DMAP question guidance:
Getting Started section

Guidance for answering questions within the Getting Started section of your data management and access plan (DMAP)

How do I use this guidance?

This page provides guidance for each of the questions in the Getting Started section.

 

To help you, after each question, there are some domain specific example answers for:

 

  • Agriculture Production
  • Livestock, and
  • Crop Breeding.

What types of data and specific datasets will be generated from the project activities and/or collected from other sources?

 

Being as detailed as you can, list the types of data that will be collected and generated by the program including 3rd party sources, and from what sources (e.g. sensor readings, interviews).  Identify any foreseeable reuse applications of the data including target users.

What data curation (e.g. organizing, describing, cleaning, enhancing, and preserving) activities are planned to enable reuse or secondary use of the datasets?

Think about tools, services and vocabularies you will use for managing reference data, data cleaning, data preservation and data sharing. Specify data types, formats, and collation/generation methods for each

Where and when will the datasets be deposited and preserved to enable reuse or secondary use and under what license?

 

How will the data be documented, organized, and preserved for long-term discoverability, accessibility, and reuse, both during and after the project? Specify any standards, metadata or vocabularies used to ensure interoperability.

Please describe any informed consents, approvals, and/or agreements that may be required to enable use or reuse of the datasets by the foundation, project collaborators, and/or third-party researchers. What steps do you plan to take to obtain such consents, approvals, and agreements?

 

What factors may impact the future reuse of data collected or published via this program for collaborators, third parties and other research programs? Consider licencing, embargo periods, approvals, privacy constraints and data-sharing agreements.

Provide a breakdown of the portion of your budget allocated to support delivering FAIR data outcomes, including data volume estimates, infrastructure needs, and costs associated with data collection, integration, transformation, and long-term maintenance past the delivery of the initial program.

 

Estimate (as a percentage of your overall program) costs will be allocated to data acquisition, integration, processing and management. Include technical, support and other human resource costs. Account for post-program maintenance.

Who will be responsible for each data management task, and how will roles and responsibilities be clearly defined and communicated?

 

Outline who will take responsibility for different data management tasks and ensure roles and responsibilities are clearly defined and communicated.

How will compliance with the data management and access plan be monitored throughout the investment?

 

Describe how you will monitor compliance with the data management and access plan throughout the investment.

Describe your investment’s FAIR data commitments and how you will implement them?

 

This question is only for data-light investments that have been directed not to complete the direction setting and implementation sections.  These investments are following the Foundational FAIR workflow (and you can find descriptions of the CGIAR: Workflows here).

 

Consider the 4 FAIR criteria:

 

  • Findable: How will your data be more discoverable to users, how will they be bale to search and find it?  What tooling (e.g. repositories or catalogs) will you use to list your data?
  • Accessible: How will data be accessed during the project and after the project?  Will data be openly accessible to all users, or will access be restricted?  How will users access the data once they’ve found it?  What procedures will be in place to manage access to restricted data, think about the process of managing access requests?
  • Interoperable: What software or tools are required to use or analyze your data?  Will you provide documentation on how to use the software and tools and provide data in an interoperable way?
  • Reusable: What licenses will you use to clarify the terms of data reuse?  Are there any conditions or restrictions on how the data can be reused?  Where can users find the full text of the license?
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