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Generic DMPs

If your funder isn't listed, or if you are not seeking external funding, please use the generic template:

Complete a generic DMP

This link will take you through to DMPonline, a tool developed by the DCC (Digital Curation Centre) to help you write DMPs. You will need to create an account and link it to your University credentials if you have not already done so.

Once successfully logged into DMPonline, click on the link 'not applicable/not listed' when asked for a funder. Enter the name of the funder or leave blank if not applicable, tick the DCC guidance box and click Create Plan to get started.

Further guidance is provided for each section, but please do get in touch at if you need any help.

This quick guide will help you find your way around DMPonline: Getting Started with DMPonline (PDF, 2,278 KB)

The generic template covers the following areas, and some useful questions to consider include:

1) Introduction and context


  • Basic project information
    • Name of project
    • Project ID
    • Grant number (may be useful to populate this when reviewing your DMP post-award)
    • PI
    • Researcher ID (e.g. ORCID)
    • Data manager/contact
    • School / research group
    • Partner Organisations
    • Funding bodies
    • University user name
    • Telephone
    • Email address
    • Project start date (approximate)
    • Duration of project
    • Date of DMP creation
  • University policies
    • University Research Data policy
    • Related policies
  • Funder policy/policies

2) Data collection


  • Use of existing data
    • Have you reviewed existing data, in the University and from third parties, to confirm that new data creation is necessary?
    • Name of existing dataset(s)
    • Name of contact(s)/responsibility for data
    • Location
    • Contents
    • Brief description
    • Estimated size
    • Any licence issues
    • Comments (any additional information (e.g. use or restrictions)
  • Creation or capture of new data
    • How will you create/capture new data?
    • What types of data will be created and how will they be created?
      • Observational (e.g. sensor data, survey data, sample data, neuroimages
      • Experimental: gene sequences, chromatograms, toroid magnetic field data.
      • Simulation: climate models, economic models
      • Derived or compiled: text and data mining, compiled database, 3D models
      • Reference: gene sequence databanks, chemical, structures, spatial data portals
    • Which file formats will you use for each type of collection and why?
    • Do these formats and software enable sharing and long-term access to the data?
    • Are there any tools or software needed to create/process/visualise this data?
    • How will you structure your names and files?
  • Organisation of the data
    • How will you handle versioning?
    • What quality assurance procedures will you adopt?
  • Any other notes or comments on data collection

3) Documentation and Metadata


  • Contextual information
    • Is the data you will be capturing/creating self-explanatory or understandable in isolation?
    • If not, what contextual details will be needed to make your data meaningful?
    • How will you produce/capture this contextual information, and in what format?
  • Documentation
    • For each type of data/material you produce what metadata will you need?
    • Can you automate the creation of this metadata and if so, how?
    • What metadata standards will you use, and why have you chosen these standards and approaches for metadata and contextual documentation?

4) Ethics and legal compliance


  • Ethical issues
    • Are there ethical and privacy issues related to the data?
    • If yes, list these issues and how you will deal with them; for example:
      • Anonymisation of personal data
      • Retention or destruction of personal data
  • Legal issues
    • Who owns the copyright and Intellectual Property Rights (IPR) to the data?
    • If more than one person owns the IPR, what agreement do you have on how this is to be handled?
    • Freedom of Information (FoI) requests
    • How will the data be licensed for reuse?
    • Are there any restrictions on the reuse of third-party data?
    • Will data sharing be postponed / restricted; e.g. to publish or seek patents?
  • Commercial issues

5) Storage, backup and security during the project


  • Anticipated data volumes
    • How much data / associated materials in electronic form do you anticipate you will collect?
    • How much data / associated materials in paper form do you anticipate you will collect?
  • Data storage
    • Where do you intend to store the data during the project, and why?
    • Whose responsibility is the storage of the data?
  • Data backup
    • How will you back up the data?
    • How regularly will backups be made?
    • Who will be responsible for making the backups?
  • Data security
    • How will you ensure the security of your (including personal / sensitive) data?
    • How will you ensure that collaborators can access your data securely?
    • If you are collecting data in the field, how will you ensure its safe transfer into your main secure systems?

6) Data sharing, access and long-term preservation


  • Sharing data
    • When will you make the data available?
    • Who do you think may use this data in the future, and for what purposes?
    • Who will ensure that data will be deposited into a suitable repository at the end of the project?
    • How will potential users find out about your data?
    • How will you pursue getting a persistent identifier (DOI) for your data?
  • Restrictions on sharing data
    • Will there be any limits / restrictions on how people can use this data?
    • What action will you take to overcome or minimise restrictions?
    • For how long do you need exclusive use of the data, and why?
    • Will a data sharing agreement be required?
  • Selecting data to keep
    • What data must be retained / destroyed for contractual, legal or regulatory purposes?
    • How will you decide which other data to keep?
    • Who will ensure that data will be deposited into a suitable repository at the end of the project?
    • How will potential users find out about your data?
    • How will you pursue getting a persistent identifier (DOI) for your data?
  • Preserving data
    • What work is required to prepare the files so they are suitable for preservation, and have you costed in the time to do this?
    • Where will you keep the data that is retained?
    • If you are responsible for the long-term storage, how will you ensure it is preserved?
    • Have you costed in time and effort to prepare the data for sharing/preservation?
    • Are there costs that your chosen repository charges for preparing and storing the data long-term? If so, have you included these in your grant application's direct costs?
    • How will you destroy data that won't be preserved?

7) Roles and responsibilities


  • Who is responsible for implementing the DMP, and ensuring it is frequently reviewed and revised where necessary?
  • Who will be responsible for each data management activity (including names and/or roles)?
  • Will (and how will) responsibilities be split across partner sites in collaborative research projects?
  • Will data ownership and responsibilities for RDM be part of any consortium agreement or contract agreed between partners?
  • Who will ensure that any published research papers include a short statement on how the underlying research data may be accessed?

8) Resources


ActivityCosts to cover
Data description Are data in a spreadsheet or database clearly marked with variable and value labels , code descriptions, missing value descriptions, etc.? Do textual data like interview transcripts need description of context; e.g. included as a heading page?
Data cleaning Data that needs to be cleaned for validity, missing values, etc.
Metadata Do structured metadata need to be created when data are shared via a data centre/archive; e.g. completing a deposit form?
Formatting and organising data Are your data files, spreadsheets, interview transcripts, etc. all in a uniform format or style? Are files, records and items in the collection clearly named with unique file names and well-organised?
Transcription Transcribing data specifically so the data can be shared and reused
Digitisation Digitising analogue or paper-based research data to increase their potential for sharing
Data storage: during the project Storing data during project
Data storage: after the project Funder(s)/discipline-specific repository/datacentre - are any charges applied by the repository/datacentre for archiving/preserving the data?


Institutional repository - if you require extra storage on top of the maximum that the University provides, how much extra storage will you require?
Data security Protecting data from unauthorised access/use or disclosure
Converting file formats Do data need to be converted to a standard or open format?
Data transfer and access Special measures needed to transfer data from mobile devices, fieldwork sites, or from home to central server, for example
Data backup University provides regular (daily) backup on all data stored centrally; otherwise costs of backing up data frequently
Anonymisation Removing identifying information or conceal the identity of participants
Data sharing Preparing data to particular standards; e.g. for documentation or format
Consent for data sharing Asking participants for their consent for data to be shared (essential for qualitative interviews; less so in quantitative surveys where data can be more easily anonymised.
Staff training Additional specialist expertise (or training for existing staff)
Operationalising data management What measures are needed to implement and operationalise data management throughout the research lifecycle?

Microsoft Word logo

Generic template (Microsoft Word version)


Microsoft Word logo

Generic template with suggested answers (Microsoft Word version)


Research Data team

University of St Andrews Library
North Street
St Andrews
KY16 9TR
Scotland, United Kingdom

Tel: (01334) 462343