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 data that is at the center -- that's the data-centric paradigm." "...there has to be some kind of paradigm shift, both in how researchers see this, but also for those who develop software for researchers, that what scientific publishing produces is not just PDFs...Through fair data, we can look at scientific publishing as this huge network of research artifacts that can be navigated, explored -- as a knowledge graph naturally -- but also recombined, reused and repurposed in different things."

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.

I interview Patrick Huck, currently staff on the Materials Project at the Lawrence Berkeley National Laboratory in Berkeley, California, United States. We talk about choices and considerations in implementing FAIR.

A FAIR implementation profile is a way to communicate how you're implementing the FAIR principles. It's a way for people, communities of practice, to share how they're addressing the FAIR principles, the choices that they've made, or considerations they've made about those choices, what enabling resources they're using to make those choices or what challenges they have, what they're planning. Also, to associate themselves with a community of practice so that people can perhaps, in similar fields, adopt similar answers to some of the questions about how do you implement FAIR.

FAIR principle R1.3: meta(data) meet domain-relevant community standards. An overview of the fundamentals of relevance and ranking in your search for standards.

The 14th of the 15 FAIR principles, R1.2: metadata and data are associated with detailed provenance. A dive into the World Wide Web Consortium (W3C) Provenance Data Model -- what are the different parts of provenance, and what are some terms that can be used in order to manage it?

FAIR Principle R1.1: Meta(data) are released with a clear and accessible data usage license. Overview of Creative Commons licenses for data and various licenses (BSD, MIT, GPL, oh my!) for code.

The 12th of the 15 FAIR principles, R1: metadata and data are richly described with a plurality of accurate and relevant attributes.

It's more powerful when our references are indexed by nature rather than by number. On the 11th of the 15 FAIR principles, I3: metadata and data include qualified references to other metadata and data.

The 10th of the 15 FAIR principles, I2: metadata and data use vocabularies that follow the FAIR principles.

About the 9th of the 15 FAIR principles, I1: (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation. You need controlled term sets, vocabularies, ontologies, thesauri, whatever you want to call it, ideally having globally unique, persistent, resolvable identifiers. And apart from these controlled vocabularies you need actual models, well-defined frameworks, to describe and structure the metadata according to those controlled vocabularies.

Data may be, or become, inaccessible by design, or on request, or by accident. While it was accessible, it may have been used by others. If someone has a reference to data by ID, can they minimally understand the nature and provenance of the data?