Machine-Centric Science

An interview about FAIR software, workflows, and virtual research environments (VREs) / science gateways with Sandra Gesing, currently a Senior Research Scientist and Scientific Outreach and Diversity, Equity, and Inclusion (DEI) Lead at the Discovery Partners Institute at the University of Illinois, Chicago.https://galaxyproject.org/https://dpi.uillinois.edu/https://sciencegateway

An interview with Christophe Blanchi, currently Executive Director of the DONA Foundation.

Vineeth is a materials scientist working on creating a knowledge graph of materials. He is new to ontologies and the semantic web in general; he'd like to understand ontologies/taxonomies and what an ontologist/taxonomist does in general. I've agreed to let him barrage me with questions until hopefully some clarity is reached.

I walk in and around a park with my dog, talking about the the DIKW (Data, Information, Knowledge, Wisdom) class of models, eventually relating this to machine-centric science.

"implementations should follow a general principle of robustness: be conservative in what you do, be liberal in what you accept from others" - Jon Postel, https://doi.org/10.17487/RFC0761, see also https://en.wikipedia.org/wiki/Robustness_principle

"The RDF graph data model...seems like the only realistic implementation at this point for the FAIR principles." "To me, FAIR data is more or less equal to Linked Data." "The software has to be built around these principles. And that's maybe quite a radical idea because for a long time, data was just like an add-on to software, right? But essentially now it's the inverse. It's the

I was thinking about FAIR-enabling resources and wanted to distinguish between things that actually have to be running in order for data to be alive and for you to actually find it, access it, interoperate with it, and reuse it, versus "one-time" things that those services will need.

Things got bad, and things got worse. I guess you will know the tune.

with apologies to Cole Porter and Robert Fletcher

I interview Shreyas Cholia, currently at the Lawrence Berkeley National Laboratory in Berkeley, California. Topics we spoke about included: data lifecycles, edge computing for data firehoses, provenance, standards, broad versus detailed domain vocabularies, scope for common APIs, and identifier leveling.