Data and Digital Outputs Management Plan (DDOMP) Guide

Full Proposal Stage

Data and Digital Outputs Management Approach

In the Data and Digital Outputs Management section of the BFGO proposal template, researchers should address the following questions and elaborate on the information provided at the Pre-Proposal Stage, where appropriate. Some proposals require development of a full DMP/DDOMP in this stage. Included here are resources for Belmont Forum researchers to address each question:


    Training videos developed especially for Belmont Forum researchers and proposers use the Data Curation Profile method to help researchers develop specific, effective data management plans for their projects:
    • What types of datasets and other digital outputs of long-term value do you expect that the project will produce or reuse?
    • How do you intend to ensure that the data and digital outputs from your project conform to the Belmont Forum Open Data Policy and Principles and the FAIR Data Principles?
      • The Belmont Forum Open Data Policy and Principles, adopted in 2015, and the FAIR Data Principles, are listed below with specific resources to aid in fulfilling each requirement:
        1. Data should be discoverable through catalogues and search engines/Findability
          • Ensure that your data (with respect to any confidentiality and legality constraints) is housed in a reputable data repository that can be accessed openly through catalogues and search engines. To find a reputable data repository for your research project, see https://www.re3data.org/. Your DDOMP should include the proposed repository/repositories in which your data will be housed. DataONE offers a module here titled "Legal and Policy Issues" to help your team consider restrictions and ethical considerations as you plan to make your data open by default.
        2. Accessible as open data by default, and made available with minimum time delay/Accessibility
          • From day one of your research project, ensure that data is being collected, processed, and maintained in the appropriate manner for the data type, and by choosing a repository early on in the research process (see the previous policy), you can ensure that you are formatting your dataset(s) for quicker submission to its respective repository/repositories. For more information and case studies on making open data by default, visit the Open Data Charter.
        3. Understandable in a way that allows researchers - including those outside the discipline of origin - to use them/Interoperability
          • Your DDOMP should clearly outline what measures your research team will take to ensure that your data is readable and accessible across audiences both within and outside of your main discipline. Steps such as naming each data category with a descriptive title, organizing the data for machine readability, and using descriptive metadata can help ensure your data are accessible. For more guidance on creating accessible data for multiple audiences, view the module housed within the ESIP Data Management Training Clearinghouse titled "Data Formats: Using Self-Describing Data Formats," linked here. Also, consider providing a data dictionary with your data to help ensure that your data's relevance is preserved when viewed by other researchers. For more information on data dictionaries, please see this guide from the University of California-Merced here.
        4. Manageable and protected from loss for future use in sustainable, trustworthy repositories/Reusability
    • Which member(s) of your team will be responsible for developing, implementing, overseeing, and updating the Data and Digital Outputs Management Plan?
      • While all members of your research team should understand and support data management best practices within your project, it is important to delegate the development, implementation, oversight, and maintenance of the DDOMP to collaborators on your team who possess the necessary skillsets and training. In your full proposal, clearly outline which members(s) will be responsible for all logistical aspects of the DDOMP and how their qualifications and background suits this role. For more resources on what to consider as you designate a data manager, please visit the following links:
      • Does your research team currently lack experience in data management training and best practices? Consider getting up to speed using our e-Infrastructures & Data Management Toolkit, which contains location- and organization-specific resources on data management training, certifications, best practices, and more. Prior to submitting a project for funding through a Belmont Forum partner organization, members of your research team should have a general understanding of data management in order to properly complete this portion of the DDOMP.
    • How do you intend to manage the data and digital outputs during the project to ensure their long-term value is protected?
      • Data management should be at the forefront of your research project from day one. Taking steps early in the process is crucial to ensuring that the long-term value of the data and digital outputs are protected. The Community Owned Digital Preservation Tool Registry (COPTR) offers a finding and evaluation tool to help researchers discover the tools they need to perform specific digital preservation tasks. For best practices and training in data management, explore the resources available in our e-Infrastructures & Data Management Toolkit, which contains location- and organization-specific resources on data management training, certifications, best practices, and more.
    • How and by whom will the data and other digital outputs be managed after the project ends to ensure their long-term accessibility?
      • Determining by whom will the data and other digital outputs be managed after the project ends is a similar process to designating managers of your DDOMP (addressed above). Using the resources below for guidance, consider the training and qualifications among members of your research team that most strongly lends itself to long-term data management:
      • Once you designate the members of the research team who will oversee management of your data and digital outputs after the project ends, begin thinking about how you will share your data to ensure their long-term accessibility. Does your research team currently lack experience in managing and sharing open data? Find more information on best practices in open data sharing in our "Sharing Data" section of the BFE-INF Toolkit here. Many researchers choose to place their data in a repository when the project commences. Below are some excellent resources for where your team may choose to store data to ensure their long-term value:
        • Toolkit of repositories and databases for open data, searchable by discipline
        • GitHub, a version-control respository allowing researchers to store open data and accompanying licensing and terms of use. It must be noted, however, that GitHub is not an archival repository and if code/outputs are made available on the platform, your DDOMP should also outline another archival repository to store your data
        • figshare, a repository for academic institutions to store, share, and manage all of their research outputs
        • Zenodo, a general-purpose open-access repository for sciences and humanities research
    • What restrictions, if any, do you anticipate could be placed on how the data and digital outputs can be accessed, mined or reused?
      • Your research team will want to include information on how your data and digital outputs will be licensed, and how the licensing outlines terms of access, mining, and reuse. For more information on best practices in licensing, please see the "Licensing" section of the BFE-INF Toolkit here.
    • How will you ensure that any data security, privacy, and intellectual property restrictions associated with datasets and digital outputs will be honored and preserved in derivative products?
      • While open data can allow increased collaborative research and scientific innovation, many researchers and stakeholders also recognize a privacy risk inherent to open data. It is important that members of your research team understand the privacy risks associated with open data and take measures in your DDOMP to address those risks. For more information on preserving privacy in open data, please read Geothink's Citizen's Guide to Open Data here. As is the case with the previous question, to ensure that your data is being used appropriately in future derivatives, your research team will want to include information on how your data and digital outputs will be licensed, and how the licensing outlines terms of access, mining, and reuse as they pertain to data security, privacy, and intellectual property restrictions. For more information on best practices in licensing, please see the "Licensing" section of the BFE-INF Toolkit here.
    • What supporting documentation and other information (e.g., metadata) do you plan to make publicly accessible to support the longer-term reuse of the data and digital outputs?
      • Providing supporting metadata with your data is imperative in supporting open data and collaborative research. For resources on creating appropriate metadata for your project, please see the "Metadata" section of the BFE-INF Toolkit here.
      • Many research projects will have supporting documentation (not including metadata) which give other researchers insights into how to reuse the data and digital outputs for continuing research. Consider using a version-control document repository, such as GitHub Pages, to store your project's supporting documentation in an accessible format.
    • How have you accounted for the costs required to manage the data and digital outputs to ensure long-term accessibility?