Interview with Patrick Huck, on implementing FAIR for computed materials data
This week on Machine-Centric Science, I interviewed Patrick Huck, currently staff on the Materials Project at the Lawrence Berkeley National Laboratory in Berkeley, California. We talk about choices and considerations in implementing FAIR.
There are show notes at the link above. Also, I tried to summarize our discussion as a draft FAIR Implementation Profile (FIP).
Career paths for people that are scientists AND software engineers.
The U.S. Department of Energy Office of Scientific and Technical Information (OSTI) DOI Service.
What gets a DOI? Granularity of resources.
Partnering with the Novel Materials Discovery (NOMAD) Laboratory for accessing raw data.
Modeling: with Python classes and with OpenAPI.
API Gateway design for authentication and authorization.
Provenance: for calculation workflows and for structure sourcing (credit to submitters!).
“I think that’s a big topic in science generally. What are the career paths for people that are software engineers that are also scientists or maybe scientists first and software engineers second, and have gone that route? It’s not like there’s H indexes for people like me in terms of publications.”
“[OSTI] provides the infrastructure for minting those DOIs and making sure that those links are always live. We’ve become over the years with now, I think 147,000 DOIs, their biggest data client.”
“We use what’s called robocrystallographer, which gets descriptions based on machine learning that we get based on the information that we calculate about that structure. And then we can take that description auto generated from our database entries and send it as metadata for the DOIs.”
“It’s kind of transparent without even knowing that there’s an API behind it. To the extent that sometimes people talk about the API and they actually mean the client. I think that’s a good thing. People in our space expect those things to be pretty transparent.”
“I don’t think that guarantees longevity on the scale of glacial times.”
“There’s a lot going on in terms of making data FAIR. It’s a little easier for making documents FAIR, like having PDFs findable. On the data level, it becomes a little bit more complicated. And I think that we should strive to get as close as possible to get to FAIR, but it might not for be feasible for every domain.”
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