Research Data Management

This guide is meant to help you navigate some common best practices when managing your research. Research data management (RDM) is the process of managing the way data is collected, processed, analyzed, preserved, and published for greater reuse by the community and the original researcher. It’s about making research materials findable, organized, documented, and safe, while also making the research process as efficient as possible.

• Short-term, RDM helps your streamline your research process and work more sustainably and efficiently
• Long-term, RDM helps ensure your research is re-usable and understandable

If you need some convincing that data management is worth your while, check out this video by our colleagues at the Data Services department in NYU's Health Sciences Library:

Data management services at NYU can help you with:

• Reviewing data management plans for your grant applications
• Selecting a repository for data and code publishing
• Citing data and code
• Identifying data management best practices for your specific project or use case (like storage and backup procedures)
• Using specific tools:
  • Open Science Framework
  • JupyterLab and Jupyter notebooks
  • Git, GitHub, and GitLab
  • OpenRefine
  • Python
  • ReproZip
  • ABBYY FineReader OCR

  • Tesseract OCR

That list is not exhaustive, so if you have a data management topic or question that you don't see listed above, please reach out to us anyway! The format of service offerings include:

• Regular workshops in the Libraries (online now)
• Embedded workshops within a course
• Workshops by request for groups (e.g. research groups, student clubs)
• 1:1 consultations (via email, videoconferencing, and in-person)
• Ask a Librarian chat service (accessible via the chat box on the left) during our open desk hours

These services are provided by:

Please refer to our consultation guidelines for an overview of our services and service expectations.

NYU defines research data as "any recorded, retrievable information necessary for the reconstruction and evaluation of reported results created in connection with the design, conduct or reporting of research performed or conducted at or under the auspices of the University and the events and processes leading to those results, regardless of the form or the media on which they may be recorded. Research data include both intangible data (statistics, finding, conclusions, etc.) and tangible data (notebooks, printouts, etc.), but not tangible research property, which is subject to a separate NYU policy."

The United States Code of Federal Regulations offers a definition researchers with federal funding should keep in mind. According to the Code of Federal Regulations, research data is, "... defined as the recorded factual material commonly accepted in the scientific community as necessary to validate research findings, but not any of the following: Preliminary analyses, drafts of scientific papers, plans for future research, peer reviews, or communications with colleagues."

You might also want to consider the following as relevant research data:

• Lab and field notebooks

• Audio interviews and transcripts

• Code books

• Spreadsheets

• Documents (text, pdf, Word)

• Photographs (digital or analog)

• Scripts and algorithms

• Workflow and methodology

• Database and database content

• Protein or gene sequences

• Specialized software

You can view a glossary of data-related terms via Cornell's guide.