Back to search

Article

The FAIRSCAPE AI-readiness Framework for Biomedical Research

2024-12-23

Abstract excerpt

<h4>Objective</h4> Biomedical datasets intended for use in AI applications require packaging with rich pre-model metadata to support model development that is explainable, ethical, epistemically grounded and FAIR (Findable, Accessible, Interoperable, Reusable). <h4>Methods</h4> We developed FAIRSCAPE, a digital commons environment, using agile methods, in close alignment with the team developing the AI-readiness...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
75052c1c-af12-59e1-a409-733624b6efb6
DOI
10.1101/2024.12.23.629818
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
The FAIRSCAPE AI-readiness Framework for Biomedical ResearchDOI 10.1101/2024.12.23.629818
Select a neighboring publication to make it the new centre.