Handling Your Materials Data for Maximum Impact Using The FAIR Data Principles

Explore an introduction of the concepts of FAIR Data Principles for materials scientists and engineers and learn how to instruct data generators and users on how to implement these principles in their everyday handling of data in this new TMS online course. In 2016, the GO FAIR international consortium of scientists and organizations published “FAIR Guiding Principles for Scientific Data Management and Stewardship” in the journal Scientific Data. This article outlined the need for scholarly data that is organized using the principles of findability, accessibility, interoperability, and reusability. By organizing scholarly data properly, it can be more easily managed and shared, particularly through machine-aided research. As highlighted in the TMS accelerator study, Building a Materials Data Infrastructure, adoption of these FAIR Data Principles is critical for the development of a highly impactful materials data infrastructure. This is especially relevant because (1) materials science is a data-intense field, (2) machine learning (which is centered about the use of data) grows rapidly in popularity and utility, and (3) optimizing materials data sharing and usage is critical to accelerating materials and manufacturing innovations.