Research Data Management and Sharing

Research data, according to the federal government, refers to “the recorded factual material commonly accepted in the scientific community as necessary to validate research findings.”  It can exist in various forms, depending on the discipline and research topic.  For example, research data can be:

• Photos or foreign-language documents, among other things, for disciplines in Arts and Humanities
• Field notes or multimedia content, among other things, for disciplines in Social Sciences
• Measurements or digital images, among other things, for STEM disciplines

Research data management and sharing consists of actions researchers take to plan, acquire, store, process, analyze, preserve, share, find, and reuse research data for their projects.  It involves how researchers handle their data (e.g., using consistent naming conventions to facilitate organization and retrieval of files) and what researchers decide to do with the data upon completion of their projects (e.g., depositing research data in an online repository for access, reuse, and preservation).
Research data management and sharing can be presented as a lifecycle because sharing research data enables reuse, supports reproducibility, and facilitates the creation of another research project.  Below is one rendition of the research data lifecycle.

Research data management and sharing presented as a lifecycle by Princeton University.

Research data management and sharing brings about a variety of benefits, including but not limited to:

• Research data is part of what is generated by scholarly activities.  It can be cited in scholarly literature and its creators receive credit for the efforts of managing and sharing it.
• Research data is fragile and can be easily lost.  A study has found that faculty members had lost research data deemed to be important in their careers.
• Research funders and publishers increasingly require research data management and sharing for funded projects and peer-reviewed publications.
• Effective data management saves time and effort in the long run, making it easier to locate, understand, and reuse data files.
• Recommended data management practices help prevent errors, which can increase the quality of analysis and research findings.  They also contribute to transparency and research integrity.
• Well-managed and shared data enables other researchers to replicate and validate findings, which reduces the costs of duplicating data collection.
• Findable and accessible data facilitates innovative reuse and may lead to groundbreaking discoveries.

Effective data management and sharing starts with preparing a well-thought-out plan and adopting recommended practices in the research process.  Cornell University has compiled a glossary that explains relevant terms in plain language.  If you have any questions, feel free to contact the Center for Digital Scholarship.