EAGER: Amplifying Community Readiness to Increase Public Access to Data

这个开放科学的探索性项目将检查并解决NSF公共访问存储库（PAR）的吸收问题，该存储库用于存放支持出版物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（例如doi）的实践是如何变化的很感兴趣。该项目将与领域和技术组织合作，以扩展这项工作的范围。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，以及他们的数据管理计划（DMP）的角色，将提供关于如何促进变革的见解——从“DMP作为一个复选框”的观点——转向使用DMP作为一个增值过程，将研究项目计划与增加的透明度联系起来，以造福公众获取。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，有开放知识网络，它有可能产生新的数据资源，以支持解决社会重大挑战。在整个数据科学和人工智能相关社区，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和可变的。利用对研究数据的NSF PAR系统的吸收可能有助于增加对研究和教育数据集的访问。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.