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Research Data Management

Planning

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?

Budget​

  • 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 https://www.ukdataservice.ac.uk/manage-data/plan/costing

Resources

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

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 on Zenodo of DMPs 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.

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 the OpenAIRE pool of services and inferred sources to make DMPs more dynamic in use and easier to be completed and published. 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.

Information on how to cost data management activities are provided below. Please refer also to specific guidelines provided by your funder.

The following guide to costing data management was created by staff at Utrecht University.

Utrecht University Costing Tool 

Reference: A. Westerhof, T.E. Pronk, A. van der Kuil , A. Mordant (2016) Data Management Cost Guide.

The UK Data Archive has also created a costing tool and checklist

UK Data Archive Costing Tool 

DCC and the OpenAIRE-Advance project have created an infographic to aid researchers in setting out the costs involved in managing and sharing their research data. It is based on the OpenAIRE guide for research data management costs and incorporates some sample costs for data storage. 

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

Research Ethics Committee (REC)

The objective of the NUI Galway Research Ethics Committee is to safeguard the health, welfare and rights of human participants and researchers (in the case of hazardous materials) in research studies and to afford dignity to the handling and treatment of biological materials, taking into account the scientific procedures and concerns of the local community. For any research proposal to gain ethical approval it must be necessary and of a design that minimises predictable risk to both the research participant and the researcher. Therefore, you may require Ethical Approval if you wish to carry out research within NUI Galway for any area that involves humans or their tissues, biological materials or hazardous substances. For further information go to the Research Office website.

Anonymisation

"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 

Guidance on how to De-identifying your data (Australian National Data Service (ANDS)

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.

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 data should be managed in line with NUI Galway Data ProtectionData Classification and Data Handling policies. 
  • Researchers must obtain and retain appropriate consent for the use of personal/sensitive personal data. Proof of consent should be available for audit as long as the University retains the data.
  • The legitimate interests of the subjects of research data must be protected as defined in legislation for the period for which University retains the data.

Read the Research Data Management Policy at NUI Galway

General Data Protection Regulation

NUI Galway Data Protection Website explains how the University manages information in line with the Data Protection Acts and GDPR

Learn more about ...

  • legal and ethical issues including informed consent, anonymisation and controlling access to data from the UK Data Service
  • data protection, rights and access from MANTRA a free online course from EDINA at the University of Edinburgh
  • GDPR and implications for researchers on webinar from Association of European Research Libraries (LIBER) 

Intellectual Property Policy at NUI Galway

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