Interdisciplinary Models
| Interdisciplinary Models | |
|---|---|
| Description | How do we define minimal information models tuned for synthesis that can interoperate across various disciplines? |
| Related Topics | Interoperability |
| Discord Channel | #interdisciplinary-models |
| Facilitator | Wayne Lutters |
| Members | Paul Itoi, Elianna DeSota, Leo Ware, Konrad Hinsen, James Howison, Wayne Lutters, Peter Murray-Rust |
What
How do we define minimal information models tuned for synthesis that can interoperate across various disciplines?
Concrete problem expressed by Peter-Murray Rust here: https://discord.com/channels/1029514961782849607/1040214388554084372/1040299259930611833: "The idea of Hypothesis testing is common in some disciplines, unknown in others. For example chemical synthesis or materials science is "can we make X?" and many sciences are exploratory - what can we see with a new telescope, plants in Antarctica, etc. You have to design your project but I suspect Hypothesis doesn't come into it."
And to a certain extent, the issue of representing/discussing the discourse of computational research (e.g., model parameters), discussed by Konrad Hinsen here: https://discord.com/channels/1029514961782849607/1038988750677606432/1039576903838859326
This connects also with Peter-Murray Rust's work on Semantic Climate (semantifying the IPCC report).
- see discussion here: https://discord.com/channels/1029514961782849607/1040057721044598788/1040060670907002973
- and here: https://discord.com/channels/1029514961782849607/1033091746139230238/1040226423346040853
And also connects to emerging discussions around interoperability and Surfacing/managing/resolving disagreements in ontologies/terms/federation
Initial discussion
Matthew, Peter, Wayne, James, Ellie, Leo
Projects discussed:
- Scraping literature in geosciences to spatially map out relevant variables and contributions across disciplines http://globe.umbc.edu/ - Materials Genome Initiative mentioned: infrastructure well-supported but still siloed - OPTIMADE: common API format between existing materials databases
"grassroot tech assemblage can work at scale. "Shoddy" now works." Shoestring budgets driving open source innovation.
"need to flourish long enough to been seen by other disciplines" Idea that each of these initiatives have a typical academic funding life of 3-7 years and then are sunset. Do they twinkle in the sky long enough to be seen by other disciplines? Core sustainability issues not just of the tools / platforms but of the motivating ideas beyond them.
"So: how do we work in a way that others can learn from in future" -- without being discouraged from starting new things, encourage high-risk, high-reward innovations.
Reaching plateau of open data --- metrics on who is using and what using for
Similar challenges in enterprise: what data do we have within an org, and who is using it? https://data.world/ vs more public initiatives like https://coleridgeinitiative.org/
Insight around longevity -- is it the infrastructure that lives on? the vision? or the relationships? Unique value of EC framework initiatives (e.g., Horizon 2020) that are as much political projects as they are scientific ones. Those connections between people and labs persists.
Discovering and forming communities of practice around datasets -- how does one person's use leave traces that others can discover? How do we align the challenges across time (ie my experience when I was grappling with a specific column in a dataset, aligned with someone doing just that a year later).
Academic model of competition rubs against open science --- both in sunk time and possessiveness of data
Disciplines have different reductionist traditions, what is the well-defined focus of study. Is this the substrate that enables cross disciplinary data engagement?
Where do people gather to have these conversations? What are the communities of practice, publication venues to share knowledge about working across the disciplines? Where do these happen within disciplines and where is the meta-science narrative developing?
Open notebook science -- show the world as you are doing it, make connections on the day of publication. Very well defined strategy with templates. http://opensourcemalaria.org/
Bold vision of what is possible, e.g. automated recombination & discovery: https://materialsproject.org, https://materialsproject.github.io/fireworks/ - would also like to plug OPTIMADE here, which is then unifying datasets between several endeavors in this field
Domain differences between contributing individual data points vs entire datasets
Can grassroots emulate giant centralisation within industrial monoliths
Analogy between web frameworks/OSS: emerging from many hands working towards similar problems
Gift economy of software applied to data? Frictionless data as an example
Collectivization as a model -- being able to push upstream to graphs at different scales
Incentivizing collectivization
Identifying key questions:
- Which solutions have worked in other domains?
- What are the differences between (ontological, socio-political, economical) domains that lend themselves to different solutions?
- Extending the concept of "discipline" to e.g., cataloguing human infrastructure (cities, roads etc), "Discipline as a search across a reasonably well defined search space"
- Alignment of primitives --- example of plants in expressed different locales and the effect on local climate
- Aligning communities of practice with a wider goal?
Possible outcomes of this group
- Compendium of practices in different fields
--- Collection of venues: where are the discussions happening now at the discipling and meta level
--- Collection of case studies around primitives in different disciplines
- Collecting ideas from other attendees from disciplines within the workshop in a survey: how would you/your field do things differently were all these things in place? Different life cycles and capturing nascent knowledge
Key themes for reporting back:
- Longevity and sustainability
- Lowering initial costs
- Designing work such that it can contribute upstream
- Differences in solutions by scientific disciplines, mechanisms of production, budgets, motivations and governance
- Alignment of primitives within a discipline: do you contribute a data point or a dataset? Different approaches required
- Synergies with other groups: interfaces, graphs, social systems