples/streamlined/02_Browse_MI.md)
+* [Search Granta MI](samples/streamlined/03_Search_MI.md)
+* [Fetch Attribute Data in Bulk](samples/streamlined/04_Fetch_Attribute_Data_in_Bulk.md)
+* [Edit Data](samples/streamlined/05_Edit_Data.md)
+* [Create and Delete Records in Bulk](samples/streamlined/06_Create_Records.md)
+* [Import Functional Data](samples/streamlined/07_Import_Functional_Data.md)
+* [Create Functional Data](samples/streamlined/08_Create_Functional_Data.md)
+* [Edit Tabular Data](samples/streamlined/09_Edit_Tabular_Data.md)
+* [Edit Pseudo-attributes](samples/streamlined/10_Edit_Pseudo-attributes.md)
+* [Add Files, Pictures, and Hyperlinks](samples/streamlined/11_Add_Files_Pictures_and_Hyperlinks.md)
+* [Add Point and Range data](samples/streamlined/12_Add_Point_Range_Data.md)
+* [Add Date, Integer and Logical data](samples/streamlined/13_Add_Date_Integer_and_Logical_Data.md)
+* [Create and Edit Tabular Data](samples/streamlined/14_Create_and_Edit_Tabular_Data.md)
+* [Export data for FEA](samples/streamlined/15_Use_Exporters_for_FEA_Export.md)
+* [Link Records](samples/streamlined/16_Link_Records.md)
+* [Layouts](samples/streamlined/17_Layouts.md)
+* [Streamlined API performance tuning](samples/streamlined/18_Streamlined_API_performance_tuning.md)
+* [Using OpenID Connect with MI Scripting Toolkit](samples/streamlined/19_Using_OIDC_With_the_STK.md)
+* [Logging](samples/streamlined/20_Logging.md)
+* [Storing Credentials Securely](samples/streamlined/21_Storing_credentials_securely.md)
+* [RecordLists and MI Scripting Toolkit](samples/streamlined/22_Record_Lists_Interoperability.md)
+* [JobQueue and MI Scripting Toolkit](samples/streamlined/23_Job_Queue_Interoperability.md)
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/toc.yml b/2025R1_Test/grantami-scripting-toolkit-dev-portal/toc.yml
index 2816d13109..b25f8f04e7 100644
--- a/2025R1_Test/grantami-scripting-toolkit-dev-portal/toc.yml
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/toc.yml
@@ -1,17 +1,14 @@
-- name: "Welcome"
+- name: "Introduction"
   href: welcome.md
-  items:
-  - name: "What's new in Version 4.1"
-    href: release_notes.md
-  - name: "Upgrading from earlier versions"
-    href: upgrading.md   
-  - name: "Scripting Toolkit and PyAnsys"
-    href: pyansys.md
-  - name: "Streamlined API Reference"
-    href: stream_api.md
-  - name: "Foundation API Reference"
-    href: GRANTA_MIScriptingToolkit.md
-  - name: "Logging"
-    href: log_files.md
-  - name: "Copyright and Trademark Information"
-    href: copyright.md
+- name: "What's new in Version 4.1"
+  href: release_notes.md
+- name: "Upgrading from earlier versions"
+  href: upgrading.md
+- name: "Scripting Toolkit and PyAnsys"
+  href: pyansys.md
+- name: "Streamlined API Reference"
+  href: stream_api.md
+- name: "Foundation API Reference"
+  href: GRANTA_MIScriptingToolkit.md
+- name: "Copyright and Trademark Information"
+  href: copyright.md

From 6f6a1b7943cc20d8e6314d75eed097d0cb689d92 Mon Sep 17 00:00:00 2001
From: Andy Grigg <mail@andygrigg.com>
Date: Thu, 31 Oct 2024 14:51:27 -0400
Subject: [PATCH 2/3] Add notebook files

---
 .../data-analytics/00_Create_scatter_plots.md | 837 ++++++++++++++++++
 .../00_Create_scatter_plots_31_0.png          | Bin 0 -> 26983 bytes
 .../00_Create_scatter_plots_34_0.png          | Bin 0 -> 26021 bytes
 .../00_Create_scatter_plots_37_0.png          | Bin 0 -> 39888 bytes
 .../00_Create_scatter_plots_40_0.png          | Bin 0 -> 82182 bytes
 .../00_Create_scatter_plots_43_0.png          | Bin 0 -> 35943 bytes
 .../00_Create_scatter_plots_48_0.png          | Bin 0 -> 34179 bytes
 .../00_Create_scatter_plots_51_1.png          | Bin 0 -> 42106 bytes
 .../00_Create_scatter_plots_54_1.png          | Bin 0 -> 115440 bytes
 .../01_Compare_datasets_graphically.md        | 614 +++++++++++++
 .../01_Compare_datasets_graphically_30_0.png  | Bin 0 -> 42433 bytes
 .../01_Compare_datasets_graphically_32_0.png  | Bin 0 -> 23946 bytes
 .../01_Compare_datasets_graphically_34_0.png  | Bin 0 -> 45226 bytes
 .../01_Compare_datasets_graphically_37_0.png  | Bin 0 -> 46276 bytes
 .../01_Compare_datasets_graphically_45_0.png  | Bin 0 -> 47193 bytes
 .../02_Plot_data_by_category.md               | 589 ++++++++++++
 .../02_Plot_data_by_category_28_0.png         | Bin 0 -> 34108 bytes
 .../02_Plot_data_by_category_36_0.png         | Bin 0 -> 46863 bytes
 .../02_Plot_data_by_category_38_0.png         | Bin 0 -> 60831 bytes
 .../03_Fit_series_data_and_import.md          | 253 ++++++
 .../03_Fit_series_data_and_import_10_0.png    | Bin 0 -> 40302 bytes
 .../03_Fit_series_data_and_import_12_0.png    | Bin 0 -> 62608 bytes
 .../04_Process_datasets_and_import.md         | 827 +++++++++++++++++
 .../04_Process_datasets_and_import_12_0.png   | Bin 0 -> 26532 bytes
 .../04_Process_datasets_and_import_17_0.png   | Bin 0 -> 24444 bytes
 .../04_Process_datasets_and_import_19_0.png   | Bin 0 -> 31433 bytes
 .../04_Process_datasets_and_import_24_0.png   | Bin 0 -> 33693 bytes
 .../04_Process_datasets_and_import_29_0.png   | Bin 0 -> 35316 bytes
 .../04_Process_datasets_and_import_32_0.png   | Bin 0 -> 44252 bytes
 .../04_Process_datasets_and_import_36_0.png   | Bin 0 -> 46219 bytes
 .../04_Process_datasets_and_import_41_0.png   | Bin 0 -> 51497 bytes
 .../04_Process_datasets_and_import_46_0.png   | Bin 0 -> 36384 bytes
 .../04_Process_datasets_and_import_51_0.png   | Bin 0 -> 30465 bytes
 .../05_Find_area_under_a_curve.md             | 614 +++++++++++++
 .../05_Find_area_under_a_curve_39_0.png       | Bin 0 -> 29259 bytes
 .../05_Find_area_under_a_curve_43_0.png       | Bin 0 -> 33039 bytes
 .../05_Find_area_under_a_curve_47_0.png       | Bin 0 -> 70447 bytes
 .../05_Find_area_under_a_curve_49_0.png       | Bin 0 -> 85512 bytes
 .../06_Calculate_statistics_for_records.md    | 283 ++++++
 ..._Calculate_statistics_for_records_12_0.png | Bin 0 -> 34135 bytes
 ...te_statistics_for_functional_attributes.md | 229 +++++
 ...tistics_for_functional_attributes_12_0.png | Bin 0 -> 292558 bytes
 .../08_Fit_model_to_series_data.md            | 361 ++++++++
 .../08_Fit_model_to_series_data_16_0.png      | Bin 0 -> 270327 bytes
 ...mpare_test_data_to_specification_values.md | 303 +++++++
 .../10_Material_datasheet_report.md           | 541 +++++++++++
 .../11_Powerpoint_summary_example.md          | 591 +++++++++++++
 .../12_Preparing_data_for_Power_BI.md         | 206 +++++
 .../data-analytics/13_Data_validation.md      | 448 ++++++++++
 .../assets/08_RambergOsgood.svg               |  30 +
 .../10_report-screenshot-header-only.png      | Bin 0 -> 36301 bytes
 .../assets/10_report-screenshot.png           | Bin 0 -> 89858 bytes
 .../assets/10_template-screenshot.png         | Bin 0 -> 28667 bytes
 ...esentation-screenshot-material-details.png | Bin 0 -> 28802 bytes
 ...esentation-screenshot-room-temperature.png | Bin 0 -> 76150 bytes
 ..._presentation-screenshot-stress-strain.png | Bin 0 -> 60295 bytes
 ...11_presentation-screenshot-title-slide.png | Bin 0 -> 18026 bytes
 .../assets/12_power-bi-dataframes-loading.PNG | Bin 0 -> 29917 bytes
 .../assets/12_power-bi-python-setup.PNG       | Bin 0 -> 20019 bytes
 .../assets/12_power-bi-python.PNG             | Bin 0 -> 6311 bytes
 ...ing_records_and_other_new_v1_5_features.md | 399 +++++++++
 .../samples/foundation/Exporting_data.md      | 293 ++++++
 .../Exporting_data_17_1.png                   | Bin 0 -> 36049 bytes
 ...rting_data_using_EngineeringDataService.md | 169 ++++
 .../Functional_data_and_importing_data.md     | 275 ++++++
 ...unctional_data_and_importing_data_15_0.png | Bin 0 -> 28766 bytes
 ...unctional_data_and_importing_data_17_1.png | Bin 0 -> 30360 bytes
 .../Importing_and_exporting_tabular_data.md   | 267 ++++++
 .../Inspecting_Record_Link_Groups.md          | 152 ++++
 .../Modifying_Record_Link_Groups.md           | 121 +++
 .../samples/foundation/Text_Search.md         |  65 ++
 .../samples/streamlined/00_Get_Started.md     | 149 ++++
 .../samples/streamlined/01_Get_Attributes.md  |  88 ++
 .../samples/streamlined/02_Browse_MI.md       | 214 +++++
 .../samples/streamlined/03_Search_MI.md       | 411 +++++++++
 .../04_Fetch_Attribute_Data_in_Bulk.md        | 142 +++
 .../samples/streamlined/05_Edit_Data.md       | 260 ++++++
 .../samples/streamlined/06_Create_Records.md  |  74 ++
 .../streamlined/07_Import_Functional_Data.md  | 253 ++++++
 .../streamlined/08_Create_Functional_Data.md  | 421 +++++++++
 .../streamlined/09_Edit_Tabular_Data.md       | 168 ++++
 .../streamlined/10_Edit_Pseudo-attributes.md  | 120 +++
 .../11_Add_Files_Pictures_and_Hyperlinks.md   | 165 ++++
 ...Add_Files_Pictures_and_Hyperlinks_20_0.jpg | Bin 0 -> 230081 bytes
 .../streamlined/12_Add_Point_Range_Data.md    | 167 ++++
 .../13_Add_Date_Integer_and_Logical_Data.md   | 115 +++
 .../14_Create_and_Edit_Tabular_Data.md        | 291 ++++++
 .../15_Use_Exporters_for_FEA_Export.md        | 287 ++++++
 .../samples/streamlined/16_Link_Records.md    | 145 +++
 .../samples/streamlined/17_Layouts.md         |  74 ++
 .../18_Streamlined_API_performance_tuning.md  | 397 +++++++++
 .../streamlined/19_Using_OIDC_With_the_STK.md |  55 ++
 .../samples/streamlined/20_Logging.md         | 102 +++
 .../21_Storing_credentials_securely.md        | 156 ++++
 .../22_Record_Lists_Interoperability.md       | 302 +++++++
 .../23_Job_Queue_Interoperability.md          | 184 ++++
 .../streamlined/assets/21_create_task.png     | Bin 0 -> 21556 bytes
 97 files changed, 13207 insertions(+)
 create mode 100644 2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/00_Create_scatter_plots.md
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diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/00_Create_scatter_plots.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/00_Create_scatter_plots.md
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index 0000000000..ad8214f6c2
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+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/00_Create_scatter_plots.md
@@ -0,0 +1,837 @@
+# Create scatter plots
+
+Export multiple attributes from a table and use the `pandas`, `matplotlib` and `seaborn` packages to compare material properties using 2D, 3D, and 4D scatter plots.
+
+This example uses data from the *MaterialUniverse* table in the *MI Training* database.
+
+## Get data from Granta MI
+
+Import the `granta` libraries and connect to Granta MI via the Service Layer using Windows authentication,
+replacing `my.server.name` with the name of your Granta MI server. Specify a database and table.
+
+
+```python
+from GRANTA_MIScriptingToolkit import granta as mpy
+
+mi = mpy.connect("http://my.server.name/mi_servicelayer", autologon=True)
+training_db = mi.get_db('MI Training')
+training_db.unit_system = 'SI (Consistent)'
+material_universe = training_db.get_table('MaterialUniverse')
+```
+
+Specify the records whose attributes you want to plot. In this case, all records in the table are used, but you can use other MI Scripting Toolkit methods to define a list of records (for example, `search_criterion()`).
+
+
+```python
+records = material_universe.all_records(include_generics=False, include_folders=False)
+```
+
+Bulk export the attributes you want to plot. (The attribute names are stored in a list for later use.)
+
+
+```python
+attributes = ['Designation', 'Base', 'Transparency', "Young's modulus", 'Density', 'Tensile strength',
+              'Electrical resistivity']
+material_universe.bulk_fetch(records, attributes=attributes)
+```
+
+## Process the data using `pandas`
+
+### Build a DataFrame
+
+A `pandas` DataFrame can be built from a `List` of `Dicts`, where each dictionary in the list corresponds to a separate row in the DataFrame. Each key-value pair in the `Dict` corresponds to a column and the cell value in that column,
+respectively.
+
+First generate the list of dictionaries, and then use this list to create a DataFrame. Here, we are using the `Record.record_guid` property as the DataFrame index, but any column with a unique value for each record can be used.
+We also sort by the Base column to guarantee a repeatable row order.
+
+
+```python
+import pandas as pd
+
+def get_attr_vals(r):
+    return {mi_attr: r.attributes[mi_attr].value for mi_attr in attributes}
+
+data = [{'guid': r.record_guid, **get_attr_vals(r)} for r in records]
+df = pd.DataFrame(data).set_index('guid').sort_values(by="Base")
+```
+
+Use the DataFrame `.head()` method to output the first few rows of the DataFrame and check the structure.
+
+
+```python
+df.head()
+```
+
+
+
+
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Designation</th>
+      <th>Base</th>
+      <th>Transparency</th>
+      <th>Young's modulus</th>
+      <th>Density</th>
+      <th>Tensile strength</th>
+      <th>Electrical resistivity</th>
+    </tr>
+    <tr>
+      <th>guid</th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>00000e41-000e-4fff-8fff-dd92ffff0000</th>
+      <td>7075</td>
+      <td>Al (Aluminum)</td>
+      <td>Opaque</td>
+      <td>{'low': 69000000000.0, 'high': 72538459777.83203}</td>
+      <td>{'low': 2781.691551208496, 'high': 2809.648036...</td>
+      <td>{'low': 386000000.0, 'high': 510000000.0}</td>
+      <td>{'low': 4.199999809265137e-08, 'high': 4.40000...</td>
+    </tr>
+    <tr>
+      <th>00000e38-000e-4fff-8fff-dd92ffff0000</th>
+      <td>7075</td>
+      <td>Al (Aluminum)</td>
+      <td>Opaque</td>
+      <td>{'low': 69000000000.0, 'high': 76000000000.0}</td>
+      <td>{'low': 2769.9999809265137, 'high': 2829.99992...</td>
+      <td>{'low': 434000000.0, 'high': 580000000.0}</td>
+      <td>{'low': 5.0999999046325684e-08, 'high': 5.3000...</td>
+    </tr>
+    <tr>
+      <th>02401a77-c3e7-4b68-875c-7be3b735b703</th>
+      <td>Tungsten Carbide</td>
+      <td>Carbide</td>
+      <td>Opaque</td>
+      <td>{'low': 599999999999.9996, 'high': 67000000000...</td>
+      <td>{'low': 15299.99999999999, 'high': 15900.00000...</td>
+      <td>{'low': 372999999.9999999, 'high': 529999999.9...</td>
+      <td>{'low': 6.309999847412099e-07, 'high': 1.00000...</td>
+    </tr>
+    <tr>
+      <th>000009cc-000e-4fff-8fff-dd92ffff0000</th>
+      <td>AISI 4130</td>
+      <td>Fe (Iron)</td>
+      <td>Opaque</td>
+      <td>{'low': 200000000000.0, 'high': 210256408691.4...</td>
+      <td>{'low': 7790.849685668945, 'high': 7869.149684...</td>
+      <td>{'low': 862000000.0, 'high': 1241000000.0}</td>
+      <td>{'low': 2e-07, 'high': 2.5e-07}</td>
+    </tr>
+    <tr>
+      <th>000009cb-000e-4fff-8fff-dd92ffff0000</th>
+      <td>AISI 4130</td>
+      <td>Fe (Iron)</td>
+      <td>Opaque</td>
+      <td>{'low': 200000000000.0, 'high': 210256408691.4...</td>
+      <td>{'low': 7794.244766235352, 'high': 7872.578620...</td>
+      <td>{'low': 621000000.0, 'high': 686368408.203125}</td>
+      <td>{'low': 2e-07, 'high': 2.5e-07}</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+### Process any range data
+
+A DataFrame cell can contain any data type we choose to store in it. However, plotting packages such as `matplotlib`
+require numerical values to be in a standard format. Some MI Scripting Toolkit data types, such as
+ranges represented as dictionaries, are not in that format and have to be processed before plotting.
+
+Below, we define a `granta_mean` function which takes a single `dict` as input and calculates either the arithmetic or
+geometric mean (depending on the signs of the input values).
+
+
+```python
+import math
+
+def granta_mean(value):
+    if value is None:
+        return None
+    if 'high' not in value:
+        return value['low']
+    if 'low' not in value:
+        return value['high']
+    product = value['low'] * value['high']
+    if product > 0:
+        gm = math.sqrt(product)
+        if value['low'] > 0:
+            return gm
+        else:
+            return -gm
+    else:
+        return sum(value.values())/2
+```
+
+Apply the `granta_mean` function to each cell in the *Density*, *Young's modulus*, *Tensile strength*, and *Electrical
+resistivity* columns. Store the results in a new DataFrame, and check the output to ensure the data processing worked as
+expected.
+
+
+```python
+df_processed = df[['Density', "Young's modulus", 'Tensile strength', 'Electrical resistivity']].map(granta_mean)
+df_processed.sort_values(by="Density").head()
+```
+
+
+
+
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Density</th>
+      <th>Young's modulus</th>
+      <th>Tensile strength</th>
+      <th>Electrical resistivity</th>
+    </tr>
+    <tr>
+      <th>guid</th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0000132e-000e-4fff-8fff-dd92ffff0000</th>
+      <td>922.225549</td>
+      <td>2.236068e+08</td>
+      <td>3.162278e+07</td>
+      <td>1.000000e+01</td>
+    </tr>
+    <tr>
+      <th>0000157d-000e-4fff-8fff-dd92ffff0000</th>
+      <td>1049.571352</td>
+      <td>2.408319e+09</td>
+      <td>3.872983e+07</td>
+      <td>9.949874e+13</td>
+    </tr>
+    <tr>
+      <th>00001584-000e-4fff-8fff-dd92ffff0000</th>
+      <td>1079.953746</td>
+      <td>1.999375e+09</td>
+      <td>4.289522e+07</td>
+      <td>9.949874e+13</td>
+    </tr>
+    <tr>
+      <th>00001ca7-000e-4fff-8fff-dd92ffff0000</th>
+      <td>1095.415362</td>
+      <td>7.509658e+05</td>
+      <td>8.289964e+06</td>
+      <td>3.160000e+10</td>
+    </tr>
+    <tr>
+      <th>00001570-000e-4fff-8fff-dd92ffff0000</th>
+      <td>1134.988977</td>
+      <td>1.308128e+10</td>
+      <td>1.064425e+08</td>
+      <td>3.162278e-02</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+Since the columns now contain numeric data, we can use the `.describe()` method to provide some initial insights into
+the distribution of values in the table. (This also confirms that the `granta_mean` function worked as expected.)
+
+
+```python
+df_processed.describe()
+```
+
+
+
+
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Density</th>
+      <th>Young's modulus</th>
+      <th>Tensile strength</th>
+      <th>Electrical resistivity</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>count</th>
+      <td>29.000000</td>
+      <td>2.900000e+01</td>
+      <td>2.900000e+01</td>
+      <td>2.900000e+01</td>
+    </tr>
+    <tr>
+      <th>mean</th>
+      <td>3056.118133</td>
+      <td>7.441616e+10</td>
+      <td>3.107848e+08</td>
+      <td>2.184037e+15</td>
+    </tr>
+    <tr>
+      <th>std</th>
+      <td>3201.106148</td>
+      <td>1.256091e+11</td>
+      <td>4.631297e+08</td>
+      <td>4.056391e+15</td>
+    </tr>
+    <tr>
+      <th>min</th>
+      <td>922.225549</td>
+      <td>7.509658e+05</td>
+      <td>8.289964e+06</td>
+      <td>4.298837e-08</td>
+    </tr>
+    <tr>
+      <th>25%</th>
+      <td>1184.905061</td>
+      <td>2.408319e+09</td>
+      <td>3.863832e+07</td>
+      <td>1.689970e-06</td>
+    </tr>
+    <tr>
+      <th>50%</th>
+      <td>1369.963508</td>
+      <td>1.308128e+10</td>
+      <td>6.816744e+07</td>
+      <td>4.000000e+13</td>
+    </tr>
+    <tr>
+      <th>75%</th>
+      <td>2799.839234</td>
+      <td>8.692341e+10</td>
+      <td>4.446234e+08</td>
+      <td>1.000000e+15</td>
+    </tr>
+    <tr>
+      <th>max</th>
+      <td>15597.115118</td>
+      <td>6.340347e+11</td>
+      <td>1.775671e+09</td>
+      <td>1.000000e+16</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+### Process any discrete or short text data
+
+The *Designation* column is based on a short-text attribute, which is represented as a string and can be
+used as is.
+
+The columns *Transparency* and *Base* are based on discrete attributes. Since they are single-valued, they contain
+strings and so can also be used as is.
+
+
+```python
+df_processed[['Transparency', 'Base', 'Designation']] = df[['Transparency', 'Base', 'Designation']]
+df_processed.sort_values(by="Density").head()
+```
+
+
+
+
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Density</th>
+      <th>Young's modulus</th>
+      <th>Tensile strength</th>
+      <th>Electrical resistivity</th>
+      <th>Transparency</th>
+      <th>Base</th>
+      <th>Designation</th>
+    </tr>
+    <tr>
+      <th>guid</th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0000132e-000e-4fff-8fff-dd92ffff0000</th>
+      <td>922.225549</td>
+      <td>2.236068e+08</td>
+      <td>3.162278e+07</td>
+      <td>1.000000e+01</td>
+      <td>Opaque</td>
+      <td>Other</td>
+      <td>Leather</td>
+    </tr>
+    <tr>
+      <th>0000157d-000e-4fff-8fff-dd92ffff0000</th>
+      <td>1049.571352</td>
+      <td>2.408319e+09</td>
+      <td>3.872983e+07</td>
+      <td>9.949874e+13</td>
+      <td>Opaque</td>
+      <td>Polymer</td>
+      <td>Acrylonitrile Butadiene Styrene (Extrusion)</td>
+    </tr>
+    <tr>
+      <th>00001584-000e-4fff-8fff-dd92ffff0000</th>
+      <td>1079.953746</td>
+      <td>1.999375e+09</td>
+      <td>4.289522e+07</td>
+      <td>9.949874e+13</td>
+      <td>Transparent</td>
+      <td>Polymer</td>
+      <td>Acrylonitrile Butadiene Styrene (Transparent, ...</td>
+    </tr>
+    <tr>
+      <th>00001ca7-000e-4fff-8fff-dd92ffff0000</th>
+      <td>1095.415362</td>
+      <td>7.509658e+05</td>
+      <td>8.289964e+06</td>
+      <td>3.160000e+10</td>
+      <td>Translucent</td>
+      <td>Polymer</td>
+      <td>Polyvinyl chloride elastomer (ultra high molec...</td>
+    </tr>
+    <tr>
+      <th>00001570-000e-4fff-8fff-dd92ffff0000</th>
+      <td>1134.988977</td>
+      <td>1.308128e+10</td>
+      <td>1.064425e+08</td>
+      <td>3.162278e-02</td>
+      <td>Opaque</td>
+      <td>Polymer</td>
+      <td>Acrylonitrile Butadiene Styrene/20% PAN Carbon...</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+### Specify column units
+
+`DataFrame` doesn't have a built-in method for specifying the units of a column. For this script, we will create a separate `dict` that provides a simple mapping between column headings and units.
+
+
+```python
+units = {mi_attr: material_universe.attributes[mi_attr].unit for mi_attr in attributes
+         if material_universe.attributes[mi_attr].unit}
+units
+```
+
+
+
+*Previous cell output:*
+```output
+{"Young's modulus": 'Pa',
+ 'Density': 'kg/m^3',
+ 'Tensile strength': 'Pa',
+ 'Electrical resistivity': 'ohm.m'}
+```
+
+
+## Create scatter plots using `pandas`
+
+This snippet of code is optional, and is included to increase the size of all plots generated in this
+notebook:
+
+
+```python
+import matplotlib.pyplot as plt
+plt.rcParams['figure.dpi'] = 125
+```
+
+Use the built-in `.plot()` method to plot the data in your DataFrame.
+
+By default, `.plot()` uses `matplotlib` to generate plots. Text wrapped in dollar signs is processed as LaTeX-style math text.
+
+
+```python
+density_unit = units['Density']
+ym_unit = units["Young's modulus"]
+density_label = rf'Density / ${density_unit}$'
+ym_label = rf'$E$ / ${ym_unit}$'
+
+_ = df_processed.plot(x='Density',
+                      y="Young's modulus",
+                      kind='scatter',
+                      s=50,
+                      loglog=True,
+                      xlabel=density_label,
+                      ylabel=ym_label,
+                      title = 'E vs Density using DataFrame.plot()')
+```
+
+
+    
+![png](00_Create_scatter_plots_files/00_Create_scatter_plots_31_0.png)
+    
+
+
+## Create scatter plots with `matplotlib`
+
+Alternatively, you can use `matplotlib` directly with the DataFrame as an input. For comparison, here is the same plot using `matplotlib`. The result is the same, but the figure and axes are first created with `plt.subplots()`, the axis scales and labels modified, and then the `ax.scatter()` method is used to create the plot itself. Finally, the top-level **plt** object is used to add a title.
+
+
+```python
+import matplotlib.pyplot as plt
+
+fig, ax = plt.subplots()
+ax.set_yscale('log')
+ax.set_xscale('log')
+ax.set_ylabel(ym_label)
+ax.set_xlabel(density_label)
+ax.scatter(data=df_processed, x='Density', y="Young's modulus", s=50)
+_ = ax.set_title('E vs Density using ax.scatter()')
+```
+
+
+    
+![png](00_Create_scatter_plots_files/00_Create_scatter_plots_34_0.png)
+    
+
+
+### Plotting data by category (discrete values)
+
+Using `matplotlib` directly allows us to use the full power and flexibility of the package. For example, you can create a plot color-coded by the value of a discrete attribute by iteratively adding scatter plots to the same set of axes.
+
+The simplest way to implement this is to use the `.groupby()` DataFrame method, which generates a set of **groupby**
+objects. These behave like individual DataFrames, and can be plotted similarly to the previous example. `matplotlib` will
+automatically increment the marker color for each new plot.
+
+In the example below, the full `DataFrame` is separated into separate `groupby` objects that share the same *Base* value. These `DataFrame` rows are each plotted on their own scatter plot with a separate color. The marker is incremented manually
+through a pre-defined list, which ensures accessibility for users with color vision deficiencies.
+
+
+```python
+fig, ax = plt.subplots()
+ax.set_yscale('log')
+ax.set_xscale('log')
+ax.set_ylabel(ym_label)
+ax.set_xlabel(density_label)
+
+markers = ["o", "s", "v", "^", "<", ">", "X"]
+
+for idx, (key, df) in enumerate(df_processed.groupby('Base')):
+    marker = markers[idx]
+    ax.scatter(data=df, x='Density', y="Young's modulus", label=key, s=50, marker=marker)
+ax.set_title('E vs Density grouped by Base (using matplotlib)')
+_ = plt.legend(title='Base')
+```
+
+
+    
+![png](00_Create_scatter_plots_files/00_Create_scatter_plots_37_0.png)
+    
+
+
+### Creating a 3D scatter plot
+
+The set-up of a 3D scatter plot is slightly different to the 2D examples above, because the z-axis must be added as a subplot. Use `figure()` and `add_subplot(projection='3d')` to create a 3D figure, then use an alternative syntax to plot the data (in previous plots, the data was added using DataFrame column names, whereas this example plots the arrays themselves).
+
+
+```python
+rho_unit = units['Electrical resistivity']
+rho_label = rf'$\rho$ / ${rho_unit}$'
+
+fig = plt.figure()
+ax = fig.add_subplot(projection='3d')
+ax.set_ylabel(ym_label)
+ax.set_xlabel(density_label)
+ax.set_zlabel(rho_label)
+
+ax.scatter(df_processed['Density'], df_processed["Young's modulus"], df_processed['Electrical resistivity'], s=50)
+_ = ax.set_title(r'$\rho$ vs E vs Density')
+```
+
+
+    
+![png](00_Create_scatter_plots_files/00_Create_scatter_plots_40_0.png)
+    
+
+
+### Adding a continuous color axis to a 2D scatter plot
+
+Add a continuous color axis using the `c` and `cmap` arguments to the `plot.scatter()` constructor. 
+
+This example uses the optional `norm` argument to specify an alternative method of mapping numeric values to a color in the
+colormap. The **color_norm** variable contains a logarithmic normalization which ensures color variation is used across all values of electrical resistivity.
+
+The `viridis` colormap is perceptually uniform (monotonically increases in luminance) so is accessible to users with color vision deficiencies.
+
+
+```python
+import matplotlib.colors as colors
+
+rho_unit = units['Electrical resistivity']
+rho_label = rf'$\rho$ / ${rho_unit}$'
+
+fig, ax = plt.subplots()
+ax.set_yscale('log')
+ax.set_xscale('log')
+ax.set_ylabel(ym_label)
+ax.set_xlabel(density_label)
+
+res_min = df_processed['Electrical resistivity'].min()
+res_max = df_processed['Electrical resistivity'].max()
+color_norm = colors.LogNorm(vmin=res_min, vmax=res_max)
+
+sc = plt.scatter(data=df_processed,
+                 x='Density',
+                 y="Young's modulus",
+                 c="Electrical resistivity",
+                 s=50, cmap='viridis',
+                 norm=color_norm)
+cbar = plt.colorbar(sc)
+cbar.set_label(rho_label, rotation=270, labelpad=20)
+_ = ax.set_title(r'E vs Density and $\rho$ (continuous marker color)')
+```
+
+
+    
+![png](00_Create_scatter_plots_files/00_Create_scatter_plots_43_0.png)
+    
+
+
+### Plotting data by category (binned point or range values)
+
+For this plot, we first use `numpy.geomspace()` to define 5 logarithmically-spaced bins and `DataFrame.cut()` to map the
+continuous *Electrical resistivity* values to those categories.
+
+
+```python
+import numpy as np
+min_value = df_processed['Electrical resistivity'].min()
+max_value = df_processed['Electrical resistivity'].max()
+
+# The number of values is calculated between the two values, and is one more than the required number of bins
+spacing = np.geomspace(min_value, max_value, 6)
+
+df_processed['Electrical resistivity (binned)'] = pd.cut(df_processed['Electrical resistivity'],
+                                                         spacing,
+                                                         labels=['Very low', 'Low', 'Medium', 'High', 'Very high'])
+df_processed.head()
+```
+
+
+
+
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Density</th>
+      <th>Young's modulus</th>
+      <th>Tensile strength</th>
+      <th>Electrical resistivity</th>
+      <th>Transparency</th>
+      <th>Base</th>
+      <th>Designation</th>
+      <th>Electrical resistivity (binned)</th>
+    </tr>
+    <tr>
+      <th>guid</th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>00000e41-000e-4fff-8fff-dd92ffff0000</th>
+      <td>2795.634849</td>
+      <td>7.074711e+10</td>
+      <td>4.436891e+08</td>
+      <td>4.298837e-08</td>
+      <td>Opaque</td>
+      <td>Al (Aluminum)</td>
+      <td>7075</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>00000e38-000e-4fff-8fff-dd92ffff0000</th>
+      <td>2799.839234</td>
+      <td>7.241547e+10</td>
+      <td>5.017171e+08</td>
+      <td>5.199038e-08</td>
+      <td>Opaque</td>
+      <td>Al (Aluminum)</td>
+      <td>7075</td>
+      <td>Very low</td>
+    </tr>
+    <tr>
+      <th>02401a77-c3e7-4b68-875c-7be3b735b703</th>
+      <td>15597.115118</td>
+      <td>6.340347e+11</td>
+      <td>4.446234e+08</td>
+      <td>7.943551e-07</td>
+      <td>Opaque</td>
+      <td>Carbide</td>
+      <td>Tungsten Carbide</td>
+      <td>Very low</td>
+    </tr>
+    <tr>
+      <th>000009cc-000e-4fff-8fff-dd92ffff0000</th>
+      <td>7829.901810</td>
+      <td>2.050641e+11</td>
+      <td>1.034283e+09</td>
+      <td>2.236068e-07</td>
+      <td>Opaque</td>
+      <td>Fe (Iron)</td>
+      <td>AISI 4130</td>
+      <td>Very low</td>
+    </tr>
+    <tr>
+      <th>000009cb-000e-4fff-8fff-dd92ffff0000</th>
+      <td>7833.313776</td>
+      <td>2.050641e+11</td>
+      <td>6.528666e+08</td>
+      <td>2.236068e-07</td>
+      <td>Opaque</td>
+      <td>Fe (Iron)</td>
+      <td>AISI 4130</td>
+      <td>Very low</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+Then, we can use `.groupby()` to plot each bin with a different color and shape of marker. A `cycler.cycler` object specifies which colors will be used for each separate scatter plot.
+
+
+```python
+from cycler import cycler
+
+fig, ax = plt.subplots()
+ax.set_yscale('log')
+ax.set_xscale('log')
+ax.set_ylabel(ym_label)
+ax.set_xlabel(density_label)
+
+ax.set_prop_cycle(cycler('color', ['blue', 'green', 'yellow', 'orange', 'red']))
+
+for idx, (key, df) in enumerate(df_processed.groupby('Electrical resistivity (binned)', observed=False)):
+    marker = markers[idx]
+    ax.scatter(data=df, x='Density', y="Young's modulus", label=key, s=50, marker=marker)
+plt.legend(title='Electrical resistivity', loc='lower right')
+_ = ax.set_title(r'E vs Density and $\rho$ (discrete marker color)')
+```
+
+
+    
+![png](00_Create_scatter_plots_files/00_Create_scatter_plots_48_0.png)
+    
+
+
+## Create scatter plots with `seaborn`
+
+An alternative to using `matplotlib` directly is to use `seaborn`, a wrapper of `matplotlib` that aims to make it easier to create professional-looking plots. 
+
+For example, here is the *E vs rho* plot grouped by 'Base' in `seaborn`. Note that the plot itself is defined in one line, and we can specify DataFrame columns to use for the marker `hue` and `style` with no additional steps. The legend is created automatically.
+
+
+```python
+from itertools import cycle, islice
+import seaborn as sns
+
+ax = sns.scatterplot(data=df_processed, x="Density", y="Young's modulus", hue="Base", style="Base", s=50)
+ax.set_xscale('log')
+ax.set_xlabel(density_label)
+ax.set_yscale('log')
+ax.set_ylabel(ym_label)
+ax.set_title('E vs Density grouped by Base (using seaborn)')
+```
+
+
+
+*Previous cell output:*
+```output
+Text(0.5, 1.0, 'E vs Density grouped by Base (using seaborn)')
+```
+
+
+
+    
+![png](00_Create_scatter_plots_files/00_Create_scatter_plots_51_1.png)
+    
+
+
+### Plotting in *n* dimensions (pair plots)
+
+The `seaborn` package doesn't expose `matplotlib`'s 3D scatter plotting functionality. However, it can produce stacked (paired) plots of any dimensionality as required. The example below shows a comparison across 4 continuous dimensions. 
+
+To avoid having to set the label of each axis, we've created a new DataFrame with the desired axis labels as column names. The labelling is then done automatically.
+
+Although `sns.pairplot` doesn't have a `style` argument, you can provide a list of markers instead. This must be the same length as the number of categories. Here, we use itertools to cycle through the marker style list to get the correct number.
+
+
+```python
+ftu_unit = units['Tensile strength']
+ftu_label = rf'$F_{{tu}}$ / ${ftu_unit}$'
+
+column_mapping = {'Density': density_label,
+                  "Young's modulus": ym_label,
+                  'Tensile strength': ftu_label,
+                  'Electrical resistivity': rho_label}
+df_pairplot = df_processed.rename(columns=column_mapping)
+
+fig = plt.figure()
+base_count = df_pairplot['Base'].nunique()
+base_markers = list(islice(cycle(markers), 0, base_count))
+g = sns.pairplot(vars=[density_label, ym_label, ftu_label, rho_label],
+                 data=df_pairplot,
+                 hue="Base",
+                 markers=base_markers,
+                 diag_kind='None')
+
+for i in range(len(g.axes)):
+    for j in range(len(g.axes[i])):
+        g.axes[i,j].set_xscale('log')
+        g.axes[i,j].set_yscale('log')
+_ = g.fig.suptitle(r'Pair plot comparing Density, E, $F_{{{tu}}}$ and $\rho$, grouped by Base', y=1.03)
+```
+
+*Previous cell output:*
+```output
+<Figure size 800x600 with 0 Axes>
+```
+
+
+    
+![png](00_Create_scatter_plots_files/00_Create_scatter_plots_54_1.png)
+    
+
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/00_Create_scatter_plots_files/00_Create_scatter_plots_31_0.png b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/00_Create_scatter_plots_files/00_Create_scatter_plots_31_0.png
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diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/01_Compare_datasets_graphically.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/01_Compare_datasets_graphically.md
new file mode 100644
index 0000000000..bc6cd2e04f
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/01_Compare_datasets_graphically.md
@@ -0,0 +1,614 @@
+# Compare datasets graphically
+
+Export AM build information and tensile test results from different tables in the *MI Training* database, process the data using a `pandas` DataFrame, and plot summaries of the data using `matplotlib` and `seaborn`.
+
+## Get data from Granta MI
+
+Connect to MI and specify a database.
+
+
+```python
+from GRANTA_MIScriptingToolkit import granta as mpy
+
+mi = mpy.connect("http://my.server.name/mi_servicelayer", autologon=True)
+training_db = mi.get_db('MI Training')
+training_db.unit_system = 'Metric'
+```
+
+Specify the records and attributes you want to export from the first table. 
+
+For this example, we need *Build ID* and *Travel Speed* for all records in the *AM Builds* table.
+
+
+```python
+am_builds = training_db.get_table('AM Builds')
+build_records = am_builds.all_records()
+build_attributes = ['Build ID', 'Travel Speed']
+am_builds.bulk_fetch(build_records, attributes=build_attributes)
+```
+
+Specify the records you want to export from the second table. Here, a link group is used to export only records linked to records in the first table. 
+
+Alternatively, you can specify a table with no link groups to the original table, as long as you can join the two datasets later on. For example, we could search for all records created after a certain date, or tested on a certain machine.
+
+
+```python
+link_group = 'Tensile tests from this build'
+
+# Use a set comprehension to ensure we only select each record once
+tensile_test_records = {tr for br in build_records for tr in br.links[link_group]}
+tensile_test_records = list(tensile_test_records)
+```
+
+Specify the attributes you want to export from the second table. *Build ID* will be used to relate the tensile tests to the appropriate build data.
+
+
+```python
+tensile_test_data = training_db.get_table('Tensile Test Data')
+tensile_attributes = ['Build ID', 'Specimen ID', '0.2% Offset Yield Stress', 'Ultimate Tensile Strength', 'Specimen Orientation']
+tensile_test_data.bulk_fetch(tensile_test_records, attributes=tensile_attributes)
+```
+
+## Use DataFrames to pre-process and append data
+
+### Build a separate DataFrame for each table
+
+Create a DataFrame for each Granta MI table. The DataFrames are built from two separate lists of dictionaries:
+
+
+```python
+def get_attr_vals(attributes, r):
+    return {mi_attr: r.attributes[mi_attr].value for mi_attr in attributes}
+
+build_data = [get_attr_vals(build_attributes, r) for r in build_records]
+tensile_data = [get_attr_vals(tensile_attributes, r) for r in tensile_test_records]
+```
+
+Create a DataFrame `build_df` that contains the build records. Set the DataFrame index to be the *Build ID* attribute value,
+and sort by the index to guarantee a repeatable row order.
+We use `.dropna()` to ensure that only fully populated records are kept in the dataframe.
+
+
+```python
+import pandas as pd
+
+build_df = pd.DataFrame(build_data).dropna().set_index('Build ID').sort_index()
+build_df.head()
+```
+
+
+
+
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Travel Speed</th>
+    </tr>
+    <tr>
+      <th>Build ID</th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>2013-04-15 CLAD-2321 01</th>
+      <td>0.0086</td>
+    </tr>
+    <tr>
+      <th>2013-04-16 CLAD-2321 02</th>
+      <td>0.0096</td>
+    </tr>
+    <tr>
+      <th>2013-04-17 CLAD-2321 03</th>
+      <td>0.0106</td>
+    </tr>
+    <tr>
+      <th>2013-04-17 CLAD-2321 04</th>
+      <td>0.0116</td>
+    </tr>
+    <tr>
+      <th>2013-04-17 CLAD-2321 05</th>
+      <td>0.0126</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+Create a second DataFrame, `tensile_df`, that contains the tensile test results. Again, set the index to be the *Build ID* attribute value
+and sort by both this index and the *Specimen ID*.
+
+
+```python
+tensile_df = pd.DataFrame(tensile_data).set_index('Build ID').sort_values(by=["Build ID", "Specimen ID"])
+tensile_df.head()
+```
+
+
+
+
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Specimen ID</th>
+      <th>0.2% Offset Yield Stress</th>
+      <th>Ultimate Tensile Strength</th>
+      <th>Specimen Orientation</th>
+    </tr>
+    <tr>
+      <th>Build ID</th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>2013-04-15 CLAD-2321 01</th>
+      <td>L13L12AA11T</td>
+      <td>127.300</td>
+      <td>126.81</td>
+      <td>AT</td>
+    </tr>
+    <tr>
+      <th>2013-04-15 CLAD-2321 01</th>
+      <td>L13L12AA13T</td>
+      <td>121.125</td>
+      <td>121.77</td>
+      <td>LT</td>
+    </tr>
+    <tr>
+      <th>2013-04-15 CLAD-2321 01</th>
+      <td>L13L12AA15T</td>
+      <td>121.315</td>
+      <td>124.47</td>
+      <td>LT</td>
+    </tr>
+    <tr>
+      <th>2013-04-15 CLAD-2321 01</th>
+      <td>L13L12AA17T</td>
+      <td>121.885</td>
+      <td>122.76</td>
+      <td>LT</td>
+    </tr>
+    <tr>
+      <th>2013-04-15 CLAD-2321 01</th>
+      <td>L13L12AA1T</td>
+      <td>126.730</td>
+      <td>130.32</td>
+      <td>L</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+### Join the dataframes
+
+Use the `dataframe.join()` method to create a DataFrame with both sets of data. The join type will default to left-join if not specified; this is fine in our example because the records were linked via a smart link in Granta MI, and so the set of indices is the same for both DataFrames.
+
+
+```python
+df = build_df.join(tensile_df)
+df.head()
+```
+
+
+
+
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Travel Speed</th>
+      <th>Specimen ID</th>
+      <th>0.2% Offset Yield Stress</th>
+      <th>Ultimate Tensile Strength</th>
+      <th>Specimen Orientation</th>
+    </tr>
+    <tr>
+      <th>Build ID</th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>2013-04-15 CLAD-2321 01</th>
+      <td>0.0086</td>
+      <td>L13L12AA11T</td>
+      <td>127.300</td>
+      <td>126.81</td>
+      <td>AT</td>
+    </tr>
+    <tr>
+      <th>2013-04-15 CLAD-2321 01</th>
+      <td>0.0086</td>
+      <td>L13L12AA13T</td>
+      <td>121.125</td>
+      <td>121.77</td>
+      <td>LT</td>
+    </tr>
+    <tr>
+      <th>2013-04-15 CLAD-2321 01</th>
+      <td>0.0086</td>
+      <td>L13L12AA15T</td>
+      <td>121.315</td>
+      <td>124.47</td>
+      <td>LT</td>
+    </tr>
+    <tr>
+      <th>2013-04-15 CLAD-2321 01</th>
+      <td>0.0086</td>
+      <td>L13L12AA17T</td>
+      <td>121.885</td>
+      <td>122.76</td>
+      <td>LT</td>
+    </tr>
+    <tr>
+      <th>2013-04-15 CLAD-2321 01</th>
+      <td>0.0086</td>
+      <td>L13L12AA1T</td>
+      <td>126.730</td>
+      <td>130.32</td>
+      <td>L</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+### Refine the data
+
+Extract simple numeric and string values from the MI Scripting Toolkit data types.
+
+
+```python
+df_processed = df[['Travel Speed', '0.2% Offset Yield Stress', 'Ultimate Tensile Strength']].copy()
+df_processed['Specimen Orientation'] = df['Specimen Orientation']
+df_processed['Specimen ID'] = df['Specimen ID']
+df_processed.head()
+```
+
+
+
+
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Travel Speed</th>
+      <th>0.2% Offset Yield Stress</th>
+      <th>Ultimate Tensile Strength</th>
+      <th>Specimen Orientation</th>
+      <th>Specimen ID</th>
+    </tr>
+    <tr>
+      <th>Build ID</th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>2013-04-15 CLAD-2321 01</th>
+      <td>0.0086</td>
+      <td>127.300</td>
+      <td>126.81</td>
+      <td>AT</td>
+      <td>L13L12AA11T</td>
+    </tr>
+    <tr>
+      <th>2013-04-15 CLAD-2321 01</th>
+      <td>0.0086</td>
+      <td>121.125</td>
+      <td>121.77</td>
+      <td>LT</td>
+      <td>L13L12AA13T</td>
+    </tr>
+    <tr>
+      <th>2013-04-15 CLAD-2321 01</th>
+      <td>0.0086</td>
+      <td>121.315</td>
+      <td>124.47</td>
+      <td>LT</td>
+      <td>L13L12AA15T</td>
+    </tr>
+    <tr>
+      <th>2013-04-15 CLAD-2321 01</th>
+      <td>0.0086</td>
+      <td>121.885</td>
+      <td>122.76</td>
+      <td>LT</td>
+      <td>L13L12AA17T</td>
+    </tr>
+    <tr>
+      <th>2013-04-15 CLAD-2321 01</th>
+      <td>0.0086</td>
+      <td>126.730</td>
+      <td>130.32</td>
+      <td>L</td>
+      <td>L13L12AA1T</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+Finally, create a `units` dict so you can create plots with meaningful axis labels.
+
+
+```python
+attr_defs = [am_builds.attributes[attr] for attr in build_attributes] + \
+            [tensile_test_data.attributes[attr] for attr in tensile_attributes]
+units = {attr.name: attr.unit for attr in attr_defs if attr.unit}
+units
+```
+
+
+
+*Previous cell output:*
+```output
+{'Travel Speed': 'm/s',
+ '0.2% Offset Yield Stress': 'MPa',
+ 'Ultimate Tensile Strength': 'MPa'}
+```
+
+
+## Plot the data
+
+This code snippet is optional, and is included to increase the size of all plots generated in this
+notebook.
+
+
+```python
+import matplotlib.pyplot as plt
+plt.rcParams['figure.dpi'] = 125
+```
+
+### Investigate the relationship between two properties
+
+Create a scatter plot comparing *Ultimate Tensile Strength* of samples to the *Travel Speed* used during the build.
+
+
+```python
+import seaborn as sns
+
+travel_speed_unit = units['Travel Speed']
+ftu_unit = units["Ultimate Tensile Strength"]
+travel_speed_label = rf'Travel Speed / ${travel_speed_unit}$'
+ftu_label = rf'$F_{{tu}}$ / ${ftu_unit}$'
+
+ax = sns.scatterplot(data=df_processed, x="Travel Speed", y="Ultimate Tensile Strength", alpha=0.75, s=100)
+ax.set_xlabel(travel_speed_label)
+ax.set_ylabel(ftu_label)
+_ = ax.set_title('$F_{{tu}}$ vs Travel Speed')
+```
+
+
+    
+![png](01_Compare_datasets_graphically_files/01_Compare_datasets_graphically_30_0.png)
+    
+
+
+There is a clear dependence of the *Ultimate Tensile Strength* on the *Travel Speed*. It is also clear that *Travel Speed*
+is an independent variable, with the values chosen for each build falling into a set of well-defined bins. 
+
+Instead of judging the distribution by eye, we can use box plots to describe the distribution of values.
+
+
+```python
+ax = sns.boxplot(data=df_processed, x="Travel Speed", y="Ultimate Tensile Strength")
+ax.set_xlabel(travel_speed_label)
+ax.set_ylabel(ftu_label)
+_ = ax.set_title('$F_{{tu}}$ vs Travel Speed (Box Plot)')
+```
+
+
+    
+![png](01_Compare_datasets_graphically_files/01_Compare_datasets_graphically_32_0.png)
+    
+
+
+For a more detailed view of the distribution, we can replace the box plots with violin plots.
+
+
+```python
+ax = sns.violinplot(data=df_processed, x="Travel Speed", y="Ultimate Tensile Strength")
+ax.set_xlabel(travel_speed_label)
+ax.set_ylabel(ftu_label)
+_ = ax.set_title('$F_{{tu}}$ vs Travel Speed (Violin Plot)')
+```
+
+
+    
+![png](01_Compare_datasets_graphically_files/01_Compare_datasets_graphically_34_0.png)
+    
+
+
+### Investigate the impact of a third property
+
+The orientation of each specimen during the test was also exported, but not included on the plots above. 
+
+The simplest way to visualize this data is to add it to the original scatter plot as a color axis.
+
+
+```python
+ax = sns.scatterplot(data=df_processed,
+                     x="Travel Speed",
+                     y="Ultimate Tensile Strength",
+                     alpha=0.75,
+                     s=100,
+                     hue='Specimen Orientation',
+                     style='Specimen Orientation')
+ax.set_xlabel(travel_speed_label)
+ax.set_ylabel(ftu_label)
+_ = ax.set_title('$F_{{tu}}$ vs Travel Speed Grouped by Specimen Orientation')
+```
+
+
+    
+![png](01_Compare_datasets_graphically_files/01_Compare_datasets_graphically_37_0.png)
+    
+
+
+This scatter plot contains different markers for each orientation, but is difficult to interpret. Some difference in
+distribution is visible for each orientation, but it's difficult to discern any detail.
+
+A clearer representation would be to add the orientation to the violin plot above. This can be done with a 'split' violin plot, but only for two values of *Specimen Orientation*. We can remove the 'AT' results from the dataset, but first we want to check how many data points would be removed using the `DataFrame.value_counts()` method.
+
+
+```python
+df_processed['Specimen Orientation'].value_counts()
+```
+
+
+
+*Previous cell output:*
+```output
+Specimen Orientation
+L     34
+LT    32
+AT     7
+Name: count, dtype: int64
+```
+
+
+There are only 7 values for 'AT', which is less than 10% of the overall dataset. 
+
+The code below creates a series of `True` or `False` values in `rows_to_keep` (set to `True` if the value is in the specified list). `rows_to_keep` is then passed as a selector into the DataFrame, which maps the list onto the DataFrame rows and returns a new DataFrame with only the `True` rows included.
+
+
+```python
+rows_to_keep = df_processed['Specimen Orientation'].map(lambda x: x in ['L', 'LT'])
+df_L_or_LT = df_processed[rows_to_keep]
+df_L_or_LT.head()
+```
+
+
+
+
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Travel Speed</th>
+      <th>0.2% Offset Yield Stress</th>
+      <th>Ultimate Tensile Strength</th>
+      <th>Specimen Orientation</th>
+      <th>Specimen ID</th>
+    </tr>
+    <tr>
+      <th>Build ID</th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>2013-04-15 CLAD-2321 01</th>
+      <td>0.0086</td>
+      <td>121.125</td>
+      <td>121.77</td>
+      <td>LT</td>
+      <td>L13L12AA13T</td>
+    </tr>
+    <tr>
+      <th>2013-04-15 CLAD-2321 01</th>
+      <td>0.0086</td>
+      <td>121.315</td>
+      <td>124.47</td>
+      <td>LT</td>
+      <td>L13L12AA15T</td>
+    </tr>
+    <tr>
+      <th>2013-04-15 CLAD-2321 01</th>
+      <td>0.0086</td>
+      <td>121.885</td>
+      <td>122.76</td>
+      <td>LT</td>
+      <td>L13L12AA17T</td>
+    </tr>
+    <tr>
+      <th>2013-04-15 CLAD-2321 01</th>
+      <td>0.0086</td>
+      <td>126.730</td>
+      <td>130.32</td>
+      <td>L</td>
+      <td>L13L12AA1T</td>
+    </tr>
+    <tr>
+      <th>2013-04-15 CLAD-2321 01</th>
+      <td>0.0086</td>
+      <td>126.065</td>
+      <td>125.37</td>
+      <td>L</td>
+      <td>L13L12AA3T</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+Re-run `.value_counts()` from before to confirm the change was successful.
+
+
+```python
+df_L_or_LT['Specimen Orientation'].value_counts()
+```
+
+
+
+*Previous cell output:*
+```output
+Specimen Orientation
+L     34
+LT    32
+Name: count, dtype: int64
+```
+
+
+Specify `split=True` in the `sns.violinplot` constructor to show the two distributions side-by-side. The dependence
+on *Specimen Orientation* is now much clearer.
+
+
+```python
+ax = sns.violinplot(data=df_L_or_LT,
+                    x="Travel Speed",
+                    y="Ultimate Tensile Strength",
+                    hue="Specimen Orientation",
+                    split=True,
+                    saturation=0.5)
+ax.set_xlabel(travel_speed_label)
+ax.set_ylabel(ftu_label)
+ax.legend(title='Specimen Orientation', loc='lower center')
+_ = ax.set_title('$F_{{tu}}$ vs Travel Speed and Specimen Orientation (Split Violin Plot)')
+```
+
+
+    
+![png](01_Compare_datasets_graphically_files/01_Compare_datasets_graphically_45_0.png)
+    
+
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/01_Compare_datasets_graphically_files/01_Compare_datasets_graphically_30_0.png b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/01_Compare_datasets_graphically_files/01_Compare_datasets_graphically_30_0.png
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diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/02_Plot_data_by_category.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/02_Plot_data_by_category.md
new file mode 100644
index 0000000000..1a6c6471cf
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/02_Plot_data_by_category.md
@@ -0,0 +1,589 @@
+# Plot data by category
+
+Export pedigree data and tensile test results for composite materials, process the data in a `pandas` DataFrame, and plot summaries of the data (including layup information) using `matplotlib` and `seaborn`.
+
+## Get data from Granta MI
+
+Connect to Granta MI and specify a database.
+
+
+```python
+from GRANTA_MIScriptingToolkit import granta as mpy
+
+mi = mpy.connect("http://my.server.name/mi_servicelayer", autologon=True)
+training_db = mi.get_db('MI Training')
+training_db.unit_system = 'Metric'
+```
+
+Specify the records and attributes you want to export from the first table. We want to use attributes describing the layup for all records in the *Composite Pedigree* table.
+
+
+```python
+comp_pedigree = training_db.get_table('Composite Pedigree')
+pedigree_records = comp_pedigree.all_records()
+pedigree_attributes = ['Panel number', 'Ply count', 'Panel thickness, average (measured)',
+                       'Ply thickness, average (measured)']
+comp_pedigree.bulk_fetch(pedigree_records, attributes=pedigree_attributes)
+```
+
+Specify the records you want to export from the second table. Here, a link group is used to export only records linked to records in the first table.
+
+Alternatively, you can specify a table with no link groups to the original table, as long as you can join the two datasets later on. For example, we could search for all records created after a certain date, or tested on a certain machine.
+
+
+```python
+link_group = 'Tensile test data'
+
+# Use a set comprehension to ensure we only select each record once
+tensile_test_records = {tr for pr in pedigree_records for tr in pr.links[link_group]}
+tensile_test_records = list(tensile_test_records)
+```
+
+Specify the attributes you want to export from the second table. *Panel number* will be used to relate the tensile tests to the layup data.
+
+
+```python
+tensile_test_data = training_db.get_table('Tensile Test Data')
+tensile_attributes = ['Panel number', 'Specimen ID', "Young's Modulus (11-axis)", 'Ultimate Tensile Strength',
+                      'Test Type']
+tensile_test_data.bulk_fetch(tensile_test_records, attributes=tensile_attributes)
+```
+
+## Use DataFrames to pre-process and append the data
+
+### Build a separate DataFrame for each table
+
+Create a DataFrame for each Granta MI table. The DataFrames are built from two separate lists of dictionaries:
+
+
+```python
+import pandas as pd
+from functools import partial
+
+def get_attr_vals(attributes, r):
+    return {mi_attr: r.attributes[mi_attr].value for mi_attr in attributes}
+
+get_pedigree_vals = partial(get_attr_vals, pedigree_attributes)
+get_tensile_vals = partial(get_attr_vals, tensile_attributes)
+
+pedigree_data = [get_pedigree_vals(r) for r in pedigree_records]
+tensile_data = [get_tensile_vals(r) for r in tensile_test_records]
+```
+
+Create a DataFrame `pedigree_df` that contains the pedigree records. Set the DataFrame index to be the *Panel number* attribute value
+and sort by the index.
+
+
+```python
+pedigree_df = pd.DataFrame(pedigree_data).set_index('Panel number').sort_index()
+pedigree_df.head()
+```
+
+
+
+
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Ply count</th>
+      <th>Panel thickness, average (measured)</th>
+      <th>Ply thickness, average (measured)</th>
+    </tr>
+    <tr>
+      <th>Panel number</th>
+      <th></th>
+      <th></th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>LBJ13</th>
+      <td>6</td>
+      <td>1.397000</td>
+      <td>0.232029</td>
+    </tr>
+    <tr>
+      <th>LBJ14</th>
+      <td>6</td>
+      <td>1.431636</td>
+      <td>0.238044</td>
+    </tr>
+    <tr>
+      <th>LBJ42</th>
+      <td>6</td>
+      <td>1.438564</td>
+      <td>0.240007</td>
+    </tr>
+    <tr>
+      <th>LBJ53</th>
+      <td>6</td>
+      <td>1.385455</td>
+      <td>0.231279</td>
+    </tr>
+    <tr>
+      <th>LBJ62</th>
+      <td>6</td>
+      <td>1.410677</td>
+      <td>0.234618</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+Create a second DataFrame `tensile_df` that contains the tensile test results. Again, set the index to be the *Panel number* attribute value
+and sort by both the index and the *Specimen ID*.
+
+
+```python
+tensile_df = pd.DataFrame(tensile_data).set_index('Panel number').sort_values(by=["Panel number", "Specimen ID"])
+tensile_df.head()
+```
+
+
+
+
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Specimen ID</th>
+      <th>Young's Modulus (11-axis)</th>
+      <th>Ultimate Tensile Strength</th>
+      <th>Test Type</th>
+    </tr>
+    <tr>
+      <th>Panel number</th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>LBJ13</th>
+      <td>LBJ1311A</td>
+      <td>47.594514</td>
+      <td>1582.870937</td>
+      <td>0° tension</td>
+    </tr>
+    <tr>
+      <th>LBJ13</th>
+      <td>LBJ1312A</td>
+      <td>47.353197</td>
+      <td>1542.122918</td>
+      <td>0° tension</td>
+    </tr>
+    <tr>
+      <th>LBJ13</th>
+      <td>LBJ1313A</td>
+      <td>47.387671</td>
+      <td>1612.952766</td>
+      <td>0° tension</td>
+    </tr>
+    <tr>
+      <th>LBJ14</th>
+      <td>LBJ1413A</td>
+      <td>[]</td>
+      <td>1613.614663</td>
+      <td>0° tension</td>
+    </tr>
+    <tr>
+      <th>LBJ14</th>
+      <td>LBJ1415A</td>
+      <td>[]</td>
+      <td>1611.201497</td>
+      <td>0° tension</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+### Join the dataframes
+
+Use the `dataframe.join()` method to create a DataFrame with both sets of data. The join type will default to a left-join if not specified; this is fine in our case because the records were linked via a smart link in Granta MI, and so the set of indices is the same for both DataFrames.
+
+
+```python
+df = pedigree_df.join(tensile_df)
+df.head()
+```
+
+
+
+
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Ply count</th>
+      <th>Panel thickness, average (measured)</th>
+      <th>Ply thickness, average (measured)</th>
+      <th>Specimen ID</th>
+      <th>Young's Modulus (11-axis)</th>
+      <th>Ultimate Tensile Strength</th>
+      <th>Test Type</th>
+    </tr>
+    <tr>
+      <th>Panel number</th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>LBJ13</th>
+      <td>6</td>
+      <td>1.397000</td>
+      <td>0.232029</td>
+      <td>LBJ1311A</td>
+      <td>47.594514</td>
+      <td>1582.870937</td>
+      <td>0° tension</td>
+    </tr>
+    <tr>
+      <th>LBJ13</th>
+      <td>6</td>
+      <td>1.397000</td>
+      <td>0.232029</td>
+      <td>LBJ1312A</td>
+      <td>47.353197</td>
+      <td>1542.122918</td>
+      <td>0° tension</td>
+    </tr>
+    <tr>
+      <th>LBJ13</th>
+      <td>6</td>
+      <td>1.397000</td>
+      <td>0.232029</td>
+      <td>LBJ1313A</td>
+      <td>47.387671</td>
+      <td>1612.952766</td>
+      <td>0° tension</td>
+    </tr>
+    <tr>
+      <th>LBJ14</th>
+      <td>6</td>
+      <td>1.431636</td>
+      <td>0.238044</td>
+      <td>LBJ1413A</td>
+      <td>[]</td>
+      <td>1613.614663</td>
+      <td>0° tension</td>
+    </tr>
+    <tr>
+      <th>LBJ14</th>
+      <td>6</td>
+      <td>1.431636</td>
+      <td>0.238044</td>
+      <td>LBJ1415A</td>
+      <td>[]</td>
+      <td>1611.201497</td>
+      <td>0° tension</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+Finally, create a `units` dict so you can create plots with meaningful axis labels.
+
+
+```python
+attr_defs = [comp_pedigree.attributes[attr] for attr in pedigree_attributes] + \
+            [tensile_test_data.attributes[attr] for attr in tensile_attributes]
+units = {attr.name: attr.unit for attr in attr_defs if attr.unit}
+units
+```
+
+
+
+*Previous cell output:*
+```output
+{'Panel thickness, average (measured)': 'mm',
+ 'Ply thickness, average (measured)': 'mm',
+ "Young's Modulus (11-axis)": 'GPa',
+ 'Ultimate Tensile Strength': 'MPa'}
+```
+
+
+## Plot the data
+
+This code snippet is optional, and is included to increase the size of all plots generated in this
+notebook.
+
+
+```python
+import matplotlib.pyplot as plt
+plt.rcParams['figure.dpi'] = 125
+```
+
+### Investigate the relationship between two properties
+
+Create a scatter plot with `matplotlib` comparing *Ultimate Tensile Strength* of the panels to *Ply thickness, average*.
+
+
+```python
+import matplotlib.pyplot as plt
+
+ply_thickness_unit = units['Ply thickness, average (measured)']
+ftu_unit = units["Ultimate Tensile Strength"]
+ply_thickness_label = rf'Average ply thickness / ${ply_thickness_unit}$'
+ftu_label = rf'$F_{{tu}}$ / ${ftu_unit}$'
+
+fig, ax = plt.subplots()
+ax.scatter(data=df, x="Ply thickness, average (measured)", y="Ultimate Tensile Strength", alpha=0.75, s=100)
+ax.set_xlabel(ply_thickness_label)
+ax.set_ylabel(ftu_label)
+_ = ax.set_title(r'$F_{tu}$ vs Average ply thickness')
+```
+
+
+    
+![png](02_Plot_data_by_category_files/02_Plot_data_by_category_28_0.png)
+    
+
+
+There is a significant difference between the results at 1200 - 1600 MPa and the group below 100 MPa. We need to identify what causes this difference, and then represent the data in a clearer way.
+
+### Investigate the impact of other properties
+
+First, examine the underlying data; use the `DataFrame.describe()` method to view a statistical summary of the data in each column.
+
+
+```python
+df.describe(include='all')
+```
+
+
+
+
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Ply count</th>
+      <th>Panel thickness, average (measured)</th>
+      <th>Ply thickness, average (measured)</th>
+      <th>Specimen ID</th>
+      <th>Young's Modulus (11-axis)</th>
+      <th>Ultimate Tensile Strength</th>
+      <th>Test Type</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>count</th>
+      <td>67.000000</td>
+      <td>67.000000</td>
+      <td>67.000000</td>
+      <td>67</td>
+      <td>67</td>
+      <td>67.000000</td>
+      <td>67</td>
+    </tr>
+    <tr>
+      <th>unique</th>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>67</td>
+      <td>19</td>
+      <td>NaN</td>
+      <td>2</td>
+    </tr>
+    <tr>
+      <th>top</th>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>LBJ1311A</td>
+      <td>[]</td>
+      <td>NaN</td>
+      <td>0° tension</td>
+    </tr>
+    <tr>
+      <th>freq</th>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>1</td>
+      <td>49</td>
+      <td>NaN</td>
+      <td>56</td>
+    </tr>
+    <tr>
+      <th>mean</th>
+      <td>6.492537</td>
+      <td>1.534167</td>
+      <td>0.235604</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>1288.110261</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>std</th>
+      <td>1.119701</td>
+      <td>0.294450</td>
+      <td>0.005039</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>557.743977</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>min</th>
+      <td>6.000000</td>
+      <td>1.379415</td>
+      <td>0.229948</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>44.629768</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>25%</th>
+      <td>6.000000</td>
+      <td>1.385455</td>
+      <td>0.231279</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>1353.613343</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>50%</th>
+      <td>6.000000</td>
+      <td>1.410677</td>
+      <td>0.234618</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>1545.653034</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>75%</th>
+      <td>6.000000</td>
+      <td>1.438564</td>
+      <td>0.240007</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>1603.610369</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>max</th>
+      <td>9.000000</td>
+      <td>2.213429</td>
+      <td>0.245727</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>1639.559637</td>
+      <td>NaN</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+The *Test Type* column summary shows that the most frequent value is '0° tension' (as expected), but there are 9 occurrences of a second test type.
+
+To identify all possible values in the *Test Type* column, use `DataFrame['Test Type'].unique()`.
+
+
+```python
+df['Test Type'].unique()
+```
+
+
+
+*Previous cell output:*
+```output
+array(['0° tension', '90° tension'], dtype=object)
+```
+
+
+Although there is only one populated attribute for *Ultimate Tensile Strength* and *Young's modulus*, the *Test Type* column shows that the tensile tests were performed in two different directions. 
+
+Since composite materials are often highly orthotropic, the direction of the test must be taken into account when plotting the results. We can filter the DataFrame based on the *Test Type* column when plotting:
+
+
+```python
+ftu_0_label = rf'$F_{{tu}}, 0^{{\circ}}$ / ${ftu_unit}$'
+df_0_tension = df[df['Test Type'] == '0° tension']
+
+fig, ax = plt.subplots()
+ax.scatter(data=df_0_tension,
+           x="Ply thickness, average (measured)",
+           y="Ultimate Tensile Strength",
+           alpha=0.75,
+           s=100)
+ax.set_xlabel(ply_thickness_label)
+ax.set_ylabel(ftu_0_label)
+_ = ax.set_title(r'$F_{tu}, 0^{{\circ}}$ vs Average ply thickness')
+```
+
+
+    
+![png](02_Plot_data_by_category_files/02_Plot_data_by_category_36_0.png)
+    
+
+
+We can also overlay the 90° *Ultimate Tensile Strength* results, using `.twinx()` to plot them on a separate y-axis.
+
+
+```python
+ftu_90_label = rf'$F_{{tu}}, 90^{{\circ}}$ / ${ftu_unit}$'
+df_90_tension = df[df['Test Type'] == '90° tension']
+
+fig, ax1 = plt.subplots()
+ax1.scatter(data=df_0_tension,
+            x="Ply thickness, average (measured)",
+            y="Ultimate Tensile Strength",
+            color="blue",
+            marker="o",
+            alpha=0.75,
+            s=100)
+ax2 = plt.twinx()
+ax2.scatter(data=df_90_tension,
+            x="Ply thickness, average (measured)",
+            y="Ultimate Tensile Strength",
+            color="orange",
+            marker="x",
+            alpha=0.75,
+            s=100)
+
+ax1.set_xlabel(ply_thickness_label)
+ax1.set_ylabel(ftu_0_label)
+ax2.set_ylabel(ftu_90_label)
+
+h1, l1 = ax1.get_legend_handles_labels()
+h2, l2 = ax2.get_legend_handles_labels()
+
+ax1.legend(h1+h2, [ftu_0_label, ftu_90_label], loc='lower right')
+_ = ax1.set_title(r'$F_{tu}, 0^{{\circ}}$ and $90^{{\circ}}$ vs Average ply thickness')
+```
+
+
+    
+![png](02_Plot_data_by_category_files/02_Plot_data_by_category_38_0.png)
+    
+
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/02_Plot_data_by_category_files/02_Plot_data_by_category_28_0.png b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/02_Plot_data_by_category_files/02_Plot_data_by_category_28_0.png
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diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/03_Fit_series_data_and_import.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/03_Fit_series_data_and_import.md
new file mode 100644
index 0000000000..6de8d3c79f
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/03_Fit_series_data_and_import.md
@@ -0,0 +1,253 @@
+# Fit series data and import fit results
+
+Analyze high-cycle fatigue test data and fit a fatigue model using SciPy, using an approach suitable for automation
+with either MI Data Flow or as a simple scheduled task (cron job).
+
+## Connect to Granta MI
+
+Specify a database and tables.
+
+
+```python
+from GRANTA_MIScriptingToolkit import granta as mpy
+
+mi = mpy.connect("http://my.server.name/mi_servicelayer", autologon=True)
+database = mi.get_db('MI Training')
+statistics_table = database.get_table('Fatigue Statistical Data')
+test_data_table = database.get_table('Fatigue Test Data')
+```
+
+## Search for records with statistical data
+
+This script will perform a fatigue analysis on the first record whose *Statistical Analysis Status* is set to 'In Progress', but it can easily be generalised to operate on multiple statistical data records.
+
+First, perform a criteria search to find the target record.
+
+
+```python
+criterion_type = statistics_table.attributes['Material type'].search_criterion(contains='Aluminum alloys')
+criterion_status = statistics_table.attributes['Statistical Analysis Status'].search_criterion(contains='In Progress')
+records = statistics_table.search_for_records_where([criterion_type, criterion_status])
+print('Found ' + str(len(records)) + ' corresponding HCF test results.')
+
+r = records[0]
+print('Analyzing fatigue data for "' + str(r.name) + '".')
+```
+*Previous cell output:*
+```output
+Found 1 corresponding HCF test results.
+Analyzing fatigue data for "7075 T6, R=0, T=430F".
+```
+## Get the source data for the record
+
+Get the *R-Ratio* and *Test Temperature* values from the record, and fetch the corresponding test results. Then read the attribute values from the test result records into a `pandas` DataFrame for plotting and analysis.
+
+
+```python
+import pandas as pd
+import statistics
+
+r_ratio = statistics.mean(r.attributes['Target R-Ratio Range'].value.values())
+test_temperature = statistics.mean(r.attributes['Test Temperature Range'].value.values())
+material_id = r.attributes['Alloy'].value + '-' + r.attributes['Condition'].value + '/' + r.attributes['Form'].value
+
+criterion_r_ratio = test_data_table.attributes['Target r-ratio'].search_criterion(less_than=r_ratio,
+                                                                                  greater_than=r_ratio)
+criterion_temperature = test_data_table.attributes['Test temperature'].search_criterion(less_than=test_temperature,
+                                                                                        greater_than=test_temperature)
+criterion_material_id = test_data_table.attributes['Material ID'].search_criterion(contains=material_id)
+records = test_data_table.search_for_records_where([criterion_r_ratio, criterion_temperature, criterion_material_id])
+
+print("Found " + str(len(records)) + " corresponding HCF test results.")
+
+t_results = ["Cyclic life", "Maximum stress", "Fatigue failure"]
+
+rs_dict = {attribute: [r.attributes[attribute].value for r in records] for attribute in t_results}
+rs_dict.update({"ShortName": [r.short_name for r in records]})
+
+rs_df = pd.DataFrame(rs_dict)
+```
+*Previous cell output:*
+```output
+Found 66 corresponding HCF test results.
+```
+## Fit the data
+
+Fit the fatigue test data using the SciPy `curve_fit()` function, and create a new DataFrame for the fitted data and the lower bound.
+
+
+```python
+from scipy.optimize import curve_fit
+import numpy as np
+
+def fatigue_func(x, A1, A2):
+    return 10 **(A2 * np.log10(x) - A1)
+                   
+(A1, A2), pcov = curve_fit(fatigue_func, rs_df['Cyclic life'], rs_df['Maximum stress'],
+                           bounds=([-500.0, -500.0], [500.0, 500.0]))
+
+min_coefficient = 0.9
+x = np.geomspace(rs_df['Cyclic life'].min(), rs_df['Cyclic life'].max(), rs_df['Cyclic life'].count())
+fitted_series = 10 **(A2 * np.log10(x) - A1)
+
+fitted_df = pd.DataFrame(index=x,
+                         data=np.transpose([fitted_series, min_coefficient*fitted_series]),
+                         columns=['Fitted Data', 'Minimum']).reset_index().melt(id_vars=['index'])\
+                    .rename(columns={'index': 'Cyclic life', 'value': 'Maximum stress', 'variable': 'Type'})
+
+fitted_df.head()
+```
+
+
+
+
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Cyclic life</th>
+      <th>Type</th>
+      <th>Maximum stress</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>5000.000000</td>
+      <td>Fitted Data</td>
+      <td>527.961774</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>5870.634799</td>
+      <td>Fitted Data</td>
+      <td>520.792043</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>6892.870589</td>
+      <td>Fitted Data</td>
+      <td>513.719678</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>8093.105189</td>
+      <td>Fitted Data</td>
+      <td>506.743355</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>9502.332991</td>
+      <td>Fitted Data</td>
+      <td>499.861771</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+## Plot the data
+
+Create a scatter plot of the source data (*Maximum stress* vs *Number of cycles*).
+
+
+```python
+import seaborn as sns
+from matplotlib import pyplot as plt
+
+plt.rcParams['figure.dpi'] = 125
+
+fig, ax = plt.subplots()
+ax = sns.scatterplot(data=rs_df, y='Maximum stress', x='Cyclic life', hue='Fatigue failure', ax=ax)
+
+plt.title("Fatigue data")
+plt.xlabel("Number of cycles")
+plt.ylabel("Max. stress / " + test_data_table.attributes['Maximum stress'].unit)
+plt.show()
+```
+
+
+    
+![png](03_Fit_series_data_and_import_files/03_Fit_series_data_and_import_10_0.png)
+    
+
+
+Add the fitted curve and the minimum curve to the plot, and set the x-axis scale to logarithmic.
+
+
+```python
+sns.lineplot(data=fitted_df, x='Cyclic life', y='Maximum stress', hue='Type', ax=ax)
+ax.set(xscale='log')
+
+fig
+```
+
+
+
+
+    
+![png](03_Fit_series_data_and_import_files/03_Fit_series_data_and_import_12_0.png)
+    
+
+
+
+## Import the fitted data to Granta MI
+
+Replace the data in the original record's functional attribute *Maximum Stress v Cycles (Equivalent Stress)* with the fitted data. Use `Session.update()` to push the changes to Granta MI.
+
+
+```python
+fit = r.attributes['Maximum Stress v Cycles (Equivalent Stress)']
+fit.clear()
+fit.unit = test_data_table.attributes['Maximum stress'].unit
+
+for _, row in fitted_df.iterrows():
+    fit.add_point({'y': row['Maximum stress'],
+                   'Number of Cycles': row['Cyclic life'],
+                   'Data Type': row['Type'],
+                   'Stress Ratio': r_ratio})
+for _, row in rs_df.iterrows():
+    fit.add_point({'y': row['Maximum stress'],
+                   'Number of Cycles': row['Cyclic life'],
+                   'Data Type': 'Test Data',
+                   'Stress Ratio': r_ratio})
+
+fit.series_linestyles[3] = 'Markers'
+
+r.attributes["Equivalent Stress, A1"].value = A1
+r.attributes["Equivalent Stress, A2"].value = A2
+r.attributes["Statistical Analysis Status"].value = "Complete"
+r.set_attributes([
+    r.attributes["Equivalent Stress, A1"],
+    r.attributes["Equivalent Stress, A2"],
+    # The status is not updated; this allows the notebook to be re-run.
+    #r.attributes["Statistical Analysis Status"],
+    fit
+])
+fit.value
+mi.update([r])
+print('Updated attributes in ' + str(r.name) + '.')
+```
+*Previous cell output:*
+```output
+Updated attributes in 7075 T6, R=0, T=430F.
+```
+## Create traceability links
+
+Add a record link group, and use `Session.update_links()` to push the changes to Granta MI.
+
+
+```python
+rec_link_group = r.links['Fatigue Test Data']
+for rsi in records:
+    rec_link_group.add(rsi)
+r.set_links('Fatigue Test Data', rec_link_group)
+mi.update_links([r])
+print('Updated links in ' + str(r.name) + '.')
+```
+*Previous cell output:*
+```output
+Updated links in 7075 T6, R=0, T=430F.
+```
\ No newline at end of file
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/03_Fit_series_data_and_import_files/03_Fit_series_data_and_import_10_0.png b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/03_Fit_series_data_and_import_files/03_Fit_series_data_and_import_10_0.png
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diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/04_Process_datasets_and_import.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/04_Process_datasets_and_import.md
new file mode 100644
index 0000000000..850afa8669
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+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/04_Process_datasets_and_import.md
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+# Process datasets and import results into Granta MI
+
+Load raw data from a *.csv* file and run a typical tensile data analysis to extract values for *Ultimate Tensile Strength*, *Young's Modulus* and *Elongation at failure*. Import the results into Granta MI.
+
+## Load data from CSV
+
+The raw *Load* vs *Displacement* data is stored in a CSV file. Import the data directly into a DataFrame using the `pandas` function `.read_csv()`.
+
+
+```python
+import pandas as pd
+
+df_raw = pd.read_csv('supporting_files/04_raw_test_data.csv')
+df_raw.head()
+```
+
+
+
+
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Displacement [mm]</th>
+      <th>Load [N]</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>0.000000</td>
+      <td>0.000</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>0.005282</td>
+      <td>273.392</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>0.005282</td>
+      <td>307.173</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>0.006010</td>
+      <td>331.741</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>0.006738</td>
+      <td>352.556</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+The convention in this example is that all properties are in SI units unless otherwise specified. Create a new DataFrame, converting *Displacement* to SI units and renaming both columns.
+
+
+```python
+df = pd.DataFrame()
+df['Displacement'] = df_raw['Displacement [mm]'] / 1e3
+df['Load'] = df_raw['Load [N]']
+df.head()
+```
+
+
+
+
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Displacement</th>
+      <th>Load</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>0.000000</td>
+      <td>0.000</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>0.000005</td>
+      <td>273.392</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>0.000005</td>
+      <td>307.173</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>0.000006</td>
+      <td>331.741</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>0.000007</td>
+      <td>352.556</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+## Process the data
+
+### Plot Load-Displacement data
+
+This code snippet is optional, and is included to increase the size of all plots generated in this notebook.
+
+
+```python
+import matplotlib.pyplot as plt
+plt.rcParams['figure.dpi'] = 125
+```
+
+Use `matplotlib` to plot the Load-Displacement curve. For this plot, we've divided `df.Load` by `1e3` so the y-axis is displayed in kN.
+
+
+```python
+import matplotlib.pyplot as plt
+
+fig, ax = plt.subplots()
+ax.plot(df.Displacement, df.Load / 1e3)
+ax.set_xlabel('Displacement / m')
+ax.set_ylabel('Load / kN')
+_ = ax.set_title('Load vs displacement')
+```
+
+
+    
+![png](04_Process_datasets_and_import_files/04_Process_datasets_and_import_12_0.png)
+    
+
+
+### Convert Load-Displacement to Stress-Strain
+
+Converting the Load-Displacement curve into Stress-Strain requires the gauge length and area, given here as constants in SI units. In a full workflow, these values can be loaded from a source file. 
+
+Calculate the *Stress* and *Strain* and store as new columns in the DataFrame.
+
+
+```python
+GAUGE_WIDTH = 0.0150068
+GAUGE_THICKNESS = 0.0015077
+GAUGE_LENGTH = 0.07997724
+
+df['Strain'] = df.Displacement / GAUGE_LENGTH
+gauge_area = GAUGE_WIDTH * GAUGE_THICKNESS
+df['Stress'] = df.Load / gauge_area
+df.head()
+```
+
+
+
+
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Displacement</th>
+      <th>Load</th>
+      <th>Strain</th>
+      <th>Stress</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>0.000000</td>
+      <td>0.000</td>
+      <td>0.000000</td>
+      <td>0.000000e+00</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>0.000005</td>
+      <td>273.392</td>
+      <td>0.000066</td>
+      <td>1.208322e+07</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>0.000005</td>
+      <td>307.173</td>
+      <td>0.000066</td>
+      <td>1.357626e+07</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>0.000006</td>
+      <td>331.741</td>
+      <td>0.000075</td>
+      <td>1.466210e+07</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>0.000007</td>
+      <td>352.556</td>
+      <td>0.000084</td>
+      <td>1.558207e+07</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+Now create a plot of Stress-Strain:
+
+
+```python
+fig, ax = plt.subplots()
+ax.plot(df.Strain, df.Stress / 1e6, zorder=0)
+ax.set_xlabel(r'$\epsilon$')
+ax.set_ylabel(r'$\sigma$ / MPa')
+ax.set_xlim(left=0)
+ax.set_ylim(bottom=0)
+
+_ = ax.set_title('Stress vs Strain')
+```
+
+
+    
+![png](04_Process_datasets_and_import_files/04_Process_datasets_and_import_17_0.png)
+    
+
+
+Since the plastic region extends over a large strain range, it is difficult to see detail in the elastic region. 
+
+Create an inset and plot the elastic and yield region only.
+
+
+```python
+# [left, bottom, width, height] relative to the figure
+ax_inset = fig.add_axes([0.25, 0.2, 0.4, 0.35])
+
+ax_inset.plot(df.Strain, df.Stress / 1e6, label='Tensile response', zorder=0)
+ax_inset.set_xlim(0, 0.004)
+ax_inset.set_xticks([0, 0.004])
+
+ax_inset.set_ylim(0, 400)
+ax_inset.set_yticks([0, 400])
+
+ax_inset.tick_params(axis='both', which='major', labelsize=8)
+fig
+```
+
+
+
+
+    
+![png](04_Process_datasets_and_import_files/04_Process_datasets_and_import_19_0.png)
+    
+
+
+
+### Calculate Ultimate tensile strength
+
+*Ultimate tensile strength* can be calculated by taking the maximum value of the `df.Stress` column. 
+
+Calculate and store the tensile strength in SI units and MPa (the latter will be used later).
+
+
+```python
+tens_strength = df.Stress.max()
+tens_strength_MPa = tens_strength / 1e6
+print(f'Ultimate tensile strength: {tens_strength_MPa:.2f} MPa')
+```
+*Previous cell output:*
+```output
+Ultimate tensile strength: 581.13 MPa
+```
+Add a marker and annotation on the stress-strain plot above, showing the location and value of the *Ultimate tensile
+strength*.
+
+
+```python
+strain_at_uts = df.Strain[df.Stress == tens_strength].iloc[0]
+ax.scatter(x=[strain_at_uts], y=[tens_strength_MPa], color='black', marker='x', alpha=0.75, s=50, zorder=1)
+ax.annotate(rf'$\sigma_{{ts}}$: {tens_strength_MPa:.2f} MPa',
+            xy=(strain_at_uts, tens_strength_MPa),
+            xytext=(0,-14),
+            textcoords='offset points',
+            fontsize=8)
+fig
+```
+
+
+
+
+    
+![png](04_Process_datasets_and_import_files/04_Process_datasets_and_import_24_0.png)
+    
+
+
+
+### Calculate Young's modulus
+
+Calculate *Young's modulus* by taking the gradient of the Stress-Strain curve between 5% and 25% of the total tensile strength (chordal fit method). There is no interpolation, because the Stress-Strain values closest to the 5% and 25% bounds are used.
+
+
+```python
+LOWER_FRACTION = 0.05
+UPPER_FRACTION = 0.25
+
+def get_stress_strain_at_fraction_of_tensile_strength(fraction):
+    stress_bound = tens_strength * fraction
+    stress_difference = df.Stress - stress_bound
+    nearest_row_number = stress_difference.abs().argsort()[0]
+    nearest_row = df.iloc[nearest_row_number]
+    return nearest_row.Strain, nearest_row.Stress
+    
+lower_strain, lower_stress = get_stress_strain_at_fraction_of_tensile_strength(LOWER_FRACTION)
+upper_strain, upper_stress = get_stress_strain_at_fraction_of_tensile_strength(UPPER_FRACTION)
+
+youngs_modulus = (upper_stress - lower_stress) / (upper_strain - lower_strain)
+print(f"Young's modulus ({LOWER_FRACTION:.0%}-{UPPER_FRACTION:.0%} tensile strength): {youngs_modulus / 1e9:.2f} GPa")
+```
+*Previous cell output:*
+```output
+Young's modulus (5%-25% tensile strength): 204.65 GPa
+```
+Add the chordal fit to the inset plot.
+
+
+```python
+stress_linear_fit = df.Strain * youngs_modulus
+ax_inset.plot(df.Strain, stress_linear_fit / 1e6, 'r--', label="Young's modulus", zorder=-1)
+
+fig
+```
+
+
+
+
+    
+![png](04_Process_datasets_and_import_files/04_Process_datasets_and_import_29_0.png)
+    
+
+
+
+### Calculate 0.2% offset yield stress
+
+The offset yield stress is determined by calculating the intercept between the chordal fit line and the stress response.
+
+First add the offset chordal fit line to the plot, to show where the yield stress lies.
+
+
+```python
+STRAIN_OFFSET = 0.002
+
+strain_offset = df.Strain + STRAIN_OFFSET
+ax_inset.plot(strain_offset, stress_linear_fit / 1e6, 'k:', label=f'0.2% offset', zorder=-1)
+
+handles, labels = ax_inset.get_legend_handles_labels()
+legend_position = (0.575, 0.85)
+ax.legend(handles, labels, bbox_to_anchor=legend_position, prop={'size': 8})
+fig
+```
+
+
+
+
+    
+![png](04_Process_datasets_and_import_files/04_Process_datasets_and_import_32_0.png)
+    
+
+
+
+Calculate the difference between the solid blue and dotted black lines, then find the values immediately before and after the intercept. Perform a linear interpolation to find the exact value of the yield stress.
+
+
+```python
+df['find_intercept'] = df.Stress - youngs_modulus * (df.Strain - STRAIN_OFFSET)
+first_row_index_above_intercept = \
+    df.loc[df.find_intercept < 0].sort_values(by=['find_intercept'], ascending=False).index[0]
+y_1 = df.Stress.iloc[first_row_index_above_intercept]
+x_1 = df.find_intercept.iloc[first_row_index_above_intercept]
+
+last_row_index_below_intercept = \
+    df.loc[df.find_intercept >= 0].sort_values(by=['find_intercept'], ascending=True).index[0]
+y_0 = df.Stress.iloc[last_row_index_below_intercept]
+x_0 = df.find_intercept.iloc[last_row_index_below_intercept]
+
+yield_stress = y_0 + (-x_0) * (y_1 - y_0) / (x_1 - x_0)
+strain_at_yield = yield_stress / youngs_modulus + STRAIN_OFFSET
+
+yield_stress_MPa = yield_stress / 1e6
+print(f'0.2% offset yield stress: {yield_stress:.2f} MPa')  
+```
+*Previous cell output:*
+```output
+0.2% offset yield stress: 335555698.60 MPa
+```
+Plot the *0.2% offset yield stress* on the Stress-Strain plot. This time, plot the marker on both the full and inset
+axes, and annotate the inset.
+
+
+```python
+ax.scatter(x=[strain_at_yield], y=[yield_stress_MPa], color='black', marker='1', alpha=0.75, s=50, zorder=1)
+ax_inset.scatter(x=[strain_at_yield], y=[yield_stress_MPa], color='black', marker='1', alpha=0.75, s=50, zorder=1)
+ax_inset.annotate(rf'$\sigma_y$: {yield_stress_MPa:.2f} MPa',
+            xy=(strain_at_yield, yield_stress_MPa),
+            xytext=(-60,6),
+            textcoords='offset points',
+            fontsize=8)
+fig
+```
+
+
+
+
+    
+![png](04_Process_datasets_and_import_files/04_Process_datasets_and_import_36_0.png)
+    
+
+
+
+### Calculate Elongation at failure
+
+The data has been pre-processed before import, so the last point in the data series is the failure point.
+
+
+```python
+stress_at_break = df.Stress.iloc[-1]
+total_strain_at_break = df.Strain.iloc[-1]
+elastic_strain_at_break = stress_at_break / youngs_modulus
+elongation = total_strain_at_break - elastic_strain_at_break
+print(f'Elongation at failure: {elongation:.2%}')  
+```
+*Previous cell output:*
+```output
+Elongation at failure: 22.27%
+```
+Plot the *Elongation at failure* on the full-range axes, and add a line between the failure point and the
+elongation value. The legend updates to include all lines on both sets of axes.
+
+
+```python
+ax.plot([total_strain_at_break, elongation], [stress_at_break / 1e6, 0], "y-.", label='Elongation', zorder=-1)
+ax.scatter(x=[total_strain_at_break, elongation],
+           y=[stress_at_break / 1e6, 0],
+           color='black',
+           marker='2',
+           alpha=0.75,
+           s=50,
+           zorder=1)
+
+inset_handles, inset_labels = ax_inset.get_legend_handles_labels()
+ax_handles, ax_labels = ax.get_legend_handles_labels()
+handles = inset_handles + ax_handles
+labels = inset_labels + ax_labels
+ax.get_legend().remove()
+ax.legend(handles, labels, bbox_to_anchor=legend_position, prop={'size': 8})
+
+ax.annotate(rf'$\epsilon_f$: {elongation:.3}',
+            xy=(elongation, 0),
+            xytext=(-40,5),
+            textcoords='offset points',
+            fontsize=8)
+fig
+```
+
+
+
+
+    
+![png](04_Process_datasets_and_import_files/04_Process_datasets_and_import_41_0.png)
+    
+
+
+
+### Calculate True Stress-Strain
+
+Since there is a 1:1 relationship between engineering and true stress-strain values, *True Stress-Strain* is added to the DataFrame as additional columns.
+
+
+```python
+import numpy as np
+
+ts_position = df.Stress[df.Stress == tens_strength].index[0]
+
+# true strain = ln(1 + eng strain)
+df['True_Strain'] = np.log(1 + df.Strain[df.Strain.index <= ts_position])
+
+# true stress = eng stress * (1 + eng strain)
+df['True_Stress'] = df.Stress[df.Stress.index <= ts_position] * (1 + df.True_Strain)
+df.head()
+```
+
+
+
+
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Displacement</th>
+      <th>Load</th>
+      <th>Strain</th>
+      <th>Stress</th>
+      <th>find_intercept</th>
+      <th>True_Strain</th>
+      <th>True_Stress</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>0.000000</td>
+      <td>0.000</td>
+      <td>0.000000</td>
+      <td>0.000000e+00</td>
+      <td>4.093098e+08</td>
+      <td>0.000000</td>
+      <td>0.000000e+00</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>0.000005</td>
+      <td>273.392</td>
+      <td>0.000066</td>
+      <td>1.208322e+07</td>
+      <td>4.078764e+08</td>
+      <td>0.000066</td>
+      <td>1.208402e+07</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>0.000005</td>
+      <td>307.173</td>
+      <td>0.000066</td>
+      <td>1.357626e+07</td>
+      <td>4.093694e+08</td>
+      <td>0.000066</td>
+      <td>1.357715e+07</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>0.000006</td>
+      <td>331.741</td>
+      <td>0.000075</td>
+      <td>1.466210e+07</td>
+      <td>4.085924e+08</td>
+      <td>0.000075</td>
+      <td>1.466320e+07</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>0.000007</td>
+      <td>352.556</td>
+      <td>0.000084</td>
+      <td>1.558207e+07</td>
+      <td>4.076494e+08</td>
+      <td>0.000084</td>
+      <td>1.558338e+07</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+Create a new plot to compare Engineering and True Stress-Strain.
+
+
+```python
+fig, ax = plt.subplots()
+ax.plot(df.Strain, df.Stress / 1e6, label='Engineering Stress', linestyle='--')
+ax.plot(df.True_Strain, df.True_Stress / 1e6, label='True Stress')
+ax.set_xlabel(r'$\epsilon$')
+ax.set_ylabel(r'$\sigma$ / MPa')
+ax.set_xlim(left=0)
+ax.set_ylim(bottom=0)
+ax.legend()
+_ = ax.set_title('Engineering and True Stress vs Strain')
+```
+
+
+    
+![png](04_Process_datasets_and_import_files/04_Process_datasets_and_import_46_0.png)
+    
+
+
+### Calculate True Stress-True Plastic Strain
+
+Create a new DataFrame to store the *True Stress-True Plastic Strain*, since the x axis is different to *Stress-Strain*.
+
+
+```python
+df_plastic = pd.DataFrame()
+plastic_strain = df.True_Strain - df.True_Stress / youngs_modulus - STRAIN_OFFSET
+df_plastic['Strain'] = plastic_strain[plastic_strain > 0]
+
+# Get only the stress values with a corresponding strain
+df_plastic['Stress'] = df.True_Stress.align(df_plastic.Strain, join='right')[0]
+
+# Manually add the first point Strain = 0, Stress = yield stress
+first_point = pd.DataFrame({'Strain': [0], 'Stress': [yield_stress]})
+df_plastic = pd.concat([first_point, df_plastic], ignore_index=True)
+df_plastic.head()
+```
+
+
+
+
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Strain</th>
+      <th>Stress</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>0.000000</td>
+      <td>3.355557e+08</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>0.000008</td>
+      <td>3.370594e+08</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>0.000029</td>
+      <td>3.374152e+08</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>0.000050</td>
+      <td>3.377409e+08</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>0.000067</td>
+      <td>3.380495e+08</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+Plot *True Stress-True Plastic Strain*. Force the x limit to be 0, but not the y limit (the curve starts at the *Yield stress*).
+
+
+```python
+df_plastic['Stress_MPa'] = df_plastic.Stress / 1e6
+
+fig, ax = plt.subplots()
+ax.plot(df_plastic.Strain, df_plastic.Stress_MPa, label='True Stress')
+ax.set_xlabel(r'$\epsilon$')
+ax.set_ylabel(r'$\sigma$ / MPa')
+ax.set_xlim(left=0)
+_ = ax.set_title('True Stress vs True Plastic Strain')
+```
+
+
+    
+![png](04_Process_datasets_and_import_files/04_Process_datasets_and_import_51_0.png)
+    
+
+
+## Import data and results into Granta MI
+
+Finally, import the raw data, processed data and results into a new Granta MI record. 
+
+Connect to Granta MI and specify a database and table.
+
+
+```python
+from GRANTA_MIScriptingToolkit import granta as mpy
+mi = mpy.connect("http://my.server.name/mi_servicelayer", autologon=True)
+db = mi.get_db('MI Training')
+tensile_test_table = db.get_table('Tensile Test Data')
+```
+
+Create a new record to import the data into, using `datetime.now()` to ensure the record name is unique. 
+
+At this stage, the record has only been created in the MI Scripting Toolkit. The changes will be pushed to Granta MI when `session.update()` is called at the end of the notebook.
+
+
+```python
+from datetime import datetime
+
+now = datetime.now().strftime("%c")
+record_name = 'Tensile analysis {}'.format(now)
+
+record = tensile_test_table.path_from(None, ['Data Analytics'], record_name)
+```
+
+### Create and populate point attributes
+
+Define a function to write a dict of attribute name-value pairs to point attributes on the record. Each dict will
+contain values with a single unit.
+
+
+```python
+def write_points(attrs, unit):
+    for name, value in attrs.items():
+        a = record.attributes[name]
+        a.value = value
+        a.unit = unit
+        record.set_attributes([a])
+```
+
+Build the dicts of attribute name-value pairs and call the function above on each. (The strain values are dimensionless but
+will be imported as a percentage, so they are multiplied by 100 before import.)
+
+
+```python
+stress_attrs = {'Ultimate Tensile Strength': tens_strength,
+                "Young's Modulus (11-axis)": youngs_modulus,
+                '0.2% Offset Yield Stress': yield_stress}
+strain_attrs = {'Strain at Ultimate Tensile Strength': strain_at_uts * 100,
+                'Elongation': elongation * 100,
+                'Strain at Tensile Failure': total_strain_at_break * 100}
+length_attrs = {'Gauge Length': GAUGE_LENGTH,
+                'Gauge Width': GAUGE_WIDTH,
+                'Gauge Thickness': GAUGE_THICKNESS}
+area_attrs = {'Gauge Area': gauge_area}
+
+write_points(stress_attrs, 'Pa')
+write_points(strain_attrs, '%')
+write_points(length_attrs, 'm')
+write_points(area_attrs, 'm^2')
+```
+
+### Create and populate text attributes
+
+Define a similar function for text attributes:
+
+
+```python
+def write_text(attrs):
+    for name, value in attrs.items():
+        a = record.attributes[name]
+        a.value = value
+        record.set_attributes([a])
+```
+
+Define the dict of text attributes, and call the function above. We've defined the attribute values manually, but they could easily be read from the source file.
+
+
+```python
+range_text = f'Linear fit between [{LOWER_FRACTION:.1%}:{UPPER_FRACTION:.1%}] of Tensile strength'
+text_attrs = {"Young's Modulus Calculation": 'Chord',
+              'Test Type': 'Tensile',
+              'Testing Standards': 'ASTM E8',
+              'Alloy': 'AMS 6520',
+              'Modulus Calculation Range': range_text}
+write_text(text_attrs)
+```
+
+### Create and populate functional attributes
+
+Finally, define a function to import functional attributes.
+
+
+```python
+def write_functional(attr_name, y_unit, x_unit, x_name, y_values, x_values):
+    a = record.attributes[attr_name]
+    a.unit = y_unit
+    a.parameters[x_name].unit = x_unit
+    a.xaxis[x_name] = True
+    for x, y in zip(x_values, y_values):
+        a.add_point({'y': y, x_name: x})
+    record.set_attributes([a])
+```
+
+Call the function for each curve generated in this script.
+
+
+```python
+write_functional(attr_name='Load vs Displacement',
+                 y_unit='N',
+                 x_unit='m',
+                 x_name='Displacement',
+                 y_values=df.Load,
+                 x_values=df.Displacement)
+
+write_functional(attr_name='Tensile Response (11 axis)',
+                 y_unit='Pa',
+                 x_unit='strain',
+                 x_name='Strain',
+                 y_values=df.Stress,
+                 x_values=df.Strain)
+
+write_functional(attr_name='True Stress vs True Plastic Strain',
+                 y_unit='Pa',
+                 x_unit='strain',
+                 x_name='Strain',
+                 y_values=df_plastic.Stress,
+                 x_values=df_plastic.Strain)
+```
+
+### Write your changes to Granta MI
+
+
+```python
+mi.update([record])
+```
+
+
+
+*Previous cell output:*
+```output
+[<Record long name: Tensile analysis Wed Oct 30 16:00:28 2024>]
+```
+
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/04_Process_datasets_and_import_files/04_Process_datasets_and_import_12_0.png b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/04_Process_datasets_and_import_files/04_Process_datasets_and_import_12_0.png
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diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/05_Find_area_under_a_curve.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/05_Find_area_under_a_curve.md
new file mode 100644
index 0000000000..24d8966155
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+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/05_Find_area_under_a_curve.md
@@ -0,0 +1,614 @@
+# Find the area under a curve
+
+Calculate the area under a set of temperature-dependent stress-strain curves with `numpy` and `scipy`, and determine how the toughness and energy stored in the material up to the yield point varies with temperature.
+
+## Connect to Granta MI
+
+Create a session, and specify a database and table.
+
+
+```python
+from GRANTA_MIScriptingToolkit import granta as mpy
+
+mi = mpy.connect("http://my.server.name/mi_servicelayer", autologon=True)
+training_db = mi.get_db('MI Training')
+training_db.unit_system = 'SI (Consistent)'
+design_data = training_db.get_table('Design Data')
+```
+
+Specify the record you want to export using a lookup value (requires MI Scripting Toolkit 2.3 or later).
+
+
+```python
+record = design_data.get_record_by_lookup_value(attribute_name='Common Name', lookup_value='250 Grade Maraging')
+```
+
+Get the attribute data you need from the record. (*Yield Stress* and *Tensile Modulus* are needed to calculate the yield point at each temperature.)
+
+
+```python
+stress_strain = record.attributes['Tensile Stress/Strain, L']
+yield_stress = record.attributes['Tens. Yield Stress (L-dir) with Temp.']
+youngs_modulus = record.attributes['Tensile Modulus (L-dir) with Temp.']
+```
+
+## Use a DataFrame to prepare the data
+
+### Build a DataFrame for each functional attribute
+
+MI Scripting Toolkit's list-of-lists format for functional data can be loaded directly into a DataFrame, as long as you separate the datum into the first list item (the column headers) and the subsequent list items (the datapoints).
+
+
+```python
+import pandas as pd
+
+def functional_to_dataframe(attr):
+    headers = attr.value[0]
+    func_data = attr.value[1:]
+    df = pd.DataFrame(func_data, columns=headers)
+    return df
+
+df_stress_strain = functional_to_dataframe(stress_strain)
+df_yield_stress = functional_to_dataframe(yield_stress)
+df_youngs_modulus = functional_to_dataframe(youngs_modulus)
+df_stress_strain.head()
+```
+
+
+
+
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Y min (Tensile Stress/Strain, L [Pa])</th>
+      <th>Y max (Tensile Stress/Strain, L [Pa])</th>
+      <th>Strain [strain]</th>
+      <th>Temperature [°C]</th>
+      <th>Time [s]</th>
+      <th>Other []</th>
+      <th>Stress/Strain Curve Type []</th>
+      <th>Estimated point?</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>0.000000e+00</td>
+      <td>0.000000e+00</td>
+      <td>0.00000</td>
+      <td>-73.332999</td>
+      <td>1800.0</td>
+      <td>None</td>
+      <td>Yield</td>
+      <td>False</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>3.964485e+08</td>
+      <td>3.964485e+08</td>
+      <td>0.00209</td>
+      <td>-73.332999</td>
+      <td>1800.0</td>
+      <td>None</td>
+      <td>Yield</td>
+      <td>False</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>7.136074e+08</td>
+      <td>7.136074e+08</td>
+      <td>0.00376</td>
+      <td>-73.332999</td>
+      <td>1800.0</td>
+      <td>None</td>
+      <td>Yield</td>
+      <td>False</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>1.030766e+09</td>
+      <td>1.030766e+09</td>
+      <td>0.00544</td>
+      <td>-73.332999</td>
+      <td>1800.0</td>
+      <td>None</td>
+      <td>Yield</td>
+      <td>False</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>1.268635e+09</td>
+      <td>1.268635e+09</td>
+      <td>0.00669</td>
+      <td>-73.332999</td>
+      <td>1800.0</td>
+      <td>None</td>
+      <td>Yield</td>
+      <td>False</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+### Refine the data
+
+Define a function that takes a DataFrame and performs the following transformations as needed:
+
+* Removes columns that aren't required, including the duplicated y-axis column
+
+* Renames the x and y columns
+
+* Copies columns
+
+
+```python
+def clean_dataframe(df, y_name, x_name, columns_to_drop=None, columns_to_keep=None):
+    if not columns_to_drop:
+        columns_to_drop = []
+    if not columns_to_keep:
+        columns_to_keep = []
+    new_df = df.drop(columns=[f'Y max ({y_name})']+[f'Y min ({y_name})']+columns_to_drop)
+    new_df['y'] = df[[f'Y max ({y_name})', f'Y min ({y_name})']].mean(axis=1)
+    new_df = new_df.rename(columns={x_name: 'x'})
+    new_df[columns_to_keep] = df[columns_to_keep]
+    return new_df
+```
+
+Use this function to create 'clean' versions of all three DataFrames.
+
+
+```python
+df_stress_strain_clean = clean_dataframe(df=df_stress_strain, 
+                                         y_name='Tensile Stress/Strain, L [Pa]', 
+                                         x_name='Strain [strain]', 
+                                         columns_to_drop=['Y max (Tensile Stress/Strain, L [Pa])',
+                                                          'Time [s]',
+                                                          'Estimated point?',
+                                                          'Other []',
+                                                          'Stress/Strain Curve Type []'], 
+                                         columns_to_keep=['Temperature [°C]'])
+df_stress_strain_clean = df_stress_strain_clean.rename(columns={'Temperature [°C]': 'Temperature'})
+df_stress_strain_clean.head()
+```
+
+
+
+
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>x</th>
+      <th>Temperature</th>
+      <th>y</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>0.00000</td>
+      <td>-73.332999</td>
+      <td>0.000000e+00</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>0.00209</td>
+      <td>-73.332999</td>
+      <td>3.964485e+08</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>0.00376</td>
+      <td>-73.332999</td>
+      <td>7.136074e+08</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>0.00544</td>
+      <td>-73.332999</td>
+      <td>1.030766e+09</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>0.00669</td>
+      <td>-73.332999</td>
+      <td>1.268635e+09</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+
+```python
+df_yield_stress_clean = clean_dataframe(df=df_yield_stress, 
+                                        y_name='Tens. Yield Stress (L-dir) with Temp. [Pa]', 
+                                        x_name='Temperature [°C]', 
+                                        columns_to_drop=['Y max (Tens. Yield Stress (L-dir) with Temp. [Pa])',
+                                                         'Time [s]',
+                                                         'Estimated point?',
+                                                         'Other []'])
+df_yield_stress_clean.head()
+```
+
+
+
+
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>x</th>
+      <th>y</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>-195.555602</td>
+      <td>2.034443e+09</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>-184.444403</td>
+      <td>1.994060e+09</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>-173.333296</td>
+      <td>1.957518e+09</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>-162.221999</td>
+      <td>1.924320e+09</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>-151.110999</td>
+      <td>1.894286e+09</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+
+```python
+df_youngs_modulus_clean = clean_dataframe(df=df_youngs_modulus, 
+                                          y_name='Tensile Modulus (L-dir) with Temp. [Pa]', 
+                                          x_name='Temperature [°C]', 
+                                          columns_to_drop=['Y max (Tensile Modulus (L-dir) with Temp. [Pa])',
+                                                           'Time [s]',
+                                                           'Estimated point?',
+                                                           'Other []'])
+df_youngs_modulus_clean.head()
+```
+
+
+
+
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>x</th>
+      <th>y</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>-73.332999</td>
+      <td>1.891060e+11</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>-62.222006</td>
+      <td>1.886130e+11</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>-51.110999</td>
+      <td>1.879552e+11</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>-40.000006</td>
+      <td>1.872244e+11</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>-28.888998</td>
+      <td>1.864384e+11</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+## Calculate the area under the curves
+
+### Calculate the yield strain for each temperature
+
+We need to integrate from x = 0 to the yield strain value. To calculate the yield strain, we require the exact *Young's modulus* and *Yield stress* at each temperature.
+
+Our data may not have a value at that temperature, so use the `interp1d` function to interpolate between datapoints.
+
+
+```python
+from scipy.interpolate import interp1d
+
+modulus_x = df_youngs_modulus_clean.x
+modulus_y = df_youngs_modulus_clean.y
+f_modulus = interp1d(modulus_x, modulus_y, kind='cubic')
+
+yield_x = df_yield_stress_clean.x
+yield_y = df_yield_stress_clean.y
+f_yield = interp1d(yield_x, yield_y, kind='cubic')
+```
+
+Define a function, `get_yield_strain_at()`, to calculate the yield strain from the interpolated values of *Young's modulus* and *Yield stress*. Assume an offset strain value of 0.02%.
+
+
+```python
+OFFSET_STRAIN = 0.0002
+
+def get_yield_strain_at(temp):
+    modulus = f_modulus(temp)
+    yield_stress = f_yield(temp)
+    yield_strain = yield_stress / modulus + OFFSET_STRAIN
+    return yield_strain
+```
+
+### Re-sample the stress-strain curve up to the yield strain point
+
+Define a function to generate a re-sampled set of x-y points for the stress-strain curve, up to the yield strain value. 
+
+Use `get_yield_strain_at()` to calculate the x limit of the resampling, then create a new set of linearly-spaced x values up to this limit. Then use `interp1d` to calculate the corresponding set of y values.
+
+
+```python
+import numpy as np
+
+def resample_stress_strain(x, y, x_t):
+    yield_strain = get_yield_strain_at(x_t)
+    xnew = np.linspace(0, yield_strain, num=101, endpoint=True)
+    f = interp1d(x, y, kind='cubic')
+    ynew = f(xnew)
+    return xnew, ynew
+```
+
+### Perform the numerical integration
+
+Define a function that uses Simpson's rule to perform the numerical integration on the resampled data points.
+
+
+```python
+from scipy.integrate import simpson
+
+def area_under_stress_strain_curve(temp, df_single_temp):    
+    x = df_single_temp.x
+    y = df_single_temp.y
+    xnew, ynew = resample_stress_strain(x, y, temp)
+    result = simpson(y=ynew, x=xnew)
+    return result
+```
+
+### Run the calculations for each stress-strain curve
+
+Use the `DataFrame.groupby()` function to generate sub-DataFrames for each unique temperature value. Iterate over these, storing the results of `area_under_stress_strain_curve()` in a dict.
+
+
+```python
+def_groupedby_temperature = df_stress_strain_clean.groupby('Temperature')
+results = [{'x': t, 'y': area_under_stress_strain_curve(t, df)} for t, df in def_groupedby_temperature]
+df_results = pd.DataFrame(results)
+df_results
+```
+
+
+
+
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>x</th>
+      <th>y</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>-73.332999</td>
+      <td>8.500279e+06</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>26.666986</td>
+      <td>7.931328e+06</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>148.889001</td>
+      <td>7.063304e+06</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>315.555994</td>
+      <td>6.036317e+06</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>426.667017</td>
+      <td>5.433286e+06</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>537.777979</td>
+      <td>2.680041e+06</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+## Plot the results
+
+This code snippet is optional, and is included to increase the size of all plots generated in this notebook.
+
+
+```python
+import matplotlib.pyplot as plt
+plt.rcParams['figure.dpi'] = 125
+```
+
+### Plot Deformation Energy vs Temperature
+
+Plot the accumulated energy (or *Deformation Energy* $U_T$) vs *Temperature*. As temperature increases, the deformation energy stored up to the yield point is reduced.
+
+
+```python
+from matplotlib import pyplot as plt
+
+energy_per_volume_unit = 'Jm^{{-3}}'
+temperature_unit = '°C'
+x_label = rf'$T$ / ${temperature_unit}$'
+y_label = rf'$U_T$ / ${energy_per_volume_unit}$'
+
+fig, ax = plt.subplots()
+ax.plot(df_results['x'], df_results['y'])
+ax.set_ylabel(y_label)
+ax.set_xlabel(x_label)
+_ = ax.set_title('Deformation Energy $U_T$ vs Temperature')
+```
+
+
+    
+![png](05_Find_area_under_a_curve_files/05_Find_area_under_a_curve_39_0.png)
+    
+
+
+### Compare Deformation Energy at temperature extrema
+
+The area under the high- and low-temperature *Stress-Strain* curves can be shown graphically, providing a
+more visual comparison. 
+
+Create a basic *Stress-Strain* plot at both temperatures, dividing the plotted y values by $10^6$ and changing the
+y-axis units to MPa. Add the legend manually.
+
+
+```python
+# Low temperature
+t_low = df_stress_strain_clean.Temperature.min()
+df_ss_t_low = df_stress_strain_clean[df_stress_strain_clean.Temperature == t_low]
+
+# High temperature
+t_high = df_stress_strain_clean.Temperature.max()
+df_ss_t_high = df_stress_strain_clean[df_stress_strain_clean.Temperature == t_high]
+
+x_label = rf'$\epsilon$'
+y_label = rf'$\sigma$ / MPa'
+
+fig, ax = plt.subplots()
+ax.set_ylabel(y_label)
+ax.set_xlabel(x_label)
+
+ax.plot(df_ss_t_low.x, df_ss_t_low['y'] / 1e6, color='blue', label=f'T = {t_low:.2f} °C')
+ax.plot(df_ss_t_high['x'], df_ss_t_high['y'] / 1e6, color='red', label=f'T = {t_high:.2f} °C')
+
+handles, labels = ax.get_legend_handles_labels()
+_ = ax.legend(handles, labels)
+```
+
+
+    
+![png](05_Find_area_under_a_curve_files/05_Find_area_under_a_curve_43_0.png)
+    
+
+
+Next, use the functions you defined above to calculate the stress-strain curves up to the yield stress.
+
+
+```python
+# Low temperature
+xnew_t_low, ynew_t_low = resample_stress_strain(df_ss_t_low.x, df_ss_t_low.y / 1e6, t_low)
+
+# High temperature
+xnew_t_high, ynew_t_high = resample_stress_strain(df_ss_t_high.x, df_ss_t_high.y / 1e6, t_high)
+```
+
+Use `ax.fill_between()` to fill the space between the resampled curves and the x-axis, and set the lower limit for both axes to 0. (`fig` forces jupyter to re-plot the existing chart.)
+
+
+```python
+ax.fill_between(xnew_t_low, 0, ynew_t_low, facecolor="none", hatch="...", edgecolor="blue")
+ax.fill_between(xnew_t_high, 0, ynew_t_high, facecolor="none", hatch="\\", edgecolor="red")
+
+ax.set_xlim(left=0)
+ax.set_ylim(bottom=0)
+
+fig
+```
+
+
+
+
+    
+![png](05_Find_area_under_a_curve_files/05_Find_area_under_a_curve_47_0.png)
+    
+
+
+
+Finally, add a chart title and annotate the filled areas with the corresponding *Deformation Energy*. 
+
+To render the *Deformation Energy* value in standard form, we've defined a function which returns the mantissa and exponent, and inserted them into LaTeX-formatted strings.
+
+
+```python
+energy_low = df_results.y[0]
+energy_high = df_results.y.iloc[-1]
+
+import math
+
+def get_mantissa_and_exponent(x):
+    exponent = int(math.floor(math.log10(abs(x))))
+    mantissa = x / 10**exponent
+    return f'{mantissa:.2f}', f'{exponent}'
+
+arrow_props = {'facecolor': 'black', 'arrowstyle': '-'}
+
+ax.annotate(r'$U_T = {0} \times 10^{{{1}}} \enspace Jm^{{-3}}$'.format(*get_mantissa_and_exponent(energy_low)),
+            xy=(0.008, 125),
+            xytext=(0.010, 140),
+            arrowprops=arrow_props)
+
+ax.annotate(r'$U_T = {0} \times 10^{{{1}}} \enspace Jm^{{-3}}$'.format(*get_mantissa_and_exponent(energy_high)),
+            xy=(0.004, 15),
+            xytext=(0.010, 35),
+            arrowprops=arrow_props)
+ax.set_title('Stress vs strain of 250 Maraging Steel')
+fig
+```
+
+
+
+
+    
+![png](05_Find_area_under_a_curve_files/05_Find_area_under_a_curve_49_0.png)
+    
+
+
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/05_Find_area_under_a_curve_files/05_Find_area_under_a_curve_39_0.png b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/05_Find_area_under_a_curve_files/05_Find_area_under_a_curve_39_0.png
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diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/06_Calculate_statistics_for_records.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/06_Calculate_statistics_for_records.md
new file mode 100644
index 0000000000..ccd6a5a54c
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/06_Calculate_statistics_for_records.md
@@ -0,0 +1,283 @@
+# Calculate statistics for a set of records
+
+Summarise (roll up) test results into new statistical data records. 
+
+In this example, the tests are stored in *Tensile Test Data* table and the statistical data is stored in *Tensile Statistical Data*. Since the sample data is arranged by specimen, the mean and other statistics will be stored by specimen in the new record.
+
+Statistical data is commonly stored using meta-attributes. Here, we store the mean of each property as the attribute value, and statistical results (*Minimum*, *Maximum*, *Median*, *Range* and *Standard Deviation*) as its meta-attributes whenever possible.
+
+## Define a function which rolls up an attribute
+
+Create a function to calculate statistics for, and store the values on the meta-attributes of, a target attribute.
+
+The `rollup_point_attribute()` method below is a general example which will work whether meta-attributes are present or not, or if the number of samples is too small to provide meaningful statistics. It checks if each named meta-attribute exists, then sets existing meta-attributes and updates them by calling `Record.set_attributes()`.
+
+To simplify your own roll-up, you can:
+
+* Make sure there are meta-attributes for roll-up statistics on all relevant attributes when setting up a Granta MI schema.
+* Make sure all the target attributes in your script have the same meta-attributes.
+* Set up a workflow where all statistical data records are created new.
+
+
+```python
+import statistics
+from typing import List
+from GRANTA_MIScriptingToolkit import granta as mpy
+
+def rollup_point_attribute(source_attributes: List[mpy.AttributeValue],
+                           target_attribute: mpy.AttributeValue, record: mpy.Record) -> None:
+    assert all([isinstance(attr.value, float) for attr in source_attributes]), \
+        "Multi-valued points are not supported by this script"
+
+    values = [attr.value for attr in source_attributes if attr.value]
+
+    if len(values) == 0:
+        return
+
+    target_attribute.unit = source_attributes[0].unit
+    target_attribute.value = statistics.mean(values)
+    updated_meta_attributes = []
+
+    if 'Minimum' in target_attribute.meta_attributes:
+        target_attribute.meta_attributes['Minimum'].value = min(values)
+        target_attribute.meta_attributes['Minimum'].unit = target_attribute.unit
+        updated_meta_attributes.append('Minimum')
+
+    if 'Maximum' in target_attribute.meta_attributes:
+        target_attribute.meta_attributes['Maximum'].value = max(values)
+        target_attribute.meta_attributes['Maximum'].unit = target_attribute.unit
+        updated_meta_attributes.append('Maximum')
+
+    if 'Median' in target_attribute.meta_attributes:
+        target_attribute.meta_attributes['Median'].value = statistics.median(values)
+        target_attribute.meta_attributes['Median'].unit = target_attribute.unit
+        updated_meta_attributes.append('Median')
+
+    if 'Number of Samples' in target_attribute.meta_attributes:
+        target_attribute.meta_attributes['Number of Samples'].value = len(values)
+        updated_meta_attributes.append('Number of Samples')
+
+    if 'Range' in target_attribute.meta_attributes:
+        target_attribute.meta_attributes['Range'].value = max(values) - min(values)
+        target_attribute.meta_attributes['Range'].unit = target_attribute.unit
+        updated_meta_attributes.append('Range')
+
+    if len(values) > 2 and 'Standard Deviation' in target_attribute.meta_attributes:
+        target_attribute.meta_attributes['Standard Deviation'].value = statistics.stdev(values)
+        target_attribute.meta_attributes['Standard Deviation'].unit = target_attribute.unit
+        updated_meta_attributes.append('Standard Deviation')
+
+    record.set_attributes([target_attribute] + 
+                          [target_attribute.meta_attributes[updated_meta] for updated_meta in updated_meta_attributes])
+```
+
+## Define a function which copies attribute values
+
+Define a `copy_attribute()` function which copies an attribute value from the source records, for example a *Specimen ID* stored in a short-text attribute, or a test temperature. 
+
+This example supports several attribute types, and could easily be extended to support others such as discrete or hyperlink.
+
+
+```python
+def copy_attribute(source_attributes: List[mpy.AttributeValue],
+                   target_attribute: mpy.AttributeValue, record: mpy.Record) -> None:
+    if target_attribute.type == 'POIN':
+        assert all([isinstance(attr.value, float) for attr in source_attributes]), \
+            "Multi-valued points are not supported by this script"
+        values = set([attr.value for attr in source_attributes if attr.value])
+        assert len(values) == 1, "Values must be identical to copy, received '{0}'".format(', '.join(values))
+        target_attribute.value = values.pop()
+    elif target_attribute.type == 'DISC':
+        # No support for multivalued
+        if any(attr.is_multivalued for attr in source_attributes):
+            raise TypeError("No support for multivalued")
+        values = set([attr.value for attr in source_attributes if attr.value])
+        assert len(values) == 1, "Values must be identical to copy, received '{0}'".format(', '.join(values))
+        target_attribute.value = values.pop()
+    else:
+        values = set([attr.value for attr in source_attributes if attr.value])
+        assert len(values) == 1, "Values must be identical to copy, received '{0}'".format(', '.join(values))
+        target_attribute.value = values.pop()
+    record.set_attributes([target_attribute])
+```
+
+## Get test data from Granta MI
+
+Composite data in Granta MI is stored according to layup orientation and *Specimen ID*. The roll-up process retains
+the overall structure of the records, but creates a single new record for each specimen.
+
+Connect to Granta MI and fetch the folder corresponding to the composite *3M, S-Glass Unitape S2/SP381*:
+
+
+```python
+mi = mpy.connect("http://my.server.name/mi_servicelayer", autologon=True)
+db = mi.get_db('MI Training')
+test_table = db.get_table('Tensile Test Data')
+statistics_table = db.get_table('Tensile Statistical Data')
+
+material_record = test_table.search_for_records_by_name('3M, S-Glass Unitape S2/SP381')[0]
+print(material_record)
+```
+*Previous cell output:*
+```output
+<Record long name: 3M, S-Glass Unitape S2/SP381>
+```
+To make processing easier, convert the structure of the data from
+
+    Material => Orientation => RTD => Specimen => Test result
+
+to a dictionary with each layer indexed by name (removing the redundant layers):
+
+    { Orientation name :
+        { Specimen name :
+            [ Test 1,
+              Test 2 ...
+            ]
+        }
+    }
+
+Although this structure is sufficient for this example, a network library or tree wrapper class might be better suited to dealing with advanced tree-traversal.
+
+
+```python
+orientation_records = material_record.children
+test_records = {
+    orientation.name: {
+        specimen.name: specimen.children for specimen in
+        test_table.get_records_from_path(orientation, ['RTD'], use_short_names=True)}
+    for orientation in orientation_records
+}
+```
+
+Define the attributes you want to calculate statistics for, and those you want to copy from the test records.
+
+To fetch large numbers of records or attributes efficiently, use the `table.bulk_fetch()` method (see [Example 18 - Streamlined API Performance Tuning](../streamlined/18_Streamlined_API_performance_tuning.ipynb)).
+
+
+```python
+attributes_to_rollup = ["Ultimate Tensile Strength",
+                        "Young's Modulus (11-axis)"]
+attributes_to_copy = ["Test Temperature",
+                      "Test Type",
+                      "Material designation",
+                      "Composite system type",
+                      "Test Environment"]
+
+test_table.bulk_fetch([test
+                       for orientation in test_records.values()
+                       for specimen in orientation.values()
+                       for test in specimen],
+                       attributes_to_copy + attributes_to_rollup)
+```
+
+Create box-plots for three of the specimens using the `seaborn` plotting libraries, first 'flattening' the data into a usable list of `numpy` arrays.
+
+Alternatively, you could convert the data into a `pandas` DataFrame at this point (useful if you needed to process the data further).
+
+
+```python
+import seaborn as sns
+import numpy as np
+import matplotlib.pyplot as plt
+
+plt.rcParams['figure.dpi'] = 125
+
+specimens_to_plot = ['LBJ42', 'LBJ53', 'LBJ62']
+specimen_data = {specimen_name: test_records['0° tension'][specimen_name] for specimen_name in specimens_to_plot}
+plot_data = [
+    np.array([test.attributes['Ultimate Tensile Strength'].value for test in specimen])
+    for specimen in specimen_data.values()
+]
+
+y_unit = test_table.attributes['Ultimate Tensile Strength'].unit
+
+sns.set(context='notebook', style='ticks')
+f, ax = plt.subplots()
+sns.boxplot(data=plot_data, width=0.6)
+sns.stripplot(data=plot_data, size=4, palette='dark:.3', linewidth=0)
+
+ax.tick_params(axis='x')
+ax.set_ylabel('Ultimate Tensile Strength / {0}'.format(y_unit))
+ax.set_xlabel('Specimen')
+ax.set_title('Tensile Strength Distribution')
+ax.yaxis.grid(True)
+plt.show()
+```
+
+
+    
+![png](06_Calculate_statistics_for_records_files/06_Calculate_statistics_for_records_12_0.png)
+    
+
+
+Perform the roll-up by creating the new records and iterating through the attributes defined above, copying or rolling up as required. Here we've used the `table.path_from()` method to create a folder path and each new roll-up record.
+
+Finally, use `Session.update()` to write the new record to the server, then set and update links between the new statistical data record and the test records it summarises.
+
+
+```python
+import datetime
+timestamp = datetime.datetime.now().isoformat()
+for orientation, specimens in test_records.items():
+    for specimen, test_runs in specimens.items():
+        rollup_record = statistics_table.path_from(None,
+                                                   ['Epoxy/Glass', '3M, S-Glass Unitape S2/SP381',
+                                                    timestamp,
+                                                    orientation,
+                                                    'RTD'],
+                                                   specimen,
+                                                   color=mpy.RecordColor.Aqua)
+        for rollup in attributes_to_rollup:
+            source_attributes = [test_run.attributes[rollup] for test_run in test_runs]
+            try:
+                target_attribute = rollup_record.attributes[rollup]
+                rollup_point_attribute(source_attributes, target_attribute, rollup_record)
+            except AssertionError:
+                continue
+            except KeyError:
+                print("No attribute in target table to roll attribute '{0}' into.".format(rollup))
+                continue
+
+        for copy_attr in attributes_to_copy:
+            source_attributes = [test_run.attributes[copy_attr] for test_run in test_runs]
+            try:
+                target_attribute = rollup_record.attributes[copy_attr]
+                copy_attribute(source_attributes, target_attribute, rollup_record)
+            except AssertionError:
+                continue
+            except KeyError:
+                print("No attribute in target table to copy attribute '{0}' into.".format(copy_attr))
+                continue
+
+        rollup_record = mi.update([rollup_record])[0]
+        rollup_record.set_links('Tensile Test Data', test_runs)
+
+        rollup_record = mi.update_links([rollup_record])[0]
+        print("Rollup completed for the specimen '{0}', view this record at '{1}'".format(rollup_record.name,
+                                                                                          rollup_record.viewer_url))
+```
+*Previous cell output:*
+```output
+Rollup completed for the specimen 'LBU15', view this record at 'http://my.server.name/mi/datasheet.aspx?dbKey=MI_Training&recordHistoryGuid=d74117a9-2eca-4f3f-a885-b720922a3b88'
+```*Previous cell output:*
+```output
+Rollup completed for the specimen 'LBU14', view this record at 'http://my.server.name/mi/datasheet.aspx?dbKey=MI_Training&recordHistoryGuid=70abae89-40b8-43a6-9f4c-a402cf96a448'
+```*Previous cell output:*
+```output
+Rollup completed for the specimen 'LBJ83', view this record at 'http://my.server.name/mi/datasheet.aspx?dbKey=MI_Training&recordHistoryGuid=210de5fe-aa26-4b46-88a6-0e7ba93ed59b'
+```*Previous cell output:*
+```output
+Rollup completed for the specimen 'LBJ62', view this record at 'http://my.server.name/mi/datasheet.aspx?dbKey=MI_Training&recordHistoryGuid=4c7e08a5-068d-4f6a-8a3a-994c57401143'
+```*Previous cell output:*
+```output
+Rollup completed for the specimen 'LBJ53', view this record at 'http://my.server.name/mi/datasheet.aspx?dbKey=MI_Training&recordHistoryGuid=1144a60f-8ba1-44d4-a46a-636cd831810d'
+```*Previous cell output:*
+```output
+Rollup completed for the specimen 'LBJ42', view this record at 'http://my.server.name/mi/datasheet.aspx?dbKey=MI_Training&recordHistoryGuid=a6def104-0ec6-49a7-9ea2-092f69cdd492'
+```*Previous cell output:*
+```output
+Rollup completed for the specimen 'LBJ14', view this record at 'http://my.server.name/mi/datasheet.aspx?dbKey=MI_Training&recordHistoryGuid=14e10817-8063-44fa-8690-c3721f6cbc87'
+```*Previous cell output:*
+```output
+Rollup completed for the specimen 'LBJ13', view this record at 'http://my.server.name/mi/datasheet.aspx?dbKey=MI_Training&recordHistoryGuid=9017b9ea-cd7f-47de-a03e-eec99efb3031'
+```
\ No newline at end of file
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/06_Calculate_statistics_for_records_files/06_Calculate_statistics_for_records_12_0.png b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/06_Calculate_statistics_for_records_files/06_Calculate_statistics_for_records_12_0.png
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diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/07_Calculate_statistics_for_functional_attributes.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/07_Calculate_statistics_for_functional_attributes.md
new file mode 100644
index 0000000000..6ffb590c94
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+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/07_Calculate_statistics_for_functional_attributes.md
@@ -0,0 +1,229 @@
+# Calculate statistics for functional data attributes
+
+Calculate statistical data, such as mean tensile response curves and confidence intervals, from functional data attributes in test records. Import the roll-up data into Granta MI.
+
+## Get test data from Granta MI
+
+Connect to Granta MI and specify a database and tables.
+
+
+```python
+from GRANTA_MIScriptingToolkit import granta as mpy
+
+mi = mpy.connect("http://my.server.name/mi_servicelayer", autologon=True)
+db = mi.get_db('MI Training')
+test_table = db.get_table('Tensile Test Data')
+statistics_table = db.get_table('Tensile Statistical Data')
+```
+
+Records in these tables are organised by test temperature, so we can fetch all children of a carefully selected folder in *Tensile Test Data*.
+
+Use `table.bulk_fetch()` to efficiently populate the attributes for each test record and filter out records that do not have values for the attributes required by this analysis.
+
+
+```python
+def bulk_fetch_attributes_and_return_populated_records(table, records, attributes):
+    table.bulk_fetch(records, attributes)
+    populated_records = [
+        record for record in records
+        if not any([record.attributes[attr].is_empty() for attr in attributes])
+    ]
+    return populated_records
+
+material_form_folders = test_table.get_records_from_path(None, ["High Alloy Steels", "AMS 6520"])
+plate_folder = next(folder for folder in material_form_folders if folder.name == "Plate")
+
+test_records = plate_folder.get_descendants()
+test_records = bulk_fetch_attributes_and_return_populated_records(
+    test_table, test_records, [
+        'Specimen ID',
+        'Tensile Response (11 axis)',
+        'Test Temperature'
+    ]
+)
+temperature_unit = test_table.attributes['Test Temperature'].unit
+temperature_column_name = f'Test Temperature [{temperature_unit}]'
+```
+
+Create a `pandas` DataFrame and populate it with data from the functional attribute *Tensile Response (11 axis)*, as well as *Specimen ID* and *Test Temperature*. 
+
+To produce a usable DataFrame, the functional data must:
+
+1. Have the headers removed
+2. Be converted from a range into a single value
+
+
+```python
+import pandas as pd
+df_response = pd.DataFrame()
+for record in test_records:
+    response_attr = record.attributes['Tensile Response (11 axis)']
+    specimen_id = record.attributes['Specimen ID'].value
+    test_temperature = record.attributes['Test Temperature'].value
+
+    df_functional_data = pd.DataFrame(response_attr.value[1:], columns=response_attr.value[0])
+
+    df_current = pd.concat([
+        df_functional_data['Strain [% strain]'],
+        df_functional_data[['Y min (Tensile Response (11 axis) [MPa])',
+                            'Y max (Tensile Response (11 axis) [MPa])']]
+            .mean(axis=1)
+            .rename('Tensile Response (11 axis) [MPa]')],
+        axis=1)
+    df_current['Specimen ID'] = specimen_id
+    df_current[temperature_column_name] = round(test_temperature, 1)
+    df_response = pd.concat([df_response, df_current])
+```
+
+## Process the data and calculate statistics
+
+For easier plotting, interpolate each series onto the union of all the parameter values (`pandas` uses the strain parameter as an index for the union). Any `NaN` inside the populated area for this column is interpolated by the `interpolate()` method with `limit_area='inside'`.
+
+Group the data by *Test Temperature*, then use `scipy` to calculate the 95% confidence intervals by passing the count, mean and standard deviation of the grouped data.
+
+Use the `.T` property to transpose the data frame before the `.groupby()` operation and after the `.count()`, `.mean()`, and `.std()` operations.
+
+
+```python
+import scipy.stats as st
+df_interpolated = df_response.pivot(index='Strain [% strain]',
+                                    values='Tensile Response (11 axis) [MPa]',
+                                    columns=['Specimen ID', temperature_column_name]) \
+    .interpolate(method='index', limit_direction='both', limit_area='inside')
+
+grouped_by = df_interpolated.T.groupby(level=temperature_column_name)
+confidence_intervals = st.t.interval(0.95, grouped_by.count().T, loc=grouped_by.mean().T, scale=grouped_by.std(numeric_only=True).T)
+```
+
+Convert the transformed data into an aggregate DataFrame for plotting and import, taking the minimum, maximum, confidence intervals and mean at each strain value and temperature.
+
+
+```python
+df_all_plots = pd.DataFrame()
+
+for temperature_index, temperature in enumerate(grouped_by.groups):
+    # Convert the confidence intervals into a DataFrame
+    df_cis = pd.DataFrame(data=zip(*[[row[temperature_index] for row in confidence_intervals[0]],
+                                     [row[temperature_index] for row in confidence_intervals[1]]]),
+                          index=df_interpolated.index).rename(columns={0: 'lower_ci', 1: 'upper_ci'})
+    # Combine with the other derived quantities
+    df_plot = pd.concat([grouped_by.mean().T[temperature].rename('mean'),
+                         grouped_by.min().T[temperature].rename('min'),
+                         grouped_by.max().T[temperature].rename('max'),
+                         df_cis], axis=1).dropna()
+    # Polish up the DataFrame for aggregation
+    df_plot['temperature'] = temperature
+    df_plot = df_plot.rename_axis('strain')
+    df_all_plots = pd.concat([df_all_plots, df_plot.reset_index()])
+```
+
+Plot the aggregated data using `matplotlib`, creating a grid of subplots with identical (shared) axes to easily compare the tensile response at different temperatures.
+
+
+```python
+import matplotlib.pyplot as plt
+
+plt.rcParams['figure.dpi'] = 125
+
+fig, axs = plt.subplots(3, 2, figsize=(9, 14), sharex='all', sharey='all')
+
+for plot_index, temperature in enumerate(grouped_by.groups):
+    ax = axs[plot_index // 2][plot_index % 2]
+
+    # Get the rows corresponding to the current temperature
+    df_plot = df_all_plots.loc[df_all_plots['temperature'] == temperature]
+
+    ax.plot(df_plot['strain'], df_plot['mean'], label='Mean')
+    ax.fill_between(df_plot['strain'],
+                    df_plot['lower_ci'],
+                    df_plot['upper_ci'],
+                    label='95% Confidence Interval',
+                    hatch='\\', facecolor='C0', edgecolor='C0', alpha=0.25)
+    ax.fill_between(df_plot['strain'],
+                    df_plot['min'],
+                    df_plot['max'],
+                    label='Observed range', hatch='.', facecolor='none', edgecolor='C3')
+
+    ax.set_title("Tensile response at {0} {1}".format(round(temperature, 0), temperature_unit))
+    ax.grid('major')
+
+plt.setp(axs[-1, :], xlabel='Strain / % strain')
+plt.setp(axs[:, 0], ylabel='Tensile Response (11 axis) / MPa')
+plt.legend()
+plt.tight_layout()
+plt.show()
+```
+
+
+    
+![png](07_Calculate_statistics_for_functional_attributes_files/07_Calculate_statistics_for_functional_attributes_12_0.png)
+    
+
+
+## Import roll-up data into Granta MI
+
+Define two helper functions: one that resamples a DataFrame linearly based on a target column's values, and one that appends a series of range values to a `FloatFunctionalAttributeValue`, skipping any `NaN` values.
+
+(Although `pandas` has a built-in `resample()` function, it only operates on time-series data.)
+
+
+```python
+import numpy as np
+
+def subsample_dataframe(df, x_axis, n_samples):
+    new_index = np.linspace(df[x_axis].min(), df[x_axis].max(), n_samples)
+    df.index = df[x_axis]
+    df_subsampled = df.reindex(df.index.union(new_index)).interpolate('index').reindex(index=new_index)
+    df_subsampled.index = range(n_samples)
+    return df_subsampled
+
+def append_series(functional_attribute, x_data, y_low_data, y_high_data=None, data_type=None):
+    if y_high_data is None:
+        y_high_data = y_low_data
+    for x, y_low, y_high in zip(x_data, y_low_data, y_high_data):
+        if pd.isna(y_low) or pd.isna(y_high):
+            continue
+        functional_attribute.value.append([y_low, y_high, x, data_type, False])
+```
+
+Import the roll-up data into Granta MI. Use `path_from()` to create a folder path with the new statistical data record at the end node, and populate the *Test Temperature* and *Tensile Response Analysis (11 axis)* attributes using the helper functions.
+
+Write the new record to Granta MI using `Session.update()`, then link the newly-created attribute to the relevant test results. Finally, write the new links to Granta MI using `Session.update_links()`.
+
+
+```python
+import datetime
+
+t_statistics = db.get_table('Tensile Statistical Data')
+timestamp = datetime.datetime.now().isoformat()
+for plot_index, temperature in enumerate(grouped_by.groups):
+    # Create a record to store the imported data
+    rec = t_statistics.path_from(starting_node=None, tree_path=['Data Analytics', timestamp],
+                                 end_node='Functional Rollup ({0} {1})'.format(round(temperature, 0), temperature_unit),
+                                 color=mpy.RecordColor.Olive)
+    
+    temp_attribute = rec.attributes['Test Temperature']
+    temp_attribute.value = temperature
+    temp_attribute.unit = temperature_unit
+
+    func_attr = rec.attributes['Tensile Response Analysis (11 axis)']
+    func_attr.unit = 'MPa'
+
+    # Get the rows corresponding to the current temperature
+    df_current = df_all_plots.loc[df_all_plots['temperature'] == temperature]
+
+    df_current = subsample_dataframe(df_current, 'strain', 101)
+    append_series(func_attr, df_current['strain'], df_current['mean'], data_type='Mean')
+    append_series(func_attr, df_current['strain'], df_current['lower_ci'], df_current['upper_ci'], data_type='95% Confidence Interval')
+    append_series(func_attr, df_current['strain'], df_current['min'], df_current['max'], data_type='Range')
+
+    rec.set_attributes([temp_attribute, func_attr])
+    rec = mi.update([rec])[0]
+
+    associated_test_records = [
+        test_record for test_record in test_records
+        if round(test_record.attributes['Test Temperature'].value, 1) == temperature
+    ]
+    rec.set_links('Tensile Test Data', associated_test_records)
+    mi.update_links([rec])
+```
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/07_Calculate_statistics_for_functional_attributes_files/07_Calculate_statistics_for_functional_attributes_12_0.png b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/07_Calculate_statistics_for_functional_attributes_files/07_Calculate_statistics_for_functional_attributes_12_0.png
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diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/08_Fit_model_to_series_data.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/08_Fit_model_to_series_data.md
new file mode 100644
index 0000000000..4c86857fdb
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/08_Fit_model_to_series_data.md
@@ -0,0 +1,361 @@
+# Fit a non-linear model to series data
+
+In this example, we will fit the Ramberg-Osgood material model to steel tensile test results to demonstrate how to
+compute model parameters, and common steps in a tensile analysis.
+
+## Connect to Granta MI
+
+Specify a database and table.
+
+Set the unit system explicitly to ensure repeatable results.
+
+
+```python
+from GRANTA_MIScriptingToolkit import granta as mpy
+
+mi = mpy.connect("http://my.server.name/mi_servicelayer", autologon=True)
+db = mi.get_db(db_key='MI_Training')
+
+db.unit_system = 'Metric'
+db.absolute_temperatures = True
+test_data_table = db.get_table('Tensile Test Data')
+```
+
+## Get the folder containing test records
+
+Records in the *Tensile Test Data* table are stored in folders grouped by *Test Temperature*. Get the parent folder
+to iterate through the different values of *Test Temperature*.
+
+
+```python
+plate_record = test_data_table.get_records_from_path(None, ["High Alloy Steels", "AMS 6520"])[0]
+temperature_test_groups = plate_record.children
+
+print(f"Found {len(temperature_test_groups)} different Test Temperatures")
+print(temperature_test_groups)
+```
+*Previous cell output:*
+```output
+Found 6 different Test Temperatures
+[<Record long name: 1000°F>, <Record long name: -110°F>, <Record long name: 300°F>, <Record long name: 600°F>, <Record long name: 800°F>, <Record long name: Room Temperature °F>]
+```
+## Fetch relevant attributes for the analysis using the Bulk Export tool
+
+The attributes *Test Temperature* and *Tensile Response (11 axis)* are required for our analysis. Export both
+attributes plus the Pseudo-Attribute `Name` for efficiency. Filter out records that do not have values for the required attributes.
+Store the record objects in a dictionary keyed by the
+test temperature, and extract the unit symbols for use when displaying results.
+
+
+```python
+test_temperature = test_data_table.attributes["Test Temperature"]
+temperature_unit = test_temperature.unit
+tensile_response = test_data_table.attributes["Tensile Response (11 axis)"]
+tensile_response_unit = tensile_response.unit
+strain_unit = tensile_response.parameters["Strain"].unit
+record_name = mpy.PseudoAttributeDefinition(name="name")
+
+test_record_dict = {}
+for test_group in temperature_test_groups:
+    test_records = test_group.children
+    fetcher = mpy.AttributeFetcher(test_data_table,
+                                   attributes=[test_temperature, tensile_response, record_name],
+                                   records=test_records)
+    fetcher.fetch_attribute_data()
+
+    test_records = [
+        test_record for test_record in test_records
+        if not test_record.attributes["Test Temperature"].is_empty()
+        and not test_record.attributes["Tensile Response (11 axis)"].is_empty()
+    ]
+
+    temperature_attribute = test_records[0].attributes["Test Temperature"]
+    bucket_temperature = temperature_attribute.value
+    test_record_dict[bucket_temperature] = {"records": test_records}
+    print(f"Fetched attributes for {len(test_records)} records at {bucket_temperature:.1f}{temperature_unit}")
+```
+*Previous cell output:*
+```output
+Fetched attributes for 6 records at 810.9K
+```*Previous cell output:*
+```output
+Fetched attributes for 6 records at 194.3K
+```*Previous cell output:*
+```output
+Fetched attributes for 6 records at 422.0K
+```*Previous cell output:*
+```output
+Fetched attributes for 6 records at 588.7K
+```*Previous cell output:*
+```output
+Fetched attributes for 6 records at 699.8K
+```*Previous cell output:*
+```output
+Fetched attributes for 6 records at 296.1K
+```
+## Define the Ramberg-Osgood model
+
+The Ramberg-Osgood model expresses the total stress as a function of the strain, the Elastic Modulus and two model
+parameters, conventionally `K` and `n`:
+
+![Strain = Stress/Elastic Modulus + K\*(Stress/Elastic Modulus)\^n](assets/08_RambergOsgood.svg 'Ramberg-Osgood equation')
+
+
+```python
+import numpy as np
+
+
+def ramberg_osgood_model(stress_: np.ndarray, modulus_: float, k_: float, n_: float) -> np.ndarray:
+    hook_strain = stress_ / modulus_
+    return hook_strain + k_ * hook_strain ** n_
+```
+
+## Define a helper function to fit the model to a series
+
+Using the `scipy.optimize` package, fit the Ramberg-Osgood model to the stress-strain data at each temperature.
+
+Provide initial values and constrain the parameters to positive and realistic values. These values are material 
+dependent - for Steel we use the following:
+
+| Parameter | Minimum | Maximum    | Initial Value |
+|-----------|---------|------------|---------------|
+| Modulus   | 1 MPa   | 10,000 MPa | 1,000 MPa     |
+| K         | 0       | 100        | 1             |
+| n         | 0       | 100        | 1             |
+
+The goodness of fit can be estimated by inspecting the diagonal of the covariance matrix, which is returned by the
+`curve_fit` function. Take the square root to obtain the sum-of-squares error for each model parameter.
+
+
+```python
+from typing import Tuple, List
+from scipy.optimize import curve_fit
+
+
+def fit_ramberg_osgood_model(stress_strain_data: np.ndarray) -> Tuple[np.ndarray, Tuple[float, float, float]]:
+    parameter_bounds = ((1, 0, 0), (1e5, 1e2, 1e2))
+    f_init = (1e3, 1, 1)
+
+    stress = stress_strain_data[:, 0]
+    strain = stress_strain_data[:, 1]
+
+    fitted_parameters, covariance = curve_fit(
+        ramberg_osgood_model,
+        stress,
+        strain,
+        f_init,
+        bounds=parameter_bounds
+    )
+
+    # Compute the sum-of-squared error for each model parameter
+    sse = np.sqrt(np.diag(covariance))
+
+    return fitted_parameters, sse
+```
+
+## Define additional helper functions
+
+Define three more helper functions to make the main loop easier to understand:
+
+* `get_response_data` iterates over the provided records and extracts the contents of the `Tensile Response (11 axis)`
+   attribute into a numpy array.
+* `compute_fitted_data` takes the fitted model parameters and a stress range and computes the predicted stress-strain
+   curve. This is used to plot the model results.
+* `compute_yield_point` takes a fitted stress-strain curve and the elastic modulus and performs a basic fit to
+   calculate the 0.2% Offset Yield Stress and Strain of the material.
+
+
+```python
+def get_response_data(records: List[mpy.Record]) -> np.ndarray:
+    # Iterate through records, extract the appropriate attribute and ensure the units are set correctly
+    test_response_attributes = []
+    for record in records:
+        tensile_response = record.attributes['Tensile Response (11 axis)']
+        test_response_attributes.append(tensile_response)
+
+    # Convert the AttributeValue objects into numpy arrays:
+    # 1. Trim off the first row, which contains the column headers
+    # 2. Cast each element to float (numpy treats these values as an object unless told otherwise)
+    test_response_data = [np.array(attribute.value[1:]).astype(float) for attribute in test_response_attributes]
+
+    # The MI Scripting Toolkit stores all functional data as ranges. Since both values are the same,
+    # take the first.
+    stress_strain_response = [np.column_stack((curve[1:, 0], curve[1:, 2])) for curve in test_response_data]
+    return stress_strain_response
+
+
+def compute_fitted_data(min_stress: float, max_stress: float, modulus: float, k: float, n: float) -> np.ndarray:
+    num_fit_points = 100
+    model_stress = np.linspace(min_stress, max_stress, num_fit_points)
+    model_strain = ramberg_osgood_model(model_stress, modulus, k, n)
+    return np.column_stack((model_stress, model_strain))
+
+
+def compute_yield_point(stress_strain_data: np.ndarray, modulus: float) -> Tuple[float, float]:
+    # Compute the difference between the provided stress-strain curve and the offset elastic stress-strain curve
+    diff_stress = stress_strain_data[:, 0] - modulus * (stress_strain_data[:, 1] - 0.2)
+
+    # Find the strain value where the offset elastic line intersects with the stress-strain curve
+    yield_strain = np.interp(0, -diff_stress, stress_strain_data[:, 1])
+
+    # Compute the yield stress
+    yield_stress = modulus * (yield_strain - 0.2)
+    return yield_stress, yield_strain
+```
+
+## Fit to the data
+
+For each set of test records, extract the stress-strain curve and fit to the Ramberg-Osgood model using the helper 
+functions defined above. Combine all the test runs into one dataset and fit the model to the aggregate data.
+
+In your own scripts, you will also need to take into account multiple series, range data, and other features of your
+data such as `Estimated` and `Not-Applicable` flags.
+
+
+```python
+import pprint
+
+for test_temperature, tests_at_temperature in sorted(test_record_dict.items()):
+    response_data = get_response_data(tests_at_temperature["records"])
+    aggregate_response_data = np.vstack(response_data)
+
+    parameters, sse = fit_ramberg_osgood_model(aggregate_response_data)
+    modulus, k, n = parameters
+
+    min_stress = min(aggregate_response_data[:, 0])
+    max_stress = max(aggregate_response_data[:, 0])
+    model_stress_strain = compute_fitted_data(min_stress, max_stress, modulus, k, n)
+
+    yield_stress, yield_strain = compute_yield_point(model_stress_strain, modulus)
+
+    tests_at_temperature["response_data"] = response_data
+    tests_at_temperature["fitted_model_data"] = model_stress_strain
+    tests_at_temperature["model_parameters"] = {
+        "modulus": modulus,
+        "yield_point": (yield_strain, yield_stress),
+        "k": k,
+        "n": n
+    }
+    print(f"Fit parameters at Temperature {test_temperature:.1f}{temperature_unit}:")
+    pprint.pprint(tests_at_temperature["model_parameters"])
+    print("\n")
+```
+*Previous cell output:*
+```output
+Fit parameters at Temperature 194.3K:
+{'k': np.float64(0.14863494441724703),
+ 'modulus': np.float64(2062.0202035684933),
+ 'n': np.float64(8.791864160782794),
+ 'yield_point': (np.float64(1.2342202393471564),
+                 np.float64(2132.5830284732797))}
+
+
+Fit parameters at Temperature 296.1K:
+{'k': np.float64(0.1791326818346526),
+ 'modulus': np.float64(1981.6619869624521),
+ 'n': np.float64(9.114593900403749),
+ 'yield_point': (np.float64(1.2120542799247618),
+                 np.float64(2005.5494952695572))}
+
+
+Fit parameters at Temperature 422.0K:
+{'k': np.float64(0.30688860274078),
+ 'modulus': np.float64(1904.622968058874),
+ 'n': np.float64(8.456684372086439),
+ 'yield_point': (np.float64(1.1505510048627243),
+                 np.float64(1810.4412761729873))}
+
+
+Fit parameters at Temperature 588.7K:
+{'k': np.float64(0.46386736088140446),
+ 'modulus': np.float64(1825.4403056571182),
+ 'n': np.float64(8.486132455089072),
+ 'yield_point': (np.float64(1.1055238461378185),
+                 np.float64(1652.9797264736287))}
+
+
+Fit parameters at Temperature 699.8K:
+{'k': np.float64(0.4788380313466644),
+ 'modulus': np.float64(1720.6433395354948),
+ 'n': np.float64(7.1629878083927006),
+ 'yield_point': (np.float64(1.0851718213011277),
+                 np.float64(1523.0649986662886))}
+
+
+Fit parameters at Temperature 810.9K:
+{'k': np.float64(3.0310550950006268),
+ 'modulus': np.float64(1501.671776811084),
+ 'n': np.float64(6.911013554765233),
+ 'yield_point': (np.float64(0.8747695429959347),
+                 np.float64(1013.2823785687083))}
+```
+## Plot the results
+
+Plot the source data for each temperature run, with common axis scales to make comparison easier.
+
+In addition to the response data and the fitted model, plot the elastic stress-strain line and the yield point at
+each temperature.
+
+
+```python
+import matplotlib.pyplot as plt
+
+plt.rcParams['figure.dpi'] = 125
+fig, axs = plt.subplots(3, 2, figsize=(9, 14), sharex='all', sharey='all')
+
+fig.supylabel(f"Stress / {tensile_response_unit}")
+fig.supxlabel(f"Strain / {strain_unit}")
+
+current_plot_index = 0
+
+for test_temperature, tests_at_temperature in sorted(test_record_dict.items()):
+    response_data = tests_at_temperature["response_data"]
+    model_parameters = tests_at_temperature["model_parameters"]
+
+    ax_current = axs[current_plot_index // 2][current_plot_index % 2]
+    ax_current.set_title(f"Tensile Response at {test_temperature:.1f}{temperature_unit}")
+
+    # Plot source data series
+    for series in response_data:
+        ax_current.plot(
+            series[:, 1], series[:, 0], alpha=0.3
+        )
+
+    # Plot fitted model
+    fitted_data = tests_at_temperature["fitted_model_data"]
+    fit_curve = ax_current.plot(fitted_data[:, 1], fitted_data[:, 0], '--', label="Fitted Model")
+
+    # Add the yield point
+    yield_strain, yield_stress = model_parameters["yield_point"]
+    yield_curve = ax_current.scatter(yield_strain, yield_stress, label="Yield Point")
+    ax_current.annotate(
+        f"Yield stress - {yield_stress:.0f}{tensile_response_unit}\nStrain at yield - {yield_strain:.3}{strain_unit}",
+        xy=model_parameters["yield_point"],
+        xytext=(-50, -60),
+        textcoords='offset points',
+        size=8,
+        arrowprops={
+            "arrowstyle": "->",
+            "connectionstyle": "arc3,rad=.2"
+        }
+    )
+
+    # Add the elastic response line
+    elastic_strain_region = [0., yield_strain]
+    elastic_stress = [0., model_parameters["modulus"] * yield_strain]
+    elastic_curve = ax_current.plot(elastic_strain_region, elastic_stress, '-.', label="Elastic Response")
+
+    # Add Legend to last subplot (where there is more whitespace)
+    if current_plot_index == 5:
+        ax_current.legend()
+
+    current_plot_index = current_plot_index + 1
+
+plt.tight_layout()
+```
+
+
+    
+![png](08_Fit_model_to_series_data_files/08_Fit_model_to_series_data_16_0.png)
+    
+
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/08_Fit_model_to_series_data_files/08_Fit_model_to_series_data_16_0.png b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/08_Fit_model_to_series_data_files/08_Fit_model_to_series_data_16_0.png
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diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/09_Compare_test_data_to_specification_values.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/09_Compare_test_data_to_specification_values.md
new file mode 100644
index 0000000000..69257f239d
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/09_Compare_test_data_to_specification_values.md
@@ -0,0 +1,303 @@
+# Compare test data to specification values
+
+The following script demonstrates how to compare raw test data to specification values that define acceptable ranges
+for the test data. In this example, the specification values are stored in a separate record that is linked to the
+test data.
+
+## Connect to Granta MI
+
+Connect to Granta MI and specify the database and table containing the test results.
+
+Since this operation is likely to be performed as a batch operation, see the notebook on
+[Storing Credentials Securely](../streamlined/21_Storing_credentials_securely.ipynb) for more information how to
+connect to Granta MI securely in a batch Python script.
+
+
+```python
+from GRANTA_MIScriptingToolkit import granta as mpy
+
+mi = mpy.connect("http://my.server.name/mi_servicelayer", autologon=True)
+db = mi.get_db("MI Training")
+test_data_table = db.get_table("Tensile Test Data")
+print(test_data_table)
+```
+*Previous cell output:*
+```output
+<Table name: Tensile Test Data, subsets: {'Tensile Test Data'}>
+```
+## Find test data to process
+Before we start, we need to find the **Test Data** records we want to process by applying the following criteria:
+   1. The test record has test data which has not previously been compared to a specification.
+   2. The test record is linked to a specification record.
+
+### Criteria 1: Test data that has not been compared to a specification
+First, define the attributes that contain the relevant test data, and the name of the meta-attribute that contains
+specification comparison results (if they exist).
+A dictionary is used for test data attributes so they can be mapped to the corresponding specification attributes.
+
+
+```python
+COMPARISON_ATTRIBUTES = {"0.02% Offset yield stress (specification value)": "0.02% Offset Yield Stress",
+                         "0.2% Offset yield stress (specification value)": "0.2% Offset Yield Stress",
+                         "Tensile modulus (specification value)": "Young's Modulus (11-axis)",
+                         "Ultimate tensile strength (specification value)": "Ultimate Tensile Strength",
+                         }
+SPEC_STATUS_META_ATTRIBUTE = "Specification status"
+```
+
+Next, define an _unprocessed records_ function that takes an attribute name as input and returns the list of records
+where the attribute is populated, but the meta-attribute is empty.
+
+
+```python
+from typing import List
+
+
+def find_unprocessed_records_for_attribute(test_attribute_name: str) -> List[mpy.Record]:
+    test_attribute_definition = test_data_table.attributes[test_attribute_name]
+    spec_result_attribute_definition = test_attribute_definition.meta_attributes[SPEC_STATUS_META_ATTRIBUTE]
+
+    test_result_attribute_has_value = test_attribute_definition.search_criterion(exists=True)
+    spec_comparison_result_is_empty = spec_result_attribute_definition.search_criterion(exists=False)
+    combined_criteria = [test_result_attribute_has_value, spec_comparison_result_is_empty]
+
+    unprocessed_records = test_data_table.search_for_records_where(combined_criteria)
+    return unprocessed_records
+```
+
+Iterate over each attribute defined in the `ATTRIBUTES` constant to get the previously unprocessed records for each
+one. Use a dictionary with the `record.record_guid` as the key to ensure you only get unique records (a single
+record may be missing multiple attributes, and we only want to process each record once).
+
+
+```python
+unique_unprocessed_records = {}
+
+for test_attribute in COMPARISON_ATTRIBUTES.values():
+    unprocessed_records_for_attribute = find_unprocessed_records_for_attribute(test_attribute)
+    unique_unprocessed_records.update({r.record_guid: r for r in unprocessed_records_for_attribute})
+```
+
+Finally, extract the `record` values from the `dict` and confirm the number of unprocessed records identified.
+
+
+```python
+unprocessed_records = list(unique_unprocessed_records.values())
+print(f"{len(unprocessed_records)} unprocessed records.")
+```
+*Previous cell output:*
+```output
+176 unprocessed records.
+```
+### Criteria 2: Test data with linked specifications
+To compare test data to specification values, each unprocessed record must also be linked to a specification record
+containing those values. Therefore, we need to filter the `unprocessed_records` list to include only those records
+that have a link to a **Specification Values** record.
+
+Use the `test_data_table.bulk_link_fetch()` method to improve performance by fetching links for all records in a
+single operation. The link group name is stored in a constant, since we will need to refer to it later.
+
+
+```python
+SPEC_LINK_GROUP_NAME = "Specification Values"
+
+test_data_table.bulk_link_fetch(link_groups=[SPEC_LINK_GROUP_NAME], records=unprocessed_records)
+records_to_process = [r for r in unprocessed_records if r.links[SPEC_LINK_GROUP_NAME]]
+print(f"{len(records_to_process)} unprocessed records with links to specifications.")
+```
+*Previous cell output:*
+```output
+6 unprocessed records with links to specifications.
+```
+## Compare test data to specification values
+Now check each record in the `records_to_process` list against its linked specification.
+
+### Pre-fetch the data
+First, pre-fetch all the attributes in the *Specification Values* and *Tensile Test Data* tables that are required for
+the analysis. (This step is optional, but helps improve performance for large datasets.)
+Pre-fetching data using the `bulk_fetch()` operation generally requires three types of objects: a list of attributes,
+a list of records, and the table to fetch the data from.
+
+For the test data, we already have the `test_data_table` object and the list of records. However, we need to construct
+the list of attributes. We want to export the following:
+
+- The *Specimen ID* attribute, used for printing status information while performing the comparison
+- The list of test data attributes defined as values in the `COMPARISON_ATTRIBUTES` dictionary
+- The *Specification Status* meta-attribute
+
+The cell below builds this list of attributes, and then uses `bulk_fetch()` to fetch the data.
+
+
+```python
+SPECIMEN_ID_ATTRIBUTE = "Specimen ID"
+specimen_id_attribute_object = test_data_table.attributes[SPECIMEN_ID_ATTRIBUTE]
+test_data_attributes = [specimen_id_attribute_object]
+
+for attribute_name in COMPARISON_ATTRIBUTES.values():
+    attribute = test_data_table.attributes[attribute_name]
+    meta_attribute = attribute.meta_attributes[SPEC_STATUS_META_ATTRIBUTE]
+    test_data_attributes.extend([attribute, meta_attribute])
+    
+test_data_table.bulk_fetch(records=records_to_process, attributes=test_data_attributes)
+print(f"Bulk fetched {len(test_data_attributes)} attributes across {len(records_to_process)} records")
+```
+*Previous cell output:*
+```output
+Bulk fetched 9 attributes across 6 records
+```
+For the specification values we know the attributes we need, but we do not have a list of records or a table object.
+The following steps must be performed:
+1. Create a single flat list of all linked specification records. Use a `set` as the intermediary container to only
+   include unique records.
+2. Extract the table name from one of the specification records and create a `specification_table` object.
+3. Use the `specification_table` object to bulk fetch the data.
+
+
+```python
+# Step 1: Generate a single list of unique linked specification records
+spec_records = set()
+for r in records_to_process:
+    spec_records.update(r.links[SPEC_LINK_GROUP_NAME])
+spec_records = list(spec_records)
+    
+# Step 2: Get the specification table object
+specification_table_name = spec_records[0].table_name
+specification_table = db.get_table(specification_table_name)
+
+# Step 3: Bulk fetch the data
+specification_data_attributes = list(COMPARISON_ATTRIBUTES.keys())
+specification_table.bulk_fetch(records=spec_records, attributes=specification_data_attributes)
+print(f"Fetched {len(specification_data_attributes)} attributes across {len(spec_records)} records")
+```
+*Previous cell output:*
+```output
+Fetched 4 attributes across 1 records
+```
+### Comparison logic
+Next, write a function that compares the test data attribute and the specification attribute and returns the result
+as a string. This function includes an initial check to ensure values exist for both attributes. If either attribute
+does not have a value, return `None`.
+
+
+```python
+from typing import Optional
+
+
+def compare_test_to_spec(test_attribute: mpy.AttributePoint, spec_attribute: mpy.AttributeRange) -> Optional[str]:
+    if test_attribute.is_empty() or spec_attribute.is_empty():
+        return None
+    test_value = test_attribute.value
+    low_spec_value = spec_attribute.value["low"] or float("-inf")
+    high_spec_value = spec_attribute.value["high"] or float("inf")
+    
+    if low_spec_value <= test_value <= high_spec_value:
+        return "Test meets specification"
+    else:
+        return "Test does not meet specification"
+```
+
+### Compare the data
+Finally, iterate over all records in the `records_to_process` list and all attributes in the `COMPARISON_ATTRIBUTES`
+constant. For each combination of record and attribute, call the function defined above and store the result in the 
+*Specification Status* meta-attribute. The comparison is skipped if the meta-attribute is already populated.
+
+
+```python
+records_to_update = []
+
+for test_record in records_to_process:
+    test_specimen_id = test_record.attributes[SPECIMEN_ID_ATTRIBUTE].value
+    print(f"{test_specimen_id}")
+    
+    spec_record = next(iter(test_record.links[SPEC_LINK_GROUP_NAME]))
+    modified_attributes = []
+    for spec_attribute_name, test_attribute_name in COMPARISON_ATTRIBUTES.items():
+        test_attribute = test_record.attributes[test_attribute_name]
+        spec_status_meta_attribute = test_attribute.meta_attributes[SPEC_STATUS_META_ATTRIBUTE]  
+        if not spec_status_meta_attribute.is_empty():
+            print(f" - {test_attribute_name} skipped: <Already populated>")
+            continue
+        
+        spec_attribute = spec_record.attributes[spec_attribute_name]
+        status = compare_test_to_spec(test_attribute, spec_attribute)
+        if not status:
+            print(f" - {test_attribute_name} skipped: <No data>")
+            continue
+        
+        print(f" - {test_attribute_name} result: {status}")
+        spec_status_meta_attribute.value = status
+        modified_attributes.append(spec_status_meta_attribute)
+    if modified_attributes:
+        test_record.set_attributes(modified_attributes)
+        records_to_update.append(test_record)
+```
+*Previous cell output:*
+```output
+MTS-615756
+ - 0.02% Offset Yield Stress result: Test meets specification
+ - 0.2% Offset Yield Stress skipped: <No data>
+ - Young's Modulus (11-axis) result: Test meets specification
+ - Ultimate Tensile Strength skipped: <No data>
+MTS-615766
+ - 0.02% Offset Yield Stress result: Test meets specification
+ - 0.2% Offset Yield Stress skipped: <No data>
+ - Young's Modulus (11-axis) result: Test does not meet specification
+ - Ultimate Tensile Strength skipped: <No data>
+MTS-615736
+ - 0.02% Offset Yield Stress result: Test does not meet specification
+ - 0.2% Offset Yield Stress skipped: <No data>
+ - Young's Modulus (11-axis) result: Test meets specification
+ - Ultimate Tensile Strength skipped: <No data>
+MTS-615776
+ - 0.02% Offset Yield Stress result: Test meets specification
+ - 0.2% Offset Yield Stress skipped: <No data>
+ - Young's Modulus (11-axis) result: Test does not meet specification
+ - Ultimate Tensile Strength skipped: <No data>
+MTS-615726
+ - 0.02% Offset Yield Stress result: Test does not meet specification
+ - 0.2% Offset Yield Stress skipped: <No data>
+ - Young's Modulus (11-axis) result: Test meets specification
+ - Ultimate Tensile Strength skipped: <No data>
+MTS-615746
+ - 0.02% Offset Yield Stress result: Test meets specification
+ - 0.2% Offset Yield Stress skipped: <No data>
+ - Young's Modulus (11-axis) result: Test meets specification
+ - Ultimate Tensile Strength skipped: <No data>
+```
+## Push the results to Granta MI
+The final step is to update the processed records in Granta MI. Once the cell below has completed, click the link to
+view the results in MI Viewer.
+
+
+```python
+if records_to_update:
+    modified_records = mi.update(records_to_update)
+    print(f"{len(modified_records)} records modified. Links to the MI Viewer datasheets are provided below.")
+    for modified_record in modified_records:
+        print(f"{modified_record.attributes[SPECIMEN_ID_ATTRIBUTE].value}"
+              f": {modified_record.viewer_url}")
+else:
+    print("No records modified")
+```
+*Previous cell output:*
+```output
+6 records modified. Links to the MI Viewer datasheets are provided below.
+MTS-615756: http://my.server.name/mi/datasheet.aspx?dbKey=MI_Training&recordHistoryGuid=d8cf380b-b48e-43b2-be5b-d150415f3691
+MTS-615766: http://my.server.name/mi/datasheet.aspx?dbKey=MI_Training&recordHistoryGuid=fdd81e11-bcdd-4ed3-bf37-6ed12f7e2162
+MTS-615736: http://my.server.name/mi/datasheet.aspx?dbKey=MI_Training&recordHistoryGuid=d2d7b359-49d4-4028-b75c-1a79ccb56892
+MTS-615776: http://my.server.name/mi/datasheet.aspx?dbKey=MI_Training&recordHistoryGuid=d5c2f875-7822-44dc-88b3-ffdf22f7a60f
+MTS-615726: http://my.server.name/mi/datasheet.aspx?dbKey=MI_Training&recordHistoryGuid=6bcb5814-9ff5-4df1-8c92-984474d4ca74
+MTS-615746: http://my.server.name/mi/datasheet.aspx?dbKey=MI_Training&recordHistoryGuid=c0130467-8847-4e22-aab2-d5ca2b6308fd
+```
+## Clean up the database
+Uncomment the bottom line and run this cell to revert the changes made in this script. This is useful if you wish to
+run the script again.
+
+
+```python
+for record in records_to_process:
+    for test_attribute in COMPARISON_ATTRIBUTES.values():
+        spec_attr = record.attributes[test_attribute].meta_attributes[SPEC_STATUS_META_ATTRIBUTE]
+        record.clear_attributes([spec_attr])
+# mi.update(records_to_process)
+```
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/10_Material_datasheet_report.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/10_Material_datasheet_report.md
new file mode 100644
index 0000000000..f29184af44
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/10_Material_datasheet_report.md
@@ -0,0 +1,541 @@
+# Generate a Material Datasheet Report using MI Scripting Toolkit and `python-docx`
+
+This example shows how to use the `python-docx` library to generate a datasheet report as a Word
+docx file. At a high level, the notebook goes through the following steps:
+
+1. Define the data to be included in the report
+2. Export the data
+3. Load the report template
+4. Add single-valued attributes to the report
+5. Add functional data to the report
+6. Save the report
+
+The final step is optional and uses the `docx2pdf` library to convert the docx file to pdf.
+However, this step requires Microsoft Word to be installed on the computer running Python. If Word
+is not available, this step is skipped.
+
+## 1. Define the data to be included in the report
+
+First, define some constants that identify the record and attributes to be included in the report.
+The following cell identifies a record by Database Key, Table name, and Record History GUID, but
+an alternative technique could be used to identify a record, for example searching.
+
+
+```python
+DATABASE = "MI_Training"
+TABLE = "Design Data"
+RECORD_HISTORY_GUID = "5b1df472-a92e-4317-b9a8-e1672335bc2e"
+UNIT_SYSTEM = "SI (Consistent)"
+```
+
+The next cells define the attributes to be included in each section of the report. Apart from the
+Datasheet Title attribute, these are dictionaries which map an attribute name to a label included
+in the report.
+
+
+```python
+DATASHEET_TITLE_ATTRIBUTE = "Common Name"
+```
+
+
+```python
+HEADER_ATTRIBUTES = {
+    "Thickness": "Thickness",
+    "Product Form": "Form",
+    "Condition": "Condition",
+}
+```
+
+
+```python
+SINGLE_VALUED_PROPERTIES = {
+    "Density": "ρ",
+    "Thermal Conductivity": "k",
+    "Thermal Expansion": "a",
+    "Specific Heat": "c",
+    "Modulus, L": "E (L-dir)",
+    "Poisson's Ratio, L": "ν (L-dir)",
+    "Yield Strength, L": "σ (yield, L-dir)",
+    "Ultimate Tensile Strength, L": "σ (ult., L-dir)",
+    "Elongation, L": "ε (L-dir)",
+}
+```
+
+The `GRAPHS` dictionary maps functional attributes to a 4-tuple, where each element of the tuple
+refers to:
+
+1. The chart title
+2. The y-axis label
+3. The x-axis label
+4. Optional: For multiple-series plots, a 2-tuple of the Parameter name and the title of the
+   legend in the report
+
+
+```python
+GRAPHS = {
+    "Thermal Conductivity with Temp.": ("Thermal conductivity vs Temperature", "k", "T", None),
+    "Thermal Expansion with Temp.": ("Thermal expansion vs Temperature", "a", "T", None),
+    "Specific Heat with Temp.": ("Specific heat vs Temperature", "c", "T", None),
+    "Tensile Stress/Strain, L": ("Tensile stress vs Strain and Temperature", "σ", "ε", ("Temperature", "T")),
+}
+```
+
+## 2. Export the data
+
+Export the data for all the required attributes for the 'released' version of the record.
+
+
+```python
+from GRANTA_MIScriptingToolkit import granta as mpy
+
+mi = mpy.connect("http://my.server.name/mi_servicelayer", autologon=True)
+db = mi.get_db(db_key=DATABASE)
+db.unit_system = UNIT_SYSTEM
+table = db.get_table(TABLE)
+record = db.get_record_by_id(hguid=RECORD_HISTORY_GUID)
+
+# To get the released version of the record, iterate through all versions of the record
+# and keep only the version which has the 'Released' `release_state`.
+for record_version in record.all_versions.values():
+    if record_version.release_state == "Released":
+        released_record = record_version
+
+# Create a list of all attributes by appending the keys of the three dictionaries defined above to
+# the datasheet title attribute
+all_attributes = [DATASHEET_TITLE_ATTRIBUTE] + list(HEADER_ATTRIBUTES.keys()) + list(SINGLE_VALUED_PROPERTIES.keys()) + list(GRAPHS.keys())
+
+table.bulk_fetch(attributes=all_attributes, records=[released_record])
+```
+
+## 3. Load the report template
+
+The report template is created in Microsoft Word, and the 'python-docx' library is used to
+populate the template with data exported from Granta MI.
+
+Import the `python-docx` library and create a `Document` object using the `docx.Document`
+function. The function accepts an optional string as the relative path to a template file, which
+is used to create the `Document` object. In this example the relative path to the template file is
+`"supporting_files/report_template.docx"`, and the `Document` object is stored in the `report`
+variable.
+
+`docx.Document` can also be called without arguments, which creates an empty Word document
+instead.
+
+
+```python
+from docx import Document
+
+report = Document(docx="supporting_files/10_report_template.docx")
+```
+
+<div class="alert alert-info">
+
+**Info:**
+
+As `python-docx` does not implement all the layout and style functionality in Microsoft Word,
+specifying a pre-defined template allows greater control over the layout of the document and more
+concise code overall.
+</div>
+
+A screenshot of the template is shown below:
+
+![The Word template header section, showing a company logo and tables with placeholder values.](assets/10_template-screenshot.png 'Word template')
+
+## 4. Add single-valued attributes to the report
+
+This section covers adding single-valued and range attributes to the report. The report makes use
+of tables to present single-valued data, and so the general approach is as follows:
+
+1. Identify the table in the report template to be modified
+2. Format the attribute value as a string
+3. Add a new row to the table (if required)
+4. Add the value to the table and apply the appropriate style
+
+### Define a helper function to write a value to a cell
+
+First, define a function that writes a value to a cell. Use the
+`docx.table._Cell.iter_inner_content()` to access the `Paragraph` object within the cell. The
+`Paragraph.text` and `Paragraph.style` properties are then used to modify the cell content and
+style respectively.
+
+
+```python
+from typing import Optional
+from docx import table
+
+def write_value_to_cell(cell: table._Cell, value: str, style: Optional[str]=None) -> None:
+    paragraph = next(cell.iter_inner_content())
+    paragraph.text = value
+    paragraph.style = style
+```
+
+### Write the datasheet title 
+
+The first table in the template contains identifying information about the material. The first row
+contains the title, and subsequent rows contain additional information about the material.
+
+The cell below does the following:
+
+1. Identifies the target table based on its location in the document.
+2. Identifies the target cell based on the row and column coordinates.
+3. Creates the title string based on the `DATASHEET_TITLE_ATTRIBUTE` attribute value and the
+   record version number.
+4. Writes the value to the cell and applies the `"Material Name"` style as defined in the Word
+   template document.
+
+
+```python
+# report.tables is an ordered list of the tables in the report template, in document order
+header_table = report.tables[0]
+cell = header_table.cell(row_idx=0, col_idx=1)
+title_attribute_value = released_record.attributes[DATASHEET_TITLE_ATTRIBUTE].value
+record_version_number = released_record.version_number
+title = f"{title_attribute_value} [v{record_version_number}]"
+write_value_to_cell(cell, title, "Material Name")
+```
+
+Modifying the `report` object does not provide any feedback. The `report` can be saved to disk as
+a `docx` file at any point by using the `Document.save` method.
+
+First, ensure the output directory exists. This directory will already exist if a notebook
+was run previously that saved a file to disk.
+
+
+```python
+from pathlib import Path
+output_folder = Path("./output")
+output_folder.mkdir(exist_ok=True)
+```
+
+Next, save the report.
+
+
+```python
+report.save("output/10_report_header_only.docx")
+```
+
+Open the `report_header_only.docx` file to see the report template populated with the
+`DATASHEET_TITLE_ATTRIBUTE` attribute value. A screenshot of the report with the title added is
+shown below:
+
+![The header section of a Word document containing a populated title cell.](assets/10_report-screenshot-header-only.png 'Word report with header')
+
+The report will not be saved to disk again until the report is complete. If you want to see the
+effects of individual operations on the report, use the ``report.save`` method to save the report
+to disk at any point and open the file in Microsoft Word.
+
+### Write 'Header' attributes
+
+The header attributes are written to additional rows in the first table. The general process for
+writing these attributes is the same as for the datasheet title attribute, but with the addition
+of iterating through multiple header attributes. For each header attribute, a new row is added to
+the table, and the attribute label and value are written to the new row.
+
+First, define a function that formats an attribute value to a suitable string format depending on
+the attribute type. The default format specifier `:.3g` uses a general format with at most 3
+digits of precision.
+
+
+```python
+def format_attribute(attribute: mpy.AttributeValue, format_spec: str = ".3g", include_unit: bool = False) -> Optional[str]:
+    value = attribute.value
+    # A null value may either be None or an empty list
+    if not value and value != 0:
+        return None
+
+    # Switch based on the attribute type
+    if attribute.type == "RNGE":
+        formatted_value = f"{value['low']:{format_spec}} - {value['high']:{format_spec}}"
+    elif attribute.type == "POIN":
+        # Single-valued point only
+        if isinstance(attribute.value, (float, int)):
+            formatted_value = f"{value:{format_spec}}"
+        else:
+            raise NotImplementedError("Multi-valued point attribute not supported.")
+    elif attribute.type in ["STXT", "LTXT"]:
+        formatted_value = value
+    else:
+        raise NotImplementedError(f'Attribute type "{attribute.type}" not supported.')
+
+    # Include the unit in the output if requested and if it exists
+    if include_unit and attribute.unit:
+        return f"{formatted_value} {attribute.unit}"
+    else:
+        return formatted_value
+```
+
+Next, iterate through each header attribute and write the appropriately formatted value to the
+header table.
+
+
+```python
+# The HEADER_ATTRIBUTES dictionary contains pairs of attribute names and report labels.
+for idx, (attribute_name, label) in enumerate(HEADER_ATTRIBUTES.items()):
+
+    # The 0th row contains the datasheet title
+    # Each header attribute is therefore written to the 'idx+1'th row
+    target_row = idx + 1
+
+    # If the target row is beyond the end of the table, add a new row
+    if target_row > len(header_table.rows) - 1:
+        header_table.add_row()
+
+    # Format the attribute value as a string with the unit included
+    attribute = released_record.attributes[attribute_name]
+    value = f"{format_attribute(attribute, include_unit=True)}"
+
+    # Write the label to the cell in column 0 with the style "Property Label"
+    label_cell = header_table.cell(target_row, 0)
+    write_value_to_cell(label_cell, label, "Property Label")
+
+    # Write the value to the cell in column 1 with the style "Property Value"
+    prop_cell = header_table.cell(target_row, 1)
+    write_value_to_cell(prop_cell, value, "Property Value")
+```
+
+### Write numeric attributes
+
+Next, write the numeric attributes to the report. This step is similar to the header attributes
+above, except the value and unit are written to separate cells instead of being joined in a
+formatted string.
+
+Additionally, the table also contains two attributes per row, so some additional calculations are
+required to determine the row number.
+
+
+```python
+# This table is the second table in the `Document` object.
+property_table = report.tables[1]
+
+for idx, (attribute_name, label) in enumerate(SINGLE_VALUED_PROPERTIES.items()):
+    # This table contains two attributes per row
+    # Calculate the row and column offset for this attribute
+
+    # First two attributes written to row 0, second two to row 1, etc.
+    target_row = idx // 2
+
+    # Even attributes written to columns 0, 1, 2, odd attributes to columns 3, 4, 5
+    target_column_offset = (idx % 2) * 3
+
+    if target_row > len(property_table.rows) - 1:
+        property_table.add_row()
+
+    attribute = released_record.attributes[attribute_name]
+
+    # Use standard form for numeric attributes
+    value = format_attribute(attribute, format_spec=".4e", include_unit=False)
+    unit = attribute.unit
+
+    # If there is no value for this attribute, set the value and unit to a dash
+    if value is None:
+        value = unit = "-"
+    elif unit == "":
+        unit = "-"
+
+    # Write the label to the cell in the leftmost column (column 0 or 3) with the style "Property Label"
+    label_cell = property_table.cell(target_row, target_column_offset)
+    write_value_to_cell(label_cell, label, "Property Label")
+
+    # Write the value to the cell in the center column (column 1 or 4) with the style "Property Value"
+    value_cell = property_table.cell(target_row, target_column_offset+1)
+    write_value_to_cell(value_cell, value, "Property Value")
+
+    # Write the unit to the cell in the rightmost column (column 2 or 5) with the style "Property Unit"
+    unit_cell = property_table.cell(target_row, target_column_offset+2)
+    write_value_to_cell(unit_cell, unit, "Property Unit")
+```
+
+## 5. Add functional data to the report
+
+Plot functional attributes with `seaborn` and `matplotlib`, convert the plots to image files, and
+then embed these in the report. Note: This section assumes the user is familiar with `seaborn` and
+`matplotlib`. For more examples and guidance in using these libraries see the previous Data
+Analytics examples.
+
+First, define a helper function to convert a `matplotlib` plot to a `png` file.
+
+
+```python
+import io
+from matplotlib import pyplot
+
+def convert_plot_to_png(plt: pyplot) -> io.BytesIO:
+    fig = plt.get_figure()
+    buffer = io.BytesIO()
+    fig.savefig(buffer, format="png")
+    fig.clf()
+    buffer.seek(0)
+    return buffer
+```
+
+Next, import `seaborn` and set the plot style.
+
+
+```python
+import seaborn as sns
+
+sns.set_theme(
+    palette=["black"],  # Datasheet reports are typically black and white
+    rc={"figure.figsize": (7, 5)}  # This size produces plots suitable for printing
+)
+sns.set_style("ticks")  # Include tickmarks on axes
+```
+
+Now, define a function to create a plot for a functional attribute with a specified title, axis
+labels, and optional constraint information.
+
+
+```python
+import io
+
+import pandas as pd
+
+def make_graph(
+    attribute: mpy.AttributeFunctional,
+    plot_title: str,
+    y_label: str,
+    x_label: str,
+    constraint_param_name: Optional[str] = None,
+    constraint_label: Optional[str] = None,
+) -> io.BytesIO:
+    
+    value = attribute.value
+
+    # This dictionary will be used to construct a dataframe
+    data = {"y": list(), "x": list(), "constraint": list()}
+    
+    # If a constraint_param_name was provided, set a CONSTRAINT flag to True
+    if constraint_param_name:
+        CONSTRAINT = True
+    else:
+        CONSTRAINT = False
+
+    # Get the position of the constraint parameter in the functional data value object
+    if CONSTRAINT:
+        param_idx = attribute.constraint_column_index[constraint_param_name]
+    else:
+        param_idx = None
+        parameter_unit = None
+
+    # Add the functional data to the dataframe dictionary
+    for row in value[1:]:
+        data["y"].append(row[0])
+        data["x"].append(row[2])
+        if CONSTRAINT:
+            data["constraint"].append(f"{row[param_idx]:.3g}")
+
+    if not CONSTRAINT:
+        # Remove the constraint key from the dictionary before converting to a dataframe
+        data.pop("constraint")
+
+    # Create the dataframe
+    df = pd.DataFrame(data)
+
+    # The commands to create a plot are slightly different if there are multiple series
+    if CONSTRAINT:
+        plt = sns.lineplot(
+            data=df,
+            x="x",
+            y="y",
+            style="constraint",
+            markers=True,
+        )
+    else:
+        plt = sns.lineplot(
+            data=df,
+            x="x",
+            y="y",
+            marker='o',
+        )
+    
+    x_unit = attribute.xaxis_parameter.unit
+    y_unit = attribute.unit
+    plt.set(
+        title=plot_title,
+        xlabel=f"{x_label} / {x_unit}",
+        ylabel=f"{y_label} / {y_unit}",
+    )
+    
+    # If we have a constraint, add a legend
+    if CONSTRAINT:
+        parameter_unit = attribute.parameters[constraint_param_name].unit
+        legend_title = f"{constraint_label} [{parameter_unit}]"
+        plt.legend(title=legend_title)
+    
+    png = convert_plot_to_png(plt)
+    return png
+```
+
+Finally, use this function to create a plot image for each functional attribute and add it to the
+report. The plots are added to the end of the document, and automatically flow to a new page if
+the previous page is filled.
+
+
+```python
+for attribute_name, (title, y_label, x_label, constraint_info) in GRAPHS.items():
+    attribute = released_record.attributes[attribute_name]
+    png = make_graph(
+        attribute=attribute,
+        plot_title=title,
+        y_label=y_label,
+        x_label=x_label,
+        # Use a ternary expression to only access the constraint_info tuple if it is not None
+        constraint_param_name=constraint_info[0] if constraint_info else None,
+        constraint_label=constraint_info[1] if constraint_info else None,
+    )
+    report.add_picture(png)
+```
+
+*Previous cell output:*
+```output
+<Figure size 700x500 with 0 Axes>
+```
+
+## 6. Save the report
+
+The values and plots have been added to the `Document` object, but these changes have not yet been
+saved to disk. To save the file, use the `Document.save` method to create a docx file at the path
+specified.
+
+
+```python
+report.save("output/10_report.docx")
+```
+
+A screenshot in Word is shown below (only the first page is shown):
+
+![The first page of a populated Word document showing a company logo, populated data tables, and a plot of thermal conductivity vs temperature.](assets/10_report-screenshot.png 'Word report')
+
+## Optional: Convert the file to PDF (not available in HTML documentation)
+
+This final step uses the `docx2pdf` library to convert the docx file to pdf. The `docx2pdf`
+library requires a local installation of Word, and so is not supported on Linux.
+
+The output of this step is not available in the HTML documentation, but will be visible if you run
+this notebook in an environment with Word installed.
+
+
+```python
+from docx2pdf import convert
+try:
+    from pythoncom import com_error
+except ModuleNotFoundError:  # pythoncom not supported on Linux
+    pass
+
+try:
+    convert("output/10_report.docx", "output/10_report.pdf")
+    pdf_created = True
+except NotImplementedError:  # Raised on Linux
+    pdf_created = False
+except com_error:  # Word is not installed
+    pdf_created = False
+```
+
+Display the PDF using the browser's PDF engine.
+
+
+```python
+if pdf_created:
+    from IPython.display import IFrame, display
+    display(IFrame("output/10_report.pdf", width=600, height=900))
+```
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/11_Powerpoint_summary_example.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/11_Powerpoint_summary_example.md
new file mode 100644
index 0000000000..b9dcb625e7
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/11_Powerpoint_summary_example.md
@@ -0,0 +1,591 @@
+# Generate a Material Summary Presentation using MI Scripting Toolkit and `python-pptx`
+
+This example shows how to use the `python-pptx` library to generate a Microsoft PowerPoint
+presentation. At a high level, the notebook goes through the following steps:
+
+1. Define the data to be included in the presentation
+2. Export the data
+3. Load the presentation template
+4. Add single-valued attributes to the presentation
+5. Add functional data to the presentation
+6. Save the presentation
+
+## 1. Define the data to be included in the presentation
+
+First, define some constants that identify the record and attributes to be included in the
+presentation. The following cell identifies a record by Database Key, Table name, and Record
+History GUID, but an alternative technique could be used to identify a record, for example
+searching.
+
+
+```python
+DATABASE = "MI_Training"
+TABLE = "Design Data"
+RECORD_HISTORY_GUID = "5b1df472-a92e-4317-b9a8-e1672335bc2e"
+UNIT_SYSTEM = "SI (Consistent)"
+```
+
+The next cells define the attributes to be included in each section of the presentation. Apart
+from the Presentation Title attribute, these are dictionaries which map an attribute name to a
+label included in the presentation.
+
+
+```python
+PRESENTATION_TITLE_ATTRIBUTE = "Common Name"
+```
+
+
+```python
+DESCRIPTION_ATTRIBUTES = {
+    "Common Name": "Name",
+    "Thickness": "Thickness",
+    "Product Form": "Form",
+    "Condition": "Condition",
+}
+```
+
+
+```python
+SINGLE_VALUED_PROPERTIES = {
+    "Density": "Density",
+    "Thermal Conductivity": "Thermal conductivity",
+    "Thermal Expansion": "Thermal expansion",
+    "Specific Heat": "Specific heat",
+    "Modulus, L": "Tensile modulus (L-dir)",
+    "Poisson's Ratio, L": "Poisson's ratio (L-dir)",
+    "Yield Strength, L": "Yield strength (yield, L-dir)",
+    "Ultimate Tensile Strength, L": "Ultimate tensile strength (ult., L-dir)",
+    "Elongation, L": "Elongation (L-dir)",
+}
+```
+
+The `GRAPHS` dictionary maps functional attributes to a 4-tuple, where each element of the tuple
+refers to:
+
+1. The chart title
+2. The y-axis label
+3. The x-axis label
+4. Optional: For multiple-series plots, a 2-tuple of the Parameter name and the title of the
+   legend in the report
+
+
+```python
+GRAPHS = {
+    "Thermal Conductivity with Temp.": ("Thermal conductivity vs Temperature", "Thermal conductivity", "Temperature", None),
+    "Thermal Expansion with Temp.": ("Thermal expansion vs Temperature", "Thermal expansion", "Temperature", None),
+    "Specific Heat with Temp.": ("Specific heat vs Temperature", "Specific heat", "Temperature", None),
+    "Tensile Stress/Strain, L": ("Stress vs Strain and Temperature", "Stress", "Strain", ("Temperature", "Temperature")),
+}
+```
+
+## 2. Export the data
+
+Export the data for all the required attributes for the 'released' version of the record.
+
+
+```python
+from GRANTA_MIScriptingToolkit import granta as mpy
+
+mi = mpy.connect("http://my.server.name/mi_servicelayer", autologon=True)
+db = mi.get_db(db_key=DATABASE)
+db.unit_system = UNIT_SYSTEM
+table = db.get_table(TABLE)
+record = db.get_record_by_id(hguid=RECORD_HISTORY_GUID)
+
+# To get the released version of the record, iterate through all versions of the record
+# and keep only the version which has the 'Released' `release_state`.
+for record_version in record.all_versions.values():
+    if record_version.release_state == "Released":
+        released_record = record_version
+
+# Create a list of all attributes by appending the keys of the three dictionaries defined above to
+# the datasheet title attribute
+all_attributes = [PRESENTATION_TITLE_ATTRIBUTE] + list(DESCRIPTION_ATTRIBUTES.keys()) + list(SINGLE_VALUED_PROPERTIES.keys()) + list(GRAPHS.keys())
+
+table.bulk_fetch(attributes=all_attributes, records=[released_record])
+```
+
+## 3. Load the presentation template
+
+The report template is created in Microsoft PowerPoint, and the 'python-pptx' library is used to
+populate the template with data exported from Granta MI.
+
+Import the `python-pptx` library and create a `Presentation` object using the `pptx.Presentation`
+function. The function accepts an optional string as the relative path to a template file, which
+is used to create the `Presentation` object. In this example the relative path to the template
+file is `"supporting_files/presentation_template.pptx"`, and the `Presentation` object is stored
+in the `prs` variable.
+
+`pptx.Presentation` can also be called without arguments, which creates an empty Presentation
+instead.
+
+
+```python
+from pptx import Presentation
+
+prs = Presentation(pptx="supporting_files/11_presentation_template.pptx")
+```
+
+<div class="alert alert-info">
+
+**Info:**
+
+As `python-pptx` does not implement all the layout and style functionality in Microsoft
+PowerPoint, specifying a pre-defined template allows greater control over the layout of the
+presentation and more concise code overall.
+</div>
+
+The presentation file used above is an empty `pptx` file, not a `potx` file.
+The presentation file contains no slides, but does contain a PowerPoint
+theme, which will be used throughout this example.
+
+## 4. Add single-valued attributes to the presentation
+
+This section covers adding single-valued and range attributes to the presentation.
+The presentation makes use of a combination of bulleted lists and tables to present
+single-valued data, and each approach will be handled separately.
+
+## Create and populate a title slide
+
+The `Presentation` object currently refers to an empty PowerPoint presentation.
+First, create a new slide based on the 'Title' slide layout, and then
+populate the title and sub-title content shapes.
+
+The `Presentation.slide_layouts` list contains the slide layouts in the same
+order as they appear in the 'Slide Master' view in PowerPoint. There is no
+convenient way to determine the required layout programmatically, so instead
+get the slide layout from PowerPoint and hardcode it here. Then add a new
+slide based on that layout.
+
+
+```python
+TITLE_LAYOUT_POSITION = 0
+title_layout = prs.slide_layouts[TITLE_LAYOUT_POSITION]
+title_slide = prs.slides.add_slide(title_layout)
+```
+
+Next, populate the values on the new slide. In PowerPoint, all content is contained
+within a shape. When editing a new slide, most shapes are *placeholders*, which means
+their content is undefined. Generally, adding content to a placeholder will turn it
+into a full shape.
+
+To add text to a placeholder, simply use the `text` property.
+
+
+```python
+material_name = released_record.attributes[PRESENTATION_TITLE_ATTRIBUTE].value
+record_version_number = released_record.version_number
+
+title = f"{material_name} [v{record_version_number}]"
+# Since the title placeholder is so commonly used, it is available via the title property
+title_slide.shapes.title.text = title
+
+# The subtitle placeholder does not have a dedicated property. To determine the 
+# correct placeholder shape, iterate through each one and identify it by name.
+# If the same placeholder is used multiple times, the idx value is static for
+# each layout and can be used directly once identified.
+subtitle = placeholder_idx = None
+for shape in title_slide.placeholders:
+    print('%d %s' % (shape.placeholder_format.idx, shape.name))
+    if shape.name.startswith("Subtitle"):
+        placeholder_idx = shape.placeholder_format.idx
+        subtitle = shape
+# subtitle = placeholders[placeholder_idx] would return the same placeholder shape object
+subtitle.text = "Design Review"
+```
+*Previous cell output:*
+```output
+0 Title 1
+1 Subtitle 2
+```
+Modifying the `Presentation` object does not provide any feedback. The `prs` variable can be
+saved to disk as a `pptx` file at any point by using the `Presentation.save` method.
+
+First, ensure the output directory exists. This directory will already exist if a notebook
+was run previously that saved a file to disk.
+
+
+```python
+from pathlib import Path
+output_folder = Path("./output")
+output_folder.mkdir(exist_ok=True)
+```
+
+Next, save the presentation.
+
+
+```python
+prs.save("output/11_presentation_title_only.pptx")
+```
+
+Open the `presentation_title_only.pptx` file to see the presentation template populated with
+the `PRESENTATION_TITLE_ATTRIBUTE` attribute value. A screenshot of the presentation title
+slide is shown below:
+
+![A PowerPoint title slide showing the name of the material.](assets/11_presentation-screenshot-title-slide.png 'Presentation title slide')
+
+The presentation will not be saved to disk again until the report is complete. If you want to see
+the effects of individual operations on the presentation, use the ``prs.save`` method to save the
+presentation to disk.
+
+## Modify the presentation properties
+
+PowerPoint presentations contain metadata describing the presentation
+content. These are exposed in the `Presentation.core_properties`
+property, and are modified below to specify values for the title,
+version, author, and comments.
+
+
+```python
+prs.core_properties.title = f"{material_name} Design Review"
+prs.core_properties.version = f"Version {record_version_number}"
+prs.core_properties.author = "Granta MI"
+prs.core_properties.comments = "Autogenerated by MI Scripting Toolkit and python-pptx"
+```
+
+## Material details slide
+
+The material description attributes will be written to a general content slide layout,
+with each separate attribute formatted as an entry in a bulleted list. The same
+high-level process will be followed as with the title slide: identify the layout,
+create the slide, and then populate the slide with the exported data.
+
+First however, define a function that formats an attribute value to a suitable string format
+depending on the attribute type. The default format specifier `:.3g` uses a general format with at
+most 3 digits of precision.
+
+
+```python
+from typing import Optional
+
+def format_attribute(attribute: mpy.AttributeValue, format_spec: str = ".3g", include_unit: bool = False) -> Optional[str]:
+    value = attribute.value
+    # A null value may either be None or an empty list
+    if not value and value != 0:
+        return None
+
+    # Switch based on the attribute type
+    if attribute.type == "RNGE":
+        formatted_value = f"{value['low']:{format_spec}} - {value['high']:{format_spec}}"
+    elif attribute.type == "POIN":
+        # Single-valued point only
+        if isinstance(attribute.value, (float, int)):
+            formatted_value = f"{value:{format_spec}}"
+        else:
+            raise NotImplementedError("Multi-valued point attribute not supported.")
+    elif attribute.type in ["STXT", "LTXT"]:
+        formatted_value = value
+    else:
+        raise NotImplementedError(f'Attribute type "{attribute.type}" not supported.')
+
+    # Include the unit in the output if requested and if it exists
+    if include_unit and attribute.unit:
+        return f"{formatted_value} {attribute.unit}"
+    else:
+        return formatted_value
+```
+
+Next, create the content slide and populate with the exported data.
+
+
+```python
+# Create a new slide based on the 'Title and content' layout
+TITLE_AND_CONTENT_LAYOUT_POSITION = 1
+title_and_content_layout = prs.slide_layouts[TITLE_AND_CONTENT_LAYOUT_POSITION]
+material_details_slide = prs.slides.add_slide(title_and_content_layout)
+
+# Add the slide title
+material_details_slide.shapes.title.text = "Material details"
+
+# The main placeholder on this slide is the second placeholder, so access
+# directly using the idx.
+content_placeholder = material_details_slide.placeholders[1]
+
+# A content placeholder is a general placeholder that can contain any type of
+# content. In this case we will add text content, so accessing the text_frame
+# property converts it to a text shape.
+text_frame = content_placeholder.text_frame
+
+# Empty the text frame, so we know there is no content present
+text_frame.clear()
+
+# The first attribute will be handled slightly differently, so use this
+# variable to track if we are processing the first attribute or not.
+is_first_attribute = True
+
+# The DESCRIPTION_ATTRIBUTES dictionary contains pairs of attribute names and labels.
+for idx, (attribute_name, label) in enumerate(DESCRIPTION_ATTRIBUTES.items()):
+    # Format the attribute value as a string with the unit included
+    attribute = released_record.attributes[attribute_name]
+    value = format_attribute(attribute, include_unit=True)
+
+    # The bullet point text is the label, two tab characters, and the formatted value
+    bullet_text = f"{label}:\t\t{value}"
+
+    # An empty text frame always contains exactly one paragraph
+    # For the first attribute, write the value to this paragraph
+    if is_first_attribute:
+        text_frame.text = bullet_text
+        is_first_attribute = False
+    # For subsequent attributes, add a new paragraph, which creates a new bullet point
+    # Then write the value to this newly created paragraph.
+    else:
+        p = text_frame.add_paragraph()
+        p.text = bullet_text
+```
+
+A screenshot of the generated slide is shown below.
+
+![A PowerPoint slide titled "Material details" with a bulleted list of material properties.](assets/11_presentation-screenshot-material-details.png 'Presentation material details slide')
+
+## Room temperature properties slide
+
+Next, write the room-temperature material properties to the presentation. This step is similar
+to the material detail attributes above, except the data is written to a table with separate
+label, value, and unit columns.
+
+First, create the new slide as before, but this time populate the table placeholder with a
+table containing 3 columns and a number of rows equal to the number of exported attributes.
+Then set the width of the columns in inches, based on some pre-computed sensible values.
+
+
+```python
+from pptx.util import Mm
+from pptx.enum.text import PP_ALIGN
+
+# The presentation template contains a custom 'Room-temperature properties' layout
+# This custom layout is at position 10 in PowerPoint
+ROOM_TEMP_LAYOUT_POSITION = 9
+room_temp_prop_layout = prs.slide_layouts[ROOM_TEMP_LAYOUT_POSITION]
+room_temp_prop_slide = prs.slides.add_slide(room_temp_prop_layout)
+room_temp_prop_slide.shapes.title.text = "Room-temperature properties"
+
+table_placeholder = None
+for p in room_temp_prop_slide.placeholders:
+    if p.name.startswith("Table Placeholder"):
+        table_placeholder = p
+
+# Convert the table placeholder to a full table, and specify the number of
+# rows and columns required.
+row_count = len(SINGLE_VALUED_PROPERTIES)
+graphic_frame = table_placeholder.insert_table(rows=row_count, cols=3)
+property_table = graphic_frame.table
+
+# The 'first_row' and 'first_col' properties define whether the first row and
+# first column of the table are formatted distinctively, typically for header
+# rows and columns. This table has no header rows or columns, so set these
+# properties to False.
+property_table.first_row = property_table.first_col = False
+
+# The first column contains the property label. Set to 177.8 mm (7 in) wide
+property_table.columns[0].width = Mm(177.8)
+# The second and third columns contains the value and unit. Set both to 57.15 (2.25 in) wide
+property_table.columns[1].width = property_table.columns[2].width = Mm(57.15)
+```
+
+Next, iterate through each property, format the value appropriately, and write the label,
+value, and unit to the table.
+
+
+```python
+for (attribute_name, label), row in zip(SINGLE_VALUED_PROPERTIES.items(), property_table.rows):
+    attribute = released_record.attributes[attribute_name]
+    
+    # Use standard form for numeric attributes
+    value = format_attribute(attribute, format_spec=".4e", include_unit=False)
+    unit = attribute.unit
+
+    # If there is no value for this attribute, set the value and unit to a dash
+    if value is None:
+        value = unit = "-"
+    elif unit == "":
+        unit = "-"
+
+    # Write the label to the cell in the leftmost column (column 0 or 3) with the style "Property Label"
+    row.cells[0].text = label
+
+    # Write the value to the cell in the center column (column 1 or 4) with the style "Property Value"
+    value_cell = row.cells[1]
+    value_cell.text = value
+    value_cell.text_frame.paragraphs[0].font.name = "Consolas"
+    value_cell.text_frame.paragraphs[0].alignment = PP_ALIGN.RIGHT
+
+    # Write the unit to the cell in the rightmost column (column 2 or 5) with the style "Property Unit"
+    unit_cell = row.cells[2]
+    unit_cell.text = unit
+```
+
+A screenshot of the generated slide is shown below.
+
+![A PowerPoint slide titled "Room temperature properties" with a tabulated list of material properties.](assets/11_presentation-screenshot-room-temperature.png 'Room temperature properties')
+
+## 5. Add functional data to the presentation
+
+Plot functional attributes with `seaborn` and `matplotlib`, convert the plots to image files, and
+then embed these in the presentation. Note: This section assumes the user is familiar with
+`seaborn` and `matplotlib`. For more examples and guidance in using these libraries see the
+previous Data Analytics examples.
+
+First, define a helper function to convert a `matplotlib` plot to a `png` file.
+
+
+```python
+import io
+from matplotlib import pyplot
+
+def convert_plot_to_png(plt: pyplot) -> io.BytesIO:
+    fig = plt.get_figure()
+    buffer = io.BytesIO()
+    # transparent=True means the chart background is transparent, not white
+    # This is useful when inserting content into a presentation
+    fig.savefig(buffer, format="png", transparent=True)
+    fig.clf()
+    buffer.seek(0)
+    return buffer
+```
+
+Next, import `seaborn` and set the plot style.
+
+
+```python
+import seaborn as sns
+
+sns.set_theme(
+    rc={"figure.figsize": (13.33, 6.29)}  # The size in inches of the image placeholder in the PowerPoint template
+)
+sns.set_style("ticks")  # Include tickmarks on axes
+```
+
+Now, define a function to create a plot for a functional attribute with specified axis
+labels and optional constraint information.
+
+
+```python
+import io
+
+import pandas as pd
+
+def make_graph(
+    attribute: mpy.AttributeFunctional,
+    y_label: str,
+    x_label: str,
+    constraint_param_name: Optional[str] = None,
+    constraint_label: Optional[str] = None,
+) -> io.BytesIO:
+    
+    value = attribute.value
+
+    # This dictionary will be used to construct a dataframe
+    data = {"y": list(), "x": list(), "constraint": list()}
+    
+    # If a constraint_param_name was provided, set a CONSTRAINT flag to True
+    if constraint_param_name:
+        CONSTRAINT = True
+    else:
+        CONSTRAINT = False
+
+    # Get the position of the constraint parameter in the functional data value object
+    if CONSTRAINT:
+        param_idx = attribute.constraint_column_index[constraint_param_name]
+    else:
+        param_idx = None
+        parameter_unit = None
+
+    # Add the functional data to the dataframe dictionary
+    for row in value[1:]:
+        data["y"].append(row[0])
+        data["x"].append(row[2])
+        if CONSTRAINT:
+            data["constraint"].append(f"{row[param_idx]:.3g}")
+
+    if not CONSTRAINT:
+        # Remove the constraint key from the dictionary before converting to a dataframe
+        data.pop("constraint")
+
+    # Create the dataframe
+    df = pd.DataFrame(data)
+
+    # The commands to create a plot are slightly different if there are multiple series
+    if CONSTRAINT:
+        plt = sns.lineplot(
+            data=df,
+            x="x",
+            y="y",
+            style="constraint",
+            hue="constraint",
+            markers=True,
+        )
+    else:
+        plt = sns.lineplot(
+            data=df,
+            x="x",
+            y="y",
+            marker='o',
+        )
+    
+    x_unit = attribute.xaxis_parameter.unit
+    y_unit = attribute.unit
+    plt.set(
+        xlabel=f"{x_label} / {x_unit}",
+        ylabel=f"{y_label} / {y_unit}",
+    )
+    
+    # If we have a constraint, add a legend
+    if CONSTRAINT:
+        parameter_unit = attribute.parameters[constraint_param_name].unit
+        legend_title = f"{constraint_label} [{parameter_unit}]"
+        plt.legend(title=legend_title)
+    
+    png = convert_plot_to_png(plt)
+    return png
+```
+
+Finally, use this function to create a plot image for each functional attribute and add it to the
+presentation. Each plot is added to a separate slide based on a custom slide layout designed for
+representing ``seaborn`` plots.
+
+
+```python
+# The presentation template contains a custom 'Graph' layout
+# This custom layout is at position 11 in PowerPoint
+GRAPH_LAYOUT_POSITION = 10
+graph_layout = prs.slide_layouts[GRAPH_LAYOUT_POSITION]
+
+for attribute_name, (title, y_label, x_label, constraint_info) in GRAPHS.items():
+    attribute = released_record.attributes[attribute_name]
+    png = make_graph(
+        attribute=attribute,
+        y_label=y_label,
+        x_label=x_label,
+        # Use a ternary expression to only access the constraint_info tuple if it is not None
+        constraint_param_name=constraint_info[0] if constraint_info else None,
+        constraint_label=constraint_info[1] if constraint_info else None,
+    )
+    # Create a new slide based on the graph layout
+    graph_slide = prs.slides.add_slide(graph_layout)
+    
+    # Set the title of the slide to be the graph title
+    graph_slide.shapes.title.text = title
+    for shape in graph_slide.placeholders:
+        if shape.name.startswith("Picture Placeholder"):
+            # Add the graph to the Picture Placeholder
+            shape.insert_picture(png)
+```
+
+*Previous cell output:*
+```output
+<Figure size 1333x629 with 0 Axes>
+```
+
+A screenshot of the stress-strain slide is shown below.
+
+![A PowerPoint slide titled "Stress vs Strain and Temperature" with a graph showing a stress-strain response at multiple temperatures.](assets/11_presentation-screenshot-stress-strain.png 'Presentation material stress-strain')
+
+## 6. Save the presentation
+
+Save a final version of the presentation at the path specified.
+
+
+```python
+prs.save("output/11_presentation.pptx")
+```
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/12_Preparing_data_for_Power_BI.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/12_Preparing_data_for_Power_BI.md
new file mode 100644
index 0000000000..3a5d151ce5
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+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/12_Preparing_data_for_Power_BI.md
@@ -0,0 +1,206 @@
+# Preparing data for Power BI
+
+This notebook illustrates using the MI Scripting Toolkit in a Power BI project. It provides two example code snippets.
+The first one can be used as a starting point and shows how to extract the following datasets:
+
+- Databases
+- Tables
+- Attributes and associated revision information
+
+The data provided in these datasets can be used to determine:
+
+- What attributes have been created in a table over a time period?
+- What attributes have been modified in a table over a time period?
+
+The second snippet is detailed further down the example.
+
+## Basic usage
+### Pre-requisites
+
+* A valid Python interpreter as defined by the [Microsoft documentation](https://learn.microsoft.com/en-us/power-bi/connect-data/desktop-python-scripts).
+* The MI Scripting Toolkit must be installed for the configured Python interpreter selected in Power BI's Python Scripting options.
+
+### Security
+When adding a Python script to Power BI, the script is persisted in the Power BI project. When using Power BI in a production
+environment, do not store credentials in plain text within the script.
+If possible, use methods of authentication that do not require storing sensitive information (such as using Windows
+Authentication via `autologon=True`) or refer to the Streamlined API example notebook `Storing Credentials Securely`.
+
+### Dataset relationships
+Power BI automatically attempts to define relationships between datasets based on the column names. For example, both
+the DataFrames ``databases`` and ``tables`` include a column ``Database.Key``, which is automatically recognized as a
+relationship.
+
+In Granta MI, it is possible for some identifiers to not be unique across the whole system: for example, two tables
+in different databases may share the same name or GUID. This is common occurrence in cases where databases have been
+duplicated. As a result, it is sometimes necessary to build a new unique identifier by concatenating existing columns
+for dataset relationship to be successfully established.
+
+### Access control
+Granta MI schema definition and data held in Granta MI might be subject to access control rules. Be aware of the
+access control rules applicable within your organization before exporting data and publishing it through a Power BI
+report, to avoid breaching organizational data confidentiality policies.
+
+### Configuration steps
+
+1. Create a new ``Python Script`` Source in Power BI via the ``Get Data`` button.
+
+   ![The list of data providers is filtered to show only Python, and "Python script" is selected.](assets/12_power-bi-python.PNG 'Power BI "Get Data" selection')
+2. Copy the content of the script cell below into the ``Script`` input.
+
+   ![The "Script" input containing MI Scripting Toolkit Python code.](assets/12_power-bi-python-setup.PNG 'Power BI "Python script" input')
+3. Update the connection URL and authentication method appropriately.
+4. Click `OK` to validate the script definition. Power BI will execute the script and report errors, if any.
+5. Select which DataFrames to import as datasets. Click the `Load` button to confirm.
+
+   ![A list of Pandas DataFrame objects are shown on the left, and a preview of the "tables" DataFrame is shown on the right.](assets/12_power-bi-dataframes-loading.PNG 'Power BI DataFrame navigator')
+6. Verify that column types and dataset relationships are correctly defined.
+7. Build your report visualizations.
+
+At the time of writing, Power BI can only import ``pandas.DataFrame`` objects from the script as datasets. If you
+modify the provided code snippet, ensure that all data to be imported into Power BI is in a DataFrame, and that all
+DataFrames have unique variable names.
+
+
+```python
+from GRANTA_MIScriptingToolkit import granta as mpy
+import pandas
+
+mi = mpy.connect("http://my.server.name/mi_servicelayer", autologon=True)
+
+# Create a DataFrame of all databases.
+_databases = [
+    {
+        "Database.Key": db.db_key,
+        "Database.Name": db.name,
+        "Database.Guid": db.guid,
+    }
+    for db in mi.dbs
+]
+databases = pandas.DataFrame.from_records(_databases)
+
+# Create a DataFrame of all tables in all databases.
+_tables = [
+    {
+        "Database.Key": table.db_key,
+        "Table.Name": table.name,
+        "Table.Guid": table.guid,
+    }
+    for db in mi.dbs for table in db.tables
+]
+tables = pandas.DataFrame.from_records(_tables)
+
+# Create a DataFrame of all attributes in all tables in all databases.
+_all_attributes = [
+    {
+        "Database.Key": table.db_key,
+        "Table.Guid": table.guid,
+        "Attribute.Name": attribute.name,
+        "Attribute.Type": attribute.type,
+        "Attribute.Unit": attribute.unit,
+        "Attribute.Created": attribute.history.created_at,
+        "Attribute.LastModified": attribute.history.last_modified_at,
+    }
+    for db in mi.dbs for table in db.tables for attribute in table.attributes.values()
+]
+all_attributes = pandas.DataFrame.from_records(_all_attributes)
+```
+
+## Advanced usage
+
+The following code snippet illustrates how to retrieve additional revision information from Granta MI. Unlike the previous code snippet, these operations are specific to a database and table. The ``MI Training`` database and ``MaterialUniverse`` table are used as examples.
+
+Record revision information and data revision information for all records in the table can be used to determine:
+    * How many records have been created in a table in a set time period?
+    * What records have been updated?
+    * What attribute values have been updated in a set time period?
+    * What attributes are most populated in the table?
+    * Whether an attribute is being used?
+
+The first script provided in this example notebook does not perform intensive operations, and therefore performance considerations are not necessary.
+However, the second snippet is likely to be a time expensive operation: it scales with the number of attributes and the number of records. For large databases, it might require a few minutes to complete. See the next section for performance considerations and guidance.
+
+
+```python
+# Create a DataFrame with all records from the configured target table, including their revision information.
+DB_KEY = "MI_Training"
+TABLE_NAME = "MaterialUniverse"
+
+example_table = mi.get_db(db_key=DB_KEY).get_table(TABLE_NAME)
+example_records = example_table.all_records()
+
+example_table.bulk_fetch(
+    example_records,
+    attributes=[
+        mpy.RecordProperties.short_name,
+        mpy.RecordProperties.name,
+        mpy.RecordProperties.table_name,
+        mpy.RecordProperties.created_on,
+        mpy.RecordProperties.last_modified_on,
+        mpy.RecordProperties.created_by,
+        mpy.RecordProperties.last_modified_by,
+    ]
+)
+_example_records_properties = [
+    {
+        "Database.Key": record.db_key,
+        "Table.Name": record.table_name,
+        "Record.Guid": record.record_guid,
+        "Record.Name": record.name,
+        "Record.Created": record.created_on,
+        "Record.Creator": record.created_by,
+        "Record.LastModified": record.last_modified_on,
+        "Record.LastModifier": record.last_modified_by,
+    }
+    for record in example_records
+]
+example_records_revision = pandas.DataFrame.from_records(_example_records_properties)
+
+# Create a DataFrame with the data revision information for all attributes for all records in the example table.
+example_table.bulk_fetch_data_revision_history(example_records)
+_example_records_data_revision = [
+    {
+        "Record.Guid": record.record_guid,
+        "Attribute.Name": attribute_name,
+        "Data.IsPopulated": attribute_revision.is_populated,
+        "Data.VersionNumber": attribute_revision.data_version_number,
+        "Data.CreatedInRecordVersion": attribute_revision.created_in_record_version,
+        "Data.RetiredInRecordVersion": attribute_revision.retired_in_record_version,
+        "Data.Created": attribute_revision.history.created_at if attribute_revision.history is not None else None,
+        "Data.Creator": attribute_revision.history.created_by if attribute_revision.history is not None else None,
+        "Data.NumberOfUpdates": attribute_revision.history.update_count if attribute_revision.history is not None else None,
+        "Data.LastModified": attribute_revision.history.last_modified_at if attribute_revision.history is not None else None,
+        "Data.LastModifier": attribute_revision.history.last_modified_by if attribute_revision.history is not None else None,
+    }
+    for record in example_records for attribute_name, attribute_revision in record.data_revision_history.items()
+]
+example_records_data_revision = pandas.DataFrame.from_records(_example_records_data_revision)
+```
+
+### Performance considerations
+
+It is important to note that Power BI evaluates Python script queries multiple times:
+* First to validate the source when it is first defined
+* Then to display the preview of DataFrames that can be loaded
+* Then, when refreshing the report data, the Python script is evaluated again for each DataFrame loaded as a query
+
+This has an impact on the overall report performance and this section aims to help users understand common pitfalls and to list possible areas of performance optimization. There is no universal advice that could apply to all Power BI projects, and it is required to identify the factors that might affect performance in specific use cases.
+
+#### Factors impacting report performance
+There are a number of factors to take into account when choosing a strategy to get data into Power BI from Python scripts using the MI Scripting Toolkit:
+
+* **Time cost associated with an operation:** Some operations (here defined as a sequence of steps that results in a DataFrame) require a longer time to process. For example, exporting data for multiple attributes for all records in a table is likely to be lengthy. On the other hand, the first code snippet included in this example is likely to run quickly for most users.
+* **Database and table size:** Some MI Scripting Toolkit operations scale with the number of records, for example exporting data for records.
+* **Number of different datasets to extract:** When loading DataFrames from a Python script, Power BI duplicates the script in each resulting query. Upon refreshing the project's data, all queries are re-run. The more DataFrames are loaded from a script, the more times the script is re-run. For example, if the snippet above is added to a project and both the ``example_records_revision`` and ``example_records_data_revision`` DataFrames are loaded as queries, then refreshing the project will run the script twice, each time running the two operations. If a third operation is added to the script and the computed DataFrame is loaded as a new query, then refreshing the project will run the script three times, each performing the three operations, etc.
+
+#### Other concerns
+* **Content of the report:** Depending on the type of reporting performed in Power BI, it might not be necessary to retrieve data for all records. For example, if the aim of the report is to display metrics of record modifications from the last week only, the list of relevant records can be obtained by performing a criteria search on the ``lastModified`` pseudo-attribute, instead of relying on the method ``Table.all_records()``. The added expense of the search operation is likely to be negligeable compared to the cost associated with exporting unnecessary record data.
+* **Frequency at which the report requires updating:** the more frequently data needs to be refreshed, the less tolerance there is for poor performance. On one end of the spectrum, if the report is an one-off operation, it can be best to avoid using Python script sources in Power BI. The script can be run separately in Python, DataFrames persisted to disk using ``.to_csv()`` and loaded into Power BI using a ``Text\CSV`` source. This approach ensures the script is only run once.
+* **Report publication:** Whether the Power BI report is published to the online Power BI service, whether it needs to be shared with other users, whether the other users need to be able to refresh the data has consequences on which connectors can be used.
+
+#### Mitigation options
+Demonstrating how these can be implemented is beyond the scope of this example, but below are options to explore:
+
+* **Using a distinct Python source query per operation:** Because Power BI runs the Python script for each extracted DataFrame, it can be more efficient to have separate scripts, each responsible for generating a single DataFrame. There will be some repetition, such as creating a session, and identifying the correct database and table, but the overall performance can be improved, as lengthy operations are not repeated.
+* **Writing data to files:** In some circumstances, using Python to write data to a file (for example via ``DataFrame.to_csv()``) and loading the file in Power BI might be more efficient. However this affects data can be refreshed, as in this scenario Power BI cannot re-run the script.
+* **Disabling parallel refresh of queries:** Power BI settings include the ability to disable ``Parallel loading of tables``. Doing so does not prevent unnecessary script re-runs, but it prevents the concurrent execution of many scripts, which could degrade performance for all queries by overloading the Granta MI server.
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/13_Data_validation.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/13_Data_validation.md
new file mode 100644
index 0000000000..4dc8ddbcc3
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/data-analytics/13_Data_validation.md
@@ -0,0 +1,448 @@
+# Data validation using Scripting Toolkit
+
+This notebook shows a best-practice approach to data validation in Granta MI by defining a set of simple data
+validation as individual Python functions.
+
+Note: The examples shown here do not cover all the possible ways in which data stored in Granta MI could be validated,
+but show a framework which could be extended to perform regular checking of data in a Granta MI deployment.
+
+The approach described in this notebook can be split into three phases:
+
+1. Define the validation rules
+2. Validate the data
+3. Output the results
+
+## 1. Define the validation rules
+
+Before we define the rules, import the Scripting Toolkit so we can use Python type hints.
+
+
+```python
+from GRANTA_MIScriptingToolkit import granta as mpy
+```
+
+### check_attribute_is_populated
+
+Next, define the first data validation rule. This rule will check that an attribute is populated.
+
+
+```python
+def check_attribute_is_populated(record: mpy.Record, attribute_name: str) -> tuple[bool, str | None]:
+    attribute = record.attributes[attribute_name]
+    if attribute.is_empty(): 
+        return (False, f'Attribute "{attribute_name}" is not populated for record "{record.name}"')
+
+    return (True, None)
+```
+
+All validation rules will have a similar structure, in that they are a function with the same parameters:
+
+* ``record``: a Scripting Toolkit ``Record`` object.
+* ``attribute_name``: the name of the attribute to be validated.
+
+The functions also all return a ``tuple`` containing the result as a boolean value (``True`` is a successful check,
+and ``False`` is a failure), and an optional message as a ``str``. The message describes the failure, and so is
+omitted if the check passed.
+
+### check_attribute_is_positive
+
+Now define a second validation rule, which checks that the attribute is positive.
+
+
+```python
+def check_attribute_is_positive(record: mpy.Record, attribute_name: str) -> tuple[bool, str | None]:
+    attribute = record.attributes[attribute_name]
+    is_positive = attribute.value > 0
+    if not is_positive:
+        return (False, f'Attribute "{attribute_name}" is not positive for record "{record.name}"')
+
+    return (True, None)
+```
+
+This check will fail with an unhandled exception if ``attribute.value`` is non-numeric or empty. The case where the
+attribute value is empty will be handled by only running this check if the ``check_attribute_is_populated`` check was
+successful. The case where the attribute value is non-numeric should be avoided at runtime by ensuring this check is
+only applied to numeric attributes.
+
+### check_attribute_is_monotonically_increasing
+
+Finally, define a more complex validation rule that checks that the data in series functional attribute is
+monotonically increasing for each individual series.
+
+
+```python
+def check_attribute_is_monotonically_increasing(record: mpy.Record, attribute_name: str) -> tuple[bool, str | None]:
+    attribute = record.attributes[attribute_name]
+    
+    # Initialize values for the previous datapoint and series
+    previous_y = previous_x = -float("inf")
+    current_series = 0
+
+    # Iterate over each row in the database, splitting the data by series
+    for row in attribute.data_with_series_number[1:]:
+        y = row[0]  # First value is always the y attribute
+        x = row[2]  # Third value is always the x parameter
+        series = row[-1]  # The final value is the series number
+
+        # If the series has changed, then update the series value
+        if series != current_series:
+            current_series = series
+
+        # If the series hasn't changed, then check values
+        elif y <= previous_y or x <= previous_x:
+            return (
+                False,
+                f'Attribute "{attribute_name}", series {series} is not monotonically increasing for record '
+                f'"{record.name}"'
+            )
+    
+        # Update the previous values
+        previous_y = y
+        previous_x = x
+    return (True, None)
+```
+
+Again, the case where the attribute is not a series functional attribute or is empty is unhandled.
+
+### Define test cases
+
+Finally, we now create a dictionary to describe the test cases, formed by mapping each attribute to the sequence of
+validation rules to be applied to that attribute.
+
+For example, for text attributes the check only ensures that they are populated, but numeric attributes are checked to
+ensure that the value is both populated and then that the value is positive. Note that validation rules will be
+checked in order.
+
+
+```python
+test_cases = {
+    "Common Name": [check_attribute_is_populated],
+    "Thickness": [check_attribute_is_populated],
+    "Statistical Basis": [check_attribute_is_populated],
+    "Density": [check_attribute_is_populated, check_attribute_is_positive],
+    "Thermal Conductivity": [check_attribute_is_populated, check_attribute_is_positive],
+    "Yield Strength, L": [check_attribute_is_populated, check_attribute_is_positive],
+    "Tensile Stress/Strain, L": [check_attribute_is_populated, check_attribute_is_monotonically_increasing],
+}
+```
+
+Note that because the function names are not followed by ``()``, the functions are not invoked. This dictionary only
+contains *references* to the validation functions, it does not include the *results*.
+
+## 2. Validate the data
+
+After defining our validation rules, we can run them against the database.
+
+First, connect to Granta MI and fetch the records to validate. This example uses the ``table.all_records()`` method
+and then filters out example records and unreleased v1 records. Alternatively, any approach could be used that
+produces a list of records, for example searching for a last modified date, or using a Record List.
+
+
+```python
+mi = mpy.connect("http://my.server.name/mi_servicelayer", autologon=True)
+db = mi.get_db("MI Training")
+table = db.get_table("Design Data")
+
+# Exclude example records
+records = [r for r in table.all_records() if "Example" not in r.name]
+
+# Exclude unreleased v1 records
+mi.bulk_fetch_release_states(records)
+records = [r for r in records if not (r.release_state == "Unreleased" and r.version_number == 1)]
+records
+```
+
+
+
+*Previous cell output:*
+```output
+[<Record long name: 250 Grade Maraging, Maraged at 900F, Plate, Thickness: 0.1875 to 0.251 in, AMS 6520, S basis>,
+ <Record long name: Aluminum alloys, 7075-T6, Sheet>,
+ <Record long name: Titanium alloys, Ti-6Al-4V>,
+ <Record long name: Nickel alloys, Inconel 718, Forging>]
+```
+
+
+Next, use the keys of the ``test_cases`` dictionary defined in the previous section to bulk fetch the data required
+for these checks.
+
+
+```python
+attribute_names = list(test_cases.keys())
+table.bulk_fetch(records=records, attributes=attribute_names)
+```
+
+Finally, run each validation rule in sequence against each record, and store the result in a pandas DataFrame. The
+results are run in such a way that if a check against a specific record/attribute combination fails, each subsequent
+check will be skipped.
+
+
+```python
+import pandas as pd
+
+results = []
+
+# Iterate over all records and attributes
+for record in records:
+    for attribute_name, validation_rules in test_cases.items():
+
+        # The first time we test this attribute, the previous result is None
+        previous_result = None
+
+        # Iterate over all validation rules for this attribute
+        for rule in validation_rules:
+            
+            # These column values describe the check that will be performed.
+            check_result = {
+                "Record Name": record.name,
+                "Record URL": record.viewer_url,
+                "Attribute": attribute_name,
+                "Check": rule.__name__,
+            }
+
+            # If the previous check in this sequence failed, then skip all
+            # subsequent checks.
+            if previous_result is False:
+                check_result["Result"] = None
+                check_result["Message"] = "Skipped"
+
+            # If the previous check did not fail, then run the check and record
+            # the results.
+            else:
+                result, message = rule(record=record, attribute_name=attribute_name)
+                check_result["Result"] = result
+                check_result["Message"] = message
+                previous_result = result
+
+            # Add the results to the list
+            results.append(check_result)
+
+df_results = pd.DataFrame(data=results)
+```
+
+## 3. Output the results
+
+The results are stored in a DataFrame, so they can be easily displayed in the notebook. The code below uses the
+``.value_counts()`` method applied to the **Result** column to show a summary of the results. Specify ``dropna=False``
+to include ``None`` results, i.e. tests that were skipped.
+
+
+```python
+df_results.Result.value_counts(dropna=False)
+```
+
+
+
+*Previous cell output:*
+```output
+Result
+True     29
+False     9
+None      6
+Name: count, dtype: int64
+```
+
+
+Filter out the passed tests, leaving only the failed or skipped tests, and display the result.
+
+
+```python
+df_failures_skipped = df_results.loc[df_results.Result != True]
+df_failures_skipped
+```
+
+
+
+
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Record Name</th>
+      <th>Record URL</th>
+      <th>Attribute</th>
+      <th>Check</th>
+      <th>Result</th>
+      <th>Message</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>11</th>
+      <td>Aluminum alloys, 7075-T6, Sheet</td>
+      <td>http://my.server.name/mi/datasheet.aspx?dbKey=...</td>
+      <td>Common Name</td>
+      <td>check_attribute_is_populated</td>
+      <td>False</td>
+      <td>Attribute "Common Name" is not populated for r...</td>
+    </tr>
+    <tr>
+      <th>13</th>
+      <td>Aluminum alloys, 7075-T6, Sheet</td>
+      <td>http://my.server.name/mi/datasheet.aspx?dbKey=...</td>
+      <td>Statistical Basis</td>
+      <td>check_attribute_is_populated</td>
+      <td>False</td>
+      <td>Attribute "Statistical Basis" is not populated...</td>
+    </tr>
+    <tr>
+      <th>16</th>
+      <td>Aluminum alloys, 7075-T6, Sheet</td>
+      <td>http://my.server.name/mi/datasheet.aspx?dbKey=...</td>
+      <td>Thermal Conductivity</td>
+      <td>check_attribute_is_populated</td>
+      <td>False</td>
+      <td>Attribute "Thermal Conductivity" is not popula...</td>
+    </tr>
+    <tr>
+      <th>17</th>
+      <td>Aluminum alloys, 7075-T6, Sheet</td>
+      <td>http://my.server.name/mi/datasheet.aspx?dbKey=...</td>
+      <td>Thermal Conductivity</td>
+      <td>check_attribute_is_positive</td>
+      <td>None</td>
+      <td>Skipped</td>
+    </tr>
+    <tr>
+      <th>18</th>
+      <td>Aluminum alloys, 7075-T6, Sheet</td>
+      <td>http://my.server.name/mi/datasheet.aspx?dbKey=...</td>
+      <td>Yield Strength, L</td>
+      <td>check_attribute_is_populated</td>
+      <td>False</td>
+      <td>Attribute "Yield Strength, L" is not populated...</td>
+    </tr>
+    <tr>
+      <th>19</th>
+      <td>Aluminum alloys, 7075-T6, Sheet</td>
+      <td>http://my.server.name/mi/datasheet.aspx?dbKey=...</td>
+      <td>Yield Strength, L</td>
+      <td>check_attribute_is_positive</td>
+      <td>None</td>
+      <td>Skipped</td>
+    </tr>
+    <tr>
+      <th>20</th>
+      <td>Aluminum alloys, 7075-T6, Sheet</td>
+      <td>http://my.server.name/mi/datasheet.aspx?dbKey=...</td>
+      <td>Tensile Stress/Strain, L</td>
+      <td>check_attribute_is_populated</td>
+      <td>False</td>
+      <td>Attribute "Tensile Stress/Strain, L" is not po...</td>
+    </tr>
+    <tr>
+      <th>21</th>
+      <td>Aluminum alloys, 7075-T6, Sheet</td>
+      <td>http://my.server.name/mi/datasheet.aspx?dbKey=...</td>
+      <td>Tensile Stress/Strain, L</td>
+      <td>check_attribute_is_monotonically_increasing</td>
+      <td>None</td>
+      <td>Skipped</td>
+    </tr>
+    <tr>
+      <th>24</th>
+      <td>Titanium alloys, Ti-6Al-4V</td>
+      <td>http://my.server.name/mi/datasheet.aspx?dbKey=...</td>
+      <td>Statistical Basis</td>
+      <td>check_attribute_is_populated</td>
+      <td>False</td>
+      <td>Attribute "Statistical Basis" is not populated...</td>
+    </tr>
+    <tr>
+      <th>31</th>
+      <td>Titanium alloys, Ti-6Al-4V</td>
+      <td>http://my.server.name/mi/datasheet.aspx?dbKey=...</td>
+      <td>Tensile Stress/Strain, L</td>
+      <td>check_attribute_is_populated</td>
+      <td>False</td>
+      <td>Attribute "Tensile Stress/Strain, L" is not po...</td>
+    </tr>
+    <tr>
+      <th>32</th>
+      <td>Titanium alloys, Ti-6Al-4V</td>
+      <td>http://my.server.name/mi/datasheet.aspx?dbKey=...</td>
+      <td>Tensile Stress/Strain, L</td>
+      <td>check_attribute_is_monotonically_increasing</td>
+      <td>None</td>
+      <td>Skipped</td>
+    </tr>
+    <tr>
+      <th>38</th>
+      <td>Nickel alloys, Inconel 718, Forging</td>
+      <td>http://my.server.name/mi/datasheet.aspx?dbKey=...</td>
+      <td>Thermal Conductivity</td>
+      <td>check_attribute_is_populated</td>
+      <td>False</td>
+      <td>Attribute "Thermal Conductivity" is not popula...</td>
+    </tr>
+    <tr>
+      <th>39</th>
+      <td>Nickel alloys, Inconel 718, Forging</td>
+      <td>http://my.server.name/mi/datasheet.aspx?dbKey=...</td>
+      <td>Thermal Conductivity</td>
+      <td>check_attribute_is_positive</td>
+      <td>None</td>
+      <td>Skipped</td>
+    </tr>
+    <tr>
+      <th>42</th>
+      <td>Nickel alloys, Inconel 718, Forging</td>
+      <td>http://my.server.name/mi/datasheet.aspx?dbKey=...</td>
+      <td>Tensile Stress/Strain, L</td>
+      <td>check_attribute_is_populated</td>
+      <td>False</td>
+      <td>Attribute "Tensile Stress/Strain, L" is not po...</td>
+    </tr>
+    <tr>
+      <th>43</th>
+      <td>Nickel alloys, Inconel 718, Forging</td>
+      <td>http://my.server.name/mi/datasheet.aspx?dbKey=...</td>
+      <td>Tensile Stress/Strain, L</td>
+      <td>check_attribute_is_monotonically_increasing</td>
+      <td>None</td>
+      <td>Skipped</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+Alternatively, write the complete set of results to Excel, making it easy to review and share the validation results
+with other Granta MI data stakeholders.
+
+First, ensure the output directory exists. This directory will already exist if a notebook
+was run previously that saved a file to disk.
+
+
+```python
+from pathlib import Path
+output_folder = Path("./output")
+output_folder.mkdir(exist_ok=True)
+```
+
+Next, save the validation results.
+
+
+```python
+df_results.to_excel("output/13_validation_results.xlsx")
+```
+
+## Next steps
+
+The data validation framework shown here illustrates a scalable and extensible approach to validating data in Granta
+MI with the Scripting Toolkit. It shows only very basic data validation operations though, and could be extended in
+the following ways:
+
+* Additional text validation rules. For example, validating spelling and grammar in long text attributes.
+* Additional numeric validation rules. For example, comparing attribute values to each other, or to some generally
+  accepted value range.
+* Additional functional validation rules. For example, checking that a functional attribute evaluated at room
+  temperature is equal to a certain attribute.
+* Automatically modifying the database to ensure consistency.
+* Running the script automatically. For more information see
+  [Storing Credentials Securely](../streamlined/21_Storing_credentials_securely.ipynb).
+* Tracking previous failures to provide a delta between multiple validation runs.
+* Using a Python testing framework like [pytest](https://docs.pytest.org/en/stable/) to organize test cases
+  and provide result reports.
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HcmV?d00001

diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/foundation/Deleting_records_and_other_new_v1_5_features.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/foundation/Deleting_records_and_other_new_v1_5_features.md
new file mode 100644
index 0000000000..5b56f02784
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/foundation/Deleting_records_and_other_new_v1_5_features.md
@@ -0,0 +1,399 @@
+# New Features in MI Scripting Toolkit v1.5
+
+## Deleting records (DeleteOrWithdrawIfLatestRecordVersion)
+
+Let's start by creating a new record (Note: added receiveTimeout to the MISession)
+
+
+```python
+import GRANTA_MIScriptingToolkit as gdl
+from time import strftime
+import datetime
+
+session = gdl.GRANTA_MISession('http://my.server.name/mi_servicelayer/', autoLogon=True, receiveTimeout=5000)
+
+raw_record_name = 'STK_1_5_Import_Test'
+rec_name = raw_record_name + '_' + strftime("%Y-%m-%d %H:%M:%S")
+table_name = 'Tensile Test Data'
+subset_name = table_name
+db_key = 'MI_Training'
+
+import_service = session.dataImportService
+browse_service = session.browseService
+export_service = session.dataExportService
+
+test_table_ref = gdl.TableReference(name=table_name, DBKey=db_key)
+partial_table_ref = gdl.PartialTableReference(tableName=table_name)
+subset_ref = gdl.SubsetReference(DBKey=db_key, partialTableReference=partial_table_ref, name=subset_name)
+table_root_ref = browse_service.GetRootNode(gdl.GetRootNode(table=test_table_ref)).rootNode.recordReference
+
+import_record = gdl.ImportRecord(recordName=rec_name, importAttributeValues=None, isFolder=False,
+                                 existingRecord=table_root_ref, subsetReferences=[subset_ref])
+
+import_request = gdl.SetRecordAttributesRequest(importRecords=[import_record])
+imported_records = import_service.SetRecordAttributes(import_request).recordsImported
+imported_record_ref = imported_records[0].recordReference
+print('Imported record "{}" to table "{}"'.format(imported_records[0].longName, table_name))
+```
+*Previous cell output:*
+```output
+Imported record "STK_1_5_Import_Test_2024-10-30 16:11:33" to table "Tensile Test Data"
+```
+Now let's delete it!
+
+
+```python
+delete_record = gdl.DeleteOrWithdrawRecord(recordReference=imported_record_ref)
+records_to_delete = [delete_record]
+
+delete_records_request = gdl.DeleteOrWithdrawIfLatestRecordVersionRequest(deleteOrWithdrawRecords=records_to_delete)
+delete_records_response = import_service.DeleteOrWithdrawIfLatestRecordVersion(delete_records_request)
+print('Record {} was deleted'.format(imported_records[0].longName))
+```
+*Previous cell output:*
+```output
+Record STK_1_5_Import_Test_2024-10-30 16:11:33 was deleted
+```
+## ResolveReferences
+
+Let's see if we can modify a record...
+
+
+```python
+modfied_record_guid = '4269f1bc-df6b-4a6d-870c-ef931728f37b'
+modified_record_ref = gdl.RecordReference(DBKey=db_key, recordGUID=modfied_record_guid)
+
+resolve_refs_request = gdl.ResolveReferencesRequest(entities=[modified_record_ref])
+
+can_modify_int = browse_service.ResolveReferences(resolve_refs_request).entityResolutions[0].canWrite
+if can_modify_int == 0:
+    print('Yes, we can edit this record.')
+else:
+    print("No, we don't have permission to edit this record.")
+```
+*Previous cell output:*
+```output
+Yes, we can edit this record.
+```
+## lastModifier and last modifiedDate pseudo-attributes
+
+If we can modify the record, let's make a small change to it...
+
+
+```python
+if can_modify_int == 0:
+    attribute_project_notes = 'Project Notes'
+    attribute_val = 'Project was updated: {0}'.format(datetime.datetime.now())
+    attribute_ref = gdl.AttributeReference(name=attribute_project_notes, DBKey=db_key, partialTableReference=partial_table_ref)
+    import_attribute_val = gdl.ImportAttributeValue(longTextDataValue=gdl.LongTextDataType(value=attribute_val),
+                                                    attributeReference=attribute_ref) 
+    update_record = gdl.ImportRecord(recordName=None,
+                                     importRecordMode='Update',
+                                     importAttributeValues=[import_attribute_val],
+                                     existingRecord=modified_record_ref)
+
+    update_request = gdl.SetRecordAttributesRequest(importRecords=[update_record])
+    updated_records = import_service.SetRecordAttributes(update_request).recordsImported
+    updated_record_ref = updated_records[0].recordReference
+    updated_record_name = updated_records[0].longName
+    print('Updated record "{}"'.format(updated_record_name))
+else:
+    print("Nothing to do - we can't modify the record!")
+```
+*Previous cell output:*
+```output
+Updated record "MTS-615723"
+```
+Now let's retrieve the record creator, createdDate, lastModifier and modifiedDate
+
+
+```python
+pseudo_creator = gdl.AttributeReference(pseudoAttribute=gdl.AttributeReference.MIPseudoAttributeReference.creator,
+                                        DBKey=db_key, partialTableReference=partial_table_ref)
+pseudo_created_date = gdl.AttributeReference(pseudoAttribute=gdl.AttributeReference.MIPseudoAttributeReference.createdDate,
+                                             DBKey=db_key, partialTableReference=partial_table_ref)
+pseudo_last_modifier = gdl.AttributeReference(pseudoAttribute=gdl.AttributeReference.MIPseudoAttributeReference.lastModifier,
+                                              DBKey=db_key, partialTableReference=partial_table_ref)
+pseudo_modified_date = gdl.AttributeReference(pseudoAttribute=gdl.AttributeReference.MIPseudoAttributeReference.modifiedDate,
+                                              DBKey=db_key, partialTableReference=partial_table_ref)
+attribute_refs = [pseudo_creator, pseudo_created_date, pseudo_last_modifier, pseudo_modified_date]
+
+export_request = gdl.GetRecordAttributesByRefRequest(recordReferences=[modified_record_ref],
+                                                     attributeReferences=attribute_refs)
+
+exported_data = export_service.GetRecordAttributesByRef(export_request).recordData
+for record in exported_data:
+    creator = [a.shortTextDataType.value for a in record.attributeValues if a.attributeName == '[creator]'][0]
+    created_date = [a.dateTimeDataType.value for a in record.attributeValues if a.attributeName == '[createdDate]'][0]
+    modifier = [a.shortTextDataType.value for a in record.attributeValues if a.attributeName == '[lastModifier]'][0]
+    modified_date = [a.dateTimeDataType.value for a in record.attributeValues if a.attributeName == '[modifiedDate]'][0]
+    print('"{}" imported the record on {}'.format(creator, created_date))
+    print('The record was last modified on {} by "{}"'.format(modified_date, modifier))
+```
+*Previous cell output:*
+```output
+"Ansys Granta 1" imported the record on 2023-09-20T12:11:05.873
+The record was last modified on 2024-10-30T16:11:34.413 by "ANSYS\mi-sw-admin"
+```
+## GetUnitConversions
+
+For the above record, let's export the Young's Modulus in the Metric system
+
+
+```python
+attribute_name = "Young's Modulus (11-axis)"
+unit_context_metric = gdl.UnitConversionContext(unitSystem='Metric')
+
+modulus_attribute_ref = gdl.AttributeReference(name=attribute_name, DBKey=db_key, partialTableReference=partial_table_ref)
+export_request = gdl.GetRecordAttributesByRefRequest(recordReferences=[modified_record_ref],
+                                                     attributeReferences=[modulus_attribute_ref])
+
+exported_data = export_service.GetRecordAttributesByRef(export_request).recordData
+modulus_value = [[a.pointDataType.points[0].value for a in r.attributeValues] for r in exported_data][0][0]
+modulus_metric_symbol = [[a.pointDataType.unitSymbol for a in r.attributeValues] for r in exported_data][0][0]
+print("Young's modulus = {} {}".format(modulus_value, modulus_metric_symbol))
+```
+*Previous cell output:*
+```output
+Young's modulus = 174.0004272460938 GPa
+```
+Now let's convert its value with all available conversions in Granta MI
+
+
+```python
+import pandas
+
+unit_conversions_request = gdl.GetUnitConversionsRequest(DBKey=db_key, unitSymbols=[modulus_metric_symbol])
+
+source_units = browse_service.GetUnitConversions(unit_conversions_request).sourceUnits
+conversion_targets = [c.conversions for c in source_units]
+factors_and_offsets = [[(c.factor, c.offset) for c in c_target] for c_target in conversion_targets][0]
+symbols = [[c.targetSymbol for c in c_target] for c_target in conversion_targets][0]
+results = [modulus_value * factor + offset for factor, offset in factors_and_offsets]
+equations = ['{} * {} + {}'.format(modulus_value, factor, offset) for factor, offset in factors_and_offsets]
+
+factors, offsets = zip(*factors_and_offsets)
+df = pandas.DataFrame([factors, offsets, equations, results])
+df_flipped = df.transpose()
+df_flipped.columns = ['factor', 'offset', 'equation', 'converted result']
+df_flipped.index = symbols
+df_flipped.style
+```
+
+
+
+
+<style type="text/css">
+</style>
+<table id="T_e4f16">
+  <thead>
+    <tr>
+      <th class="blank level0" >&nbsp;</th>
+      <th id="T_e4f16_level0_col0" class="col_heading level0 col0" >factor</th>
+      <th id="T_e4f16_level0_col1" class="col_heading level0 col1" >offset</th>
+      <th id="T_e4f16_level0_col2" class="col_heading level0 col2" >equation</th>
+      <th id="T_e4f16_level0_col3" class="col_heading level0 col3" >converted result</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th id="T_e4f16_level0_row0" class="row_heading level0 row0" >10^6 psi</th>
+      <td id="T_e4f16_row0_col0" class="data row0 col0" >0.145038</td>
+      <td id="T_e4f16_row0_col1" class="data row0 col1" >0.000000</td>
+      <td id="T_e4f16_row0_col2" class="data row0 col2" >174.0004272460938 * 0.14503773773039605 + 0.0</td>
+      <td id="T_e4f16_row0_col3" class="data row0 col3" >25.236628</td>
+    </tr>
+    <tr>
+      <th id="T_e4f16_level0_row1" class="row_heading level0 row1" >ksi</th>
+      <td id="T_e4f16_row1_col0" class="data row1 col0" >145.037738</td>
+      <td id="T_e4f16_row1_col1" class="data row1 col1" >0.000000</td>
+      <td id="T_e4f16_row1_col2" class="data row1 col2" >174.0004272460938 * 145.03773773039603 + 0.0</td>
+      <td id="T_e4f16_row1_col3" class="data row1 col3" >25236.628332</td>
+    </tr>
+    <tr>
+      <th id="T_e4f16_level0_row2" class="row_heading level0 row2" >psi</th>
+      <td id="T_e4f16_row2_col0" class="data row2 col0" >145037.737730</td>
+      <td id="T_e4f16_row2_col1" class="data row2 col1" >0.000000</td>
+      <td id="T_e4f16_row2_col2" class="data row2 col2" >174.0004272460938 * 145037.73773039604 + 0.0</td>
+      <td id="T_e4f16_row2_col3" class="data row2 col3" >25236628.331896</td>
+    </tr>
+    <tr>
+      <th id="T_e4f16_level0_row3" class="row_heading level0 row3" >MGO</th>
+      <td id="T_e4f16_row3_col0" class="data row3 col0" >125663.706106</td>
+      <td id="T_e4f16_row3_col1" class="data row3 col1" >0.000000</td>
+      <td id="T_e4f16_row3_col2" class="data row3 col2" >174.0004272460938 * 125663.70610560982 + 0.0</td>
+      <td id="T_e4f16_row3_col3" class="data row3 col3" >21865538.551704</td>
+    </tr>
+    <tr>
+      <th id="T_e4f16_level0_row4" class="row_heading level0 row4" >Pa</th>
+      <td id="T_e4f16_row4_col0" class="data row4 col0" >1000000000.000000</td>
+      <td id="T_e4f16_row4_col1" class="data row4 col1" >0.000000</td>
+      <td id="T_e4f16_row4_col2" class="data row4 col2" >174.0004272460938 * 1000000000.0 + 0.0</td>
+      <td id="T_e4f16_row4_col3" class="data row4 col3" >174000427246.093811</td>
+    </tr>
+    <tr>
+      <th id="T_e4f16_level0_row5" class="row_heading level0 row5" >MPa</th>
+      <td id="T_e4f16_row5_col0" class="data row5 col0" >1000.000000</td>
+      <td id="T_e4f16_row5_col1" class="data row5 col1" >0.000000</td>
+      <td id="T_e4f16_row5_col2" class="data row5 col2" >174.0004272460938 * 1000.0 + 0.0</td>
+      <td id="T_e4f16_row5_col3" class="data row5 col3" >174000.427246</td>
+    </tr>
+    <tr>
+      <th id="T_e4f16_level0_row6" class="row_heading level0 row6" >J/m^3</th>
+      <td id="T_e4f16_row6_col0" class="data row6 col0" >1000000000.000000</td>
+      <td id="T_e4f16_row6_col1" class="data row6 col1" >0.000000</td>
+      <td id="T_e4f16_row6_col2" class="data row6 col2" >174.0004272460938 * 1000000000.0 + 0.0</td>
+      <td id="T_e4f16_row6_col3" class="data row6 col3" >174000427246.093811</td>
+    </tr>
+    <tr>
+      <th id="T_e4f16_level0_row7" class="row_heading level0 row7" >MJ/m^3</th>
+      <td id="T_e4f16_row7_col0" class="data row7 col0" >1000.000000</td>
+      <td id="T_e4f16_row7_col1" class="data row7 col1" >0.000000</td>
+      <td id="T_e4f16_row7_col2" class="data row7 col2" >174.0004272460938 * 1000.0 + 0.0</td>
+      <td id="T_e4f16_row7_col3" class="data row7 col3" >174000.427246</td>
+    </tr>
+    <tr>
+      <th id="T_e4f16_level0_row8" class="row_heading level0 row8" >erg/cm^3</th>
+      <td id="T_e4f16_row8_col0" class="data row8 col0" >10000000000.000002</td>
+      <td id="T_e4f16_row8_col1" class="data row8 col1" >0.000000</td>
+      <td id="T_e4f16_row8_col2" class="data row8 col2" >174.0004272460938 * 10000000000.000002 + 0.0</td>
+      <td id="T_e4f16_row8_col3" class="data row8 col3" >1740004272460.938477</td>
+    </tr>
+    <tr>
+      <th id="T_e4f16_level0_row9" class="row_heading level0 row9" >ft.lbf/in^3</th>
+      <td id="T_e4f16_row9_col0" class="data row9 col0" >12086.478144</td>
+      <td id="T_e4f16_row9_col1" class="data row9 col1" >0.000000</td>
+      <td id="T_e4f16_row9_col2" class="data row9 col2" >174.0004272460938 * 12086.478144199671 + 0.0</td>
+      <td id="T_e4f16_row9_col3" class="data row9 col3" >2103052.360991</td>
+    </tr>
+    <tr>
+      <th id="T_e4f16_level0_row10" class="row_heading level0 row10" >kJ/m^3</th>
+      <td id="T_e4f16_row10_col0" class="data row10 col0" >1000000.000000</td>
+      <td id="T_e4f16_row10_col1" class="data row10 col1" >0.000000</td>
+      <td id="T_e4f16_row10_col2" class="data row10 col2" >174.0004272460938 * 1000000.0 + 0.0</td>
+      <td id="T_e4f16_row10_col3" class="data row10 col3" >174000427.246094</td>
+    </tr>
+    <tr>
+      <th id="T_e4f16_level0_row11" class="row_heading level0 row11" >inHg</th>
+      <td id="T_e4f16_row11_col0" class="data row11 col0" >295299.714445</td>
+      <td id="T_e4f16_row11_col1" class="data row11 col1" >0.000000</td>
+      <td id="T_e4f16_row11_col2" class="data row11 col2" >174.0004272460938 * 295299.7144451761 + 0.0</td>
+      <td id="T_e4f16_row11_col3" class="data row11 col3" >51382276.479110</td>
+    </tr>
+    <tr>
+      <th id="T_e4f16_level0_row12" class="row_heading level0 row12" >Ba</th>
+      <td id="T_e4f16_row12_col0" class="data row12 col0" >10000000000.000000</td>
+      <td id="T_e4f16_row12_col1" class="data row12 col1" >0.000000</td>
+      <td id="T_e4f16_row12_col2" class="data row12 col2" >174.0004272460938 * 10000000000.0 + 0.0</td>
+      <td id="T_e4f16_row12_col3" class="data row12 col3" >1740004272460.937988</td>
+    </tr>
+    <tr>
+      <th id="T_e4f16_level0_row13" class="row_heading level0 row13" >hPa</th>
+      <td id="T_e4f16_row13_col0" class="data row13 col0" >10000000.000000</td>
+      <td id="T_e4f16_row13_col1" class="data row13 col1" >0.000000</td>
+      <td id="T_e4f16_row13_col2" class="data row13 col2" >174.0004272460938 * 10000000.0 + 0.0</td>
+      <td id="T_e4f16_row13_col3" class="data row13 col3" >1740004272.460938</td>
+    </tr>
+    <tr>
+      <th id="T_e4f16_level0_row14" class="row_heading level0 row14" >mb</th>
+      <td id="T_e4f16_row14_col0" class="data row14 col0" >10000000.000000</td>
+      <td id="T_e4f16_row14_col1" class="data row14 col1" >0.000000</td>
+      <td id="T_e4f16_row14_col2" class="data row14 col2" >174.0004272460938 * 10000000.0 + 0.0</td>
+      <td id="T_e4f16_row14_col3" class="data row14 col3" >1740004272.460938</td>
+    </tr>
+    <tr>
+      <th id="T_e4f16_level0_row15" class="row_heading level0 row15" >bar</th>
+      <td id="T_e4f16_row15_col0" class="data row15 col0" >10000.000000</td>
+      <td id="T_e4f16_row15_col1" class="data row15 col1" >0.000000</td>
+      <td id="T_e4f16_row15_col2" class="data row15 col2" >174.0004272460938 * 10000.0 + 0.0</td>
+      <td id="T_e4f16_row15_col3" class="data row15 col3" >1740004.272461</td>
+    </tr>
+    <tr>
+      <th id="T_e4f16_level0_row16" class="row_heading level0 row16" >atm</th>
+      <td id="T_e4f16_row16_col0" class="data row16 col0" >9869.232667</td>
+      <td id="T_e4f16_row16_col1" class="data row16 col1" >0.000000</td>
+      <td id="T_e4f16_row16_col2" class="data row16 col2" >174.0004272460938 * 9869.232667160128 + 0.0</td>
+      <td id="T_e4f16_row16_col3" class="data row16 col3" >1717250.700677</td>
+    </tr>
+    <tr>
+      <th id="T_e4f16_level0_row17" class="row_heading level0 row17" >torr</th>
+      <td id="T_e4f16_row17_col0" class="data row17 col0" >7500637.554192</td>
+      <td id="T_e4f16_row17_col1" class="data row17 col1" >0.000000</td>
+      <td id="T_e4f16_row17_col2" class="data row17 col2" >174.0004272460938 * 7500637.554192106 + 0.0</td>
+      <td id="T_e4f16_row17_col3" class="data row17 col3" >1305114139.047523</td>
+    </tr>
+    <tr>
+      <th id="T_e4f16_level0_row18" class="row_heading level0 row18" >lbf/ft^2</th>
+      <td id="T_e4f16_row18_col0" class="data row18 col0" >20885434.233177</td>
+      <td id="T_e4f16_row18_col1" class="data row18 col1" >0.000000</td>
+      <td id="T_e4f16_row18_col2" class="data row18 col2" >174.0004272460938 * 20885434.233177025 + 0.0</td>
+      <td id="T_e4f16_row18_col3" class="data row18 col3" >3634074479.792996</td>
+    </tr>
+    <tr>
+      <th id="T_e4f16_level0_row19" class="row_heading level0 row19" >HV</th>
+      <td id="T_e4f16_row19_col0" class="data row19 col0" >101.971621</td>
+      <td id="T_e4f16_row19_col1" class="data row19 col1" >0.000000</td>
+      <td id="T_e4f16_row19_col2" class="data row19 col2" >174.0004272460938 * 101.97162129779282 + 0.0</td>
+      <td id="T_e4f16_row19_col3" class="data row19 col3" >17743.105673</td>
+    </tr>
+    <tr>
+      <th id="T_e4f16_level0_row20" class="row_heading level0 row20" >kgf/mm^2</th>
+      <td id="T_e4f16_row20_col0" class="data row20 col0" >101.971621</td>
+      <td id="T_e4f16_row20_col1" class="data row20 col1" >0.000000</td>
+      <td id="T_e4f16_row20_col2" class="data row20 col2" >174.0004272460938 * 101.97162129779282 + 0.0</td>
+      <td id="T_e4f16_row20_col3" class="data row20 col3" >17743.105673</td>
+    </tr>
+    <tr>
+      <th id="T_e4f16_level0_row21" class="row_heading level0 row21" >dyn/cm^2</th>
+      <td id="T_e4f16_row21_col0" class="data row21 col0" >10000000000.000000</td>
+      <td id="T_e4f16_row21_col1" class="data row21 col1" >0.000000</td>
+      <td id="T_e4f16_row21_col2" class="data row21 col2" >174.0004272460938 * 10000000000.0 + 0.0</td>
+      <td id="T_e4f16_row21_col3" class="data row21 col3" >1740004272460.937988</td>
+    </tr>
+    <tr>
+      <th id="T_e4f16_level0_row22" class="row_heading level0 row22" >ft.lbf/ft^3</th>
+      <td id="T_e4f16_row22_col0" class="data row22 col0" >20885434.233177</td>
+      <td id="T_e4f16_row22_col1" class="data row22 col1" >0.000000</td>
+      <td id="T_e4f16_row22_col2" class="data row22 col2" >174.0004272460938 * 20885434.23317702 + 0.0</td>
+      <td id="T_e4f16_row22_col3" class="data row22 col3" >3634074479.792995</td>
+    </tr>
+    <tr>
+      <th id="T_e4f16_level0_row23" class="row_heading level0 row23" >in.lbf/in^3</th>
+      <td id="T_e4f16_row23_col0" class="data row23 col0" >145037.737730</td>
+      <td id="T_e4f16_row23_col1" class="data row23 col1" >0.000000</td>
+      <td id="T_e4f16_row23_col2" class="data row23 col2" >174.0004272460938 * 145037.73773039604 + 0.0</td>
+      <td id="T_e4f16_row23_col3" class="data row23 col3" >25236628.331896</td>
+    </tr>
+    <tr>
+      <th id="T_e4f16_level0_row24" class="row_heading level0 row24" >J/cm^3</th>
+      <td id="T_e4f16_row24_col0" class="data row24 col0" >1000.000000</td>
+      <td id="T_e4f16_row24_col1" class="data row24 col1" >0.000000</td>
+      <td id="T_e4f16_row24_col2" class="data row24 col2" >174.0004272460938 * 1000.0000000000001 + 0.0</td>
+      <td id="T_e4f16_row24_col3" class="data row24 col3" >174000.427246</td>
+    </tr>
+    <tr>
+      <th id="T_e4f16_level0_row25" class="row_heading level0 row25" >kN/cm^2</th>
+      <td id="T_e4f16_row25_col0" class="data row25 col0" >100.000000</td>
+      <td id="T_e4f16_row25_col1" class="data row25 col1" >0.000000</td>
+      <td id="T_e4f16_row25_col2" class="data row25 col2" >174.0004272460938 * 100.0 + 0.0</td>
+      <td id="T_e4f16_row25_col3" class="data row25 col3" >17400.042725</td>
+    </tr>
+    <tr>
+      <th id="T_e4f16_level0_row26" class="row_heading level0 row26" >Msi</th>
+      <td id="T_e4f16_row26_col0" class="data row26 col0" >0.145038</td>
+      <td id="T_e4f16_row26_col1" class="data row26 col1" >0.000000</td>
+      <td id="T_e4f16_row26_col2" class="data row26 col2" >174.0004272460938 * 0.14503773773039605 + 0.0</td>
+      <td id="T_e4f16_row26_col3" class="data row26 col3" >25.236628</td>
+    </tr>
+    <tr>
+      <th id="T_e4f16_level0_row27" class="row_heading level0 row27" >N/mm^2</th>
+      <td id="T_e4f16_row27_col0" class="data row27 col0" >1000.000000</td>
+      <td id="T_e4f16_row27_col1" class="data row27 col1" >0.000000</td>
+      <td id="T_e4f16_row27_col2" class="data row27 col2" >174.0004272460938 * 1000.0 + 0.0</td>
+      <td id="T_e4f16_row27_col3" class="data row27 col3" >174000.427246</td>
+    </tr>
+  </tbody>
+</table>
+
+
+
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/foundation/Exporting_data.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/foundation/Exporting_data.md
new file mode 100644
index 0000000000..230baa192c
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/foundation/Exporting_data.md
@@ -0,0 +1,293 @@
+# Exporting data
+
+Export the values of Ultimate tensile strength, Modulus, Baseline fiber volume, and Poisson's ratio for a set of records from the MI Training database.
+
+This example demonstrates:
+
+* Retrieving a list of databases from a Granta MI server
+* Browsing the tables of a Granta MI database
+* Searching a Granta MI database using the *CriteriaSearch* operation
+* Retrieving values for attributes of a record
+
+## Create a Granta MI Session
+
+Import the GRANTA_MIScriptingToolkit package, and create a connection to a Granta MI server.
+
+
+```python
+import GRANTA_MIScriptingToolkit as gdl
+
+session = gdl.GRANTA_MISession('http://my.server.name/mi_servicelayer', autoLogon=True)
+```
+
+## Get the Databases
+
+Access the browse service from the session and execute the *GetDatabases* method.
+
+
+```python
+browseService = session.browseService
+databases = browseService.GetDatabases().databases
+
+print("Found {0} databases on the Granta MI Server".format(len(databases)))
+for d in databases:
+    print("Database key: {0.DBKey}, Database name: {0.volumeName}".format(d))
+```
+*Previous cell output:*
+```output
+Found 1 databases on the Granta MI Server
+Database key: MI_Training, Database name: MI Training
+```
+Use pandas to display the available databases.
+
+
+```python
+import pandas as pd
+
+df = pd.DataFrame({'DBKey': [db.DBKey for db in databases],
+                  'DBName': [db.volumeName for db in databases]})
+df
+```
+
+
+
+
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>DBKey</th>
+      <th>DBName</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>MI_Training</td>
+      <td>MI Training</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+## Get the tables in a database
+
+Use the *GetTables* method from the browse service to see what tables are available in the MI Training database.
+
+
+```python
+dbKey = "MI_Training"
+
+tables = browseService.GetTables(gdl.GetTables(DBKey=dbKey)).tableDetails
+
+print("Found {0} tables in database {1}".format(len(tables), dbKey))
+print("Printing the first 5")
+for t in tables[:5]:
+    print("Table name: {0}".format(t.tableReference.name))
+```
+*Previous cell output:*
+```output
+Found 20 tables in database MI_Training
+Printing the first 5
+Table name: MaterialUniverse
+Table name: ProcessUniverse
+Table name: Restricted Substances
+Table name: Legislations and Lists
+Table name: Design Data
+```
+## Searching a database
+
+Search MI Training for all records in the Tensile Test Data table which have a defined *Young's modulus (11-axis) (normalized)*
+
+
+```python
+table = 'Tensile Test Data'
+attribute = "Young's modulus (11-axis) (normalized)"
+
+tableRef = gdl.PartialTableReference(tableName=table)
+attrRef = gdl.AttributeReference(name=attribute, DBKey=dbKey, partialTableReference=tableRef)
+searchCriterion = gdl.RecordSearchCriterion(searchAttribute=attrRef, existsSearchValue=gdl.ExistsSearchValue())
+request = gdl.CriteriaSearch(DBKey=dbKey, searchCriteria=[searchCriterion])
+
+searchResults = session.searchService.CriteriaSearch(request).searchResults
+```
+
+Print the *shortName* and *longName* of the first 5 records returned by the search.
+
+
+```python
+df2 = pd.DataFrame({'ShortName': [r.shortName for r in searchResults],
+                     'LongName': [r.longName  for r in searchResults]})
+df2.head()
+```
+
+
+
+
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>ShortName</th>
+      <th>LongName</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>LBJ8311A</td>
+      <td>S-Glass Unitape S2/SP381, 3M, 0° tension, Spec...</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>LBJ6213A</td>
+      <td>S-Glass Unitape S2/SP381, 3M, 0° tension, Spec...</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>LBJ4211A</td>
+      <td>S-Glass Unitape S2/SP381, 3M, 0° tension, Spec...</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>LBJ4213A</td>
+      <td>S-Glass Unitape S2/SP381, 3M, 0° tension, Spec...</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>LBJ6214A</td>
+      <td>S-Glass Unitape S2/SP381, 3M, 0° tension, Spec...</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+## Export data from records
+
+Create attribute references for the attributes you want to export and export data from all records.
+
+
+```python
+attributes = ["Young's Modulus (11-axis) (normalized)", "Ultimate tensile strength (normalized)",
+              "Baseline fiber volume", "Elastic Poisson's Ratio (12-plane)"]
+
+attrRefs = [gdl.AttributeReference(name=a, DBKey=dbKey, partialTableReference=tableRef) for a in attributes]
+recordRefs = [r.recordReference for r in searchResults]
+request = gdl.GetRecordAttributesByRefRequest(recordReferences=recordRefs, attributeReferences=attrRefs)
+       
+recordData = session.dataExportService.GetRecordAttributesByRef(request).recordData
+```
+
+Print the values of the attributes from the first 5 exported records. Note that some records may not have values for
+all attributes.
+
+
+```python
+s = [None]*len(df2)
+for attribute in attributes:
+    for idx, record in enumerate(recordData):
+        attrValue = next((x for x in record.attributeValues if x.attributeName == attribute), None)
+        s[idx] = attrValue.pointDataType.points[0].value if attrValue else None
+    df2[attribute] = s
+    
+df2.head()
+```
+
+
+
+
+<div>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>ShortName</th>
+      <th>LongName</th>
+      <th>Young's Modulus (11-axis) (normalized)</th>
+      <th>Ultimate tensile strength (normalized)</th>
+      <th>Baseline fiber volume</th>
+      <th>Elastic Poisson's Ratio (12-plane)</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>LBJ8311A</td>
+      <td>S-Glass Unitape S2/SP381, 3M, 0° tension, Spec...</td>
+      <td>48.428779</td>
+      <td>1556.643278</td>
+      <td>53.473</td>
+      <td>0.272</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>LBJ6213A</td>
+      <td>S-Glass Unitape S2/SP381, 3M, 0° tension, Spec...</td>
+      <td>47.911673</td>
+      <td>1392.975515</td>
+      <td>53.473</td>
+      <td>0.266</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>LBJ4211A</td>
+      <td>S-Glass Unitape S2/SP381, 3M, 0° tension, Spec...</td>
+      <td>48.028883</td>
+      <td>1535.152318</td>
+      <td>53.473</td>
+      <td>0.242</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>LBJ4213A</td>
+      <td>S-Glass Unitape S2/SP381, 3M, 0° tension, Spec...</td>
+      <td>48.042673</td>
+      <td>1601.486784</td>
+      <td>53.473</td>
+      <td>0.259</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>LBJ6214A</td>
+      <td>S-Glass Unitape S2/SP381, 3M, 0° tension, Spec...</td>
+      <td>46.387931</td>
+      <td>1322.511090</td>
+      <td>53.473</td>
+      <td>0.264</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+Create a plot of the exported values
+
+
+```python
+import matplotlib
+
+df2.plot.scatter(x="Ultimate tensile strength (normalized)", y="Young's Modulus (11-axis) (normalized)", loglog=False,
+                 c="Elastic Poisson's Ratio (12-plane)", colormap="afmhot", norm=matplotlib.colors.LogNorm(),
+                 grid=True, figsize=(10, 7))
+```
+
+
+
+*Previous cell output:*
+```output
+<Axes: xlabel='Ultimate tensile strength (normalized)', ylabel="Young's Modulus (11-axis) (normalized)">
+```
+
+
+
+    
+![png](Exporting_data_files/Exporting_data_17_1.png)
+    
+
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/foundation/Exporting_data_files/Exporting_data_17_1.png b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/foundation/Exporting_data_files/Exporting_data_17_1.png
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diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/foundation/Exporting_data_using_EngineeringDataService.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/foundation/Exporting_data_using_EngineeringDataService.md
new file mode 100644
index 0000000000..1e3028ff43
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/foundation/Exporting_data_using_EngineeringDataService.md
@@ -0,0 +1,169 @@
+# Exporting data using the Engineering Data Service
+
+Find a list of FEA exporters for a record in a Granta MI database, and export relevant parameter values.
+
+This example demonstrates:
+
+- Retrieving a list of available exporters
+- Retrieving a list exporters applicable to a specific record in a Granta MI database
+- Get the parameters that can be exported for a specific exporter and record combination
+- Run the FEA exporter on the specific record
+
+## Create a Granta MI Session
+
+Import the GRANTA_MIScriptingToolkit package, and create a connection to a Granta MI server.
+
+
+```python
+import GRANTA_MIScriptingToolkit as gdl
+
+session = gdl.GRANTA_MISession('http://my.server.name/mi_servicelayer', autoLogon=True)
+```
+
+## List the available exporters
+Retrieve the list of FEA exporters that will export data from the database MI_Training into Ansys Workbench format.
+
+
+```python
+dbKey = "MI_Training"
+request = gdl.GetAvailableExportersRequest(DBKey=dbKey, package="ANSYS Workbench", matchDB=True)
+
+print("\nOutput of available Exporters for Ansys Workbench")
+response = session.engineeringDataService.GetAvailableExporters(request)
+
+for exporter in response.exporters:
+    print("{0} ({1}) - {2}".format(exporter.name, exporter.package, exporter.description))
+```
+*Previous cell output:*
+```output
+
+Output of available Exporters for Ansys Workbench
+```*Previous cell output:*
+```output
+458E9A7E-C268-4ED0-9CC1-FF7438521B4F (ANSYS Workbench) - Exports linear, temperature-independent, isotropic data to the Ansys Workbench format
+CE8DCFA2-B3EE-4545-8D3E-82810FA92AFC (ANSYS Workbench) - Exports linear, temperature-dependent, isotropic data to the Ansys Workbench format
+4B0B1EA3-8760-43DF-8060-2C79CA471D4C (ANSYS Workbench) - Exports linear, temperature-independent, isotropic with simple failure data to the Ansys Workbench format
+```
+Get a record by name.
+
+
+```python
+req = gdl.RecordNameSearchRequest(
+    recordName="Nickel alloys, Inconel 718, Forging",
+    table=gdl.TableReference(DBKey=dbKey, name="Design Data"),
+    searchShortNames=True
+)
+resp = session.searchService.RecordNameSearch(req)
+print("Found {0} record(s)".format(len(resp.searchResults)))
+rec = resp.searchResults[0].recordReference
+```
+*Previous cell output:*
+```output
+Found 1 record(s)
+```
+## Get exporters for a specific record
+Use the engineering data service to find valid FEA exporters for Inconel 718.
+
+
+```python
+request = gdl.ExportersForRecordsRequest(records=[rec])
+resp = session.engineeringDataService.ExportersForRecords(request)
+print("\nOutput of exporters for Inconel 718")
+for  exporter in resp.records[0].exporters:
+    print("{0} ({1}) - {2}".format(exporter.name, 
+                                   exporter.package, 
+                                   exporter.description))
+```
+*Previous cell output:*
+```output
+
+Output of exporters for Inconel 718
+71CE1C21-FDEA-4119-B481-81BDC41BD900 (Abaqus 6) - Exports temperature dependent, isotropic data to the Abaqus format.
+5C560880-4FD3-4E5C-992B-4B6CEF6A055A (Abaqus 6) - Exports temperature independent, isotropic data to the Abaqus 6 format.
+911AF055-B388-439A-8AF6-EB18480E2D80 (Abaqus 6) - Linear, temperature-independent, isotropic, simple failure
+3AE2BEA5-B1DB-45D3-A431-48915B8D1317 (Abaqus 6) - Linear, temperature-independent, isotropic, simple failure with thermal expansion
+722E5C46-3633-4B72-BF93-74E8112C20C3 (Abaqus 6) - Exports temperature dependent, isotropic data to the Abaqus 6 format.
+B653C213-8BEB-42A7-8512-5F340EEBFAB4 (Abaqus 6) - Exports temperature independent, isotropic data to the Abaqus 6 format.
+458E9A7E-C268-4ED0-9CC1-FF7438521B4F (ANSYS Workbench) - Exports linear, temperature-independent, isotropic data to the Ansys Workbench format
+CE8DCFA2-B3EE-4545-8D3E-82810FA92AFC (ANSYS Workbench) - Exports linear, temperature-dependent, isotropic data to the Ansys Workbench format
+4B0B1EA3-8760-43DF-8060-2C79CA471D4C (ANSYS Workbench) - Exports linear, temperature-independent, isotropic with simple failure data to the Ansys Workbench format
+```
+## Get a list of parameters that can be exported
+Get the parameters that can be exported from Inconel 718 using an exporter that supports them.
+
+
+```python
+exporter = resp.records[0].exporters[7]
+req = gdl.GetExporterParametersRequest(records=[rec], exporterKey=exporter.key)
+expParams = session.engineeringDataService.GetExporterParameters(req)
+for attrib in expParams.records[0].attributes:
+    print(attrib.attribute.name)
+    for param in attrib.parameters:
+        print("\t" + param.name)
+```
+*Previous cell output:*
+```output
+Tensile Modulus (L-dir) with Temp.
+	Temperature
+	Time
+	Other
+```
+## Perform an FEA Export
+Get all applicable attributes for this record.
+
+
+```python
+req = gdl.GetRecordAttributesRequest(recordReferences=[rec])
+attribs = session.browseService.GetRecordAttributes(req)
+```
+
+If exporting a functional data attribute, you also need to define a parameter value to evaluate the attribute at.
+Here, a parameter is fixed at 1.337.
+
+
+```python
+myParam = expParams.records[0].attributes[0].parameters[0]
+pwv = gdl.ParameterReferenceAndValue(parameterValue=gdl.ParameterValue(numericValue=1.337),
+                                     parameter=myParam.parameterReference)
+
+pv = gdl.UnittedParameterValue(unitSymbol=myParam.unit.unitSymbol, 
+                               parameterWithValues=pwv)
+```
+
+Run the FEA exporter, and print the output.
+
+
+```python
+expReq = gdl.ExportRecordDataRequest(
+    attributeReferences=[a.attribute.attribute for a in attribs.recordAttributes], 
+    records=[rec], 
+    exporterKey=exporter.key,
+    parameterValues=[pv]
+)
+
+
+resp = session.engineeringDataService.ExportRecordData(expReq)
+
+print(resp.text[:200] + "...")
+```
+*Previous cell output:*
+```output
+<?xml version="1.0" encoding="utf-8"?>
+<EngineeringData version="14.0.0.367" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
+  <Materials>
+    <MatML_Doc xsi:noNamespaceSchemaLocation="http:...
+```
+Write the output to file. First create an output directory if it doesn't exist, and then save the XML file into that
+directory.
+
+
+```python
+from pathlib import Path
+Path("./output").mkdir(exist_ok=True)
+```
+
+
+```python
+with open("output/engineering_data.xml", "wb") as f:
+    f.write(resp.text.encode("utf-8"))
+```
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/foundation/Functional_data_and_importing_data.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/foundation/Functional_data_and_importing_data.md
new file mode 100644
index 0000000000..1261fa4379
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/foundation/Functional_data_and_importing_data.md
@@ -0,0 +1,275 @@
+# Functional data and importing data
+
+Export tensile stress-strain data from the MI_Training database, and fit a curve to the exported data. Create a new record using parameter values extracted from the fitted curve, and upload this record to the database.
+
+This example demonstrates:
+
+- Browsing for records
+- Extracting functional data from a Granta MI database
+- Importing a new record to a Granta MI database
+
+## Create a Granta MI Session
+
+Import the GRANTA_MIScriptingToolkit package, and create a connection to a Granta MI server.
+
+
+```python
+import datetime
+import sys
+import GRANTA_MIScriptingToolkit as gdl
+
+session = gdl.GRANTA_MISession('http://my.server.name/mi_servicelayer', autoLogon=True)
+```
+
+## Browse for records
+
+Browse for a record located within the Tensile Statistical Data table in the MI_Training database.
+
+Set the folder path for the record to browse to as: High Alloy Steel / AMS 6520 / Plate / Room Temperature °F.
+
+
+```python
+dbKey = 'MI_Training'
+table = 'Tensile Statistical Data'
+folderPath = """High Alloy Steel/AMS 6520/Plate/Room Temperature °F"""
+```
+
+Get the root node of the table of interest.
+
+
+```python
+tableReference = gdl.TableReference(DBKey = dbKey, name = table)
+treeRecord = session.browseService.GetRootNode(gdl.GetRootNode(tableReference)).rootNode
+```
+
+Find the record of interest, *AMS 6520, Plate, Room Temperature °F*.
+
+
+```python
+folders = folderPath.split('/')
+
+for folder in folders:
+    treeRecords = session.browseService.GetChildNodes(gdl.GetChildNodes(parent=treeRecord.recordReference)).treeRecords
+    treeRecord = next((r for r in treeRecords if r.shortName == folder), None)
+    if treeRecord:
+        print("Found treeRecord: {0}".format(treeRecord.shortName))
+    else:
+        print("Unable to find folder: {0}".format(folder))
+        sys.exit()
+```
+*Previous cell output:*
+```output
+Found treeRecord: High Alloy Steel
+Found treeRecord: AMS 6520
+Found treeRecord: Plate
+Found treeRecord: Room Temperature °F
+```
+## Exporting data
+
+Export *Tensile Response (11 axis)* data from the record of interest.
+
+
+```python
+attribute = 'Tensile Response (11 axis)'
+
+partialTableRef = gdl.PartialTableReference(tableName = table)
+attrRef = gdl.AttributeReference(name = attribute, DBKey = dbKey, partialTableReference = partialTableRef)
+request = gdl.GetRecordAttributesByRefRequest(recordReferences = [treeRecord.recordReference], attributeReferences = [attrRef])
+
+data = session.dataExportService.GetRecordAttributesByRef(request).recordData[0]
+```
+
+Check the data type of the *Tensile Response (11 axis)* attribute is FLOAT_FUNCTIONAL_SERIES.
+
+
+```python
+value = data.attributeValues[0]
+print("Attribute Name: {0.attributeName}, Type {0.dataType}".format(value))
+```
+*Previous cell output:*
+```output
+Attribute Name: Tensile Response (11 axis), Type FLOAT_FUNCTIONAL_SERIES
+```
+Extract the data from each curve into a list.
+
+
+```python
+graph = value.floatFunctionalSeriesDataType.graph
+series = graph.series
+
+curves = []
+
+for curve in series:
+    points = curve.XYPoints.XYPoints
+    x = [point.parameterValue.numericValue for point in points]
+    y = [point.Y for point in points]
+    curves.append([x,y])
+    
+print(curves)
+```
+*Previous cell output:*
+```output
+[[[0.0, 0.884463, 0.889755, 0.890251, 0.890748, 0.891246, 0.922673, 0.937986, 0.96068, 0.986228, 1.00829, 1.02755, 1.0414, 1.05956, 1.07409, 1.08701, 1.11642, 1.14737, 1.17464, 1.20351, 1.23038], [0.0, 1731.86, 1733.01, 1733.87, 1734.72, 1735.58, 1786.9, 1809.62, 1840.22, 1870.26, 1892.73, 1910.05, 1921.33, 1934.84, 1944.71, 1952.89, 1969.64, 1984.99, 1996.96, 2008.33, 2017.92]]]
+```
+Plot the curves using the matplotlib package.
+
+
+```python
+import matplotlib.pyplot as plt
+
+x_label = '{param.name} ({param.unit.unitSymbol})'.format(param = graph.XAxisParameter)
+y_label = 'Tensile stress 10^6 (Pa)'
+
+fig = plt.figure()
+ax = fig.add_subplot(111)
+ax.set_xlabel(x_label)
+ax.set_ylabel(y_label)
+ax.set_title('Tensile Response (11 axis)')
+
+for curve in curves:
+    ax.plot(curve[0], curve[1])
+```
+
+
+    
+![png](Functional_data_and_importing_data_files/Functional_data_and_importing_data_15_0.png)
+    
+
+
+Fit a Ramberg-Osgood function to the data.
+
+
+```python
+import matplotlib.pyplot as plt
+from scipy.optimize import leastsq
+import numpy as np
+
+
+def ramberg_osgood(p, stress):
+    return (stress/p[0])*(1.0 + p[1]*(stress/p[2])**(p[3]-1))
+
+
+def error_function(p, stress, strain):
+    return ramberg_osgood(p, stress) - strain
+
+
+strain = 0.01 * np.array(curves[0][0])
+stress = np.array(curves[0][1])
+
+p0 = (2.0e5, 0.0, 1.0, 24.0)
+
+plsq = leastsq(error_function, p0[:], args=(stress, strain))
+
+fig = plt.figure()
+ax = fig.add_subplot(111)
+ax.set_xlabel(x_label)
+ax.set_ylabel(y_label)
+
+ax.plot(ramberg_osgood(plsq[0], stress), stress, label='Fit')
+ax.plot(strain, stress, marker='o', linestyle=' ', label='Data')
+
+ax.set_title('Ramberg-Osgood fit')
+ax.legend()
+```
+
+
+
+*Previous cell output:*
+```output
+<matplotlib.legend.Legend at 0x7f00bd6e5ee0>
+```
+
+
+
+    
+![png](Functional_data_and_importing_data_files/Functional_data_and_importing_data_17_1.png)
+    
+
+
+## Importing data
+
+Create a new record using the attribute values for Young's modulus obtained from the fitted curve, and import this record into the database.
+
+Identify the folder to upload the new record to. Browse the record tree to get the record reference for that folder.
+
+
+```python
+importDBKey = 'MI_Training'
+importTableName = 'Tensile Statistical Data'
+importFolderPath = """High Alloy Steel/AMS 6520"""
+now = datetime.datetime.now()
+currentTime = now.strftime("%d/%m/%Y %H:%M:%S")
+importRecordName = f"AMS 6520 - With Fitted Data @{currentTime}"
+
+importFolders = importFolderPath.split('/')
+
+importTableReference = gdl.TableReference(DBKey=importDBKey, name=importTableName)
+treeRecord = session.browseService.GetRootNode(gdl.GetRootNode(table=importTableReference)).rootNode
+
+for folder in importFolders:
+    treeRecords = session.browseService.GetChildNodes(gdl.GetChildNodes(parent=treeRecord.recordReference)).treeRecords
+    treeRecord = next((r for r in treeRecords if r.shortName == folder), None)
+    if treeRecord:
+        print("Found treeRecord folder: {0}".format(treeRecord.shortName))
+    else:
+        print("Unable to find import folder: {0}".format(folder))
+        sys.exit()
+```
+*Previous cell output:*
+```output
+Found treeRecord folder: High Alloy Steel
+Found treeRecord folder: AMS 6520
+```
+Define the record attribute to include in the new record.
+
+
+```python
+modulusAttributeReference = gdl.AttributeReference(
+    name="Young's Modulus (11-axis)",
+    DBKey=importDBKey,
+    partialTableReference=gdl.PartialTableReference(tableName = importTableName)
+)
+```
+
+Set values and units for the attributes of the new record.
+
+
+```python
+fittedEValue = plsq[0][0]
+modulusValue = gdl.PointValueWithParameters(value=fittedEValue)
+
+modulusPointValue = gdl.PointDataType(unitSymbol='psi', points=[modulusValue])
+
+importModulusValue = gdl.ImportAttributeValue(attributeReference=modulusAttributeReference, pointDataValue=modulusPointValue)
+```
+
+Create a reference to the subset that the new record will be added to.
+
+
+```python
+subsetReference = gdl.SubsetReference(
+    name='Statistical Test Data',
+    DBKey=importDBKey,
+    partialTableReference=gdl.PartialTableReference(tableName=importTableName)
+)
+```
+
+Import the record to the Granta MI database.
+
+
+```python
+importRecord = gdl.ImportRecord(recordName=importRecordName,
+                                existingRecord=treeRecord.recordReference,
+                                subsetReferences=[subsetReference],
+                                importAttributeValues=[importModulusValue])
+
+setRecordAttributesRequest = gdl.SetRecordAttributesRequest(importRecords=[importRecord])
+setRecordAttributesResponse = session.dataImportService.SetRecordAttributes(setRecordAttributesRequest)
+
+for record in setRecordAttributesResponse.recordsImported:
+    print("Imported record {0} to Granta MI database".format(record.shortName))
+```
+*Previous cell output:*
+```output
+Imported record AMS 6520 - With Fitted Data @30/10/2024 16:12:22 to Granta MI database
+```
\ No newline at end of file
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/foundation/Functional_data_and_importing_data_files/Functional_data_and_importing_data_15_0.png b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/foundation/Functional_data_and_importing_data_files/Functional_data_and_importing_data_15_0.png
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diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/foundation/Importing_and_exporting_tabular_data.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/foundation/Importing_and_exporting_tabular_data.md
new file mode 100644
index 0000000000..3d05210d44
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/foundation/Importing_and_exporting_tabular_data.md
@@ -0,0 +1,267 @@
+# Importing and exporting tabular data
+
+View tabular data information, and export the values from a record. Create a new record containing tabular data, and import this into a Granta MI database. 
+
+This example demonstrates:
+
+- Get tabular data attribute information from a Granta MI database
+- Export tabular data from a record
+- Import a new record and contains tabular data
+- Update tabular data for a record in a database
+
+## Create a Granta MI Session
+
+Import the GRANTA_MIScriptingToolkit package, and create a connection to a Granta MI server.
+
+
+```python
+import datetime
+import GRANTA_MIScriptingToolkit as gdl
+
+session = gdl.GRANTA_MISession('http://my.server.name/mi_servicelayer', autoLogon=True)
+```
+
+## Get tabular data attribute information
+
+Search for the record "Shore A75, flame retarded" in the "MI_Training" database.
+
+
+```python
+dbKey = "MI_Training"
+tableName = "MaterialUniverse"
+recordName = "Shore A75, flame retarded"
+
+req = gdl.RecordNameSearchRequest(caseSensitiveNames=False, searchShortNames=True, recordName=recordName)
+req.table = gdl.TableReference(DBKey=dbKey, name=tableName)
+resp = session.searchService.RecordNameSearch(req)
+record = resp.searchResults[0]
+```
+
+Use the browse service to get the column headers for the tabular data attribute "Specifications".
+
+
+```python
+attribName = "Restricted substances that may be associated with this material"
+
+a = gdl.AttributeReference(name=attribName, 
+                           partialTableReference=gdl.PartialTableReference(tableName=tableName), 
+                           DBKey=dbKey)
+
+resp = session.browseService.GetAttributeDetails(gdl.GetAttributeDetailsRequest([a]))
+for col in resp.attributeDetails[0].tabular.columns:
+    print(col.name)
+```
+*Previous cell output:*
+```output
+Substance name
+CAS number
+Amount
+Substance rating
+Legislation name
+Legislation rating
+Effective date
+Category
+Function
+Comments
+```
+## Export tabular data
+
+Perform a data export request to get column data for the tabular data attribute.
+
+
+```python
+dataExportRequest = gdl.GetRecordAttributesByRefRequest(recordReferences=[record.recordReference],
+                                                        attributeReferences=[a])
+
+dataExportResponse = session.dataExportService.GetRecordAttributesByRef(dataExportRequest)
+myRecordData = dataExportResponse.recordData
+
+for rec in myRecordData:
+    for attr in rec.attributeValues:
+        if attr.attributeName == attribName:
+            if not attr.dataType == "TABL":
+                raise TypeError("No tables found! Check your record.")
+            myTable = attr.tabularDataType
+            print("Table found.")
+```
+*Previous cell output:*
+```output
+Table found.
+```
+Print the data exported from the Granta MI database for this record. Note that attribute values, row cells, and list items all have a dataType attribute to help you pick the right data member to inspect.
+
+
+```python
+i = 1
+for row in myTable.tabularDataRows:
+    if len(row.cells[0].listDataValue.items) > 0:
+        print("Row {0}:".format(i))
+        print("\t{0}".format(row.cells[0].listDataValue.items[0].shortTextDataValue.value))
+        print("\t{0}".format(row.cells[1].listDataValue.items[0].shortTextDataValue.value))
+        print("\t{0}-{1}".format(row.cells[2].rangeDataValue.low, row.cells[2].rangeDataValue.high))
+        print("\t{0}".format(row.cells[3].discreteDataValue.discreteValues[0].value))
+        print("\t{0}".format(row.cells[4].shortTextDataValue.value))
+        i = i + 1
+```
+*Previous cell output:*
+```output
+Row 1:
+	Bis (2-ethyl(hexyl)phthalate) (DEHP)
+	117-81-7
+	0.0-50.0
+	To be phased-out
+	
+Row 2:
+	Dibutyl phthalate
+	84-74-2
+	0.0-50.0
+	To be phased-out
+	
+Row 3:
+	Alkanes, C10-13, chloro;  (Short chain chlorinated paraffins )
+	85535-84-8
+	0.0-50.0
+	High risk of phase-out
+```
+## Import tabular data
+
+Search the database for a parent record under which you can create a new record. 
+
+
+```python
+dbKey = "MI_Training"
+tableName = "Training Exercise for Import"
+recordName = "Metal"
+
+req = gdl.RecordNameSearchRequest(caseSensitiveNames=False, searchShortNames=True, recordName=recordName)
+req.table = gdl.TableReference(DBKey=dbKey, name=tableName)
+resp = session.searchService.RecordNameSearch(req)
+destination = resp.searchResults[0]
+```
+
+Create an empty data structure, and populate it with tabular data values.
+
+
+```python
+newTable = gdl.TabularDataType()
+newTable.AddColumn("Order")
+newTable.AddColumn("Notes")
+
+
+newRow = newTable.CreateRow()
+newInt = gdl.IntegerDataType(value=1)
+newRow.cells[0].data = newInt
+
+newText = gdl.LongTextDataType(value="Some notes about a material")
+newRow.cells[1].data = newText
+
+print("Created a table with cell data {0} and {1}".format(newInt.value, newText.value))
+```
+*Previous cell output:*
+```output
+Created a table with cell data 1 and Some notes about a material
+```
+Create a new import attribute, "Characterization of this material", that contains these tabular data values.
+
+
+```python
+attribName = "Characterization of this material"
+
+a = gdl.AttributeReference(name=attribName,
+                           partialTableReference=gdl.PartialTableReference(tableName=tableName),
+                           DBKey=dbKey)
+
+importAtribute = gdl.ImportAttributeValue(attributeReference=a)
+importAtribute.tabularDataValue = newTable
+```
+
+Create a new import record.
+
+
+```python
+now = datetime.datetime.now()
+currentTime = now.strftime("%d/%m/%Y %H:%M:%S")
+recName = "Import-test@{0}".format(currentTime)
+
+importRecord = gdl.ImportRecord(recordName=recName,
+                                existingRecord=destination.recordReference,
+                                importAttributeValues=[importAtribute])
+print(recName)
+```
+*Previous cell output:*
+```output
+Import-test@30/10/2024 16:12:47
+```
+Import the record to a Granta MI database.
+
+
+```python
+setRecordAttributesRequest = gdl.SetRecordAttributesRequest(importRecords=[importRecord])
+response = session.dataImportService.SetRecordAttributes(setRecordAttributesRequest)
+recordsImported = response.recordsImported
+
+print("{0} records imported".format(len(recordsImported)))
+```
+*Previous cell output:*
+```output
+1 records imported
+```
+## Update the record
+
+Retrieve the row ID of the data that you want to update.
+
+
+```python
+dataExportRequest = gdl.GetRecordAttributesByRefRequest(recordReferences=[response.recordsImported[0].recordReference],  
+                                                        attributeReferences=[a])
+dataExportResponse = session.dataExportService.GetRecordAttributesByRef(dataExportRequest)
+myRecordData = dataExportResponse.recordData[0]
+attribute = myRecordData.attributeValues[0].tabularDataType
+
+aid = attribute.tabularDataRows[0].Id
+
+print("This row's ID is {0}".format(aid))
+```
+*Previous cell output:*
+```output
+This row's ID is 94427
+```
+Create a table containing the values you want to update. You only need to specify the columns that contain updated data values.
+
+
+```python
+updatedValue = gdl.IntegerDataType(value=newInt.value * 1000)
+print("Updated previous value to {0}".format(updatedValue.value))
+
+tableUpdates = gdl.TabularDataType()
+tableUpdates.AddColumn("Order")
+
+changetype = gdl.TabularDataType.ChangeType.Update
+
+rowChange = tableUpdates.CreateUpdateRow(changetype, aid)
+rowChange.cells[0].data = updatedValue
+```
+*Previous cell output:*
+```output
+Updated previous value to 1000
+```
+Import the updated data into Granta MI using SetRecordAttributes.
+
+
+```python
+attribute = gdl.ImportAttributeValue(attributeReference=a)
+attribute.tabularDataValue = tableUpdates
+
+updateRecord = gdl.ImportRecord(importRecordMode="Update", 
+                                existingRecord=recordsImported[0].recordReference,
+                                importAttributeValues=[attribute])
+
+setRecordAttributesRequest = gdl.SetRecordAttributesRequest(importRecords=[updateRecord])
+response = session.dataImportService.SetRecordAttributes(setRecordAttributesRequest)
+
+print("Use MI Viewer to view {0} and check that the Order in 'Characterization of this material' is {1}".format(recName, updatedValue.value))
+```
+*Previous cell output:*
+```output
+Use MI Viewer to view Import-test@30/10/2024 16:12:47 and check that the Order in 'Characterization of this material' is 1000
+```
\ No newline at end of file
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/foundation/Inspecting_Record_Link_Groups.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/foundation/Inspecting_Record_Link_Groups.md
new file mode 100644
index 0000000000..003207bc19
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/foundation/Inspecting_Record_Link_Groups.md
@@ -0,0 +1,152 @@
+# Inspecting record link groups 
+Find which record link groups are in a specified database, and find which records are contained in a specified record link group.
+
+This example demonstrates:
+
+- Retrieving a list of record link groups from a database
+
+- Retrieve a list of records within a specified record link group
+
+## Create a Granta MI session
+Import the GRANTA_MIScriptingToolkit package, and create a connection to a Granta MI server.
+
+
+```python
+import GRANTA_MIScriptingToolkit as gdl
+
+session = gdl.GRANTA_MISession('http://my.server.name/mi_servicelayer', autoLogon=True)
+
+print("Session created")                                                               
+```
+*Previous cell output:*
+```output
+Session created
+```
+## Retrieve record link groups from a given database
+You can use GetRecordLinkGroups to retrieve information about all the link groups in a given database.
+Find the record link groups and the group IDs for groups in the MI_Training database.
+
+
+```python
+dbKey = "MI_Training"
+tableName = "Metals Pedigree"
+
+req = gdl.GetRecordLinkGroups(DBKey=dbKey)
+grlg_resp = session.browseService.GetRecordLinkGroups(req)
+
+groups = {} # Will be populated as RLG name -> RLG object
+for i, r in enumerate(grlg_resp.recordLinkGroups):
+    if i < 20: # Print first 20 group names
+        print("{0}: id={1}".format(r.name, r.reference.recordLinkGroupIdentity))
+    if r.fromTable.name == tableName:
+        groups[r.name] = r
+```
+*Previous cell output:*
+```output
+Training Exercise: id=116
+Tensile Statistical Data: id=106
+Design Data: id=105
+Tensile Test Data: id=104
+Metals Pedigree: id=102
+Specification Values: id=127
+Tensile Statistical Data: id=103
+Fatigue Test Data: id=125
+Fatigue Statistical Data: id=126
+Test Data: id=128
+AM builds using this batch: id=123
+Tensile Test Data: id=101
+Tensile test data: id=8
+Material batch: id=124
+Tensile tests from this build: id=10
+Machine learning: Build parameters: id=11
+MaterialUniverse: id=115
+Smart Link to MaterialUniverse: id=3
+```
+## List the records within a record link group
+Search for records containing "ICS-46634". Subsequent steps will use only these search results.
+
+
+```python
+def TextSearch(session, text, dbKey):
+    """
+    Simple wrapper around textsearch
+    """
+    search = session.searchService
+    s = gdl.SimpleTextSearch()
+    s.DBKey = dbKey
+    s.searchValue = text
+    s.populateGUIDs = True
+    resp = search.SimpleTextSearch(s)
+    return resp
+
+results = TextSearch(session, "ICS-46634", dbKey)
+```
+
+Collate the record references from the search results. Use GetLinkedRecords to specify the record link group "Tensile Test Data".
+
+
+```python
+recs = [r.recordReference for r in results.searchResults]
+
+name = "Tensile Test Data"
+g = groups[name]
+
+req = gdl.GetLinkedRecordsRequest(recordLinkGroups=[g.reference],
+                                  recordReferences=recs)
+
+glr_resp = session.browseService.GetLinkedRecords(req)
+```
+
+Collate and list the number of results from the record link group.
+
+
+```python
+links = [] # list of ordered pairs of refs
+for sr in glr_resp.sourceRecords:
+    for rlg in sr.recordLinkGroups.recordLinkGroups:
+        for tr in rlg.linkedRecords:
+            links.append((sr.record, tr.recordReference))
+print("{0} links found from {1} source records".format(len(links), len(recs)))
+```
+*Previous cell output:*
+```output
+9 links found from 1 source records
+```
+Use other functions in the MI Scripting Toolkit to retrieve the names of the test data records within the record link group.
+
+
+```python
+# Prepare to use GetTreeRecords to find record names corresponding to record references
+idsToFetch = set([pair[0].identity for pair in links])      # source records
+idsToFetch.update([pair[1].identity for pair in links])     # also add target records
+
+# create ref objects for all source/target records
+recordRefs = [gdl.RecordReference(DBKey=dbKey, identity=recId) for recId in idsToFetch]
+
+# use GetTreeRecords to find record names
+req = gdl.GetTreeRecordsRequest(records=recordRefs)
+resp = session.browseService.GetTreeRecords(req)
+nameLookup = {tr.recordReference.identity:tr.shortName for tr in resp.treeRecords}
+
+# Get and print record names
+linkPairs = []
+for r in links:
+    linkPairs.append((nameLookup[r[0].identity], nameLookup[r[1].identity]))
+    
+print("Records linked by '{0}':".format(name))
+for pair in linkPairs:
+    print("{0:<30}-> {1}".format(pair[0], pair[1]))
+```
+*Previous cell output:*
+```output
+Records linked by 'Tensile Test Data':
+Batch 921                     -> MTS-615722
+Batch 921                     -> MTS-615721
+Batch 921                     -> MTS-615723
+Batch 921                     -> MTS-615724
+Batch 921                     -> MTS-615725
+Batch 921                     -> MTS-615726
+Batch 921                     -> MTS-615734
+Batch 921                     -> MTS-615735
+Batch 921                     -> MTS-615736
+```
\ No newline at end of file
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/foundation/Modifying_Record_Link_Groups.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/foundation/Modifying_Record_Link_Groups.md
new file mode 100644
index 0000000000..727b7ca547
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/foundation/Modifying_Record_Link_Groups.md
@@ -0,0 +1,121 @@
+# Modifying record link groups
+
+Create a record link between two records in a Granta MI database.
+
+This example demonstrates:
+
+- Retrieve a list of record groups within a Granta MI database
+- Import a new record
+- Create a link between the new record and another record
+
+## Prerequisites
+Import required Python packages, and define two custom functions. The functions will:
+
+1. Format the current time (used to set the record name)
+2. Perform a record name search
+
+
+```python
+import datetime
+
+def NowString():
+    return datetime.datetime.now().strftime("%d/%m/%Y %H:%M:%S")
+
+def FindRecord(gdlSession, dbKey, tableName, recordName):
+    """ 
+    Finds a record by name
+    :returns: :py:mod:`SearchResult <GRANTA_MIScriptingToolkit.SearchResult>`
+    """
+    req = gdl.RecordNameSearchRequest(caseSensitiveNames=False, recordName=recordName, populateGUIDs=True, searchShortNames=True, searchFullNames=True)
+    req.table = gdl.TableReference(DBKey=dbKey, name=tableName)
+    resp = gdlSession.searchService.RecordNameSearch(req)
+    return resp.searchResults[0]
+```
+
+## Create a Granta MI session
+Import the GRANTA_MIScriptingToolkit package, and create a connection to a Granta MI server.
+
+
+```python
+import GRANTA_MIScriptingToolkit as gdl
+
+session = gdl.GRANTA_MISession('http://my.server.name/mi_servicelayer', autoLogon=True)
+```
+
+##  Create a link between a current record and a new record
+Find the record link groups that can be created within MaterialUniverse.
+
+
+```python
+dbkey = "MI_Training"
+tableName = "MaterialUniverse"
+
+req = gdl.GetRecordLinkGroups(DBKey=dbkey)
+
+grlg_resp = session.browseService.GetRecordLinkGroups(req)
+
+groups = {}
+for r in grlg_resp.recordLinkGroups:
+    if r.fromTable.name == tableName:
+        print("{0}: id={1}".format(r.name, r.reference.recordLinkGroupIdentity))
+        groups[r.name] = r
+```
+*Previous cell output:*
+```output
+Training Exercise: id=116
+```
+Import a new record into the "Training Exercise for Import" table, with the current time as the record name. This will be the target record to create a link to later.
+
+
+```python
+grn_req = gdl.GetRootNode(table=groups[u'Training Exercise'].toTable)
+grn_resp = session.browseService.GetRootNode(grn_req)
+
+ir = gdl.ImportRecord(recordName=NowString(),
+                      existingRecord=grn_resp.rootNode.recordReference)
+req = gdl.SetRecordAttributesRequest(importRecords=[ir], importErrorMode=gdl.GRANTA_Constants.ImportErrorMode.Fault)
+resp = session.dataImportService.SetRecordAttributes(req)
+target = resp.recordsImported[0]
+```
+
+Set "PMMA (cast sheet)" in MaterialUniverse as the source record to create a link from.
+
+
+```python
+sourceResult = FindRecord(session, dbkey, "MaterialUniverse", "PMMA (cast sheet)")
+source = sourceResult.recordReference
+```
+
+Use the ModifyRecordLinks operation to create a link called "Training Exercise" from "PMMA (cast sheet)" to the new imported record.
+
+
+```python
+destinationRec = gdl.NotatedTargetRecord(record=target.recordReference, notes="This will work")
+mySourceRec = gdl.NotatedTargetedSourceRecord(sourceRecord=source,
+                                              targetRecords=[destinationRec])
+linkThisRecord = gdl.LinkRecords(sourceRecords=[mySourceRec])
+recordLinksMod = gdl.RecordLinkModifications(linkRecords=[linkThisRecord])
+
+req = gdl.ModifyRecordLinksRequest(recordLinkGroupReference=groups[u'Training Exercise'].reference,
+                                   recordLinkModifications=recordLinksMod,
+                                   importErrorMode=gdl.GRANTA_Constants.ImportErrorMode.Fault)
+mrlresp = session.dataImportService.ModifyRecordLinks(req)
+```
+
+Print the record names and GUIDs for the newly-linked records. You can also check this new link in MI Viewer, by viewing the link on the "PMMA (cast sheet)" datasheet.
+
+
+```python
+print("Created {0} link(s)".format(len(mrlresp.recordLinkChanges.linked)))
+
+change = mrlresp.recordLinkChanges.linked[0]
+print("{0} ({1}) -> {2} ({3})".format(change.record.recordReference.recordGUID,
+                                      sourceResult.shortName,
+                                      change.targetRecords[0].recordReference.recordGUID,
+                                      ir.recordName))
+```
+*Previous cell output:*
+```output
+Created 1 link(s)
+000016f6-000e-4fff-8fff-dd92ffff0000 (Cast sheet) -> d7c5f5d9-4169-4877-81f7-f9b56f5ba94b (30/10/2024 16:13:15)
+```
\ No newline at end of file
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/foundation/Text_Search.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/foundation/Text_Search.md
new file mode 100644
index 0000000000..12a38a7af7
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/foundation/Text_Search.md
@@ -0,0 +1,65 @@
+# Text search
+
+Search a Granta MI database for records containing a specified text value.
+
+This example demonstrates:
+
+* Creating a connection to a Granta MI server
+* Retrieving Granta MI databases from the server
+* Searching a Granta MI database with the *SimpleTextSearch* operation
+
+## Create a Granta MI Session
+
+Import the GRANTA_MIScriptingToolkit package, and create a connection to a Granta MI server.
+
+
+```python
+import GRANTA_MIScriptingToolkit as gdl
+
+session = gdl.GRANTA_MISession('http://my.server.name/mi_servicelayer', autoLogon=True)
+```
+
+## Select a database
+
+View the databases available on your Granta MI server.
+
+
+```python
+databases = session.browseService.GetDatabases().databases
+
+print("Found {0} databases on the Granta MI Server".format(len(databases)))
+for d in databases:
+    print("Database key: {0.DBKey}, Database name: {0.volumeName}".format(d))
+```
+*Previous cell output:*
+```output
+Found 1 databases on the Granta MI Server
+Database key: MI_Training, Database name: MI Training
+```
+## Search a database
+
+Search in the MI Training database for records containing the text *Leather*.
+
+
+```python
+dbKey = 'MI_Training'
+searchText = 'leather'
+
+simpleTextSearch = gdl.SimpleTextSearch(searchValue=searchText, DBKey=dbKey)
+simpleTextSearchResponse = session.searchService.SimpleTextSearch(simpleTextSearch)
+```
+
+Print the results returned by the search.
+
+
+```python
+for result in simpleTextSearchResponse.searchResults:
+    print('{0}'.format(result.shortName))
+```
+*Previous cell output:*
+```output
+Leather
+Potassium chromate [7789-00-6]
+Alkanes, C10-13, chloro;  (Short chain chlorinated paraffins ) [85535-84-8]
+Exercise Model Answers.ipynb
+```
\ No newline at end of file
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/00_Get_Started.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/00_Get_Started.md
new file mode 100644
index 0000000000..7aeca0c79e
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/00_Get_Started.md
@@ -0,0 +1,149 @@
+# Get Started 
+Load MI Scripting Toolkit, connect to your MI Session, and select a database and table.
+
+## Connect to MI
+Import the `granta` libraries and connect to Granta MI via the Service Layer using Windows authentication,
+replacing `my.server.name` with the name of your Granta MI server.
+
+
+```python
+from GRANTA_MIScriptingToolkit import granta as mpy
+
+mi = mpy.connect("http://my.server.name/mi_servicelayer", autologon=True)
+```
+
+## Select a database
+All the sample scripts use the *MI Training* database.
+
+
+```python
+my_db = mi.get_db(db_key="MI_Training")
+```
+
+Set a unit system, and choose whether to use absolute or relative temperatures (Kelvin/Rankine or Celsius/Fahrenheit).
+
+
+```python
+my_db.unit_system = 'UK Imperial'
+my_db.absolute_temperatures = False
+```
+
+## Select a table
+Select *MaterialUniverse* and print its number of attributes. 
+
+
+```python
+my_table = my_db.get_table("MaterialUniverse")
+print("Table {0} has {1} attributes defined".format(my_table.name, len(my_table.attributes)))
+```
+*Previous cell output:*
+```output
+Table MaterialUniverse has 424 attributes defined
+```
+Print the definition of an attribute within your table.
+
+
+```python
+print("The definition of the Density attribute in {0} is {1}".format(my_table.name, my_table.attributes["Density"]))
+```
+*Previous cell output:*
+```output
+The definition of the Density attribute in MaterialUniverse is <Attribute Definition, name: Density, type: RNGE, unit: lb/in^3>
+```
+## Find a record
+Search for a record by name (only exact matches for short or long name will be returned), and print information to help you locate and view it in MI applications.
+
+
+```python
+print("Finding Aluminum, 7075, wrought, T73...")
+my_record = my_table.search_for_records_by_name("Aluminum, 7075, wrought, T73")[0]
+```
+*Previous cell output:*
+```output
+Finding Aluminum, 7075, wrought, T73...
+```
+
+```python
+print("Found this record:")
+my_record
+```
+*Previous cell output:*
+```output
+Found this record:
+```
+
+
+*Previous cell output:*
+```output
+<Record long name: Aluminum, 7075, wrought, T73>
+```
+
+
+
+```python
+print("in database/table:")
+my_record.db_key+"/"+my_record.table_name
+```
+*Previous cell output:*
+```output
+in database/table:
+```
+
+
+*Previous cell output:*
+```output
+'MI_Training/MaterialUniverse'
+```
+
+
+
+```python
+print("at this point in the tree:")
+"=>".join(my_record.path)
+```
+*Previous cell output:*
+```output
+at this point in the tree:
+```
+
+
+*Previous cell output:*
+```output
+'Metals and alloys=>Non-ferrous=>Aluminum=>Wrought=>7000 series (Zn-alloyed)=>7075'
+```
+
+
+
+```python
+print("with data like this:")
+my_table.bulk_fetch([my_record], attributes=['Mg (magnesium)'])
+my_record.attributes['Mg (magnesium)']
+```
+*Previous cell output:*
+```output
+with data like this:
+```
+
+
+*Previous cell output:*
+```output
+<RangeValue name: Mg (magnesium), value: {'low': 2.5, 'high': 2.5}, high_value_is_inclusive: True, low_value_is_inclusive: True, unit: %>
+```
+
+
+
+```python
+print("and you can see it in MI Viewer here:")
+my_record.viewer_url
+```
+*Previous cell output:*
+```output
+and you can see it in MI Viewer here:
+```
+
+
+*Previous cell output:*
+```output
+'http://my.server.name/mi/datasheet.aspx?dbKey=MI_Training&recordHistoryGuid=7682acfe-46ca-4adf-94f7-6fa678debed1'
+```
+
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/01_Get_Attributes.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/01_Get_Attributes.md
new file mode 100644
index 0000000000..092236f894
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/01_Get_Attributes.md
@@ -0,0 +1,88 @@
+# Get Attributes
+Find out about the database schema by accessing the `attributes` property of the **Table** object.
+
+## Connect to MI
+
+
+```python
+from GRANTA_MIScriptingToolkit import granta as mpy
+
+mi = mpy.connect('http://my.server.name/mi_servicelayer', autologon=True)
+```
+
+## Get table 
+Get a database, then set the unit system for the **Database** object.
+
+
+```python
+db = mi.get_db(db_key='MI_Training')
+db.unit_system = 'Metric'
+db.unit_system
+```
+
+
+
+*Previous cell output:*
+```output
+'Metric'
+```
+
+
+Get a table from the database.
+
+
+```python
+tab = db.get_table('Training Exercise for Import')
+tab
+```
+
+
+
+*Previous cell output:*
+```output
+<Table name: Training Exercise for Import, subsets: {'Materials'}>
+```
+
+
+## Access the attribute definitions 
+These are associated with the **Table** object through the `attributes` property,
+which returns a dictionary of **AttributeDefinition** objects.
+
+
+```python
+print('{:30.30} - {:^10.10} - {:^10.10} - {:^10.10}'.format('Name', 'Type', 'Unit', 'Meta?'))
+print('-'*70)
+for name, att in tab.attributes.items():
+    output = '{:30.30} - {:^10.10} - {:^10.10} - {:^10.10}'.format(name, att.type, att.unit, str(att.is_meta))
+    print(output)
+```
+*Previous cell output:*
+```output
+Name                           -    Type    -    Unit    -   Meta?   
+----------------------------------------------------------------------
+```*Previous cell output:*
+```output
+Base                           -    DISC    -            -   False   
+Composition                    -    STXT    -            -   False   
+Density                        -    POIN    -   kg/m^3   -   False   
+Description                    -    LTXT    -            -   False   
+Document                       -    FILE    -            -   False   
+Electrical Properties          -    DISC    -            -   False   
+Material                       -    STXT    -            -   False   
+More Information               -    HLNK    -            -   False   
+Tensile Modulus                -    RNGE    -    GPa     -   False   
+Tensile Strength               -    RNGE    -    MPa     -   False   
+Transparent                    -    LOGI    -            -   False   
+Typical Use                    -    PICT    -            -   False   
+Young's Modulus                -    FUNC    -    GPa     -   False   
+Stress-strain Chart            -    FUNC    -    MPa     -   False   
+Creep Rupture                  -    FUNC    -    MPa     -   False   
+Environmental Resistance       -    DSFN    -            -   False   
+Design Allowable Data          -    TABL    -            -   False   
+Atomic number                  -    INPT    -            -   False   
+Date of datasheet release      -    DTTM    -            -   False   
+Wikipedia page                 -    HLNK    -            -   False   
+Environmental performances ove -    TABL    -            -   False   
+Characterization of this mater -    TABL    -            -   False   
+Yield Strength                 -    POIN    -    MPa     -   False
+```
\ No newline at end of file
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/02_Browse_MI.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/02_Browse_MI.md
new file mode 100644
index 0000000000..8e6e083e18
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/02_Browse_MI.md
@@ -0,0 +1,214 @@
+# Browse Granta MI
+Use various methods to get records from Granta MI.
+
+This notebook shows three methods of browsing for records in Granta MI:
+
+* Get records by internal Granta MI identifier
+* Get records by an exact match on a short text attribute value
+* Get records by navigating the tree structure
+
+## Connect to MI
+
+
+```python
+from GRANTA_MIScriptingToolkit import granta as mpy
+mi = mpy.connect('http://my.server.name/mi_servicelayer', autologon=True)
+db = mi.get_db(db_key='MI_Training')
+```
+
+## Access a record by specifying an internal ID
+Get a record with a specific identifier, for example history GUID.
+
+
+```python
+record_by_guid = db.get_record_by_id(hguid='bf5e6054-6cad-4c9d-ad7a-adfa124c504b')
+record_by_guid
+```
+
+
+
+*Previous cell output:*
+```output
+<Record long name: Soda barium glass>
+```
+
+
+## Access a record by specifying a unique short text value
+Get a record with a unique short-text attribute value using the `Table.get_record_by_lookup_value()` method.
+
+
+```python
+tensile_test_data = db.get_table('Tensile Test Data')
+record_by_value = tensile_test_data.get_record_by_lookup_value('Specimen ID', 'MTS-615722')
+record_by_value
+```
+
+
+
+*Previous cell output:*
+```output
+<Record long name: MTS-615722>
+```
+
+
+**Note:** This method can only return a single record; if multiple records have the same value an exception is raised. Uncomment the line below to see the error.
+
+
+```python
+# tensile_test_data.get_record_by_lookup_value('Testing Standards', 'ASTM E8')
+```
+
+## Browse for records by navigating the tree structure
+The following examples show two methods of browsing the Granta MI tree structure in Python. These examples use the
+*MaterialUniverse* table.
+
+
+```python
+material_universe = db.get_table('MaterialUniverse')
+```
+
+### Iterative browsing
+Browse iteratively by getting the immediate children of each record in turn. First, get the top-level folders of the table with the `Table.children` property:
+
+
+```python
+material_universe_folders = material_universe.children
+["Name: {}, Type: {}".format(child.name, child.type) for child in material_universe_folders]
+```
+
+
+
+*Previous cell output:*
+```output
+['Name: Ceramics and glasses, Type: Folder',
+ 'Name: Hybrids: composites, foams, honeycombs, natural materials, Type: Folder',
+ 'Name: Metals and alloys, Type: Folder',
+ 'Name: Polymers: plastics, elastomers, Type: Folder',
+ 'Name: NEW RECORDS, Type: Folder']
+```
+
+
+Then filter for a specific folder and access the `Record.children` property to find its children:
+
+
+```python
+ceramics_and_glasses = [child for child in material_universe_folders if child.short_name == 'Ceramics and glasses'][0]
+["Name: {}, Type: {}".format(child.name, child.type) for child in ceramics_and_glasses.children]
+```
+
+
+
+*Previous cell output:*
+```output
+['Name: Glasses, Type: Folder', 'Name: Technical Ceramics, Type: Folder']
+```
+
+
+Repeat the process to navigate the tree structure until you reach your records of interest.
+
+
+```python
+glasses = [child for child in ceramics_and_glasses.children if child.short_name == 'Glasses'][0]
+alumino_silicate = [child for child in glasses.children if child.short_name == 'Alumino silicate'][0]
+["Name: {}, Type: {}".format(child.name, child.type) for child in alumino_silicate.children]
+```
+
+
+
+*Previous cell output:*
+```output
+['Name: Alumino silicate - 1720, Type: Record',
+ 'Name: Alumino silicate - 1723, Type: Record',
+ 'Name: Lithium aluminosilicate, Type: Record',
+ 'Name: Soda barium glass, Type: Record']
+```
+
+
+### Access a tree location directly
+
+The `table.get_records_from_path()` method returns all the records at the end of a specified path.
+It accepts wildcards at any level.
+
+Get all records that are great-grandchildren of folders with the short name *Ceramics and glasses* and also have a
+parent with the short name *Baryta*.
+
+
+```python
+recs = material_universe.get_records_from_path(None,
+                                               ['Ceramics and glasses', None, 'Baryta'],
+                                               use_short_names=True)
+recs
+```
+
+
+
+*Previous cell output:*
+```output
+[<Record long name: Barium silicate>]
+```
+
+
+Get all records that are great-grandchildren of the folder *Ceramics and glasses*.
+
+
+```python
+recs = material_universe.get_records_from_path(ceramics_and_glasses, [None, None])
+recs
+```
+
+
+
+*Previous cell output:*
+```output
+[<Record long name: Alumino silicate - 1720>,
+ <Record long name: Alumino silicate - 1723>,
+ <Record long name: Lithium aluminosilicate>,
+ <Record long name: Soda barium glass>,
+ <Record long name: Barium silicate>,
+ <Record long name: Tungsten carbide>]
+```
+
+
+### Access all descendant records within a folder
+Use the `Record.get_descendants()` method to retrieve all records that are a descendant of the specified record
+in a single step.
+
+
+```python
+all_ceramics_and_glasses = ceramics_and_glasses.get_descendants()
+all_ceramics_and_glasses
+```
+
+
+
+*Previous cell output:*
+```output
+[<Record long name: Alumino silicate - 1720>,
+ <Record long name: Alumino silicate - 1723>,
+ <Record long name: Lithium aluminosilicate>,
+ <Record long name: Soda barium glass>,
+ <Record long name: Barium silicate>,
+ <Record long name: Tungsten carbide>]
+```
+
+
+Print the results and whether the object is a record or folder.
+
+
+```python
+print('{:^30.30} | {:^30.30} | {:^30.30}'.format('Record Name', 'Short Name', 'Record / Folder?'))
+print('-'*96)
+for r in all_ceramics_and_glasses:
+    print('{:^30.30} | {:^30.30} | {:^30.30}'.format(str(r.name), r.short_name, r.type))
+```
+*Previous cell output:*
+```output
+         Record Name           |           Short Name           |        Record / Folder?       
+------------------------------------------------------------------------------------------------
+   Alumino silicate - 1720     |    Alumino silicate - 1720     |             Record            
+   Alumino silicate - 1723     |    Alumino silicate - 1723     |             Record            
+   Lithium aluminosilicate     |    Lithium aluminosilicate     |             Record            
+      Soda barium glass        |       Soda barium glass        |             Record            
+       Barium silicate         |        Barium silicate         |             Record            
+       Tungsten carbide        |        Tungsten carbide        |             Record
+```
\ No newline at end of file
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/03_Search_MI.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/03_Search_MI.md
new file mode 100644
index 0000000000..f28f535e7b
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/03_Search_MI.md
@@ -0,0 +1,411 @@
+# Search Granta MI
+Use the three available search methods to search for records.
+
+All three methods can be performed at either the Session, Database, or Table levels:
+
+* Simple text search
+* Record name search
+* Criteria search
+
+## Connect to MI
+
+
+```python
+from GRANTA_MIScriptingToolkit import granta as mpy
+mi = mpy.connect('http://my.server.name/mi_servicelayer', autologon=True)
+db = mi.get_db(db_key='MI_Training')
+db.unit_system = "UK Imperial"
+material_universe = db.get_table('MaterialUniverse')
+```
+
+## Simple text search
+Search for records which contain the string anywhere in the record.
+
+
+```python
+normalized_material_records = material_universe.search_for_records_by_text('Normalized')
+normalized_material_records
+```
+
+
+
+*Previous cell output:*
+```output
+[<Record long name: Low alloy steel, AISI 4130, air melted, normalized>]
+```
+
+
+## Record name search
+Search for records by name. Only exact matches to the long or short name of a record are returned.
+
+
+```python
+sodium_barium_glass_records = material_universe.search_for_records_by_name('Soda barium glass')
+sodium_barium_glass_records
+```
+
+
+
+*Previous cell output:*
+```output
+[<Record long name: Soda barium glass>]
+```
+
+
+## Criteria searches
+Search criteria are constructed from **AttributeDefinition** objects using the `search_criterion()` method.
+
+This search type is typically used in 7 different scenarios:
+
+* Text searches
+* Numeric searches
+* Date searches
+* Boolean searches
+* Tabular searches
+* Searching for the presence of any value
+* Combining search criteria
+
+
+### Text searches
+In these examples, the criteria are that the attribute *Base* in the *MaterialUniverse* table:
+
+* Contains iron (`contains=`)
+* Does not contain iron (`does_not_contain=`)
+* Contains one of three specified metals (`contains_any=`)
+* Contains all three specified metals (`contains_all=`)
+
+
+```python
+base_attribute = material_universe.attributes['Base']
+```
+
+All materials that contain Iron:
+
+
+```python
+base_contains_iron = base_attribute.search_criterion(contains='Fe (Iron)')
+base_contains_iron
+```
+
+
+
+*Previous cell output:*
+```output
+<SearchCriterion: Base - CONTAINS - 'Fe (Iron)'>
+```
+
+
+
+```python
+iron_records = material_universe.search_for_records_where([base_contains_iron])
+iron_records
+```
+
+
+
+*Previous cell output:*
+```output
+[<Record long name: Low alloy steel, AISI 4130, air melted, quenched & tempered>,
+ <Record long name: 250 maraging steel, maraged at 900F>,
+ <Record long name: Low alloy steel, AISI 4130, air melted, normalized>]
+```
+
+
+All materials that **do not** contain Iron:
+
+
+```python
+base_does_not_contain_iron = base_attribute.search_criterion(does_not_contain='Fe (Iron)')
+iron_free_records = material_universe.search_for_records_where([base_does_not_contain_iron])
+iron_free_records[:5]
+```
+
+
+
+*Previous cell output:*
+```output
+[<Record long name: Ceramics and glasses>,
+ <Record long name: Soda barium glass>,
+ <Record long name: Aluminum, 7075, wrought, T73>,
+ <Record long name: Titanium, alpha-beta alloy, Ti-6Al-4V, aged>,
+ <Record long name: Titanium, alpha-beta alloy, Ti-6Al-4V, annealed, generic>]
+```
+
+
+All materials that contain **one of** Iron, Aluminum **or** Titanium:
+
+
+```python
+base_contains_any_metal = base_attribute.search_criterion(contains_any=['Fe (Iron)', 'Al (Aluminum)', 'Ti (Titanium)'])
+base_any_metal_records = material_universe.search_for_records_where([base_contains_any_metal])
+base_any_metal_records
+```
+
+
+
+*Previous cell output:*
+```output
+[<Record long name: Low alloy steel, AISI 4130, air melted, quenched & tempered>,
+ <Record long name: Aluminum, 7075, wrought, T73>,
+ <Record long name: Titanium, alpha-beta alloy, Ti-6Al-4V, aged>,
+ <Record long name: Titanium, alpha-beta alloy, Ti-6Al-4V, annealed, generic>,
+ <Record long name: Titanium, alpha-beta alloy, Ti-6Al-4V, solution treated & aged>,
+ <Record long name: 250 maraging steel, maraged at 900F>,
+ <Record long name: Low alloy steel, AISI 4130, air melted, normalized>,
+ <Record long name: Aluminum, 7075, wrought, T6>]
+```
+
+
+All materials that contain Iron, Aluminum **and** Titanium:
+
+
+```python
+base_contains_all_metals = base_attribute.search_criterion(contains_all=['Fe (Iron)', 'Al (Aluminum)', 'Ti (Titanium)'])
+base_all_metals_records = material_universe.search_for_records_where([base_contains_all_metals])
+base_all_metals_records
+```
+
+
+
+*Previous cell output:*
+```output
+[]
+```
+
+
+### Numeric searches
+In this example, the criteria are that the attribute *Yield Strength, L* in the *Design Data* table is either greater than or less than 145 ksi.
+
+
+```python
+design_data = db.get_table('Design Data')
+yield_strength_l_attribute = design_data.attributes['Yield Strength, L']
+```
+
+Materials with a *Yield Strength* **less than** 145 ksi:
+
+
+```python
+yield_strength_l_attribute.unit
+low_yield_strength = yield_strength_l_attribute.search_criterion(less_than=145.0)
+low_yield_strength_records = design_data.search_for_records_where([low_yield_strength])
+low_yield_strength_records
+```
+
+
+
+*Previous cell output:*
+```output
+[<Record long name: Titanium alloys, Ti-6Al-4V>]
+```
+
+
+Materials with a *Yield Strength* **greater than** 145 ksi:
+
+
+```python
+high_yield_strength = yield_strength_l_attribute.search_criterion(greater_than=145.0)
+high_yield_strength_records = design_data.search_for_records_where([high_yield_strength])
+high_yield_strength_records
+```
+
+
+
+*Previous cell output:*
+```output
+[<Record long name: Nickel alloys, Inconel 718, Forging>,
+ <Record long name: 250 Grade Maraging, Maraged at 900F, Plate, Thickness: 0.1875 to 0.251 in, AMS 6520, S basis>]
+```
+
+
+### Date searches
+In this example, the search is for records in the *Legislation and Lists* table with an *Effective Date* between 1970 and 2000.
+
+
+```python
+legislations = db.get_table('Legislations and Lists')
+effective_date_attribute = legislations.attributes['Effective date']
+```
+
+
+```python
+import datetime
+start_date = datetime.datetime(1970, 1, 1)
+end_date = datetime.datetime(2000, 1, 1)
+effective_date_1970_2000 = effective_date_attribute.search_criterion(between_dates=(start_date, end_date))
+legislations_1970_2000 = legislations.search_for_records_where([effective_date_1970_2000])
+legislations_1970_2000
+```
+
+
+
+*Previous cell output:*
+```output
+[<Record long name: TSCA Section 6>]
+```
+
+
+### Boolean searches
+Search for records with a certain Boolean value. In this example, the search is for all materials where RoHS compliant grades are available.
+
+
+```python
+RoHS_grades_attribute = material_universe.attributes['RoHS (EU) compliant grades?']
+
+RoHS_grades_true = RoHS_grades_attribute.search_criterion(contains=True)
+materials_with_rohs_grades = material_universe.search_for_records_where([RoHS_grades_true])
+materials_with_rohs_grades[:5]
+```
+
+
+
+*Previous cell output:*
+```output
+[<Record long name: Soda barium glass>,
+ <Record long name: Low alloy steel, AISI 4130, air melted, quenched & tempered>,
+ <Record long name: Aluminum, 7075, wrought, T73>,
+ <Record long name: Titanium, alpha-beta alloy, Ti-6Al-4V, aged>,
+ <Record long name: Titanium, alpha-beta alloy, Ti-6Al-4V, annealed, generic>]
+```
+
+
+### Combining criteria
+The `search_for_records_where` method takes a list of criteria, allowing criteria to be combined as long as they relate to attributes in the same table.
+
+
+```python
+metals_with_rohs_grades = material_universe.search_for_records_where([base_contains_any_metal, RoHS_grades_true])
+metals_with_rohs_grades
+```
+
+
+
+*Previous cell output:*
+```output
+[<Record long name: Low alloy steel, AISI 4130, air melted, quenched & tempered>,
+ <Record long name: Aluminum, 7075, wrought, T73>,
+ <Record long name: Titanium, alpha-beta alloy, Ti-6Al-4V, aged>,
+ <Record long name: Titanium, alpha-beta alloy, Ti-6Al-4V, annealed, generic>,
+ <Record long name: 250 maraging steel, maraged at 900F>,
+ <Record long name: Low alloy steel, AISI 4130, air melted, normalized>,
+ <Record long name: Titanium, alpha-beta alloy, Ti-6Al-4V, solution treated & aged>,
+ <Record long name: Aluminum, 7075, wrought, T6>]
+```
+
+
+### Searching for the existence of data
+We can also define criteria that search for records which have *any* value for the specified attribute, with no restriction on the data.
+
+
+```python
+restricted_substances = db.get_table('Restricted Substances')
+SVHC_criterion = restricted_substances.attributes['SVHC criterion']
+
+svhc_exists = SVHC_criterion.search_criterion(exists=True)
+svhc_materials = restricted_substances.search_for_records_where([svhc_exists])
+svhc_materials
+```
+
+
+
+*Previous cell output:*
+```output
+[<Record long name: Bis (2-ethyl(hexyl)phthalate) (DEHP) [117-81-7]>,
+ <Record long name: Alkanes, C10-13, chloro;  (Short chain chlorinated paraffins ) [85535-84-8]>,
+ <Record long name: Potassium chromate [7789-00-6]>,
+ <Record long name: Dibutyl phthalate [84-74-2]>,
+ <Record long name: Arsenic trioxide [1327-53-3]>]
+```
+
+
+### Searching for tabular data
+Searching tabular data works in exactly the same way as searching attributes, except the criteria require an additional argument to specify the applicable column. This example finds all records in the *MaterialUniverse* table that contain at least one tabular row, and where both the following are true:
+
+* The 'Substance name' column contains the text value 'Alkanes'
+* The 'Amount' column contains a value greater than 10.0 % (the criterion value is defined in the current unit system)
+
+
+```python
+declaration = material_universe.attributes['Restricted substances that may be associated with this material']
+
+substance_contains_alkanes = declaration.search_criterion(contains='Alkanes', in_column='Substance name')
+amount_gt_10 = declaration.search_criterion(greater_than=10.0, in_column='Amount')
+
+affected_materials = material_universe.search_for_records_where([substance_contains_alkanes, amount_gt_10])
+affected_materials
+```
+
+
+
+*Previous cell output:*
+```output
+[<Record long name: PVC-elastomer (Shore A35)>,
+ <Record long name: PVC-elastomer (Shore A75, flame retarded)>,
+ <Record long name: PVC-elastomer (Shore A55)>,
+ <Record long name: PVC-elastomer (Shore A75)>]
+```
+
+
+Print the results of the tabular search.
+
+
+```python
+print('{:^55.55} | {:^55.55}'.format('Record Name', 'Short Name'))
+print('-'*113)
+for r in affected_materials:
+    print('{:^55.55} | {:^55.55}'.format(str(r.name), r.short_name))
+```
+*Previous cell output:*
+```output
+                      Record Name                       |                       Short Name                       
+-----------------------------------------------------------------------------------------------------------------
+               PVC-elastomer (Shore A35)                |                        Shore A35                       
+       PVC-elastomer (Shore A75, flame retarded)        |                Shore A75, flame retarded               
+               PVC-elastomer (Shore A55)                |                        Shore A55                       
+               PVC-elastomer (Shore A75)                |                        Shore A75
+```
+### Searching with pseudo-attributes
+Some pseudo-attributes (also known as record properties) can be used as search criteria, they behave as ordinary
+attributes and permit the same criteria to be used. This example
+searches for tensile test results for **MTS** samples (High Alloy Steels) that were created during September 2023.
+
+
+```python
+tensile_test_table = db.get_table("Tensile Test Data")
+
+name_criterion = mpy.SearchCriterion(mpy.RecordProperties.name, "CONTAINS", "MTS")
+
+start_datetime = datetime.datetime(year=2023, month=9, day=1)
+end_datetime = datetime.datetime(year=2023, month=9, day=30)
+created_criterion = mpy.SearchCriterion(
+    mpy.RecordProperties.created_on, "BETWEEN", (start_datetime, end_datetime)
+)
+
+created_records = tensile_test_table.search_for_records_where([name_criterion, created_criterion])
+
+print(f"{len(created_records)} records found. Displaying first 5...")
+print()
+print('{:^55.55} | {:^55.55}'.format('Record Name', 'Created Date'))
+print('-'*113)
+for r in created_records[:5]:
+    print('{:^55.55} | {:^55.55}'.format(
+        str(r.name), r.created_on.strftime("%Y/%m/%d %H:%M:%S")))
+```
+*Previous cell output:*
+```output
+36 records found. Displaying first 5...
+
+                      Record Name                       |                      Created Date                      
+-----------------------------------------------------------------------------------------------------------------
+```*Previous cell output:*
+```output
+                      MTS-615736                        |                   2023/09/20 12:11:05                  
+                      MTS-615776                        |                   2023/09/20 12:11:05                  
+                      MTS-615755                        |                   2023/09/20 12:11:05
+```*Previous cell output:*
+```output
+                      MTS-615765                        |                   2023/09/20 12:11:05                  
+                      MTS-615723                        |                   2023/09/20 12:11:05
+```
+Pseudo-attributes are not table specific and can be used in database criteria searches via
+``Database.search_for_records_where()``. In this case, search results can include records from multiple tables.
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/04_Fetch_Attribute_Data_in_Bulk.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/04_Fetch_Attribute_Data_in_Bulk.md
new file mode 100644
index 0000000000..7cd1eac5c5
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/04_Fetch_Attribute_Data_in_Bulk.md
@@ -0,0 +1,142 @@
+# Fetch Attribute Data in Bulk
+By default, all attribute values are dynamically retrieved from the server when `Record.attributes` is accessed. When
+operating on many records, this can result in poor performance, as unnecessary data might be exported for each record
+and as export requests are performed for each individual record.
+By grouping records into fewer requests through batching, and allowing the export of only a subset of attributes,
+`bulk_fetch()` can be used to enhance performance.
+
+## Connect to MI 
+Specify a database and table.
+
+
+```python
+from GRANTA_MIScriptingToolkit import granta as mpy
+
+mi = mpy.connect('http://my.server.name/mi_servicelayer', autologon=True)
+db = mi.get_db(db_key='MI_Training')
+db.unit_system = 'Metric'
+db.absolute_temperatures = True
+tab = db.get_table('MaterialUniverse')
+```
+
+## Define a list of records to fetch
+For example, by Record GUID.
+
+
+```python
+guids = ['00001192-000e-4fff-8fff-dd92ffff0000',
+         '00001194-000e-4fff-8fff-dd92ffff0000',
+         '0000119b-000e-4fff-8fff-dd92ffff0000',
+         '00000e38-000e-4fff-8fff-dd92ffff0000',
+         '00000e41-000e-4fff-8fff-dd92ffff0000',
+         '000009cb-000e-4fff-8fff-dd92ffff0000']
+```
+
+Get each record using its Record GUID, using the `get_records_by_ids` method.
+
+
+```python
+records = tab.get_records_by_ids([{"vguid": guid} for guid in guids])
+records
+```
+
+
+
+*Previous cell output:*
+```output
+[<Record long name: Titanium, alpha-beta alloy, Ti-6Al-4V, aged>,
+ <Record long name: Titanium, alpha-beta alloy, Ti-6Al-4V, annealed, generic>,
+ <Record long name: Titanium, alpha-beta alloy, Ti-6Al-4V, solution treated & aged>,
+ <Record long name: Aluminum, 7075, wrought, T6>,
+ <Record long name: Aluminum, 7075, wrought, T73>,
+ <Record long name: Low alloy steel, AISI 4130, air melted, normalized>]
+```
+
+
+## Fetch attribute data values 
+Fetch the *Base*, *Density* and *Young's modulus* attribute values for each record, and print the results.
+Default units can be overridden before export using the `table.set_display_unit()` method.
+
+
+```python
+tab.set_display_unit("Young's modulus", "ksi")
+tab.bulk_fetch(records=records, attributes=["Base", "Density", "Young's modulus"])
+```
+
+
+```python
+density_unit = tab.attributes['Density'].unit
+youngs_mod_unit = tab.attributes["Young's modulus"].unit
+print('{:50.50} | {:^21} | {:^21} | {:^18}'.format('Name',
+                                                   'Density / ' + density_unit,
+                                                   "Young's modulus / " + youngs_mod_unit,
+                                                   'Base'))
+print('-'*120)
+for record in records:
+    output = '{:50.50} | {:^10.2f}-{:^10.2f} | {:^10.2f}-{:^10.2f} | {:^20}'.format(record.name,
+        record.attributes['Density'].value['low'],
+        record.attributes['Density'].value['high'],
+        record.attributes["Young's modulus"].value['low'],
+        record.attributes["Young's modulus"].value['high'],
+        str(record.attributes['Base'].value))
+    print(output)
+```
+*Previous cell output:*
+```output
+Name                                               |   Density / kg/m^3    | Young's modulus / ksi |        Base       
+------------------------------------------------------------------------------------------------------------------------
+Titanium, alpha-beta alloy, Ti-6Al-4V, aged        |  4420.00  - 4430.00   |  16099.19 - 17259.49  |    Ti (Titanium)    
+Titanium, alpha-beta alloy, Ti-6Al-4V, annealed, g |  4406.64  - 4450.93   |  15954.15 - 17259.49  |    Ti (Titanium)    
+Titanium, alpha-beta alloy, Ti-6Al-4V, solution tr |  4406.64  - 4450.93   |  15954.15 - 16969.42  |    Ti (Titanium)    
+Aluminum, 7075, wrought, T6                        |  2770.00  - 2830.00   |  10007.60 - 11022.87  |    Al (Aluminum)    
+Aluminum, 7075, wrought, T73                       |  2781.69  - 2809.65   |  10007.60 - 10520.81  |    Al (Aluminum)    
+Low alloy steel, AISI 4130, air melted, normalized |  7794.24  - 7872.58   |  29007.55 - 30495.11  |      Fe (Iron)
+```
+## Fetch meta-attributes data values
+Meta-attributes can be included in a bulk export operation by specifying their `AttributeDefinition`, which can be
+obtained from the parent attribute definition.
+Accessing a meta-attribute that has not been exported will raise an error, even if the parent attribute has been
+exported.
+Fetch the attribute *Food contact* and associated meta-attribute *Notes* for each record and print the results.
+
+
+```python
+import textwrap
+```
+
+
+```python
+food_contact = tab.attributes['Food contact']
+food_contact_notes = food_contact.meta_attributes['Notes']
+```
+
+
+```python
+tab.bulk_fetch(records=records, attributes=[food_contact, food_contact_notes])
+```
+
+
+```python
+print('{:50.50} | {:^18} | {:^42}'.format(
+    'Name', 'Food contact', 'Notes')
+)
+print('-'*120)
+for record in records:
+    output = '{:50.50} | {:^18} | {:^42}'.format(
+        record.name,
+        record.attributes['Food contact'].value,
+        textwrap.shorten(str(record.attributes['Food contact'].meta_attributes['Notes'].value), width=42),
+    )
+    print(output)
+```
+*Previous cell output:*
+```output
+Name                                               |    Food contact    |                   Notes                   
+------------------------------------------------------------------------------------------------------------------------
+Titanium, alpha-beta alloy, Ti-6Al-4V, aged        |        Yes         |                    None                   
+Titanium, alpha-beta alloy, Ti-6Al-4V, annealed, g |        Yes         |                    None                   
+Titanium, alpha-beta alloy, Ti-6Al-4V, solution tr |        Yes         |                    None                   
+Aluminum, 7075, wrought, T6                        |        Yes         |   Not valid for use in France and [...]   
+Aluminum, 7075, wrought, T73                       |        Yes         |   Not valid for use in France and [...]   
+Low alloy steel, AISI 4130, air melted, normalized |        Yes         |                    None
+```
\ No newline at end of file
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/05_Edit_Data.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/05_Edit_Data.md
new file mode 100644
index 0000000000..01c55e9245
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/05_Edit_Data.md
@@ -0,0 +1,260 @@
+# Edit Data
+Create and edit a **Record** object and write your changes to the MI server.
+
+This notebook describes how to edit text-based attributes. See the following notebooks for additional attribute types:
+
+- [Point and range attributes](./12_Add_Point_Range_Data.ipynb)
+- [Date, integer, and logical attributes](./13_Add_Date_Integer_and_Logical_Data.ipynb)
+- [Functional attributes](./07_Import_Functional_Data.ipynb)
+- [Tabular attributes](./09_Edit_Tabular_Data.ipynb)
+- [File, picture, and hyperlink attributes](./11_Add_Files_Pictures_and_Hyperlinks.ipynb)
+- [Pseudo-attributes](./10_Edit_Pseudo-attributes.ipynb)
+
+## Connect to MI
+Get a database and table.
+
+
+```python
+from datetime import datetime
+from GRANTA_MIScriptingToolkit import granta as mpy
+
+mi = mpy.connect('http://my.server.name/mi_servicelayer', autologon=True)
+db = mi.get_db(db_key='MI_Training')
+db.unit_system = 'SI (Consistent)'
+db.absolute_temperatures = True
+tab = db.get_table('Design Data')
+```
+
+## Create a new record and path
+Create a path in the table from a starting folder, in this case, the top level folder, using `path_from()`.
+Specify `end_node` to create a new **Record** object at the end of the path with that name.
+
+
+```python
+now = datetime.now().strftime("%c")
+record_name = 'STK Example 5:{}'.format(now)
+record = tab.path_from(None, tree_path=['High Alloy Steel', 'AMS 6520'], end_node=record_name)
+record, record.parent
+```
+
+
+
+*Previous cell output:*
+```output
+(<Record long name: STK Example 5:Wed Oct 30 16:04:21 2024>,
+ <Record long name: AMS 6520>)
+```
+
+
+## Edit the record's attributes
+Get some attributes associated with the record.
+
+
+```python
+tab.bulk_fetch([record], attributes=['Common Name',
+                                     'Product Form',
+                                     'Statistical Basis',
+                                     'Available mechanical properties'])
+common_name = record.attributes['Common Name']
+product_form = record.attributes['Product Form']
+statistical_basis = record.attributes['Statistical Basis']
+available_properties = record.attributes['Available mechanical properties']
+```
+
+Edit their data values.
+
+
+```python
+common_name.value = 'STK Example 5 Test Material'
+product_form.value = 'Plate'
+```
+
+*Statistical Basis* is a single valued discrete attribute, and must be set with a string.
+
+
+```python
+print(f"{statistical_basis.name} supports multivalued data? {statistical_basis.is_multivalued}")
+statistical_basis.value = 'S basis'
+```
+*Previous cell output:*
+```output
+Statistical Basis supports multivalued data? False
+```
+*Available mechanical properties* is a multivalued discrete attribute. It can be set either with a string or a list of
+strings for each value.
+
+
+```python
+print(f"{available_properties.name} supports multivalued data? {available_properties.is_multivalued}")
+available_properties.value = ['Tensile', 'Compression']
+```
+*Previous cell output:*
+```output
+Available mechanical properties supports multivalued data? True
+```
+## Set the record's release status
+If the record is being created in a version-controlled table,
+decide whether the record should be released after creation.
+The record is created in the **unreleased** state
+to allow more data to be added later without creating a new version.
+
+
+```python
+record.flag_for_release = False
+```
+
+## Write your changes to MI
+First, specify the attributes on the record which you want to update on the server.
+
+
+```python
+record.set_attributes([common_name, product_form, statistical_basis, available_properties])
+```
+
+Then write the changes to MI. The list of updated **Record** objects is returned.
+
+
+```python
+record = mi.update([record])[0]
+print(f'Record Name: {record.name}, State: {record.release_state}')
+```
+*Previous cell output:*
+```output
+Record Name: STK Example 5:Wed Oct 30 16:04:21 2024, State: Unreleased
+```
+## 'Not Applicable' flag
+Further edits can be made to the same **Record** object. In this case, the *Condition* attribute is not relevant,
+so the `is_applicable` property is set to False. This sets the attribute to 'Not Applicable' in Granta MI.
+
+
+```python
+condition = record.attributes['Condition']
+condition.is_applicable = False
+```
+
+Set the new attributes to update, and update again to write the latest changes to the server. Release the record.
+
+
+```python
+record.set_attributes([condition])
+record.flag_for_release = True
+record = mi.update([record])[0]
+
+print(f'Record Name: {record.name}, State: {record.release_state}')
+```
+*Previous cell output:*
+```output
+Record Name: STK Example 5:Wed Oct 30 16:04:21 2024, State: Released
+```
+Check the `Record.all_versions` property for a dictionary of all versions of the record. The dictionary
+contains a single version, confirming that only a single version was created in Granta MI.
+
+
+```python
+record.all_versions
+```
+
+
+
+*Previous cell output:*
+```output
+{'v1': <Record long name: STK Example 5:Wed Oct 30 16:04:21 2024>}
+```
+
+
+## Meta-attributes
+Meta-attributes are edited similarly to attributes, except that meta-attributes are a property of the parent
+attribute, as opposed to the table. It is strongly recommended to ensure the parent attribute has a value if the
+meta-attribute is being set; the behavior of a populated meta-attribute with an empty parent attribute is undefined.
+
+If the parent attribute doesn't already have a value, set the parent attribute value.
+
+
+```python
+mooney_rivlin = record.attributes['Mooney-Rivlin']
+```
+
+*Mooney-Rivlin* is a logical attribute. It is set to a Python boolean value.
+
+
+```python
+mooney_rivlin.value = False
+```
+
+Now set the meta-attribute value.
+
+
+```python
+mooney_rivlin_comments = mooney_rivlin.meta_attributes['Comments']
+mooney_rivlin_comments.value = "Material does not exhibit hyperelastic behavior."
+```
+
+Finally, set both the attribute and meta-attribute to update, and update the record.
+
+
+```python
+record.set_attributes([mooney_rivlin, mooney_rivlin_comments])
+record.flag_for_release = True
+record = mi.update([record])[0]
+
+print(f'Record Name: {record.name}, State: {record.release_state}')
+```
+*Previous cell output:*
+```output
+Record Name: STK Example 5:Wed Oct 30 16:04:21 2024, State: Released
+```
+Check the `Record.all_versions` property again. The dictionary contains two versions, confirming that updating the
+Mooney-Rivlin attribute has created and released a second version of the record.
+
+
+```python
+record.all_versions
+```
+
+
+
+*Previous cell output:*
+```output
+{'v1': <Record long name: STK Example 5:Wed Oct 30 16:04:21 2024>,
+ 'v2': <Record long name: STK Example 5:Wed Oct 30 16:04:21 2024>}
+```
+
+
+## Output the record's attributes
+The attribute values are accessed via the same properties used for assignment.
+*Statistical Basis* and *Available mechanical properties* are discrete attributes. For multivalued discrete attributes the value is a list of strings, otherwise the value is a string.
+*Condition* is set to 'Not Applicable'; this is checked when printing the attribute value.
+
+
+```python
+common_name = record.attributes['Common Name']
+print(f'Common Name: {common_name.value}')
+
+product_form = record.attributes['Product Form']
+print(f'Product Form: {product_form.value}')
+
+statistical_basis = record.attributes['Statistical Basis']
+print(f'Statistical Basis: {statistical_basis.value}')
+
+available_properties = record.attributes['Available mechanical properties']
+result = ", ".join([prop for prop in available_properties.value])
+print(f'Available mechanical properties: {result}')
+
+condition = record.attributes['Condition']
+result = condition.value if condition.is_applicable else '<Not Applicable>'
+print(f'Condition: {result}')
+
+mooney_rivlin = record.attributes['Mooney-Rivlin']
+mooney_rivlin_comment = mooney_rivlin.meta_attributes['Comments']
+result = 'Yes' if mooney_rivlin.value else 'No'
+print(f'Mooney-Rivlin: {result}, comments: {mooney_rivlin_comment.value}')
+```
+*Previous cell output:*
+```output
+Common Name: STK Example 5 Test Material
+Product Form: Plate
+Statistical Basis: S basis
+Available mechanical properties: Tensile, Compression
+Condition: <Not Applicable>
+Mooney-Rivlin: No, comments: Material does not exhibit hyperelastic behavior.
+```
\ No newline at end of file
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/06_Create_Records.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/06_Create_Records.md
new file mode 100644
index 0000000000..ec2c532787
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/06_Create_Records.md
@@ -0,0 +1,74 @@
+# Create and Delete Records in Bulk
+Create records and delete (or withdraw) them in bulk.
+
+## Specify a table
+
+
+```python
+from datetime import datetime
+from GRANTA_MIScriptingToolkit import granta as mpy
+
+mi = mpy.connect('http://my.server.name/mi_servicelayer', autologon=True)
+table = mi.get_db(db_key="MI_Training").get_table("Files for Training")
+```
+
+## Create records
+Decide which folder the new records will be added to (you can use the table itself as a parent for a 'top-level' record).
+
+
+```python
+parent = table.search_for_records_by_name("Other")[0]
+```
+
+Create five new **Record** objects.
+
+
+```python
+now = datetime.now().strftime("%c")
+recordNames = ['STK Example 6:{} - {}'.format(now, i) for i in range(5)]
+new_records = [table.create_record(n, parent=parent, subsets={"All files"}) for n in recordNames]
+new_records
+```
+
+
+
+*Previous cell output:*
+```output
+[<Record long name: STK Example 6:Wed Oct 30 16:04:50 2024 - 0>,
+ <Record long name: STK Example 6:Wed Oct 30 16:04:50 2024 - 1>,
+ <Record long name: STK Example 6:Wed Oct 30 16:04:50 2024 - 2>,
+ <Record long name: STK Example 6:Wed Oct 30 16:04:50 2024 - 3>,
+ <Record long name: STK Example 6:Wed Oct 30 16:04:50 2024 - 4>]
+```
+
+
+## Write your changes to MI 
+The new records are created on the server when update() is called.
+
+
+```python
+recs = mi.update(new_records)
+
+print("New records:")
+for rec in recs:
+    print(rec.viewer_url)
+```
+*Previous cell output:*
+```output
+New records:
+http://my.server.name/mi/datasheet.aspx?dbKey=MI_Training&recordHistoryGuid=0b547c6b-64ae-4d18-87e5-2959f5e97a49
+http://my.server.name/mi/datasheet.aspx?dbKey=MI_Training&recordHistoryGuid=0952c6b1-4f37-45fd-b6a5-590097d91d79
+http://my.server.name/mi/datasheet.aspx?dbKey=MI_Training&recordHistoryGuid=2dfdfcda-6348-4558-b5e2-87959e05663b
+http://my.server.name/mi/datasheet.aspx?dbKey=MI_Training&recordHistoryGuid=34028202-d7d7-4c0b-9615-0357fa60cd96
+http://my.server.name/mi/datasheet.aspx?dbKey=MI_Training&recordHistoryGuid=950e8ce8-bd04-4051-89e2-dc56b1c28733
+```
+## Delete the records
+``Session.bulk_delete_or_withdraw_records`` accepts any list of records (e.g. results of a search, ``Table.all_records()``, ``Record.children``, etc.).
+If the table is version-controlled, the records are withdrawn instead.
+Unlike record creation, this method interacts directly with the server. The records are deleted or withdrawn when the
+method is called.
+
+
+```python
+mi.bulk_delete_or_withdraw_records(recs)
+```
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/07_Import_Functional_Data.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/07_Import_Functional_Data.md
new file mode 100644
index 0000000000..4774c66fa1
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/07_Import_Functional_Data.md
@@ -0,0 +1,253 @@
+# Import Functional Data
+Import CSV data into a functional attribute and update the parameters and header units.
+
+## Import required libraries
+Connect to MI and specify a table.
+
+
+```python
+from datetime import datetime
+import csv
+from GRANTA_MIScriptingToolkit import granta as mpy
+
+mi = mpy.connect('http://my.server.name/mi_servicelayer', autologon=True)
+db = mi.get_db(db_key='MI_Training')
+tab = db.get_table('Design Data')
+```
+
+## Create a new record to store your data
+Define names for a new record, and an attribute on that record.
+
+
+```python
+now = datetime.now().strftime("%c")
+recordName = 'STK Example 7:{}'.format(now)
+funcAttributeName = 'Tensile Stress/Strain, L'
+```
+
+Create the new **Record** object.
+
+
+```python
+rec = tab.create_record(recordName, subsets={'Design Data'})
+rec
+```
+
+
+
+*Previous cell output:*
+```output
+<Record long name: STK Example 7:Wed Oct 30 16:05:16 2024>
+```
+
+
+Get the unpopulated functional attribute for the new record, so we can populate it.
+
+
+```python
+func = rec.attributes[funcAttributeName]
+func
+```
+
+
+
+*Previous cell output:*
+```output
+<FloatFunctionalSeriesValue name: Tensile Stress/Strain, L, with 0 points>
+```
+
+
+## Import test data
+The tensile test data to import is stored in `example.csv`.
+Read in each row of the CSV data, and add the data to the functional attribute point-by-point.
+
+
+```python
+with open('supporting_files/07_example_data_for_import.csv') as csvfile:
+    reader = csv.DictReader(csvfile, delimiter=',')
+    for row in reader:
+        func.add_point({'y': float(row['Tensile Stress [ksi]']),
+                        'Strain': float(row['Strain [%]']),
+                        'Temperature': float(row['Temperature [Celsius]']),
+                        'Stress/Strain Curve Type': str(row['Curve Type']),
+                        'Estimated?': bool(row['Estimated?'])})
+```
+
+Access the `func.value` property to see the underlying data structure of the attribute represented as a list of lists.
+`func.data` shows all parameters supported by the attribute. Parameters that were not populated during the import have
+been set to **None**.
+
+
+```python
+func.value
+```
+
+
+
+*Previous cell output:*
+```output
+[['Y min (Tensile Stress/Strain, L [ksi])',
+  'Y max (Tensile Stress/Strain, L [ksi])',
+  'Strain [% strain]',
+  'Temperature [°C]',
+  'Time [hr]',
+  'Other []',
+  'Stress/Strain Curve Type []',
+  'Estimated point?'],
+ [0.0, 0.0, 0.0, -73.0, None, None, 'Yield', True],
+ [396.0, 396.0, 0.209, -73.0, None, None, 'Yield', True],
+ [714.0, 714.0, 0.376, -73.0, None, None, 'Yield', True],
+ [1031.0, 1031.0, 0.544, -73.0, None, None, 'Yield', True],
+ [1269.0, 1269.0, 0.669, -73.0, None, None, 'Yield', True],
+ [1427.0, 1427.0, 0.753, -73.0, None, None, 'Yield', True],
+ [1507.0, 1507.0, 0.795, -73.0, None, None, 'Yield', True],
+ [1570.0, 1570.0, 0.83, -73.0, None, None, 'Yield', True],
+ [1633.0, 1633.0, 0.867, -73.0, None, None, 'Yield', True],
+ [1681.0, 1681.0, 0.897, -73.0, None, None, 'Yield', True],
+ [1721.0, 1721.0, 0.925, -73.0, None, None, 'Yield', True],
+ [1752.0, 1752.0, 0.952, -73.0, None, None, 'Yield', True],
+ [1784.0, 1784.0, 0.983, -73.0, None, None, 'Yield', True],
+ [1808.0, 1808.0, 1.012, -73.0, None, None, 'Yield', True],
+ [1824.0, 1824.0, 1.034, -73.0, None, None, 'Yield', True],
+ [1840.0, 1840.0, 1.059, -73.0, None, None, 'Yield', True],
+ [1855.0, 1855.0, 1.087, -73.0, None, None, 'Yield', True],
+ [1871.0, 1871.0, 1.121, -73.0, None, None, 'Yield', True],
+ [1887.0, 1887.0, 1.159, -73.0, None, None, 'Yield', True],
+ [1903.0, 1903.0, 1.204, -73.0, None, None, 'Yield', True],
+ [1919.0, 1919.0, 1.256, -73.0, None, None, 'Yield', True],
+ [1935.0, 1935.0, 1.318, -73.0, None, None, 'Yield', True],
+ [1951.0, 1951.0, 1.39, -73.0, None, None, 'Yield', True],
+ [1958.0, 1958.0, 1.432, -73.0, None, None, 'Yield', True],
+ [1966.0, 1966.0, 1.476, -73.0, None, None, 'Yield', True],
+ [0.0, 0.0, 0.0, 27.0, None, None, 'Yield', True],
+ [371.0, 371.0, 0.203, 27.0, None, None, 'Yield', True],
+ [667.0, 667.0, 0.365, 27.0, None, None, 'Yield', True],
+ [964.0, 964.0, 0.527, 27.0, None, None, 'Yield', True],
+ [1186.0, 1186.0, 0.649, 27.0, None, None, 'Yield', True],
+ [1334.0, 1334.0, 0.73, 27.0, None, None, 'Yield', True],
+ [1408.0, 1408.0, 0.771, 27.0, None, None, 'Yield', True],
+ [1468.0, 1468.0, 0.805, 27.0, None, None, 'Yield', True],
+ [1527.0, 1527.0, 0.839, 27.0, None, None, 'Yield', True],
+ [1571.0, 1571.0, 0.868, 27.0, None, None, 'Yield', True],
+ [1608.0, 1608.0, 0.895, 27.0, None, None, 'Yield', True],
+ [1638.0, 1638.0, 0.92, 27.0, None, None, 'Yield', True],
+ [1668.0, 1668.0, 0.95, 27.0, None, None, 'Yield', True],
+ [1690.0, 1690.0, 0.978, 27.0, None, None, 'Yield', True],
+ [1705.0, 1705.0, 0.999, 27.0, None, None, 'Yield', True],
+ [1720.0, 1720.0, 1.024, 27.0, None, None, 'Yield', True],
+ [1734.0, 1734.0, 1.053, 27.0, None, None, 'Yield', True],
+ [1749.0, 1749.0, 1.087, 27.0, None, None, 'Yield', True],
+ [1764.0, 1764.0, 1.126, 27.0, None, None, 'Yield', True],
+ [1779.0, 1779.0, 1.174, 27.0, None, None, 'Yield', True],
+ [1794.0, 1794.0, 1.23, 27.0, None, None, 'Yield', True],
+ [1808.0, 1808.0, 1.297, 27.0, None, None, 'Yield', True],
+ [1823.0, 1823.0, 1.378, 27.0, None, None, 'Yield', True],
+ [1831.0, 1831.0, 1.424, 27.0, None, None, 'Yield', True],
+ [1838.0, 1838.0, 1.475, 27.0, None, None, 'Yield', True],
+ [0.0, 0.0, 0.0, 149.0, None, None, 'Yield', True],
+ [328.0, 328.0, 0.187, 149.0, None, None, 'Yield', True],
+ [590.0, 590.0, 0.337, 149.0, None, None, 'Yield', True],
+ [852.0, 852.0, 0.486, 149.0, None, None, 'Yield', True],
+ [1048.0, 1048.0, 0.598, 149.0, None, None, 'Yield', True],
+ [1179.0, 1179.0, 0.673, 149.0, None, None, 'Yield', True],
+ [1245.0, 1245.0, 0.711, 149.0, None, None, 'Yield', True],
+ [1297.0, 1297.0, 0.741, 149.0, None, None, 'Yield', True],
+ [1349.0, 1349.0, 0.773, 149.0, None, None, 'Yield', True],
+ [1389.0, 1389.0, 0.798, 149.0, None, None, 'Yield', True],
+ [1421.0, 1421.0, 0.822, 149.0, None, None, 'Yield', True],
+ [1448.0, 1448.0, 0.845, 149.0, None, None, 'Yield', True],
+ [1474.0, 1474.0, 0.872, 149.0, None, None, 'Yield', True],
+ [1493.0, 1493.0, 0.898, 149.0, None, None, 'Yield', True],
+ [1507.0, 1507.0, 0.918, 149.0, None, None, 'Yield', True],
+ [1520.0, 1520.0, 0.943, 149.0, None, None, 'Yield', True],
+ [1533.0, 1533.0, 0.971, 149.0, None, None, 'Yield', True],
+ [1546.0, 1546.0, 1.006, 149.0, None, None, 'Yield', True],
+ [1559.0, 1559.0, 1.047, 149.0, None, None, 'Yield', True],
+ [1572.0, 1572.0, 1.098, 149.0, None, None, 'Yield', True],
+ [1585.0, 1585.0, 1.16, 149.0, None, None, 'Yield', True],
+ [1598.0, 1598.0, 1.236, 149.0, None, None, 'Yield', True],
+ [1611.0, 1611.0, 1.329, 149.0, None, None, 'Yield', True],
+ [1618.0, 1618.0, 1.384, 149.0, None, None, 'Yield', True],
+ [0.0, 0.0, 0.0, 316.0, None, None, 'Yield', True],
+ [307.0, 307.0, 0.182, 316.0, None, None, 'Yield', True],
+ [553.0, 553.0, 0.328, 316.0, None, None, 'Yield', True],
+ [799.0, 799.0, 0.473, 316.0, None, None, 'Yield', True],
+ [984.0, 984.0, 0.582, 316.0, None, None, 'Yield', True],
+ [1107.0, 1107.0, 0.655, 316.0, None, None, 'Yield', True],
+ [1168.0, 1168.0, 0.692, 316.0, None, None, 'Yield', True],
+ [1217.0, 1217.0, 0.722, 316.0, None, None, 'Yield', True],
+ [1266.0, 1266.0, 0.753, 316.0, None, None, 'Yield', True],
+ [1303.0, 1303.0, 0.78, 316.0, None, None, 'Yield', True],
+ [1334.0, 1334.0, 0.804, 316.0, None, None, 'Yield', True],
+ [1359.0, 1359.0, 0.828, 316.0, None, None, 'Yield', True],
+ [1383.0, 1383.0, 0.856, 316.0, None, None, 'Yield', True],
+ [1402.0, 1402.0, 0.882, 316.0, None, None, 'Yield', True],
+ [1414.0, 1414.0, 0.903, 316.0, None, None, 'Yield', True],
+ [1426.0, 1426.0, 0.927, 316.0, None, None, 'Yield', True],
+ [1439.0, 1439.0, 0.955, 316.0, None, None, 'Yield', True],
+ [1451.0, 1451.0, 0.988, 316.0, None, None, 'Yield', True],
+ [1463.0, 1463.0, 1.027, 316.0, None, None, 'Yield', True],
+ [1475.0, 1475.0, 1.073, 316.0, None, None, 'Yield', True],
+ [1488.0, 1488.0, 1.129, 316.0, None, None, 'Yield', True],
+ [1500.0, 1500.0, 1.195, 316.0, None, None, 'Yield', True],
+ [1512.0, 1512.0, 1.275, 316.0, None, None, 'Yield', True],
+ [1519.0, 1519.0, 1.321, 316.0, None, None, 'Yield', True],
+ [1525.0, 1525.0, 1.372, 316.0, None, None, 'Yield', True],
+ [0.0, 0.0, 0.0, 427.0, None, None, 'Yield', True],
+ [303.0, 303.0, 0.192, 427.0, None, None, 'Yield', True],
+ [545.0, 545.0, 0.345, 427.0, None, None, 'Yield', True],
+ [787.0, 787.0, 0.499, 427.0, None, None, 'Yield', True],
+ [969.0, 969.0, 0.617, 427.0, None, None, 'Yield', True],
+ [1090.0, 1090.0, 0.704, 427.0, None, None, 'Yield', True],
+ [1151.0, 1151.0, 0.753, 427.0, None, None, 'Yield', True],
+ [1199.0, 1199.0, 0.798, 427.0, None, None, 'Yield', True],
+ [1247.0, 1247.0, 0.85, 427.0, None, None, 'Yield', True],
+ [1284.0, 1284.0, 0.895, 427.0, None, None, 'Yield', True],
+ [1314.0, 1314.0, 0.938, 427.0, None, None, 'Yield', True],
+ [1338.0, 1338.0, 0.977, 427.0, None, None, 'Yield', True],
+ [1362.0, 1362.0, 1.02, 427.0, None, None, 'Yield', True],
+ [1381.0, 1381.0, 1.056, 427.0, None, None, 'Yield', True],
+ [1393.0, 1393.0, 1.082, 427.0, None, None, 'Yield', True],
+ [1405.0, 1405.0, 1.11, 427.0, None, None, 'Yield', True],
+ [1417.0, 1417.0, 1.139, 427.0, None, None, 'Yield', True],
+ [1429.0, 1429.0, 1.171, 427.0, None, None, 'Yield', True],
+ [1441.0, 1441.0, 1.204, 427.0, None, None, 'Yield', True],
+ [1453.0, 1453.0, 1.24, 427.0, None, None, 'Yield', True],
+ [1465.0, 1465.0, 1.278, 427.0, None, None, 'Yield', True],
+ [1478.0, 1478.0, 1.319, 427.0, None, None, 'Yield', True],
+ [1490.0, 1490.0, 1.363, 427.0, None, None, 'Yield', True],
+ [1496.0, 1496.0, 1.386, 427.0, None, None, 'Yield', True]]
+```
+
+
+The example data uses Celsius for temperature, but the database default is Kelvin.
+Change the parameter unit before import to take account of this.
+
+
+```python
+func.parameters['Temperature'].unit = '°C'
+```
+
+After changing the units, it is good practice to update the header units in `func.data` so that the units displayed in
+MI applications are correct.
+
+
+```python
+func.update_header_units()
+```
+
+## Write your changes to MI
+
+
+```python
+rec.set_attributes([func])
+mi.update([rec])
+```
+
+
+
+*Previous cell output:*
+```output
+[<Record long name: STK Example 7:Wed Oct 30 16:05:16 2024>]
+```
+
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/08_Create_Functional_Data.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/08_Create_Functional_Data.md
new file mode 100644
index 0000000000..105c4c9de5
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/08_Create_Functional_Data.md
@@ -0,0 +1,421 @@
+# Create Functional Data
+Populate a functional attribute with data fitted using the Python `numpy` library.
+
+## Import libraries and define a polynomial fit function
+This example populates a functional attribute for *Ultimate Tensile Strength vs Temperature* in one table from a polynomial fit of the individual attributes in another table.
+
+
+```python
+from datetime import datetime
+import numpy as np
+from GRANTA_MIScriptingToolkit import granta as mpy
+
+def My4degPolyFitFunc(x, a, b, c, d, e):
+    return a*np.power(x, 4) + b*np.power(x, 3) + c*np.power(x, 2) + d*x + e
+```
+
+## Specify database and table
+The source data will come from the *Tensile Statistical Data* table.
+
+
+```python
+mi = mpy.connect('http://my.server.name/mi_servicelayer', autologon=True)
+
+db = mi.get_db(db_key='MI_Training')
+db.unit_system ='Metric'
+db.absolute_temperatures = True
+
+table = db.get_table('Tensile Statistical Data')
+```
+
+## Export test data
+Find *High Alloy Steel* > *AMS 6520* > *Plate* records in which both the *Ultimate Tensile Strength* and *Test Temperature* attributes are populated.
+
+
+```python
+records = table.get_records_from_path(None, ["High Alloy Steel", "AMS 6520", "Plate"])
+```
+
+Extract the attribute values from the returned records into x and y values.
+
+
+```python
+table.bulk_fetch(records, attributes=['Test Temperature', 'Ultimate Tensile Strength'])
+populated_records = [
+    r for r in records
+    if not r.attributes['Test Temperature'].is_empty()
+    and not r.attributes['Ultimate Tensile Strength'].is_empty()
+]
+```
+
+
+```python
+x_values = [r.attributes['Test Temperature'].value for r in populated_records]
+y_values = [r.attributes['Ultimate Tensile Strength'].value for r in populated_records]
+```
+
+## Fit the test data
+Fit a fourth-order polynomial to your x and y data.
+
+
+```python
+coeffs = np.polyfit(x_values, y_values, 4)
+```
+
+Generate x and y values for the fitted equation, using the function you defined at the start.
+
+
+```python
+x_fit = np.linspace(np.amin(x_values), np.amax(x_values), 20)
+y_fit = My4degPolyFitFunc(x_fit, *coeffs)
+```
+
+## Create a record to store the data in 
+The resulting functional data will be written into the *Metals* subset of the *Design Data* table,
+using the same unit system.
+
+
+```python
+designdata = db.get_table('Design Data')
+designdata.subsets.clear()
+designdata.subsets.add('Metals')
+```
+
+Create a new record to store your functional data.
+
+
+```python
+now = datetime.now().strftime("%c")
+recordName = 'STK Example 8:{}'.format(now)
+record = designdata.create_record(recordName)
+record.color = mpy.RecordColor.Green
+```
+
+Access the (empty) functional attribute, and view its column headers.
+
+
+```python
+func = record.attributes['Tens. Ult. Stress (L-dir) with Temp.']
+func.column_headers
+```
+
+
+
+*Previous cell output:*
+```output
+['Y min (Tens. Ult. Stress (L-dir) with Temp. [MPa])',
+ 'Y max (Tens. Ult. Stress (L-dir) with Temp. [MPa])',
+ 'Temperature [K]',
+ 'Time [hr]',
+ 'Other []',
+ 'Data Type Lab []',
+ 'Estimated point?']
+```
+
+
+## Populate the functional attribute
+Add the test data to the functional attribute point-by-point, then view the attribute data. Column headers can be
+omitted if they aren't required to represent the data.
+
+
+```python
+for x, y in zip(x_values, y_values):
+    func.add_point({'Temperature': x, 'y': y, 'Data Type Lab': 'Test Data'})
+func.value
+```
+
+
+
+*Previous cell output:*
+```output
+[['Y min (Tens. Ult. Stress (L-dir) with Temp. [MPa])',
+  'Y max (Tens. Ult. Stress (L-dir) with Temp. [MPa])',
+  'Temperature [K]',
+  'Time [hr]',
+  'Other []',
+  'Data Type Lab []',
+  'Estimated point?'],
+ [1263.0048828125,
+  1263.0048828125,
+  810.9284057617188,
+  None,
+  None,
+  'Test Data',
+  False],
+ [2399.146240234375,
+  2399.146240234375,
+  194.2612762451172,
+  None,
+  None,
+  'Test Data',
+  False],
+ [2078.31, 2078.31, 422.0389938964844, None, None, 'Test Data', False],
+ [1848.140014648438,
+  1848.140014648438,
+  588.7060546875,
+  None,
+  None,
+  'Test Data',
+  False],
+ [1734.37646484375,
+  1734.37646484375,
+  699.8172607421875,
+  None,
+  None,
+  'Test Data',
+  False],
+ [2189.89, 2189.89, 294.2613938964844, None, None, 'Test Data', False]]
+```
+
+
+Then add the fitted data to the functional attribute point-by-point, and view the attribute data with series number as an extra column.
+
+
+```python
+for x, y in zip(x_fit, y_fit):
+    func.add_point({'Temperature': x, 'y': y, 'Data Type Lab': 'Fitted Data'})
+func.data_with_series_number
+```
+
+
+
+*Previous cell output:*
+```output
+[['Y min (Tens. Ult. Stress (L-dir) with Temp. [MPa])',
+  'Y max (Tens. Ult. Stress (L-dir) with Temp. [MPa])',
+  'Temperature [K]',
+  'Time [hr]',
+  'Other []',
+  'Data Type Lab []',
+  'Estimated point?',
+  'Series number'],
+ [1263.0048828125,
+  1263.0048828125,
+  810.9284057617188,
+  None,
+  None,
+  'Test Data',
+  False,
+  1],
+ [2399.146240234375,
+  2399.146240234375,
+  194.2612762451172,
+  None,
+  None,
+  'Test Data',
+  False,
+  1],
+ [2078.31, 2078.31, 422.0389938964844, None, None, 'Test Data', False, 1],
+ [1848.140014648438,
+  1848.140014648438,
+  588.7060546875,
+  None,
+  None,
+  'Test Data',
+  False,
+  1],
+ [1734.37646484375,
+  1734.37646484375,
+  699.8172607421875,
+  None,
+  None,
+  'Test Data',
+  False,
+  1],
+ [2189.89, 2189.89, 294.2613938964844, None, None, 'Test Data', False, 1],
+ [np.float64(2392.1796767662518),
+  np.float64(2392.1796767662518),
+  np.float64(194.2612762451172),
+  None,
+  None,
+  'Fitted Data',
+  False,
+  2],
+ [np.float64(2333.081679240432),
+  np.float64(2333.081679240432),
+  np.float64(226.71744095651727),
+  None,
+  None,
+  'Fitted Data',
+  False,
+  2],
+ [np.float64(2274.74066356839),
+  np.float64(2274.74066356839),
+  np.float64(259.17360566791734),
+  None,
+  None,
+  'Fitted Data',
+  False,
+  2],
+ [np.float64(2219.0030335134543),
+  np.float64(2219.0030335134543),
+  np.float64(291.62977037931745),
+  None,
+  None,
+  'Fitted Data',
+  False,
+  2],
+ [np.float64(2167.2202125230733),
+  np.float64(2167.2202125230733),
+  np.float64(324.0859350907175),
+  None,
+  None,
+  'Fitted Data',
+  False,
+  2],
+ [np.float64(2120.248643728818),
+  np.float64(2120.248643728818),
+  np.float64(356.5420998021176),
+  None,
+  None,
+  'Fitted Data',
+  False,
+  2],
+ [np.float64(2078.4497899463804),
+  np.float64(2078.4497899463804),
+  np.float64(388.9982645135177),
+  None,
+  None,
+  'Fitted Data',
+  False,
+  2],
+ [np.float64(2041.6901336755727),
+  np.float64(2041.6901336755727),
+  np.float64(421.4544292249178),
+  None,
+  None,
+  'Fitted Data',
+  False,
+  2],
+ [np.float64(2009.3411771003305),
+  np.float64(2009.3411771003305),
+  np.float64(453.91059393631787),
+  None,
+  None,
+  'Fitted Data',
+  False,
+  2],
+ [np.float64(1980.2794420887074),
+  np.float64(1980.2794420887074),
+  np.float64(486.366758647718),
+  None,
+  None,
+  'Fitted Data',
+  False,
+  2],
+ [np.float64(1952.8864701928812),
+  np.float64(1952.8864701928812),
+  np.float64(518.822923359118),
+  None,
+  None,
+  'Fitted Data',
+  False,
+  2],
+ [np.float64(1925.0488226491502),
+  np.float64(1925.0488226491502),
+  np.float64(551.2790880705181),
+  None,
+  None,
+  'Fitted Data',
+  False,
+  2],
+ [np.float64(1894.1580803779325),
+  np.float64(1894.1580803779325),
+  np.float64(583.7352527819182),
+  None,
+  None,
+  'Fitted Data',
+  False,
+  2],
+ [np.float64(1857.1108439837685),
+  np.float64(1857.1108439837685),
+  np.float64(616.1914174933183),
+  None,
+  None,
+  'Fitted Data',
+  False,
+  2],
+ [np.float64(1810.3087337553218),
+  np.float64(1810.3087337553218),
+  np.float64(648.6475822047184),
+  None,
+  None,
+  'Fitted Data',
+  False,
+  2],
+ [np.float64(1749.6583896653735),
+  np.float64(1749.6583896653735),
+  np.float64(681.1037469161184),
+  None,
+  None,
+  'Fitted Data',
+  False,
+  2],
+ [np.float64(1670.571471370828),
+  np.float64(1670.571471370828),
+  np.float64(713.5599116275185),
+  None,
+  None,
+  'Fitted Data',
+  False,
+  2],
+ [np.float64(1567.9646582127116),
+  np.float64(1567.9646582127116),
+  np.float64(746.0160763389187),
+  None,
+  None,
+  'Fitted Data',
+  False,
+  2],
+ [np.float64(1436.2596492161715),
+  np.float64(1436.2596492161715),
+  np.float64(778.4722410503188),
+  None,
+  None,
+  'Fitted Data',
+  False,
+  2],
+ [np.float64(1269.3831630904717),
+  np.float64(1269.3831630904717),
+  np.float64(810.9284057617188),
+  None,
+  None,
+  'Fitted Data',
+  False,
+  2]]
+```
+
+
+Adjust the series linestyles (`series_linestyles` is a dictionary, indexed with integers).
+
+
+```python
+func.series_linestyles[1] = 'Markers'
+func.series_linestyles[2] = 'Lines'
+func.series_linestyles
+```
+
+
+
+*Previous cell output:*
+```output
+{1: 'Markers', 2: 'Lines'}
+```
+
+
+## Write your changes to MI
+Set the attributes you've modified to update, and write the new record to the server.
+
+
+```python
+record.set_attributes([func])
+mi.update([record])
+```
+
+
+
+*Previous cell output:*
+```output
+[<Record long name: STK Example 8:Wed Oct 30 16:05:46 2024>]
+```
+
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/09_Edit_Tabular_Data.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/09_Edit_Tabular_Data.md
new file mode 100644
index 0000000000..ede756c087
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/09_Edit_Tabular_Data.md
@@ -0,0 +1,168 @@
+# Edit Tabular Data
+Edit and compare tabular data using the Python `numpy` library.
+
+In this example, there is some existing tensile data for a material (AMS 6520, Plate, 1000°F).
+Some of that data is of particular interest and we want to separate it from the old data and
+put it in a record of its own.
+
+## Connect to MI
+Specify a database and table.
+
+
+```python
+from datetime import datetime
+import numpy as np
+from GRANTA_MIScriptingToolkit import granta as mpy
+
+mi = mpy.connect('http://my.server.name/mi_servicelayer', autologon=True)
+dbase = mi.get_db(db_key='MI_Training')
+table = dbase.get_table('Tensile Statistical Data')
+```
+
+## Locate existing data
+Find the record and attribute that contains the original data.
+
+
+```python
+record = table.search_for_records_by_name('AMS 6520, Plate, 1000°F')[0]
+samples = record.attributes['Tensile test data used in this rollup']
+```
+
+Select some samples of interest.
+
+
+```python
+focus_samples = ['MTS-615731', 'MTS-615741', 'MTS-615771']
+```
+
+Get their **Record** objects from the tabular attribute containing the tensile test data.
+
+
+```python
+focus_recs = [samples.linked_records[s][0] for s in focus_samples]
+```
+
+Get their row indices in the tabular data structure.
+
+
+```python
+focus_rows = [samples['Linking Value (Specimen ID)'].index(samp) for samp in focus_samples]
+```
+
+## Extract and analyse the data
+Extract the sample data, and establish how different these particular samples are from the remainder of the set.
+
+
+```python
+focus_youngs = [samples["Young's Modulus (11-axis)", i] for i in focus_rows]
+rmain_youngs = [samples["Young's Modulus (11-axis)", i] for i in range(6) if i not in focus_rows]
+
+rmain_mean = np.mean(rmain_youngs)
+focus_mean = np.mean(focus_youngs)
+rmain_mean, focus_mean
+```
+
+
+
+*Previous cell output:*
+```output
+(np.float64(142.74677022298178), np.float64(146.3169097900391))
+```
+
+
+
+```python
+print('Percentage difference between the two means is: '
+      '{} %'.format(100*abs(rmain_mean-focus_mean)/(0.5*(rmain_mean+focus_mean))))
+```
+*Previous cell output:*
+```output
+Percentage difference between the two means is: 2.470140535743884 %
+```
+## Create a new record and tabular attribute
+Create a new record for your data of interest.
+
+
+```python
+now = datetime.now().strftime("%c")
+recordName = 'STK Example 9:{}'.format(now)
+new_rec = table.create_record(recordName, parent=record.parent, subsets={'Statistical Test Data'})
+```
+
+Select the tabular attribute to write to.
+
+
+```python
+new_samples = new_rec.attributes['Tensile test data used in this rollup']
+new_samples
+```
+
+
+
+*Previous cell output:*
+```output
+<TabularValue name: Tensile test data used in this rollup (not loaded)>
+```
+
+
+Link the samples to the new tabular datum.
+
+
+```python
+for focus_sample in focus_samples:
+    new_samples.add_row(linking_value=focus_sample)
+new_samples
+```
+
+
+
+*Previous cell output:*
+```output
+<TabularValue name: Tensile test data used in this rollup, shape: 9 x 3>
+```
+
+
+Set the new tabular attribute to update, and write the new record to MI.
+
+
+```python
+new_rec.set_attributes([new_samples])
+new_rec = mi.update([new_rec])[0]
+```
+
+## Link to the original record
+The original record is linked to the records you just linked to the tabular data. Link the new record to the original, too.
+
+(Records must exist on the server to be linked together, which means the record must be pushed to the server before it can be linked to other records.)
+
+
+```python
+new_rec.set_links('Tensile Test Data', set(focus_recs))
+new_rec.links
+```
+
+
+
+*Previous cell output:*
+```output
+{'Tensile Test Data': {<Record long name: MTS-615731>,
+  <Record long name: MTS-615741>,
+  <Record long name: MTS-615771>}}
+```
+
+
+## Update the new record's links on the server
+Like changes to data, changes to links also need to be pushed to the server. Unlike attributes, links do not need to be flagged for update (there is no equivalent to `set_attributes()` for links).
+
+
+```python
+mi.update_links([new_rec])
+```
+
+
+
+*Previous cell output:*
+```output
+[<Record long name: STK Example 9:Wed Oct 30 16:06:18 2024>]
+```
+
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/10_Edit_Pseudo-attributes.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/10_Edit_Pseudo-attributes.md
new file mode 100644
index 0000000000..abfd2374f5
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/10_Edit_Pseudo-attributes.md
@@ -0,0 +1,120 @@
+# Edit Pseudo-attributes
+This example illustrates how to view the pseudo-attributes values (also known as record properties) 
+and how to modify the value of an editable pseudo-attribute.
+
+## Connect to MI
+Specify a database and table.
+
+
+```python
+from GRANTA_MIScriptingToolkit import granta as mpy
+
+mi = mpy.connect('http://my.server.name/mi_servicelayer', autologon=True)
+db = mi.get_db(db_key='MI_Training')
+tab = db.get_table('Training Exercise for Import')
+```
+
+## Find a record and view its pseudo-attributes
+Search for a record (use the first result returned).
+
+
+```python
+recs = tab.search_for_records_by_name('Ti')
+record = recs[0]
+record
+```
+
+
+
+*Previous cell output:*
+```output
+<Record long name: Ti (Titanium)>
+```
+
+
+Examine the pseudo-attributes on the record.
+
+
+```python
+print('{:30.30} = {}'.format("name", record.name))
+print('{:30.30} = {}'.format("short_name", record.short_name))
+print('{:30.30} = {}'.format("subsets", record.subsets))
+print('{:30.30} = {}'.format("type", record.type))
+print('{:30.30} = {}'.format("color", record.color))
+print('{:30.30} = {}'.format("history_identity", record.history_identity))
+print('{:30.30} = {}'.format("version_number", record.version_number))
+print('{:30.30} = {}'.format("table_name", record.table_name))
+print('{:30.30} = {}'.format("is_writable", record.is_writable))
+print('{:30.30} = {}'.format("parent_record_short_name", record.parent_record_short_name))
+print('{:30.30} = {}'.format("parent_record_name", record.parent_record_name))
+print('{:30.30} = {}'.format("parent_record_history_identity", record.parent_record_history_identity))
+print('{:30.30} = {}'.format("released_on", record.released_on))
+print('{:30.30} = {}'.format("last_modified_on", record.last_modified_on))
+print('{:30.30} = {}'.format("created_on", record.created_on))
+print('{:30.30} = {}'.format("last_modified_by", record.last_modified_by))
+print('{:30.30} = {}'.format("created_by", record.created_by))
+```
+*Previous cell output:*
+```output
+name                           = Ti (Titanium)
+short_name                     = Ti
+subsets                        = {'Materials'}
+type                           = Record
+color                          = Red
+history_identity               = 121491
+version_number                 = None
+table_name                     = Training Exercise for Import
+is_writable                    = True
+parent_record_short_name       = Metal
+parent_record_name             = Metal
+parent_record_history_identity = 121213
+released_on                    = 2023-09-20 12:11:05.873000+00:00
+last_modified_on               = 2023-09-20 12:11:05.873000+00:00
+created_on                     = 2023-09-20 12:11:05.873000+00:00
+last_modified_by               = Ansys Granta 1
+created_by                     = Ansys Granta 1
+```
+If a pseudo-attribute value is accessed on a record and it has not yet been retrieved from the server, all
+pseudo-attributes for the record are dynamically retrieved from the server. To prevent dynamic retrieval of all
+pseudo-attributes, use ``Table.bulk_fetch`` to retrieve their values prior to accessing the property on the record.
+``RecordProperties`` defines all available pseudo-attribute definitions that can be used in a bulk export.
+
+## Edit pseudo-attribute values.
+Only a few pseudo-attributes can be edited:
+
+* ``Record.name``
+* ``Record.short_name``
+* ``Record.color``
+* ``Record.subsets``
+
+To edit the value of a pseudo-attribute, modify the property on the ``Record`` object directly.
+For example, change the ``color`` of the record to white.
+
+
+```python
+record.color = mpy.RecordColor.White
+record.color
+```
+
+
+
+*Previous cell output:*
+```output
+RecordColor.White
+```
+
+
+Write your changes to MI (pseudo-attributes do not need to be flagged for update using `set_attribute()`).
+
+
+```python
+mi.update([record])
+```
+
+
+
+*Previous cell output:*
+```output
+[<Record long name: Ti (Titanium)>]
+```
+
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/11_Add_Files_Pictures_and_Hyperlinks.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/11_Add_Files_Pictures_and_Hyperlinks.md
new file mode 100644
index 0000000000..1342cf4b42
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/11_Add_Files_Pictures_and_Hyperlinks.md
@@ -0,0 +1,165 @@
+# Add Files, Pictures, and Hyperlinks
+Add File, Picture, and Hyperlink attribute types to MI.
+
+## Connect to MI
+Specify a database and table.
+
+
+```python
+from datetime import datetime
+import pathlib
+from GRANTA_MIScriptingToolkit import granta as mpy
+
+mi = mpy.connect('http://my.server.name/mi_servicelayer', autologon=True)
+db = mi.get_db(db_key='MI_Training')
+tab = db.get_table('Tensile Test Data')
+```
+
+## Create a new record 
+Define a path in the table from a starting folder (in this case the top level folder) using `path_from()`.
+If the path does not exist, the required folders will be created.
+Specify `end_node` to create a new **Record** object at the end of the path with that name.
+
+
+```python
+now = datetime.now().strftime("%c")
+record_name = 'STK Example 11:{}'.format(now)
+record = tab.path_from(None, tree_path=['High Alloy Steels', 'AMS 6520', 'Plate', '300°F'], end_node=record_name)
+record
+```
+
+
+
+*Previous cell output:*
+```output
+<Record long name: STK Example 11:Wed Oct 30 16:07:16 2024>
+```
+
+
+Access the (empty) **AttributeFile**, **AttributePicture**, and **AttributeHyperlink** objects.
+
+
+```python
+new_file = record.attributes['Test File 1']
+new_pict = record.attributes['Image 2']
+test_method_link = record.attributes['Standard Tension Testing Method']
+test_method_link
+```
+
+
+
+*Previous cell output:*
+```output
+<HyperlinkValue name: Standard Tension Testing Method, url: None>
+```
+
+
+## Add files to the record
+To add a file to a file attribute (or a picture to a picture attribute) there are two approaches.
+
+Create a **File** or **Picture** object, and set the `binary_data` property directly with either a file buffer or
+bytes object. (Useful when you have a file already loaded in your script.)
+
+
+```python
+file_object = mpy.File()
+file_path = pathlib.Path('supporting_files/11_example_file_for_import.txt')
+with open(file_path, 'rb') as file_buffer:
+    file_object.binary_data = file_buffer.read()
+file_object.file_name = file_path.name
+file_object.description = 'This is an example file'
+new_file.object = file_object
+new_file
+```
+
+
+
+*Previous cell output:*
+```output
+<FileValue name: Test File 1, file_name: 11_example_file_for_import.txt, description: This is an exampl...>
+```
+
+
+Use the **AttributePicture.load()** method to load a file from disk with a provided file name. (Useful when you have
+a file saved to disk).
+
+
+```python
+picture = pathlib.Path('supporting_files/11_example_image_for_import.jpg')
+new_pict.load(picture)
+new_pict
+```
+
+
+
+*Previous cell output:*
+```output
+<PictureValue name: Image 2, populated: True>
+```
+
+
+## Add hyperlinks to the record
+To add a hyperlink to a hyperlink attribute, set the `value` property. Optionally, you can also set the `hyperlink_display`
+and/or `hyperlink_description` properties.
+
+
+```python
+test_method_link.value = 'https://www.astm.org/Standards/E8'
+test_method_link.hyperlink_display = 'New'
+test_method_link.hyperlink_description = 'Specification'
+test_method_link
+```
+
+
+
+*Previous cell output:*
+```output
+<HyperlinkValue name: Standard Tension Testing Method, url: https://www.astm.org/Standards/E8>
+```
+
+
+Set the new attributes to update, and write the changes to MI. Specifying `include_binary_data_in_refresh = True` means that
+the picture data will be included in the resulting `Record` object, rather than a URL reference to the picture data.
+
+
+```python
+record.set_attributes([new_file, new_pict, test_method_link])
+record = mi.update([record], include_binary_data_in_refresh=True)[0]
+```
+
+## Save files attached to a record
+Verify your changes by first extracting the new image from the record, then saving it to disk.
+You can either access the `value` directly or provide a file path to the `save()` method.
+
+First, ensure the output directory exists. This directory will already exist if a notebook
+was run previously that saved a file to disk.
+
+
+```python
+from pathlib import Path
+output_folder = Path("./output")
+output_folder.mkdir(exist_ok=True)
+```
+
+Next, save the picture to disk.
+
+
+```python
+new_pict = record.attributes['Image 2']
+from IPython.display import Image
+Image(new_pict.value)
+```
+
+
+
+
+    
+![jpeg](11_Add_Files_Pictures_and_Hyperlinks_files/11_Add_Files_Pictures_and_Hyperlinks_20_0.jpg)
+    
+
+
+
+
+```python
+new_pict.save(path='output/11_exported_picture.jpg')
+```
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/11_Add_Files_Pictures_and_Hyperlinks_files/11_Add_Files_Pictures_and_Hyperlinks_20_0.jpg b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/11_Add_Files_Pictures_and_Hyperlinks_files/11_Add_Files_Pictures_and_Hyperlinks_20_0.jpg
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diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/12_Add_Point_Range_Data.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/12_Add_Point_Range_Data.md
new file mode 100644
index 0000000000..d0a36a46f3
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/12_Add_Point_Range_Data.md
@@ -0,0 +1,167 @@
+# Add Point and Range data
+Work with point and range attribute types, including multi-valued points and open-ended ranges.
+
+## Connect to MI
+Get a database and table.
+
+
+```python
+from datetime import datetime
+from GRANTA_MIScriptingToolkit import granta as mpy
+
+mi = mpy.connect('http://my.server.name/mi_servicelayer', autologon=True)
+db = mi.get_db(db_key='MI_Training')
+db.unit_system = 'Metric'
+db.absolute_temperatures = False
+tab = db.get_table('Composite Design Data')
+```
+
+## Create a new record and path
+Define a path in the table from a starting folder (in this case the top level folder) using `path_from()`.
+If the path does not exist, the required folders will be created.
+Specify `end_node` to create a new **Record** object at the end of the path with that name.
+
+
+```python
+now = datetime.now().strftime("%c")
+record_name = 'STK Example 12:{}'.format(now)
+record = tab.path_from(None, tree_path=['Epoxy / Glass', '3M, S-Glass Unitape S2/SP381', '[0]'], end_node=record_name)
+record, record.parent
+```
+
+
+
+*Previous cell output:*
+```output
+(<Record long name: STK Example 12:Wed Oct 30 16:07:43 2024>,
+ <Record long name: 3M, S-Glass Unitape S2/SP381, [0]>)
+```
+
+
+## Edit the record's attributes
+Fetch point and range attributes for editing.
+
+
+```python
+tab.bulk_fetch([record], attributes=['Test temperature',
+                                     '0° tension modulus - measured',
+                                     'Resin content',
+                                     'Fiber volume'])
+test_temperature = record.attributes['Test temperature']
+modulus = record.attributes['0° tension modulus - measured']
+resin_content = record.attributes['Resin content']
+fiber_volume = record.attributes['Fiber volume']
+```
+
+### Point attributes
+Assign a list of numeric values to the `points` property.
+
+If multiple values are assigned, you must also assign a list of dictionaries containing the parameter values to the
+`parameters` property. (The two lists must be the same length.)
+Here, a single parameter called *Basis* is used to discriminate between the two point values.
+
+
+```python
+test_temperature.value = 23
+test_temperature.unit = '°C'
+```
+
+
+```python
+modulus.value = [8, 7.5]
+modulus.unit = 'GPa'
+modulus.parameters = [{'Basis': 'Mean'}, {'Basis': 'A-basis'}]
+```
+
+### Range attributes
+Access the `value` property directly and assign either a dictionary or tuple for high and low values.
+Omitting either the 'low' or 'high' value creates an open-ended range.
+
+
+```python
+resin_content.value = {'low': 28, 'high': 30}
+resin_content.unit = 'wt%'
+```
+
+
+```python
+fiber_volume.value = (None, 62.0)
+fiber_volume.unit = '%'
+```
+
+The high and low values can separately be flagged as either inclusive (≤ and ≥) or exclusive
+(< and >). Ranges default to inclusive.
+
+
+```python
+fiber_volume.high_value_is_inclusive = False
+print(fiber_volume.high_value_is_inclusive)
+```
+*Previous cell output:*
+```output
+False
+```
+## Write your changes to MI
+First, specify the attributes on the record which you want to update on the server. Then write the changes to MI.
+The list of updated **Record** objects is returned.
+
+
+```python
+record.set_attributes([resin_content, test_temperature, fiber_volume, modulus])
+record = mi.update([record])[0]
+```
+
+## Output the record's attributes
+Access the attribute values via the same properties you used to assign them.
+
+
+```python
+test_temperature = record.attributes['Test temperature']
+print("Test temperature: {0} {1}".format(test_temperature.value, test_temperature.unit))
+
+modulus = record.attributes['0° tension modulus - measured']
+print('0° tension modulus: ', end="")
+formatted_points = ["{0} {1} ({2})".format(point, modulus.unit, modulus.parameters[idx]['Basis'])
+                    for idx, point in enumerate(modulus.value)]
+print(", ".join(formatted_points))
+```
+*Previous cell output:*
+```output
+Test temperature: 23.0 °C
+0° tension modulus: 7.5 GPa (A-basis), 8.0 GPa (Mean)
+```
+Define a function to format ranges as strings with the appropriate inclusive or exclusive inequality
+symbols.
+
+
+```python
+def format_range(range_value):
+    low_value = range_value.value['low']
+    if low_value is not None:
+        low_inequality = "≤" if range_value.low_value_is_inclusive else "<"
+    else:
+        low_inequality = ""
+    low_range = f"{low_value} {low_inequality}" if low_value else ""
+
+    high_value = range_value.value['high']
+    if high_value is not None:
+        high_inequality = "≤" if range_value.high_value_is_inclusive else "<"
+    else:
+        high_inequality = ""
+    high_range = f"{high_inequality} {high_value}" if high_value else ""
+
+    unit_symbol = range_value.unit
+    return f"{low_range} x {high_range} {unit_symbol}".strip()
+
+
+resin_content = record.attributes['Resin content']
+print(format_range(resin_content))
+
+fiber_volume = record.attributes['Fiber volume']
+print(format_range(fiber_volume))
+```
+*Previous cell output:*
+```output
+28.0 ≤ x ≤ 30.0 wt%
+x < 62.0 %
+```
\ No newline at end of file
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/13_Add_Date_Integer_and_Logical_Data.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/13_Add_Date_Integer_and_Logical_Data.md
new file mode 100644
index 0000000000..a75249e580
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/13_Add_Date_Integer_and_Logical_Data.md
@@ -0,0 +1,115 @@
+# Add Date, Integer and Logical data 
+Work with date, integer, and logical attribute types.
+
+## Connect to MI
+Get a database and table.
+
+
+```python
+from datetime import datetime
+from GRANTA_MIScriptingToolkit import granta as mpy
+
+mi = mpy.connect('http://my.server.name/mi_servicelayer', autologon=True)
+db = mi.get_db(db_key='MI_Training')
+db.unit_system = 'Metric'
+db.absolute_temperatures = False
+tab = db.get_table('AM Builds')
+```
+
+## Create a new record and path
+Define a path in the table from a starting folder (in this case the top level folder) using `path_from()`.
+If the path does not exist, the required folders will be created.
+Specify `end_node` to create a new **Record** object at the end of the path with that name.
+
+
+```python
+now = datetime.now().strftime("%c")
+record_name = 'STK Example 13:{}'.format(now)
+record = tab.path_from(None, tree_path=['Blown Powder (Laser beam)', 'Ti-6Al-4V'], end_node=record_name)
+record, record.parent
+```
+
+
+
+*Previous cell output:*
+```output
+(<Record long name: STK Example 13:Wed Oct 30 16:08:10 2024>,
+ <Record long name: Ti-6Al-4V>)
+```
+
+
+## Edit the record's attributes
+Fetch date, integer and logical attributes for editing.
+
+
+```python
+tab.bulk_fetch([record], attributes=['Date of Beam Profile Diagnostic',
+                                     'Date of Build',
+                                     'Maximum Number of Layers',
+                                     'Built in Closed Chamber?'])
+diagnostic_date = record.attributes['Date of Beam Profile Diagnostic']
+build_date = record.attributes['Date of Build']
+number_of_layers = record.attributes['Maximum Number of Layers']
+closed_chamber = record.attributes['Built in Closed Chamber?']
+```
+
+### Date attributes
+Use the `value` property to set the date value of the attribute with either a **datetime** object or a properly
+formatted string.
+
+
+```python
+diagnostic_date.value = '2020-07-12'
+build_date.value = datetime.now()
+```
+
+### Integer attributes
+Use the `value` property to set the integer value of the attribute.
+
+
+```python
+number_of_layers.value = 5
+```
+
+### Logical attributes
+Use the `value` property to set the logical value of the attribute.
+
+
+```python
+closed_chamber.value = True
+```
+
+## Write your changes to MI
+First, specify the attributes on the record which you want to update on the server. Then write the changes to MI. The list of updated **Record** objects is returned.
+
+
+```python
+record.set_attributes([diagnostic_date, build_date, number_of_layers, closed_chamber])
+record = mi.update([record])[0]
+```
+
+## Output the record's attributes
+Access the attribute values via the same properties you used to assign them.
+
+
+```python
+datetime_format = "%b %d %Y"
+diagnostic_date = record.attributes['Date of Beam Profile Diagnostic']
+print("Date of Beam Profile Diagnostic: {0}".format(diagnostic_date.value.strftime(datetime_format)))
+
+build_date = record.attributes['Date of Build']
+print("Date of Build: {0}".format(build_date.value.strftime(datetime_format)))
+
+number_of_layers = record.attributes['Maximum Number of Layers']
+print("Maximum Number of Layers: {0}".format(number_of_layers.value))
+
+closed_chamber = record.attributes['Built in Closed Chamber?']
+print("Built in Closed Chamber?: {0}".format('Yes' if closed_chamber.value else 'No'))
+```
+*Previous cell output:*
+```output
+Date of Beam Profile Diagnostic: Jul 12 2020
+Date of Build: Oct 30 2024
+Maximum Number of Layers: 5
+Built in Closed Chamber?: Yes
+```
\ No newline at end of file
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/14_Create_and_Edit_Tabular_Data.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/14_Create_and_Edit_Tabular_Data.md
new file mode 100644
index 0000000000..44022b2fd7
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/14_Create_and_Edit_Tabular_Data.md
@@ -0,0 +1,291 @@
+# Create and Edit Tabular Data
+Create a new tabular attribute, add, delete and swap rows, and edit the units.
+
+## Create a new record
+Connect to MI and specify a database and table.
+
+
+```python
+from datetime import datetime
+from GRANTA_MIScriptingToolkit import granta as mpy
+
+mi = mpy.connect('http://my.server.name/mi_servicelayer', autologon=True)
+db = mi.get_db(db_key='MI_Training')
+tab = db.get_table('Tensile Test Data')
+```
+
+Create a new record.
+
+
+```python
+now = datetime.now().strftime("%c")
+recordName = 'STK Example 14:{}'.format(now)
+rec = tab.create_record(recordName, subsets={'In Progress'})
+```
+
+## Access a Tabular attribute
+Access the (empty) Tabular attribute *Workflow history*.
+
+
+```python
+hist = rec.attributes['Workflow history']
+hist
+```
+
+
+
+*Previous cell output:*
+```output
+<TabularValue name: Workflow history (not loaded)>
+```
+
+
+Inspect the tabular datum. Using the method `show()` shows the table in ASCII format.
+
+
+```python
+hist.show()
+```
+*Previous cell output:*
+```output
+             User              |              Date              |            Comments            |      Linking Value (None)     
+---------------------------------------------------------------------------------------------------------------------------------
+---------------------------------------------------------------------------------------------------------------------------------
+```
+Look at the `__repr__` of the attribute. There are currently no rows in the table.
+
+
+```python
+hist
+```
+
+
+
+*Previous cell output:*
+```output
+<TabularValue name: Workflow history, shape: 4 x 0>
+```
+
+
+Tabular attributes can be loaded with the `load()` method (there is no data to load in this example):
+
+
+```python
+hist.load()
+```
+
+## Add rows and data
+
+Add three (empty) rows to the Tabular object, and view the table in an ACSII-style format.
+
+
+```python
+hist.add_row()
+hist.add_row()
+hist.add_row()
+hist.show()
+```
+*Previous cell output:*
+```output
+             User              |              Date              |            Comments            |      Linking Value (None)     
+---------------------------------------------------------------------------------------------------------------------------------
+             None              |              None              |              None              |  '<linking value not chosen>' 
+             None              |              None              |              None              |  '<linking value not chosen>' 
+             None              |              None              |              None              |  '<linking value not chosen>' 
+---------------------------------------------------------------------------------------------------------------------------------
+```
+Data is added by accessing the `tabular` property. The column name should be provided as a string followed by the index of the row. 
+Start by populating the first row:
+
+
+```python
+hist['User', 0] = 'Your username'
+hist['Date', 0] = '2019-01-01'  # dates should always be provided in the format YYY-MM-DD
+hist['Comments', 0] = 'This is an example comment'
+hist.value[:][0]
+```
+
+
+
+*Previous cell output:*
+```output
+['Your username',
+ '2019-01-01',
+ 'This is an example comment',
+ '<linking value not chosen>']
+```
+
+
+Populate the second row:
+
+
+```python
+hist['User', 1] = 'Another username'
+hist['Date', 1] = '2019-01-02'
+hist['Comments', 1] = 'This is another example comment'
+hist.value[:][1]
+```
+
+
+
+*Previous cell output:*
+```output
+['Another username',
+ '2019-01-02',
+ 'This is another example comment',
+ '<linking value not chosen>']
+```
+
+
+Then populate the third row. The data is stored locally, so the linking values are displayed as `<linking value not chosen>`.
+
+
+```python
+hist['User', 2] = 'Another username'
+hist['Date', 2] = '2019-01-02'
+hist['Comments', 2] = 'This is another example comment'
+hist.value[:][2]
+```
+
+
+
+*Previous cell output:*
+```output
+['Another username',
+ '2019-01-02',
+ 'This is another example comment',
+ '<linking value not chosen>']
+```
+
+
+
+```python
+hist.show()
+hist.value
+```
+*Previous cell output:*
+```output
+             User              |              Date              |            Comments            |      Linking Value (None)     
+---------------------------------------------------------------------------------------------------------------------------------
+       'Your username'         |          '2019-01-01'          |  'This is an example comment'  |  '<linking value not chosen>' 
+      'Another username'       |          '2019-01-02'          | 'This is another example c...' |  '<linking value not chosen>' 
+      'Another username'       |          '2019-01-02'          | 'This is another example c...' |  '<linking value not chosen>' 
+---------------------------------------------------------------------------------------------------------------------------------
+```
+
+
+*Previous cell output:*
+```output
+[['Your username',
+  '2019-01-01',
+  'This is an example comment',
+  '<linking value not chosen>'],
+ ['Another username',
+  '2019-01-02',
+  'This is another example comment',
+  '<linking value not chosen>'],
+ ['Another username',
+  '2019-01-02',
+  'This is another example comment',
+  '<linking value not chosen>']]
+```
+
+
+## Edit the table
+Rows can be moved using the `swap_rows()` method.
+
+
+```python
+hist.value[:][:2]
+```
+
+
+
+*Previous cell output:*
+```output
+[['Your username',
+  '2019-01-01',
+  'This is an example comment',
+  '<linking value not chosen>'],
+ ['Another username',
+  '2019-01-02',
+  'This is another example comment',
+  '<linking value not chosen>']]
+```
+
+
+
+```python
+hist.swap_rows(0, 1)
+```
+
+
+```python
+hist.value[:][:2]
+```
+
+
+
+*Previous cell output:*
+```output
+[['Another username',
+  '2019-01-02',
+  'This is another example comment',
+  '<linking value not chosen>'],
+ ['Your username',
+  '2019-01-01',
+  'This is an example comment',
+  '<linking value not chosen>']]
+```
+
+
+Rows can also be deleted; for example, one of the two duplicate rows in this table.
+
+
+```python
+hist.delete_row(0)
+```
+
+## Access the units
+Each **Tabular** object is associated with a **TabularUnits** object which stores the units for the Tabular data. View (and edit) these units through the `tabular.units` property.
+
+
+```python
+hist.units
+```
+
+
+
+*Previous cell output:*
+```output
+<Tabular Units, shape: 2 x 3>
+```
+
+
+Access the complete set of units for the tabular datum
+
+
+```python
+hist.units.data
+```
+
+
+
+*Previous cell output:*
+```output
+[['', '', '', None], ['', '', '', None]]
+```
+
+
+## Write your changes to MI
+Set your new attribute to update, and write the changes to the server.
+
+
+```python
+rec.set_attributes([hist])
+rec = mi.update([rec])[0]
+```
+
+## Notes about Tabular data:
+ * Point-type values in Tabular cannot have parameters, and are always lists.
+ * Any changes to linked data will be ignored on import.
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/15_Use_Exporters_for_FEA_Export.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/15_Use_Exporters_for_FEA_Export.md
new file mode 100644
index 0000000000..789c775cca
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/15_Use_Exporters_for_FEA_Export.md
@@ -0,0 +1,287 @@
+# Export data for FEA
+Use MI exporters to export records for use with FEA, CAE or CAD packages.
+
+## Find exporters
+Check which exporters are available for a specific table.
+
+
+```python
+from GRANTA_MIScriptingToolkit import granta as mpy
+
+mi = mpy.connect('http://my.server.name/mi_servicelayer', autologon=True)
+db = mi.get_db(db_key='MI_Training')
+table = db.get_table('Design Data')
+
+exporters_in_table = table.get_available_exporters(package='ANSYS Workbench')
+
+print("\nOutput of available ANSYS Workbench Exporters")
+for exporter in exporters_in_table:
+    print("{0} ({1}) - {2}".format(exporter.name, exporter.package, exporter.description))
+```
+*Previous cell output:*
+```output
+
+Output of available ANSYS Workbench Exporters
+458E9A7E-C268-4ED0-9CC1-FF7438521B4F (ANSYS Workbench) - Exports linear, temperature-independent, isotropic data to the Ansys Workbench format
+CE8DCFA2-B3EE-4545-8D3E-82810FA92AFC (ANSYS Workbench) - Exports linear, temperature-dependent, isotropic data to the Ansys Workbench format
+4B0B1EA3-8760-43DF-8060-2C79CA471D4C (ANSYS Workbench) - Exports linear, temperature-independent, isotropic with simple failure data to the Ansys Workbench format
+```
+Check which exporters are applicable to a specific record.
+
+
+```python
+rec = table.search_for_records_by_name('250 Grade Maraging, Maraged at 900F, Plate, Thickness: 0.1875 to 0.251 in, AMS 6520, S basis')[0]
+applicable_exporters = rec.get_available_exporters()
+print("\nOutput of exporters for 250 Grade Maraging steel:")
+
+for exporter in applicable_exporters:
+    print("{0} ({1}) - {2}".format(exporter.name,
+                                   exporter.package,
+                                   exporter.description))
+```
+*Previous cell output:*
+```output
+
+Output of exporters for 250 Grade Maraging steel:
+71CE1C21-FDEA-4119-B481-81BDC41BD900 (Abaqus 6) - Exports temperature dependent, isotropic data to the Abaqus format.
+5C560880-4FD3-4E5C-992B-4B6CEF6A055A (Abaqus 6) - Exports temperature independent, isotropic data to the Abaqus 6 format.
+911AF055-B388-439A-8AF6-EB18480E2D80 (Abaqus 6) - Linear, temperature-independent, isotropic, simple failure
+3AE2BEA5-B1DB-45D3-A431-48915B8D1317 (Abaqus 6) - Linear, temperature-independent, isotropic, simple failure with thermal expansion
+722E5C46-3633-4B72-BF93-74E8112C20C3 (Abaqus 6) - Exports temperature dependent, isotropic data to the Abaqus 6 format.
+B653C213-8BEB-42A7-8512-5F340EEBFAB4 (Abaqus 6) - Exports temperature independent, isotropic data to the Abaqus 6 format.
+458E9A7E-C268-4ED0-9CC1-FF7438521B4F (ANSYS Workbench) - Exports linear, temperature-independent, isotropic data to the Ansys Workbench format
+CE8DCFA2-B3EE-4545-8D3E-82810FA92AFC (ANSYS Workbench) - Exports linear, temperature-dependent, isotropic data to the Ansys Workbench format
+4B0B1EA3-8760-43DF-8060-2C79CA471D4C (ANSYS Workbench) - Exports linear, temperature-independent, isotropic with simple failure data to the Ansys Workbench format
+```
+## Working with parameters
+Some exporters support parameters. The exported parameters have default values but they can also be set manually.
+
+Get the required parameters for an exporter.
+
+
+```python
+exporter_to_use = rec.get_available_exporters(package="Abaqus 6",
+                                              model="Linear, temperature-dependent, isotropic, thermal, plastic")[0]
+
+parameters_required = exporter_to_use.get_parameters_required_for_export([rec])
+print(parameters_required)
+```
+*Previous cell output:*
+```output
+{'Time': <ExporterParameter name: Time, type: Numeric, unit: hr>}
+```
+Set the parameter values and perform the export to obtain the material card for use with FEA software. Provide one or more records to the exporter, up to the maximum number specified in the exporter configuration file. The records will be exported to the same material card.
+
+
+```python
+parameter_values = {"Time": 100.0}
+for parameter_name in parameters_required.keys():
+    parameters_required[parameter_name].value_for_exporters = parameter_values[parameter_name]
+
+material_card = exporter_to_use.run_exporter([rec], parameter_defs=parameters_required)
+print(material_card)
+```
+*Previous cell output:*
+```output
+**
+**Granta Material Name: 250 Grade Maraging, Maraged at 900F, Plate, Thickness: 0.1875 to 0.251 in, AMS 6520, S basis
+**Model Type: Linear, temperature-dependent, isotropic, thermal, plastic
+**Unit System: SI (Consistent)
+**Export User: ANSYS\mi-sw-admin
+**Export DateTime: 2024-10-30T12:09:30.6901993-04:00
+**Database Name: MI Training
+**Table Name: Design Data
+**Material Record History Id: 20673
+**Density: 7.92e+3 kg/m^3
+**Thermal conductivity with temperature: 37 datapoints W/m.°C
+**Thermal expansion coefficient with temperature: 103 datapoints strain/°C
+**Tensile strength, yield with temperature: 0 datapoints Pa
+**Tensile modulus with temperature: 0 datapoints Pa
+**Poissons ratio: 3.10e-1
+**Thermal diffusivity with temperature: [No Value]
+**
+**Comments
+**
+**LinearIsoTempThermalPlastic.xslt: Transformed CAE material model data
+**(Linear, temperature-dependent, isotropic, thermal, plastic) to Abaqus 6 format.
+**
+**
+*MATERIAL, NAME = A250_Grade_Maraging_Maraged_at
+**
+*DENSITY
+7.92e+3
+**
+**
+**ELASTIC
+** [No Value]
+**
+**PLASTIC
+** [No Value]
+**
+*EXPANSION, ZERO=2.93e+2
+5.41e-6, 1.09e+1
+5.58e-6, 2.20e+1
+5.75e-6, 3.32e+1
+5.92e-6, 4.43e+1
+6.09e-6, 5.54e+1
+6.25e-6, 6.65e+1
+6.41e-6, 7.76e+1
+6.57e-6, 8.87e+1
+6.73e-6, 9.98e+1
+6.88e-6, 1.11e+2
+7.03e-6, 1.22e+2
+7.17e-6, 1.33e+2
+7.31e-6, 1.44e+2
+7.45e-6, 1.55e+2
+7.58e-6, 1.66e+2
+7.71e-6, 1.78e+2
+7.83e-6, 1.89e+2
+7.95e-6, 2.00e+2
+8.07e-6, 2.11e+2
+8.18e-6, 2.22e+2
+8.30e-6, 2.33e+2
+8.40e-6, 2.44e+2
+8.51e-6, 2.55e+2
+8.61e-6, 2.66e+2
+8.71e-6, 2.78e+2
+8.81e-6, 2.89e+2
+8.91e-6, 3.00e+2
+9.00e-6, 3.11e+2
+9.10e-6, 3.22e+2
+9.19e-6, 3.33e+2
+9.28e-6, 3.44e+2
+9.36e-6, 3.55e+2
+9.45e-6, 3.66e+2
+9.53e-6, 3.78e+2
+9.61e-6, 3.89e+2
+9.69e-6, 4.00e+2
+9.77e-6, 4.11e+2
+9.84e-6, 4.22e+2
+9.91e-6, 4.33e+2
+9.98e-6, 4.44e+2
+1.01e-5, 4.55e+2
+1.01e-5, 4.66e+2
+1.02e-5, 4.78e+2
+1.02e-5, 4.89e+2
+1.03e-5, 5.00e+2
+1.03e-5, 5.11e+2
+1.04e-5, 5.22e+2
+1.04e-5, 5.33e+2
+1.05e-5, 5.44e+2
+1.05e-5, 5.55e+2
+1.06e-5, 5.66e+2
+1.06e-5, 5.78e+2
+1.06e-5, 5.89e+2
+1.07e-5, 6.00e+2
+1.07e-5, 6.11e+2
+1.07e-5, 6.22e+2
+1.07e-5, 6.33e+2
+1.07e-5, 6.44e+2
+1.08e-5, 6.55e+2
+1.08e-5, 6.66e+2
+1.08e-5, 6.78e+2
+1.08e-5, 6.89e+2
+1.08e-5, 7.00e+2
+1.08e-5, 7.11e+2
+1.08e-5, 7.22e+2
+1.08e-5, 7.33e+2
+1.08e-5, 7.44e+2
+1.08e-5, 7.55e+2
+1.08e-5, 7.66e+2
+1.08e-5, 7.78e+2
+1.08e-5, 7.89e+2
+1.08e-5, 8.00e+2
+1.07e-5, 8.11e+2
+1.07e-5, 8.22e+2
+1.06e-5, 8.33e+2
+1.06e-5, 8.44e+2
+1.05e-5, 8.55e+2
+1.04e-5, 8.66e+2
+1.02e-5, 8.78e+2
+1.00e-5, 8.89e+2
+9.86e-6, 9.00e+2
+9.66e-6, 9.11e+2
+9.45e-6, 9.22e+2
+9.24e-6, 9.33e+2
+9.03e-6, 9.44e+2
+8.84e-6, 9.55e+2
+8.68e-6, 9.66e+2
+8.54e-6, 9.78e+2
+8.42e-6, 9.89e+2
+8.34e-6, 1.00e+3
+8.28e-6, 1.01e+3
+8.25e-6, 1.02e+3
+8.25e-6, 1.03e+3
+8.27e-6, 1.04e+3
+8.31e-6, 1.06e+3
+8.37e-6, 1.07e+3
+8.44e-6, 1.08e+3
+8.54e-6, 1.09e+3
+8.66e-6, 1.10e+3
+8.82e-6, 1.11e+3
+9.02e-6, 1.12e+3
+9.29e-6, 1.13e+3
+9.67e-6, 1.14e+3
+**
+**
+*CONDUCTIVITY
+2.51e+1, 3.00e+2
+2.54e+1, 3.11e+2
+2.56e+1, 3.22e+2
+2.59e+1, 3.33e+2
+2.62e+1, 3.44e+2
+2.65e+1, 3.55e+2
+2.67e+1, 3.66e+2
+2.70e+1, 3.78e+2
+2.73e+1, 3.89e+2
+2.75e+1, 4.00e+2
+2.78e+1, 4.11e+2
+2.80e+1, 4.22e+2
+2.83e+1, 4.33e+2
+2.85e+1, 4.44e+2
+2.87e+1, 4.55e+2
+2.89e+1, 4.66e+2
+2.91e+1, 4.78e+2
+2.93e+1, 4.89e+2
+2.94e+1, 5.00e+2
+2.96e+1, 5.11e+2
+2.98e+1, 5.22e+2
+2.99e+1, 5.33e+2
+3.00e+1, 5.44e+2
+3.02e+1, 5.55e+2
+3.03e+1, 5.66e+2
+3.04e+1, 5.78e+2
+3.05e+1, 5.89e+2
+3.05e+1, 6.00e+2
+3.06e+1, 6.11e+2
+3.07e+1, 6.22e+2
+3.08e+1, 6.33e+2
+3.08e+1, 6.44e+2
+3.09e+1, 6.55e+2
+3.09e+1, 6.66e+2
+3.10e+1, 6.78e+2
+3.10e+1, 6.89e+2
+3.11e+1, 7.00e+2
+**
+**
+**SPECIFIC HEAT
+** [No Value]
+*NODE
+1, 0.0, 0.0, 0.0
+*ELEMENT, TYPE=CPE4, ELSET=EALL
+1, 1, 2, 5, 4
+```
+Save the exported data using the settings provided in the .exp file. The filename, extension, and file encoding scheme are left as their defaults.
+
+
+```python
+path_to_save = "./"
+file_name = 'Example_Export'
+exporter_to_use.save(path_to_save, file_name="Example_Export")
+file_extension = exporter_to_use.default_file_extension
+print("Exporter output saved to \"{}{}.{}\"".format(path_to_save, file_name, file_extension))
+```
+*Previous cell output:*
+```output
+Exporter output saved to "./Example_Export.inp"
+```
\ No newline at end of file
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/16_Link_Records.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/16_Link_Records.md
new file mode 100644
index 0000000000..969dbc38d2
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/16_Link_Records.md
@@ -0,0 +1,145 @@
+# Link Records
+Create and edit smart and static links between records.
+
+## Create a new record
+Connect to MI and get a database and table.
+
+
+```python
+from datetime import datetime
+from GRANTA_MIScriptingToolkit import granta as mpy
+
+mi = mpy.connect('http://my.server.name/mi_servicelayer', autologon=True)
+db = mi.get_db(db_key='MI_Training')
+tab = db.get_table('Training Exercise for Import')
+```
+
+Create a new record in the subset *Materials*.
+
+
+```python
+now = datetime.now().strftime("%c")
+recordName = 'STK Example 16:{}'.format(now)
+rec = tab.create_record(recordName, subsets={'Materials'})
+```
+
+## Smart links
+A smart link is configured in this database to link records with matching values for the attribute *Base*.
+Setting a value for *Base* on the new record will link it to all existing records with the same value for that attribute.
+
+Set the value of *Base* to *Oxide*, then write the changes to MI. At this point, the new record is created on the server.
+
+
+```python
+base = rec.attributes['Base']
+base.value = 'Oxide'
+rec.set_attributes([base])
+rec = mi.update([rec])[0]
+```
+
+View the smart links that have just been created to the new record.
+
+
+```python
+rec.links
+```
+
+
+
+*Previous cell output:*
+```output
+{'MaterialUniverse': set(),
+ 'Smart Link to MaterialUniverse': {<Record long name: Alumino silicate - 1720>,
+  <Record long name: Alumino silicate - 1723>,
+  <Record long name: Barium silicate>,
+  <Record long name: Lithium aluminosilicate>,
+  <Record long name: Soda barium glass>}}
+```
+
+
+## Static links
+A static link can be created between two existing records, including cross-database.
+
+Get a record from *MaterialUniverse* to link to your newly-created record.
+
+
+```python
+mu_rec = db.get_record_by_id(hguid='bf5e6054-6cad-4c9d-ad7a-adfa124c504b')
+```
+
+Add the *MaterialUniverse* record to the link group on the new record.
+
+
+```python
+new_linked_recs = rec.links['MaterialUniverse']
+new_linked_recs.add(mu_rec)
+rec.set_links('MaterialUniverse', new_linked_recs)
+```
+
+Write your changes to MI (use `update_links()` for changes to links, not `update()`).
+
+
+```python
+rec = mi.update_links([rec])[0]
+```
+
+View your new link on the list of record links.
+
+
+```python
+print(rec.links)
+```
+*Previous cell output:*
+```output
+{'MaterialUniverse': {<Record long name: Soda barium glass>}, 'Smart Link to MaterialUniverse': {<Record long name: Alumino silicate - 1720>, <Record long name: Soda barium glass>, <Record long name: Lithium aluminosilicate>, <Record long name: Barium silicate>, <Record long name: Alumino silicate - 1723>}}
+```
+## Associated Records
+Associated Records are a way of traversing tabular links multiple steps at a time. This example finds all materials
+impacted by a certain legislation by building an associated record chain between three tables.
+
+First define the starting table and record. The association chain starts on the relevant record in the *Legislations and
+Lists* table.
+
+
+```python
+starting_table = db.get_table('Legislations and Lists')
+starting_record = starting_table.get_record_by_lookup_value('Legislation ID', 'Candidate_AnnexXV')
+```
+
+Next, set the tabular attribute that forms the first step in the chain. In this case, it is the tabular attribute
+that links the *Restricted Substances* table to the *Legislations and Lists* table.
+
+
+```python
+rs_table = db.get_table('Restricted Substances')
+first_step = rs_table.attributes['Legislations restricting its use']
+```
+
+Then set the tabular attribute that forms the second step in the chain: *Restricted substances associated with this
+material*.
+
+
+```python
+materialuniverse_table = db.get_table('MaterialUniverse')
+second_step = materialuniverse_table.attributes['Restricted substances associated with this material']
+```
+
+Finally, use the `get_associated_records()` method on the `starting_record` **Record** object to extract the records at
+the end of the association chain.
+
+
+```python
+associated_records = starting_record.get_associated_records(materialuniverse_table,
+                                                   link_direction='Reverse',
+                                                   attribute_path=[first_step, second_step])
+associated_records
+```
+
+
+
+*Previous cell output:*
+```output
+[<Record long name: PVC-elastomer (Shore A35)>,
+ <Record long name: Barium silicate>]
+```
+
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/17_Layouts.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/17_Layouts.md
new file mode 100644
index 0000000000..1145a45d14
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/17_Layouts.md
@@ -0,0 +1,74 @@
+# Layouts
+
+This notebook shows how to access layouts through the Streamlined API.
+
+## Connect to Granta MI
+
+
+```python
+from GRANTA_MIScriptingToolkit import granta as mpy
+mi = mpy.connect('http://my.server.name/mi_servicelayer', autologon=True)
+db = mi.get_db(db_key='MI_Training')
+```
+
+## Access a layout and inspect its properties
+
+The `Table.layouts` property contains a **dict** of all layouts in the table, indexed by the layout name. Show the
+first two elements in this dictionary.
+
+
+```python
+table = db.get_table('MaterialUniverse')
+list(table.layouts.items())[:2]
+```
+
+
+
+*Previous cell output:*
+```output
+[('Polymers', <TableLayout: Polymers; categories: 23>),
+ ('Metals', <TableLayout: Metals; categories: 18>)]
+```
+
+
+Each layout contains a list of categories. Select a particular **Layout** object and view the first two categories
+within the layout.
+
+
+```python
+polymers_layout = table.layouts['Polymers']
+polymers_layout.categories[:2]
+```
+
+
+
+*Previous cell output:*
+```output
+['General properties', 'Composition overview']
+```
+
+
+The `attributes_by_category` property contains a **dict** of attributes indexed by their category. Display the
+attributes in the first two categories.
+
+
+```python
+list(polymers_layout.attributes_by_category.items())[:2]
+```
+
+
+
+*Previous cell output:*
+```output
+[('General properties',
+  ['Designation', 'Tradenames', 'Density', 'Price', 'Material form']),
+ ('Composition overview',
+  ['Composition (summary)',
+   'Polymer class',
+   'Polymer type',
+   'Polymer type full name',
+   '% filler (by weight)',
+   'Filler type',
+   'Additive'])]
+```
+
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/18_Streamlined_API_performance_tuning.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/18_Streamlined_API_performance_tuning.md
new file mode 100644
index 0000000000..6a0487519f
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/18_Streamlined_API_performance_tuning.md
@@ -0,0 +1,397 @@
+# Streamlined API performance tuning
+
+
+## Introduction
+
+This Notebook provides some tips and tricks on improving the performance of the Streamlined API.
+
+The Streamlined API is a productivity layer that wraps the Foundation Layer. It typically loads a lot of information for the user in anticipation that the user will need it, which  makes exploratory work in interactive sessions (such as in Jupyter Notebooks) very powerful as a tool. The examples below show some coding approaches that may improve the speed and performance of your programs.
+
+
+## Disclaimer
+
+Execution times for some of the functions are shown, however, note that performance depends fundamentally on your hardware setup and connection speed, and on previously-executed code in the same Notebook, amongst other factors. We cannot guarantee that the saved outputs in this Notebook will match what you will see, and they are intended primarily as a guide rather than a benchmark.
+
+## Establishing a Session
+
+The first steps in any MI Scripting Toolkit script are to make a connection to a Granta MI Service Layer, then fetch a database and table. The three objects, and the fastest ways of acquiring them, are as follows:
+
+1. `Session` - `mpy.connect()` is the fastest way to create a session.
+2. `Database` - `Session.get_db()` is the fastest way to fetch a database
+3. `Table` - `Database.get_table()` is the fastest way to fetch a table
+
+`get_db` and `get_table`, however, actually fetch *all* the databases and *all* the tables (for the selected database) and just return the one that was asked for. The results are then cached; this means those calls to the Service Layer will not be repeated when the objects are accessed again. You can see the effect of this when re-running the cell below multiple times.
+
+
+```python
+from GRANTA_MIScriptingToolkit import granta as mpy
+import time
+
+mi = mpy.connect('http://my.server.name/mi_servicelayer', autologon=True)
+# all tables & databases are cached when requested
+start = time.time()
+db = mi.get_db(db_key='MI_Training')
+tab = db.get_table('Design Data')
+milestone1 = time.time()
+db = mi.get_db(db_key='MI_Training')
+tab = db.get_table('Design Data')
+end = time.time()
+
+print('{:4.4f} s - Time elapsed to reach the first milestone'.format(milestone1 - start))
+print('{:4.4f} s - Time elapsed between milestone 1 and the end'.format(end - milestone1))
+
+# ALL are cached, so subsequent requests for different tables will still be quick.
+start = time.time()
+tab = db.get_table('MaterialUniverse')
+end = time.time()
+print('{:4.4f} s - Time elapsed getting the MaterialUniverse table'.format(end - milestone1))
+```
+*Previous cell output:*
+```output
+0.3607 s - Time elapsed to reach the first milestone
+0.0001 s - Time elapsed between milestone 1 and the end
+0.0004 s - Time elapsed getting the MaterialUniverse table
+```
+## Locating records
+
+There are many search methods in the Streamlined API that will return records that match a user-defined attribute-based criterion or text-based criterion.
+
+There are, however, some methods that get records from a table and also populate caches which can be useful. For example, if you have some very specific filtering that you want to apply to all records that cannot be found using the existing functionality, such as a list of all records with 3 or more children. In this case, you could use `Table.all_records()` to return a list of all records in the table and populate the children of each record, in one call. You would then apply your own search filters to the record list to execute a search.
+
+
+```python
+start = time.time()
+recs = tab.all_records(include_folders=True, include_generics=True)
+milestone1 = time.time()
+filtered_recs = [r for r in recs if len(r.children) > 3]
+end = time.time()
+print('{:4.4f} s - Time elapsed to reach the first milestone'.format(milestone1 - start))
+print('{:4.4f} s - Time elapsed between milestone 1 and the end'.format(end - milestone1))
+```
+*Previous cell output:*
+```output
+0.1820 s - Time elapsed to reach the first milestone
+0.0001 s - Time elapsed between milestone 1 and the end
+```
+
+```python
+filtered_recs
+```
+
+
+
+*Previous cell output:*
+```output
+[<Record long name: Alumino silicate glass>,
+ <Record long name: PVC-elastomer (Polyvinyl Chloride elastomer)>,
+ <Record long name: PMMA - unfilled>]
+```
+
+
+## Creating records
+
+Records are normally created using the `table` object through `Table.create_record`, however, when creating a record at a specific (known) location in the tree, it can be time consuming to fetch the record objects in the path needed to assign the correct parent. For this use case, we have added two methods which take a path (or paths) through the tree and traverse it, creating any nodes that don't already exist along the way.
+
+These two methods exist on the `Table` object:
+
+1. `Table.path_from` - Check for the existence of a path through the table and create it if it does not exist
+2. `Table.paths_from` - Create multiple paths in one call
+
+
+```python
+import datetime
+
+# create a new top-level record
+tab = db.get_table('Training Exercise for Import')
+rec = mi.update([tab.create_record('Top-level folder {}'.format(str(datetime.datetime.now())), folder=True)],
+                                   refresh_attributes=False)[0]
+rec.children
+```
+
+
+
+*Previous cell output:*
+```output
+[]
+```
+
+
+
+```python
+end_leaf = tab.path_from(rec, ['our', 'new', 'path'], end_node='and optional record', color=mpy.RecordColor.Fuchsia)
+end_leaf.path
+```
+
+
+
+*Previous cell output:*
+```output
+['Top-level folder 2024-10-30 16:10:04.245965', 'our', 'new', 'path']
+```
+
+
+
+```python
+# any folders that already exist in a path will not be altered, even if we alter the arguments
+end_leaf = tab.path_from(rec, ['our', 'new', 'route'], end_node='and optional record', color=mpy.RecordColor.Silver)
+end_leaf.path
+```
+
+
+
+*Previous cell output:*
+```output
+['Top-level folder 2024-10-30 16:10:04.245965', 'our', 'new', 'route']
+```
+
+
+## Accessing attributes and links
+
+The first time you call `Record.attributes`, the Streamlined API checks to see if the `attributes` property has been populated yet. If not, it will automatically  populate it with all the attributes on the record. This is inefficient if you are only interested in one or two attributes. The same is true of `links`. We provide two `Table` methods which can be used to ensure only the objects you are interested in are fetched.
+
+* `Table.bulk_fetch` - fetches specified attributes for specified records
+    * If an attribute is not populated, an empty attribute object is added to the dictionary instead
+* `Table.bulk_link_fetch` - fetches specified link groups for specified records
+
+These will fetch data for _just_ the attributes and records you specify; the properties will be populated in the record objects you provide and the methods do not return anything.
+
+The most efficient way to access attributes and links is using these two methods.
+
+
+```python
+tab = db.get_table('MaterialUniverse')
+recs = tab.all_records()
+
+# bulk_fetch (& all the bulk_xxx methods) can be parallelized by the MI Scripting Toolkit IF you have the cores available
+start = time.time()
+# populate all attributes on every record
+for r in recs:
+    _ = r.attributes
+    r.decache_attributes
+milestone1 = time.time()
+
+# populate all attributes on every record using bulk_fetch
+tab.bulk_fetch(recs)
+
+milestone2 = time.time()
+# just populate Base and Young's Modulus on every record
+tab.bulk_fetch(recs, attributes=['Base', "Young's modulus"], batch_size=5, parallelise=False)
+
+milestone3 = time.time()
+# Parallelise the previous command
+tab.bulk_fetch(recs, attributes=['Base', "Young's modulus"], batch_size=5, parallelise=True, max_num_threads=3)
+
+end = time.time()
+print('{:4.4f} s - Time elapsed to reach the first milestone'.format(milestone1 - start))
+print('{:4.4f} s - Time elapsed between milestone 1 and 2'.format(milestone2 - milestone1))
+print('{:4.4f} s - Time elapsed between milestone 2 and 3'.format(milestone3 - milestone2))
+print('{:4.4f} s - Time elapsed between milestone 2 and the end'.format(end - milestone3))
+```
+*Previous cell output:*
+```output
+8.5419 s - Time elapsed to reach the first milestone
+1.9429 s - Time elapsed between milestone 1 and 2
+0.4992 s - Time elapsed between milestone 2 and 3
+0.1357 s - Time elapsed between milestone 2 and the end
+```
+
+```python
+for r in recs:
+    print(r.attributes['Base'].value, r.attributes['Young\'s modulus'].value, r.name)
+```
+*Previous cell output:*
+```output
+Oxide {'low': 84.8, 'high': 89.1} Alumino silicate - 1720
+Oxide {'low': 83.9, 'high': 88.1} Alumino silicate - 1723
+Oxide {'low': 84.0, 'high': 88.0} Lithium aluminosilicate
+Oxide {'low': 57.3, 'high': 75.9} Soda barium glass
+Oxide {'low': 66.30000305175781, 'high': 69.69999694824219} Barium silicate
+Carbide {'low': 599.9999999999997, 'high': 670.0000000000003} Tungsten carbide
+Fe (Iron) {'low': 183.0, 'high': 192.38461303710938} 250 maraging steel, maraged at 900F
+Fe (Iron) {'low': 200.0, 'high': 210.25640869140625} Low alloy steel, AISI 4130, air melted, normalized
+Fe (Iron) {'low': 200.0, 'high': 210.25640869140625} Low alloy steel, AISI 4130, air melted, quenched & tempered
+Al (Aluminum) {'low': 69.0, 'high': 76.0} Aluminum, 7075, wrought, T6
+Al (Aluminum) {'low': 69.0, 'high': 72.53845977783203} Aluminum, 7075, wrought, T73
+Ti (Titanium) {'low': 111.0, 'high': 119.0} Titanium, alpha-beta alloy, Ti-6Al-4V, aged
+Ti (Titanium) {'low': 110.0, 'high': 119.0} Titanium, alpha-beta alloy, Ti-6Al-4V, annealed, generic
+Ti (Titanium) {'low': 110.0, 'high': 117.0} Titanium, alpha-beta alloy, Ti-6Al-4V, solution treated & aged
+Polymer {'low': 0.0006011593504808843, 'high': 0.0009381033014506102} PVC-elastomer (Shore A35)
+Polymer {'low': 0.002864203881472349, 'high': 0.0029362563509494066} PVC-elastomer (Shore A55)
+Polymer {'low': 0.00871698185801506, 'high': 0.011692171916365623} PVC-elastomer (Shore A75)
+Polymer {'low': 0.0035647887270897627, 'high': 0.005562817677855492} PVC-elastomer (Shore A75, flame retarded)
+Polymer {'low': 12.399999618530273, 'high': 13.800000190734863} ABS (20% carbon fiber, EMI shielding, conductive)
+Polymer {'low': 5.099999904632568, 'high': 6.070000171661377} ABS (20% glass fiber, injection molding)
+Polymer {'low': 5.796008586883545, 'high': 6.0} ABS (20% glass fiber, injection molding, flame retarded)
+Polymer {'low': 6.800000190734863, 'high': 8.300000190734863} ABS (30% glass fiber, injection molding)
+Polymer {'low': 2.0, 'high': 2.9000000953674316} ABS (extrusion)
+Polymer {'low': 1.9500000476837158, 'high': 2.049999952316284} ABS (transparent, injection molding)
+Polymer {'low': 2.700000047683716, 'high': 2.9000000953674316} PMMA (cast sheet)
+Polymer {'low': 2.4100000858306885, 'high': 3.799999952316284} PMMA (heat resistant)
+Polymer {'low': 1.600000023841858, 'high': 3.299999952316284} PMMA (impact modified)
+Polymer {'low': 2.240000009536743, 'high': 3.240000009536743} PMMA (molding and extrusion)
+None None PMMA - unfilled
+Other {'low': 0.10000000149011612, 'high': 0.5} Leather
+```
+
+```python
+tab = db.get_table('Metals Pedigree')
+recs = tab.all_records(include_folders=False)
+
+start = time.time()
+
+# manually fetch the links on each record, one record at a time
+for r in recs:
+    _ = r.links
+    r.decache_links
+milestone1 = time.time()
+
+# bulk fetch all links on every record
+tab.bulk_link_fetch(recs)
+
+milestone2 = time.time()
+
+# bulk_fetch (& all the bulk_xxx methods) can be parallelized by the MI Scripting Toolkit IF you have the cores available
+tab.bulk_link_fetch(recs,
+                    link_groups=['Tensile Test Data'],
+                    batch_size=5,
+                    parallelise=False)
+milestone3 = time.time()
+
+# parallelise
+tab.bulk_link_fetch(recs,
+                    link_groups=['Tensile Test Data'],
+                    batch_size=5,
+                    parallelise=True,
+                    max_num_threads=3)
+end = time.time()
+print('{:4.4f} s - Time elapsed to reach the first milestone'.format(milestone1 - start))
+print('{:4.4f} s - Time elapsed between milestone 1 and 2'.format(milestone2 - milestone1))
+print('{:4.4f} s - Time elapsed between milestone 2 and 3'.format(milestone3 - milestone2))
+print('{:4.4f} s - Time elapsed between milestone 2 and the end'.format(end - milestone3))
+```
+*Previous cell output:*
+```output
+1.3176 s - Time elapsed to reach the first milestone
+0.4944 s - Time elapsed between milestone 1 and 2
+0.9680 s - Time elapsed between milestone 2 and 3
+0.3884 s - Time elapsed between milestone 2 and the end
+```
+
+```python
+for r in recs:
+    print('{} links on record "{}"'.format(len(r.links['Tensile Test Data']), r.name))
+```
+*Previous cell output:*
+```output
+10 links on record "Batch 948"
+9 links on record "Batch 943"
+9 links on record "Batch 921"
+8 links on record "Batch 978"
+0 links on record "2X2301A"
+```
+## Import records
+
+There is one method to import/update records: `Session.update`. In MI Scripting Toolkit 2.1, we added an additional argument which can be used to streamline calls to `Session.update` in specific cases. Normally, after importing records, the MI Scripting Toolkit will fetch the newly imported data on those records to return the results of your changes back to you. However, if you set `refresh_attributes` to `False`, it won't do this, and you can fetch attributes you're interested in (if you want to) using `bulk_fetch`.
+
+
+```python
+tab = db.get_table('MaterialUniverse')
+start = time.time()
+# Let's create a record and give it some data on a single attribute
+new_rec1 = tab.create_record('My new record {}'.format(str(datetime.datetime.now())))
+a = new_rec1.attributes['Density']
+a.points = [1.3]
+new_rec1.set_attributes([a])
+# This will re-fetch the record's attributes after setting them, in case anything has changed on the server!
+new_rec1 = mi.update([new_rec1])[0]
+milestone1 = time.time()
+
+# Do the same again
+new_rec2 = tab.create_record('My new record {}'.format(str(datetime.datetime.now())))
+a = new_rec2.attributes['Density']
+a.points = [1.3]
+new_rec2.set_attributes([a])
+# This time we won't refetch the attributes because we only care about the attribute we edited: 'Density'
+new_rec2 = mi.update([new_rec2], refresh_attributes=False)[0]
+# Instead we do it ourselves in an additional line of code
+tab.bulk_fetch([new_rec2], attributes=['Density'])
+end = time.time()
+
+# How long did both processes take?
+print('{:4.4f} s - Time elapsed to reach the first milestone'.format(milestone1 - start))
+print('{:4.4f} s - Time elapsed between milestone 1 and the end'.format(end - milestone1))
+```
+*Previous cell output:*
+```output
+0.4290 s - Time elapsed to reach the first milestone
+0.1977 s - Time elapsed between milestone 1 and the end
+```
+## Deleting records & release states
+
+In addition to the methods shown above, record deletion and fetching of release states can also be performed in bulk. Unlike link fetching and attribute fetching, these are **NOT** table-specific methods, and so they exist as methods on the `Session` class. This means, for example, that records can be deleted from multiple tables and databases simultaneously. Both `Session`-level bulk methods can be parallelized and batched like their `Table`-level siblings. The two methods are called:
+
+1. `Session.bulk_fetch_release_states`
+1. `Session.bulk_delete_or_withdraw_records`
+
+On the whole, these methods are less pervasive, but are still worth demonstrating should you ever need to use them!
+
+
+```python
+# Let's take some of the record's we've created in this example, fetch their release states, then delete them!
+# but let's split them into two batches, and compare the times taken doing things individually versus in bulk
+individuals = [new_rec1, new_rec2]
+bulk_batch = [end_leaf, rec]
+
+print(' Individual Records ')
+print(' ------------------ ')
+print()
+start = time.time()
+for r in individuals:
+    print(r.release_state)
+milestone1 = time.time()
+for r in individuals:
+    r.delete_or_withdraw_record_on_server()
+end = time.time()
+print('{:4.4f} s - Time to fetch release states'.format(milestone1 - start))
+print('{:4.4f} s - Time to manually delete the records'.format(end - milestone1))
+
+print()
+
+print(' Bulk batched Records ')
+print(' -------------------- ')
+print()
+start = time.time()
+mi.bulk_fetch_release_states(bulk_batch)
+for r in bulk_batch:
+    print(r.release_state)
+milestone1 = time.time()
+mi.bulk_delete_or_withdraw_records(bulk_batch)
+end = time.time()
+print('{:4.4f} s - Time to bulk fetch release states'.format(milestone1 - start))
+print('{:4.4f} s - Time to bulk delete the records'.format(end - milestone1))
+```
+*Previous cell output:*
+```output
+ Individual Records 
+ ------------------ 
+
+Unversioned
+Unversioned
+```*Previous cell output:*
+```output
+0.1595 s - Time to fetch release states
+0.4368 s - Time to manually delete the records
+
+ Bulk batched Records 
+ -------------------- 
+
+Unversioned
+Unversioned
+```*Previous cell output:*
+```output
+0.0495 s - Time to bulk fetch release states
+0.4456 s - Time to bulk delete the records
+```
+## Conclusion
+
+The optimizations shown above are not significant when working with small amounts of data, and the ease of use provided by the Streamlined API makes it a very powerful tool for small operations. However, as you scale up the operations being undertaken to hundreds, thousands, and tens of thousands of records, the time saved by using these more efficient methods becomes more significant.
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/19_Using_OIDC_With_the_STK.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/19_Using_OIDC_With_the_STK.md
new file mode 100644
index 0000000000..1fb5dadce7
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/19_Using_OIDC_With_the_STK.md
@@ -0,0 +1,55 @@
+# Using OpenID Connect with MI Scripting Toolkit
+
+Please contact Ansys Granta Technical Support for information on supported OpenID Connect Identity Providers, and for configuration and setup documentation.
+
+## Access Granta MI using OpenID Connect
+
+In MI Scripting Toolkit 2.2 or later, OIDC authentication is exposed through the Session class in the same way as other authentication options. For interactive use, this is as simple as setting `oidc=True` in the constructor.
+
+
+```python
+from GRANTA_MIScriptingToolkit import granta as mpy
+
+# Connect to MI using OpenID Connect; this will open a browser and prompt the user to log in.
+# Access MI and list the available databases.
+mi = mpy.connect('https://my.server.name/mi_servicelayer', oidc=True)
+print(mi.dbs)
+```
+*Previous cell output:*
+```output
+[<Database: MI Training, unit system: Database>]
+```
+## Unattended use
+For unattended use, OIDC tokens can be stored securely in the OS credential manager. For Windows this is the Windows
+Credential Locker, for linux this may be Freedesktop Secret Service or the dbus KWallet. Tokens can be persisted with
+the `Session.persist_oidc_token()` method.
+
+
+```python
+# We can access the tokens provided by the OpenID identity provider using a private property of the session object.
+print(mi._Session__auth_token)
+
+mi.persist_oidc_token()
+
+# Clear the current Session
+del(mi)
+```
+*Previous cell output:*
+```output
+gaUDsgUrOiJSUzI1NiIsImtpZCI6IkVlMlQ0Y2F4U1RkaVFDTmEyMTcyUWciLCJ0eXAiOiJhdCtqd3QifQ.eyJuYmYiOjE1OTE3ODEwNzAsImV4cCI6MTU5MTc4MjI3MCwiaXNzIjoiaHR0cHM6Ly9taWF1dGgtaWRwLmdyYW50YWRlc2lnbi5sb2NhbDo1MDAwIiwiYXVkIjoibWkiLCJjbGllbnRfaWQiOiJtaUNsaWVudCIsInN1YiI6Ik1JX1VTRVJfNyIsImF1dGhfdGltZSI6MTU5MTc4MTA2OSwiaWRwIjoibG9jYWwiLCJzY29wZSI6WyJvcGVuaWQiLCJwcm9maWxlIiwibWkiLCJvZmZsaW5lX2FjY2VzcyJdLCJhbXIiOlsicHdkIl19.D2PM4ofOZYDH-yQfo_2K1K33OpdmuhsEp3yt_p0wQeN8WmCBVrF4CFfWNlKUBbiQHlQlUUNEjze14Z-nkogpJafT37ltVzIxF0AEHfHsZpB4WwUMLPoDP3fvQ9cO3N66LuiKaD5LUhnalFZTiwZs4xCJhAMcWpYkaSNAEcR_TX-3do-iDmHqXqXbvqHa-2PACgh5k3nVl4sa92d69_OFzLEQA-vuBBzhMiM9aecK6M-2CBxPQn26rG8f1_L0siZC_qAhw0lKj-NTh6Eeyd-BqfpSEvBvUOCUDzoDGvK9e6e-PPwikOJKa4LtBUgt4mmfo9UaqLpraHp2nvp8v2J3LQ
+```
+OpenID Connect tokens can then be extracted from the store and used to create a new Session object with no user interaction.
+
+
+```python
+mi = mpy.Session("https://my.server.name/mi_servicelayer", oidc=True, use_stored_token=True)
+
+print(mi._Session__auth_token)
+
+print(mi.dbs)
+```
+*Previous cell output:*
+```output
+gaUDsgUrOiJSUzI1NiIsImtpZCI6IkVlMlQ0Y2F4U1RkaVFDTmEyMTcyUWciLCJ0eXAiOiJhdCtqd3QifQ.eyJuYmYiOjE1OTE3ODEwNzAsImV4cCI6MTU5MTc4MjI3MCwiaXNzIjoiaHR0cHM6Ly9taWF1dGgtaWRwLmdyYW50YWRlc2lnbi5sb2NhbDo1MDAwIiwiYXVkIjoibWkiLCJjbGllbnRfaWQiOiJtaUNsaWVudCIsInN1YiI6Ik1JX1VTRVJfNyIsImF1dGhfdGltZSI6MTU5MTc4MTA2OSwiaWRwIjoibG9jYWwiLCJzY29wZSI6WyJvcGVuaWQiLCJwcm9maWxlIiwibWkiLCJvZmZsaW5lX2FjY2VzcyJdLCJhbXIiOlsicHdkIl19.D2PM4ofOZYDH-yQfo_2K1K33OpdmuhsEp3yt_p0wQeN8WmCBVrF4CFfWNlKUBbiQHlQlUUNEjze14Z-nkogpJafT37ltVzIxF0AEHfHsZpB4WwUMLPoDP3fvQ9cO3N66LuiKaD5LUhnalFZTiwZs4xCJhAMcWpYkaSNAEcR_TX-3do-iDmHqXqXbvqHa-2PACgh5k3nVl4sa92d69_OFzLEQA-vuBBzhMiM9aecK6M-2CBxPQn26rG8f1_L0siZC_qAhw0lKj-NTh6Eeyd-BqfpSEvBvUOCUDzoDGvK9e6e-PPwikOJKa4LtBUgt4mmfo9UaqLpraHp2nvp8v2J3LQ
+[<Database: MI Training, unit system: Database>]
+```
\ No newline at end of file
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/20_Logging.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/20_Logging.md
new file mode 100644
index 0000000000..4252ecb773
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/20_Logging.md
@@ -0,0 +1,102 @@
+# Logging
+
+## Writing Scripting Toolkit logs to the console
+
+The Foundation layer defines a logger with the name `GDL` that can be used to capture logs about the requests made
+to Granta MI.
+
+To use this logger, first fetch the logger object either via the `get_foundation_logger` function, or by using the
+Python logging module directly. 
+
+
+```python
+import logging
+
+from GRANTA_MIScriptingToolkit import granta as mpy
+
+logger = mpy.get_foundation_logger()
+
+# Alternatively, the logger can be fetched by name.
+logger = logging.getLogger("GDL")
+```
+
+Next, configure and attach a `StreamHandler` to the logger. A `StreamHandler` writes log messages to `stderr`.
+
+
+```python
+console_handler = logging.StreamHandler()
+string_format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+formatter = logging.Formatter(string_format)
+console_handler.setFormatter(formatter)
+
+logger.addHandler(console_handler)
+```
+
+Finally, set the logger level to DEBUG to ensure all messages are displayed. The log level defines the *minimum*
+log level to be handled by this logger.
+
+
+```python
+logger.setLevel(logging.DEBUG)
+```
+
+To test that the logging has been set up successfully, perform some Scripting Toolkit operations. The log messages
+generated by the Foundation Layer are shown on the console or in the notebook, depending on how this script is
+executed.
+
+
+```python
+mi = mpy.connect("http://my.server.name/mi_servicelayer", autologon=True)
+my_db = mi.get_db(db_key="MI_Training")
+```
+*Previous cell output:*
+```output
+2024-10-30 16:10:48,965 - GDL - DEBUG - Executing Browse_GetDatabases() command, attempt 1.
+```*Previous cell output:*
+```output
+2024-10-30 16:10:49,001 - GDL - INFO - Browse_GetDatabases() command succeeded on attempt 1 after 0.0351679939776659s.
+```
+## Writing Scripting Toolkit logs to a file
+
+If you would like to write logs to disk, you can use a `FileHandler`. In this example, use a timestamp to define a
+unique file name for each execution of the script.
+
+
+```python
+from datetime import datetime
+
+file_name = f"scripting_toolkit_{datetime.now().strftime('%Y-%m-%d-%H%M%S')}.log"
+file_handler = logging.FileHandler(filename=file_name)
+formatter = logging.Formatter(string_format)
+file_handler.setFormatter(formatter)
+
+logger.addHandler(file_handler)
+```
+
+To test that the log files are being generated, perform some more Scripting Toolkit operations. The log messages will
+be available both on the console (or in the notebook) and in a file on disk.
+
+
+```python
+tables = my_db.tables
+```
+*Previous cell output:*
+```output
+2024-10-30 16:10:49,012 - GDL - DEBUG - Executing Browse_GetTables() command, attempt 1.
+```*Previous cell output:*
+```output
+2024-10-30 16:10:49,273 - GDL - INFO - Browse_GetTables() command succeeded on attempt 1 after 0.2604787079617381s.
+```
+If required, you can also define additional loggers for your own log messages, which may have different handlers and
+may log at different levels.
+
+For more details on managing logging, including additional handler types with more advanced file management
+capabilities, consult the [Python logging documentation](https://docs.python.org/3/library/logging.html).
+
+## Additional Scripting Toolkit logging
+
+The MI Scripting Toolkit underlying C libraries also perform their own additional logging separate to the Python
+logging. These logs can be found in the following locations:
+
+* Windows: `%LOCALAPPDATA%\Granta Design\MIScriptingToolkit`
+* Linux: `~/.appdata/Granta Design/MIScriptingToolkit`
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/21_Storing_credentials_securely.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/21_Storing_credentials_securely.md
new file mode 100644
index 0000000000..c56a0fa46d
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/21_Storing_credentials_securely.md
@@ -0,0 +1,156 @@
+# Storing Credentials Securely
+
+When writing Python code to be executed in a batch mode, it is important to ensure that the credentials required to connect to Granta MI (and any other server) are stored securely.
+
+## Storing an Identity in Windows Task Scheduler for Autologon
+
+If the server running Granta MI is on the same domain as the machine running the Python script, and the script is intended to be run automatically on a schedule, Task Scheduler can be used to store the identity that will run the script. The MI Scripting Toolkit `connect()` function can then be used with `autologon=True`, and the script will connect to Granta MI using the identity specified in the Task Scheduler task.
+
+### Creating a Scheduled Task
+
+The screenshot below shows what a typical Scheduled Task will look like when configured to run as a specific user. In this case, `python_user` is the user that the script will run as. Clicking **OK** will display a password prompt to store the password for that user.
+
+!["Create Task" options, with the task configured to run as "python_user", and "Run whether user is logged on or not" selected.](assets/21_create_task.png '"Create task" options')
+
+#### Triggers
+
+Use the *Triggers* tab to configure when the script will execute. Generally this is specified in terms of certain days of the week and at a certain time of day.
+
+#### Actions
+
+Use the *Actions* tab to add the location of the Python executable and the script to be executed. In Windows, the Python executable is typically available either with `py` or `python`. If these are not available, you will need to specify the full path to the executable (accessible in Python by running `sys.executable`).
+
+If you are executing your script in a virtual environment, we recommend writing a batch script that first activates the virtual environment before running the script. In this case, the *Actions* tab would reference the batch script, which in turn executes the Python script.
+
+### Considerations
+
+- We recommend running the script as a service principal which has been granted the permission "Log On As Batch Job".
+- Make sure you select the radio button **Run whether user is logged on or not**, since in general the service principal will not be logged in.
+- Uncheck the **Do not store password** box, since the session will need to access a remote Granta MI server. This means that if the password changes, it will need to be re-entered in Task Scheduler.
+
+## Using `keyring` to Store Credentials for Password-Based Authentication
+
+There are a number of scenarios where it does not make sense to use Task Scheduler in this way, for example:
+- The script is running in Linux
+- The script needs to run on-demand by a user without access to Task Scheduler
+- The script needs to store credentials for a non-Windows account
+
+In these scenarios, the `keyring` Python package is recommended ([keyring PyPI page](https://pypi.org/project/keyring/)). It has cross-platform support by leveraging the [Windows Credential Locker](https://docs.microsoft.com/en-us/windows/uwp/security/credential-locker) in Windows or the [Secret Service API](https://www.gnu.org/software/emacs/manual/html_node/auth/Secret-Service-API.html) in Linux, which in turn relies on one of a number of different credential storage methods depending on the environment. In Ubuntu 18.04, the GNOME Password Manager is used.
+
+### Considerations
+
+Before proceeding, be aware of the following considerations when using `keyring`.
+
+- `keyring` itself does not store the credentials, it relies on an external back end. Environments other than Windows and Ubuntu may provide these, but their configuration can be more complex. See the `keyring` [documentation](https://github.com/jaraco/keyring) for more details.
+- In Windows, all credentials stored by a user are visible to that user. As a result, any credentials stored by a shared or service account are visible to everyone with access to that account.
+- In Ubuntu, credentials are only accessible if a password was used to log in. If the user was logged in automatically, the credential store must be unlocked before it can be accessed.
+- `keyring` has a plug-in architecture, which is used to provide alternative (and potentially unsecured) back ends. However, these require installing additional packages from PyPI and manually selecting an alternative back end.
+
+### Installing keyring
+
+Installing `keyring` is typically done in the same way in both Windows and Ubuntu 18.04, despite the different credential storage back ends. The package can be installed using pip:
+
+```
+pip install keyring
+```
+
+You can confirm the back end in use by running the following on the command line:
+
+```
+keyring --list-backends
+```
+
+In Windows, this produces the following output, which indicates that the Windows Credential Locker will be used by default. If for some reason it is unavailable, `keyring` will fail, thus ensuring that credentials will not inadvertently be stored using an unsecured method:
+
+```
+keyring.backends.Windows.WinVaultKeyring (priority: 5)
+keyring.backends.fail.Keyring (priority: 0)
+keyring.backends.chainer.ChainerBackend (priority: -1)
+```
+
+Running the same command on Ubuntu 18.04 produces a similar output.
+
+```
+keyring.backends.SecretService.Keyring (priority: 5)
+keyring.backends.chainer.ChainerBackend (priority: -1)
+keyring.backends.fail.Keyring (priority: 0)
+```
+
+### Storing Credentials Using the Command Line
+
+The simplest way to store a credential using `keyring` is to use the command line.
+
+The following command invokes the `keyring` Python package on the command line, and creates a new credential in the appropriate back end for the username `granta-admin-user` within the service `my-python-script`. We recommend setting the service to the name of the Python package.
+
+```batch
+keyring set my-python-script granta-admin-user
+```
+
+You will then be prompted for a password. Enter a password to store it securely using either Windows Credential Locker or Secret Service.
+
+### Storing Credentials Programmatically
+
+It is sometimes useful to store credentials programmatically in Python. This is the approach used in MI Scripting Toolkit to securely persist OpenID Connect (OIDC) tokens. The cells below demonstrate how to perform the same operation as above, but in Python:
+
+
+```python
+import keyring
+
+keyring.set_password("my-python-script", "granta-admin-user", "my_secret_password")
+```
+
+### Retrieving Credentials Programmatically
+
+Credentials are retrieved programmatically similarly to how they are set.
+
+
+```python
+keyring.get_password("my-python-script", "granta-admin-user")
+```
+
+
+
+*Previous cell output:*
+```output
+'my_secret_password'
+```
+
+
+This password can now be provided to the MI Scripting Toolkit `connect()` method, or to any other Python function that requires a credential or other secret.
+
+### Updating Credentials
+
+Credentials are updated by simply setting the password for the same username and service.
+
+
+```python
+keyring.set_password("my-python-script", "granta-admin-user", "my_super_secret_password")
+
+keyring.get_password("my-python-script", "granta-admin-user")
+```
+
+
+
+*Previous cell output:*
+```output
+'my_super_secret_password'
+```
+
+
+### Deleting Credentials
+
+Passwords can be deleted with the `delete_password` function.
+
+
+```python
+keyring.delete_password("my-python-script", "granta-admin-user")
+```
+
+Now calling `get_password()` will not retrieve the password, since it has been deleted.
+
+
+```python
+keyring.get_password("my-python-script", "granta-admin-user")
+```
+
+Credentials can also be deleted using the appropriate app, either Credential Manager in Windows, or Passwords and Keys in Ubuntu. They can also be deleted using the `keyring` command line interface.
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/22_Record_Lists_Interoperability.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/22_Record_Lists_Interoperability.md
new file mode 100644
index 0000000000..8f78af4b17
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/22_Record_Lists_Interoperability.md
@@ -0,0 +1,302 @@
+# RecordLists and MI Scripting Toolkit
+
+This example demonstrates how to use the Granta MI RecordLists library with the Scripting
+Toolkit. The RecordLists library provides programmatic access to the Favorites
+feature in Granta MI, which allows users to create and access lists of records stored on the
+Granta MI server.
+
+The example demonstrates two use scenarios:
+
+1. Create a record list and add Streamlined API `Record` objects to it.
+2. Fetch a record list and create Streamlined API `Record` objects from the contents.
+
+These use cases are then repeated to show how similar steps are performed for version-controlled
+records.
+
+This example does not cover all aspects of the RecordLists package. Full API documentation
+and additional examples are available [here](https://recordlists.grantami.docs.pyansys.com).
+
+<div class="alert alert-info">
+
+**Info:**
+
+Running this notebook requires the `ansys-grantami-recordlists` package.
+See the 'PyGranta Dependencies' section of the 'Example Notebooks'
+documentation page for more details.
+</div>
+
+## Create a record list from Streamlined API `Record` objects
+
+First create an MI Scripting Toolkit session.
+
+### Create a list of Streamlined API `Record` objects
+
+
+```python
+from GRANTA_MIScriptingToolkit import granta as mpy
+
+SERVICE_LAYER_URL = "http://my.server.name/mi_servicelayer"
+mi = mpy.connect(SERVICE_LAYER_URL, autologon=True)
+```
+
+Search for all *MaterialUniverse* records with a density above a certain threshold. The
+`Table.search_for_records_where` method returns a list of Streamlined API `Record` objects.
+
+
+```python
+db = mi.get_db(db_key="MI_Training")
+db.unit_system = "SI (Consistent)"
+material_universe = db.get_table("MaterialUniverse")
+
+criterion = material_universe.attributes["Density"].search_criterion(greater_than=7000.0)
+results = material_universe.search_for_records_where([criterion])
+results
+```
+
+
+
+*Previous cell output:*
+```output
+[<Record long name: Low alloy steel, AISI 4130, air melted, quenched & tempered>,
+ <Record long name: Tungsten carbide>,
+ <Record long name: 250 maraging steel, maraged at 900F>,
+ <Record long name: Low alloy steel, AISI 4130, air melted, normalized>]
+```
+
+
+### Create an 'ansys-grantami-recordlists' client
+Use the `Connection` class to connect to create Record List client.
+
+
+```python
+from ansys.grantami.recordlists import Connection, RecordListItem
+
+api_client = Connection(SERVICE_LAYER_URL).with_autologon().connect()
+record_list = api_client.create_list(name="High density materials")
+record_list
+```
+
+
+
+*Previous cell output:*
+```output
+<RecordList name: High density materials>
+```
+
+
+### Add Streamlined API Record objects to a Record List
+A `RecordList` object is a collection of `RecordListItem` objects, where each `RecordListItem`
+refers to a specific Granta MI record. Therefore, to add records to a RecordList, we must first
+create a list of `RecordListItem` objects from Streamlined API Record objects.
+
+
+```python
+items = [
+    RecordListItem(
+        database_guid=record.table.database.guid,
+        table_guid=record.table.guid,
+        record_history_guid=record.history_guid,
+    )
+    for record in results
+]
+```
+
+Now, use the `RecordList.add_items_to_list` method to add the `RecordListItem` objects to the
+RecordList. This method returns all items now contained in the RecordList.
+
+
+```python
+record_list_items = api_client.add_items_to_list(record_list, items)
+record_list_items
+```
+
+
+
+*Previous cell output:*
+```output
+[<RecordListItem(database_guid='43a43640-4919-428a-bac9-16efbc4ce6ed', record_history_guid='8cf5911d-b08c-4893-880a-838cccd5ea12')>,
+ <RecordListItem(database_guid='43a43640-4919-428a-bac9-16efbc4ce6ed', record_history_guid='d0237d48-ddd2-4916-b24c-86ba51f25b52')>,
+ <RecordListItem(database_guid='43a43640-4919-428a-bac9-16efbc4ce6ed', record_history_guid='1ca9e494-a011-4023-970c-d63c8a48c1a7')>,
+ <RecordListItem(database_guid='43a43640-4919-428a-bac9-16efbc4ce6ed', record_history_guid='f425c026-2e6b-486a-a342-ee3ea79fdb0c')>]
+```
+
+
+## Get Streamlined API `Record` objects from a Record List
+
+The method `RecordList.add_items_to_list` called above returned the list of `RecordListItem`
+objects, and so the following cell is not strictly necessary. However, for completeness, the
+following cell identifies a RecordList by name and gets the contents of the list.
+
+
+```python
+record_lists = api_client.get_all_lists()
+record_list = [r for r in record_lists if r.name == "High density materials"][0]
+record_list_items = api_client.get_list_items(record_list)
+record_list_items
+```
+
+
+
+*Previous cell output:*
+```output
+[<RecordListItem(database_guid='43a43640-4919-428a-bac9-16efbc4ce6ed', record_history_guid='8cf5911d-b08c-4893-880a-838cccd5ea12')>,
+ <RecordListItem(database_guid='43a43640-4919-428a-bac9-16efbc4ce6ed', record_history_guid='d0237d48-ddd2-4916-b24c-86ba51f25b52')>,
+ <RecordListItem(database_guid='43a43640-4919-428a-bac9-16efbc4ce6ed', record_history_guid='1ca9e494-a011-4023-970c-d63c8a48c1a7')>,
+ <RecordListItem(database_guid='43a43640-4919-428a-bac9-16efbc4ce6ed', record_history_guid='f425c026-2e6b-486a-a342-ee3ea79fdb0c')>]
+```
+
+
+Use the `Session.get_records_by_ids` method to instantiate multiple `Record` objects at once.
+
+This method requires database keys, but the `RecordListItem` objects only contain database GUIDs.
+To convert between the two:
+
+1. Use the `Session.dbs_by_guid` dictionary to get the `Database` object given its GUID
+2. Access the `Database.db_key` property to get the database key.
+
+<div class="alert alert-warning">
+
+**Warning:**
+
+It is a valid Granta MI configuration to have multiple databases with the same GUID. In this
+situation, `Session.dbs_by_guid` will raise a `KeyError`. In this case, use `Session.get_db()`
+and provide additional arguments to identify a single database, for example the database name.
+</div>
+
+
+```python
+identifiers = [
+    {
+        "db_key": mi.dbs_by_guid[item.database_guid].db_key,
+        "hguid": item.record_history_guid,
+    }
+    for item in record_list_items
+]
+records = mi.get_records_by_ids(identifiers)
+records
+```
+
+
+
+*Previous cell output:*
+```output
+[<Record long name: Low alloy steel, AISI 4130, air melted, normalized>,
+ <Record long name: 250 maraging steel, maraged at 900F>,
+ <Record long name: Low alloy steel, AISI 4130, air melted, quenched & tempered>,
+ <Record long name: Tungsten carbide>]
+```
+
+
+## Version-controlled records
+### Create a record list from version-controlled Streamlined API `Record` objects
+Search for all *Design Data* records with a density above a certain threshold.
+
+
+```python
+design_data = db.get_table("Design Data")
+
+criterion = design_data.attributes["Density"].search_criterion(greater_than=7000.0)
+results = design_data.search_for_records_where([criterion])
+```
+
+Since the *Design Data* table is version-controlled, each `Record` object represents a particular
+version of a Granta MI record. This can be seen by accessing the ``version_number``
+property.
+
+
+```python
+for r in results:
+    print(f"'{r.name}'", ", version: ",  r.version_number)
+```
+*Previous cell output:*
+```output
+'Nickel alloys, Inconel 718, Forging' , version:  1
+'250 Grade Maraging, Maraged at 900F, Plate, Thickness: 0.1875 to 0.251 in, AMS 6520, S basis' , version:  3
+```
+When creating a `RecordListItem` that represents a version-controlled record, the `record_version`
+parameter is optional. If it is specified, the `RecordListItem` represents that specific version.
+If it is omitted, the `RecordListItem` tracks the latest available version of that record.
+
+In this example, provide the version number of the latest version.
+
+
+```python
+version_controlled_items = [
+    RecordListItem(
+        database_guid=record.table.database.guid,
+        table_guid=record.table.guid,
+        record_history_guid=record.history_guid,
+        record_version=record.version_number,
+    )
+    for record in results
+]
+record_list_items = api_client.add_items_to_list(record_list, version_controlled_items)
+record_list_items
+```
+
+
+
+*Previous cell output:*
+```output
+[<RecordListItem(database_guid='43a43640-4919-428a-bac9-16efbc4ce6ed', record_history_guid='8cf5911d-b08c-4893-880a-838cccd5ea12')>,
+ <RecordListItem(database_guid='43a43640-4919-428a-bac9-16efbc4ce6ed', record_history_guid='d0237d48-ddd2-4916-b24c-86ba51f25b52')>,
+ <RecordListItem(database_guid='43a43640-4919-428a-bac9-16efbc4ce6ed', record_history_guid='1ca9e494-a011-4023-970c-d63c8a48c1a7')>,
+ <RecordListItem(database_guid='43a43640-4919-428a-bac9-16efbc4ce6ed', record_history_guid='f425c026-2e6b-486a-a342-ee3ea79fdb0c')>,
+ <RecordListItem(database_guid='43a43640-4919-428a-bac9-16efbc4ce6ed', record_history_guid='5b1df472-a92e-4317-b9a8-e1672335bc2e', record_version=3)>,
+ <RecordListItem(database_guid='43a43640-4919-428a-bac9-16efbc4ce6ed', record_history_guid='ce294339-e59d-4be1-a96c-f9e92adb71ac', record_version=1)>]
+```
+
+
+## Get Streamlined API `Record` objects from a Record List with version-controlled items
+As in the non-versioned example, use the `Session.get_records_by_ids` method to instantiate the
+`Record` objects. In this case though, we provide both the `hguid` and `vguid` parameters for
+each record:
+
+* The `hguid` (or record history GUID) refers to all versions of a record, and generally resolves
+to the latest available version that the user can access.
+* The `vguid` (record version GUID or record GUID) refers to a specific version of a record.
+
+If a `record_version` was provided when creating a `RecordListItem`, the
+`RecordListItem.record_guid` property will be populated. If however, the `RecordListItem` was
+created without a `record_version`, the `RecordListItem.record_guid` property will be empty. In
+both cases the `record_history_guid` is populated.
+
+Since the `Session.get_records_by_ids` prioritizes the `vguid` parameter over the `hguid`
+parameter, we can provide both:
+
+* If the `RecordListItem.record_guid` property exists, `Session.get_records_by_ids` will resolve
+the specific version of the record.
+* If the `RecordListItem.record_guid` property does not exist, `Session.get_records_by_ids` will
+identify the latest available version for the user.
+
+The following code therefore correctly identifies items specified both with and without a version.
+
+
+```python
+identifiers = [
+    {
+        "db_key": mi.dbs_by_guid[item.database_guid].db_key,
+        "hguid": item.record_history_guid,
+        "vguid": item.record_guid
+    }
+    for item in record_list_items
+]
+records = mi.get_records_by_ids(identifiers)
+record_versions = [r.version_number for r in records]
+
+for rec, ver in zip(records, record_versions):
+    print(f"{rec.name}{' [v' + str(ver) + ']' if ver else ''} ({rec.table.name})")
+```
+*Previous cell output:*
+```output
+Low alloy steel, AISI 4130, air melted, normalized (MaterialUniverse)
+250 maraging steel, maraged at 900F (MaterialUniverse)
+Low alloy steel, AISI 4130, air melted, quenched & tempered (MaterialUniverse)
+Tungsten carbide (MaterialUniverse)
+250 Grade Maraging, Maraged at 900F, Plate, Thickness: 0.1875 to 0.251 in, AMS 6520, S basis [v3] (Design Data)
+Nickel alloys, Inconel 718, Forging [v1] (Design Data)
+```
+
+```python
+api_client.delete_list(record_list)
+```
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/23_Job_Queue_Interoperability.md b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/23_Job_Queue_Interoperability.md
new file mode 100644
index 0000000000..b30231771d
--- /dev/null
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/23_Job_Queue_Interoperability.md
@@ -0,0 +1,184 @@
+# JobQueue and MI Scripting Toolkit
+
+## Introduction
+
+This example demonstrates how to use the Granta MI JobQueue library with the Scripting
+Toolkit. The JobQueue library replaces the AsyncJobQueue component previously available within
+the MI Scripting Toolkit, and allows interaction with the Granta MI Job Queue via Python.
+
+The JobQueue library supports import and export operations. This example demonstrates how to use
+the MI Scripting Toolkit to define the records exported to Excel using an Excel template.
+
+This example does not cover all aspects of the JobQueue package. Full API documentation
+and additional examples are available [here](https://jobqueue.grantami.docs.pyansys.com).
+
+<div class="alert alert-info">
+
+**Info:**
+
+Running this notebook requires the `ansys-grantami-jobqueue` package.
+See the 'PyGranta Dependencies' section of the 'Example Notebooks'
+documentation page for more details.
+</div>
+
+## Define the records to export
+
+First create an MI Scripting Toolkit session.
+
+
+```python
+from GRANTA_MIScriptingToolkit import granta as mpy
+
+SERVICE_LAYER_URL = "http://my.server.name/mi_servicelayer"
+mi = mpy.connect(SERVICE_LAYER_URL, autologon=True)
+```
+
+Next, access all records in a specific tree location. We will need the database key later, so
+store it in a constant.
+
+
+```python
+DB_KEY = "MI_Training"
+db = mi.get_db(db_key=DB_KEY)
+tensile_test_data = db.get_table("Tensile Test Data")
+
+records = tensile_test_data.get_records_from_path(
+    starting_node=None,
+    tree_path=["High Alloy Steels", "AMS 6520", "Plate", "300°F"]
+)
+records
+```
+
+
+
+*Previous cell output:*
+```output
+[<Record long name: MTS-615723>,
+ <Record long name: MTS-615733>,
+ <Record long name: MTS-615743>,
+ <Record long name: MTS-615753>,
+ <Record long name: MTS-615763>,
+ <Record long name: MTS-615773>]
+```
+
+
+## Create an 'ansys-grantami-jobqueue' client
+Use the `Connection` class to connect to create a JobQueue client.
+
+
+```python
+from ansys.grantami.jobqueue import Connection, ExcelExportJobRequest, ExportRecord
+
+api_client = Connection(SERVICE_LAYER_URL).with_autologon().connect()
+api_client
+```
+
+
+
+*Previous cell output:*
+```output
+<JobQueueApiClient: url=http://my.server.name/mi_servicelayer">
+```
+
+
+## Export the records to Excel
+All JobQueue operations are performed by first creating a `JobRequest` object, and then submitting
+that object to the server.
+
+An ``ExcelExportJobRequest`` object requires the following:
+
+* A name and description
+* The path to an Excel export template, provided as either a string or a `pathlib.Path` object
+* A list of `ExportRecord` objects, which contain a record history identity and optionally a
+  record version
+* The database key for the provided `ExportRecord` objects
+
+First, create the list of `ExportRecord` objects from the list of `Record` objects.
+
+
+```python
+export_records = [ExportRecord(r.history_identity) for r in records]
+export_records
+```
+
+
+
+*Previous cell output:*
+```output
+[ExportRecord(record_history_identity=120726, record_version=None),
+ ExportRecord(record_history_identity=120727, record_version=None),
+ ExportRecord(record_history_identity=120728, record_version=None),
+ ExportRecord(record_history_identity=120729, record_version=None),
+ ExportRecord(record_history_identity=120730, record_version=None),
+ ExportRecord(record_history_identity=120731, record_version=None)]
+```
+
+
+Next, create the Excel export request.
+
+
+```python
+from pathlib import Path
+
+request = ExcelExportJobRequest(
+    name="Example export",
+    description="A demonstration of how to export data to Excel using Granta MI JobQueue",
+    template_file=Path("supporting_files/23_Export_Template.xlsx"),
+    database_key=DB_KEY,
+    records=export_records,
+)
+request
+```
+
+
+
+*Previous cell output:*
+```output
+<ExcelExportJobRequest: name: "Example export">
+```
+
+
+Finally, use the `JobQueue.create_job_and_wait` method to submit the `ExcelExportJobRequest`
+object to the server. This method returns once the job is complete.
+
+
+```python
+job = api_client.create_job_and_wait(job_request=request)
+job
+```
+
+
+
+*Previous cell output:*
+```output
+<AsyncJob: name: "Example export", status: "JobStatus.Succeeded">
+```
+
+
+## Access the exported data
+Once the job is complete, the list of files generated as part of the operation can be accessed via
+the ``output_file_names`` property.
+
+
+```python
+job.output_file_names
+```
+
+
+
+*Previous cell output:*
+```output
+['Example export.log',
+ 'ExportedRecords-Tensile Test Data.xlsx',
+ 'summary.json']
+```
+
+
+The binary content of an individual file can be accessed with the ``get_file_content`` method, or
+alternatively the file can be saved to disk with the ``download_file`` method. See the JobQueue
+API documentation for a demonstration of these methods.
+
+
+```python
+api_client.delete_jobs([job])
+```
diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/assets/21_create_task.png b/2025R1_Test/grantami-scripting-toolkit-dev-portal/samples/streamlined/assets/21_create_task.png
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From 076cf7a2ed7003e7c5b0b7f0814ff3de98074094 Mon Sep 17 00:00:00 2001
From: Andy Grigg <andrew.grigg@ansys.com>
Date: Thu, 31 Oct 2024 14:53:11 -0400
Subject: [PATCH 3/3] Revert docfx.json changes

---
 .../grantami-scripting-toolkit-dev-portal/docfx.json        | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/2025R1_Test/grantami-scripting-toolkit-dev-portal/docfx.json b/2025R1_Test/grantami-scripting-toolkit-dev-portal/docfx.json
index 515b752342..6d42743684 100644
--- a/2025R1_Test/grantami-scripting-toolkit-dev-portal/docfx.json
+++ b/2025R1_Test/grantami-scripting-toolkit-dev-portal/docfx.json
@@ -1,8 +1,8 @@
 {
     "build": {
       "globalMetadata": {
-        "title": "Granta scripting toolkit documentation 4.2",
-        "summary": "Granta scripting toolkit documentation",
+        "title": "Granta scripting toolkit documentation 4.1",
+        "summary": "",
         "version": "4.1",
         "product": "Granta",
         "programming language": "Python",
@@ -10,4 +10,4 @@
         "physics": "Materials Data"
       }
     }
- }
\ No newline at end of file
+ }