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Research Data Alliance Ideate Incubator (RDA-I2) : Conceptualizing an Interdisciplinary Research Framework for Strengthening Community Impact and Advancing an Innovation Platform

Research Data Alliance Ideate Incubator (RDA-I2) : Conceptualizing an Interdisciplinary Research Framework for Strengthening Community Impact and Advancing an Innovation Platform
研究数据联盟创意孵化器 (RDA-I2):构思跨学科研究框架,以加强社区影响力和推进创新平台
批准号:
1934649
负责人:
Rebecca Koskela
金额:
$220.2万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-02-01 至 2023-11-30

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中文摘要
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英文摘要
The Research Data Alliance (RDA) is a global organization providing a community infrastructure of researchers, data scientists, librarians, practitioners who work within and across disciplines to identify and solve grand challenges of data sharing and interoperability. This project augments the RDA infrastructure with an incubator framework that can be used to accelerate data sharing and data-driven innovation, by providing targeted community support and strategic dissemination of community outputs. The goals are to establish mechanisms to integrate and link work across different RDA working groups and improve the discoverability and usability of RDA outputs.The project includes a pilot facilitation training program for RDA group chairs, to share strategies and tactics for building better connections across efforts. In a quickly-scaling organization, the chairs are uniquely placed as a consistent, visible leadership presence for all group participants. By equipping chairs with skills to lead more inclusive session discussions that give space to the knowledge of the many participants, RDA can better leverage the Plenary meetings to connect groups. A second goal of the project is to increase the intelligibility and discoverability of RDA outputs, to prepare for broader adoption of the RDA work. RDA's current process of documenting and tracking approved and supported RDA outputs makes it difficult to capture the full array of work associated with RDA, including publications, posters, lectures, webinars, white-papers, and adoption stories. Developing a strategy to better track output development and adoption would enhance the capability to support all group work and to monitor, measure, and disseminate the impact of RDA group outputs within the wider scientific community. In addition, enhancing the documentation of organizational outputs would improve the ability of group collaboration and cross-fertilization of ideas, as outputs are reused and adapted to additional purposes, methods, and audiences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
Data-driven Recommendation System Construction of an Online Medical Platform Based on the Fusion of Information
Development of a Linear Stochastic Model for Wind Field Reconstruction from Limited Measurement Data
  • 批准号:
    --
  • 项目类别:
    --
  • 资助金额:
    40万元
  • 批准年份:
    2020
  • 负责人:
    Vikrant Gupta
  • 依托单位:
基于Linked Open Data的Web服务语义互操作关键技术
  • 批准号:
    61373035
  • 项目类别:
    面上项目
  • 资助金额:
    77.0万元
  • 批准年份:
    2013
  • 负责人:
    冯志勇
  • 依托单位: