A data management plan (DMP) is a critical part of your grant application which outlines how you will collect, organize, manage, store, secure, backup, preserve, and share your data. But what is data? Read all about how NYU and the federal government defines research data here.
The particular requirements of a DMP will vary among funding agencies, so it is best to always consult the agency's resources for their specific needs. However there are a few common attributes to all data management plans, including:
NYU's Policy on Retention of and Access to Research Data outlines in some detail NYU's position on the research output of its community members. This policy dictates that NYU must retain research data in enough detail and for a long enough period of time to respond to questions about accuracy, authenticity, primacy, and compliance with laws and regulations governing the conduct of the research and to establish priority for patentable items. This policy also states the PI is responsible for determining what needs to be retained under this policy.
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
