Generating FAIR research data in experimental tribology

Author:

Garabedian Nikolay T.ORCID,Schreiber Paul J.ORCID,Brandt Nico,Zschumme Philipp,Blatter Ines L.ORCID,Dollmann Antje,Haug ChristianORCID,Kümmel Daniel,Li Yulong,Meyer Franziska,Morstein Carina E.ORCID,Rau Julia S.,Weber Manfred,Schneider Johannes,Gumbsch PeterORCID,Selzer Michael,Greiner ChristianORCID

Abstract

AbstractSolutions 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.

Publisher

Springer Science and Business Media LLC

Subject

Library and Information Sciences,Statistics, Probability and Uncertainty,Computer Science Applications,Education,Information Systems,Statistics and Probability

Reference46 articles.

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