About the Data Catalog

The NYU Data Catalog facilitates researchers’ discovery of data by providing a searchable and browsable online collection of datasets. Rather than functioning as a data repository, the catalog is a digital way-finder for researchers looking for datasets relevant to their work. It includes datasets generated by NYU researchers as well as publically available and licensed datasets that are managed by external organizations, e.g. the Bureau of Labor Statistics.

The NYU Data Catalog is designed to:

  • Increase the visibility of research data generated by NYU researchers
  • Facilitate collaboration across departments and institutes at NYU
  • Help NYU researchers locate and understand datasets generated at external organizations
  • Support the re-use of research data in secondary analysis

If you are interested in submitting a dataset to the NYU Data Catalog, would like to suggest additional datasets for inclusion, or are willing to serve as a local expert, please use the Contact Us form.

The code used to create the NYU Data Catalog is open source and available via GitHub. Documentation and further information is available via OSF. If you would like to create a similar catalog, please use the Contact Us form to learn more about the multi-institution Data Discovery Collaboration.

Including the NYU Data Catalog in DMPs

If you are writing a grant application and plan to share your data (via a public repository, lab hosted server, by request, etc.) and make it discoverable with the NYU Data Catalog, below is sample language that can be inserted into your data sharing plan or the data sharing section of your data management plan (DMP). For NIH Data Management and Sharing plans, this text can be included under Element 4.

  • Data from this project will be described with rich metadata in the NYU Data Catalog (https://datacatalog.med.nyu.edu/) to increase findability and usefulness of the datasets.

  • OR

  • Data from this project will be described in the NYU Data Catalog (https://datacatalog.med.nyu.edu/) with rich metadata (including: description, keywords, format of dataset, instrumentation or software utilized/required, and information about who can access each dataset and how) to increase findability and usefulness of datasets.

The Data Discovery Collaboration

The Data Discovery Collaboration was created to facilitate the discovery of biomedical research data that are difficult to find. The DDC is a multi-institutional consortium that has implemented local projects, programs, or technologies to index and make available data. This collaboration brings a cross-institutional perspective to addressing usability, data sharing workflows, metadata, and outreach for improving data discovery efforts.

The Mission of the DDC:

  • To enhance discovery of data and other research products in order to maximize their value

To learn more about our accomplishments, our publications, and how to join, please visit the DDC website.

Sharing Datasets

Researchers can now view and share descriptions of datasets that will be exclusively shown to other NYU faculty and staff! If you would like to share your data only with NYU faculty and staff, please contact the NYU Data Catalog at datacatalog@nyulangone.org.

The Genome Technology Center has adapted this feature to help researchers share descriptions of pre-publication data with NYU colleagues while retaining control of who accesses the data itself.

This internal, NYU-only data discovery and sharing component for researchers enables PIs to share pre-publication data to collaborate with NYU colleagues and learn about their ongoing work. Descriptions of pre-publication datasets will be made available to NYU faculty and staff only, and access to the data is only granted with the PI’s consent.

As an added incentive to share, all who participate in the data sharing initiative will receive a free upgrade to their NYU Langone-network iPhone to the latest generation of iPhone, currently iPhone14, which is unavailable to others on the NYU Langone plan.

To opt in, you can either:

  • Indicate your interest through a new checkbox on the Genome Technology Center iLab project request form
  • Complete this REDCap form to have NYU Data Catalog staff contact you with further information

For any questions, please contact the NYU Data Catalog at datacatalog@nyulangone.org.

Meet the Team

Nicole

Nicole Contaxis

Data Services Librarian, Lead of Data Discovery

Nicole Contaxis, MLIS MA is the Lead for Data Discovery and the NYU Data Catalog at the NYU Health Sciences Library. She works alongside the research community to improve data sharing and discovery. Her areas of interest include data sharing and governance, data ethics, and community engagement. Nicole is a former National Digital Stewardship Resident at the National Library of Medicine. She received her MLIS from UCLA, and her MA in Bioethics from NYU.

Michelle

Michelle Yee

Data Catalog Coordinator

Michelle Yee, MPH, engages with NYU researchers working in the areas of clinical and population health to promote their work and encourage collaborative opportunities. Michelle has prior experience in clinical research within NYU Langone through the Clinical and Translational Science Institute and a MPH in Epidemiology from NYU School of Global Public Health.

Ummea

Ummea Urmi

Data Catalog Coordinator

Ummea Urmi, MS, works with NYU researchers in the basic sciences to make their data more discoverable through the NYU Data Catalog. Ummea has previously worked at a neuroscience lab at Columbia University through the Howard Hughes Medical Institute. She received her MS in Toxicology from St. John’s University for which she was the recipient of the Clare Boothe Luce Fellowship.

Ian

Ian Lamb

Senior Solutions Developer

Ian Lamb is a full-stack web developer at the NYU Health Sciences Library and is the principal developer of our data catalog. He focuses on building friendly and usable systems to advance the institution’s clinical, educational, and research goals.

Icons on the homepage are made by Vectors Market, Gregor Cresnar, and monkik, from www.flaticon.com.