Repo for the 2022 OHBM Education 1/2 day course entitled: "How to Write a Re-Executable Publication"
When: June 19th, 1pm-5pm local time
Where: Glasgow, Scotland, Scottish Event Campus (SEC), Alsh Room
The goal of this 1/2 day (4 hour) course is to have the students DO a re-executable publication.
- Julie Bates - University of assachusetts Chan Medical School
- Dorota Jarecka - MIT
- Peer Herholz - McGill and ???
- Stephan Heunis - Institute of Neuroscience and Medicine, Forschungszentrum Jülich
- Neda Jahanshad - Universty of Southern California
- Stephen Strother
- David Kennedy - University of assachusetts Chan Medical School
- JB Poline - McGill
- A computer with internet and browser
- A GitHub Account (Need one, go here!)
1:00 - 1:05 (5 minutes) Introduction to the course (DNK/JBP) - Link to slides - Link to video
1:05 - 1:20 (15 minutes) What is a ReproPub and why would we want one? (JFB) - Link to slides - Link to video
1:20 - 1:35 (15 Minutes) Introduction to the publication you are going to do (NJ) - Link to slides - Link to video
- ENIGMA has some Parkinson’s Disease (PD) results
- You have some ‘new’ PD data
- Do you see what ENIGMA did?
1:35 - 1:50 (15 minutes) What are containers, in general (DJ) - Link to slides - Link to video
1:50 - 2:05 (15 minutes) How to make a specific “simple container” (containing FSL) (PH) - Link to slides - Link to video
2:05 - 2:20 (15 minutes) Brief intro to DataLad (aka DataLad does it all...) (SH) - Link to slides - Link to video
- installing data
- running containers
- publishing results
2:20 - 4:30 “Just do it” - Exercise
- Here are your command lines
- Here is the JupyterHub
- Do it, lots of ReproStaff around to help answer questions or problems
2:30 - 2:45 Break
4:30 - 4:45 (15 minutes) Meta and mega analysis of your results (NJ+DNK) - Link to slides - Link to video
4:45 - 5:00 (15 minutes) Recap and Summary (SS) - Link to slides - Link to video
What do we mean by 'do a re-executable' publication?
- DataLad install a particular dataset - DataLad@NITRC
- DataLad containers-run a particular container (that generates some derived images and results in NIDM)
- DataLad "Publish" the resulting dataset
- pynidm query the results, and run a specific statistical test - PyNIDM@NITRC
- "Publish" the NIDM results to the ReproLake