EAGER: Amplifying Community Readiness to Increase Public Access to Data

这个开放科学的探索性项目将研究和解决NSF公共访问存储库（PAR）的吸收，用于支持出版物中结果验证的研究数据集的元数据记录，或用于出版物中描述的数据资源。特别是，人们对使用数据标识符（如DOI）的做法如何变化感兴趣。该项目将与领域和技术组织合作，以扩大这项工作的范围。研究人员目前的数据生产，规划存款或暴露这些数据，以及他们的数据管理计划（Data Management Plan）的作用，将提供有关如何促进变化的见解-远离“数据管理作为一个复选框”的观点-走向使用数据管理计划作为一个增值过程，将研究项目规划与提高透明度联系起来，以利于公众访问。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，有机会通过获得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，有开放知识网络，它们有可能产生新的数据资源，以支持解决社会重大挑战的方案。在数据科学和人工智能相关的社区中，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、访问和选择这些数据集的资源是分散和可变的。利用NSF PAR系统的研究数据的摄取可以促进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，并已被认为是值得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的支持。

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.