nipreps · josephmje · Apr 4, 2021 · Mar 23, 2021 · Apr 1, 2021 · Apr 1, 2021
diff --git a/docs/_config.yml b/docs/_config.yml
@@ -1,8 +1,12 @@
 # Book settings
-title: "dMRIPrep"
-author: The dMRIPrep developers
+title: "NiPreps"
+author: The NiPreps developers
 copywright: 2020
-logo: images/logo.svg
+
+# Sphinx customizations
+sphinx:
+  extra_extensions:
+    - sphinx_exercise
 
 # Execution settings
 execute:

diff --git a/docs/_toc.yml b/docs/_toc.yml
@@ -1,8 +1,13 @@
 # Table of content
 # Learn more at https://jupyterbook.org/customize/toc.html
-#
+
 - file: welcome
-- file: dmriprep/dmriprep
-- file: dmriprep/gradient_table
-- file: dmriprep/sdc
-- file: dmriprep/hmc
+- part: nipreps
+  chapters:
+  - file: nipreps/nipreps
+  - file: nipreps/community_development
+- part: dmriprep
+  chapters:
+  - file: dmriprep/dmriprep
+  - file: dmriprep/gradient_table
+  - file: dmriprep/hmc
diff --git a/docs/dmriprep/dmriprep.md b/docs/dmriprep/dmriprep.md
@@ -1,24 +1,9 @@
 # About dMRIPrep
 
-The pre-processing of dMRI involves numerous steps to reduce noise, remove artifacts, and standardize the data before fitting a particular model or carrying out tractography.
-
-Generally, researchers create ad-hoc pre-processing workflows for each dataset, building upon a large inventory of available tools.
-The complexity of these workflows has snowballed with rapid advances in acquisition parameters and processing steps.
+The pre-processing of dMRI data involves numerous steps to reduce noise, remove artifacts, and standardize the data before fitting a particular model or carrying out tractography.
 
 dMRIPrep is an analysis-agnostic tool that addresses the challenge of robust and reproducible pre-processing for whole-brain dMRI data.
 
-## Motivation
-
-The development and fast adoption of fMRIPrep [^esteban2019] have revealed that neuroscientists need tools that simplify their research workflow, provide visual reports and checkpoints, and engender trust in the tool itself.
-
-In Botvinik et al., 2020 [^botvinik2020], 70 independent teams were tasked with analyzing the same fMRI dataset and testing 9 hypothesis.
-The study demonstrated the huge amount of variability in analytic approaches as *no two teams* chose identical workflows.
-One encouraging finding was that 48% of teams chose to pre-process the data using fMRIPrep.
-
-A similar predicament exists in the field of dMRI analysis.
-There has been a lot of effort in recent years to compare the influence of various pre-processing steps on tractography and structural connectivity [^oldham2020] [^schilling2019] and harmonize different datasets [^tax2019].
-All of this points to a need for creating a standardized pipeline for pre-processing dMRI data that will reduce methodological variability and enable comparisons between different datasets and downstream analysis decisions.
-
 ## Development
 
 In his 2019 ISMRM talk [^veraart2019], Jelle Veraart polled the developers of some of the major dMRI analysis software packages.
@@ -57,97 +42,8 @@ Proposed dMRI pre-processing workflow
 Current list of contributors to dMRIPrep
 ```
 
-## Key Features
-
-There are several other dMRI pre-processing pipelines being developed.
-Below are some of dMRIPrep's key features.
-
-### 1. Part of the NiPreps organization
-
-```{image} ../images/sashimi.jpg
-:name: sashimi
-:width: 200px
-:align: right
-```
-
-NiPreps are a collection of tools that work as an extension of the scanner in that they minimally pre-process the data and make them "safe to consume" for analysis - kinda like *sashimi*!
-
-NiPreps pipelines are also:
-- robust to different datasets
-- easy to use (containerized software environment that can be run with single command)
-- reproducible
-- "glass box" architecture (all code/decisions visible on GitHub)
-- regularly maintained
-
-```{figure} ../images/nipreps-chart.svg
-:name: nipreps_chart
-
-Pipelines maintained by the NiPreps community
-```
-
-### 2. Automated workflows based on BIDS configuration
-
-dMRIPrep only imposes a single constraint on the input dataset - being compliant with BIDS (Brain Imaging Data Structure).
-BIDS enables consistency in how neuroimaging data is structured and ensures that the necessary metadata is complete.
-This also minimizes human intervention in running the pipeline as it is able to adapt to the unique features of the input data and make decisions about whether a particular processing step is appropriate or not.
-
-- head motion correction algorithm based on shell sampling
-  - FSL eddy or Sparse Fascicle Model (SFM) for single-shell
-  - 3D-SHORE for multi-shell/Cartesian grid
-- distortion correction strategy based on input fieldmap
-- parsing phase encoding direction and total readout time for applying distortion correction
-- shell distribution - algorithms that require information redundancy cannot be applied to sparse sampling schemes
-
-### 3. Quality control reportlets
-
-```{figure} ../images/dwi_reportlet.gif
-:name: reportlet
-```
-
-### 4. Continuous integration and deployment
-
-```{tabbed} unittest
-Checks whether a function or class method behaves as expected.
-
-![unittest](../images/unittest.png)
-
-```
-
-```{tabbed} doctest
-Also checks whether code behaves as expected and serves as an example for how to use the code.
-
-![doctest1](../images/doctest1.png)
-
-![doctest2](../images/doctest2.png)
-
-```
-
-```{tabbed} integration test
-Checks the behaviour of a system (multiple pieces of code).
-Can also be used to determine whether the system is behaving suboptimally.
-
-![integration_test](../images/integration_test.png)
-
-```
-
-```{tabbed} build test
-Checks that code or software environment can be compiled and deployed.
-
-![build_test](../images/build_test.png)
-
-```
-
 ---
-## References
 
-[^esteban2019]: Esteban, O., Markiewicz, C.J., Blair, R.W. et al. fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat Methods 16, 111–116 (2019). https://doi.org/10.1038/s41592-018-0235-4
-
-[^botvinik2020]: Botvinik-Nezer, R., Holzmeister, F., Camerer, C.F. et al. Variability in the analysis of a single neuroimaging dataset by many teams. Nature 582, 84–88 (2020). https://doi.org/10.1038/s41586-020-2314-9
-
-[^oldham2020]: Oldham, S., Arnatkevic̆iūtė, A., Smith, R.W., et al. The efficacy of different preprocessing steps in reducing motion-related confounds in diffusion MRI connectomics. NeuroImage 222 117252 (2020). https://doi.org/10.1016/j.neuroimage.2020.117252
-
-[^schilling2019]: Schilling, K. G., Daducci, A., Maier-Hein, K., Poupon, C., Houde, J. C., Nath, V., Anderson, A. W., Landman, B. A., & Descoteaux, M. (2019). Challenges in diffusion MRI tractography - Lessons learned from international benchmark competitions. Magnetic resonance imaging, 57, 194–209. https://doi.org/10.1016/j.mri.2018.11.014
-
-[^tax2019]: Tax, C. M., Grussu, F., Kaden, E., Ning, L., Rudrapatna, U., John Evans, C., St-Jean, S., Leemans, A., Koppers, S., Merhof, D., Ghosh, A., Tanno, R., Alexander, D. C., Zappalà, S., Charron, C., Kusmia, S., Linden, D. E., Jones, D. K., & Veraart, J. (2019). Cross-scanner and cross-protocol diffusion MRI data harmonisation: A benchmark database and evaluation of algorithms. NeuroImage, 195, 285–299. https://doi.org/10.1016/j.neuroimage.2019.01.077
+## References
 
-[^veraart2019]: Image Processing: Possible Guidelines for Standardization & Clinical Applications. https://www.ismrm.org/19/program_files/MIS15.htm
+[^veraart2019]: Image Processing: Possible Guidelines for Standardization & Clinical Applications. https://www.ismrm.org/19/program_files/MIS15.htm
diff --git a/docs/dmriprep/gradient_table.md b/docs/dmriprep/gradient_table.md
@@ -217,4 +217,4 @@ $ dwigradcheck -fslgrad ../../data/sub-02_dwi.bvec ../../data/sub-02_dwi.bval ..
 19.49            0                (2, 1, 0)           scanner
 19.45            2                (2, 1, 0)           image
 19.43         none                (1, 2, 0)           scanner
-```
+```
diff --git a/docs/dmriprep/sdc.md b/docs/dmriprep/sdc.md