Journal of Data Science, Statistics, and Visualisation The journal welcomes contributions to practical aspects of data science, statistics and visualisation, and in particular those which are linking and integrating these subject areas. Papers should thus be oriented towards a very wide scientific audience, and can cover topics such as machine learning and statistical learning, the visualisation and verbalisation of data, big data infrastructures and analytics, interactive learning, advanced computing, and other important themes. JDSSV is an open access journal that charges no author fees. The journal now has a new review process aimed at reducing the turnaround time between initial submission and publication of accepted papers to three months.
JDSSV is currently indexed by DBLP and Google Scholar. And it is currently pending on Scopus.
- What is text doing in a data visualisation?
by Paul Murrell on 6 May, 2026This article discusses the role that text elements play in a data visualisation. Based on existing knowledge and frameworks for data visualization, we develop […]
- Plotting correlated databy Lukas Koch on 28 April, 2026
A very common task in data visualization is to plot many data points with some measured y-value as a function of fixed x-values. Uncertainties on the y-values […]
- Optimal subsampling for linear models with heteroscedasticityby Jiayi Zheng, Dongqi Fu, Ziqiao Xu, Nicholas Rios on 2 February, 2026
In recent years, the size of datasets has dramatically increased. This has encouraged the use of subsampling, where only a subset of the full dataset is used […]
- Graphical tools for visualizing cellwise and casewise outliersby Mehdi Hirari, Mia Hubert, Peter Rousseeuw on 30 December, 2025
Principal component analysis (PCA) and other dimension reduction methods can be affected by cellwise and casewise outliers. Several approaches have been […]
- Bridging the inference gap in multimodal variational autoencodersby Agathe Senellart, Stéphanie Allassonnière on 13 November, 2025
From medical diagnosis to autonomous vehicles, many critical applicationsrely on the integration of multiple heterogeneous data modalities. […]
- Kernel outlier detectionby Can Hakan Dagidir, Mia Hubert, Peter J. Rousseeuw on 27 June, 2025
new anomaly detection method called kernel outlier detection (KOD) is proposed.It is designed to address challenges of outlier detection in […]
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