Open mslw opened 2 years ago
I'm not sure if there's space in the course to cover this, but I think something that could be a useful part of such training is a walk though of how to use DataLad in practice for evolving research datasets. What I've found is that the existing DataLad resources are great for understanding its core capabilities and commands and to know which options are available for data hosting and more (and how to configure/implement them). But I think there's a lack of good training resources to help people with the practical challenges of deciding when and why to implement DataLad in a specific way, or when to do which steps (and what to be cognisant of when doing so), especially at varying stages of the life cycle of research data.
Practical examples that my limited experience allows me to think of atm:
datalad create -f
all the raw data, then rerun the standardisation scripts on all data? Or should I create a datalad dataset from the data once everything is standardised?Perhaps something to discuss further if others also feel there's a need for something like this.
Sorry for keeping this silent in a while. For me it's definitely worth discussing further - especially in terms of whether we want some of the more-general issues discussed within the last (4th) module of the "core" workshop, or whether we want to create an additional module to talk about these things (potentially to be used for more advanced workshops) or maybe put this content elsewhere. Main question for me is whether discussion of these questions will be informative to people who have just learned the very basics about DataLad.
Good point. I think they might first be confronted with such challenges once they have had time to work with datalad or tried it on their own large dataset. Perhaps a more useful way to structure this type of lesson is via a practical walk-through, where the questions (and subsequent answers) present themselves logically and chronologically through the storyline. The combination of this approach with the above mentioned questions feels like it might be more suited to a more advanced audience, but I think such an approach (i.e. practical walkthrough with a challenge+solution-based storyline) could also work well for beginner topics. Could be an idea for the 4th module, whatever the topic ends up being.
During planning, we arrived at four modules, which would introduce the following (datalad commands are listed, but they also represent concepts):
Module 1 (day 1, first half) [Content tracking with datalad]
Module 2 (day 1, second half) [Structuring data]
Module 3 (day 2, first half) [Dataset management]]
Module 4 (day2, second half) [remote cooperation]
Later ideas include: