Principles of scientific data visualization

Maryna Korshevniuk


University Medical Centre Groningen



Scientific visualization is the act of displaying raw scientific data as visuals as an external assistance to help scientists better analyze massive data sets and discover insights that statistical approaches alone would miss. Cleaning data, studying data structure, discovering outliers and odd groupings, identifying trends and clusters, spotting local patterns, assessing modeling output, and presenting results are all things that data visualization may help with. Checking data quality and assisting analysts in becoming familiar with the structure and properties of the data before them is critical for exploratory data analysis and data mining.

Despite the above arguments that prove the importance of proper data visualisation, usually quality and clarity of data visualisation is neglected and underestimated. To overcome this, scientists should be familiar with the main approaches and handy tools for visualising data. This talk will highlight the main principles and tools for effective data representation.

Key references:

Midway, S.R. (2020). Principles of Effective Data Visualization. Patterns, [online] 1(9), p.100141. Available at:

Nayak, S. and Iwasa, J.H. (2019). Preparing scientists for a visual future. EMBO reports, [online] 20(11). Available at: [Accessed 29 Oct. 2021].


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