Type-Based Automation of Scientific Data Management

科学数据互操作性和重用的一种方法是通过全局的、持久的和唯一标识的数据类型，这些数据类型可以被组合来表征研究数据集。 这个项目建议使用持久标识符（PID）来识别数据类型。PID解析为指定元数据（如数据的来源）的结构化和记录方式的记录。 基本前提是，机器可解释数据是实现数据公平性（可查找性，可访问性，互操作性和重用）的关键目标，因为全球范围内的数据发现取决于数字形式信息的自动处理。 一种基于类型的数据可解释性方法，在数据类型的粒度上利用持久化ID，可以颠覆互联网，并刺激FAIR数据的新工具生态系统。这一试点工作包括通过以下方式对这一方法进行评估：通过构建临界质量的用例。该项目由国家科学基金会公共访问计划支持，该计划由NSF高级网络基础设施办公室代表基金会管理。该奖项反映了NSF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进行评估，被认为值得支持审查标准。

An approach to scientific data interoperability and reuse is through global, persistent, and uniquely identified data types that can be assembled to characterize research data sets. This project proposes to identify data types using persistent identifiers (PIDs). The PIDs resolve to records that specify the way in which metadata, such as the provenance of the data, is structured and recorded. The basic premise is that machine interpretable data is a critical goal to achieving FAIRness (findability, accessibility, interoperability, and reuse) of data as data discovery at a global scale depends on automated processing of the information in digital form. A type based approach to data interpretability that utilizes persistent IDs at the granularity of data types can overturn the Internet and stimulate an ecosystem of new tools for FAIR data. This pilot effort involves evaluating the approach through, in part, by constructing a critical mass of use cases.This project is supported by the National Science Foundation Public Access Initiative which is managed by the NSF Office of Advanced Cyberinfrastructure on behalf of the Foundation.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.