You can find here guidance on addressing data management in grant applications, and information about the requirements of some funders.
If you are required to complete a data management plan (DMP) as part of a grant application, this must be reviewed by the Research Data Manager prior to submission. This only applies where this University is the lead applicant.
You should ensure that a draft of your DMP is made available to the Research Data Manager for review and comment no later than 5 working days before any internal deadline or the application submission date. Please include the rest of the research proposal to provide context (even if this is a rough draft).
You will need to consider data management even if the funder does not ask for a DMP, and think about any requirements for storage, high performance computing, and long-term preservation and sharing of data, and associated costs where relevant. Funders will expect you to plan to deposit data that support project findings in a suitable data repository for long-term preservation and access, unless there is a valid legal, ethical or commercial reason why they cannot be shared. Sharing of data supporting project findings is also a requirement of the University’s Research Data Management Policy.
You can contact the Research Data Manager, via email or by booking a consultation, at any stage if you require advice and support in writing your data management plan.
The Resarch Data Service holds some examples of good quality DMPs submitted by University resarchers to selected funders which can be shared with members of the University on a confidential basis. Please ask us if you would like to see an example of a DMP written for your target funder.
You may also find DMPs included in applications made by University of Reading researchers stored in the Successful proposal library (login required).
Bear in mind that specific examples should be used as inspirations rather than models to be followed. Individual DMPs may have weaknesses as well as strengths, and variable relevance to your own research context. Applications in the Successful proposal library are not included on the basis of the quality of the DMP.
A data management plan (DMP) is any section of a grant application where the applicant is required to discuss the collection, management, preservation and sharing of research data associated with the proposed research. Funders will specify their requirements for this section in the scheme notes or application guidance. Some funders provide a template or a set of headings that can be used to structure the plan.
Funders ask applicants to demonstrate a considered approach to data management because they expect the researchers they fund to collect and manage their data responsibly, and to ensure these data are preserved and made as accessible as possible when the research findings are published. Many funders have data sharing policies which set out their expectations: see the Policies section for details.
DMPs requirements can vary. Shorter DMPs tend to focus on identifying data outputs and the means by which they will be preserved and shared. Longer DMPs may ask about aspects of data management during the project, such as methods for data collection, curation and quality control, procedures for secure storage and sharing of data, plans for addressing legal and ethical matters relating to research participants, and management of intellectual property.
A DMP can also help you identify where there may be costs for storage of data during the project, for computing resources required to generate/process data, and for archiving of data on project completion. These costs may need to be included in the project budget. The Research Data Manager and your Research Development Manager can help you identify costs and include them in your budget.
Common elements that may need to be addressed in grant application DMPs are highlighted here. For more information on the different aspec ts of research data management discussed here, you can refer to the relevant section of the Resarch data handbook.
The funders will always ask you to specify the planned data outputs. You should describe your data outputs in terms of data type, content (key variables and characteristics: what you will record/measure, etc.) and approximate expected quantity (e.g. number of measurements, model runs, survey sample size, total volume of data if substantial). It may be useful to specify the format or formats in which the data will be stored. For long-term preservation, open formats (such as CSV) or widely-used proprietary formats should be used where possible. Some data repositories may require or recommend submission of data in specific formats. We provide guidance on file formats.
Any code you may write to process or analyse data should also be described. If code will be required to replicate your final results, it should be preserved and made accessible to others as well as your data.
You may be ask to provide information about planned methods for data collection and management, how data will be documented, and procedures for data validation and quality control.
You may want to provide information about proposed instruments and methods for data collection. Instruments may include hardware, software and paper-based instruments, e.g. data collection forms, lab notebooks. If you will be using any experimental facilities, e.g. the ISIS neutron and muon source, or research infrastructure, such as the NERC JASMIN supercomputing service, this should be indicated.
You should plan to use standard procedures wherever possible in your research methods, and to use standard data and metadata formats to record the information you collect. Standards promote reproducibility of research, and understanding and re-use of data. Some data respositories may provide guidance on suitable formats for long-term data preservation or require submission of data in specified formats, so it is always worth reviewing the submission guidelines of the repository you plan to deposit project data in..
Quality control should be considered at all stages of your data workflows, from point of capture, through all subsequent processing, to ensure accuracy and consistency of data. Refer to our guidance on quality control for more information.
Data collected/held at the University should be stored using University-managed infrastructure, which will provide information security, replication in separate data centres, automated backup and file recovery. In practice this means planning to use OneDrive, Teams, or network-based services such as the Research Data Storage service for large volumes of data or UoR REDCap (which can be used to collect consent and data from participants). You may need to address storage of non-digital data, e.g. signed consent forms, in appropriate secure environments.
If data are acquired using specialist infrastructure, such the JASMIN supercomputing environment, raw data may be stored in the facility infrastructure, and data copied or extracted locally as required.
If the project team includes external collaborators, methods of enabling secure shared access to data should be considered, for example, by means of a project Teams site or by setting up an external user account.
If you have computing-intensive requirements, custom specifications of CPU, memory, storage and GPU can be purchased from the University on a pro rata basis. Information is available from DTS.
Note that the high-volume Research Data Storage and the Reading Research Cloud are chargeable services. If you plan to use these, you should cost your specification and include costs in your budget.
Refer to our guidance on data storage for more information.
If you will be collecting data from research participants, you may be asked to discuss technical and organisational measures that will be used to maintain data securely during the project in compliance with ethical obligrations and data protection law, and the methods you will use to ensure that relevant research data can be safely and ethically shared in support of project findings.
Measures to be used during the project will include collecting data in accordance with standard ethical procedures, using appropriate secure infrastructure and access management to store and and share data safely within authorised networks, and applying risk management measures such as link-coding or pseudonymisation of data to minimise the risk of disclosure. Refer to our guidance on information security and research ethics for more information.
Methods that can be used to enable sharing of data collected from participants include using appropriate informed consent procedures that notify intention to preserve and share research data, anonymising data, and, where data present a higher level of risk and are not suitable for sharing as anonymised open data, archiving them in a suitable controlled-access repository. Refer to our guidance on research ethics and data preservation and sharing for more information.
You may be asked to state who will own IP rights (IPR) in data outputs generated by the project. Where they are created by University employees, the rights-holder will be the University. Where data outputs are jointly created by employees of more than one institution, IPR will be shared.
Where secondary sources will be used, you should investigate existing IPR in these sources to ensure they will not inhibit use of the data for the purposes of the project. If terms of use or licence conditions are not clearly stated, these should be investigated with the data provider. Where you plan to produce derived outputs that may incorporate or build on existing resources, you should seek permission to share these where possible.
Where copyright materials are contributed by research participants (e.g. photographs) you should ask them to transfer copyright in the materials to the University or to grant the University a licence to use and distribute the materials. Refer to our guide on participant-created IP for more information.
Funders expect that primary data collected in support of project findings will be deposited in a suitable data repository, and made available, under open licence where possible, no later than publication of related findings. They may specify an expectation that data will made FAIR (Findable, Accessible, Interoperable and Re-usable). In practice this is achieved by depositing data in a repository.
This expectation also applies to data collected from reserch participants. In order to enable sharing of these data, you would be expected to use appropriate consent procedures that notify the intention to share data, anonymisation techniques to render data safe for sharing, and, where data will present a higher level of risk and are not suitable for sharing as anonymised open data, plan to archive them in a suitable controlled-access repository (see below). Refer to our guidance on research ethics for more information on managing and sharing participant data.
You should identify the repository or repositories that will be used to preserve and enable access to your data when your research findings are released. Some funders, such as NERC and ESRC, also manage data centres that applicants may be expected or encouraged to use; but most will not require the use of a specific repository.
You should plan to use relevant external services where these exist (such as the repositories of the European Bioinformatics Institute for biological study data, the UK Data Servivce ReShare repository for social and behavioural science data, etc.). You may also plan to deposit data in the University's Research Data Archive.
If controlled access to higher-risk participant data is required, this can be provided by the UK Data Service ReShare repository and the Research Data Archive, as well as some other repositories. Refer to our guidance on controlled-access repositories for more information.
Most data repository services are free to use, but there are some exceptions. See the Costs tab for further information.
You should also plan to archive any code written that is required to replicate results. Code repository platforms such as GitHub are good solutions for the ongoing management and sharing of code, but they are not preservation solutions, so deposit in a data repository should also be planned. GitHub has an integration with the Zenodo repository, which will archive new releases to Zenodo, where they will be preserved and assigned DOIs that can be used for version-specific citation from project findings. Small scripts, e.g. for data processing and analysis, can be archived alongside data files in any data repository.
Data should be made accessible no later than publication of the related findings, unless there is a reason why release needs to be delayed. For example, if there is a commercial pathway for the research and data are part of the information for which IP protection will be sought, their release may be delayed until protection is secured. You would be advised to consult the Intellecutal Property and Commercialisation office if you need to determine whether access to data may need to be restricted, and if so at what stage it will be possible for them to be released.
A typical example of a statement addresseing data preservation and sharing might be as follows:
You may need to include costs for use of the Research Data Storage service or the Research Cloud computing platform. Information about these services and guidance on identifying a suitable specification for your requirements is provided by DTS. We provide an overview of data storage options.
Archiving costs may also need to be included in your grant budget. Most data centres/data repositories do not routinely charge for deposit of data. Possible exceptions include:
You can refer to our guidance on choosing a data repository for more information, or contact us for advice.
One of the first things you should do when you receive an award is create a DMP as a practical tool for the research project. This may build on a DMP that was created as part of the application, but to be of practical use it will need to be developed in more detail, and kept up to date throughout the project.
We provide a Post-award data management guide (PDF) to help PIs and project teams address a project’s data management requirements when a project is being set up. You can also refer to the section on Data management planning in the Research data handbook.
Creating a project DMP is something you should do for any project, irrespective of the funder's requirements. But note that some funders make a DMP a required deliverable for funded projects:
UKRI's Common principles on data policy set out shared expectations relating to the preservation and sharing of research data generated in funded projects. UKRI expects researchers to make their research data openly available with as few restrictions as possible in a timely and responsible manner. These expectations apply even where research councils do not have their own research data policy and where councils or specific funding opportunities do not not require a DMP.
Costs associated with research data management and sharing should be included in the application, as appropriate.
| Funder | Requirements |
|---|---|
| UKRI/cross-council (e.g. FLF) | DMP must comply with UKRI's data sharing policy, and the policy and guidance of the research council most applicable to the type(s) of research data that will be generated. Requirements specific to the award may be stated in the funding opportunity guidance. |
| AHRC | No DMP required at application. |
| BBSRC | DMP must comply with BBSRC data sharing policy, which includes guidance on what to include in the DMP. |
| EPSRC | DMP not required at application. |
| ESRC | DMP must comply with ESRC research data policy and should be structured following ESRC DMP guidance. |
| MRC | DMP must comply with MRC data sharing policy and follow the MRC data management plan template. |
| NERC | DMP must comply with NERC data policy and follow NERC data management plan guidance. |
| STFC | DMP must comply with STFC scientific data policy and follow STFC data management plan guidance. |
Open Science expectations apply to all research funded under the EU's Horizon Europe programme, including Marie Skłodowska-Curie Actions (MSCA) and European Resarch Council (ERC) grants, although requirements at application vary. All awarded Horizon Europe projects (including ERC grants) are required to submit a DMP deliverable within the first six months of the grant, and will be expected to plan for sharing of relevant data in accordance with FAIR Principles.
The main expectations are outlined below. You can refer to the Horizon Europe open science and research data management guide (PDF) for full information.
Applications within the main Horizon Europe programme must include sections in part B 1.2 of the application form, each up to one page in length, describing how appropriate open science practices will be implemented as part of the project’s methodology, and how research data and outputs other than publications generated/collected during the project will be managed.
Applications for MSCA funding must include a section in part B 1.2 of the application form, up to half a page in length, describing how appropriate open science practices will be implemented as part of the project’s methodology, and how research data and outputs other than publications generated/collected during the project will be managed.
ERC grant applications are evaluated on the criteria of scientific excellence alone, and there is no requirement to address open science and data management in the application.
There are five mandatory open science practices within the Horizon Europe programme which must be addressed in applications:
There are also recommended open science practices endorsed by Horizon Europe. Adoption of these, where relevant to your project, will result in a higher evaluation score. These are:
As well as any data-related costs, costs for publishing Open Access articles should be included in your budget where UoR is expected to be the corresponding institution for an article, and where journals are not covered by the University's publisher agreements. As a ballpark figure you can estimate a cost of £3,500 including VAT per article.
Applicants to many British Academy funding schemes, including co-funded schemes, are required to address data management and/or data sharing by completing either a Digital Resource/Deposit of Datasets or (for schemes co-funded by the Royal Society) an Outline of a Data Management and Data Sharing Plan section in the application form.
The standard application form for British Academy funding schemes includes the following sections.
Digital Resource
If the primary product of the research will be a digital resource have you obtained guidance on appropriate standards and methods? (Y/N)
Deposit of Datasets
Please provide details of how and where any electronic or digital data (including datasets) developed during the project will be stored, along with details on the appropriate methods of access. (500 words)
The scheme notes guidance for this section is as follows:
Digital resources created as a result of research funded by the Academy should be deposited in an appropriately accessible repository. Of course, we do not expect confidential data to be readily available.
If applicable to your project, you will need to provide details of how and where any electronic or digital data (including datasets) developed during the project will be stored, along with details on the appropriate methods of access.
Applicants should ensure that any necessary technical advice is obtained before commencing work that involves the creation of digital resources. Please confirm whether the primary product of the research will be a digital resource, and if so how and where it will be deposited.
The British Academy co-funds some schemes, including the BA/Royal Academy of Engineering/Royal Society/Leverhulme APEX Awards, and Newton International Fellowships. The Royal Society guidance on completing the Outline of a Data Management and Data Sharing Plan applies for these schemes and should be referred to.
Applicants to Royal Society funding schemes are required to complete an Outline of a Data Management and Data Sharing Plan section in the application form. The requirement is the same for all funding schemes, including co-funded schemes, such as Newton International Fewllowships co-funded with the British Academy.
There is a 200-word limit for this section, so you need not go into detail. The most important thing is to clearly identify your data outputs and the data repository or repositories you will use to preserve and share them on completion of the research.
The Royal Society supports science as an open enterprise and is committed to ensuring that data outputs from research supported by the Society are made publicly available in a managed and responsible manner, with as few restrictions as possible. Data outputs should be deposited in an appropriate, recognised, publicly available repository, so that others can verify and build upon the data, which is of public interest. To fully realise the benefits of publicly available data they should be made intelligently open by fulfilling the requirements of being discoverable, accessible, intelligible, assessable and reusable.
The Royal Society does not dictate a set format for data management and sharing plans. Where they are required, applicants should structure their plan in a manner most appropriate to the proposed research. The information submitted in plans should focus specifically on how the data outputs will be managed and shared, detailing the repositories where data will be deposited. In considering your approach for data management and sharing, applicants should consider the following:
If the proposed research will generate data that is of significant value to the research community, then please provide details of your data management and sharing plan. (200 words max.)
Wellcome ask for an outputs management plan to be submitted with applications for funding. This is somewhat broader in scope than most DMPs, in that it is expected to include any software outputs, materials (such as reagents, cell cultures, specimens, etc.) and intellectual property that will be generated by the project in addition to data. The requirements for data and software are to discuss:
Wellcome provides very comprehensive guidance on completing the outputs management plan, covering data, software, materials and IP, and including examples of what they consider to be good plans from previous applicants.
