使用机器可操作数据管理计划的互连系统-黑客马拉松报告

Q2 Computer Science

Data Science Journal Pub Date : 2021-01-01 DOI:10.5334/dsj-2021-035

João Cardoso, L. J. Castro, Tomasz Miksa

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引用次数: 2

摘要

机器可操作的数据管理计划(dmp)的通用标准允许自动交换、集成和验证dmp中提供的信息。在本文中，我们报告了由研究数据联盟组织的黑客马拉松，来自21个国家的89名参与者在用例上合作，探索标准在不同环境中的效用。这项工作包括工具和服务的集成、资助者模板映射和新序列化的开发。本文总结了黑客马拉松期间取得的成果，并提供了进一步资源的指针。若昂卡多佐

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Interconnecting Systems Using Machine-Actionable Data Management Plans – Hackathon Report

The common standard for machine-actionable Data Management Plans (DMPs) allows for automatic exchange, integration, and validation of information provided in DMPs. In this paper, we report on the hackathon organised by the Research Data Alliance in which a group of 89 participants from 21 countries worked collaboratively on use cases exploring the utility of the standard in different settings. The work included integration of tools and services, funder templates mapping, and development of new serialisations. This paper summarises the results achieved during the hackathon and provides pointers to further resources.

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来源期刊

Data Science Journal Computer Science-Computer Science (miscellaneous)

CiteScore

5.40

自引率

0.00%

发文量

审稿时长

10 weeks

期刊介绍： The Data Science Journal is a peer-reviewed electronic journal publishing papers on the management of data and databases in Science and Technology. Details can be found in the prospectus. The scope of the journal includes descriptions of data systems, their publication on the internet, applications and legal issues. All of the Sciences are covered, including the Physical Sciences, Engineering, the Geosciences and the Biosciences, along with Agriculture and the Medical Science. The journal publishes papers about data and data systems; it does not publish data or data compilations. However it may publish papers about methods of data compilation or analysis.