# Improving Research Efficiency through Better Descriptors > **NIH NIH P41** · UNIV OF MASSACHUSETTS MED SCH WORCESTER · 2021 · $365,499 ## Abstract TR&D Project 2: Improving Research Efficiency through Better Descriptors (DESCRIBE) SUMMARY: The scale and complexity of neuroimaging research have grown exponentially over the last three decades and have enabled new insights into human cognition in health and disease and development of new imaging hardware, processing, and informatics technologies. As new information has proliferated into the research ecosystem, there is a need to integrate this knowledge from publications, data sources, and analysis tools. This integration has been hampered by limited harmonization of description across these digital outputs. During the current period, this Technology Research and Development Project, TR&D2, has addressed some of these challenges. We extended the Neuroimaging Data Model (NIDM) - a descriptor framework built on top of the World Wide Web Consortium's Provenance Data Model (W3C-PROV) and backed by community- developed ontologies. Using such standards we also created a set of technologies with our ReproNim projects and partners to enable reproducible analytics, to harmonize data and results, and to gather standardized provenance. This proposal aims to increase research efficiency and overall trust in scientific findings through better description of digital objects and better provenance of analytics. To accomplish these overarching goals, we will: 1) Formalize detailed and structured descriptors of all stages of a neuroimaging research workflow. This is critical for interpreting and trusting scientific results. 2) Develop a resource to create and disseminate Findable, Accessible, Interoperable, Reusable (FAIR) and robust scientific workflows. This will enable users to trust and reuse existing and well-tested analyses, as well as disseminate their own scripts when such analyses are not available. 3) Extend and harden existing ReproNim technologies in coordination with the community. We will integrate our technologies through developers of other tools, thus making our technologies more accessible to those who have limited technical experience. This effort will be complemented by training and support for different user experience levels and use cases. We will deliver a set of technologies that allows researchers to harmonize their output by design, from assessment and imaging data collection to final results. These technologies will also support consolidation and reuse of existing workflows, with new processes being developed only when necessary. Finally, our tools will support community-based generation, curation, and management of standardized information. We will carry out this work in collaboration with the other ReproNim technology research and development projects, and our collaborative and service projects. Together, we will help researchers become more effective through increased efficiency in every facet of the research lifecycle. TR&D2 technologies support the overall mission of ReproNim to improve the way neuroimaging research is performed ... ## Key facts - **NIH application ID:** 10334136 - **Project number:** 2P41EB019936-06A1 - **Recipient organization:** UNIV OF MASSACHUSETTS MED SCH WORCESTER - **Principal Investigator:** David Nelson Kennedy - **Activity code:** P41 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2021 - **Award amount:** $365,499 - **Award type:** 2 - **Project period:** 2016-04-15 → 2026-08-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10334136 ## Citation > US National Institutes of Health, RePORTER application 10334136, Improving Research Efficiency through Better Descriptors (2P41EB019936-06A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10334136. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*