Research Data Management

Data is anything you perform analysis on.

Data can:

• Be both digital and physical (i.e. computer files and paper survey responses)
• Be from many fields - sciences, humanities, etc
• Include research notes or lab notebooks, survey responses, software, code, measurements, images, audio, video, and physical samples.

Definition from "Data Management for Researchers" by Kristin Briney: https://pelagicpublishing.com/products/data-management-for-researchers-briney

Research data management (RDM) will help you:

• Save valuable time and resources
• Preserve your data
• Maintain data integrity
• Meet grant requirements
• Promote new discoveries
• Support open access and open data

RDM is the compilation of small practices that make your data:

• Easier to find & understand
• Less likely to be lost
• More likely to be usable during a project or 10 years later

Most importantly you don't want to lose your data!

Research Data types Include:

Quantitative vs. Qualitative

• Quantitative - numeric data that answers the questions: how many? how much? how often? (social sciences, physics, natural sciences, health sciences etc.)
• Qualitative - descriptive in nature and deals with the quality or characteristic of things. It is collected using questionnaires, interviews, or observation. It could be notes taken during a focus group, or responses from an open-ended questionnaire, for example. (anthropology, history, social sciences, etc.)

Primary vs. secondary data

• Primary data (or raw data) - data that is collected by the researcher for a particular project. This is original data that is collected  from an experiment or observation. Gathered and maintained by the researcher. 
• Secondary data - data originally created by someone else. For instance, census data or data via open access repositories. The researcher will still identify, analyze, and disseminate the data. 

Other types of data

• Experimental data - derived from controlled, randomized experiments
• Observational data - gathered in instances where it is not possible to conduct a controlled experiment
• Longitudinal (long range) data -  collected for two or more distinct periods. The subjects or cases analyzed are the same, or at least comparable, from one period to the next; and the analysis involves some comparison of data between or among periods.
• Computational data - the output of a computer that has taken a large set of data and run it through a simulation

Resources:

• Data Management Glossary

  Cornell University's Research Data Management Service has complied a basic glossary of data management terms.

• DataONE Data Management Skillbuilding Hub

  The DataONE Best Practices database provides individuals with recommendations on how to effectively work with their data through all stages of the data lifecycle.

• Data Sharing & Management Snafu in 3 Short Acts

  A data management horror story by Karen Hanson, Alisa Surkis and Karen Yacobucci. This is what shouldn't happen when a researcher makes a data sharing request! Topics include storage, documentation, and file formats. Video below.

The Open Access Accelerator: 

Image: "Infographic Illustration on significance of Nelson Memo" by Unknown. From "Breaking Barriers: How Open Access and the Nelson Memo Are Transforming Research" by Naveen Mukala. University of Texas at Arlington. All Rights Reserved.

Notice: As of July 1st, 2025, the NIH (National Institute of Health) Public Access Policy goes into effect. This requires all federally funded research or research findings to be:

1) Submitted to PubMed Central openly and without embargo upon acceptance for publication.
2) Acknowledgement or statement of federal funding within the submitted manuscript
3) Application of a standard license that makes government funded research publicly available through PubMed Central without embargo upon official date of publication.

The purpose of this policy is to increase public confidence in federally funded research, while "also ensuring that the investments made by taxpayers produce replicable, reproducible, and generalizable results that benefit all Americans." (Jay Battacharya, Director of the NIH, 2025). Read more about the acceleration of this Open Access Policy at the NIH.

For the full 2024 NIH Public Access Policy Report, please visit the 2024 NIH Public Access Policy Notice: NOT-OD-25-047

Please contact The UMB Office of Research & Sponsored Programs (ORSP) with specific questions about this timeline and requirements.