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EAGER: Collaborative Research: Supporting Public Access to Supplemental Scholarly Products Generated from Grant Funded Research

EAGER: Collaborative Research: Supporting Public Access to Supplemental Scholarly Products Generated from Grant Funded Research
EAGER:合作研究:支持公众获取资助研究产生的补充学术产品
批准号:
1649555
负责人:
Victoria Stodden
金额:
$6.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2016
资助国家:
美国
项目状态:
已结题
起止时间:
2016-09-01 至 2018-08-31

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中文摘要
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英文摘要
This EAGER project addresses the urgent need to better understand the research community's Data Management Plan (DMP) requirements and, based on this understanding, provides an open software tool that helps investigators generate structured and machine-readable Data Management Plans that fulfill both the researcher's need to easily deliver a standardized set of information to the funder, and the funder's need to analyze the information contained in DMPs. This allows funders to identify trends in data and software submission, repository use patterns, and carry out other analyses that can assist in understanding community use patterns and needs. The Principal Investigators (PIs) will leverage an existing DMP Tool built for the geosciences community by initially assessing the DMPs not only of the geosciences, but also the biological and social, behavioral, and economic sciences and upgrading the DMP Tool to serve those communities as well. Ultimately, the team will work to determine if this upgraded DMP Tool is extendable and scalable to all science, engineering, and educational research funded by the National Science Foundation. If successful, this will ultimately enhance the reproducibility and reuse of scientific research and help improve public access to supplementary scholarly products from federally funded research.The National Science Foundation has required Data Management Plans (DMPs) for all grant proposals submitted for review since 2011. The DMPs submitted thus far are mostly free text and do not follow any specified format or structure and because of this limitation, current DMPs are not easy to compare or analyze. Consistent and comprehensive structured and machine-readable DMPs may substantially advance understanding of the data management landscape and address gaps that will improve data access which will lead to enhanced re-use and more reproducible science. The PIs propose to modify and upgrade the DMP Tool that has been developed and operated by IEDA (Interdisciplinary Earth Data Alliance) to serve the broadest research communities possible. This DMP Tool gathers relevant data management planning information from investigators in a structured manner into a relational database that can be mined and analyzed. As part of this project, they will analyze the information from the more than 1,350 DMPs that have already been generated with the IEDA DMP Tool to understand gaps, successes, and patterns of use. They will initially focus on the DMP requirements of science communities funded by the National Science Foundation's GEO, BIO, and SBE directorates, and use the results of this research to guide the development and prototyping of the extended version of the IEDA DMP Tool. They will subsequently focus on other directorates.
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Collaborative Research: PPoSS: Planning: Performance Scalability, Trust, and Reproducibility: A Community Roadmap to Robust Science in High-throughput Applications
  • 批准号:
    2138770
  • 项目类别:
    Standard Grant
  • 资助金额:
    $3.0万
  • 财政年份:
    2021
  • 负责人:
    Victoria Stodden
  • 依托单位:
EAGER: Preserve/Destroy Decisions for Simulation Data in Computational Physics and Beyond
  • 批准号:
    2138773
  • 项目类别:
    Standard Grant
  • 资助金额:
    $30.0万
  • 财政年份:
    2021
  • 负责人:
    Victoria Stodden
  • 依托单位:
EAGER: Reproducibility and Cyberinfrastructure for Computational and Data-Enabled Science
  • 批准号:
    2138776
  • 项目类别:
    Standard Grant
  • 资助金额:
    $30.0万
  • 财政年份:
    2021
  • 负责人:
    Victoria Stodden
  • 依托单位:
Collaborative Research: PPoSS: Planning: Performance Scalability, Trust, and Reproducibility: A Community Roadmap to Robust Science in High-throughput Applications
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