Special Issue "Trends and Opportunities in Visualization and Visual Analytics"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: 28 February 2021.

Special Issue Editor

Prof. Dr. Fernando V. Paulovich
Guest Editor
Associate Professor and Canada Research Chair in Data Visualization, Faculty of Computer Science, Dalhousie University, Halifax, Canada
Interests: information visualization; visual analytics; visual data mining; explainable machine learning; machine learning or data mining interpretability

Special Issue Information

Dear Colleagues,

Over the past few decades, significant advances in data production, storage, and dissemination are promoting a paradigm shift in science and our society towards more data-driven processes and decision-making. In this scenario, visualization tools and techniques are becoming popular, giving their inherent ability to ease communication and increase user trust. Many areas that habitually use data mining and machine learning solutions are now starting to adopt visualization as part of their analytical pipelines.

From physics, biology, and chemistry areas to data democratization initiatives and applications of machine learning interpretability, visualization is becoming essential when users play a central role in the analytical process. If the goal is to understand decisions made by machines or to help users to comprehend different phenomena based on data, interactive visual representations are becoming pervasive, creating novel research opportunities, and highlighting new trends in the field.

This Special Issue is aimed at industrial and academic researchers applying visualization methods to help people take full advantage of their data collections to interpret complex phenomena or make more informed decisions. The key areas of this Special Issue include, but are not limited to the following:

  • Visual data analysis and knowledge discovery
  • Visual data mining
  • Graph visualization
  • Visual analytical reasoning
  • High-dimensional data and dimensionality reduction
  • Text, document, and social media visualization
  • Data management and knowledge representation
  • Explainable machine learning by visualization
  • Data-driven storytelling
  • Machine learning interpretability
  • Human-in-the-loop processing
  • Interactive data mining and machine learning
  • Progressive analytics
  • Analytics in the fields of scholarly data, digital libraries, multimedia, scientific data, and social data
  • Physics, chemistry, and biology visualization tools and applications

Prof. Dr. Fernando Paulovich
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • Information visualization
  • visual analytics
  • machine learning interpretability
  • visual data mining
  • visualization tools and applications

Published Papers (1 paper)

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Open AccessArticle
Interactive Visual Analysis of Mass Spectrometry Imaging Data Using Linear and Non-Linear Embeddings
Information 2020, 11(12), 575; https://doi.org/10.3390/info11120575 - 09 Dec 2020
Mass spectrometry imaging (MSI) is an imaging technique used in analytical chemistry to study the molecular distribution of various compounds at a micro-scale level. For each pixel, MSI stores a mass spectrum obtained by measuring signal intensities of thousands of mass-to-charge ratios ( [...] Read more.
Mass spectrometry imaging (MSI) is an imaging technique used in analytical chemistry to study the molecular distribution of various compounds at a micro-scale level. For each pixel, MSI stores a mass spectrum obtained by measuring signal intensities of thousands of mass-to-charge ratios (m/z-ratios), each linked to an individual molecular ion species. Traditional analysis tools focus on few individual m/z-ratios, which neglects most of the data. Recently, clustering methods of the spectral information have emerged, but faithful detection of all relevant image regions is not always possible. We propose an interactive visual analysis approach that considers all available information in coordinated views of image and spectral space visualizations, where the spectral space is treated as a multi-dimensional space. We use non-linear embeddings of the spectral information to interactively define clusters and respective image regions. Of particular interest is, then, which of the molecular ion species cause the formation of the clusters. We propose to use linear embeddings of the clustered data, as they allow for relating the projected views to the given dimensions. We document the effectiveness of our approach in analyzing matrix-assisted laser desorption/ionization (MALDI-2) imaging data with ground truth obtained from histological images. Full article
(This article belongs to the Special Issue Trends and Opportunities in Visualization and Visual Analytics)
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