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Data Services workstations are available for walk-in use whenever the library stacks are open.
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COVID - 19 Service Status
Data Services has shifted to virtual services for the Summer 2020 sessions. During our normal working hours, we will respond to requests via e-mail and will attempt to offer guidance via Zoom when necessary.
This guide lays out practical considerations and information to aid you in managing your research throughout its' lifecycle, including the steps you will take to collect, safeguard, archive, and make available the data used for the research in question.
Many key granting organizations, like NSF, NIH, NEH and more, now require submitters to include a Data Management Plan as part of their application. In short, these plans outline the best practices in data management that you will apply throughout the course of your grant. You can see some more background on this issue, or get started by selecting a tab at the top of the page.
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
Documents (text, pdf, Word)
Photographs (digital or analog)
Scripts and algorithms
Workflow and methodology
Database and database content
Protein or gene sequences