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Research Data Management: Getting Started

What counts as data?

Observational: data captured in real-time, usually irreplaceable (e.g., censor data, telemetry, survey data, sample data, neuroimages)

Experimental: data from lab equipment, often reproducible, but can be expensive (e.g., gene sequences, chromatograms, toroid magnetic field data)

Simulation: data generated from test models where model and metadata (inputs) are more important than output data (e.g., climate models, economic models)

Derived or compiled: data that is reproducible, but very expensive (e.g., text and data mining, compiled database, 3D models, data gathered from public documents)

Evaluate your data needs

Data description

  1. What type of data will be produced? Will it be reproducible? What would happen if it got lost or became unusable later?
  2. How much data will there be? How quickly will it grow? How often will it change? Once archives/stored, what kind of access will be needed to use it?
  3. Who will use the data now, and in the future?
  4. Who controls the data (PI, student, lab, CUNY, funding agency)? What intellectual property considerations might apply?
  5. How long should the data be retained? How long would you expect it to be useful, e.g. through the end of grant/experiment, 3-5 years, 10-20 years, permanently?


  1. Is there good project and data documentation?
  2. What directory and file naming conventions will be used?
  3. What project and data identifiers will be assigned?
  4. What file formats are used? Are they standards-based or proprietary?
  5. Are there tools or software needed to create/process/visualize the data? Are the tools or software proprietary?
  6. Is there an ontology or other community standard for data sharing/integration?

Access, Sharing, and Re-use

  1. Any special privacy or security requirements? e.g., personal data, high-security data
  2. Any sharing requirements? e.g., funder data sharing policy
  3. Any other funder requirements? e.g., data management plan in grant proposals
  4. What is your storage and backup strategy?
  5. When will it be shared and where? How broadly will it be shared? Are there I/O throughput issues with respect to the size of the datasets?
  6. Who in the research group will be responsible for data management?

CITI Training

All CUNY faculty members, postdoctoral scholars, graduate and undergraduate students involved in research are required to complete the CITI RCR training within six weeks of initiating their research. A list of Research Integrity Officers by campus is available on the CUNY website.

This guide was developed by the CUNY Office of Library Services and is based on (and, in some cases, pulls from) guides created at the libraries at the CUNY Graduate Center, New York University, Massachusetts Institute of Technology, University of Massachusetts, University of Michigan, and Stanford University.