FAIR Data Package of a Tribological Showcase Pin-on-Disk Experiment

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 (<em>included in the newest version of the dataset</em>) 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为评估通过受控词汇表、本体论（ontology）与电子实验记录本（Electronic Lab Notebook，ELN）集成生成FAIR（Findable, Accessible, Interoperable and Reusable，可发现、可访问、可互操作、可复用）数据的可行性，本数据集尽可能详尽地展示了一项摩擦学实验的实施流程。本次展示实验采用润滑销盘式布置，法向载荷为15牛，运行速度范围为20至170毫米每秒。借助本数据集，我们旨在为实验摩擦学领域的FAIR数据发布提供可行范式。 https://www.nature.com/articles/s41597-022-01429-9 —— Garabedian, N.T., Schreiber, P.J., Brandt, N., Greiner, C. 等 数据集快速入门指南详见README.txt（包含于本数据集最新版本中） **摘要**：实验摩擦学中的FAIR科研数据生成。《科学数据》9卷，第315页（2022年） 尽管整合前沿数据科学技术是诸多依赖现象学与经验主义的学科极具前景的科研路径，但当前实验摩擦学领域仍缺乏用于生成FAIR数据及元数据的数字化解决方案。此外，摩擦学极强的跨学科性，或许是缺乏社区通用的数据与元数据标准、且高度依赖定制化工作流与设备的主要原因。 本研究首先勾勒出可规模化生成FAIR数据的示例框架，其次提供了一套用于销盘式摩擦学实验的FAIR数据集展示包。最终经整理的数据集包含2008个键值对与1696条逻辑公理，其生成依托四项工作：(1) 与虚拟研究环境开发者的深度协作；(2) 众包受控词汇表；(3) 本体构建；(4) 多款看似小型的数字化工具的开发与应用。借此，本研究展示了一系列可规模化且无侵入性的技术，将实验摩擦学数据的留存寿命、可靠性与复用性提升至远超常规出版流程的水平。 https://youtu.be/xwCpRDnPFvs —— 《实验摩擦学中的FAIR科研数据生成：将科研结果适配机器学习》 https://doi.org/10.5281/zenodo.5720626 —— 摩擦学展示用销盘式实验FAIR数据集包 或访问 https://github.com/nick-garabedian/TriboDataFAIR-Ontology、https://fairsharing.org/3597 —— TriboDataFAIR 本体 https://doi.org/10.5281/zenodo.5720218 或 https://github.com/nick-garabedian/SurfTheOWL —— SurfTheOWL https://kadi4mat.iam-cms.kit.edu/ —— Kadi4Mat 虚拟研究环境与电子实验记录本