Have you ever been in one of the following situations?

1) Writing your first neuroimaging paper but unsure what level of detail to put in your methods section.

2) Reviewing a paper and being unsure what information is missing in that paper.
3) Wanting to replicate some study, reading the paper and finding there is not enough information in the methods section to do that or even to actually make sense of the results.
4) Comparing your results to those of a paper, but not finding enough information about how the authors got there. So you can’t assess where the differences between your numbers and theirs are coming from.
5) Having to look up into your MRI dataset acquisition parameters to write up part of your methods.
And then having to repeat yourself by copy-pasting parts of your methods section when filling in a journal checklist or uploading your results on @VaultNeuro.
6) Doing a meta-analysis and having to design a system to list the methods section of each study

7) Wanting to create pre-registration but not knowing what to put in it.

#ReinventTheWheel
Well we hope that this app can help with a lot of those in the long term.
Hoping to help with this problem, the COBIDAS committee of @OHBM was created. It extended previous guidelines to create a report listing what should actually go into a methods section.

COBIDAS: https://www.humanbrainmapping.org/i4a/pages/index.cfm?pageid=3728

The report: https://www.biorxiv.org/content/10.1101/054262v
These guidelines are a great resource and they come with a very useful checklist in the form of a table at the end. The only drawback: it is a 30 PAGES LONG checklist...
And many items only concern some very specific use case ("why do I have to flip through the 2 pages for fMRI if I do tractography?")
So... Some of us decided to make this more user friendly. This was started at the @OhbmOpen hackathon in Rome in 2019. We got together and started breaking down those 30 pages into a spreadsheet.
So now those 30 pages have been turned into a "much more manageable" checklist spreadsheet with... 462 items (and growing).

If you want to get dizzy you can have a look at it here:
https://docs.google.com/spreadsheets/d/1dCXP0MTK3DjY09ZFd7FXgv0Ngx16_YJwVBiXOeQbTho/edit?usp=sharing
How is that any better? Well I am glad you asked!

This format now becomes machine-readable. We can feed that into some app to render a checklist. And that brings us to the other aspect of that project. How to visualize this checklist?
This project aims to create a standardized library of questionnaires using linked-data and has a user interface to display those questionnaires and collect data.
And that’s pretty much all we were looking for to get that checklist running.

https://ohbm.github.io/cobidas/#/ 
At the moment the app is just a proof of concept based on the 90 metadata items one can fill in when uploading their neuroimaging results to @VaultNeuro. But we have been working on extending that to 200 items from our mother of all spreadsheets very soon.
You can click your way through the different sections of the checklist. There is a bare minimum of input validation. The app tries to minimize which items you are “exposed” to. You can export the results in a very simple text file that encapsulates “all” of your method section.
The next steps (beyond expanding the number of items)?
1) Using that simple machine-readable text file to automatically write large chunks of the method section--like fMRIprep does for you, depending on the options you chose to run it.

https://fmriprep.org/en/stable/citing.html
2) Minimize as much as possible the time it takes to fill in the checklist by making sure that users don’t have to scroll through irrelevant items and by allowing the app to read from a @BIDSstandard dataset to fill in automatically many aspects of the list.
So a special thanks to all the people who have so much time looking at spreadsheets that they ended up crying tears of blood.

@cassgvp @IRuotsa @fedeadolfi @akeshavan_ @Tim_van_Mourik @davidwmoreau @ZSjoerds @angietepp @martinagvilas @k_wiebels @johalgermissen @DorienHuijser
A special thanks to @ten_photos for connecting me with the amazing work done by Sanu and @satra_ on the Reproschema @ReproNim
And in case you want more methods checklists, you might want to check:

1) The transparency checklist @BalazsAczel
http://www.shinyapps.org/apps/TransparencyChecklist/

2) The CLAIM checklist for AI in medical imaging @neuroccino
https://claim.shinyapps.io/CLAIM/ 
3) The CREDnf checklist for cognitive-beharioural neurofeedback studies @rt_thibault
https://crednf.shinyapps.io/CREDnf/ 
You can follow @RemiGau.
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