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.

NIH (National Institute of Health) amended their research plan form, including updated Research Data Management requirements

• Visit the NIH Data Management and Sharing Policy website for details on their new (January 25, 2023) Data Management and Sharing Policy: Data Management and Sharing Policy | Data Sharing (nih.gov).
• Review the UMB Office of Research & Sponsored Programs (OSRP) NIH 2023 Data Management and Sharing Policy information sheet on the UMB ORSP website, or view the PDF below.