Research Data Management

(What is it, why it is important and what needs to be done).

Data often outlives the projects that create them. Increasingly funders request or require that their funding recipients create and follow plans for managing data, storing or preserving it for the future and making some or all of their data open access. It is accepted that not all data will be available for sharing due to commercial or ethical considerations so there may well be an opt-out clause in the funder’s policy.

What it is

Research data may be created by an individual researcher, group or contributed by a third party to a research group. Some examples are

Observational: data captured in real time that is usually unique. Eg. Sensing data, survey data, field recordings, sample data: Experimental: data captured from lab equipment that is often reproducible-examples are Gene sequences, magnetic field data: Models or simulations: data generated from test models where the model and metadata may be more important than output data from the model-examples are  climate models, economic models: Derived or complied: resulting from processing or combining “raw” data often reproducible: Reference or Canonical: a static or organic conglomeration or collection of datasets probably published and curated-examples are Gene sequence databanks, collections of letters, historical images.

Types of outputs to be considered

Documents, spreadsheets

Scanned laboratory notebooks, field notebooks, diaries

Online Questionnaires, transcripts, surveys or codebooks

Digital audiotapes, videotapes or other digital media

Scanned photographs or films

Transcribed Test Responses

Database contents (video, audio, text, images)

Digital Models, algorithms, scripts

Contents of an application (input, output, log files for analysis software, simulation software, schemas)

Documented methodologies and workflows

Records of standard operating procedures and protocols


Why manage Data?

Data is a digital asset and needs to be managed so as

  1. To ensure research integrity and validation of results.
  2. To increase research efficiency.
  3. To facilitate data security and minimise the risk of loss.
  4. To ensure wider dissemination, increased impact and online sharing.
  5. To enable research continuity through secondary data use.
  6. To comply with funders’ requirements. 

Having a good plan means you can find and understand your data when you need to, there is continuity if people leave or join the project, you avoid unnecessary duplication, you link your data and publications together and ultimately your research is more visible and has greater impact (data attracts citations).  For the Sciences the expectation of the data is that it can be used for factual purposes and to validate the research. For the Arts & Humanities it is more about evidence for the production of new knowledge (this may very well be more subjective, ephemeral and tacit). If data is not managed properly, it can quickly become lost or unusable because of obsolete file formats, hardware etc.



Data Management Plan

A data management plan or DMP is a formal document that is developed at the start of the research project. Data management is an ongoing process and planning in the early stages makes the whole endeavour easier. If you are asked for a plan in a funder proposal, consider it as a preliminary outline which will be developed during your research. A plan will deal with the following questions

1  What research data am I creating or collecting?

2  Who will be responsible/take ownership of each aspect of the plan?

3  What policies (funder/institutional) will apply to the plan?

4  How will the data be organised (file naming conventions, file versioning)?

5  How will the data be documented during the collection and analysis stages of the research?

6  How will I backup, store and secure my data?

7  What equipment and facilities are needed?

8  Who will have ownership/rights to my data (especially important if this is collaborative research)?

9   How will I preserve the data once the research project is finished?

Your plan will cover initiation of the research, mid-term review and final review. Remember your plan like your research will evolve and may need amending as the direction of the research changes.

Ethical Considerations

A number of ethical and legal requirements will apply to the management of research data particularly when the research involves people.

 Ethical considerations include the purpose and nature of the research itself. Much research data about people can be shared ethically and legally if researchers avail of informed consent, anonymisation and controlled access.


It is recommended that you use one of these checklists to ensure that you cover all the essential areas

UCD Library Data Management Checklist  

The Digital Curation Centre Data Management Checklist

Compiling a data management plan

You can use an online tool like the Dmponline from the Digital Curation Centre in the UK and this is recommended as you can share your plan with others in your research group and they can also contribute to the plan. This software will also allow you to manage your plan online and they have templates for some of the big Funders in the UK.

To use the tool you need not be based in the UK but you must register. This is simply done by filling in your email and your institution. There is a drop down list of Institutions and if your institution is not listed there, simply use “other institution” and fill in the name.    You can register and create an account and the software will prompt you through the process. You can also manage your plans online. There is a short video introduction on the home screen that will show you how to use Dmponline.

Data Management Plan for a Funding Proposal.


A really good source of information for this whole area is the Digital Curation Centre and most of the information here is taken from their website.

Increasingly Funders are mandating that data must be made openly available as well as publications.  The Horizon 2020 funding call has an Open Research Data Pilot which will be monitored during the life of the program with a view to further developing EC policy on open research.

If you are asked for a plan when making a funding proposal, consider it as a preliminary outline plan for a comprehensive Data Management Plan which will be developed during the course of the research. 

What do Funders expect from the preliminary outline?

A funder will expect data plans to outline how the data will be created, managed, shared and preserved justifying any restrictions that need to be applied. Strict word counts may be applied so you need to be clear and concise. Funders typically expect a succinct summary submitted as part of the “case for support” or in an allocated section of the application form. Take care not to repeat information here or to provide details unrelated to data management.

Consult and collaborate widely. Ask for advice from colleagues, research offices, library, local IT, legal, ethics, repositories.

Justify your decisions: generally the funder will not specify particular file formats, standards or methodologies that you are expected to use. However, you need to choose and demonstrate that the selections you have made are the appropriate ones, both for the project and for the future. You also will need to make a convincing case with regards to restrictions on data sharing.

Be prepared to implement: you need to convince the funder that you understand their requirements and have realistic plans in place to meet these. These plans should be clear and achievable. Clearly defined roles and responsibilities will help so be very clear about who will do what, how and when.


Detail what data you will create and explain why you have opted for particular formats, standards and methodologies. Be aware that the choices you make here will make it easier or harder to share your data. Using standard or widely adopted formats will make your data interoperable and more easily shared. Open or non-proprietary data formats are preferable. If you are depositing your data into a subject/discipline or institutional archive, check what the preferred formats are.

You will need to state the data outputs you expect to generate. This means stating the volume, type, content, quality and format of the final dataset. Outline the metadata, documentation or other supporting material that must accompany the data for it to be understood properly. State the standards and methodologies that you will use to collect and manage this data.

Point out the relationship to other data available in other repositories; existing data sources that will be used, gaps between available data and your research project, the added value that your data will provide in relation to existing data.

Documentation and Metadata

This is really important as it allows your data to be understood and discovered by others. You must capture the contextual details about how and why the data was created. Metadata describes the data in detail (think of it like a description in a catalogue). There are various standards that can be used for this, check with the library or your colleagues for the one most appropriate for your discipline.

Make a strong case for any restrictions on sharing

You should justify any embargo periods or restrictions on sharing your data. Remember there is an expectation that publically funded research data will be openly available as soon as possible.

If using human subjects, you will be guided by formal ethical review and outline the steps you will take to protect the research participants. Show that you have weighed up the reasons to share or not share and in this context negotiated informed consent of the participants may be one way forward. You should also show that you are aware of relevant legislation such as the Data Protection Acts. Some guidance to the Acts is provided here

Data Ownership

Show that you are aware of this issue. You must demonstrate that you have looked for advice on and addressed all copyright, license or other rights issues that might arise.

Anticipate how other users might avail of your data

If you can, anticipate the type of users who might avail of your data and address their needs when deciding how to make the data available. Remember your objective is to make it as easy as possible for them to access the data. The funders will welcome clarity around access so be clear about where, when and how your data will be made available. You may want to license your data using a Creative Commons License.  Where possible use an appropriate disciplinary database, data centre or institutional repository. You will find a list of such repositories at DataCite  or  and you can upload data to the Institutional Repository Arrow@dit.


Describe how you plan to store securely store the data. Security will need to be stronger for any sensitive data you collect than for licensed data.  If you are using an online serve know where your data is housed and if this is legally permissible. The more important your data and the more it is used, the more regularly it needs to be backed up. If your project has multiple partners, specify the responsibilities for data management and curation within the research teams operating in all partner institutions.


Some useful resources:

UCD Library: Research Data Management Guide:

DCC, Funders’ data plan requirements, URL:

20 Wellcome Trust. (n.d.). Guidance for Researchers: Developing a Data Management and Sharing Plan. Retrieved 8 August 2011, from

UKDA. (2011). Managing and Sharing Data: best practice for researchers:


Yvonne Desmond,

Manager, Library Central Services Unit

21 April, 2015

Contact:, tel. 4027807

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