The BRAIN Initiative data-sharing ecosystem: Characteristics, challenges, benefits, and opportunities.

IF 6.4 1区生物学 Q1 BIOLOGY eLife Pub Date : 2024-11-27 DOI:10.7554/eLife.94000

Sudhanvan Iyer, Kathryn Maxson Jones, Jill O Robinson, Nicole R Provenza, Dominique Duncan, Gabriel Lázaro-Muñoz, Amy L McGuire, Sameer A Sheth, Mary A Majumder

{"title":"The BRAIN Initiative data-sharing ecosystem: Characteristics, challenges, benefits, and opportunities.","authors":"Sudhanvan Iyer, Kathryn Maxson Jones, Jill O Robinson, Nicole R Provenza, Dominique Duncan, Gabriel Lázaro-Muñoz, Amy L McGuire, Sameer A Sheth, Mary A Majumder","doi":"10.7554/eLife.94000","DOIUrl":null,"url":null,"abstract":"In this paper, we provide an overview and analysis of the BRAIN Initiative data-sharing ecosystem. First, we compare and contrast the characteristics of the seven BRAIN Initiative data archives germane to data sharing and reuse, namely data submission and access procedures and aspects of interoperability. Second, we discuss challenges, benefits, and future opportunities, focusing on issues largely specific to sharing human data and drawing on N = 34 interviews with diverse stakeholders. The BRAIN Initiative-funded archive ecosystem faces interoperability and data stewardship challenges, such as achieving and maintaining interoperability of data and archives and harmonizing research participants' informed consents for tiers of access for human data across multiple archives. Yet, a benefit of this distributed archive ecosystem is the ability of more specialized archives to adapt to the needs of particular research communities. Finally, the multiple archives offer ample raw material for network evolution in response to the needs of neuroscientists over time. Our first objective in this paper is to provide a guide to the BRAIN Initiative data-sharing ecosystem for readers interested in sharing and reusing neuroscience data. Second, our analysis supports the development of empirically informed policy and practice aimed at making neuroscience data more findable, accessible, interoperable, and reusable.","PeriodicalId":11640,"journal":{"name":"eLife","volume":"13 ","pages":""},"PeriodicalIF":6.4000,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"eLife","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.7554/eLife.94000","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOLOGY","Score":null,"Total":0}

引用次数: 0

Abstract

In this paper, we provide an overview and analysis of the BRAIN Initiative data-sharing ecosystem. First, we compare and contrast the characteristics of the seven BRAIN Initiative data archives germane to data sharing and reuse, namely data submission and access procedures and aspects of interoperability. Second, we discuss challenges, benefits, and future opportunities, focusing on issues largely specific to sharing human data and drawing on N = 34 interviews with diverse stakeholders. The BRAIN Initiative-funded archive ecosystem faces interoperability and data stewardship challenges, such as achieving and maintaining interoperability of data and archives and harmonizing research participants' informed consents for tiers of access for human data across multiple archives. Yet, a benefit of this distributed archive ecosystem is the ability of more specialized archives to adapt to the needs of particular research communities. Finally, the multiple archives offer ample raw material for network evolution in response to the needs of neuroscientists over time. Our first objective in this paper is to provide a guide to the BRAIN Initiative data-sharing ecosystem for readers interested in sharing and reusing neuroscience data. Second, our analysis supports the development of empirically informed policy and practice aimed at making neuroscience data more findable, accessible, interoperable, and reusable.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

BRAIN 计划数据共享生态系统：特点、挑战、益处和机遇。

本文概述并分析了 BRAIN 计划数据共享生态系统。首先，我们比较和对比了与数据共享和再利用相关的七个 BRAIN 计划数据档案的特点，即数据提交和访问程序以及互操作性的各个方面。其次，我们讨论了挑战、益处和未来机遇，重点关注与人类数据共享密切相关的问题，并参考了对不同利益相关者的 34 次访谈。由脑神经网络计划资助的档案生态系统面临着互操作性和数据管理方面的挑战，例如实现和维护数据与档案的互操作性，以及协调研究参与者对多个档案中人类数据访问层级的知情同意。然而，这种分布式档案生态系统的一个好处是，更专业化的档案馆能够适应特定研究群体的需求。最后，随着时间的推移，多个档案馆为网络进化提供了充足的原材料，以满足神经科学家的需求。本文的首要目标是为对神经科学数据共享和再利用感兴趣的读者提供一份 BRAIN 计划数据共享生态系统指南。其次，我们的分析有助于制定基于经验的政策和实践，使神经科学数据更易于查找、访问、互操作和重用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

eLife BIOLOGY-

CiteScore

12.90

自引率

3.90%

发文量

3122

审稿时长

17 weeks

期刊介绍： eLife is a distinguished, not-for-profit, peer-reviewed open access scientific journal that specializes in the fields of biomedical and life sciences. eLife is known for its selective publication process, which includes a variety of article types such as: Research Articles: Detailed reports of original research findings. Short Reports: Concise presentations of significant findings that do not warrant a full-length research article. Tools and Resources: Descriptions of new tools, technologies, or resources that facilitate scientific research. Research Advances: Brief reports on significant scientific advancements that have immediate implications for the field. Scientific Correspondence: Short communications that comment on or provide additional information related to published articles. Review Articles: Comprehensive overviews of a specific topic or field within the life sciences.