Sharing and collaborating on scientific data is not a solved problem. The FAIR principles express hope for meeting the challenge of making digital research objects (data, models, workflows, etc.) findable, accessible, interoperable, and reusable. However, we can’t centralize everything; science is fundamentally distributed – it’s a Web.
When you do comparisons and benchmarking against prior art, how do you unify – semantically, not just format-wise – datasets that are physically distributed? When you complete (a part of) a study, how do you help the data you “share” actually be received and used?
Is scientific data sharing and collaboration important to you? I want to help people like you (continue to) make decisions that result in sustainable data stewardship and that dramatically reduce the likelihood that other people re-do your work due to not finding information, not trusting it when they find it, or just not being able to understand it because it’s missing context.
I plan to send short notes, each taking no more than a minute or two to read, about the intersection between what is interesting to me and what is important to you.
My current goal, though September 2021, is to send 1-3 notes per week, and to develop a tool to help you make a better upcoming decision by reducing uncertainty.