Our group presented at this year’s Information Visualisation Conference (iV 2022) two papers entitled “Visual Analytics for Systematic Reviews According to PRISMA” and “Visual Collaboration – An Approach for Visual Analytical Collaborative Research”. In addition, Professor Kawa Nazemi, the head of the research group, was a co-chair of the conference and chaired the program committee and various sessions and tracks. Due to the ease of COVID travel restrictions, we were able to participate in person Vienna. The iV 2022 is an international conference that aims to provide a foundation for integrating information visualization’s human-centered, technological and strategic aspects to promote international exchange, cooperation, and development.

Paper #1: Visual Analytics for Systematic Reviews According to PRISMA

Lead author: Lennart B. Sina

Systematic reviews play an essential role in various disciplines. Particularly, in biomedical sciences, systematic reviews according to a predefined schema and protocol are how related literature is analyzed. Although a protocol-based systematic review is replicable and provides the required information to reproduce each step and refine them, such a systematic review is time-consuming and may get complex. To face this challenge, automatic methods can be applied that support researchers in their systematic analysis process. The combination of artificial intelligence for automatic information extraction from scientific literature with interactive visualizations as a Visual Analytics system can lead to sophisticated analysis and protocoling of the review process. We introduce in this paper a novel Visual Analytics approach and system that enables researchers to visually search and explore scientific publications and generate a protocol based on the PRISMA protocol and the PRISMA statement.

Link to paper/fulltext: DOI: 10.1109/IV56949.2022.00059

Paper #2: Visual Collaboration – An Approach for Visual Analytical Collaborative Research

Lead author: Midhad Blazevic


Studies have shown that collaboration in scientific fields is rising and considered enormously important. However, collaboration has proved to be challenging for various reasons, among others, the requirements for human-machine workflows. The importance of scientific collaboration lies in the complexity of the challenges that are faced today. The more complex the challenge, the more scientists should work together. The current form of collaboration in the scientific community is not as intelligent as it should be. Scientists have to multitask with various applications, often losing cognitive focus. Collaboration itself is very nearsighted as it is usually conducted not solely based on expertise but instead on social or local networks. We introduce a single-source visual collaboration approach based on learning methods in this work. We use machine learning and natural language processing approaches to improve the traditional research and development process and create a system that facilitates and encourages collaboration based on expertise, enhancing the research collaboration process in many ways. Our approach combines collaborative Visual Analytics with enhanced collaboration techniques to support researchers from different disciplines

Link to paper/fulltext: DOI: 10.1109/IV56949.2022.00057