RDM Overview: Research Data and FAIR Principles

Research Data

Research data refers to the information collected, observed, or generated during the course of a research project. This data can be quantitative or qualitative and includes raw data, processed data, and metadata. It serves as the foundation for analysis, interpretation, and validation of research findings. Examples of research data types include survey results, experimental measurements, observational records, interview transcripts, images, audio recordings, and simulation data.

Research data formats can vary widely and include spreadsheets (e.g., CSV, Excel), text files (e.g., TXT, DOC), images (e.g., JPEG, PNG), audio files (e.g., WAV, MP3), and databases (e.g., SQL). Research data could include but not limited to the following formats:

	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

The FAIR Principles

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

	Findable	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 the automatic discovery of datasets and services, so this is an essential component of the FAIRification process.
	Accessible	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.
	Interoperable	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.
	Reusable	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.

A video about FAIR Principles

  For enquiries, please contact the Library's Research Data Management Services of the Research Support and Scholarly Communication Section at lbrdms@cityu.edu.hk