Interdisciplinary Models: Difference between revisions

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EllieDeSota

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 Maintaining semantic bibliography for a grant is a great form of legitimate peripheral participation
 We plan to try this out for preprints in climate. Sensemaking structure to 15,000 refs
+=== DAY 2 ===
+question how can we use AI processing to discover non-explicit connections?
+- claim - the simplest job to get here is to create an index.
+question what are the venues where we can use open/interdisciplinary science?
+- evidence - women for open climate already exists and would would support this
+claim - we conceptualize inter-disciplinarity as multiple groups doing their primary research together.
+- evidence - (counter to truth of claim) Django as an example. This is collaboration btwn website builders making the infrastructure. Or scikitlearn etc. None of these are collaboration on their primary activity.
+-  question - how might we ensure that these groups can collaborate on projects that aren't their direct work?
+claim - there is a disconnect between the team level incentives and the global level need for collaborative infrastructure
+- evidence synthesis centers couldn't resolve this algorithmically, and so simply needed to create a space where everyone came in to resolve the connections.
+Centralized pool for resources - the university used to have machine labs or computing centers meaning that the university cuts it back bc its too costly
+- Documenting these stories.
+claim - Teaching mission of the university is missing, I think they should engage in teaching during setting up these resources.
+- evidence - super computing centers used to do this, but then get's lost.
+Research Software Engineering groups -
+- They get attached to grants and become the software engineering groups.
+- evidence - the escience program in UK moved into software sustainability institute.
+- - There was an atmosphere that this was super interesting vs a dedicated national service.
+- evidence - the grid - came out of particle physics. Everyone needs to buy into it. It's been largely overtaken by major software companies.
+claim - libraries have attempted to make sure that you can manage all the different data but scientists need to know super deeply what the data is in itself, and a library can't actually provide this service. You NEED domain specialists. Similarly, there can't just be a 'random' person who is focused on integrating data -> they won't be able to give you this info.
+- evidence - how platforms emerge. Coring + tipping. The breadth of view is so large that they aren't going to spot the core of the data.
+- evidence - cambridge at the institutional level can enforce data sharing. BUT sharing it isn't necessarily what makes it useful.
+- Claim - we're not making the most of the networks that we have right now, it feels like you need a single person who has the knowledge to share and integrate data to the 'institutional' repository and then the institutional repository can be integrated at the more global level.
+- is there not enough interest for this to happen?
+- claim - main motivation for open source is to reduce the maintenance cost of things we build on in the future.
+- evidence - when you write a grant application, you write a lit review but then when you need to write an updated lit review -> you need to bring a 1.5 year out of date lit review that you haven't explored.
+OPEN QUESTIONS -
+- How do we identify the goldilocks level - wide enough infrastructure to allow for everyone to be on the infrastructure, and narrow enough for specific groups to be able to customize to their ideal needs.
+- evidence - data analysis, genome searching, these are places where everyone from many disciplines come together to ensure that you have a shared tech which brings ppl together.