Generating FAIR research data in experimental tribology

TLDR

FAIR data generation in experimental tribology is currently lacking, yet it promises scalable data science and deeper insight into friction and wear, though community‑wide standards and reliance on custom workflows pose major challenges. This paper outlines a sample framework for scalable FAIR data generation and presents a showcase FAIR data package from a pin‑on‑disk tribological experiment. The framework was built through collaboration with a virtual research environment, crowd‑sourced controlled vocabularies, ontology development, and a suite of small‑scale digital tools. The resulting curated dataset contains 2,008 key‑value pairs and 1,696 logical axioms, demonstrating scalable, non‑intrusive techniques that extend the life, reliability, and reusability of tribological data beyond typical publication practices.

Abstract

Solutions for the generation of FAIR (Findable, Accessible, Interoperable, and Reusable) data and metadata in experimental tribology are currently lacking. Nonetheless, FAIR data production is a promising path for implementing scalable data science techniques in tribology, which can lead to a deeper understanding of the phenomena that govern friction and wear. Missing community-wide data standards, and the reliance on custom workflows and equipment are some of the main challenges when it comes to adopting FAIR data practices. This paper, first, outlines a sample framework for scalable generation of FAIR data, and second, delivers a showcase FAIR data package for a pin-on-disk tribological experiment. The resulting curated data, consisting of 2,008 key-value pairs and 1,696 logical axioms, is the result of (1) the close collaboration with developers of a virtual research environment, (2) crowd-sourced controlled vocabulary, (3) ontology building, and (4) numerous - seemingly - small-scale digital tools. Thereby, this paper demonstrates a collection of scalable non-intrusive techniques that extend the life, reliability, and reusability of experimental tribological data beyond typical publication practices.