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"Idealized" versus "good-enough" processing stream #26
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A fun exercise, thanks! Very much inline with our now elderly https://github.com/ReproNim/simple_workflow container of which I have now reused locally for "a script that calculates subcortical volumes" ;) It is also well aligns with the http://www.repronim.org/5steps . As for particulars, I think a custom heudiconv heuristic could perform the "search" and conversion. So overall a simplified, datalad-centric workflow could be something like
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@jdkent - continuing on the datalad theme, one place where the nidm model could be integrated is how datalad stores the input, process, output relationships. or as an exporter from the git log. regarding the workflows themselves, reproducibility would come from making them niflows, as you started with the simple1 example. more broadly, the same data typically gets used for many experiments. different pieces are used to test different hypotheses. thus the graph model of data does make a lot of sense. perhaps the idealized to good enough spectrum can be refactored a bit through the lens of the goal of the workflow. highlighting points where things can make a difference. as an example, there is a piece of software that kevin (in my group) is using that only works if the dicoms are converted via spm rather than dcm2niix. |
I'm curious what would be considered an "idealized" reproducible processing stream, and what is a "good enough" reproducible processing stream, and identify the tools/skills needed to complete a "good enough" reproducible analysis. I have some hypothesized steps and some tools listed to complete those steps.
Sparse Learner's Profile
Starting from the top where a PI (or someone) hands you a bunch of dicoms and asks you get subcortical volumes from the structural scans (but there are other currently irrelevant dicoms as well). The PI also wants to be able to run your analysis and wants the data to be publicly available (assuming all IRB/data sharing agreements are satisfied)
An Idealized Processing Pipeline
I imagine we would be using datalad to record all our data/code/processing steps, and always be using/developing containers from the beginning. I'm not exactly sure where/how to place NIDM annotations of data/results or what tool I should use (PyNIDM?).
Good Enough Processing Pipeline
Removed datalad from the processing stream, removed testing, removed niflows, but still want to use desired software from within a container.
I would like feedback on both the "Idealized" and "Good Enough" analyses since I am not as knowledgeable as I would like to be on designing processing pipelines. I may not be most up to date on what are the hot/new tools versus what will get the job done.
Once we pin what we would like workshop attendees to be able to do (and hopefully this matches with what they wish to do as well), then I think we will have an easier time elucidating necessary skills and modifying episodes to make sure they help build these skills.
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