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Research Services

Research data management

Research data can take many forms, both digital and non-digital. It may refer to primary data collected first hand (such as interview scripts) or it may refer to secondary research created by others.

  • Text (word) documents or spreadsheets
  • Qualitative interview transcripts
  • Audio-visual material (including film and photographs)
  • Creative material (including artwork)
  • Fieldwork (including notebooks and log books)
  • Code, algorithms and scripts.

What is Research Data Management?

Research data management (RDM) refers to the process of planning and managing research data throughout the research process (known as the as the data life cycle).

JISC define RDM as the process of keeping data...

well-organised and documented, so that it is easy to share with other researchers and the public. 

Graphical representation of the research data lifecycle

Why Manage your Research Data?

There are many reasons why it’s a good idea to manage your research data. Well organised research data makes your project:

  • More efficient: Managing and storing your neatly and logically saves time and effort in the long run.
  • Minimises Risk: Managing your data effectively minimizes the chances of your data being lost or of breaching confidentiality.
  • Maximises Value: Makes it easier for others to use your data, increasing rigour and transparency and reproducibility.

More information about research data management and the data lifecycle can be found in JISC's RDM toolkit.

Planning your Research Data Management

If you are undertaking a research project that involves research data, you will need to create a research data management plan. If your research is funded, this may be mandated by your research funder. You may also be required to write a research data management plan as part of a grant application. 

The Digital Curation Centre provide a great resource that you can use when considering how to make a data management plan and showcase some examples of data management plans. 

Policy Summary:

  1. Researchers should consider data creation, management, and sharing in a Data Management Plan (DMP).
  2. DMPs are best practice for all research—whether funded or unfunded.
  3. The primary responsibility for managing research data lies with the researcher.
  4. Where research data may include personal information about identifiable individuals, data protection laws must be followed.
  5. Researchers should adhere to their DMP throughout the research process.
  6. Research metadata will be published (where appropriate) for permanent citation in Lincoln’s Repository.
  7. Access to research data should be provided with appropriate safeguards in place.
  8. It is the researcher’s responsibility to ensure that data supporting published findings are submitted to a suitable repository for long-term preservation and public access.

If your research is funded—such as through a UK Research and Innovation (UKRI) council—you may also need to follow your funder's Research Data Management (RDM) policy.

Below are links to RDM policies from commonly used funders:

When preparing a Data Management Plan, you should consider not only your funder's requirements but also the FAIR principles. These principles are widely recognised as a standard for good research data management.

FAIR stands for:

  • Findable – Data should be easy to locate by humans and machines, with rich metadata and a persistent identifier (e.g. DOI).
  • Accessible – Data should be retrievable using standard protocols and include clear conditions for access and reuse.
  • Interoperable – Data should be compatible with other datasets and tools, using standard formats and vocabularies.
  • Reusable – Data should be well-described and documented to enable reuse by others, with appropriate licences and provenance information.

Applying these principles helps ensure that your data is useful not only during your project but also for future researchers and stakeholders.

Working with Data

When you have created your research data management plan, it is important that you stick to it since there are many issues that you will have to navigate when working with your data:

It is normally recommended practice that you store your data using the university’s cloud storage.

If possible, store data on more than one type of media.

If you are making notes, you may like to use electronic research notebooks.

Make sure you back your data up regularly.

You will need to consider what format you want to save your data with. You will need to think about:

Your data’s longevity: (will formats facilitate future use)?

The Library of Congress recommends output types which can be found on their website.

It is also important to organise your data effectively. The video below provides an introduction to basic principles of organising your research data

Consider creating a hierarchy of folders, more information can be found on the UK data service website.

Make sure that you create devise a consistent and logical way of naming folders and files.

Ensure that you have a good command tracking newer versions of data (known as version control).

 

You will also need to consider how much meta-data you need to create. Metadata refers to information that describes your data. The JISC toolkit for research data management recommends topics below as considerations for creating meta-data:

  • The aims of your project.
  • The methods used to collect data.
  • The contents of your data.
  • The folder structure and file naming conventions.
  • The data processing techniques used.
  • The modifications made to the initial data throughout the project.
  • Data validation and other quality assurance processes.
  • Roles and responsibilities within the project.
  • Details on identifiers, licensing, and sensitive information.

You will also need to understand issues relating to data protection and the handling of sensitive information. If you are collecting personally identifiable information (PII) you will need to consider the type of data that your research discloses.

There are different types of personally identifiable information:

  • Direct identifiers: information that, on its own, allows you to identify an individual. This includes names, email addresses including one’s name, fingerprints, facial photos, etc. This information presents a high risk.
  • Strong indirect identifiers: information that allows you to identify an individual through minimal effort. This includes postal addresses, telephone numbers, email addresses not including one’s name, URL of personal pages, etc. This information presents a moderate risk.
  • Indirect identifiers: information that allows you to identify an individual when linked with other available information. This includes background information on people, such as age, location, gender, and job title. This information presents a low risk.

You should address indirect identifiers as thoroughly as you would the direct ones.

You should also be aware of special legal protection given to specific types of data:

  • Racial or ethnic origin
  • Political opinions
  • Religious or philosophical beliefs
  • Trade union membership
  • Genetic data
  • Biometric data
  • Health
  • Sex life or sexual orientation

Taken from JISC toolkit for RDM under CC-BY-NC-ND License.

In response to these measures, you may wish to anonymise your data. More information can be found on the UK data service management website.

When your data is ready to share, you will need to think about how to share it and how to license it. You may deposit your data in a repository, a data journal, or publish it alongside your research.

Data can be uploaded to the Repository. Guidance on how to deposit datasets in the repository can be found on the repository's help blog. A list of other data repositories and journals can be found from Nature and TRAC.

When sharing your data, you should consider how to license it. More information can be found under copyright and licensing.