Data Management Plans (DMPs)
Why research data management and plans?
A data management plan helps achieve optimal handling, organising, documenting and enhancing of research data. It is particularly important for facilitating data sharing, ensuring the sustainability and accessibility of data in the long-term and allowing data to be reused for future research.
For the effective management of data, planning must start when research is being designed and needs to consider both how data will be managed during the research and how they will be shared afterwards. This involves thinking critically about the sharing of research data, what might limit or prohibit data sharing, and whether any steps can be taken to remove such limitations.
Good practice is also to regularly update the data management plan as research progresses, for example during six-monthly project meetings. That way, the plan becomes important documentation and a quality assurance statement for your research data in the long term. Data Service
Plan, design & organise research processes
Researchers need to plan, design & organise their research process:
plan what data you need to collect, and how to collect it
decide what data will be used in your work and how will it be represented
plan how and where your research data will be stored for preservation and reuse
decide how your data may be made discoverable, accessed, shared and terms of re-use
There are the core elements of Data Management Plans (DMPs) and as such, are to be initiated at the start of any research project/undertaking. Well-developed DMPs increases research efficiency.
Creating a DMP is considered good research practice. Decisions made early on in the research project helps researchers save time, consider the necessary resources and costs. These will be required for funding/grant applications
A good DMP takes into account the applicable regulations and data policies, and considers the whole research data lifecycle. Ghent University
Rhodes University DMP requirements
The usual checklist for institutional requirements at the start of a research project include:
The title of the research project
The nature and purpose of the research project
Provide a researcher ID e.g. ORCID
Related policies e.g. department, institution, standards
Increasingly, Funders & Publishers require grant-holders to submit a DMP both at the preparation/early stages, and after the project is concluded.
More and more research funders require a short data management statement or plan as part of the grant proposal process, and a full-blown DMP after funding has been approved. Ghent University
Planning helps to focus on the resources and funding needed to implement good data management practices.
It also helps to clarify, at an early stage, individual and institutional roles and responsibilities.
Planning is also essential to facilitate compliance with ethical codes and data protection laws.
Many public research funders require a data management and sharing plan as part of research grant applications. They also expect data to be shared. UK Data Service
What to cover in your DMP may also depend on your funder. Many research funders provide their own DMP template.
MANTRA - John MacInnes - Importance of data management planning
4 May 2012
MANTRA - Richard Rodger - Advising PhD students on data management planning
30 May 2014
Where can researchers find assistance and support with writing a DMP?
Researchers are often unfamiliar with the support and services available to them
Listed below are some guides for researchers:
Training
UK Data Service - interactive RDM training hub
Rhodes University Research Support Units
Some Universities and Institutions have a mandate for all researchers to provide DMPs for each research project. Rhodes University does not have a DMP mandate.
However, it is strongly advocated that all Masters & PhD students, and researchers develop DMPS at the start of the research project
Online tools
DMP Tool. Create Data Management Plans that meet researchers' requirements and promotes research
DMPonline.be is an online planning tool to help you write an effective DMP based on an institutional or funder template.
Examples of DMPs to suit all academic disciplines
Sample data management plans can be viewed at:
Data Management Sample Plans (The Odum Institute, UNC Chapel Hill - Data Archive) ?
Sample NSF Data Management Plans (University of Michigan Library)
Sample Data Management Plan for Depositing Data with ICPSR (Inter-university Consortium for Social and Political Research: Data Management and Curation)
Data Management Plans - Biology, Chemistry (New England Collaborative Data Management Curriculum, Lamar Soutter Library, University of Massachusetts Medical School
Data Management Sample Plan and Guidance (University of Pittsburgh, Pymatuning Laboratory of Ecology (PLE))
Write a data management plan (MIT Libraries: Data management)
Data Management Planning (Data One)
Not sure what to write in your DMP? Have a look at example DMPs from other research projects (but keep in mind that not all have been reviewed for quality!).
You can find public DMPs via the following sources:
Examples on the Digital Curation Centre (DCC) website
DMPs published in the Research Ideas and Outcomes open access journal
Public Horizon 2020 DMPs collected by OpenAIRE Austria
Some example DMPs from Ghent University researchers:
Engineering PhD DMP (cluster (Bioscience) Engineering; FWO template)
C-PlaNet DMP (cluster (Bioscience) Engineering; H2020 template)
Search Schemes for Sequence Alignment DMP (cluster (Bioscience) Engineering; FWO template)
Statistical Inference DMP (cluster Natural Sciences; FWO template)
Actors You Can Trust DMP (cluster Natural Sciences; FWO template)
Origin of Cosmic Dust DMP (cluster Natural Sciences; ERC template)
Health Promotion DMP (cluster Life Sciences & Medicine; FWO template)
Getting a Grip DMP (cluster Law, Arts & Humanities; UGent generic template)
Examples of DMPs from GOFAIR
Data Stewardship Wizard created by ELIXIR CZ and NL
DMPonline of the Digital Curation Centre (DCC), UK
DMPTool of University of California Curation Center of the California Digital Library (CDL), USA
RDMO Research Data Management Organiser of the German Research Foundation, Germany
Data Management Plan Catalogue of the LIBER Research Data Management Working Group
Practical Guide on Research Data Management , developed by experts from Science Europe Member Organisations
How do you write a DMP?
DMP Checklist
Data management plans (DMPs) are documents prepared by researchers as they are planning a project and writing a grant proposal.
Use a checklist to develop a data management plan
The DCC checklist was developed to guide researchers through the step-by-step process to managing data from the funding application, through the research process, publication and finally post-publication
The checklist helps researchers to think through the key issues relating to research data management and any costs involved. It provides a very comprehensive list of questions to be considered across ten headings. It also contains a series of guidance notes designed to assist in the completion of a DMP, often accompanied by hyperlinks to useful and authoritative resources
Keep in mind
It is good practice to review the DMP throughout the research process
The UK Data Service data management checklist can point you to key matters to consider in a data management and sharing plan.
For more insight into the questions you should ask and answer, check out Data Management Checklist (UK Data Archive). University of Pittsburgh
Data Collection
What Data will be collected?
The collection of data is dependent on the type of research project.
What data will be collected
Roles and responsibilities for data management on your research team. University of Pittsburgh
Assign roles and responsibilities to relevant parties. UK Data Service
How will the data/samples be collected and analysed?
How will the samples be collected and analyzed?
Whether you collect new data or reuse existing data. University of Pittsburgh
The kind of data collected and its format. University of Pittsburgh
The quantity of data collected. University of Pittsburgh
Data Tools
These are the general questions that could be asked when researchers are deciding what to write about the data collection tools they plan to use
What resources will be required for data collection?
What resources will be required for data collection? University of Pittsburgh
Who will be responsible for data management and what additional resources may be required. Ghent University
Assign roles and responsibilities to relevant parties. UK Data Service
Design data management according to the needs and purpose of the research. UK Data Service
Aim to incorporate data management measures as an integral part of your research cycle. UK Data Service
Data Management
These are the general questions that could be asked when researchers are deciding what to write about their data management plans
How will the data be organised?
How data will be organized within a file?
What file formats will be used?
What types of data products will be generated?
How will the data be organized, described and labeled? University of Pittsburgh
How will version control be handled?
How will version control be handled? DCC checklist
How will the versions be tracked? University of Pittsburgh
How will the data be backed up, and how often?
How will data be backed up, and how often? DCC checklist
How data will be stored and backed up throughout the research project? Ghent University
Know your institution’s policies and services, such as storage and backup strategy, research integrity framework, Intellectual Property rights policy, and any data sharing facilities like an institutional repository. UK Data Service
Data Repository
A Data Repository is a data archiving centre.
Select a data repository that is most appropriate for the data you will generate and for the community that will make use of the data
Open Data repositories
Check with the repository about requirements for submission, including required data documentation, metadata standards, and any possible restrictions on reuse
Rhodes Digital Commons (Research Data)
Rhodes University subscribes to Figshare as the institutional research data repository
Institutionally generated research data is stored and archived in the repository
Data Documentation
In order for the research data to be easily discovered, it should be documented appropriately.
By getting into the practice of documenting data at the very start of the process, researchers will save a great deal of time and resources in the long run
Creating comprehensive data documentation is easiest when begun at the onset of a project and continued throughout the research. UK Data Service
How will the data be documented?
Check with the repository about requirements for submission, including required data documentation, metadata standards, and any possible restrictions on reuse
Organizing and describing or labeling the data. University of Pittsburgh
Metadata: descriptions and standards
Metadata is "data that provides information about other data", but not the content of the data, such as the text of a message or the image itself. There are many distinct types of metadata, including: Descriptive metadata – the descriptive information about a resource. It is used for discovery and identification. Wikipedia
Data Sharing
Develop a plan for sharing data with the project team, with other collaborators, and with the broader research community
Data release
Consider conditions for data release
Target release dates
How will the data be released?
Data sharing policy
Know your institution’s policies and services, such as storage and backup strategy, research integrity framework, Intellectual Property rights policy, and any data sharing facilities like an institutional repository. UK Data Service
Data Storage
Know your institution’s policies and services, such as storage and backup strategy, research integrity framework, Intellectual Property rights policy, and any data sharing facilities like an institutional repository. UK Data Service
How will you back up your data?
Storage of active data and backup policy and implementation. University of Pittsburgh
Storage and archiving options and requirements. University of Pittsburgh
How will you manage copyright and intellectual property?
How will you manage copyright and Intellectual Property Rights (IP) issues
Who owns the data?
How will the data be licensed for reuse?
Privacy, consent, intellectual property, and security issues. University of Pittsburgh
How will the data be licensed for reuse?
Data Ethics
How any ethical and legal issues will be dealt with. Ghent University
Know your legal, ethical and other obligations regarding research data, towards research participants, colleagues, research funders and institutions. UK Data Service
Human subjects
Have you gained consent for data preservation and sharing?
How will you protect the identity of participants if required?
Privacy, consent, intellectual property, and security issues. University of Pittsburgh
How will you protect the identity of participants?
Sensitive data
How will sensitive data be handled to ensure it is stored and transferred securely
MANTRA course
The aim of this unit is to help you to think through how you will collect, store and share the wealth of research data you will collect during your research project
After completing this unit you will:
▪ Understand the basic principles of research data management and the key role that data management plays in the responsible conduct of research.
▪ Consider your research in terms of the research data lifecycle and be able to plan ahead to prepare for potential data management pitfalls.
▪ Know about the data management planning requirements of different research funders.
▪ Be aware of data management planning tools, support and guidance which are available to academic researchers.
▪ Be able to use the information in this unit to develop a data and software management plan, and to maintain it through the course of your research.