Building FAIR

Overview

Largely inspired by Erik’s index. I’m trying to hypothesize how we might sync the three different dimensions of FAIR that I see as important: cultural adoption, technical feasibility/capability, and the theory so they all contribute to a fuller science.

Theory

• The Semantic Web - ontologies as exclusive to new discoveries, or inclusive?
• The role of humans and machines in the semantic web
• Who sees the semantic web?
  • Machines - Humans
• Centralization and Decentralization in the semantic web - managing expanding content, sustainability, and discovery

Adoption

• Ways to create a norm: Moral norm, Incentivized norm, Institutional norms, default norms and their place in science
• A scientific ecosystem of metadata
• Treating Humans.

Technical Implementation

• Rich metadata automation
• Nanopublications, CEDAR, and the differences between them.
• Machine Readability, Machine Usability, Machine Actionability
• Federated Model Learning - what might it take to ensure we don’t overload individual servers
• Machine Generated for 80%, Semantic Web Annotations for the 20%
• Actual monitoring - monitoring scientific change
• Ways to get rich machine actionable data that don’t use RDF - (Knowledge Graph, etc)