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Research Data Management

This guide contains resources for learning about best practices in research data management.

Introduction

Research data management (RDM) is about handling research data effectively and appropriately throughout the life of a research project and beyond. It refers to all aspects of creating, storing, sharing and archiving data and is an essential aspect of conducting responsible research.

It includes planning for data management at the grant application stage or before the start of a project, managing data on a day-to-day basis over the lifetime of a project, and sharing and preserving data for the long term after the completion of a project.

Research data is any information that has been collected, observed, generated or created to validate original research findings. Although usually digital, research data also includes non-digital formats such as laboratory notebooks and sketchbooks.

Some examples of research data:

  • Documents, spreadsheets, databases
  • Laboratory notebooks, field notebooks, diaries
  • Questionnaires, transcripts, codebooks
  • Photographs, audio or video recordings
  • Slides, artefacts, specimens, samples
  • Models, algorithms, scripts

Data may be raw or primary (e.g. direct from measurement or collection) or derived from primary data for subsequent analysis or interpretation (e.g. cleaned up or as an extract from a larger data set), or derived from existing sources where the rights may be held by others. 

Concordat on Open Research Data, 2016

In addition to research data, the following accompanying research records may also be important to manage during and beyond the life of a project:

  • Correspondence (electronic mail and paper-based correspondence)
  • Project files
  • Grant applications
  • Ethics applications
  • Technical reports
  • Technical appendices
  • Research reports
  • Research publications
  • Signed consent forms
  • Social media communications (blogs, wikis, tweets, etc.)

(Source: Jisc; MANTRA)

Benefits of RDM

Managing research data brings many benefits, not only to the project but to future researchers and wider society.

Good data management practice:

  • Ensures research integrity and reproducibility
  • Increases your research efficiency
  • Ensures research data and records are accurate, complete, authentic and reliable
  • Saves time and resources in the long run
  • Enhances data security and minimising the risk of data loss
  • Prevents duplication of effort by enabling others to use your data
  • Complies with practices conducted in industry and commerce

(Source: MANTRA)

RDM Lifecycle

Effective data management is carried out for the entire lifecycle of the data, from the point of creation through to dissemination, publication and archiving.

(Source: UC Santa Cruz University Library)