data management plan phd

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• Open Science
• Data management
• Before research

Preparing a Data Management Plan (DMP)

A Data Management Plan is a document specifying how research data will be handled both during and after a research project . It identifies key actions and strategies to ensure that research data are of a high-quality, secure, sustainable, and – to the extent possible – accessible and reusable.

Why develop a DMP?

Creating a DMP is considered good practice for any research project using or generating data. After all, planning is the first step towards proper research data management.

Decisions made early on affect what you can do later, so good and timely planning can save you a lot of time and problems in the longer run. It also helps you consider the necessary resources and costs for data management , so you can include these in your grant applications.

In addition, you may be required to draft a DMP, for example by your research funder.

DMP requirements

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's RDM policy framework also strongly encourages all researchers to create DMPs for their projects. In addition:

• A DMP is mandatory for BOF- and IOF-funded research awarded from 2020 onwards.
• The Education & Examination Code requires all PhD students first enrolled in the academic year 2020-2021 or after to write a DMP. 
•  Some Faculties (e.g. Psychology and Educational Sciences, Law and Criminology) require PhD students and/or other researchers to submit a DMP.
• DMPs may also be required as part of the ethical approval process.
• Even if a full DMP is not required, a record of processing activities is needed to comply with the GDPR when working with personal data. This GDPR record is also required to initiate the drawing up of an agreement by Ghent University's TechTransfer team (if applicable).

Overview of funder and university-wide DMP requirements

Writing a DMP

What topics to cover.

A good DMP takes into account the applicable regulations and data policies, and considers the whole research data lifecycle .

It typically addresses the following topics :

• What data will be collected , and how.
• How data will be documented .
• How any ethical and legal issues will be dealt with.
• How data will be stored and backed up during research.
• Any plans for preserving (some of the) data beyond the project's end.
• Any plans for sharing or providing access to (some of the) data.
• Who will be responsible for data management and what additional resources may be required.

What to cover in your DMP may also depend on your funder. Many research funders provide their own DMP template . Ghent University also has various templates for researchers not writing a DMP for a specific funder. Funder and institutional templates can be accessed via DMPonline.be .

Online planning tool DMPonline.be

DMPonline.be is an online planning tool to help you write an effective DMP based on an institutional or funder template.

With the tool, you can:

• Sign in with your UGent account and create a new DMP. The tool takes you step-by-step through a number of data management questions based on a relevant DMP template. Guidance is available to help you answer the questions.
• Select a suitable DMP template and guidance depending on the requirements. The tool contains different templates and guidance associated with either UGent (generic or Faculty-specific) or external research funders (including FWO, VLAIO cSBO, BELSPO, ERC and Horizon Europe).
• Easily share plans with collaborators within and outside UGent
• Export completed plans in various file formats

Need more help? Check this (re)Search tip on how to use DMPonline.be .

A living document

Treat your DMP as a living document: created before or in the early stages of research, but updated where necessary in the course of the project. You may not know all the answers at the outset, and circumstances may change.

The final DMP

At the end of the project, the final DMP must be submitted to some funders. This final version of the DMP will contain a description of how the research data were managed during research, and how the generated/collected research data are shared and preserved.

Need help? Check this (re)Search tip on how to write a final DMP.

Example DMPs

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:

• LIBER DMP Catalogue
• 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)
• RNAI in plants DMP (cluster (Bioscience) Engineering; UGent generic 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)

DMP feedback

For various templates, evaluation grids are available to (self-)assess your completed DMP:

• UGent Generic DMP review rubric
• Horizon 2020 FAIR DMP review rubric
• Horizon Europe FAIR DMP review rubric
• FWO DMP review rubric
• ERC DMP review rubric
• BELSPO DMP review rubric

You can also request feedback on your DMP via [email protected] . If you have to submit a DMP for a funder, please allow us enough time to review your DMP. Do not wait until just before the submission deadline to get in touch!

More information

• External funder policies
• Ghent University policy
• DMPonline.be: how do I write a Data Management Plan?
• How do I complete the DMP section in my FWO application?

• Data Management Plans

What is a Data Management Plan (DMP)?

For nih (recently updated), what about other government agencies, writing a plan, having a plan reviewed, what generally goes into a dmp, anticipate the storage, infrastructure, and software needs of the project, create or adopt standard terminology and file-naming practices, set a schedule for your data management activities, assign responsibilities, think long-term.

A DMP (or DMSP, Data Management and Sharing Plan) describes what data will be acquired or generated as part of a research project, how the data will be managed, described, analyzed, and stored, and what mechanisms will be used to at the end of your project to share and preserve the data. 

One of the key advantages to writing a DMP is that it helps you think concretely about your process, identify potential weaknesses in your plans, and provide a record of what you intend to do. Developing a DMP can prompt valuable discussion among collaborators that uncovers and resolves unspoken assumptions, and provide a framework for documentation that keeps graduate students, postdocs, and collaborators on the same page with respect to practices, expectations, and policies. 

Data management planning is most effective in the early stages of a research project, but it is never too late to develop a data management plan.

How can I find out what my funding agency requires?

Most funding agencies require a DMP as part of an application for funding, but the specific requirements differ across and even within agencies. Many agencies, including the NSF and NIH, have requirements that apply generally, with some additional considerations depending on the specific funding announcement or the directorate/institute. 

Here are some resources to help identify what you’ll need:

• DMP Requirements  (PAPPG, Chapter 2, Proposal prep instructions)
• Data sharing policy  (PAPPG, Chapter 11, Post-award requirements)
• Links to directorate-specific requirements  
• Data Management and Sharing Policy Overview
• Research Covered Under the Data Management and Sharing Policy
• Writing a Data Management and Sharing Plan
• Final NIH Policy for Data Management and Sharing
• NOTE : Some specific NIH Institutes, Centers, or Offices have additional requirements for DMSPs. For example, applications to NIMH require a data validation schedule. Please check with your institute and your funding announcement to ensure all aspects expected are included in your DMSP.
• Data Sharing Policies
• General guidance and examples
• Data Sharing and Management Policies for US Federal Agencies

Need help figuring out what your agency needs? Ask a PRDS team member !

Where can I get help with writing a DMP?

With recent and upcoming changes to the research landscape, it can be tricky to determine what information is needed for your Data Management (and Sharing) Plan. As a Princeton researcher, you have several ways of obtaining support in this area

You have free access to an online tool for writing DMPs: DMPTool . You just need to sign in as a Princeton researcher, and you’ll be able to use and adapt templates, example DMPs, and Princeton-specific guidance. You can find some helpful public guidance on using DMPTool created by Arizona State University.

You are also welcome to schedule an appointment with a member of the PRDS team. While we are unable to write your DMP for you, we are happy to review your funding call and guide you through the information you will need to provide as part of your DMP

PRDS also offers free and confidential feedback on draft DMPs. If you would like to request feedback, we require:

• Your draft DMP (either via email [[email protected]] or by selecting the “Request Feedback” option on the last page of your DMP template in the DMPTool ).
• Your funding announcement.
• Your deadline to submit your grant proposal.

NOTE: Reviewing DMPs is a process and may involve several rounds of edits or a conversation between you and our team. The timeline for requesting a DMP review is as follows:

• Single-lab or single-PI grants:  no fewer than 5 business days ; 
• Complex, multi-institution grants, including Centers:  no fewer than 10 business days . 

We will make every effort to review all DMPs submitted to us, however, we cannot guarantee a thorough review if submitted after our requested time frame.

Details will vary from funder to funder, but the  Digital Curation Centre’s Checklist for a Data Management Plan  provides a useful list of questions to consider when writing a DMP:

• What data will you collect or create?       Type of data, e.g., observation, experimental, simulation, derived/compiled      Form of data, e.g., text, numeric, audiovisual, discipline- or instrument-specific      File Formats, ideally using research community standards or open format (e.g., txt, csv, pdf, jpg)
• How will the data be collected or created?
• What documentation and metadata will accompany the data?
• How will you manage any ethical issues?
• How will you manage copyright and intellectual property rights issues?
• How will the data be stored and backed up during research?
• How will you manage access and security?
• Which data should be retained, shared, and/or preserved?
• What is the long-term preservation plan for the dataset?
• How will you share the data?
• Are any restrictions on data sharing required?
• Who will be responsible for data management?
• What resources will you require to implement your plan?

Additional key things to consider

Consider the types of data that will be created or used in the project. For example, will your project…

• generate large amounts of data?
• require coordinated effort between offsite collaborators?
• use data that has licensing agreements or other restrictions on its use?
• Involve human or non-human animal subjects?

Answers to questions like these will help you accurately assess what you’ll need during the project and prevent delays during crucial stages. 

Decide on file and directory naming conventions and stick to them. Document them (either independently or as part of a standard operating procedure (SOP) document) so that any new graduate students, post-docs, or collaborators can transition smoothly into the project.

Plan and implement a back-up schedule onto shared storage in order to ensure that more than one copy of the data exists.  Periodic file and/or directory clean-ups will help keep “publication quality” data safe and accessible.

Make it clear who is responsible for what. For example, assign a data manager who can check that backup clients are functional, monitor shared directories for clean-up or archiving maintenance, and follow up with project members as needed.

Decide where your data will go after the end of the project. Data that are associated with publications need to be preserved long-term, and so it’s good to decide early on where the data will be stored (e.g. a discipline or institutional repository) and when and how it will get there. Other data may need this level of preservation as well. PRDS can help you find places to store your data and provide advice about what kinds of data to plan to keep.

• Events and Training
• Getting Started
• Guides for Good Data Management
• Data Curation
• Data Ownership
• Large Data Resources
• Data Use Agreements
• Human Subjects
• Finding Datasets
• Non-Human Animal Subjects
• Data Security
• File Organization
• READMEs for Research Data
• Research Computing Resources
• Collaboration Options
• Help with Statistics
• Publisher Requirements
• Data Repositories
• Princeton Data Commons
• Getting Started as a Princeton Data Commons Describe Contributor
• Publishing Large Datasets
• Using Globus with Princeton Data Commons (PDC)
• Storage Options
• Tools and Resources

Theses: Data Plan for your PhD

• Finding theses @ Soton
• Data Plan for your PhD
• Deposit - Faculty Office
• Deposit - PGR Manager & Pure
• Thesis Data Deposit
• Restricting Access
• Using your own publications in your thesis
• Info. for Faculty Admin
• Info. for Supervisors

DMP for your PhD research

All first year post graduate researchers should complete a data management plan for their research and include it as part of their first three month review.  There is also a Blackboard course  Data Management Plans for Doctoral Students -  mandatory for all new doctoral students - to introduce you to research data management and help you complete the plan. Log into Blackboard using your university username and password.

A data management plan or DMP is a living document that helps you consider how you will organise your data, files, research notes and other supporting documentation throughout the length of the project.  The aim is to help you find these easily, keep them safe and have sufficient documentation to be able to re-use throughout your research and beyond.

You will need to complete a preliminary data management plan in your first three months, along with your Academic Needs Analysis.  Your DMP will continue to develop as your research progresses and you will need to update and review your DMP at every progression review. ( Code of Practice for Research Degree Candidature and Supervision, )

All researchers will have data. Data can be broadly defined as 'Material intended for analysis'.  This covers many forms and formats, and is not just about digital data.

For example, 

Art History - high resolution reproductions of photographs, notebook describing context

English literature - research notes on text, textual analysis

Engineering - experimental measurements on the physical properties of liquid metals

The University also has a definition for “Research Data” in its  Research Data Management Policy  that you should consider.

A PhD DMP template and guidance on how to complete your Data Management Plan is available ( see below ). All new doctoral students should complete the Data Management Plans for Doctoral Students module on Blackboard. Contact us if you need further information or have feedback via [email protected]

Guidance on depositing your research data at the end of your doctorate can be found on the Thesis Data Deposit guide. Please also see our depositing research data videos at  https://library.soton.ac.uk/researchdata/datasetvideos

Creating your DMP

• Introduction
• DMP and Project Overview
• About your Project Data
• Making Data Findable
• Making Data Accessible
• Making Data Reusable
• Making Data Secure
• Implementing the Plan
• Example Plans

What are data management plans? A data management plan is a document that describes:

• What data will be created
• What policies will apply to the data  
• Who will own and have access to the data
• What data management practices will be used 
• What facilities and equipment will be required 
• Who will be responsible for each of these activities

Your data management plan should be written specifically for the research that you will be doing.  Our template is a guide to help you identify the key areas that you need to consider, but not all sections will apply to everyone.  You may need to seek further guidance from your supervisor, colleagues in your department or other sources on best practice in your discipline.  We provide some details of guidance available in our training section and on our general research data management pages.

Each of the tabs looks at the different topics that can be included in a data management plan.  You can move through the tabs in any order.

Describing your Project

At the start of your data management plan (DMP) it is useful to include some basic information about the research you are planning to do.  This may already exist in other documents in more detail, but for the purposes of the DMP try to summarise in as few sentences as possible.

What policies will apply?

It is important that you think about who is funding your research and whether there are any requirements that you need to meet.  Are you funded by a UK Research Council? What policies do they have on research data - see  Funder Guidance .  What does our University Research Data Management policy  and Code for Conduct for Research state is required?

Does the type of data you will be creating, using, collecting mean that you have to meet certain legal conditions?  Will you be collecting any form of personal data, (see ICO Personal Data Definition ), special category data (see ICS Special Category definition ) or is it commercially sensitive?  For example, if you are involved in population health and clinical studies research data and records minimum retention could be 20-25 years for certain types of data - see the MRC Retention framework for research data and records  for further details. 

Do you need Ethics Approval?

Anyone who is dealing with human subjects or cultural heritage (see University policies ) will require to obtain ethics approval and this must be done prior to collecting any data.  Your DMP should inform what you say in your ethics application about how you will collect, store and re-use your data.  It is important that your DMP and your ethics application are in agreement and you provide your participants with the correct information. Once you receive your ethics approval, review your data management plan and update as necessary.

Reviewing your Data Management Plan

A DMP should be a living document and should be updated as your research develops.  It should be reviewed on a regular basis and good practice would encourage that the dates of review are included in the plan itself.  Use of a version table in any document can be helpful.

What data will be created?

In your data management plan you need to provide some detail about the material you will be collecting to support your research.  This should cover how you will collect notes, supporting documentation and bibliographic management as well as your primary data. Will all your data be held electronically or will you require to maintain a print notebook to collect your observations?

Are you using Secondary Data?

Not everyone has to collect their own data, it may already have been collected and made available.  This data is known as secondary data.  Some secondary data are freely available, but other data are released with terms and conditions that you need to meet.  In some cases this may influence where you can store and analyse the data.  You need to be aware of this as you plan the work you intend to do.

How are you collecting or creating your data?

How you collect or gather the material for your research will influence what you need to do to manage them. The way you do this may alter as your research progresses and you should update your plan as required. Will you be collecting data by observing, note-taking in an archive, carrying out experiments or a mixture of these? 

How much data are you likely to have?

Knowing how much data you might create is important as it will dictate where you can store your data and whether you need to ask for additional storage from iSolutions.  It is unlikely that you can say exactly what volume of data you might create, but you will have an idea of individual file sizes.  If you will be working with word, excel documents and a reference management software library then you are likely to be dealing with megabytes or gigabytes of data. If you will be collecting high resolution images then you may end up needing to store terabytes. Estimate as early as possible and if you think you may need additional space you should discuss this with your supervisor.

What formats will you be using?

A crucial factor in being able to share data is that it is in an open format or collected using disciplinary standard software that allow export to open formats.  Consider how open the format of your data will be when selecting the software, instruments, word processing packages that you use. See the Data formats section in  Introducing Research Data  Part III for points to consider.

Who will own the data?

If you have been sponsored by a research council, government, industry or commercial body the agreement you signed may cover ownership of the data that you create.  Being aware of this early is useful as it will influence what you are able to do when you come to writing papers, sharing and depositing your data when your finish. It may also impact on where you can store your data.  

How will you make your data findable?

Using standards to capture the essential metadata is a good way to help create data that will be easy to find.  It will also make preparing for deposit in the future more straightforward.  The Research Data Alliance has a helpful list of  disciplinary metadata  and use case examples.  You can make reference to these in your plan once you know what will be most appropriate to use.

Where will you store the data during your PhD?

Where you store your data will depend on things such as the type and size of data you are collecting.  Certain types of data, such as personal , special category data (formerly referred to as sensitive data) or commercially confidential data, will require to be stored more securely than others.  This type of data generally requires to be stored on University network drives that have additional protection and not on personal computers or cloud storage (for example, Office 365, One Drive). Where you are collecting less sensitive data your choice of storage is wider.  For all storage it should in a location with good back-up procedures in place. Consult iSolutions knowledge base  for further information.

How will you name your files and folders?

It can be helpful to think about creating a procedure on how you will name your files. This is a basic step where it is useful to consider how easy it will be to interpret the name in the future.  Abbreviations can be good, but ask yourself how someone else might understand the file name should you need to share it with them. What would make it easy to know what each file contains?  While it is possible to have quite longer file names this can cause problems when you zip files. 

How will you tell one version of a file from another?

How will you be able to tell whether you are dealing with the latest version of a file? How will you manage major versus minor changes?  What if you want to return to an earlier version?  Use the data management plan to investigate what would be the optimum method for you and establish a good procedure from the beginning.  Generally the use of 'draft', 'latest' or 'final' should be avoided.  Instead consider using the data (YYYY-MM-DD) or a version number, for example, v.1.0  where the nominal value increases with major changes and decimal for minor ones.  Adding a version table at the end of a document can also be helpful.

How can you share your data?

To make data accessible is not about doing something at the end of the project, but needs to be planned for from the beginning.  During your research you are likely to have colleagues or collaborators who will need to be able to access the data - how will you do this?  Will you need a collaborative space and if so what can you use?  Does it need to be is a protected location with restricted access due to the type of data you are using? By establishing good procedures on documentation, metadata collection, file-naming and using disciplinary standards this will assist you throughout your research, as well as helping at the end.

How do you handle personal, sensitive or commercially confidential data?

If the data you are collecting contains   personal ,  special category  data (formerly referred to as sensitive data) or commercially confidential data  then sharing or transferring the files needs to be carried out in a way that does not make the data vulnerable. Data should be anonymised or pseudo-anonymised as early as possible after collection, seek disciplinary guidance prior to collection. 

The medium of transfer must be secure and where necessary encryption should be used. You may want to consider one of the following:

There may be other software available and you should check if there is a standard in your discipline. 

Transferring data via USB or external drives is not recommended, but where required these should be encrypted. Avoid using email to send files and instead use our University SafeSend service.  This offers transfer of files up to 50GB and your files can be encrypted by ticking "Encrypt every file" when creating a new drop-off - see ' How secure is SaveSend'

What data do you need to keep and what do you need to destroy?

Not all the data from a project needs to be kept and the data you collect should be reviewed regularly.  The Digital Curation Centre (2014) guide  ' Five steps to decide what data to keep: a checklist for appraising research data v.1 ' may help you to decide what to retain. It is important that you retain or discard data in line with your ethics approval.

You also need to consider what data needs to be destroyed, how you will mark the data for destruction and when this needs to happen. Destroying paper based records is relatively easy through our confidential waste system.   Destroying digital data is less so as it may need to be done so that it cannot be forensically recovered. Guidance on destroying your data is available  or contact iSolutions for advice.

Why do you need to consider the long-term storage now?

At the end of your PhD you will be encouraged to share your data as openly as possible, and as closed as necessary. To do this safely consider what you need to do to enable your data to be accessible in the future.  Knowing where the best place to store your data may inform what you need to plan for in its creation or collection.  Are you aware of any disciplinary data repositories that hold similar data?  Examples are:

• Archaeology - Archaeology Data Service  
• ESRC - UK Data Archive
• STFC -  eData   
• NERC - data centres
• Biology - GenBank
• General repository - Zenodo

Investigate what requirements these repositories have on formats, documentation etc and incorporate these into your plan. Otherwise you should plan to deposit in the University Institutional Repository . 

There are currently no costs for depositing most dataset in our Institutional Repository unless the data requires specialist archive storage or is in excess of 1TB. External repositories may have charges for depositing data. 

Who will be creating the archive?

Generally as a PhD the job of drawing together your data into a dataset ready for deposit will fall to you as the researcher.  It is not the responsibility of your supervisor, although they may be able to advise on what needs to be done.  If you are part of a larger project there may be someone designated to curate the project data.  For further assistance contact [email protected] . 

How long should the data be kept?

This will depend on a number of factors.  Your funder may have a policy that requires the data to be held for a minimum of 10 years from last use.  If you are working in certain medical areas the data may need to be held for 25 years.  There may be some restrictions on how long you can retain personal data relating to Data Protection Act 2018 (GDPR).  Significant data that has been given a persistent identifier (DOI) will be kept permanently.

What documentation or additional information needs to accompany the data?

Keeping a record of what changes you have made, when data was collected, where data was collected from, observations, definitions of what has been collected are all crucial to allowing data to be used safely and with integrity. How do you plan to do this? How will you make sure that you can match up your notes with the files they refer to?  Some programming languages such as Python and R allow you to make notes in the files about what you are doing which is really helpful.  Where this is not an option then you will need to develop your own method to make sure that processes applied to the data are recorded and available to you to refer back to later.  Creating a register of your files by type using an excel spreadsheet may be worth considering, but it should be manageable and importantly kept up-to-date.

In order for data to be reusable it requires data provenance.  Data provenance is used to document where a piece of data comes from and the process and methodology by which it is produced. It is important to confirm the authenticity of data enabling trust, credibility and reproducibility. This is becoming increasingly important, especially in the eScience community where research is data intensive and often involves complex data transformations and procedures.

• Research Data Management and Sharing - Documentation The importance of systematically documenting your research data. more... less... From the Coursera Research Data Management and Sharing course https://www.coursera.org/learn/data-management

What restrictions will need to apply?

Not all data can be made openly available.  Some data may only be shared once a data sharing agreement has been signed, while other data may not be suitable for sharing.  Funding councils encourage all data to be as open as possible and as closed as necessary. Where will your data fit with this?  What agreements do you need to be able to share your data?

When can data be made available?

Data can be deposited in our Institutional Repository  and kept as an 'entry in progress' until it is ready for publication. 

Not all data needs to be made immediately available at the end of your PhD.  It is possible to add an embargo to give yourself some additional time to find funding to continue your work and re-use your own data.  See Regulations on embargoes.

However, it is not always necessary for you to wait until the end of your PhD before depositing data.  If you write a conference or journal paper it is likely that you will be asked to make the underpinning data available.

How will you keep your data safe?

What would happen if your files became corrupted or your laptop was stolen, would you be able to restore them?  What would happen if someone was able to access your data without your knowledge or approval?  If you are holding personal  or   special category  data (formerly referred to as sensitive data) and these became public this would be a data breach with potentially serious consequences.

Dr Fitzgerald  Loss of seven years of Ebola research  

Consider carefully the impact to you and your research if these were to happen and what procedures you may need to put into place to reduce the risk of these happening.

• Research Data Management and Sharing - Data Security Ensuring your research data are kept safe from corruption, and that access is suitably controlled more... less... From the Coursera Research Data Management and Sharing course https://www.coursera.org/learn/data-management

How will you back up your data?

Good housing keeping of your data is important and this includes doing regular back ups of your data.  University storage is backed up regularly but it is important to have your own 'back up' folders, kept separately from your working files.  Back up should be done on as regular a basis as required.  This can be defined by the length of time you are prepared to repeat work lost.  You may need to back up daily, weekly or monthly depending on the nature of your research.  

• Research Data Management and Sharing - Backup Effective backup strategies for your research data. more... less... From the Coursera Research Data Management and Sharing course https://www.coursera.org/learn/data-management

As well as establishing a process for backing up your files, you should check the process of restoring your files.  You will need to check that the files restore correctly.  Having good documentation on what your files contain, what transformations or analysis has been carried out will be invaluable for this process.

How can you safely destroy data?

Destroying data, especially   personal ,  special category  data (formerly referred to as sensitive data) or commercially confidential data , is not as straightforward as just deleting the file.  Further action is required otherwise the data could be recovered.  Please read our guidance on destruction of data   and GDPR regulations .

• Data Disposal Essential guidance from the UK Data Archive on data disposal

An important part of research data management is that your plan is implemented and part of your everyday good research practice.  The plan should be a living document and reflect your practice.  You may find that some parts become redundant or that there is a better way to carry out a process so your plan should be updated. As a PhD researcher it is likely that you will be the person responsible for implementing the plan.  If your research is part of a wider research project there may be someone in the team who has been given the role and you should discuss your data management plan with them.

Having written your plan consider what actions do you need to take in order to carry it out? What further information do you need to find? Investigate what training or briefing sessions are available via PGR Manager.  If you want to enhance your data analysis skills check out material on Linked in Learning 

Over time we will add plans to this section as we get permission to share them.

• PhD DMP Example (Web Science) This is an example PhD Data Management Plan for a research project looking at learner engagement and peer support in digital environments.
• Arts and Humanities
• Science, Medicine and Engineering
• Social Sciences
• Further Reading

Courses offered by the University:

Data Management Plans for Doctoral Students -  mandatory course on for all new doctoral students. Log into Blackboard using your university username and password.

Data Management Plan: Q&A Clinic - as a follow up to the compulsary online course, the Library is running twice weekly clinics to answer your DMP queries. Book PGR Development Hub .

Data Management Plan: Why Plan?  45 minute briefing.  A Panopto recording of this course  is  available

Research Data Management: What you need to know from the start .  45 minute briefing. Book via Gradbook

Research Data Management Workshop .180 minute workshop Book via Gradbook

• Introduction to research data (visual arts) Introduction to research data in the visual arts, wirtten by Marie-Therese Gramstadt as part of the Kultur project
• Manage, improve and open up your research and data PARTHENOS training module on various aspect on data management
• VADS4R Data Management Planning A toolkit developed by the Visual Arts Data Skills for Researchers (vads4R)
• Cross-Linguistic Data Formats, advancing data sharing and re-use in comparative linguistics The Cross-Linguistic Data Formats initiative proposes new standards for two basic types of data in historical and typological language comparison (word lists, structural datasets) and a framework to incorporate more data types (e.g. parallel texts, and dictionaries). The new specification for cross-linguistic data formats comes along with a software package for validation and manipulation, a basic ontology which links to more general frameworks, and usage examples of best practices. [article]
• EMBL-EBI Training EMBL-EBI train scientists at all levels to get the most out of publicly available biological data.
• Datatree - Data Training A free online course, aimed at PhD and early career researchers, with all you need to know for research data management, along with ways to engage and share data with business, policymakers, media and the wider public. more... less... The course is for any scientist, whether you look after your own data or are guided by an organisation.
• Expert Tour Guide on Data Management A guide for social science researchers who are in an early stage of practising research data management.
• CESSDA ERIC RDM User Guides Brief guides on important topics in data management and a helpful checklist
• Guide to Social Science Data Preparation and Archiving An important guide covering the different stages of data management to enable the sharing and preserving of data in the Social Sciences
• Managing your dissertation data : Thinking ahead Maureen Haaker and Scott Summers from the UK Data Service gave this presentation. The session sought to help the students ensure transparency in the collection and writing up of their dissertation, whilst also ensuring that good practices in data management were followed. more... less... Although aimed at undergraduate dissertation it provides useful information for everyone.
• UK Data Service Prepare and Manage Data Good data management practices are essential in research, to make sure that research data are of high quality, are well organised, documented, preserved and accessible and their validity controlled at all times. This results in efficient and excelling research.
• FAIR Principlies Guidelines to improve the findability, accessibility, interoperability, and reuse of digital assets
• How to Develop a Data Management and Sharing Plan Jones, S. (2011). ‘How to Develop a Data Management and Sharing Plan’. DCC How-to Guides. Edinburgh: Digital Curation Centre. Available online: http://www.dcc.ac.uk/resources/how-guides
• MRC Retention framework for research data and records guidance on retention of research data an records resulting from population health and clinical studies
• Open Data Handbook Handbook that discusses the why, what and how of open data – why to go open, what open is, and the how to ‘open’ data.
• Open Research Data and Materials Open Science Training Handbook section on research data

Key Documents

• DMP Templates
• Deposit Guide
• Code of Conduct for Research University of Southampton policy - October 2017
• Data Protection Policy University of Southampton policy May 2018
• Data Sharing Protocol University of Southampton protocol - May 2018. [Login required]
• Ethics - Human participant policy University of Southampton policy - March 2012
• Ethics - Policy on Cultural Heritage University of Southampton policy - October 2018
• Research Data Management Policy University of Southampton policy - 2015

The template below has been provided to assist you in writing your data management plan.  Not all sections will be relevant, but you should consider carefully each section.

• Template for PhD DMP (pdf)
• Template for PhD DMP (Word)

When the time comes to deposit your data, follow the advice in our Thesis Data Deposit guide . 

Email us on: [email protected]

Who's Who in the Research Engagement Team

Research Support Guide

  research support.

• << Previous: Copyright
• Next: Deposit - Faculty Office >>
• Last Updated: Aug 30, 2024 3:42 PM
• URL: https://library.soton.ac.uk/thesis

• AUT Library
• Library Guides
• Research Support

Managing your research data

• Writing a data management plan
• Research data & data management

Research data management plan

Aut dmp tool, other guides and checklists.

• Collecting data
• Managing data
• Sharing data

A data management plan  ( DMP ) is a formal document that outlines how you will handle your data both during your research, and after the project is completed.  This ensures that data are well-managed in the present, and prepared for preservation in the future.  A DMP  is often required in grant proposals.

A research data management plan is a  living document and should be reviewed and updated regularly.

WSG format for a data management plan mentioned in the video.

Your DMP should include the a brief description about your project and how data will be managed: 

• Roles and responsibilities
• Ethics and policies/guidelines compliance 
• Types of data, data format and documentation
• Data storage, file backup and security
• Access, sharing and archiving 

AUT guidelines and policies

• Principles, policies and codes
• Data management guidelines - AUT ethical guidelines and confidentiality requirements
• AUT guide for drafting a data management plan

The  AUT Data Management Planning Tool  makes use of a platform developed and hosted by University of California Digital Library. By using this tool you will create a data management plan based on current AUT data management guidance.

Plans can be drafted on DMPTool and once complete are downloadable in PDF form for your own records. Settings in the tool allow you to control whether your plan is private, institutionally viewable or open to public view.

The questions and structure of the DMPTool have been customised for AUT researchers as part of a joint project between AUT Library and the University Research Office. If you would like to give constructive feedback on the tool please contact:  [email protected]

Sign in to AUT DMPTool:

• Enter your AUT email to the sign in box
• On the next screen, click  Sign in with Institution (SSO)

Important:  To access the AUT Template, you must select 'No funder associated with this plan or my funder is not listed' on the Create Plan page.

• Video - Creating a Data Management Plan (DMP) - Curtin University
• ANDS guide for Data management plans By Australian National Data Service.
• Example DMPs Examples on the Digital Curation Centre website (UK).
• Data management costing tool and checklist  - UK Data Service
• DDC guidance  - The Digital Curation Centre (DCC) UK
• Digital Creation Centre (DCC), Edinburgh
• UK Data Archive
• << Previous: Research data & data management
• Next: Collecting data >>
• Last Updated: Nov 21, 2023 11:28 AM
• URL: https://aut.ac.nz.libguides.com/RDM

• Jump to Main navigation
• Jump to main content
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• Norwegian website
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• Open science
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• Sensitive data
• Preregistration

If your PhD contains research data , you will have to think about how to deal with those data. In this section, you will learn about

• principles for data management
• data management plans
• how to store and archive your data
• how to provide good and sufficient metadata
• how to structure data files
• data security  

The FAIR principles were originally introduced in 2016 (Wilkinson et al., 2016), and the Research Council of Norway (NFR) states:

The FAIR guiding principles for scientific data management and stewardship are included as a main principle... (NFR, 2017)

The same principles govern the data policy in the Horizon 2020 framework, and are followed by more and more academic publishers, such as Nature Publishers . The Norwegian government also states that the FAIR principles should govern all publicly funded research in Norway (Meld. St. 25 (2016–2017) The Humanities in Norway , summary in English)

In order to comply with the FAIR guidelines, data should be

• a ccessible
• i nteroperable

One of the keys to complying with this principle is to use a decent data management plan . We will present the necessities of a good data management plan below, but first some information on the FAIR guidelines.

F indable data

To be findable, data should be uniquely and persistently identifiable, which means that it should be possible to find the same object at any point in time by using persistent links. The data should also minimally include enough basic machine-readable metadata to separate it from other data.

A ccessible data

Accessible data are those obtainable by machines and humans after appropriate authorization. Access must be granted through a well-defined protocol.

I nteroperable data

This means that data and metadata are machine-readable and formatted according to well-known vocabularies or ontologies. In other words, data must be both correct and understandable for a machine in order to be interoperable.

R e-usable data

A further requirement is re-usability, which can only be ensured if the FAI-part above are followed. In addition: metadata should be described sufficiently well to allow it to be automatically linked or integrated with other data sources. Published data should have enough metadata to enable correct referencing.

Treating your data according to the FAIR-principles will make sure that the data you collect are re-usable and verifiable for fellow researchers according to the national strategy on transparent and reproducible data . One key element in this is to make sure you think ahead when it comes to data management.

Data management plans

A data management plan (DMP) describes the data management life cycle for data to be collected, processed and/or generated. The data management plan will state how you treat data from project start to end. Note that the data management plan can be regarded as part of the research process, and should be included in the final project publication.

Public funders such as the European Commission or the Research Council of Norway require you to provide a data management plan as part of your project description, and there could very well be a note on data management in your PhD agreement. There are guidelines on what the plan should contain; here are two examples:

• The European Commission H2020 Programme Guidelines for data management plans
• The Research Council of Norway's recommendations for data management plans

As there is a growing tendency to use the FAIR-guidelines in more areas of research, you should try to use them to create a plan for your data even if your current research is not funded directly by an external party. Following the FAIR-guidelines is recommended by many of the Norwegian higher educational institutions, for example the University of Oslo data management , the University of Bergen , UiT The Arctic University of Norway or the Norwegian University of Science and Technology .

Templates for data management plans

A general data management plan contains information such as this:

• The research project, people involved, and whether it is part of a larger project.
• Who is responsible for following up the DMP and who has rights to manage and/or access the data. This could be just you, your research group, or other collaborators.
• Details of the data you are going to collect or generate. These could be observations, simulations, interviews, etc. This part could also include information on existing data, which requires that you have searched for research data created by others.
• How documentation and metadata will be created, and what file formats will be used.
• Where the data will be stored during the project, expected file size, procedures for backup, storage and archiving. The local IT department can probably support you here.
• Where the data will be archived at the end of the project, and if they will be shared.
• If the data are sensitive, how this will be handled.

It is usually best to use a DMP template and generate a plan containing the necessary information. A few different templates exist, and you could of course use the Horizon2020 template also for non-EU funded research. If your PhD needs to be registered with NSD or preapproved by REK (personal/sensitive data), the DMP generator published by NSD is probably the better choice. Note that your institution may also recommend certain templates for creating data management plans.

• The Norwegian Centre for Research Data DMP generator
• Horizon 2020 template (pages 7 - 9)

Storage and archiving

In many PhDs, the amount of data produced is small enough to be stored on your own computer, or a shared area provided by your institution if you collaborate with others. Most universities and university colleges provide an institutional home area for you to use, which is automatically backed up regularly, usually every night. If you need more disc space, or have special requirements for data storage, your local IT department can help you. Note that if you work with sensitive data, there are stricter requirements for safe storage, e.g. where to store data, encryption, passwords for access, etc.

Most institutions provide their own services for collaborative use. This is recommended to ensure storage for as long as you need, and to limit access to those who have the right of access. Dropbox and Google docs should be avoided.

You may have different needs for storage during your PhD, and for archiving when it is finished. Some data can be shared , while other data should be archived in a closed repository. If you do not have clear requirements for what archive to use, you can search research data or data repositories to find the one that best suits your data.

Documentation and metadata

Data documentation is an essential part of data management. The documentation should include what data you have collected, what methods were used, and what the research context is. If there are any limitations, this should be stated. This is especially important if you plan to share data, but is also useful for yourself, so that you are in complete control of your data sets, and do not risk having to re-do the data collection. Provide information on what the data represent and if they have been processed in any way.

Metadata are data about the data. They are often provided in addition to more extensive documentation, to give brief information on the data. This could include the author, a descriptive title of the data set, date of collection, keywords, etc. There should be enough metadata to enable you and others to understand how to use the data in the future. Some data archives or repositories have metadata forms with required fields. If at all possible you should use metadata from a widely accepted standard in your field of study; this will make your data easier to find through data search engines.

Structuring data files

It may seem obvious that data should be structured so that they are easy to understand and reuse. However, there are common mistakes like using acronyms and abbreviations that may seem easy to understand now, but turn out to make little sense later. Try to follow the guidelines below:

• Provide units of measurement when applicable.
• Use naming conventions that are easy to understand, and be consistent, with e.g. file names on the form datatype_date_location.ext. If you use acronyms and abbreviations in file names, headers, etc, explain them in the metadata.
• Avoid names that are too long.
• Avoid special characters.
• Use underscore (_) instead of space in file names, as some programmes have difficulty reading files with spaces in the file name.
• Use persistent file formats.

Technical security measures

There is no such thing as too much data security , but there is no need to make your data management more cumbersome than needed. In a low-risk setting such as this example of sensitive data , your smartphone could be the perfect tool for the job; high quality, digital files, unlimited storage, small size, high power capacity, easy to transfer the data for processing, etc. Setting up a rigorous system with rotating passwords, ever-changing encryption keys and file transfer through encrypted channels is not needed if the data are not considered as sensitive enough.

The key point is that you need to think about this before you start on the data collection. As is obvious from the examples above, there are no fixed 'security categories' when dealing with personal and sensitive data, and you will have to define your need for security yourself.

Recording of sound and video

Video and sound recordings can be highly personally identifiable, remember that even the voice of the interviewees can be enough to identify them! Cartooning, censoring and voice distortion are the only ways to anonymise such data, but this option is not viable for all kinds of projects, such as those studying spoken dialect or facial expressions. Note that anonymization should be done on a computer without internet connection. Anonymised data can be processed on a computer with internet but remember that the content of any dialogue still remains; what is said can be enough to identify people.

You need to be in full control of the equipment used for recording sound or video; you should not leave it unattended or lend it to unauthorised persons. Note that equipment with an internet connection, such as a smartphone, should never be used to record personal information unless you can ensure that all communications are shut off. Equipment without an internet connection and which uses removable storage units is generally the best option. If encryption of the storage media is not an option, make sure that the physical media are securely stored in a safe or similar, and transfer any sensitive or personal data to more secure storage, such as a computer without internet connection and encrypted harddrive. Long-time storage of such data for the purpose of further research will need specific secure long-time storage facilities, and the approval of such storage from the relevant authorities.

Transferring data

Pay attention to the way you transfer your data between units. The transfer of data should be as safe as the storage unit; for example, you cannot send highly sensitive data in emails. At the highest levels of security, an encrypted connection is needed, but a physical transfer using a wire or card-reader would also work well. If you transcribe sensitive recorded data, make sure that there is no one in the room with you, looking over your shoulder.

In cases where email is the best solution for transferring personal data, use software such as 7zip for encryption before sending the file. Sensitive data should as a general rule never be sent as attachments to email going over a normal mail server. Ask your IT department if sending emails internally in you institution is regarded as safe or not.

Processing and storing

Data sometimes need to be transferred to your desktop computer for processing. Anonymization of data from an interview is one example, but there could be numerous reasons for needing to process the raw data before analysing them. If you have data in the high-risk category, you should probably have a designated computer with permanently deactivated communications for the job. No internet or similar connections should be available on a computer used for processing high-risk data. You also need to make sure that the processing can take place in a secure place; a public reading- room or internet cafe with people looking over your shoulder should never be used.

Low-risk data could be stored on removable units, such as memory cards or USB-sticks, but there must at least be some basic level of security for personal data. Encryption of the removable storage is a possibility, or you could store media in a safe location. Higher-risk data need specific storage units using high-level security measures such as secure servers with encrypted disks and strictly regulated access, even physical access to the login-terminals themselves.

Some data can be shared, and some should indeed be shared! Sharing data is generally done through uploading, or archiving , the data into a repository . Even if your funder or your institution is a strong supporter of the FAIR principles , you are still responsible for not sharing data that should not be shared. Remember: "data should be as open as possible and as closed as necessary". Read through the short section on when not to share your data before you upload your data into an open repository. Note that uploading data into a database is not the same as sharing them; there are many options for secure storing of your data, which is not the same as sharing them. Sharing and storing are not the same.

Two of the key notions behind data sharing is that they should allow both new research and that conclusions can be verified. With this in mind, you should make a decision regarding the rawness of the data you share; for very large datasets a certain degree of processing of the data before sharing is the obvious choice, but every step of processing you take could limit the possibilities of future use.

Encryption of mobile storage media is possible on a normal Windows computer by turning on 'BitLocker' for the selected drive. If you use a Mac, encryption can be done in 'disk utility' after creating an image out of the folder you wish to encrypt. Encryption is necessary for all removable storage media containing high-risk data. The encryption key (the password) must never be accessible by any unauthorised persons and should not be stored in connection with the encrypted disk itself.

Data management vocabulary

Below you can find some commonly used Norwegian and English terms used in data management and research activities in the higher education institutions in Norway.

Words and expressions used in privacy and data protection

The following list is adapted from the Norwegian Data Protection Authority's list of words and expressions used in privacy and data protection. You can find the complete list, including an English-Norwegian version, on the site of the Data Protection Authority .

The UHR dictionary of academic terms

The Norwegian Association of Higher Education Institutions (UHR) has created a short dictionary (termbase). In this dictionary, you will find translations of more than 2000 administrative terms from the two written languages in Norway to English, and vice versa.

Useful resources

CESSDA ERIC (the Consortium of European Social Science Data Archives European Infrastructure Consortium) provides an expert tour guide on data management . The guide aims to help researchers make their data findable, understandable, sustainably accessible, and reusable.

Wilkinson, M.D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A.,...Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data , 3 , Article 160018. https://doi.org/10.1038/sdata.2016.18

• Types of reviews
• Search techniques
• The Dissertation
• Where to publish
• Submitting articles
• Co – authorship
• The Cristin system
• Citation impact

PhD on Track

• University of Michigan Library
• Research Guides

Data Management Plans (ENG)

• Templates & Examples
• Funder Mandates
• Roles & Responsibilities
• Expected Data
• Dissemination
• Storage & Preservation
• DMP Workshop

Tools for Writing DMPs

The following Microsoft Word documents are borrowed from DMPTool.org . DMPTool.org is created by a group of major research institutions to help researchers generate data management plans. These documents display the current (as of the date shown on the document) funding agency DMP guidance, followed by a listing of points from DMPTool on topics to consider when writing the relevant portion of your DMP.

• NSF-ENG Template (Word Document) DMP Template for the NSF Directorate for Engineering, from DMPTool.org
• NSF-BIO Template (Word Document) DMP Template for the NSF Directorate for Biological Sciences, from DMPTool.org
• NSF-CISE Template (Word Document) DMP Template for the NSF Directorate for Computer & Information Science & Engineering, from DMPTool.org
• NSF-AGS Template (Word Document) DMP Template for the NSF Division of Atmospheric & Geospace Sciences, from DMPTool.org
• NSF-EAR Template (Word Document) DMP Template for the NSF Division of Earth Sciences, from DMPTool.org
• NSF-AST Template (Word Document) DMP Template for the NSF Division of Astronomical Sciences, from DMPTool.org
• NSF-CHE Template DMP Template for the NSF Division of Chemistry, from DMPTool.org
• NSF-DMR Template (Word Document) DMP Template for the NSF Division of Materials Research, from DMPTool.org
• NSF-PHY Template (Word Document) DMP Template for the NSF Division of Physics, from DMPTool.org
• DoE Template (World Document) DMP template for the Department of Energy, from DMPTool.org
• DoE Template (Word Document) DMP Template for the Department of Energy, from DMPTool.org
• DoE Office of Science Template (Word Document) DMP Template for the Department of Energy, Office of Science, from DMPTool.org
• DoE-EERE Template (Word Document) DMP Template for the Department of Energy, Office of Energy Efficiency and Renewable Energy, from DMPTool.org
• DOD Template (Word Document) DMP template for the Department of Defense, from DMPTool.org
• NASA Template (Word Document) DMP Template for NASA, from DMPTool.org
• DOT Template (Word Document) DMP Template for the Department of Transportation, from DMPTool.org

Other DMP Templates

These are links to templates created for researchers at other colleges & universities.

• University of New Mexico Examples
• University of Illinois - Grainger Engineering Library Information Center
• ICPSR Natural Science Examples
• DataONE Examples

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• CAREER FEATURE
• 13 March 2018

Data management made simple

• Quirin Schiermeier

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When Marjorie Etique learnt that she had to create a data-management plan for her next research project, she was not sure exactly what to do.

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Research data management

Creating a data management plan.

This video outlines how to create a Data Management Plan (DMP) using the Curtin Data Management Planning Tool (DMP Tool).

Data management plans

A DMP is a document outlining how you intend to handle your data as you perform your research.

A DMP may ask you to consider things such as:

• What type of data you will collect
• How and where you’ll store that data
• Who will have access to it
• How you’ll address any legislative requirements​
• Any agreements about ownership of the data between collaborators
• How, where and when you’ll share your data at the end of the project

Managing the data over the whole project is very important for the success of your research and having a plan on how you’ll manage that data is equally important. Having a good DMP will also help ensure all of the elements of research data FAIRness are present - Findability, Accessibility, Interoperability and Reusability.

Curtin considers this data management plan process to be so important that completing a DMP is a required for anyone seeking to:

• Commence a higher degree by research (HDR)
• Obtain ethics approval for human or animal research
• Obtain access to the Research Drive (R: drive)

The DMP Tool linked below will help you create a DMP and update it when needed; it will also allow supervisors to review and check the plan and request the creation of R: drives for students.

Creating a Data Management Plan [00:25:19] This video covers how to create a DMP at Curtin.

Curtin Research Data Management Planning Tool The Curtin DMP tool guides the process of creating a research data management plan and ensures that important aspects of research data management are explored at the start of a research project.

Data Management Plan workshop This 1 hour hands-on session will help researchers create their data management plan using the Curtin DMP tool. It is particularly aimed at research staff and students who are preparing for their candidacy or ethics applications and is relevant for all disciplines and fields.

DMP Advice Tool This tool outlines areas of possible concern that may arise in your research that should be addressed in a DMP.

DMP Tool Help

Below are instructions for researchers and supervisors on how to resolve common issues when using the Curtin DMP Tool .

Students completing a DMP

Once you have completed all the fields, you should see this message:

This means the DMP has been submitted to your nominated supervisor. You should notify them that your DMP has been created and that they have been nominated as the supervisor.

They will need to log on to the DMP Tool with their own details and complete the steps below before Curtin Digital & Technology Solutions (DTS) will provision your Research Drive access.

DMP Process Flowchart

Student FAQs

Q. How do I setup my R: drive?

A. Once you’ve completed your plan, you should notify your supervisor that your plan is complete.

They can then check your plan and follow the steps as noted in the DMP Tool Help > Help for staff> Steps for Supervisors to complete Students’ Research Drive requests and have DTS create the storage space.

Q. I understand the questions, but I don’t know the answers to the questions the DMP Tool is asking me. Who can I talk to?

A. Your supervisor should be your first point of contact, as they will have the best knowledge of your individual research project and the common research processes and ethics issues in your discipline.

Q. I need more space on my Research Drive. How do I get it?

A. Please contact DTS as described in Student Oasis / SupportU - “Request additional storage for existing research project folders”.

Q. I need to give access to my Research Drive so my Curtin collaborators can access my data. How can I add them to my drive?

A. Please contact DTS as described in Student Oasis / SupportU - “How to request an access change for the R drive”.

Q. I still am having problems. Who can help?

A. Contact the Research Data Management team who will try to help you.

Steps for Supervisors to complete Students’ Research Drive requests

• Go to My Students’ Plans and click Generate PDF from the drop down menu and click Select .

You will need some of the information in the generated PDF to approve the request.

From the same drop down menu, click Request Storage and then click Select .

You will then be asked to provide some details about the DMP submitted by the researcher. These should be included in the generated PDF (N.B. – If the project has a finite timeframe, enter “0” at Projected Yearly Growth ).

Once the details have been entered, select Review . Check the information is correct then select Submit .

• You should then get this message indicating the researchers new DMP has been approved by you and a request will now be sent to Curtin IT for them to provision the R: drive space.

Steps for Staff to complete Staff Research Drive requests

• Go to My Data Management Plans and click Generate PDF from the drop down menu and click Select .

You will then be asked to some details about the submitted DMP. These should be included in the generated PDF (N.B. – If the project has a finite timeframe, enter “0” at Projected Yearly Growth ).

• You should then get this message. The request will now be sent to DTS for them to provision the R: drive space.

A. Please contact DTS as described in their Data and Storage - Shared Drives self-help portal (requires Curtin staff login).

Q. I need to give access to my Research Drive so my Curtin collaborators or students can access my data. How can I add them to my drive?

A. Please contact DTS as described in their Data and Storage - Research Drive self-help portal (requires Curtin staff login).

Example DMPs

These examples are fictitious DMPs in the faculties of Science and Engineering, Health Sciences, Business & Law, Humanities, Centre for Aboriginal Studies and the Vice-Chancellory, which may provide assistance.

Science and Engineering example data management plan [PDF, 80kB]

Health Sciences example data management plan [PDF, 60kB]

Business & Law example data management plan [PDF, 61kB]

Humanities example data management plan [PDF, 60kB]

Centre for Aboriginal Studies example data management plan [PDF, 59kB]

Vice Chancellory example data management plan [PDF, 59kB]

Funder requirements

Research funding bodies are concerned with obtaining the best outcomes for the research they fund. One of the ways they do this is by ensuring that researchers have a plan for their data throughout the whole research process.

An example of this is the Australian Research Council (ARC) - a key funding body of fundamental and applied research in Australia. Since 2020, the ARC has required funding applications for National Competitive Grants to have a completed data management plan before the project starts - this is to ensure that researchers are addressing the responsibilities as outlined in the Australian Code for the Responsible Conduct of Research 2018 .

It’s important to note that for most grants the ARC does not require you to submit a full, detailed DMP for assessment. However, you will need to have one in place before the start of the project and provide it to the ARC when requested.

ARC - Research Data Management Information about the ARC’s requirements around RDM

The Australian Code for the Responsible Conduct of Research 2018 The Code is a principles-based document that articulates the broad principles and responsibilities that underpin the conduct of Australian research.

More resources

Data management plans Provides information about managing and sharing research data.

The what, why and how of data management planning [00:05:30] A useful background on data management planning from Research Data Netherlands.

AIATSIS Code of Ethics for Aboriginal and Torres Strait Islander Research Researchers engaging in research relating to or involving indigenous participants should be aware of the guidance provided by the AIATSIS Code around research data.

Research Data Management

Data management plans.

A Data Management Plan (DMP) is a document describing how you will handle your data throughout your research project. It is part of good research practice and will help you plan for and manage the issues surrounding your data.

University of Liverpool researchers are expected to write a DMP for all research projects whether they are funded or not.

A DMP should be created before the project begins. It will help you identify problems before they occur so you can address them in good time. It will also be a record of the decisions you made and be a reminder when the project ends.

If you will apply for ethical approval or are handling sensitive data that requires a DPIA , it is best practice to write your DMP first.

Your DMP should be brief but specific and cover information about your research data as well as decisions for how you will manage them. If you have a research funder, they may have a specific template for you to use.

A DMP may include:

• A description of what data you will collect or create including the type, format, and quantity of the data
• Standards or methodologies for collecting, organising, or analysing the data
• How ethical or legal issues will be addressed
• Where the data will be stored
• What will happen to the data at the end of the project
• Plans for sharing the data including when, where, and how
• Roles and responsibilities among project collaborators

Remember, it is easier to manage data when you plan for it at the beginning. Many aspects are much harder to address or not possible later on.

Many research funders require a DMP as part of funding applications. See funder policies .

You can use  DMPonline to write your DMP. It is a free tool that provides many templates including those of all major UK funders as well as useful guidance and examples.

To get started:

• Go to DMPOnline. Set up an account using your University email address. Indicate UoL as your institution.
• Click Create Plan.
• Select your primary funder, if applicable, to use their template. If you do not have an associated funder, you will be given the option to use a University of Liverpool template.

You can request feedback from the Library Research Data Management team by clicking the Request Feedback tab once you have created the DMP.

You can get support and advice from the Library RDM team on writing your DMP. Email us at [email protected] .

We can also review your DMP if it is part of a funding application. We require five working days to reply, so please be sure to allow enough time before the deadline of your application. You can either email it to us or use the Request Feedback option in DMPOnline.

Educational resources and simple solutions for your research journey

Research Data Management: Tips for PhD Students and Researchers  

We produce a mind-boggling   2.5 quintillion bytes of data  each day. In fact, 90% of the data in the world today was generated over the past two years alone.1 This is a statistic that researchers and scientists can well identify with, given the fact that they, too, create and collect vast amounts of data during their work. In an age of data-driven research, the proper management of research data is essential for ensuring its integrity, accessibility, and usability. Effectively managing, collating, and archiving data is a crucial skill that early career researchers and even experienced academics need to develop.  

Research data management is the process of organizing, documenting, storing, and sharing research data. But what does research data management mean? Why is it important? Below, we will answer these questions and provide expert tips on how to develop effective research data management plans for each research project. 

Table of Contents

The importance of research data management   

While conducting research, a large amount of data is generated, which must be labeled, organized, and stored correctly to allow easy access for future use. This is where research data management comes in. It entails the organizing of different forms of data, from quantitative and qualitative, digital and physical, to raw and processed. In practice, research data includes data derived from surveys, interviews, experiments, observations, simulations, models, images, videos, audio files, software code, and working models. Research data management helps us in:  

• Ensuring the quality and integrity of our data  
• Avoiding data loss or corruption in case of unforeseen circumstances  
• Facilitating data analysis and interpretation  
• Complying with ethical and legal requirements  
• Meeting the expectations of funders, publishers, and reviewers  
• Enhancing the visibility and impact of research  
• Enabling data reuse and collaboration

In other words, efficient research data management makes research more accessible by organizing it in a structured manner that allows easy access when required.  

Developing an effective research data management plan   

A research data management plan outlines how to manage research data during research work. It is essential to ask the following questions when creating an effective research data management plan –  

• What data will we collect or generate, and how?  It is important to clearly describe the types of data collected, including their formats, structures, and relationships. 
• How will we document and describe our data?  Outline the organization scheme and file naming conventions to be used. Define data variables, units of measurement, and any associated metadata. 
• How will we protect our data from unauthorized access or misuse? A robust research data management plan includes measures to protect data integrity, confidentiality, and security. Define the level and type of security measures that will be required to protect research data. This safeguards sensitive data from unauthorized access or loss. 
• How will we share our data with others?  Identify the intended audience for data sharing and determine access restrictions if applicable. Specify the platforms or repositories where data will be deposited, along with any embargo periods or licensing requirements. 
• How will we preserve our data for the long term?  Determine the duration for which data should be retained after the completion of the project. Identify strategies for long-term data preservation, including considerations for format migration and future accessibility. 

A good research data management plan is clear, concise, and consistent. It is also flexible to accommodate changes or challenges that arise with any groundbreaking research. Here are some expert tips to help you create an effective research data management plan:  

• Start early:  Ideally, one should create a research data management plan before collecting or generating data for research. This helps in planning and avoids any potential issues later on.  
• Check established guidelines:  Different investors, institutions, journals, and disciplines generally have different requirements or standards for their research data management, as per their requirements. It is essential to go through the relevant guidelines and policies before creating a research data management plan and ensure that it is in line with institutional policies.  
• Use a template:  Several templates and tools are available online that help in creating an efficient research data management plan. For example, DMPonline is a web-based tool that provides customized templates based on the funder or institution for which you are making the research data management plan. You can also refer to the work of other researchers in your field to help create a customized template for your needs.  
• Seek feedback:  It is always a good idea to seek input from supervisors and peers on a research data management plan that may have worked well for them. You can use ideas from their experience to create a research data management plan that works well for you.  
• Review and update:  Remember that a research data management plan is not a static document. It should be reviewed and updated regularly throughout the research project to reflect changes or developments in your data or methods during research.  

Developing and implementing a sound research data management plan can help researchers and PhD students better manage their data, making it more accessible and ensuring the quality and integrity of the research data. By taking the time to plan and organize your research data management, you can streamline your research process, avoid potential issues, and improve the overall quality of your research. 

Reference:  

• Marr, B. How Much Data Do We Create Every Day? The Mind-Blowing Stats Everyone Should Read. Forbes, May 2018. Available online at   https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/?sh=aef8d3860ba9  

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• Study and research support
• Researcher support
• Research data management

Data management planning

Write a data management plan.

A data management plan (DMP) describes how you will collect, organise, analyse, preserve and share data. It ensures that you meet funder requirements.

You should complete your DMP early in the research project. This will establish good data management practice and make your data management more efficient throughout.

Many research funders require that you create a DMP as part of grant applications. DMPs vary in length depending on the funder requirement and the complexity of your data. Most plans are 1–4 pages long.

Find out more about funder requirements for research data .

What is included in a data management plan

Your DMP should describe how you will:

• Organise and describe data : What data types, formats, standards and capture methods will be used.
• Manage legal and ethical requirements : How you will safeguard sensitive data, and whether to restrict access and use.
• Store data : How you will store the data in the short- and long-term.
• Share data : How data will be shared outside the core project staff and what reuse opportunities there are.
• Manage staff resource and training : Who will be responsible for data management, what resources do you need, and is any training required.

Tools and templates are available to support you when creating your data management plan.

Examples of data management plans

These examples of data management plans (DMPs) were provided by University of Minnesota researchers. They feature different elements. One is concise and the other is detailed. One utilizes secondary data, while the other collects primary data. Both have explicit plans for how the data is handled through the life cycle of the project.

School of Public Health featuring data use agreements and secondary data analysis

All data to be used in the proposed study will be obtained from XXXXXX; only completely de-identified data will be obtained. No new data collection is planned. The pre-analysis data obtained from the XXX should be requested from the XXX directly. Below is the contact information provided with the funding opportunity announcement (PAR_XXX).

Types of data : Appendix # contains the specific variable list that will be used in the proposed study. The data specification including the size, file format, number of files, data dictionary and codebook will be documented upon receipt of the data from the XXX. Any newly created variables from the process of data management and analyses will be updated to the data specification.

Data use for others : The post-analysis data may be useful for researchers who plan to conduct a study in WTC related injuries and personal economic status and quality of life change. The Injury Exposure Index that will be created from this project will also be useful for causal analysis between WTC exposure and injuries among WTC general responders.

Data limitations for secondary use : While the data involve human subjects, only completely de-identified data will be available and used in the proposed study. Secondary data use is not expected to be limited, given the permission obtained to use the data from the XXX, through the data use agreement (Appendix #).

Data preparation for transformations, preservation and sharing : The pre-analysis data will be delivered in Stata format. The post-analysis data will also be stored in Stata format. If requested, other data formats, including comma-separated-values (CSV), Excel, SAS, R, and SPSS can be transformed.

Metadata documentation : The Data Use Log will document all data-related activities. The proposed study investigators will have access to a highly secured network drive controlled by the University of Minnesota that requires logging of any data use. For specific data management activities, Stata “log” function will record all activities and store in relevant designated folders. Standard file naming convention will be used with a format: “WTCINJ_[six letter of data indication]_mmddyy_[initial of personnel]”.

Data sharing agreement : Data sharing will require two steps of permission. 1) data use agreement from the XXXXXX for pre-analysis data use, and 2) data use agreement from the Principal Investigator, Dr. XXX XXX ([email protected] and 612-xxx-xxxx) for post-analysis data use.

Data repository/sharing/archiving : A long-term data sharing and preservation plan will be used to store and make publicly accessible the data beyond the life of the project. The data will be deposited into the Data Repository for the University of Minnesota (DRUM), http://hdl.handle.net/11299/166578. This University Libraries’ hosted institutional data repository is an open access platform for dissemination and archiving of university research data. Date files in DRUM are written to an Isilon storage system with two copies, one local to ​each of the two geographically separated University of Minnesota Data Centers​. The local Isilon cluster stores the data in such a way that the data can survive the loss of any two disks or any one node of the cluster. Within two hours of the initial write, data replication to the 2nd Isilon cluster commences. The 2nd cluster employs the same protections as the local cluster, and both verify with a checksum procedure that data has not altered on write. In addition, DRUM provides long-term preservation of digital data files for at least 10 years using services such as migration (limited format types), secure backup, bit-level checksums, and maintains a persistent DOIs for data sets, facilitating data citations. In accordance to DRUM policies, the de-identified data will be accompanied by the appropriate documentation, metadata, and code to facilitate reuse and provide the potential for interoperability with similar data sets.

Expected timeline : Preparation for data sharing will begin with completion of planned publications and anticipated data release date will be six months prior.

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College of Education and Human Development featuring quantitative and qualitative data

Types of data to be collected and shared The following quantitative and qualitative data (for which we have participant consent to share in de-identified form) will be collected as part of the project and will be available for sharing in raw or aggregate form. Specifically, any individual level data will be de-identified before sharing. Demographic data may only be shared at an aggregated level as needed to maintain confidentiality.

Student-level data including

• Pre- and posttest data from proximal and distal writing measures
• Demographic data (age, sex, race/ethnicity, free or reduced price lunch status, home language, special education and English language learning services status)
• Pre/post knowledge and skills data (collected via secure survey tools such as Qualtrics)
• Teacher efficacy data (collected via secure survey tools such as Qualtrics)
• Fidelity data (teachers’ accuracy of implementation of Data-Based Instruction; DBI)
• Teacher logs of time spent on DBI activities
• Demographic data (age, sex, race/ethnicity, degrees earned, teaching certification, years and nature of teaching experience)
• Qualitative field notes from classroom observations and transcribed teacher responses to semi-structured follow-up interview questions.
• Coded qualitative data
• Audio and video files from teacher observations and interviews (participants will sign a release form indicating that they understand that sharing of these files may reveal their identity)

Procedures for managing and for maintaining the confidentiality of the data to be shared

The following procedures will be used to maintain data confidentiality (for managing confidentiality of qualitative data, we will follow additional guidelines ).

• When participants give consent and are enrolled in the study, each will be assigned a unique (random) study identification number. This ID number will be associated with all participant data that are collected, entered, and analyzed for the study.
• All paper data will be stored in locked file cabinets in locked lab/storage space accessible only to research staff at the performance sites. Whenever possible, paper data will only be labeled with the participant’s study ID. Any direct identifiers will be redacted from paper data as soon as it is processed for data entry.
• All electronic data will be stripped of participant names and other identifiable information such as addresses, and emails.
• During the active project period (while data are being collected, coded, and analyzed), data from students and teachers will be entered remotely from the two performance sites into the University of Minnesota’s secure BOX storage (box.umn.edu), which is a highly secure online file-sharing system. Participants’ names and any other direct identifiers will not be entered into this system; rather, study ID numbers will be associated with the data entered into BOX.
• Data will be downloaded from BOX for analysis onto password protected computers and saved only on secure University servers. A log (saved in BOX) will be maintained to track when, at which site, and by whom data are entered as well as downloaded for analysis (including what data are downloaded and for what specific purpose).

Roles and responsibilities of project or institutional staff in the management and retention of research data

Key personnel on the project (PIs XXXXX and XXXXX; Co-Investigator XXXXX) will be the data stewards while the data are “active” (i.e., during data collection, coding, analysis, and publication phases of the project), and will be responsible for documenting and managing the data throughout this time. Additional project personnel (cost analyst, project coordinators, and graduate research assistants at each site) will receive human subjects and data management training at their institutions, and will also be responsible for adhering to the data management plan described above.

Project PIs will develop study-specific protocols and will train all project staff who handle data to follow these protocols. Protocols will include guidelines for managing confidentiality of data (described above), as well as protocols for naming, organizing, and sharing files and entering and downloading data. For example, we will establish file naming conventions and hierarchies for file and folder organization, as well as conventions for versioning files. We will also develop a directory that lists all types of data and where they are stored and entered. As described above, we will create a log to track data entry and downloads for analysis. We will designate one project staff member (e.g., UMN project coordinator) to ensure that these protocols are followed and documentation is maintained. This person will work closely with Co-Investigator XXXXX, who will oversee primary data analysis activities.

At the end of the grant and publication processes, the data will be archived and shared (see Access below) and the University of Minnesota Libraries will serve as the steward of the de-identified, archived dataset from that point forward.

Expected schedule for data access

The complete dataset is expected to be accessible after the study and all related publications are completed, and will remain accessible for at least 10 years after the data are made available publicly. The PIs and Co-Investigator acknowledge that each annual report must contain information about data accessibility, and that the timeframe of data accessibility will be reviewed as part of the annual progress reviews and revised as necessary for each publication.

Format of the final dataset

The format of the final dataset to be available for public access is as follows: De-identified raw paper data (e.g., student pre/posttest data) will be scanned into pdf files. Raw data collected electronically (e.g., via survey tools, field notes) will be available in MS Excel spreadsheets or pdf files. Raw data from audio/video files will be in .wav format. Audio/video materials and field notes from observations/interviews will also be transcribed and coded onto paper forms and scanned into pdf files. The final database will be in a .csv file that can be exported into MS Excel, SAS, SPSS, or ASCII files.

Dataset documentation to be provided

The final data file to be shared will include (a) raw item-level data (where applicable to recreate analyses) with appropriate variable and value labels, (b) all computed variables created during setup and scoring, and (c) all scale scores for the demographic, behavioral, and assessment data. These data will be the de-identified and individual- or aggregate-level data used for the final and published analyses.

Dataset documentation will consist of electronic codebooks documenting the following information: (a) a description of the research questions, methodology, and sample, (b) a description of each specific data source (e.g., measures, observation protocols), and (c) a description of the raw data and derived variables, including variable lists and definitions.

To aid in final dataset documentation, throughout the project, we will maintain a log of when, where, and how data were collected, decisions related to methods, coding, and analysis, statistical analyses, software and instruments used, where data and corresponding documentation are stored, and future research ideas and plans.

Method of data access

Final peer-reviewed publications resulting from the study/grant will be accompanied by the dataset used at the time of publication, during and after the grant period. A long-term data sharing and preservation plan will be used to store and make publicly accessible the data beyond the life of the project. The data will be deposited into the Data Repository for the University of Minnesota (DRUM),  http://hdl.handle.net/11299/166578 . This University Libraries’ hosted institutional data repository is an open access platform for dissemination and archiving of university research data. Date files in DRUM are written to an Isilon storage system with two copies, one local to each of the two geographically separated University of Minnesota Data Centers. The local Isilon cluster stores the data in such a way that the data can survive the loss of any two disks or any one node of the cluster. Within two hours of the initial write, data replication to the 2nd Isilon cluster commences. The 2nd cluster employs the same protections as the local cluster, and both verify with a checksum procedure that data has not altered on write. In addition, DRUM provides long-term preservation of digital data files for at least 10 years using services such as migration (limited format types), secure backup, bit-level checksums, and maintains persistent DOIs for datasets, facilitating data citations. In accordance to DRUM policies, the de-identified data will be accompanied by the appropriate documentation, metadata, and code to facilitate reuse and provide the potential for interoperability with similar datasets.

The main benefit of DRUM is whatever is shared through this repository is public; however, a completely open system is not optimal if any of the data could be identifying (e.g., certain types of demographic data). We will work with the University of MN Library System to determine if DRUM is the best option. Another option available to the University of MN, ICPSR ( https://www.icpsr.umich.edu/icpsrweb/ ), would allow us to share data at different levels. Through ICPSR, data are available to researchers at member institutions of ICPSR rather than publicly. ICPSR allows for various mediated forms of sharing, where people interested in getting less de-identified individual level would sign data use agreements before receiving the data, or would need to use special software to access it directly from ICPSR rather than downloading it, for security proposes. ICPSR is a good option for sensitive or other kinds of data that are difficult to de-identify, but is not as open as DRUM. We expect that data for this project will be de-identifiable to a level that we can use DRUM, but will consider ICPSR as an option if needed.

Data agreement

No specific data sharing agreement will be needed if we use DRUM; however, DRUM does have a general end-user access policy ( conservancy.umn.edu/pages/drum/policies/#end-user-access-policy ). If we go with a less open access system such as ICPSR, we will work with ICPSR and the Un-funded Research Agreements (UFRA) coordinator at the University of Minnesota to develop necessary data sharing agreements.

Circumstances preventing data sharing

The data for this study fall under multiple statutes for confidentiality including multiple IRB requirements for confidentiality and FERPA. If it is not possible to meet all of the requirements of these agencies, data will not be shared.

For example, at the two sites where data will be collected, both universities (University of Minnesota and University of Missouri) and school districts have specific requirements for data confidentiality that will be described in consent forms. Participants will be informed of procedures used to maintain data confidentiality and that only de-identified data will be shared publicly. Some demographic data may not be sharable at the individual level and thus would only be provided in aggregate form.

When we collect audio/video data, participants will sign a release form that provides options to have data shared with project personnel only and/or for sharing purposes. We will not share audio/video data from people who do not consent to share it, and we will not publicly share any data that could identify an individual (these parameters will be specified in our IRB-approved informed consent forms). De-identifying is also required for FERPA data. The level of de-identification needed to meet these requirements is extensive, so it may not be possible to share all raw data exactly as collected in order to protect privacy of participants and maintain confidentiality of data.

Data Management Plans: UoB Data Management Plan Templates

• Research Data Service Home
• Data Management Plans Home
• Data Management Overview
• UoB Data Management Plan Templates
• UKRI Funder Requirements
• Other Major Funder Requirements
• University Requirements
• National Legislation

On this page you will find the  University of Bath Doctoral Data Management Plan template ( for doctoral students )  and the  University of Bath Data Management Plan template. There is also guidance for supervisors on reviewing the Doctoral Data Management Plan.  

Doctoral Data Management Plan template

Use this template if you are a  doctoral student.  All doctoral students are required to submit a Data Management Plan at the point of confirmation. We provide a self-paced online training course on Writing a University of Bath Data Management Plan (requires University of Bath login) to help you start to draft your Data Management Plan.

• University of Bath Doctoral Data Management Plan template (Word document)
• University of Bath Doctoral Data Management Plan template (LaTeX)
• University of Bath Doctoral Data Management Plan template (PDF)

University of Bath Data Management Plan template

Use this template if you are a researcher and you are writing a Data Management Plan for your project where there is no funder template available . You can find information on funder requirements on the ' UKRI Funder Requirements ' and 'Other Major Funder Requirements ' tabs. The following funders have specific templates or have issued specific guidance on the contents of their Data Management Plans: UKRI funders: AHRC, BBSRC, EPSRC, ESRC, MRC, NERC and STFC can be found on the UKRI funders page  (click on relevant tab from this link). Other major funders: British Academy, Cancer Research UK, European Research Council, NIH, NIHR, Royal Society, The Academy of Medical Sciences and Wellcome Trust can be found on the other major funders page (click on the relevant tab from this link).

• University of Bath Data Management Plan template (Word document)
• University of Bath Data Management Plan template (PDF)
• University of Bath Data Management Plan template (LaTeX)
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• Next: UKRI Funder Requirements >>
• Last Updated: Sep 6, 2024 12:09 PM
• URL: https://library.bath.ac.uk/research-data/data-management-plans

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Data management plans

What is a data management plan.

A data management plan (DMP), sometimes also referred to as Technical Plan or Data Access Plan, is a document that describes how data will be collected, organised, managed, stored, secured, backed-up, preserved, and where applicable, shared. It is an important tool that facilitates and supports the creation of FAIR data .

The DMP is intended to be a living document which is updated as the project progresses.

Warning: we have been made aware that UKRI may be changing the format required for data management plans in new applications. DMPOnline are liaising with UKRI to ensure templates are updated, but in the meantime, please check the requirements on your application.

What exactly is “research data”?

Following the definition provided in the University's Research Data Management Policy , “Research data are the evidence that underpins the answer to the research question, and can be used to validate findings regardless of its form.” Thus, data covers quantitative and qualitative statements, raw data from measurements and derived data – either cleaned or extracted from your primary dataset or derived from an existing source. 

Do I need to write a data management plan?

If your research uses or creates data, yes. The University of Birmingham’s Research Data management Policy states that

“3.4 All funded research must be supported by a DMP or protocol that explicitly addresses data capture, management, integrity, confidentiality, retention, ownership, sharing and publication. This may be either a DMP submitted to the research funder as part of a research application or a document developed via the University’s DMP system after the project receives funding. Unfunded research which is likely to generate Research Data should also be supported by a DMP.”

If you are funded, there might be additional requirements that you need to meet. The main funder requirements are summarised in the data policy section . 

How do I write a data management plan?

We recommend you to use the DMPOnline   tool to create your data management plan, as it provides you with a variety of templates tailored to funder requirements as well as a basic template if you are unfunded. Guidance on using DMPOnline is available in our Introduction to using DMPonline video  (duration 7:11), and the guide to creating a DMP using DMPOnline .

If you are creating a DMP to support a research grant application, you should check whether the funder requires the use of a specific template. Your College Research Support Office can advise.

For PGR students and researchers where no funder requirement applies, the University provides a standard template. You can access it via DMPonline  or download a Word version (DOCX file - 64KB) .

Examples of data management plans

The  RIO journal  allows you to formally publish your data management plan. See their examples of published DMPs.

The Digital Curation Centre provides a collection of  example DMPs and guidance .

DMP Review Service

The Scholarly Communications Team has recently launched a Data Management Plan Review Service for Research Staff . We will review drafted data management plans before grant applications submissions and provide feedback.

 Please allow at least two weeks for our review.

 If you would like us to review your data management plan (DMP), email us a draft of your plan to: [email protected] . Also, make sure that you have included the name of the Principal Investigator, a summary of the project and the associated Funder.

DMPs for PGRs

• College of Arts and Law
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• College of Life and Environmental Sciences
• College of Medicine and Health
• College of Social Sciences

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Course for PhD students: Writing a Data Management Plan

In the course we discuss:

• which laws and codes of conduct your research should comply with
• where you are going to store your data
• where you are going to archive them for the long term
• how you can make them FAIR (Findable, Accessible, Interoperable, Reusable)

The course is tailored to the faculties of VU Amsterdam and we organize it in collaboration with the university graduate schools and PhD coordinators. On this web page you can find how the course is being organised in your faculty.

This course is intended for PhD students at the VU. If you're not doing a PhD or if you're affiliated at another institution, but are nevertheless interested in this course, please get in touch with [email protected] .

Course description

In this course, we will introduce and discuss the different aspects of RDM (Research Data Management) which typically need to be covered in a DMP, such as data description, data storage during research, sharing data with colleagues, data archiving after research and data citation. The various components of research data management will be related to the FAIR principles (Findable, Accessible, Interoperable and Reusable). We will also address the ethical and legal framework, including the GDPR (General Data Protection Regulation).

You will learn why good RDM practices are necessary and how your research benefits from that. In two interactive workshops, we will provide you with practical guidelines and instruments to manage your data properly. The workshops follow the structure of the VU DMP template. 

After the two workshops, you will write a first version of a DMP for your own research, so that you can apply the things you learn to your own project. Your DMP will be peer reviewed by another participant of the course and you will peer review someone else’s DMP. You will also receive feedback from a faculty data steward. After this round of feedback, you submit the final version of your DMP, which will be reviewed by one of the central data stewards. 

At the end of the course, you will have a solid DMP that will guide you in managing your research data throughout your project.

The course consists of the elements described below. Part of the materials need to be studied prior to the first workshop.

Preparation (reading materials): 9 hours 2 workshops of 2 hours (online or in person): 4 hours Assignment 1 | RDM Framework: 4 hours Assignment 2 | First draft DMP: 6 hours Peer review DMP: 1 hour Assignment 3 | Final DMP: 4 hours

Total 28 hours (= 1 EC)

You can receive 1 EC for this course. In order to obtain this credit, you need to meet the following requirements:

• Participate in both workshops
• Submit all assignments
• Do a peer review of someone else’s DMP
• Assignment 3 should be of sufficient quality

RDM Support Desk

+31 20 598 5367

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Data Management Plans Pilot for Cambridge PhD Students

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The Research Data Management Facility is running a pilot project with some departments to include data management plans in the assessments PhD students go through towards or at the end of their first year. We are asking students to complete a brief data management plan (DMP) and for supervisors and assessors to ensure that the student has thought about all the issues and their responses are reasonable. The Research Data Management Facility will provide support for any students, supervisors or assessors that are in need.

This page lists some sources of information that will help students to complete their data management plans.

Data Formats

In your DMP you should specify the formats that your data will be collected in or made available in. It is best to share your data using open source formats as this increases the reuseability of the data. For example, if you save your spreadsheet in a Microsoft Excel format this can create problems if the person wishing to look at your data doesn't have access to Excel. It would be much better to save it as a .csv file in this example. You might also want to think about the long term preservation potential of different formats. For example, if you have an image .tif is considered to be a better format than .jpg when it comes to preservation. For lists of recommended file formats see:

UK Data Service Recommended File Formats

Library of Congress Recommended Formats Statement

Short-Term Storage

Good research data management practices include backing up your data in at least 2 locations. You might use the University's network for this, an external hard drive or a cloud solution. It can be problematic using private cloud solutions due to their terms and conditions and you should never put personal or sensitive data on a private cloud. However, the University offers several cloud solutions that students can use with their @cam email address, which have different terms and conditions. The University Information Service also offers paid-for storage options for researchers working with large volumes of data (>2Tb). For information about University provided cloud solutions see:

UIS Data Storage for Individuals

For information about the paid-for large capacity storage options please see:

UIS Research Data Storage Services

Data Management

When considering your data management think about all the day to day things you do with your data and digital files. How will you organise your folders and files on your computer? Will you use a file naming convention  (e.g. like this file naming convention ) to make it easy to locate files later on and keep track of the lastest version? If you are collecting data in the field, will you need to digitise your notes? If so, when will you do this and how will you back it up if you are away from Cambridge? If you are doing lab work, you might want to consider using an electronic laboratory notebook as this means all your notes and experimental data are digital from the start.

Ethics and Intellectual Property

Hopefully if there are any ethical considerations to your PhD you will have picked them up before the end of your first year. If you still need advice on ethics you should visit the Research Ethics Website . Many departments have their own ethics committees and advice so it is worth checking if your department does . If you think their might be Intellectual Property issues with your research please visit the Cambridge Enterprise pages for more information. If there are ethical or IP considerations for your data you should say what you have done or are planning to do to address these.

Data Sharing and Reuse

When thinking about data sharing you should first consider what your funder requires you to do. Provided you can share your data (there are exceptions for personal/sensitive data) you will need to think about:

a) what data will you share. You should share any data that is needed to support the arguments you make in your publications. This might be the raw data or processed data - you are expected to make this judgement in line with what is most useful to others working in your discipline. You should also share any documentation that goes with the data, e.g. protocols or information about collection. Code should also be shared if this was used in the creation or manipulation of data. If you are going to share anonymised data, do you know how to carry out the anonymisation?

b) where to share your data. A subject specific repository is best. If you don't know what that is for your repository you can look it up on the re3data registry . If a discipline specific repository doesn't exist you can use a general repository, such as Apollo , the University's repository.

c) when you will share your data. Data should be shared at the same time that any publication it supports is published. Some funders will allow you to delay the sharing of the data if another paper is imminent but you shouldn't delay sharing it indefinitely.

d) is there any cost to sharing your data? The University repository allows you to deposit datasets up to 20Gb for free but anything large is charged at £4/Gb (one-off charge).

If you cannot share your data you should state the reasons for this in your plan.

Thesis Sharing

It is now a requirement of graduation that a digital copy of your thesis is given to the University repository. This doesn't mean that your thesis has to be openly available although it is encouraged. Think about any issues their might be with making your thesis available in the repository. The Office of Scholarly Communication provides detailed advice about the new requirement to deposit an electronic copy of your thesis .

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