EAGER: SMART-DMSP: Streamlining Metadata, Automation, and Research Tracking for Data Management and Sharing Plans

This project will investigate multiple strategies to enhance and support the adoption of machine-actionable Data Management and Sharing Plans (maDMSPs) in the academic environment. As federal mandates for DMSPs continue to increase, maDMSPs offer an efficient means to generate optimal DMSPs and serve as a crucial mechanism for ensuring compliance with these policies. The project focuses on structured metadata, PIDs (persistent identifiers), DOIs (digital object identifiers), and research output tracking to streamline data management and effectively implement maDMSPs. In addition, the project aims to uncover new opportunities for optimizing data management and tracking research outputs by automating processes, improving quality, and adhering to standards. The research project consists of two main work packages. The first work package aims to advance maDMSPs by retooling the DMPTool and experimenting with three key feature areas: automated updates to the DMSP, incorporating externally created DMSPs, and developing dashboards for stakeholders. The second work package explores the potential of machine learning (ML) in a maDMSP workflow, focusing on automating the conversion of narrative text documents into maDMSPs, generating narrative DMSPs from structured data inputs, and addressing privacy concerns surrounding sensitive information. 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.