监测Helmholtz中开放和公平数据的状态:HMC的数据收集和仪表板方法

Gabriel Preuß, Alexander Schmidt, Mojeeb Sedeqi, Vivien Serve, Oonagh Mannix, Markus Kubin
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摘要

在这篇文章中,我们提出了一种监测和评估亥姆霍兹协会开放和公平数据状态的综合方法。该项目是Hub Matter在亥姆霍兹元数据协作(HMC)中多方法方法的一部分。在一种收集方法中,亥姆霍兹研究人员发表的数据从文献元数据开始,从研究中心收集。与该文献相关的数据出版物使用SCHOLIX API进行识别。在自动化公平评估的第一种方法中,我们采用了由FAIRsFAIR联盟开发的F-UJI框架。收集到的信息显示在交互式仪表板中。它允许探索哪些存储库亥姆霍兹研究人员公开提供他们的数据,参与亥姆霍兹社区,并确定差距,以提高亥姆霍兹数据的公平性。该仪表板可在https://fairdashboard.helmholtz-metadaten.de上公开获取。一般的方法以及所有的程序代码都可以被所有的研究团体重用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Monitoring the State of Open and FAIR Data in Helmholtz A Data-Harvesting and Dashboard-Approach by HMC
In this contribution we present an integrated approach to monitoring and assessing the state of open and FAIR data in the Helmholtz Association. The project is part of a multi-method approach by Hub Matter in the Helmholtz Metadata Collaboration (HMC). In a harvesting-approach, data published by Helmholtz researchers is found starting from literature metadata, harvested from the research centers. Data publications linked to that literature are identified using the SCHOLIX API. In a first approach to automated FAIR assessment, we adopted the F-UJI framework, as developed by the FAIRsFAIR consortium. The information collected is presented in an interactive dashboard. It allows to explore in which repositories Helmholtz researchers make their data publicly available, to engage Helmholtz communities, and to identify gaps towards improving the FAIRness of Helmholtz data. The dashboard is publicly available on https://fairdashboard.helmholtz-metadaten.de. The general approach as well as all program code are reusable by all research communities.
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