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Data Visualisation and Digital Scholarship Research: Concepts of Data Visualisation


This guide is to provide information about data visualisation concept such as types and tools of visualisation, project examples, useful websites, etc. in conducting data visualisation projects. You are welcome to contact us if you would like to get advice or information in doing your research.

Digital Scholarship Projects

The Library welcomes CUHK faculty members and researchers to collaborate with us in conducting digital scholarship research. Please visit our Digital Scholarship Projects page for the projects conducted.

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Digital Scholarship Lab

Located on the G/F of the University Library, The Chinese University of Hong Kong, the Digital Scholarship Lab aims at providing a cutting-edge space for supporting digital scholarship research. 

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Tel.: (852) 3943 9954

What is Data Visualisation?

(Source: PBS Digital Studios. (2013). The Art of Data Visualization,


"Data Visualisation", in general, means the representation of data in a visual form such as a chart, diagram, map and infographic. The aims of data visualisation are to effectively communicate with the audience and facilitate further data analysis.

Data visualisation is indeed important for researchers. By using visual representations of information, researchers can present an enormous amount of data in clear, cohesive and quick ways. Readers can thus draw conclusions, and easily discover general patterns, outliers and exceptions from the information. 

4 Major Phases of Data Visualisation Cycle

  • Data Extraction

    Researchers should pay attention to data interoperability: the ability of the data to work with different systems or products. There are some examples of open standard formats which can communicate with different systems:

    Type of Data

    Format of Data


    PNG and SVG

    Web Pages


    Tabular Data

    XML, CSV and JSON

  • Data Cleansing and Data Transformation

    For most cases, data cleansing is a necessary process of detecting and eliminating errors, inconsistencies and duplications from data so as to enhance the quality of a database. After data cleansing, data transformation may be needed to convert data from one format into another. For example, researchers may need to convert KML data (a geo-data format used in Google Map) into JSON format (a geo-data format widely applicable to GIS software such as QGIS) for visualising data on a map.

  • Data Analysis

    Data visualisation techniques are widely applied to statistical analysis, text analysis and geospatial analysis. The advanced data visualisation methods can give researchers new insights.

    Type of Analysis

    Examples of Data Visualisation Tools

    Statistical Analysis

    Graphs and Infographics

    Text Analysis

    Word Cloud, Word Tree, Collocation Graph

    Geospatial analysis

    Map, Network Graph

  • Data Visualisation

    To achieve different goals and functionalities, researchers can use different kinds of data visualisation tools. Some common types of graphs can be found in Data Visualisation Catalogue.


Digital Scholarship Librarian

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Kitty Siu
Digital Initiatives Team
The Chinese University of Hong Kong Library, The Chinese University of Hong Kong, Shatin, N.T.
(852) 3943 9731

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