Mid-scale RI-2: The Research Data Ecosystem (RDE), a National Resource for Reproducible, Robust, and Transparent Social Science Research in the 21st Century

中型 RI-2：研究数据生态系统 (RDE)，21 世纪可重复、稳健和透明的社会科学研究的国家资源

• 批准号: 1946932
• 负责人: Margaret Levenstein
• 金额: $ 3835.7万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-15 至 2027-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1946932&HistoricalAwards=false
• 关键词: Mid; scale; RI; Research; Data

This project will implement a new platform for social and behavioral science data. Diverse types of data enable path-breaking analyses into human behavior but also present challenges of scale, sensitivity, and structure. Current barriers to conducting research include multiple incompatible standards for data, lack of interoperability, and the inherent difficulty of managing big data. There is an urgent need for new modes of access, confidentiality protection, methodological approaches, and tools so that research using a variety of data types meets accepted scientific standards. The Research Data Ecosystem (RDE) will modernize the management of data to enable a new era of interconnected research for the social and behavioral sciences. The platform will improve the quality of data-driven social and behavioral science research over the entire data life cycle. RDE will enable researchers across disciplines to conduct their work more efficiently and to create, organize, archive, access, and analyze data in ways that they cannot with existing infrastructure. RDE will make social and behavioral data more usable outside of academia by making it more findable and accessible. The project will provide training opportunities for graduate and undergraduate students and will broaden and diversify participation in the social and behavioral sciences by removing technical bottlenecks to research. This project is supported by the Foundation-wide Mid-scale Research Infrastructure program.This project will develop an integrated suite of software to advance research in the social and behavioral sciences. RDE will enable: 1) Interoperability: An integrated system for the entire research data lifecycle, so that work done early in the data lifecycle is useful at later stages, making it possible to integrate data from different sources, 2) Reproducibility: Making it easier to reproduce and build on prior research results by being able to find and re-use data and code, 3) Transparency: Providing information about provenance, including source, code, method of collection, etc. for research data, 4) Increased Efficiency of Data Sharing: Reducing burden on data producers in sharing data and ensuring that shared data are FAIR (Findable, Accessible, Interoperable, Reusable), and 5) Confidentiality Protection: Protecting confidentiality while increasing research access. To achieve these goals, the project will develop the Research Data Description Framework, a metadata specification similar to the Resource Description Framework, for describing different research data lifecycle events. RDE will include stand-alone functional components for each stage of the research lifecycle that will be interoperable with one another and with key existing research infrastructure. The platform will support social and behavioral science researchers using traditional (e.g., survey and experimental) and novel (e.g., digital trace, imaging) types of data over the entire research lifecycle, from data collection to analysis to sharing to re-discovery and re-analysis. This infrastructure will improve the quality, integrity, and safety of data while increasing accessibility to data and collaboration between users across all social science and some behavioral science disciplines.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.

该项目将实现一个新的社会和行为科学数据平台。不同类型的数据使对人类行为的开创性分析成为可能，但也提出了规模、敏感性和结构方面的挑战。目前进行研究的障碍包括多个不兼容的数据标准，缺乏互操作性，以及管理大数据的固有困难。迫切需要新的访问模式、保密保护、方法方法和工具，以便使用各种数据类型的研究符合公认的科学标准。研究数据生态系统（RDE）将使数据管理现代化，为社会科学和行为科学的互联研究开创一个新时代。该平台将在整个数据生命周期内提高数据驱动的社会和行为科学研究的质量。RDE将使跨学科的研究人员能够更有效地开展工作，并以现有基础设施无法实现的方式创建、组织、存档、访问和分析数据。RDE将使社会和行为数据更容易被发现和访问，从而使其在学术界之外更可用。该项目将为研究生和本科生提供培训机会，并将通过消除研究的技术瓶颈，扩大和多样化参与社会和行为科学。本项目由基金会范围内的中型研究基础设施计划支持。该项目将开发一套集成的软件，以推进社会科学和行为科学的研究。RDE将实现：1)互操作性：整个研究数据生命周期的集成系统，因此在数据生命周期早期所做的工作在后期阶段是有用的，从而可以集成来自不同来源的数据；2)可重复性：通过能够查找和重用数据和代码，使其更容易复制和构建先前的研究结果；3)透明性：4)提高数据共享效率：减少数据生产者共享数据的负担，确保共享数据公平（可查找、可访问、可互操作、可重用）；5)保密保护：在增加研究访问的同时保护机密性。为了实现这些目标，该项目将开发研究数据描述框架，这是一个类似于资源描述框架的元数据规范，用于描述不同的研究数据生命周期事件。RDE将为研究生命周期的每个阶段提供独立的功能组件，这些组件将彼此互操作，并与关键的现有研究基础设施互操作。该平台将支持社会和行为科学研究人员在整个研究生命周期中使用传统（例如，调查和实验）和新型（例如，数字跟踪，成像）数据类型，从数据收集到分析，再到共享，再到发现和再分析。该基础设施将提高数据的质量、完整性和安全性，同时增加所有社会科学和一些行为科学学科的用户对数据的可访问性和协作。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。