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Generating FAIR research data in experimental tribology
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...
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9203546/ http://dx.doi.org/10.1038/s41597-022-01429-9 |
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author | Garabedian, Nikolay T. Schreiber, Paul J. Brandt, Nico Zschumme, Philipp Blatter, Ines L. Dollmann, Antje Haug, Christian Kümmel, Daniel Li, Yulong Meyer, Franziska Morstein, Carina E. Rau, Julia S. Weber, Manfred Schneider, Johannes Gumbsch, Peter Selzer, Michael Greiner, Christian |
author_facet | Garabedian, Nikolay T. Schreiber, Paul J. Brandt, Nico Zschumme, Philipp Blatter, Ines L. Dollmann, Antje Haug, Christian Kümmel, Daniel Li, Yulong Meyer, Franziska Morstein, Carina E. Rau, Julia S. Weber, Manfred Schneider, Johannes Gumbsch, Peter Selzer, Michael Greiner, Christian |
author_sort | Garabedian, Nikolay T. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9203546 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-92035462022-06-18 Generating FAIR research data in experimental tribology Garabedian, Nikolay T. Schreiber, Paul J. Brandt, Nico Zschumme, Philipp Blatter, Ines L. Dollmann, Antje Haug, Christian Kümmel, Daniel Li, Yulong Meyer, Franziska Morstein, Carina E. Rau, Julia S. Weber, Manfred Schneider, Johannes Gumbsch, Peter Selzer, Michael Greiner, Christian Sci Data Article 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. Nature Publishing Group UK 2022-06-16 /pmc/articles/PMC9203546/ http://dx.doi.org/10.1038/s41597-022-01429-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Garabedian, Nikolay T. Schreiber, Paul J. Brandt, Nico Zschumme, Philipp Blatter, Ines L. Dollmann, Antje Haug, Christian Kümmel, Daniel Li, Yulong Meyer, Franziska Morstein, Carina E. Rau, Julia S. Weber, Manfred Schneider, Johannes Gumbsch, Peter Selzer, Michael Greiner, Christian Generating FAIR research data in experimental tribology |
title | Generating FAIR research data in experimental tribology |
title_full | Generating FAIR research data in experimental tribology |
title_fullStr | Generating FAIR research data in experimental tribology |
title_full_unstemmed | Generating FAIR research data in experimental tribology |
title_short | Generating FAIR research data in experimental tribology |
title_sort | generating fair research data in experimental tribology |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9203546/ http://dx.doi.org/10.1038/s41597-022-01429-9 |
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