Machine-Centric Science

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.

Show Notes

* [Materials Project](https://materialsproject.org/)
* [Environmental Systems Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE)](https://ess-dive.lbl.gov/)
* [National Microbiome Data Collaborative (NMDC)](https://microbiomedata.org/)
* [W3C Provenance (PROV) specs](https://www.w3.org/TR/prov-overview/)
* [Research Equals (R=)](https://www.researchequals.com/)
* [JSON-LD](https://json-ld.org/)
* [Ecological Metadata Language (EML)](https://eml.ecoinformatics.org/)
* [DataCite](https://datacite.org/)
* [OSTI](https://www.osti.gov/)
* [DOI](https://www.doi.org/)
* schema.org
* [OAuth](https://oauth.net/2/)
* [OpenID Connect (OIDC)](https://openid.net/connect/)
* [OpenAPI](https://www.openapis.org/)
* [REST](https://en.wikipedia.org/wiki/Representational_state_transfer)
* [IGSN](https://www.igsn.org/)
* [Data Observation Network for Earth (DataONE)](https://www.dataone.org/)
* [Frictionless Data](https://frictionlessdata.io/)

What is Machine-Centric Science?

Stories about the FAIR principles in practice, for scientists who want to compound their impacts, not their errors.