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3M.1.PRODUCT: Draft FAIR Metadata Specification



This document explains how to assess the FAIRness (Findable, Accessible, Interoperable, and Reusable) of digital research objects. It gives data providers, researchers, and Full Stacks rubrics that are specific to different types of objects such as datasets, tools or analyses, and directly relates to KC10 (FAIR guidelines and metrics)

What they achieved

This is a preliminary document that describes three 'grading' rubrics, and some information about why and how to apply them. The most established rubric, the FAIRmetrics and Guidelines, is difficult to automate, and is supplemented by two new rubrics: Datasets2Tools and Data Commons Assessment Framework (DCAF). Both Datasets2Tools and DCAF can be used on datasets, tools and canned analyses and can automatically perform a FAIRness assessment with minimal input from a human user.

Why is this valuable?

Making data Findable, Accessible, Interoperable, and Reusable is one of the core goals of the Data Commons, and these rubrics give us a way to access how close we are to those ideals. Although manual FAIRness assessments are useful, they are time-consuming and error prone. Development of rubrics that can be completed automatically by computers will be necessary as the Commons expands and begins to take on thousands of data sets.