To assess the feasibility of producing FAIR data via the integration of a controlled vocabulary, an ontology, and an ELN, this dataset demonstrates the implementation of a tribological experiment while accounting for as many details as possible. The showcase experiment had a lubricated pin-on-disk arrangement, ran at 15 N normal load and a velocity range of 20 to 170 mm/s. With this dataset, we hope to provide a possible blueprint for FAIR data publication in experimental tribology. https://www.nature.com/articles/s41597-022-01429-9 - Garabedian, N.T., Schreiber, P.J., Brandt, N., Greiner, C., et al. Quick start with the dataset in README.txt (included in the newest version of the dataset) Abstract: Generating FAIR research data in experimental tribology. Sci Data 9, 315 (2022). Digital solutions for the generation of FAIR (Findable, Accessible, Interoperable and Reusable) data and metadata in experimental tribology are currently lacking, despite the looming challenge of integrating cutting-edge data science techniques – a promising scientific route for any field that often relies on phenomenology and empiricism. Additionally, the broad interdisciplinarity of tribology is probably a main contributing factor for the lack of community-wide data and metadata standards, and the heavy reliance on custom workflows and equipment. 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. https://youtu.be/xwCpRDnPFvs - Generating FAIR Research Data in Experimental Tribology - Get Scientific Results Ready for ML https://doi.org/10.5281/zenodo.5720626 - FAIR Data Package of a Tribological Showcase Pin-on-Disk Experiment https://doi.org/10.5281/zenodo.5720198 or https://github.com/nick-garabedian/TriboDataFAIR-Ontology or https://fairsharing.org/3597 - TriboDataFAIR Ontology https://doi.org/10.5281/zenodo.5720218 or https://github.com/nick-garabedian/SurfTheOWL - SurfTheOWL https://kadi4mat.iam-cms.kit.edu/ - Kadi4Mat Virtual Research Environment and Electronic Lab Notebook

{"references": ["Garabedian, N.T., Schreiber, P.J., Brandt, N. et al. Generating FAIR research data in experimental tribology. Sci Data 9, 315 (2022). https://doi.org/10.1038/s41597-022-01429-9"]}