EAGER: Amplifying Community Readiness to Increase Public Access to Data

EAGER：加强社区准备以增加公众对数据的访问

• 批准号: 2032705
• 负责人: Melissa Cragin
• 金额: $ 15万
• 依托单位: University of California-San Diego
• 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
• 起止时间: 2020-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2032705&HistoricalAwards=false
• 关键词: EAGER; Amplifying; Community; Readiness; Increase

This exploratory project in open science will examine and address uptake of the NSF Public Access Repository (PAR) for deposition of metadata records for research datasets that support validation of results in publications, or for data resources described in publications. In particular, there is interest in how practices are changing for the use of data identifiers (e.g. DOIs). The project will partner with domain and technical organizations to extend the reach of this work. Studying researcher’s current data production, planning for deposit or exposing those data, and roles of their Data Management Plan (DMP), will provide insights on how to foster change – away from the view of “DMP as a box-checking” – toward the use of the DMP as a value-add process for linking research project planning with increased transparency for the benefit of public access. This study will begin with two different research areas: Initial work will focus on the Geosciences, and computationally-based communities (e.g. Data Science and AI/Machine Learning). For the Geosciences, there are opportunities to improve data publishing practices, by obtaining DOIs and increasing data citation. At the intersections of the Geosciences, semantic technologies, computing and informatics, there are Open Knowledge Networks which have the potential to generate new data resources to support solutions to societal grand challenges. Across the Data Science and AI-related communities, there is a need for better access to high quality datasets, models, and infrastructure; currently, resources for locating, accessing, and selecting such datasets are scattered and variable. Utilizing the uptake of the NSF PAR system for research data may facilitate increased access to datasets for research and education.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.

这个开放科学领域的探索性项目将检查并解决 NSF 公共访问存储库 (PAR) 的使用情况，用于存储支持出版物中结果验证的研究数据集的元数据记录，或出版物中描述的数据资源。人们尤其感兴趣的是数据标识符（例如 DOI）的使用实践如何发生变化。该项目将与领域和技术组织合作，以扩大这项工作的范围。研究研究人员当前的数据生产、存储或公开这些数据的计划以及其数据管理计划 (DMP) 的作用，将提供关于如何促进变革的见解——远离“DMP 作为检查框”的观点——转向使用 DMP 作为增值流程，将研究项目规划与提高透明度联系起来，以造福于公众访问。这项研究将从两个不同的研究领域开始：最初的工作将集中在地球科学和基于计算的社区（例如数据科学和人工智能/机器学习）。对于地球科学来说，有机会通过获取 DOI 和增加数据引用来改进数据发布实践。在地球科学、语义技术、计算和信息学的交叉点上，开放知识网络有潜力产生新的数据资源，以支持解决社会重大挑战。在数据科学和人工智能相关社区中，需要更好地访问高质量的数据集、模型和基础设施；目前，用于定位、访问和选择此类数据集的资源是分散且可变的。利用 NSF PAR 系统获取研究数据可能有助于增加对研究和教育数据集的访问。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。