| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 663 | 28 | 33 | 8 |
The foci of the journal are Visualization, Artificial intelligence, Information visualization, Visual analytics and Very-long-baseline interferometry. Visualization research featured in Information Visualization incorporates concerns from various other topics such as Context (language use), Information retrieval and Human–computer interaction. The journal features Human–computer interaction research that overlaps with concepts in Multimedia.
The journal holds forums on Artificial intelligence that merges themes from other disciplines such as Machine learning, Computer vision and Pattern recognition. Information Visualization links adjacent topics like Information visualization with Interactive visualization. In the journal, Analytics, Data science and World Wide Web are investigated in conjunction with one another to address concerns in Visual analytics research.
Topics in Very-long-baseline interferometry were tackled in line with various other fields like Geodetic datum and Remote sensing.
The published papers investigate areas of study like Visualization, Information visualization, Artificial intelligence, Visual analytics and Data science. The most cited papers hold forums on Visualization that merge themes from other disciplines such as Algorithm, Theoretical computer science and Human–computer interaction. While work presented in the most cited papers provide substantial information on Artificial intelligence, it also covers topics in Machine learning and Computer vision.
The aim of the journal is to expand the discussion of research in Visualization, Information retrieval, Data visualization, Recall and Artificial intelligence. The studies in Visualization featured incorporate elements of Computer graphics (images), Comprehension, Human–computer interaction, Representation (arts) and Data science. In addition to Human–computer interaction research, it aims to explore topics under Emphasis (typography) and Storytelling.
The research on Information retrieval featured in Information Visualization combines topics in other fields like Content (measure theory), Tag cloud, Representation (systemics), Selection (genetic algorithm) and Information visualization. Information visualization research presented in Information Visualization encompasses a variety of subjects, including Multivariate data visualization, Visual analytics, Preprocessor, Data structure and Profiling (information science). The research on Artificial intelligence tackled can also make contributions to studies in the areas of Order (business) and Natural language processing.
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in Information Visualization (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in Information Visualization (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 8.70% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 19.05% were posted by at least one author from the top 10 institutions publishing in the journal. Another 4.76% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 14.29% of all publications and 61.90% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
Information Visualization is a growing field addressing the need to interpret complex data sets visually. It's an interdisciplinary domain that combines aspects of computer science, data science, machine learning, and more. This broad applicability leads to plenty of career opportunities for aspiring professionals and researchers.
Potential roles within this field include data visualization engineer, business intelligence analyst, and user experience designer, among others. These roles involve creating interactive visualizations to help businesses and researchers understand and use their data more effectively.
One of the pathways into this field is through academia, where researchers can contribute to the development of new visualization techniques and technologies. For those interested in this pathway, a graduate degree might be required. For instance, how to become a teacher in Wisconsin with a master's degree could provide insights into the academic route in this field.
Ultimately, a career in Information Visualization is rewarding not only because of the ample job opportunities but also due to the potential impact on various industries. It will allow you to improve decision-making processes by turning raw data into understandable visual representations.
Matteo Golfarelli;Stefano Rizzi
(2020)Mateus Espadoto;Nina Sumiko Tomita Hirata;Alexandru C. Telea
(2020)Deokgun Park;Mohamed Suhail;Minsheng Zheng;Cody Dunne
(2021)Kiran Gadhave;Jochen Görtler;Zach Cutler;Carolina Nobre
(2021)Sriram Karthik Badam;Niklas Elmqvist
(2021)Aristides Mairena;Carl Gutwin;Andy Cockburn
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