Data Management

Research and other types of scholarly or creative activity generate data.  Whether an architect created blueprints and computer-aided renderings, or a biologist collected specimens, both of their projects generated data.  

Data management is defined as the organization, storage, access, and preservation of data in its various forms (Henderson, 2016).  Data management also includes what will be done with the data once a project is completed and how it may be shared and stored.  

Data management and data management plans are important for many reasons.  Researchers may be required to submit a DMP in their grant proposals.  Increasing numbers of journals are either encouraging or requiring researchers to share their research data for manuscripts selected for publication.  Data management also helps with data sharing and archiving.

For general tips and considerations on managing and sharing data, refer to an authoritative style, writing or research guide within your subject area; see a list in the Citations Research Guide or search the FAU Library Catalog.  For example, psychology and social science fields that use the current Publication Manual of the American Psychological Association can refer to its chapter on APA's recommendations for data management.  The current AMA Manual of Style for medicine provides some background and guidance in their section on ethical and legal considerations.

Source: Henderson, M. E. (2016). Data management: A practical guide for librarians. Rowman & Littlefield.

Data Management Training

Data Management in Higher Education

Data management and data management plans are important for many reasons. 

• Researchers may be required to submit a DMP in their grant proposals. 
• Increasing numbers of journals are either encouraging or requiring researchers to share their research data for manuscripts selected for publication. 
• New federal requirements mandate making data that was generated from federally funded projects openly available.
• Reproducibility is critical for good research.
• Organization makes working with data more efficient and creates stronger support of results.
• Re-use of the data is simplified, which means that the impact of the research is strengthened.