研究数据的长期存档。

IF 2.7 4区医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Neuroinformatics Pub Date : 2023-04-01 DOI:10.1007/s12021-023-09621-x

Cyril Pernet, Claus Svarer, Ross Blair, John D Van Horn, Russell A Poldrack

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引用次数: 2

摘要

随时访问研究数据是FAIR(可查找可访问可互操作可重用)数据共享的目标。然而，我们认为，考虑到维护存储库的货币和生态成本，继续积累和维护所有数据集以快速访问是不可持续的。在这里，我们将讨论冷数据存储的问题:何时处理数据以进行离线存储，如何在保持FAIR原则的同时进行处理，以及谁应该负责冷存档和长期保存。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

On the Long-term Archiving of Research Data.

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On the Long-term Archiving of Research Data.

Accessing research data at any time is what FAIR (Findable Accessible Interoperable Reusable) data sharing aims to achieve at scale. Yet, we argue that it is not sustainable to keep accumulating and maintaining all datasets for rapid access, considering the monetary and ecological cost of maintaining repositories. Here, we address the issue of cold data storage: when to dispose of data for offline storage, how can this be done while maintaining FAIR principles and who should be responsible for cold archiving and long-term preservation.

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来源期刊

Neuroinformatics 医学-计算机：跨学科应用

CiteScore

6.00

自引率

6.70%

发文量

审稿时长

3 months

期刊介绍： Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.