Research data comprises information that is collected, observed, or produced for the purposes of analysis as part of the research process across all disciplines.

Examples include notebooks, survey data, computer generated data, audio, film, images, coding of textual information, computational metadata, gene sequences etc.

Managing and sharing data produced as part of the research process is increasingly important. Many research funders require that research data are made as openly available as possible and align with the FAIR principles.

Research data that is Findable, Accessible, Interoperable and Reusable (FAIR) supports and enables data-driven research. The FAIR data principles, originally published in 2016, provide community based best practice guidelines which have been adopted by research institutions and funding bodies worldwide. The principles note that FAIR does not necessarily equal Open data. Data should be ‘as open as possible, and as closed as necessary’.

Funders are  increasingly requiring researchers to make their datasets openly and publicly available to ensure the funds used to create the datasets are used (and reused) most efficiently.

Research Data Management or RDM comprises the necessary actions and best practices to ensure research data is well organised, secure, sustainable, easy to find and (re)use. It includes several key data management activities such as good planning, collecting and effectively organising data, storing and backing up the data as well as preserving and sharing data. 

A key first step in the data lifecycle is drafting a research data management plan (DMP). 

Our DMP library guide offers a helpful walkthrough of the process of drafting a data management plan. A number of other guides and resources are provided further down on this page.

Finding data is not as much an issue as finding relevant or reliable data. There are a number of useful resources for different contexts and areas of research listed below under Data Sources. 

One useful starting point is Google Dataset Search.

if you reuse an openly available dataset, you should make sure to abide by the terms of the reuse licence, and cite the original data set and its creator(s).

Typically research data should be stored and made available in a trusted data repository, making it availabile for re-use, facilitating collaboration, transparency and reproducibility. A data archive is a similar concept but may have more emphasis on curation and long-term preservation. 

A data repository or archive will often provide services such as:

• Persistent identifier such as a Digital Object Identifier” or DOI
• Structured metadata through the use of a schema or template
• Allow you to apply a licence to your data
• Accept a wide range of data types
• Manage requests for data on your behalf

It can be useful to identify a suitable repository early so you can familiarise yourself with their requirements, such as file formats, metadata standards or supporting documentation. 

Re3data.org is a useful resource to locate a suitable data repository, whatever your area of research.  

Considerations when selecting a repository:

• Is it reputable?
• Is it appropriate to my discipline? e.g. Irish Social Science Data Archive or PubChem.
• Has my funders or publisher specified a repository e.g. Springer, PLOS. 
• Will it take the data you want to deposit? Is there a size limit?
• Does it provide a persistent identifier?
• Does it provide access control, where necessary, for your data?
• Does it ensure long-term preservation / curation?
• Is there a charge?

Other questions may pertain depending on your requirements. For more information see the UK’s Digital Curation Centre’s checklist.