Introduction

Jisc have produced a handy guide which explains how and why you should manage your research data.

 

Research data management involves activities throughout the research data lifecycle; planning and making decisions about the collection, organisation, storage, preservation, publishing and sharing of research data.

There is no clear consensus on a definition of research data because the nature of it can vary widely depending on the subject discipline or research funder. But in any context, research data represent the accumulation of a significant amount of effort, time, and resources.

Within the arts, research data can be evidence of an identified research activity and can include preparatory, unfinished and supportive work.

Research data can be produced using a variety of methods:

Observational: Data captured in real-time, usually unique and unrepeatable e.g. neuroimages, sample data, sensor data, survey data

Experimental: data from experimental results e.g. produced in a laboratory by applying a treatment or control condition and recording specific variables that result. The data is often reproducible but could be costly. Examples include clinical trials, gene sequences, chromatograms, microassays

Simulation: Data generated from test models, where model and metadata may be more important than output data from the model e.g. climate, mathematical or economic models

Derived or compiled: Data resulting from processing or combining 'raw' data, often reproducible but expensive e.g. text and data mining, 3D models, compiled databases, aggregate census data

Reference: A (static or organic) conglomeration or collection of smaller (peer-reviewed) datasets, most probably published and curated. For example, gene sequence databanks, chemical structures, crystallographic databases, or spatial data portals.

Many researchers still record data in non-digital formats such as in laboratory notebooks, sketches, photographic film, prints and hand-written questionnaires. More information about managing non-digital data is available here.

The University of Bristol have collated a useful glossary of terms relating to research data management.

Research data management is vital for ensuring the sustainability, discoverability and accessibility of data in the long term. It enhances the integrity and efficiency of research and facilitates data sharing, validation and re-use in accordance with legal, ethical and funder requirements.

Other benefits of research data management include:

  • Increases the impact and visibility of research, by publishing data and gaining credit through re-use and citation
  • Ensures compliance with third-party requirements, such as those stipulated by funders, the University of Salford, collaborative partners and the research community
  • Supports responsible communication of research results
  • Data security and back up is improved and the risk of data loss and breaches minimized
  • Provides opportunities for interdisciplinary collaboration
  • Enables easier location and understanding of data files
  • Avoids data duplication, and shows responsible use of public resources to fund research.

If research data management is not considered, you may be unable to fulfil your research objectives or funder requirements, and in some cases this can result in sanctioning research income.

If you have any queries about looking after your research data, please contact the Research Data Management Team.