I can assist you in improving the capture, organization, management, storage & preservation, presentation and dissemination of your research data
Virtual Office Hours
Do you have reams of research data that you need to organize and document? Do you need to ensure that it is accessible to the public and/or preserved for the long term? Are you applying for a grant that requires you to create a data management plan? If so, then this drop-in session is for you.
Stephen Klein will help you navigate the world of data management during drop-in video consultations on the second Tuesday of the each month from 2-3pm. Advance registration is possible but not required.
Click here to join during Tuesdays (the third Tuesday of the month) 2-3pm and if another person is being assisted, you'll be kept in the "waiting room" until the librarian is available. Or email Stephen for an alternative meeting time if Tuesday afternoons do not work.
CUNY Academic Commons: Data Management Tools (Note: Some of the tools listed on this page may not be appropriate for data management plans or long-term data management.)
Office of Institutional Research and Assessment (OIRA)'s Guiding Questions and General Tips for Working with Data for Program Reviews handout.
All CUNY faculty members, postdoctoral scholars, graduate and undergraduate students involved in human subjects research as key personnel must complete the applicable Basic Course (e.g. HSR for Social & Behavioral Faculty, Graduate Students, & Postdoctoral Fellows) in the protection of human subjects prior to Institutional Review Board (IRB) approval of their protocol. More info here.
Data Management Plans include information on:
DataOne's 'Primer on Data Management: What you always wanted to know' is a comprehensive guide helping users become familiar to the most relevant steps in the data lifecycle.
Observational: data captured in real-time, usually irreplaceable (e.g., censor data, telemetry, survey data, sample data, neuroimages)
Integrated and transformed: data from different sources, but transformed so disparate data ensuring data compatibility (document provenance, workflows and changes).
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)
The DMPTool is an online tool that includes data management plan templates for many of the large funding agencies that require such plans. The tool includes general guidance, links to helpful documentation, issues to consider, and specific questions to think about as you prepare your data management plan. Space is provided to compose a response for each of the main areas that your funding agency would like for you to address in your plan. You can save and come back to your plan as often as you like. When you are finished, you can export your plan in plain text format and insert it into your grant proposal.
Access, Sharing, and Re-use