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Data Management

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Data Management

The Libraries' Data Working Group supports faculty and graduate students who need to meet funder expectations for data management, analyze existing data management practices, or create new practices that best fit the needs of their research projects.

The Data Working Group currently provides the following services:

  1. Reviews of Data Management Plans for NSF, NEH, NIH, and other funding agencies.
    Contact us to request a review one week prior to your submission deadline.
    See our Data Management Plan Guidance page for more detailed information on data management planning.
  2. Workshops on data management best practices and data management plan preparation.
    Workshops provide a context for data management, review funder mandates, and cover basic best practice including file naming, metadata, backup and storage, and post-project activities such as data sharing and copyright.
  3. Consultations on metadata and standards for data format and content, policies for data sharing and accessibility, and plans for long-term access to data sets.
    Contact us to request a consultation for a specific project or course.
  4. Provision of a online location for faculty’s research outputs through ScholarWorks.
    ScholarWorks is an Open Access repository indexed by Google Scholar and can accommodate multiple research outputs, including small, static, data sets.

The Data working Group will also assist faculty and graduate students in identifying appropriate data repositories for archiving of large-scale data sets and associated research outputs and assist with material deposition.

 Why Data Management?

Data management is the systematic organization and planning for data throughout the research cycle. It encompasses a set of activities that are essential to the short- and long-term access and use of research data. It involves planning for the creation, storage, use, security, and continued access to data.

Data are the raw, analyzed, or derived results of observations, experiments, and simulations. Data can be either analog or digital and can exist in different formats, including but not limited to text, numerical, multimedia, and instrument-specific.

Management of data throughout the research life-cycle not only increases the efficiency of a research project, it also complies with expectations for the ethical conduct of research and is rapidly becoming mandatory practice for many funding agencies.

For more information about managing your data, see the DWG's Good Practice for Data Management Guide

 Three Things You Can Do Today to Help Manage Your Data

  1. Backup, backup, backup.
    Think of what it would take to reproduce your data. To make sure you don't lose it, strive to have three copies—the original master file, a local backup (e.g., on an external hard drive), and an external backup (e.g., on a networked drive or on a web-based storage service).
  2. Organize your data.
    Plan the directory structure and file naming conventions before creating your data, taking into consideration the potential need to track versions of data sets and documents. Follow any existing project-specific conventions or disciplianry standards or best practices.
  3. Document your data.
    Data documentation, also known as metadata, will help you use and understand your research data into the future. If you plan to share your data it will also help others find, use, and properly cite it. At a minimum, create a readme.txt file that includes basic documentation such as title, creator, identifier, rights/access information, dates, location, methodology, etc. (A complete list of recommended metadata to be captured is available at 


Last Edited: 11 December 2014