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Research Data Management


FAIR Data Management Plan (DMP) implies that data are:

Findable i.e. discoverable with metadata, identifiable and locatable by means of a standard identification mechanism;

Accessible i.e. always available and obtainable

Interoperable i.e. both syntactically parseable and semantically understandable, allowing data exchange and reuse between researchers, institutions, organisations or countries

Reusable i.e. sufficiently described and shared with the least restrictive licences, allowing the widest reuse possible and the least cumbersome integration with other data sources.

A Data Management Plan saves time, minimises reorganisation later, avoids duplication, increases research efficiency, provides guidelines for everyone on the research team, avoids the risk of data loss, ensures adequate preparation for data preservation, helps to quantify the resources required and ensures that others can understand and re-use your research data in the future

Components of a Data Management Plan

Information about data and data format

  • Description of data to be produced e.g.experimental, observational, physical collections, models and their outputs, simulation outputs, curriculum materials, software, images, interviews, surveys, etc
  • How data will be acquired e.g. when and where
  • How data will be processed e.g. software used, algorithms, workflows
  • File formats e.g. justification, naming convention
  • Quality assurance and control during sample  collection, analysis, and processing
  • Existing data e.g. if existing data are used, what are their origins,will your data be combined with existing data and what is the relationship between your data and existing data?
  • How data will be managed in short-term e.g. version control, backing up, security and protection, who will be responsible?

Metadata content and format

  • Documentation and reporting of data
  • Contextual details: critical information about the dataset
  • Information important for using the data
  • Descriptions of temporal and spatial details, instruments, parameters, units, files, etc. 
  • What metadata are needed e.g. any details that make data meaningful 
  • How metadata will be created and/or captured e.g. lab notebooks, GPS units, auto-saved on instrument?
  • What format will be used for the metadata e.g. standards for community, justification for format chosen

Policies for access, sharing and re-use

  • Obligations for sharing e.g funding agency, institution, other organization or legal
  • Details of data sharing e.g how long, when, how access can be gained, data collector rights
  • Ethical/privacy issues with data sharing
  • Intellectual property and copyright issues e.g. who owns the copyright, related institutional policies, funding agency policies, embargos for political/commercial reasons
  • Intended future uses/users for data
  • Citation e.g. how should data be cited when used, persistent citation?

Long-term storage and data management

  • What data will be preserved?
  • Where will it be archived e.g. most appropriate archive for data, community standards?
  • Data transformations/formats needed e.g. consider archive policies
  • Who will be responsible e.g. contact person for archive?


  • Anticipated costs e.g. time for data preparation and documentation, hardware/software for data preparation and documentation, personnel, archive costs
  • Funding for costs

Read about costing data management at UK Data Service


Jones, S. (2011). ‘How to Develop a Data Management and Sharing Plan’. DCC How-to Guides. Edinburgh: Digital Curation Centre. Available online:

For help contact the Library - details below

For advice and support please contact Trish Finnan, Digital Publishing and Data Management Librarian (trish.finnan@universityofgalway).

Trish provides a Data Management Plan review service and reads and comments on draft Data Management Plans (DMPs). DMPs for review should be sent to her well in advance of funding application deadlines.

Examples of Data Management Plans

A summary of example plans organised by research funders is provided by the Digital Curation Centre. LIBER the Association of European Research Libraries also provides a Data Management Plan Catalogue which is a central hub for DMPs from different disciplines. It also includes quality reviews of the DMPs.

The Portage Network provides examples of DMPs on Zenodo that illustrate how to document research data that is in forms that are not as easily understood as data, for example in the arts and humanities.

A collection of Horizon 2020 DMPs has been curated under the auspices of the OpenAIRE RDM taskforce and the University of Vienna Library IT ServiceThe content of the DMPs have not been quality reviewed. They are published as is and should not necessarily be taken as good practice cases.

DMPTool has some public DMPs created using it and shared publicly by their owners. They are not vetted for quality, completeness, or adherence to funder guidelines.

DMPonline has some public DMPs created using it and shared publicly by their owners. They are not vetted for quality, completeness, or adherence to funder guidelines.

DMPonline a tool to help you write your data management plan. It contains templates for most of the major funding bodies. Make sure the template you use is compatible with the requirements set out by your funder. There is a short tutorial at the link but the tool is straightforward and easy to use. Just sign In to create an account and get started. Some funders mandate the use of DMPonline, while others point to it as a useful option. You can download funder templates without logging in, but the tool provides tailored guidance and example answers from the DCC and many research organisations.

DMPTool is a service of the University of California Curation Center of the California Digital Library 

Data Stewardship Wizard from the GoFAIR organisation provides a smart questionnaire to guide you through the creation of a data stewardship plan. It includes hints, multimedia content, external resources and help.

Argos developed by OpenAIRE for Research Data Management (RDM) activities and Data Management Plans. It uses OpenAIRE guides created by the RDM Task Force to familiarize users with basic RDM concepts and guide them throughout the process of writing DMPs. It also utilises OpenAIRE services. Argos is based on the OpenDMP open source software and is available through the OpenAIRE Service catalogue and the EOSC Portal.

DMP checklists

There are many data management checklists available that explain and guide the process of creating a data management plan. The Digital Curation Centre provides a checklist as well as guidance and some examples.

If your research collects, stores or uses personal data there is a potential requirement for a Data Protection Impact Assessment (DPIA) and your draft DPIA must be submitted to the the University of Galway Data Protection Officer (email: at least 10 weeks in advance of Research Ethics Submission in order to facilitate timely review and edits.

A Data Protection Impact Assessment is a process designed to identify risks arising out of the processing of personal data and to minimise these risks as far and as early as possible. DPIAs are important tools for negating risks, and for demonstrating compliance with GDPR. Under GDPR, a Data Protection Impact Assessment is mandatory where data processing "is likely to result in a high risk to the rights and freedoms of natural persons."

In cases where it is not clear whether a Data Protection Impact Assessment is mandatory, carrying out one is good practice and a useful tool to help data controllers comply with data protection law.

Please complete this checklist to see if you are required to complete a Data Protection Impact Assessment: University of Galway Data Protection Impact Assessment Checklist

Ethical and legal requirements apply to the management of research data. Ethical oversight is the responsibility of a number of committees at the University of Galway and further information go to

The Research Ethics SharePoint site contains resources, forms and FAQs for the University of Galway research community. 


"Anonymisation is a valuable tool that allows data to be shared, whilst preserving privacy. The process of anonymising data requires that identifiers are changed in some way such as being removed, substituted, distorted, generalised or aggregated." UK Data Service

For information and guidance on both qualitative and quantitative anonymisation please refer to the following resources: 

Advice on anonymisation from the UK Data Service 

Amnesia is a data anonymization tool, that allows you to remove identifying information from data. Amnesia not only removes direct identifiers like names, SSNs etc but also transforms secondary identifiers like birth date and zip code so that individuals cannot be identified in the data. It is available as a desktop software and an online tool. See three how-to tutorials about the three main subprocesses of anonymization. The videos focus on enabling users to understand, tailor and guide the anonymization processes, while exploring the quality of the anonymized data.

UKAN UK Anonymisation Network who provide information and support about anonymisation to anyone who handles personal data and needs to share it.

ISSDA (Irish Social Science Data Archive) slides from Anonymisation workshop  (22 June 2016) relating to social research, quantitative and qualitative data.

IQDA Qualitative Data Anonymizer Tool and other resources for researchers.

Research and Innovation policies at the University of Galway are available on the Innovation Office website 

For advice and support contact the Technology Transfer / Innovation Office

Licensing your data

When making data available you will need to use a data licence to help others understand what they are allowed to do with your data.

The Digital Curation Centre (DCC) provide guidelines on How to License Research Data 

The UK Data Service provides a guideline on Rights relating to research data 

It is important to consider the financial and time resources required for data management and ensuring that data will be FAIR (Findable, Accessible, Interoperable and Reusable). The following costs should be included:

-  Resources required to prepare your data for sharing and preservation (i.e. data curation) e.g. time and people, storage and computation, creation and reuse of data, deposit and preservation 

- Explain the resources needed to deliver the data e.g. storage costs, hardware, staff time, costs of preparing data for deposit and repository charges

Information and tools to help with costing data management activities are provided below. Please refer also to specific guidelines provided by your funder.

The UK Data Archive costing tool and checklist

UK Data Archive Costing Tool 

Guide to costing data management created by staff at Utrecht University.

Utrecht University Costing Tool

For advice and support please contact Trish Finnan, Digital Publishing and Data Management Librarian (trish.finnan@universityofgalway).

Trish provides a Data Management Plan review service and reads and comments on draft Data Management Plans (DMPs). DMPs for review should be sent to her well in advance of funding application deadlines.