Research Data Management Implementations: Impact on Science

研究数据管理实施：对科学的影响

• 批准号: 1661523
• 负责人: Hakizumwami Runesha
• 金额: $ 6.99万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-05-01 至 2018-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1661523&HistoricalAwards=false
• 关键词: Research; Data; Management; Implementations; Impact

The pace and scale of modern research requires researchers to navigate a large number of complex data management activities to collect, organize, analyze, share, store, and preserve their data, while also working to make their data more open, discoverable, and reproducible. These data management activities are critical for modern research and collaborative science initiatives and maximize the utility of government funded research. Unfortunately, researchers often lack the time, resources, and expertise to perfectly execute all of these activities. While requirements for data management have been in place for six years, many researchers are still not entirely clear on what a good implementation strategy should look like or what their specific responsibilities are with respect to data management. There are few good Research Data Implementations (RDMI) exemplars and a lack of common standards and practices. Data remains difficult to manage and reproduce throughout its lifecycle, and solutions are still carried out and implemented in a piecemeal fashion.In March of 2013, an NSF-funded workshop on RDMI brought together 109 data management experts, with an additional 73 experts attending online, to discuss RDMI implementations as they relate to domain specific workflows and deployments. These discussions were very helpful to the scientific community. There is a broad community of researchers that would benefit from RDMI, but RDMI availability is currently limited to institutions and domains with the resources and specialized personnel to develop them. After 4 years, the scientific community would greatly benefit from an opportunity to come together again to reflect on what has been accomplished to date related to Data Management, share achievements and assess gaps and opportunities for further contributions, and contribute with suggestions for a future roadmap. We propose to organize a second RDMI workshop to be held in 2017. This new workshop will focus on:(a)the impact that RDMI have on science, (b) developing RDMI in partnership with research communities, and (c) increasing access to RDMI for the broader research community. Workshop organizers will invite submission of position and experience papers and encourage dialogue on these focus areas through talks, panel discussions, and breakout sessions. Individual case studies will be discussed and workshop participants, through breakout sessions, will offer feedback and suggestions that will aid in refining future RDMI implementations.

现代研究的速度和规模要求研究人员进行大量复杂的数据管理活动，以收集、组织、分析、共享、存储和保存他们的数据，同时也努力使他们的数据更加开放、可发现和可复制。这些数据管理活动对现代研究和协作科学计划至关重要，并最大限度地利用政府资助的研究。不幸的是，研究人员往往缺乏时间、资源和专业知识来完美地执行所有这些活动。虽然数据管理的需求已经存在了六年，但许多研究人员仍然不完全清楚一个好的实现策略应该是什么样子，或者他们在数据管理方面的具体职责是什么。很少有好的研究数据实现（RDMI）范例，并且缺乏通用的标准和实践。数据在整个生命周期中仍然难以管理和复制，解决方案仍然以零敲碎打的方式执行和实现。2013年3月，由nsf资助的RDMI研讨会汇集了109位数据管理专家，另外还有73位专家在线参加，讨论了与特定领域工作流和部署相关的RDMI实现。这些讨论对科学界很有帮助。有一个广泛的研究人员社区将从RDMI中受益，但是RDMI的可用性目前仅限于具有开发它们的资源和专业人员的机构和领域。4年后，科学界将有机会再次聚在一起，反思迄今为止在数据管理方面取得的成就，分享成就，评估差距和进一步贡献的机会，并为未来路线图提出建议，这将使科学界受益匪浅。我们建议在2017年举办第二次RDMI研讨会。这个新的研讨会将重点关注：(a) RDMI对科学的影响，(b)与研究社区合作开发RDMI，以及(c)为更广泛的研究社区增加对RDMI的访问。研讨会组织者将邀请提交立场和经验论文，并鼓励通过会谈、小组讨论和分组会议对这些重点领域进行对话。将讨论个别案例研究，研讨会参与者将通过分组会议提供反馈和建议，以帮助改进未来的RDMI实现。