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Research Data Management

Research Data and Management

Research data is usually described as data collected, observed or created for scientific purposes so as to produce and validate the original research findings. Depending on its discipline, the nature of research data can vary widely being textual, numerical, qualitative, quantitative, final, preliminary, physical, digital or print. 

Research data could include but not limited to the following:

  • Text or Word documents, spreadsheet
  • Laboratory notebooks, field notebooks, diaries
  • Questionnaires, transcripts, codebooks
  • Audiotapes, videotapes
  • Photographs, films
  • Test responses
  • Slides, artifacts, specimens, samples
  • Collection of digital objects acquired and generated during the process of research
  • Data files
  • Database contents including video, audio, text, images
  • Models, algorithms, scripts
  • Contents of an application such as input, output, log files for analysis software, simulation software, schemas
  • Methodologies and workflows
  • Standard operating procedures and protocols

Besides, research records along with the research data may also be important to manage during and beyond the life of a project:

  • Correspondence including electronic mail and paper-based correspondence
  • Project files
  • Grant applications
  • Ethics applications
  • Technical reports
  • Research reports
  • Master lists
  • Signed consent forms

Research data management refers to the activity of working with research data throughout the research process, from data collection, to data storage and backup, through to data sharing at the end of a research project. The graphic from the Joint Information Systems Committee provides an overview of the steps involved in research data management: 

Reference:
https://mantra.edina.ac.uk/researchdataexplained/
https://researchdata.ox.ac.uk/home/introduction-to-rdm/
https://guides.library.oregonstate.edu/research-data-services/data-management-types-formats
https://www2.le.ac.uk/services/research-data/old-2019-12-11/documents/UoL_ReserchDataDefinitions_20120904.pdf
https://www.jisc.ac.uk/guides/research-data-management

Open and FAIR Data

Government authorities, funding bodies and journals are increasingly encouraging or mandating authors to make data openly accessible without sacrificing the protection of human subjects or other valid subject privacy. In view of the volume, complexity and creation speed of data, FAIR principles are thus published to provide guidelines to improve the findability, accessibility, interoperability, and reuse of the scientific data.

Findable
The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services, so this is an essential component of the FAIRification process.   

Accessible
Once the user finds the required data, she/he needs to know how can they be accessed, possibly including authentication and authorisation.

Interoperable
The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing.

Reusable
The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings.

 

Reference:
https://www.go-fair.org/fair-principles/
https://authorservices.taylorandfrancis.com/data-sharing-policies/

Benefits of Research Data Management

Research data management is essential in responsible, high-quality and professional research. Good practice in data management will bring benefits for researchers, institutions and the wider public.

‚ÄčIncrease research impact
Making your data available to other researchers can increase the discovery, citation and impact of your research.

Save time and effort
Planning ahead for your data management needs will save you time, resource and duplication of effort.

Preserve data
Depositing your data in a repository safeguards your investment of time and resources while preserving your research contribution for you and others to use and minimising the risk of data loss.

Maintain data integrity
Managing and documenting your data throughout its life cycle will allow you and others to understand, verify and reproduce your data in the future.

Meet grant requirements
Many funding agencies now require that researchers deposit data collected as part of a research project.

Promote new discoveries
Sharing your data with other researchers can lead to new and unanticipated discoveries and provide research material for those with little or no funding.

Support open access
Be a catalyst for research and discovery. Show your support for open access by sharing your data.

 

Reference:
https://libraries.mit.edu/data-management/plan/why/
https://researchdata.ox.ac.uk/home/introduction-to-rdm/