摘要：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 workfows and equipment are some of the main challenges when it comes to adopting FAIR data practices . This paper, frst, 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.
