What is Research Data Management?

[What is research data?] [Data management and lifecycle] [Structuring/naming your data] [Metadata and documentation] [GDPR and data security] [Data storage]

What is research data?

Research data is information that has been collected, observed, generated or created to validate original research findings. It includes both digital (e.g. photographs, spreadsheets, audio recordings) and non-digital (e.g. laboratory notebooks, diaries, interview transcripts) formats. The Digital Curation Centre (DCC) glossary defines data as:

‘A reinterpretable representation of information in a formalized manner suitable for communication, interpretation, or processing. Examples of data include a sequence of bits, a table of numbers, the characters on a page, the recording of sounds made by a persons peaking, or a moon rock specimen.’ (DCC glossaryReference model for an Open Archival Information System (OAIS) (PDF)

Determining what data is generated in research project is a key step in Research Data Management (RDM), helping to plan how to look after the data through different phases of the research project and beyond.

The University of Leicester Figshare-powered research data repository describes digital materials but could also be used to create records for non-digital research data outputs.

Please contact the team if you would like to discuss requirements for non-digital data.

[What is research data?] [Data management and lifecycle] [Structuring/naming your data] [Metadata and documentation] [GDPR and data security] [Data storage]


Data management and lifecycle

Research Data Management (RDM) is a collective term describing good practices of planning, organising, documenting, sharing, preserving the data as well as data security and ethical considerations. RDM provides a framework supporting researchers to produce quality, well documented data and make it discoverable and available, either as data underpinning a publication or after the research project has finished.

The data created by current research project(s) may play a vital role in the future, potentially in a totally different discipline. Therefore, it is essential to ensure that the data will be:

  • discoverable and available on a long term storage facility (discipline/institutional repository)
  • structured in logical and consistent manner with clear labels to help navigation
  • well documented, with appropriate metadata to help understanding the data, re-using it or be able to replicate results
  • having a persistent identifier, allowing quoting the dataset and acknowledging the original author

Data management should start as early as grant proposal planning or writing. Most funders now require the Data Management Plan as a part of grant application (please see our Policies page).

[What is research data?] [Data management and lifecycle] [Structuring/naming your data] [Metadata and documentation] [GDPR and data security] [Data storage]


Structuring/naming your data

Choosing a logical and consistent way to organise and name files and folders generated during the project allows you and others to locate and use them. Planning on how to organise research data at the start of a project will save you time and frustration, prevent duplication or errors.

Tips:

  • Be consistent – create a system and stick to it.  Do not use no or multiple conventions.
  • Use of folders – apply logical structuring of files within folders relating to projects or issues, do not leave files unsorted in top level folders.
  • Structure folders hierarchically – design a hierarchy with higher level broader topics, with more specific folders within these.
  • Folder naming – folders should be named after projects and research issues, with clear meaning.
  • Current and completed work – separate current and completed work, in case of multiple contributors consider a “Current version” folder.
  • Review what you have – select and appraise, consider carefully the requirements of what you need to retain, for how long, and what can (and can’t) be destroyed/deleted.  Consider this at intervals and at the end of a project.

[What is research data?] [Data management and lifecycle] [Structuring/naming your data] [Metadata and documentation] [GDPR and data security] [Data storage]


Metadata and documentation

Metadata describes other data. It provides information about an item and its relevance so that it can easily be found when needed. Good documentation ensures your data can be:

  • Searched for and retrieved
  • Understood now and in the future
  • Properly interpreted, as relevant context is available

Metadata is part of broader contextual information that accompanies data to ensure it can be found and understood over time. The information that can be recorded can range from a detailed description of the data to explanatory material about why the data was created and how it has been used.

Deciding what information should be recorded within research metadata is a significant and complex task. However, there are established metadata standards available, some of which are discipline specific (read more at http://www.dcc.ac.uk/drupal/resources/metadata-standards). Common European Research Information Format (CERIF), the Dublin Core Metadata Initiative (DCMI) and the Data Documentation Initiative (DDI) are just a few examples of metadata standards.

Metadata can be stored as:

  • supporting information in a separate file within the folder structure
  • embedded in the file (e.g. Microsoft Office use “Properties” to record common pieces of metadata such as title, author, organisation, subjects and keywords)
  • catalogue metadata usually structured according to an international standard, used to identify and locate the data that meet the user's requirements via a web browser or web based catalogue

[What is research data?] [Data management and lifecycle] [Structuring/naming your data] [Metadata and documentation] [GDPR and data security] [Data storage]


GDPR and data security

The General Data Protection Regulation (GDPR) came to effect on 25th May 2018 replacing the UK Data Protection Act 1998. It is important to be aware of the risks of storing personal data and keeping up to date with the University guidance and procedures. For more information and training please visit the Information Assurance Services website or get in touch at ias@le.ac.uk.

Please be aware that there is now a standard University Privacy Impact Assessment process where data collection or receipt, or systems purchase or development may have privacy consequences. The University has also adopted a simple data classification model which must be applied by the research PI and/or data owner.

[What is research data?] [Data management and lifecycle] [Structuring/naming your data] [Metadata and documentation] [GDPR and data security] [Data storage]


Data storage

Please note the difference between the active research data which is being analysed and data underpinning publication(s) or end of the project data. For the latter there may be requirements from the research funder in terms of preferred repository and the timescales. Visit ‘Publish, share and cite your data’ to find out more about options available for data publication and long-term storage.

The University provides storage solutions for active research data which are secure and automatically backed up. Researchers can use the following network data drives:

It is advised to keep multiple copies of the active research data, on different storage solutions. However, remember about regular backup and keep track of the file versions stored. Automate the process where possible.

More guidance on backing up and data security can be found at Information Assurance Services pages.

[What is research data?] [Data management and lifecycle] [Structuring/naming your data] [Metadata and documentation] [GDPR and data security] [Data storage]

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