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

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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.