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Research Data Management and Curation: Start Here

This guide covers the research data management and curation resources available at the University of Alabama through a partnership of the UA Libraries, Office for Research, and Office for Information Technology.


Subject Guide


For assistance with data management plans, please email DMP Help.

What are Research Data?

The Office of Management and Budget, Circular A-110, defines data as:  

...recorded factual material commonly accepted in the scientific community as necessary to validate research findings, but not any of the following: preliminary analyses, drafts of scientific papers, plans for future research, peer reviews, or communications with colleagues. This "recorded" material excludes physical objects (e.g., laboratory samples).
Research data also do not include: (A) Trade secrets, commercial information, materials necessary to be held confidential by a researcher until they are published, or similar information which is protected under law; and (B) Personnel and medical information and similar information the disclosure of which would constitute a clearly unwarranted invasion of personal privacy, such as information that could be used to identify a particular person in a research study.

In the Humanities and related disciplines, data could be text, text corpora, audio, image, or video.  


Academic departments and individual disciplines may add other designations.  

Important UA Links

This guide provides an introduction to resources in the University for Data Management Plan creation and execution. Use the tabs above or the links below to find information on the following topics:

UA Office for Research Links:

Research Data Management and Curation

What is this?

In a nutshell, data management and curation entails structured processes to ensure that the data used and generated by research activities are well-described, documented, findable, retrievable, understandable, and preserved for some period of time.

Why should I be interested in this?

Effective data management results in no lost or corrupted data, easier identification and handling of data during projects and after, and more time for research activities. In addition, increasingly external funders require data management (and sharing) plans so that funded research can serve as foundations for future research and previous investments can continue to yield dividends.  


Data Life Cycle Models

There are many ways to visualize the life cycle of data.  Here are three prominent models: 


DataOne Data Life Cycle

Data Life Cycle-Plan, Collect, Assure, Describe, Preserve, Discover, Integrate, Analyse




MIT Data Management Cycle

Data Management Cycle-Study Concept, Data Collection, Data Processing | Data Archiving, Data Discovery, Data Analysis, Repurposing




Rutgers University Community Repository Data Life Cycle

Rutgers University Data Life Cycle

Tools Shortcuts

Government Regs

OSTP Memorandum for the Heads of Executive Departments and Agencies (March 20, 2014): Improving the Management of and Access to Scientific Collections