Information Management

Lightning Talk: Program Description Abstract

Supporting Data Harmonization and Discovery in the BRAIN Initiative

Monday, May 6
2:45 PM - 2:50 PM
Room: Columbus KL (East Tower, Ballroom/Gold Level)

Background : The NIH BRAIN Initiative is a program to fund the development and application of innovative technologies to aid in understanding the human brain. In 2017, it began funding streams to support: 1) the development of data archives, standards, and tools; 2) research teams studying brain circuit functions underlying behavior. Data science cores were required as part of the circuit function grants, and were tasked with ensuring that the FAIR Principles were considered with respect to the data collected. An NIH-supported data science consortium included the directors of the data science cores to promote collaboration and sharing of tools and resources.

Description : A librarian in an academic health sciences library serves as the data science core director for one of the circuit function grants funded in 2018, with a second librarian serving as key personnel on the core. Work within our project team includes generalizing a data model developed in one lab to capture metadata of all participating labs, and assessing data and metadata practices across these labs to improve metadata collection and harmonization and reduce the impact on existing research workflows. Across the data science consortium, we have brought a discovery-oriented perspective; there is is only one librarian among the data science core directors; the others represent the following disciplines: neuroscience (4), computer science/statistics/engineering (6), and physics (2). This has resulted in an exploration of the use of a data catalog to improve the discovery of BRAIN Initiative data.

Conclusion : The FAIR Guiding Principles address data findability, accessibility, interoperability, and reusability. While the BRAIN Initiative is funding the development of repositories, standards, and tools, which address the latter three principles, librarians bring an otherwise missing focus on discovery, and were able to propose a tool to improve findability of BRAIN Initiative datasets. Within their project team, the librarians partnered with a domain expert who had developed metadata that served the needs of a single lab, in order to generalize the metadata schema to effectively capture the metadata from all project labs.


Alisa Surkis

Assistant Director, Research Data and Metrics/Vice Chair for Research
NYU Health Sciences Library
New York, New York

Alisa Surkis, PhD, MLS is the Assistant Director for Research Data and Metrics and the Vice Chair for Research at the NYU Health Sciences Library. She serves as a co-Director for Team Science and leads the Workforce Data Capacity Core within the Biomedical Informatics Program for the NYU Clincical and Translational Science Institute. She is the Data Science Core Director for an NIH BRAIN Initiative funded project on Oxytocin Modulation of Neural Circuit Function and Behavior.


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Kevin Read

Lead, Data Discovery and Data Services Librarian
NYU Health Sciences Library
New York, New York

Kevin Read, MLIS, MAS is the Lead of Data Discovery and Data Services Librarian at NYU Langone Health. He leads the NYU Data Catalog project; an initiative to make research datasets created and used by NYU researchers more discoverable. He also leads the Data Catalog Collaboration Project, a multi-site collaboration consisting of eight academic institutions working to improve the discoverability of institutional research data using the NYU Data Catalog model.

Beyond his data discovery efforts, Kevin provides training and research support to faculty, residents, students and staff on topics including: clinical research data management, REDCap, reproducibility, and data sharing.


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Supporting Data Harmonization and Discovery in the BRAIN Initiative

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