Research data

Research data includes data, records, files and other evidence on which research conclusions are based. The simplest way of defining Research Data is as material that supports your research output.

Research data include but is not limited to:

• Results of experiments or simulations
• Statistics and measurements
• Models and software
• Observations e.g. fieldwork
• Survey results – print or online
• Interview recordings and transcripts, and coding applied to these
• Images, from cameras and scientific equipment
• Textual source materials and annotations
• Physical artefacts and samples.

There are many benefits to good data management. Below are the most common:

Meet funder requirements:

• Most funders have a policy on the management of research data which must be complied with
• Industrial collaborators may have different practices with which you will need to comply

The integrity of your research is improved and can be recognised:

• Research data and records are accurate, complete, authentic and reliable
• Data security is improved and the risk of data loss minimised
• Providing access to your datasets enables others to validate your findings
• Responsible use of public resources to fund research is demonstrated
• It supports the responsible communication of research results

Increase the impact of your research:

• Reuse of your data increases its impact
• Citation of your data acknowledges your contribution - see Piwowar, H., Day, R. and Fridsma, D. Sharing Detailed Research Data is Associated with Increased Citation Rate, 21 March 2007. Viewed 18 November 2013.

Support future use:

• Your data may be reused by researchers in other fields for different purposes
• It can be discovered in the future by others. See The Royal Society. Science as an Open Enterprise: Final Report, 21 June 2012. Viewed 18 November 2013.

The FAIR Guiding Principles (https://www.go-fair.org/fair-principles/)

In 2016, the ‘FAIR Guiding Principles for scientific data management and stewardship' were published in Scientific Data. The authors intended to provide guidelines to improve the findability, accessibility, interoperability, and reuse of digital assets. The principles emphasise machine-actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention) because humans increasingly rely on computational support to deal with data as a result of the increase in volume, complexity, and creation speed of data.

The Principles are as follows:

To be Findable:

F1. (meta)data are assigned a globally unique and persistent identifier 
F2. data are described with rich metadata (defined by R1 below) 
F3. metadata clearly and explicitly include the identifier of the data it describes 
F4. (meta)data are registered or indexed in a searchable resource

To be Accessible:

A1. (meta)data are retrievable by their identifier using a standardised communications protocol 
A1.1 the protocol is open, free, and universally implementable 
A1.2 the protocol allows for an authentication and authorisation procedure, where necessary 
A2. metadata are accessible, even when the data are no longer available

To be Interoperable:

I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation 
I2. (meta)data use vocabularies that follow FAIR principles 
I3. (meta)data include qualified references to other (meta) data

To be Reusable:

R1. meta(data) are richly described with a plurality of accurate and relevant attributes 
R1.1. (meta)data are released with a clear and accessible data usage license 
R1.2. (meta)data are associated with detailed provenance 
R1.3. (meta)data meet domain-relevant community standards