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talk-rdm.Rmd
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talk-rdm.Rmd
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---
title: "Open and reproducible data management with DataLad"
subtitle: "Talk at Freie Universität Berlin (Dept. of Education and Psychology)"
author: "Dr. Lennart Wittkuhn"
institute: "</br>Department of Psychology, University of Hamburg</br>Max Planck Institute for Human Development, Berlin</br>Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin"
date: "</br>Friday, 14<sup>th</sup> of April 2023"
#date: "Last update of slides: `r format(Sys.time(), '%H:%M | %B %d, %Y')`"
output:
xaringan::moon_reader:
css: [default, metropolis, metropolis-fonts]
#css: xaringan-themer.css
self_contained: true
yolo: false
lib_dir: libs
nature:
ratio: '16:9'
# highlightStyle: solarized-dark
highlightLines: true
countIncrementalSlides: false
citation_package: biblatex
#countdown: 60000
---
```{r, echo=FALSE, message=FALSE, warning=FALSE, results='hide'}
if (!requireNamespace("pacman")) install.packages("pacman")
packages_cran = c("here", "xaringan", "emoji")
pacman::p_load(char = packages_cran)
```
# About
--
#### About me
- `r emoji::emoji("scientist")` **Position:** PostDoc & Lab Manager at [University of Hamburg](https://www.psy.uni-hamburg.de/en/arbeitsbereiche/lern-und-veraenderungsmechanismen.html) & [MPI for Human Development](https://www.mpib-berlin.mpg.de/en) (PI: Prof. Nicolas Schuck)
- `r emoji::emoji("microscope")` **Research:** I study the role of fast neural memory reactivation ([*replay*](https://en.wikipedia.org/wiki/Hippocampal_replay)) in the human brain using fMRI
- `r emoji::emoji("mortar_board")` **Background:** BSc Psychology (TU Dresden), MSc Cognitive Neuroscience (TU Dresden), PhD Psychology (FU Berlin)
- `r emoji::emoji("bookmark")` **Roles:** Member of the MPIB's working group on research data management and open science
& ethics committee
- `r emoji::emoji("link")` **Contact:** You can connect with me via [email](mailto:wittkuhn@mpib-berlin.mpg.de), [Twitter](https://twitter.com/lnnrtwttkhn), [Mastodon](https://fediscience.org/@lnnrtwttkhn),
[GitHub](https://github.com/lnnrtwttkhn) or
[LinkedIn](https://www.linkedin.com/in/lennart-wittkuhn-6a079a1a8/)
- `r emoji::emoji("information_source")` **Info:** Find out more about my work on [my website](https://lennartwittkuhn.com/), [Google Scholar](https://scholar.google.de/) and [ORCiD](https://orcid.org/0000-0003-2966-6888)
???
- FDM und Open Science aus Sicht eines Forschenden
- Wie mit Code und Daten effektiv umgegangen werden kann
--
#### About this presentation
- `r emoji::emoji("computer")` **Slides:** Reproducible slides are publicly available at [lennartwittkuhn.com/talk-rdm](https://lennartwittkuhn.com/talk-rdm/)
- `r emoji::emoji("package")` **Software:** [RMarkdown](https://bookdown.org/yihui/rmarkdown/) with the [xaringan](https://github.com/yihui/xaringan) package, run in [Docker](https://www.docker.com/), deployed to [GitHub Pages](https://pages.github.com/) using [GitHub Actions](https://github.com/features/actions)
- `r emoji::emoji("trackball")` **DOI:** [10.5281/zenodo.5012476](http://doi.org/10.5281/zenodo.5012476) (generated using GitHub releases + Zenodo, see details [here](https://guides.github.com/activities/citable-code/))
- `r emoji::emoji("abacus")` **Source:** Source code is publicly available on GitHub at [github.com/lnnrtwttkhn/talk-rdm](https://github.com/lnnrtwttkhn/talk-rdm/)
- `r emoji::emoji("electric_plug")` **Links:** This presentation contains links to external resources. I do not take responsibility for the accuracy, legality or content of the external site or for that of subsequent links. If you notice an issue with a link, please contact me!
- `r emoji::emoji("pray")` **Contact**: I am happy for any feedback or suggestions via [email](mailto:wittkuhn@mpib-berlin.mpg.de) or [GitHub issues](https://github.com/lnnrtwttkhn/talk-rdm/issues). Thank you!
---
# Agenda
1. **Introduction**
2. **Research Workflow**
- Code Management: **Git**
- Data Management: **DataLad**
- Code & Data Sharing: **GIN**, **Nextcloud**, **OSF**, **S3**, etc.
- Example: Wittkuhn & Schuck, 2021, Nature Communications
- Live demonstrations
Optional:
- Communication: Discussion via **Issues** and collaborations via **Merge Requests**
- *(Procedures: **datalad run** and **Make**)*
- *(Environments: **renv**, **venv**, **Docker**, etc.)*
3. **Wrap-up & discussion**
???
- Kurze Einleitung in die Thematik
- Fokus auf drei Aspekte des vorgeschlagenen Workflows: Code, Daten, Kommunikation
- Verweise auf Infrastruktur innerhalb der MPG
- Anstösse für die Diskussion
---
# Credit and further reading
#### Papers
- Wilson et al. (2014). [Best practices for scientific computing](https://doi.org/10.1371/journal.pbio.1001745). *PLOS Biology*.
- Wilson et al. (2017). [Good enough practices in scientific computing](https://doi.org/10.1371/journal.pcbi.1005510). *PLOS Computational Biology*.
- Lowndes et al. (2017). [Our path to better science in less time using open data science tools](https://doi.org/10.1038/s41559-017-0160). *Nature Ecology Evolution*.
#### Talks
- Richard McElreath (2020). [Science as amateur software development](https://www.youtube.com/watch?v=zwRdO9_GGhY). YouTube
- Russ Poldrack (2020). [Toward a Culture of Computational Reproducibility](https://www.youtube.com/watch?v=XjW3t-qXAiE). YouTube
#### Handbooks
- Adina Wagner et al. (2022). [The DataLad Handbook](http://handbook.datalad.org/)
- Greg Wilson (2021). [Building software together](https://buildtogether.tech/)
- Patrick J. Mineault & The Good Research Code Handbook Community (2021). [The Good Research Code Handbook](https://goodresearch.dev)
- The Turing Way Community. (2019). [The Turing Way handbook to reproducible, ethical and collaborative data science](https://the-turing-way.netlify.app)
... and many more!
---
class: title-slide, center, middle
name: introduction
# Introduction
<!--the next --- is a horizontal line-->
---
---
exclude: true
# Motivation: "Open" Science should just be "Science"
.pull-left[
*"An article about computational science in a scientific publication is not the scholarship itself, it is merely advertising of the scholarship.
The actual scholarship is the complete software development environment and the complete set of instructions which generated the figures."*
Buckheit & Donoho (1995), paraphrasing Jon Claerbout
{{content}}
]
.pull-right[
```{r, echo=FALSE, fig.align="center", out.width="50%", fig.retina=1, fig.cap='<a href="https://wiki.seg.org/wiki/Jon_Claerbout" target="_blank">Jon Claerbout</a></br>Geophysicist at Stanford University</br>(CC-BY-SA)'}
knitr::include_graphics("https://wiki.seg.org/images/b/b0/Jon_Claerbout_headshot.jpg")
```
]
???
- "Open Science" vielgehörter Begriff
- Praktiken von Open Science (Open Access, Open Data, Open Code) entsprechen Elementen der guten wissenschaftlichen Praxis
- Ansatz: Offenheit der Offenen Wissenschaft sollte Standard sein
- Die Basis der berichteten Ergebnisse in einem Paper (Code, Daten, etc.) sollten zugänglich und nutzbar sein, sonst bleibt die eigentliche Arbeit unzugänglich, nicht überprüfbar und nicht reproduzierbar
<!--the next --- is a horizontal line-->
---
- Jon Claerbout, a distinguished exploration geophysicist at Stanford University
- He has also pointed out that we have reached a point where solutions are available - it is now possible to publish computational research that is really reproducible by others.
---
# Challenge: Computational reproducibility
> *"[...] when the same analysis steps performed on the same dataset consistently produces the same answer."*
```{r, echo=FALSE, fig.align="center", out.width="70%", fig.retina=1, fig.cap='<a href="https://the-turing-way.netlify.app/reproducible-research/overview/overview-definitions.html" target="_blank">Table of Definitions for Reproducibility</a> by <i>The Turing Way</i> (CC-BY 4.0)'}
knitr::include_graphics("https://the-turing-way.netlify.app/_images/reproducible-matrix.jpg")
```
???
- Komputationale Reproduzierbarkeit: Situation, wenn die gleiche Analyse der gleichen Daten das gleiche Ergebnis hervorbringt
- Komputationale Reproduzierbarkeit als Mindestanforderung für Forschungsarbeit
- Grundvoraussetzung für replizierbare, robuste und generalisierbare Forschung
- Oft können Ergebnisse kurze Zeit nach Publikation nicht mehr von den Originalautoren reproduziert werden
- Wie verlässlich sind die Ergebnisse? Wie effizient ist diese Art Forschung zu betreiben?
---
- **Reproducible:** A result is reproducible when the same analysis steps performed on the same dataset consistently produces the same answer.
- **Replicable:** A result is replicable when the same analysis performed on different datasets produces qualitatively similar answers.
- **Robust:** A result is robust when the same dataset is subjected to different analysis workflows to answer the same research question (for example one pipeline written in R and another written in Python) and a qualitatively similar or identical answer is produced. Robust results show that the work is not dependent on the specificities of the programming language chosen to perform the analysis.
- **Generalisable:** Combining replicable and robust findings allow us to form generalisable results. Note that running an analysis on a different software implementation and with a different dataset does not provide generalised results. There will be many more steps to know how well the work applies to all the different aspects of the research question. Generalisation is an important step towards understanding that the result is not dependent on a particular dataset nor a particular version of the analysis pipeline.
---
# Challenge: Many stages in the research cycle
```{r, echo=FALSE, fig.align="center", out.width="58%", fig.retina=1, fig.cap='<a href="https://zenodo.org/record/4906004" target="_blank"><sup>by Scriberia for The Turing Way community (CC-BY 4.0)</sup></a>'}
knitr::include_graphics("https://keeper.mpdl.mpg.de/f/3a1863ac2c2e40809c5f/?dl=1")
```
???
- Moderne Forschung ist sehr umfangreich und komplex
- Gleichzeitig gibt es hohe Ansprüche an die Forschung: Sie soll exakt sein, objektiv, transparent, nachvollziehbar und reproduzierbar sein
- Viele Schritte im Forschungsprozess über viele Jahre
- Bei jedem dieser Schritte werden i.d.R. Daten generiert und verarbeitet - wie kann man damit systematisch und effektiv umgehen?
- Formalisieren dieser Herausforderung
---
# Challenge: Many computational interactions
```{r, echo=FALSE, fig.align="center", out.width="40%", fig.retina=1}
knitr::include_graphics("https://keeper.mpdl.mpg.de/f/7094210d35484a1f8f8c/?dl=1")
```
???
- **Code:** Kleine Textdatein
- z.B. Programmiercode in Python oder R
- Manuskript in LaTeX
- **Daten:** binäre Dateeien und größere Textdateien
- MRT Daten
- größere tabellarische Daten (`.csv`)
- **Prozeduren:** Code und Daten interagieren
- Basierend auf Daten werden mit Code neue Daten produziert: Ich lese Dateien ein und produziere Grafiken
- Code produziert alleine neue Daten (z.B. in Simulationen)
- Prozeduren können und sollten dokumentiert werden bzw. maschinenausführbar implementiert werden
- **Umgebung:** Komputationale Umgebungen (Betriebssystem, Softwareversionen), die diese Interaktion von Code und Daten möglich machen und beeinflussen
- Reproduzierbare komputationale Umgebungen verhindern das "Works On My Machine"-Problem
- **Kommunikation:**
- Koordination der Arbeit mit mehrere Forschenden
- Absprachen, Entscheidungen, etc.
Fokus: Code, Daten, Kommunikation
---
- Data is produced through code (e.g., task code)
- Data is manipulated by code and new data is generated
- Mapping between input and output data
- This happens using specific software in specific versions
---
class: title-slide, center, middle
name: track-everything
# If everything is relevant ...
--
track everything
.footnote[
Credit: Adapted from [Slides on "Research Data Management with DataLad"](http://datasets.datalad.org/datalad/datalad-course/html/mpsc-introduction.html#/5) by Adina Wagner & Michael Hanke
]
---
class: title-slide, center, middle
name: workflow-git
# Workflow: Code Management using Git
<!--the next --- is a horizontal line-->
---
---
# Why we need version control for code
```{r, echo=FALSE, fig.align="center", out.width="33%", fig.retina=1, fig.cap='<a href="http://phdcomics.com/comics/archive/phd101212s.gif" target="_blank"><sup>© Jorge Cham (phdcomics.com)</sup></a>'}
knitr::include_graphics("http://phdcomics.com/comics/archive/phd101212s.gif")
```
???
- Vielen, die an einem langen Textdokument gearbeitet haben, inbesondere zusammen mit anderen, sollte dieses Problem bekannt vorkommen
- Viele Runden von Veränderungen und Anpassungen
- Speichern von Zwischenergebnissen
- Entstehung von Parallelversionen
- Bei Programmiercode: Warum funktioniert der Code in dieser Version nicht?
- Lösung: Versionskontrolle
- Hier: Implizite Versionskontrolle, die an ihre Grenzen stösst
---
exclude: false
# What is version control?
.pull-left[
```{r, echo=FALSE, fig.align="center", out.width="100%", fig.retina=1, fig.cap='<a href="https://zenodo.org/record/3695300" target="_blank"><sup>by Scriberia for The Turing Way community (CC-BY 4.0)</sup></a>'}
knitr::include_graphics("https://zenodo.org/record/3695300/files/VersionControl.jpg?download=1")
```
]
.pull-right[
```{r, echo=FALSE, fig.align="center", out.width="100%", fig.retina=1, fig.cap='<a href="https://zenodo.org/record/3695300" target="_blank"><sup>by Scriberia for The Turing Way community (CC-BY 4.0)</sup></a>'}
knitr::include_graphics("https://zenodo.org/record/3695300/files/ProjectHistory.jpg?download=1")
```
]
.center[
keep files organized</br>
keep track of changes</br>
revert changes or go back to previous versions
]
???
- Datein zu organisieren
- präziser Verlauf aller Veränderungen an einer Datei
- Änderungen zurücknehmen und zu älteren Versionen zurückkehren
---
# Version control with Git
```{r, echo=FALSE, fig.align="center", out.width="15%", fig.retina=1}
knitr::include_graphics("https://git-scm.com/images/logos/downloads/Git-Logo-2Color.png")
```
> Version control is a systematic approach to record changes made in a [...] set of files, over time. This allows you and your collaborators to track the history, see what changed, and recall specific versions later [...] ([Turing Way](https://the-turing-way.netlify.app/reproducible-research/vcs.html))
--
.pull-left[
#### Basic version control workflow
1. Create files (text, code, etc.)
1. Work on the files (change, delete or add content)
1. **Create a snapshot of the file state** (current version)
{{content}}
]
<!-- see https://stackoverflow.com/questions/46408057/incremental-slides-do-not-work-with-a-two-column-layout-->
.pull-right[
```{r, echo=FALSE, fig.align="center", out.width="55%", fig.retina=1}
# fig.cap='<a href="https://git-scm.com/book/en/v2/Getting-Started-About-Version-Control" target="_blank">Figure 3: Distributed Version Control Systems</a>'
knitr::include_graphics("https://git-scm.com/book/en/v2/images/distributed.png")
```
]
--
#### Git
- most popular **distributed version control system**
- free, [open-source](https://github.com/git) command-line tool
- Graphical User Interfaces (GUIs) exist, e.g., [GitKraken](https://www.gitkraken.com/)
- standard tool for professional software developers
???
- Back in the day: Software developers used BitKeeper to collaborate on code with colleagues
- Free access to BitKeeper was revoked after a company broke down in 2005
- A new solution was needed so Linus Torvalds coded it up
- First version after a couple of days
---
# The amazing superpowers of version control with Git
--
.pull-left[
#### Git as a distributed **version control** system
- keep track of changes in a directory (a "repository")
- take snapshots ("commits") of your repo at any time
- know the history of what was changed when by whom
- compare commits and go back to any previous state
- work on "branches" and flexibly "merge" them together
`r emoji::emoji("bulb")` **save one file and all of its history instead of multiple versions of the same file**
{{content}}
]
.pull-right[
```{r, echo=FALSE, fig.align="center", out.width="100%", fig.retina=1, fig.cap="Commit history in GitKraken"}
knitr::include_graphics("https://keeper.mpdl.mpg.de/f/8fda5b269fef4d778007/?dl=1")
```
]
--
#### Git as a **distributed** version control system
- "push" your repo to a "remote" location and share it
- host / share your repo on GitHub, GitLab or BitBucket
- work with others on the same files at the same time
- others can read / copy / edit and suggest changes
- make your repo public and openly share your work
---
exclude: true
# Code sharing via GitLab
```{r, echo=FALSE, fig.align="center", out.width="10%", fig.retina=1, fig.cap='<a href="https://gitlab.com/" target="_blank">gitlab.com</a>'}
knitr::include_graphics("https://cdn.icon-icons.com/icons2/2415/PNG/512/gitlab_original_logo_icon_146503.png")
```
> "*GitLab is **open source** software to collaborate on code. Manage git repositories with fine-grained **access controls** that keep your code secure. [...] Perform **code reviews** and **enhance collaboration** with merge requests. Each project can also have an **issue tracker** and a **wiki**.*"
--
exclude: true
#### GitLab for TU Dresden employees
- hosted [at the School of Science at TU Dresden](https://tu-dresden.de/mn/der-bereich/it-kompetenz-und-servicezentrum/gitlab-dienst?set_language=en)<sup>1</sup>
.footnote[
<sup>1</sup> Using your ZIH credentials, you should already have an account!
]
---
class: title-slide, center, middle
name: workflow-data
# Workflow: Data Management using DataLad
<!--the next --- is a horizontal line-->
---
---
# Why we need version control for data
```{r, echo=FALSE, fig.align="center", out.width="55%", fig.retina=1, fig.cap='<a href="http://phdcomics.com/comics/archive/phd052810s.gif" target="_blank"><sup>© Jorge Cham (phdcomics.com)</sup></a>'}
knitr::include_graphics("http://phdcomics.com/comics/archive/phd052810s.gif")
```
???
- Problem mangelnder Versionskontrolle gibt es auch für Daten
- Daten können sich über die Zeit verändern
- Mehrere Versionen der gleichen Datei mit unklaren Unterschieden
- Vermischung verschiedener Daten
<!--the next --- is a horizontal line-->
---
- Kann man Git verwenden? Git ist für die Versionskontrolle von kleineren, textbasierten Dateien geeignet
- Gibt es auch Versionskontrolle für Daten?
---
# What is DataLad?
```{r, echo=FALSE, fig.align="center", out.width="8%", fig.retina=1, fig.cap='<a href="https://www.datalad.org/" target="_blank">datalad.org</a>'}
knitr::include_graphics("https://avatars.githubusercontent.com/u/8927200?s=200&v=4")
```
> *"DataLad is a software tool developed to **aid with everything related to the evolution of digital objects**"*
--
- **"git for (large) data"**
- free, [open-source](https://github.com/datalad/datalad) **command-line tool**
- building on top of **git** and **git-annex**, DataLad allows you to **version control arbitrarily large files** in datasets.
- *"Arbitrarily large?"* - yes, see DataLad dataset of 80TB / 15 million files from the Human Connectome Project (see [details](https://handbook.datalad.org/en/latest/usecases/HCP_dataset.html#usecase-hcp-dataset))
- A Graphical User Interface (GUI) exists: [DataLad Gooey](http://docs.datalad.org/projects/gooey/en/latest/index.html)
--
exclude: true
#### DataLad philosophy (excerpt)
- DataLad knows only two things: Datasets and files
- DataLad minimizes custom procedures and data structures
- DataLad is developed for complete decentralization
- DataLad aims to maximize the (re-)use of existing third-party data resources and infrastructure
???
- DataLad ist domänen-unspezifisch und kann für jede Art von Daten verwendet werden
- DataLad interagiert mit verschiedenen bekannten Infrastrukturen und Hosting-Plattformen
<!--the next --- is a horizontal line-->
---
#### What is DataLad? (see the [10,000 feet](http://handbook.datalad.org/en/latest/intro/executive_summary.html) and [brief](http://handbook.datalad.org/en/latest/intro/philosophy.html) overview in the DataLad Handbook by [Wagner et al., 2020, *Zenodo*](https://doi.org/10.5281/ZENODO.3905791))
#### Human Connectome Project
> The goal of the Human Connectome Project is to build a "network map" (connectome) that will shed light on the anatomical and functional connectivity within the healthy human brain, as well as to produce a body of data that will facilitate research into brain disorders such as dyslexia, autism, Alzheimer's disease, and schizophrenia.
-- [Source: Wikipedia](https://en.wikipedia.org/wiki/Human_Connectome_Project)
---
# DataLad: What is a dataset?
> *"A dataset is a directory on a computer that DataLad manages."*
```{r, echo=FALSE, fig.align="center", out.width="40%", fig.retina=1, fig.cap='see <a href="http://handbook.datalad.org/en/latest/basics/101-101-create.html" target="_blank">DataLad Handbook: Create a new dataset</a>'}
knitr::include_graphics("http://handbook.datalad.org/en/latest/_images/dataset.svg")
```
> "*You can create new, empty datasets [..] and populate them, or transform existing directories into datasets.*"
???
- Wie bei Git, ist ein DataLad dataset ist einfach nur ein Ordner auf dem Computer der von DataLad getrackt wird
- Man kann neue, leere Datasets erstellen und befüllen oder bereits existierende Ordner mit Dateien in DataLad datasets überführen
---
# DataLad: Version control arbitrarily large files
> *"Building on top of Git and git-annex, DataLad allows you to version control arbitrarily large files in datasets."*
```{r, echo=FALSE, fig.align="center", out.width="40%", fig.retina=1, fig.cap='see <a href="http://handbook.datalad.org/en/latest/basics/101-102-populate.html" target="_blank">DataLad Handbook: How to pupulate a dataset</a>'}
knitr::include_graphics("http://handbook.datalad.org/en/latest/_images/local_wf.svg")
```
> *"[...] keep track of revisions of data of any size, and view, interact with or restore any version of your dataset [...]."*
???
- DataLad trackt Veränderungen in Dateien jeglicher Größe und speichert den Zustand in der Repository Historie ab
---
# DataLad: Dataset consumption and collaboration
> *"DataLad lets you consume datasets provided by others, and collaborate with them."*
> *"You can **install existing datasets** and update them from their sources, or create sibling datasets that you can **publish updates** to and **pull updates** from for collaboration and data sharing."*
```{r, echo=FALSE, fig.align="center", out.width="70%", fig.retina=1, fig.cap='see <a href="https://handbook.datalad.org/en/latest/basics/101-105-install.html" target="_blank">DataLad Handbook: Install an existing dataset</a>'}
knitr::include_graphics("http://handbook.datalad.org/en/latest/_images/collaboration.svg")
```
???
- Mit DataLad können bereits existierende Datensätze gecloned / installiert werden, damit weitergearbeitet werden
- Eigene Datasets können zu einer Reihe von Hosting-Platformen publiziert und mit anderen geteilt werden
---
# DataLad: Dataset linkage
> *"Datasets can contain other datasets (subdatasets), **nested arbitrarily deep.**"*
```{r, echo=FALSE, fig.align="center", out.width="70%", fig.retina=1, fig.cap='see <a href="http://handbook.datalad.org/en/latest/basics/101-106-nesting.html" target="_blank">DataLad Handbook: Nesting datasets</a>'}
knitr::include_graphics("http://handbook.datalad.org/en/latest/_images/linkage_subds.svg")
```
> *"Each dataset has an independent [...] history, but can be registered at a precise version in higher-level datasets. This allows to **combine datasets** and to perform commands recursively across a hierarchy of datasets, and it is the basis for advanced provenance capture abilities."*
???
- DataLad datasets können miteinander verlinkt werden
- DataLad speichert die präzise Version des Datensatzes, der verwendet wird
- Sehr geeignet für die Art von sequentiellen Datenverarbeitungsschritten in der Forschung
- Modularität der einzelnen Datasets erleichtert Wiederwendung
- Git user: funktioniert mit Git submodules
---
exclude: true
# DataLad: Full provenance capture and reproducibility
> *"DataLad allows to **capture full provenance**: The origin of datasets, the origin of files obtained from web sources, complete machine-readable and automatically reproducible records of how files were created [...]."*
```{r, echo=FALSE, fig.align="center", out.width="55%", fig.retina=1, fig.cap='see <a href="http://handbook.datalad.org/en/latest/usecases/provenance_tracking.html" target="_blank">DataLad Handbook: Provenance tracking</a> and <a href="http://handbook.datalad.org/en/latest/basics/basics-run.html" target="_blank">run commands</a>'}
knitr::include_graphics("http://handbook.datalad.org/en/latest/_images/reproducible_execution.svg")
```
> *"You or your collaborators can thus re-obtain or reproducibly **recompute content with a single command**, and make use of extensive provenance of dataset content **(who created it, when, and how?)**."*
---
# DataLad: Third party service integration
> *"**Export datasets to third party services** such as GitHub, GitLab, or Figshare with built-in commands."*
```{r, echo=FALSE, fig.align="center", out.width="65%", fig.retina=1, fig.cap='see <a href="http://handbook.datalad.org/en/latest/basics/basics-thirdparty.html" target="_blank">DataLad Handbook: Third-party infrastructure</a>'}
knitr::include_graphics("http://handbook.datalad.org/en/latest/_images/thirdparty.svg")
```
> *"Alternatively, you can use a **multitude of other available third party services** such as Dropbox, Google Drive, Amazon S3, owncloud, or many more that DataLad datasets are compatible with."*
???
- DataLad integriert mit einer Reihe von Platformen und Services, die in der Forschung verbreitet sind
- Dadurch ermöglicht DataLad systematische Kollaboration und Veröffentlichung von größeren Datensätzen
---
exclude: true
# DataLad: Metadata handling
> *"**Extract, aggregate, and query dataset metadata.** This allows to automatically obtain metadata according to different metadata standards (EXIF, XMP, ID3, BIDS, DICOM, NIfTI1, ...), store this metadata in a portable format, share it, and search dataset contents."*
```{r, echo=FALSE, fig.align="center", out.width="100%", fig.retina=1, fig.cap='see <a href="http://docs.datalad.org/en/stable/metadata.html" target="_blank">DataLad Handbook: Metadata</a>'}
knitr::include_graphics("http://handbook.datalad.org/en/latest/_images/metadata_prov_imaging.svg")
```
---
# Interesting bits and pieces about DataLad
#### Use DataLad within Python `r emoji::emoji("snake")` and R `r emoji::emoji("pirate_flag")`
- [DataLad Python API](http://docs.datalad.org/en/stable/modref.html): Use DataLad commands directly in your Python code!
- Install DataLad `pip install datalad` and import in your Python script `import datalad.api as dl`
- Use system commands in other languages, e.g., in R `system2("datalad status")`
--
#### Keep only what you need (aka. "How to work on two fMRI studies with a 250GB laptop")
- `datalad drop` removes the file contents completely from your dataset
- only keep whatever you like or re-obtain with `datalad get`
--
#### git-annex takes the safety of your files seriously
- Files saved under git-annex are locked against modifications
- `datalad run` automatically unlocks specified inputs / outputs
- `datalad unlock` can be used to unlock annexed content manually
- Everything that is stored under git-annex is content-locked and everything that is stored under Git is not
---
# Git vs. git-annex
.pull-left[
```{r, echo=FALSE, fig.align="center", out.width="100%", fig.retina=1, fig.cap='<a href="http://handbook.datalad.org/en/latest/basics/101-114-txt2git.html" target="_blank"><sup>DataLad Handbook: Data Safety</sup></a>'}
knitr::include_graphics("http://handbook.datalad.org/en/latest/_images/git_vs_gitannex.svg")
```
- Example: `datalad create -c text2git my_dataset`<br>→ all text files are saved under Git
- the `.gitattributes` file handles, which files are stored under git vs. git-annex (can modify manually)
- see [this chapter](http://handbook.datalad.og/en/latest/basics/basics-configuration.html#chapter-config) in the DataLad handbook
]
.pull-right[
```{r, echo=FALSE, fig.align="center", out.width="100%", fig.retina=1, fig.cap='<a href="http://handbook.datalad.org/en/latest/basics/101-138-sharethirdparty.html" target="_blank"><sup>DataLad Handbook: Beyond shared infrastructure</sup></a>'}
knitr::include_graphics("http://handbook.datalad.org/en/latest/_images/publishing_network_publishparts2.svg")
```
]
---
class: title-slide, center, middle
name: workflow-sharing
# Workflow: Data sharing using DataLad
<!--the next --- is a horizontal line-->
---
???
- Fokus, wie Infrastruktur der MPG zum Tragen kommt
---
# Data sharing and collaboration with DataLad
.center[
*"I have a dataset on my computer. How can I share it or collaborate on it?"*
]
```{r, echo=FALSE, fig.align="center", out.width="60%", fig.retina=1, fig.cap='see <a href="https://handbook.datalad.org/en/latest/basics/101-138-sharethirdparty.html" target="_blank">DataLad Handbook: Beyond shared infrastructure</a>'}
knitr::include_graphics("https://keeper.mpdl.mpg.de/f/d0b1749c4d504bb080b7/?dl=1")
```
---
# Share version-controlled datasets with DataLad
- With DataLad, you can **share data like you share code** (i.e., via repository hosting services)
- DataLad datsets can be cloned, pushed and updated from and to remote hosting services
```{r, echo=FALSE, fig.align="center", out.width="75%", fig.retina=1, fig.cap='see <a href="https://handbook.datalad.org/en/latest/basics/101-105-install.html" target="_blank">DataLad Handbook: Install an existing dataset</a>'}
knitr::include_graphics("http://handbook.datalad.org/en/latest/_images/collaboration.svg")
```
???
- Mit DataLad können wir Daten genau so mit anderen Teilen, wie wir es mit Git und Code gewohnt
---
# Interoperability with a range of hosting services
.center[DataLad is built to **maximize interoperability with a wide range of hosting services** and storage technologies]
.center[
```{r, echo=FALSE, fig.align="center", out.width="38%", fig.retina=1, fig.cap='see <a href="http://handbook.datalad.org/en/latest/basics/101-138-sharethirdparty.html" target="_blank">DataLad Handbook: Beyond shared infrastructure</a>'}
knitr::include_graphics("https://keeper.mpdl.mpg.de/f/3ff02e436e5142059461/?dl=1")
# http://datasets.datalad.org/datalad/datalad-course/pics/services_connected.png
# http://handbook.datalad.org/en/latest/_images/publishing_network_publishparts2.svg
```
]
???
- DataLad interagiert mit einer Vielzahl von Hosting-Services
- Bekannte wie GitHub, GitLab, Open Science Framework (OSF)
- Fokus auf drei Services, die von der MPG angeboten werden bzw. könnten
---
# Separate content in git vs. git-annex behind the scenes
- Datasets are exposed via private or public repository on a repository hosting service (e.g., GitLab)
- Data can't be stored in the repository hosting service but can be kept in almost any third party storage
- Publication dependencies automate pushing data content to the correct place
.center[
```{r, echo=FALSE, fig.align="center", out.width="45%", fig.retina=1, fig.cap='see <a href="http://handbook.datalad.org/en/latest/basics/101-138-sharethirdparty.html" target="_blank">DataLad Handbook: Beyond shared infrastructure</a>'}
knitr::include_graphics("https://keeper.mpdl.mpg.de/f/a3347c7a01084525a8df/?dl=1")
# http://handbook.datalad.org/en/latest/_images/publishing_network_publishparts2.svg
```
]
---
# Data sharing via GIN
```{r, echo=FALSE, fig.align="center", out.width="10%", fig.retina=1, fig.cap='<a href="https://gin.g-node.org/" target="_blank">gin.g-node.org</a>'}
knitr::include_graphics("https://gin.g-node.org/img/favicon.png")
```
> "*GIN is [...] a web-accessible repository store of your data based on git and git-annex that you can access securely anywhere you desire while keeping your data in sync, backed up and easily accessible [...]"*
.center[
DataLad plays perfectly with GIN, since both use git + git-annex (details [here](https://handbook.datalad.org/en/latest/basics/101-139-gin.html))
]
???
- Angebot des German Neuroinformatics Node (GNode) in München
- Fokus auf Neurowissenschaften, aber im Prinzip für Daten verschiedener Disziplinen verwendbar
--
#### `r emoji::emoji("sparkles")` Advantages of GIN
- free to use and open-source (could be hosted within your institution; details [here](https://gin.g-node.org/G-Node/Info/wiki/In+House))
- currently unlimited storage capacity and no restrictions on individual file size
- supports private and public repositories
- publicly funded by the Federal Ministry of Education and Research (BMBF; details [here](https://gin.g-node.org/G-Node/Info/wiki/about#support))
- servers are on German land (near Munich, Germany; cf. GDPR)
- provides Digital Object Identifiers (DOIs) (details [here](https://gin.g-node.org/G-Node/Info/wiki/DOI)) and allows free licensing (details [here](https://gin.g-node.org/G-Node/Info/wiki/Licensing))
???
- We have an *experimental* [in-house GIN instance](http://gin.mpib-berlin.mpg.de/) with 5TB that can also host annexed data
---
# Publishing a DataLad dataset to GIN in only 4 steps
--
1\. Create a dataset
```{bash, eval=FALSE}
datalad create my_dataset
```
--
2\. Save data into the dataset
```{bash, eval=FALSE}
datalad save -m "add data to dataset"
```
--
3\. Add the GIN remote (aka. "sibling") to the repository
```{bash, eval=FALSE}
datalad siblings add -d . --name gin --url git@gin.g-node.org:/my_username/my_dataset.git
```
--
4\. Transfer the dataset to GIN
```{bash, eval=FALSE}
datalad push --to gin
```
--
Done!<sup>1</sup> 🎉
<sup><sup>1</sup> To be fair, it's a bit more complex than that ... `r emoji::emoji("innocent")` (details [here](https://handbook.datalad.org/en/latest/basics/101-139-gin.html))</sup>
---
# Data sharing via Nextcloud
```{r, echo=FALSE, fig.align="center", out.width="10%", fig.retina=1, fig.cap='<a href="https://nextcloud.com/" target="_blank">nextcloud.com</a>'}
knitr::include_graphics("https://keeper.mpdl.mpg.de/f/d630458cd4124c2c9ce5/?dl=1")
```
.center[
DataLad NEXT extension allows to push data to Nextcloud (using WebDAV; details [here](http://docs.datalad.org/projects/next/en/latest/generated/datalad.api.create_sibling_webdav.html))
]
--
#### `r emoji::emoji("sparkles")` Advantages of Nextcloud
- data privacy compliant alternative to Google Drive, Dropbox, etc. (often hosted on-site)
- provided by your institution, so free to use
- supports private and public repositories
- can be used together with external collaborators
#### `r emoji::emoji("bear")` Nextcloud at FU Berlin: [Box.FU](https://box.fu-berlin.de/)
- data hosted on FU servers
- storage quota: 200 GB per employee / student (expandable)
---
# Data sharing via the Open Science Framework (OSF)
```{r, echo=FALSE, fig.align="center", out.width="10%", fig.retina=1, fig.cap='<a href="https://osf.io/" target="_blank">osf.io</a>'}
knitr::include_graphics("https://keeper.mpdl.mpg.de/f/27b4f2ac823147a7975c/?dl=1")
```
.center[
DataLad-OSF extension allows to integrate with OSF via DataLad (details [here](http://docs.datalad.org/projects/osf/en/latest/index.html))
]
--
#### `r emoji::emoji("sparkles")` Advantages of OSF
- free to use
- supports private and public repositories
- provides Digital Object Identifiers (DOIs) (details [here](https://help.osf.io/article/220-create-dois)) and allows free licensing (details [here](https://help.osf.io/article/148-licensing))
- very popular among scientists (details [here](https://www.cos.io/blog/shared-investment-in-osf-sustainability))
#### `r emoji::emoji("cloud_with_rain")` Limitations of OSF
- private and public projects projects limited to 5GB and 50GB, respectively
- maximum individual file size of 5GB
---
# Data sharing via S3 buckets (object storage)
```{r, echo=FALSE, fig.align="center", out.width="10%", fig.retina=1, fig.cap='e.g., <a href="https://www.rrz.uni-hamburg.de/en/services/datenhaltung/objektspeicher.html" target="_blank">Object Storage at UHH</a>'}
knitr::include_graphics("https://keeper.mpdl.mpg.de/f/45a664d668724e51917a/?dl=1")
```
--
.center[
> "*Object storage is a data store that originates in cloud environments and can be used to store and share data.*
> *An object consists of a unique name, the actual data, and associated metadata.*
> *In contrast to file systems, objects are not stored in a hierarchy but in a flat container (so-called buckets).*"
]
--
#### `r emoji::emoji("sparkles")` Advantages of S3 buckets
- flexible storage size for UHH employees
- data hosted on UHH servers
- S3 buckets can be configured as a [DataLad special remote](https://handbook.datalad.org/en/latest/basics/101-139-s3.html) (walkthrough in DataLad Handbook)
???
> Object storage is a data store that originates in cloud environments and can be used to store and share data. An object consists of a unique name, the actual data, and associated metadata (such as access permissions and user-defined metadata). In contrast to file systems, objects are not stored in a hierarchy but in a flat container (so-called buckets). Access is possible via HTTP-based protocols/APIs such as S3 or Swift.
---
# Data sharing via Keeper
```{r, echo=FALSE, fig.align="center", out.width="35%", fig.retina=1, fig.cap='<a href="https://keeper.mpdl.mpg.de/" target="_blank">keeper.mpdl.mpg.de</a>'}
knitr::include_graphics("https://keeper.mpdl.mpg.de/media/img/catalog/KEEPER_logo.png")
```
> "*A free service for all Max Planck employees and project partners with **more than 1TB of storage per user** for your researchdata.
> Profit from safe data storage, seamlessly integrated into your research workflow.*"
--
- \> 1 TB per Max Planck employee (expandable)
- based on the cloud-sharing service [Seafile](https://www.seafile.com/en/home/) (similar to Dropbox)
- data hosted on MPS servers
- configurable as a [DataLad special remote](http://handbook.datalad.org/en/latest/basics/101-139-dropbox.html)
???
**special remote**: git-annex concept: A protocol that defines the underlying transport of annexed files to and from places that are not Git repositories (e.g., a cloud service or external machines such as HPC systems).
---
# Data sharing via Edmond
```{r, echo=FALSE, fig.align="center", out.width="35%", fig.retina=1, fig.cap='<a href="https://edmond.mpdl.mpg.de/" target="_blank">edmond.mpdl.mpg.de</a>'}
knitr::include_graphics("https://colab.mpdl.mpg.de/mw010/images/d/db/EDMOND_hell.png")
```
> "*Edmond is a research data repository for Max Planck researchers. It is the place to store completed datasets of research data with open access. Edmond serves the publication of research data from all disciplines [...].*"
--
- based on [Dataverse](https://dataverse.org/), hosted on MPS servers
- use is free of charge
- no storage limitation (on datasets or individual files)
- flexible licensing
--
#### DataLad - Dataverse integration (details [here](http://docs.datalad.org/projects/datalad-dataverse/en/latest/index.html))
- "push" DataLad datasets (including version history, data, code, results and provenance) to Dataverse
- "clone" published DataLad datasets from Dataverse
- primarily for one-time dataset publication and consumption, not extensive collaboration
???
- nach Hackathon während des Meetings der Organization for Human Brain Mapping (OHBM) gibt es seit Juli 2022 eine DataLad-Dataverse Integration (noch nicht getestest!)
With datalad-dataverse, the entire dataset is deposited on a Dataverse installation. Internally, this is achieved by packaging the "Git" part and depositing it alongside the annexed data, similar to how the datalad-next extensions allows to do this for webdav based services.
The primary use case for dataverse siblings is dataset deposition, where only one site is uploading dataset and file content updates for others to reuse. Compared to workflows which use repository hosting services, this solution will be less flexible for collaboration (because it's not able to utilise features for controlling dataset history offered by repository hosting services, such as pull requests and conflict resolution), and might be slower (when it comes to file transfer). What it offers, however, is the ability to make the published dataset browsable like regular directories and amendable with metadata on the Dataverse instance while being cloneable through DataLad.
<!-----
#### Suggested alternatives to GIN that can be used with DataLad (selection):
- [Keeper](https://keeper.mpdl.mpg.de/) (Seafile) offers all Max Planck employees 1TB(!) of storage (expandable)
- [Open Science Framework (OSF)](https://osf.io/), popular in Psychology / Cognitive Neuroscience (see [details](http://docs.datalad.org/projects/osf/en/latest/))
-->
---
class: title-slide, center, middle
name: example
# Example: Wittkuhn & Schuck, 2021, *Nature Communications*
<!--the next --- is a horizontal line-->
---
---
# Example: Our paper
.pull-left[
```{r, echo=FALSE, fig.align="center", out.width="100%", fig.retina=1, fig.cap='<a href="https://doi.org/10.1038/s41467-021-21970-2" target="_blank">doi: 10.1038/s41467-021-21970-2</a> (accessed 18/01/23)'}
knitr::include_graphics("https://keeper.mpdl.mpg.de/f/a7540f80580b4131b22c/?dl=1")
```
#### Two-sentence summary:
> "*Non-invasive measurement of fast neural activity with spatial precision in humans is difficult.*
> *Here, the authors **show how fMRI can be used to detect sub-second neural sequences in a localized fashion** and **report fast replay of images in visual cortex** that occurred independently of the hippocampus.*"
]
--
.pull-right[
<blockquote class="twitter-tweet" data-conversation="none" data-lang="en"><p lang="en" dir="ltr">We share all code + data via <a href="https://twitter.com/gnode?ref_src=twsrc%5Etfw">@gnode</a> + <a href="https://twitter.com/hashtag/GitHub?src=hash&ref_src=twsrc%5Etfw">#GitHub</a>, version-controlled with <a href="https://twitter.com/datalad?ref_src=twsrc%5Etfw">@datalad</a> (ca. 1.5 TB): MRI in <a href="https://twitter.com/BIDSstandard?ref_src=twsrc%5Etfw">@BIDSstandard</a>, <a href="https://twitter.com/hashtag/fMRIPrep?src=hash&ref_src=twsrc%5Etfw">#fMRIPrep</a> data, <a href="https://twitter.com/hashtag/MRIQC?src=hash&ref_src=twsrc%5Etfw">#MRIQC</a> metrics, GLMs + anatomical masks, task code, decoding pipeline, statistical analyses: <a href="https://t.co/sv3Vrco7wj">https://t.co/sv3Vrco7wj</a> <a href="https://twitter.com/hashtag/OpenScience?src=hash&ref_src=twsrc%5Etfw">#OpenScience</a> 🧮 [2/n]</p>— Lennart Wittkuhn (@lnnrtwttkhn) <a href="https://twitter.com/lnnrtwttkhn/status/1372859256777670659?ref_src=twsrc%5Etfw">March 19, 2021</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
]
---
# Example: Data management using DataLad
#### From Wittkuhn & Schuck, 2021, *Nature Communications* (see [Data Availability statement](https://www.nature.com/articles/s41467-021-21970-2#data-availability)):
> *"We publicly share all data used in this study. Data and code management was realized using DataLad.*"
--
- All individual datasets can be found at: https://gin.g-node.org/lnnrtwttkhn
- Each dataset is associated with a unique URL and a Digital Object Identifier (DOI)
- Dataset structure shared to GitHub and dataset contents shared to GIN
--
#### All data?
--
- `highspeed`: superdataset of all subdatasets, incl. project documentation ([GitLab](https://git.mpib-berlin.mpg.de/wittkuhn/highspeed))
- `highspeed-bids`: MRI and behavioral data adhering to the [BIDS standard](https://bids.neuroimaging.io/)
([GitHub](https://github.com/lnnrtwttkhn/highspeed-bids),
[GIN](https://gin.g-node.org/lnnrtwttkhn/highspeed-bids),
[DOI](https://doi.org/10.12751/g-node.4ivuv8))
- `highspeed-mriqc`: MRI quality metrics and reports based on [MRIQC](https://mriqc.readthedocs.io/en/stable/)
([GitHub](https://github.com/lnnrtwttkhn/highspeed-mriqc),
[GIN](https://gin.g-node.org/lnnrtwttkhn/highspeed-mriqc),
[DOI](https://doi.org/10.12751/g-node.0vmyuh))
- `highspeed-fmriprep`: preprocessed MRI data using [fMRIPrep](https://fmriprep.org/en/stable/),
([GitHub](https://github.com/lnnrtwttkhn/highspeed-fmriprep),