esipfed / data-readiness Goto Github PK

The checklist needs some thought on CARE principles, specifically in the area of knowledge encoding and semantic depth/clarity in data and metadata. There is a massive bias toward the systems of knowledge of major data generators. As we turn data loose in the wild, we need to put much more attention into encoding where we have inherent biases, major gaps, and where interlinks with other systems of knowledges need to be assessed and encoded for use by AI (and humans for that matter).

• Add cluster information
• Add information to "How to Contribute"
• Add link to the AI-ready data blogpost from ESIP Merge

Increasingly, many analytical methods involve an AI component. As such AI-ready and analysis-ready are synonymous. Almost all analyses now involve the need to rapidly and efficiently (computational processing and human-learning energy) restructure data in multiple ways to work well with methods and tools, including AIs. I don't see this particular concept well represented in the checklist, but it seems like it could fit in the data access or data preparation areas.