Research Data Management

Here are the four most common ways to publish your data including their advantages and disadvantages.

• An Institutional Repository: the mission of an institutional repository is to permanently preserve the scholarly output of the institution.  Here at the Missouri University of Science and Technology our institutional repository is know as Scholars' Mine. Scholars'  Mine serves this function, and preserves text, audio, video, data and more.  Scholars' Mine is designed to meet the needs of scholars in all disciplines, and operates according to widely accepted standards for preservation and access.
• A Disciplinary Repository: Disciplinary repositories offer high visibility within a particular field. See the list on the left of this page for a list on disciplinary repositories. Not all repositories are committed to long-term preservation of data, and their mission and focus may change over time.  Some, are only available to subscribers. 
• Journals: Some journals publish data associated with their published articles.  This will provide good visibility, but is often tied to a journal subscription, limiting access.  Compliance with documentation standards and long-term preservation vary considerably from journal to journal.
• Data Journals: The "data journal" is an emerging alternative.  In data journals the data is the focus and the article is descriptive of the data set.  This enables the data to be cited in a very familiar form. 
• Self-publishing: Self-publishing occur through individual, institutional, or third-party websites.  The researcher assumes the  responsibility for vetting their own data for quality and documentation, as well as preserving an accessible version of the data as file formats change in the future.  Tools are emerging which focus on the broad sharing of data, while allowing individual researchers or research centers to manage their own data on a remote server.  The long-term implications are uncertain at this point.

It is not necessary to choose only one of these options. In fact, there are advantages to using multiple publishing options. Most of these options do not require an exclusive granting of rights, making it possible to deposit data in multiple locations, which both maximizes current visibility and long-term preservation simultaneously.

Citing data is highly recommended to to provide reliable access to specific datasets and to provide credit to the producers of useful data. Data citation standards are just beginning the emerging in many disciplines. In the absence of a specific standards , a data citation should include the following:

• Author or Responsible Party(such as: study PI, sample collector, government agency)
• Name of the Data Element used (e.g., a specific Table/Map/dataset with any applicable unique IDs)
• Name of the Database
• Name of the Publication ( if applicable)
• Name of the Repository (if applicable) 
• Version identifier (Study number, edition, year, version, etc.)
• Date accessed
• URL used

If specific steps were required to subset, analyze, or access the data, the citation should also include:

• parameters selected 
• software used

Additional information on citing data can be found at:

• Data-PASS on data citation
• DataCite a project to provide DOIs (web-resolvable unique identifiers) to datasets.