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