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Data Management Guide: Make a plan

 

  LIBRARY SUPPORT FOR RESEARCHERS

     DATA MANAGEMENT GUIDE: Make a plan

Data Management mainpage                                                                

Data Management Plan (DMP) is document that describes:  What data you expect to acquire or generate during a research project; How the data will be collected, documented, analyzed, stored, preserved and shared during the project; Who will be responsible for managing the data throughout the project; and When data management activities occur throughout the project.

DMP Exemplar for Mixed-Methods Studies (NEW July 2020)


Reasons for Managing and Sharing Your Data

  • Increase your research impact - making your data available to other researchers can impact the discovery and relevance of your research
  • Save time and money - planning ahead for your data management needs means accounting and anticipating what you need to avoid costly mistakes later on
  • Preserve your data - depositing your data in a repository safeguards your investment of time and resources while preserving your research contribution for you and others to use
  • Maintain data integrity - managing and documenting choices/decisions about your data throughout its lifecycle will allow you and others to understand and use your data in the future
  • Meet grant funder requirements - many funding agencies now require that researchers deposit data collected as part of a research project
  • Support Open Scholarship - utilizing creative commons licensing and deposit, you demonstrate your support for scholarly reuse

 


Write a Data Management Plan

For best practice and step-by-step guidance in creating a Data Management Plan, use:

  • DMP ASSISTANT - to create data management plans for Canadian funders; select University of Manitoba template
  • DMP TOOL - to create data management plans for US funders, such as NIH or NSF

The University of Manitoba DMP template is available at the DMP Assistant with the components listed below. The guidance that accompanies each question in the template directs you to UM specific resources and support. Registration for an account in DMP Assistant is free.

Components of a Data Management Plan

  • Data Collection -
    • Define data types: textual, tabular, graphical, numerical
    • List the file formats that will be used/created: proprietary, open standard. Software or tools required to read and/or view the data
    • Naming conventions for versions, data architecture: documentation in READ ME file, root name
  • Documentation and Metadata -
    • Define the documentation accompanying the data generated
    • Outline best practices to ensure accuracy, consistency of documentation 
    • Define Metadata standard(s) to be used
  • Storage and Backup (during project) - 
    • Define backup process - see best practice for backup
    • Outline total storage requirements (e.g. megabytes, gigabytes, terabytes) and length of retention time
    • Access and location: details of access (protection of sensitive data) and documentation
  • Data Preservation (after project) -
    • Using deposit with a permanent identifier assigned is preservation best practice
    • Elaborate on retention: duration, format, location, access parameters
    • Describe preparation processes: data selection, de-identification, data formats
  • Data Access and Reuse -
    • Stipulate the version of data (raw, processed, analyzed, final)
    • Method of storage and access - resource and access considerations
    • Levels of access: free to read v. ability to reuse
  • Responsibilities and Resources -
    • Describe the various types of access, duration that will be granted, and how it will be documented
    • Outline the costs related to the data management lifecycle, including equipment, software, dissemination (including any journal article processing charges) - see the Data Management Costing Tool for estimating what those costs may be
  • Ethics and Legal Compliance -
    • Sensitive data: describe your management processes to ensure security and restrict access 
    • Strategies for reuse: your data selection criteria, documentation; method(s) to enable access e.g. deposit 
    • Identify any/all ethical, legal and intellectual property considerations that may override access, and/or reuse to the data and what processes you have undertaken to manage them