| 项目编号: | 1745675
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| 项目名称: | EAGER: DMP Roadmap: Making Data Management Plans Actionable |
| 作者: | Guenter Waibel
|
| 承担单位: | University of California, Office of the President, Oakland
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| 批准年: | 2017
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| 开始日期: | 2017-09-01
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| 结束日期: | 2019-08-31
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| 资助金额: | 275178
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| 资助来源: | US-NSF
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| 项目类别: | Standard Grant
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| 国家: | US
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| 语种: | 英语
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| 特色学科分类: | Geosciences - Integrative and Collaborative Education and Research
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| 英文关键词: | dmp
; project
; machine-actionable
; datum
; dmp policy
; data-intensive research
; static datum management plan
; datum management practice
; prototype machine-actionable dmp
; machine-actionable hub
|
| 英文摘要: | The California Digital Library (CDL) will work with an extensive coalition of national and international collaborators to convert static data management plans (DMPs) into machine-actionable documents useful for structuring the course of research activities and communicating with other systems. Open data policies are proliferating worldwide and researchers are now required to submit DMPs with most grant proposals that describe the data they will produce and plans for sharing and preserving it. Researchers do not always know exactly what data they will produce at the beginning of a project, however. Furthermore, they have no incentives or easy methods for updating a DMP to keep things organized over the course of their research, which can lead to poor data practices and chaotic, unusable data shared at the end. DMPs in their current, static form pose similar challenges for other stakeholders across the research ecosystem, e.g., funders who must monitor compliance manually. This project will solve this problem by building out DMPRoadmap, a new, internationalized platform to reposition DMPs as true hubs of the networked research ecosystem. The principal impact of the proposed project is to transform one component of the increasingly digital research enterprise, the DMP, into an actively updated and machine-actionable hub to document and disseminate the products of research activity. Converting free-text responses to funder requirements into dynamic, verifiable data feeds will vastly improve the entire toolchain for all stakeholders and increase the velocity and availability of information across all disciplines. The software outputs of the pilot will be shared publicly with an open source license so that the community can continue to enhance and reuse the technology, extending the scholarly cyberinfrastructure in a scalable manner. All supporting data collected during the project will be made fully available for human and machine consumption. Since DMPs are rapidly becoming a global phenomenon, the outputs of the project will be of great interest to the entire research community. The breadth of the impact extends into the future of DMP policies worldwide, as machine-actionability makes change easier and continuous improvements possible. The project will provide insight into how to maximize investment in institutions and infrastructures in a manner that achieves policy goals by providing public access to government-funded research, and advances the overall public good. By advancing best practices for data-intensive research across all disciplines the project will further the goals of the scientific endeavor.
The CDL will design, develop, and prototype machine-actionable DMPs based on community generated use cases; field research on DMP and data management practices; and pilot projects conducted with disciplinary and institutional partners. The work plan encompasses iterative phases of developing, implementing, and testing with various stakeholder groups using an agile methodology. Specific use cases include implementing a set of common standards and exchange protocols for DMPs to enable information to flow between DMPs and existing research information systems (e.g., offices of research, data repositories, faculty profile systems); leveraging persistent identifiers (e.g., DOIs for articles and datasets, ORCID iDs for people) to trigger push/pull notifications across systems that enable stakeholders to plan resources, connect research outputs, automate reporting and monitoring, get credit, and promote data discoverability, reuse, and reproducibility; among others. |
| 资源类型: | 项目
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/89077
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| Appears in Collections: | 全球变化的国际研究计划
科学计划与规划
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| Recommended Citation: | |
Guenter Waibel. EAGER: DMP Roadmap: Making Data Management Plans Actionable. 2017-01-01.
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