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Data Management and Sharing Plans

This guide will take you through each of the elements you need to consider when creating a data management and sharing plan

Storing your Data (during active phase of research)

Data storage

There are a number of options to choose from when deciding where to store your data while completing your project. It is recommended that your data be saved and stored on a minimum of two options. You have several options for storage and processing of your data during the active phase of research:

  • Google Workspace accounts are available to faculty upon request and include unlimited storage in Google Drive. Google drive is for non-sensitive data types only. 
  • Faculty have access to Box which provides 100GB of storage space with security features for sensitive data storage.
  • Departmental server or storage network
  • Other Third Party Cloud storage space, for example: Dropbox, OneDrive (Use with caution! Patentable or Confidential Information should not be stored in the cloud)
  • Contact IT for further support options

Sharing & Archiving your Data

Proper preservation of your data ensures the integrity of the data remains intact for future use and easy access.  

Reasons to share your data:

  • Accelerates research and provides greater exposure to data so that scholars and citizens to make new connections and discoveries
  • Increases possibility of future research collaborations as long term preservation allows for continued access to and use of your research
  • Potential increased citation of source papers by amplifying your research and making your data discoverable (Piwowar, 2007)
  • Increases return on research investment by allowing continued re-use of data (funding agencies value this)
  • Funding agencies and journals (e.g. Nature, Science) are now implementing data sharing requirements
  • Encourages scientific enquiry and debate
Questions to consider before sharing your data:
 
  • How much and which aspects of your data will you share?  (Raw data? Analyzed?  Both?)
  • Are there privacy or security issues with your data and how will they be resolved? (Anonymization? Informed consent? Controlled access?)
  • When will you make the data available? (As soon as it's collected? Upon project completion? After publication?)
  • With whom will you share your data? (Your department? Your institution? Others in your field? Everyone?)

How to share and archive your data?

Learn about the Open Data movement! 

Licensing Data

Want to make your data open and easily shareable, but have some concerns about how exactly it is shared? Consider using the Open Data Commons to create a data license. The Open Data Commons provides a set of legal tools and licenses to help researchers publish, provide and use open data. They have three standard licenses:

You can also use the Licenses Service to get data on more than 100 open source, open data and open content licenses in JSON and API friendly format.

You might already be familiar with Creative Commons, which provides standardized licenses for a variety of works you might create. Several licenses also relate to research data management and sharing, including:

If you are interested in some type of licensing, but don't see any options listed here that meet exactly your needs. Reach out to the Scholarly Communications and Open Access Librarian with questions. 

Citing Data

Data, just like other scholarly outputs, requires citations to acknowledge the original author/producer, and to help others locate the resource. Check with the repository from which you have accessed the data first for preferred citations. If no guidance is provided, a dataset citation includes all of the same components as any other citation:

  • author -- who created this data set? Could be an individual, group, or organization. 
  • title -- does the data set have a title, or does the project have a name? 
  • year of publication - when was the data created, or published online? 
  • publisher -- for data this is often the archive where it is housed. There might be publisher and distributor of the data that both need acknowledgment. 
  • edition or version -- Is there a version number associated with the data?
  • editor -- is there an editor of this version? 
  • material designation -- what type of data is it? 
  • access information  -- a URL, DOI, or other persistent identifier)

Standards for data citations have not been codified internationally, though many data providers and distributors and some style manuals do provide guidelines. University of Michigan offers this great resource for data research data.