Research Data Management: Home
Welcome to the Research Data Management Guide!
Research data come in many shapes and forms—physical or digital, big or small, uniform or varied. They can range from lab or survey results to ethnographic notes, demographic information, interview transcripts, audiovisual material, economic indicators, government records, text mining results, and catalogue and archival records. Whatever their form, data are the evidence for your research findings and proper data management is critical for good research practice and compliance. This subject guide is meant to provide you with information on some of the most important aspects of data management. Browse the separate tabs or click on one of the links below for information on:
While many of the resources, repositories, and tools in this subject guide are geared toward the humanities and social sciences, the underlying principles will be useful to anyone managing research data. Please feel free to explore this guide or your data librarians at firstname.lastname@example.org if you have any additional questions about data management.
What is Research Data Management?
"Research data management is the organization, documentation, storage, and preservation of the data resulting from the research process, where data can be broadly defined as the outcome of experiments or observations that validate research findings, and can take a variety of forms including numerical output (quantitative data), qualitative data, documentation, images, audio, and video."
Source: National Library of Medicine
Why Manage Your Research Data?
1. Save Time
Organizing your data, backing them up, and documenting them in detail ensures that you will not waste time searching for, recovering, or deciphering your data in the future. Never assume you will remember every step of your research process; make a clear plan and document practices as you go.
2. Improve Workflows and Standardize Research Practices
Avoid having everyone on your project develop their own way of managing data. From the get-go, have a description standard that everyone uses to a common understanding of how data is stored, standardize data management practices for each project to ensure that everyone on your team is able to locate and understand the data even after they've graduated or moved on to another institution. See Data Curation and the Data Lifecycle.
3. Meet Funding Requirements
Increasingly, key funding agencies including the National Science Foundation and the National Institutes of Health are requiring data management plans (DMPs) with grant proposals as well as open data sharing for publication. Managing your data ensures that you are prepared to share your data in a way that makes it broadly available and understandable to other researchers. For more information, view the Creating Data Management Plans and Accessing and Sharing Data tabs in this guide.
4. Promote Responsible Research
Good data management enables researchers to fulfill their commitment to responsible research by making their research repeatable, reproducible, and replicable. An article is not sufficient for other scientists to validate and build upon your research; data and documentation are also necessary.
5. Enable New Discoveries and Public Accessibility
Well-managed and openly available data leads the way to new discovery. When other researchers can access and use your data, they gain the ability to ask new questions of your data that you may not have even imagined. Openly available data can be used for comparative and longitudinal research and can even be used as a training tool for new researchers. Research is also often funded by taxpayers through state and federal agencies and institutions. A growing sentiment is that this funding should be leveraged for the public good. Like government documents that are openly available by default (with obvious national security and privacy exceptions), data collected with public monies should also be accessible by the public.