A survey of researchers' needs and priorities for data sharing

Q2 Computer Science Data Science Journal Pub Date : 2021-02-22 DOI:10.31219/osf.io/njr5u
I. Hrynaszkiewicz, J. Harney, L. Cadwallader
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引用次数: 12

Abstract

PLOS has long supported Open Science. One of the ways in which we do so is via our stringent data availability policy established in 2014. Despite this policy, and more data sharing policies being introduced by other organizations, best practices for data sharing are adopted by a minority of researchers in their publications. Problems with effective research data sharing persist and these problems have been quantified by previous research as a lack of time, resources, incentives, and/or skills to share data. In this study we built on this research by investigating the importance of tasks associated with data sharing, and researchers’ satisfaction with their ability to complete these tasks. By investigating these factors we aimed to better understand opportunities for new or improved solutions for sharing data. In May-June 2020 we surveyed researchers from Europe and North America to rate tasks associated with data sharing on (i) their importance and (ii) their satisfaction with their ability to complete them. We received 728 completed and 667 partial responses. We calculated mean importance and satisfaction scores to highlight potential opportunities for new solutions to and compare different cohorts.Tasks relating to research impact, funder compliance, and credit had the highest importance scores. 52% of respondents reuse research data but the average satisfaction score for obtaining data for reuse was relatively low. Tasks associated with sharing data were rated somewhat important and respondents were reasonably well satisfied in their ability to accomplish them. Notably, this included tasks associated with best data sharing practice, such as use of data repositories. However, the most common method for sharing data was in fact via supplemental files with articles, which is not considered to be best practice.We presume that researchers are unlikely to seek new solutions to a problem or task that they are satisfied in their ability to accomplish, even if many do not attempt this task. This implies there are few opportunities for new solutions or tools to meet these researcher needs. Publishers can likely meet these needs for data sharing by working to seamlessly integrate existing solutions that reduce the effort or behaviour change involved in some tasks, and focusing on advocacy and education around the benefits of sharing data. There may however be opportunities - unmet researcher needs - in relation to better supporting data reuse, which could be met in part by strengthening data sharing policies of journals and publishers, and improving the discoverability of data associated with published articles.
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研究人员对数据共享的需求和优先事项的调查
PLOS长期以来一直支持开放科学。我们这样做的方式之一是通过2014年制定的严格的数据可用性政策。尽管有这项政策,其他组织也推出了更多的数据共享政策,但少数研究人员在其出版物中采用了数据共享的最佳做法。有效的研究数据共享问题依然存在,之前的研究将这些问题量化为缺乏时间、资源、激励和/或共享数据的技能。在这项研究中,我们通过调查与数据共享相关的任务的重要性,以及研究人员对他们完成这些任务的能力的满意度,建立了这项研究的基础。通过调查这些因素,我们旨在更好地了解共享数据的新解决方案或改进解决方案的机会。2020年5月至6月,我们对来自欧洲和北美的研究人员进行了调查,对与数据共享相关的任务进行了评分,包括(i)它们的重要性和(ii)它们对完成这些任务的能力的满意度。我们收到728份完整回复和667份部分回复。我们计算了平均重要性和满意度得分,以突出新解决方案的潜在机会,并比较不同的队列。与研究影响、资助者合规性和信用相关的任务的重要性得分最高。52%的受访者重复使用研究数据,但获得重复使用数据的平均满意度相对较低。与共享数据相关的任务被认为有些重要,受访者对自己完成这些任务的能力相当满意。值得注意的是,这包括与最佳数据共享实践相关的任务,例如使用数据存储库。然而,共享数据的最常见方法实际上是通过文章的补充文件,这并不被认为是最佳实践。我们认为,研究人员不太可能为他们对自己的能力感到满意的问题或任务寻求新的解决方案,即使许多人没有尝试这项任务。这意味着新的解决方案或工具几乎没有机会满足这些研究人员的需求。出版商可以通过无缝集成现有解决方案来满足数据共享的这些需求,这些解决方案可以减少某些任务所涉及的工作量或行为变化,并专注于围绕共享数据的好处进行宣传和教育。然而,在更好地支持数据重用方面,可能存在机会——未满足的研究人员需求——这可以通过加强期刊和出版商的数据共享政策以及提高与已发表文章相关的数据的可发现性来部分满足。
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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来源期刊
Data Science Journal
Data Science Journal Computer Science-Computer Science (miscellaneous)
CiteScore
5.40
自引率
0.00%
发文量
17
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.
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