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Research Data Management: About Research Data

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, digital or print. Research data could include but not limited to the following:

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

Besides, research-related documents 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 Technical reports
Project files Research reports
Grant applications Master lists
Ethics applications Signed consent forms

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.

Learn more about FAIR data via H2020 Programme - Guidelines on FAIR Data Management in Horizon 2020

Making 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.   


Making data openly accessible

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


Making data 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.


Increase data re-use

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.

More about Data Sharing :

For enquiries, please contact Library's Research Support and Scholarly Communication Section at: