摘要翻译：
使数据符合公平数据原则（可查找、可访问、可互操作、可重用）对许多研究人员来说仍然是一个挑战，他们不确定应该首先满足哪些标准以及如何满足这些标准。从与实验设计相关的实验数据表中，我们提出了一种可以作为研究数据管理模型的方法，允许研究人员通过满足主要公平标准而无需付出不可逾越的努力来传播他们的数据。更重要的是，这种方法旨在通过为研究人员提供工具来改善他们的数据管理实践，从而促进忠实化进程。
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英文标题：
《Making experimental data tables in the life sciences more FAIR: a
  pragmatic approach》
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作者：
Daniel Jacob (BFP), Romain David (MISTEA, ERINHA-AISBL), Sophie Aubin
  (DV-IST), Yves Gibon (BFP)
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最新提交年份：
2020
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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英文摘要：
  Making data compliant with the FAIR Data principles (Findable, Accessible, Interoperable, Reusable) is still a challenge for many researchers, who are not sure which criteria should be met first and how. Illustrated from experimental data tables associated with a Design of Experiments, we propose an approach that can serve as a model for a research data management that allows researchers to disseminate their data by satisfying the main FAIR criteria without insurmountable efforts. More importantly, this approach aims to facilitate the FAIRification process by providing researchers with tools to improve their data management practices.
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PDF链接：
https://arxiv.org/pdf/2012.09435