EAGER: Completing the Lifecycle: Developing Evidence Based Models of Research Data Sharing

有效的数据共享做法对于科学研究的成功至关重要，特别是在开放科学原则的新兴背景下。该项目将在几个具体的机构和组织背景下对数据共享活动进行详细分析，以便更好地了解支持公众获取研究数据的模式、做法、成本、障碍和工作流程。该项目是在几个主要协会的协调下进行的，这些协会是美国科学研究的主要支持者。许多学术机构提供基础设施来支持研究人员的数据共享活动。这些服务和基础设施通常分布在机构的各个管理单元中，例如校园IT、大学图书馆和研究室等。鉴于这种分散且往往无文件记录的临时性质，公众获取研究数据的真实成本并未得到很好的理解。该项目旨在阐明许多目前尚不为人所知的因素，以供公众获取研究数据。这一分析项目的技术方面如下。将通过分析通过DataCite应用程序编程接口(API)获取的元数据来完成对研究数据存储库使用模式的评估。将利用基于NIST的机构职能模型，对特定学科和特定格式的研究数据做法进行回溯性研究，以便公众获取适用于一系列机构和次级学科的研究数据。最后，将使用NAS和NIST成本模型的组合从机构收集和分析财务数据，以一致地比较数据共享活动的成本，从而产生对机构支出的概括性理解。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Effective data sharing practices are vital for the success of scientific research, especially in the emerging context of Open Science principles. This project will conduct a detailed analysis of data sharing activities in several specific institutional and organizational contexts in order to better understand the patterns, practices, costs, barriers, and workflows that support public access to research data. The project is being undertaken with coordination between several major associations which are key supporters of scientific research in the United States. Many academic institutions provide infrastructure to support researchers in data sharing activities. These services and infrastructures are often distributed across the institution, housed in various administrative units, such as campus IT, the university libraries, and the research office, among others. Given this distributed and often undocumented ad hoc nature, the true costs of public access to research data is not well understood. This project is designed to illuminate many of the currently unknown factors about the institutional landscape for public access to research data. The technical aspects of this analysis project are as follows. An assessment of research data repository use patterns will be accomplished by analyzing metadata harvested by means of the DataCite application programming interface (API). A retrospective study of discipline-specific and format-specific research data practices will be accomplished utilizing a NIST-based institutional functional model for public access to research data applied to a series of institutions and sub-disciplines. Finally, financial data will be collected and analyzed from the institutions using a combination of NAS and NIST cost models to consistently compare costs of data sharing activities and thereby produce a generalizable understanding of institutional expenditures.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.