Oregon Data Science Collaborative
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ODSC FAIR Workshop

FAIR Principles​


What are FAIR Principles?​
A universal and coincise set of community-agreed principles and practices aimed at increasing data reusability by “enhancing the ability of machines to automatically find and use data” ( [1] Wilkinson, 2016). This enhancing can be pinned upon four fundamental principles (FAIR): Findable, Accessible, Interoperable, Reusable. ​

Goal ​
Provide guidelines on developing "high quality digital publications that facilitate discovery, access, interoperability, and reuse in downstream studies" ( [1] Wilkinson, 2016). FAIR, does not attempt to provide implementation, or domain dependent specifications, but rather inform of good data management practices that allow for long-term retrieval and re-use in order to benefit multiple stakeholders (academia, industry, funding agencies, and scholarly publishers) and drive innovation forward at a higher pace. ​

There is a shift in perception where “the quality of the publication—and more importantly, the impact of the publication—is a function of its ability to be accurately and appropriately found, re-used, and cited over time, by all stakeholders, both human and mechanical”( [1] Wilkinson, 2016). ​

The Problem
Perhaps, one might still have doubts about FAIR and its necessity in one's daily work. Let us view the below clip about possible points of controversy, although there's a humoristic flair to the conversation, it allows one to gain some perspective on the problems that might arise in real life, and how FAIR can help us overcome such hurdles. ​

High Level Overview
Insert description in here ...
Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship (2016)
Sci. Data 3:160018 doi: 10.1038/sdata.2016.18

FAIR Visualization from PANOSC.

A data management horror story by Karen Hanson, Alisa Surkis and Karen Yacobucci- Video.