Garabedian, Nikolay T.Nikolay T.GarabedianSchreiber, Paul J.Paul J.SchreiberBrandt, NicoNicoBrandtZschumme, PhilippPhilippZschummeBlatter, Ines L.Ines L.BlatterDollmann, AntjeAntjeDollmannHaug, ChristianChristianHaugKümmel, DanielDanielKümmelYulong, LiLiYulongMeyer, FranziskaFranziskaMeyerMorstein, Carina E.Carina E.MorsteinRau, Julia S.Julia S.RauWeber, ManfredManfredWeberSchneider, JohannesJohannesSchneiderGumbsch, PeterPeterGumbschSelzer, MichaelMichaelSelzerGreiner, ChristianChristianGreiner2022-11-212022-11-212022https://publica.fraunhofer.de/handle/publica/42894510.1038/s41597-022-01429-9Solutions 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.enmetadatareproducibility of resultsjournal article