| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 398 | 42 | 59 | 15 |
The aim of the journal is to expand the discussion of research in Visualization, Visual analytics, Artificial intelligence, Data science and Human–computer interaction. The study of Data mining serves as the foundation of the Visualization research discussed in the journal. While it focused on Data mining, it was also able to explore topics like Multivariate statistics, Feature (computer vision) and Automatic summarization.
It holds forums on Visual analytics that merges themes from other disciplines such as Domain (software engineering), Information retrieval and Data exploration. It explores Information retrieval concepts, specifically Topic model but expands to research in TRACE (psycholinguistics). Topics in Artificial intelligence explored in Visual Informatics were investigated in conjunction with research in Machine learning, Computer vision and Pattern recognition.
Image (mathematics) and Benchmark (computing) are some topics wherein Machine learning research discussed in it have an impact. The featured Data science research zeroes in on concepts in Analytics but also tackles themes under Field (computer science), Process (engineering) and Graph (abstract data type). The studies in Interactive visualization featured incorporate elements of Interactive visual analysis and Communication design.
The most cited articles tackle a plethora of topics, such as Visual analytics, Visualization, Data science, Interactive visualization and Analytics. The most cited papers focus on Visual analytics research as part of the broader topic of Artificial intelligence. The published papers explore issues in Visualization which can be linked to other research areas like HTML5 and Human–computer interaction.
The journal investigates studies in Visualization, Visual analytics, Data science, Algorithm and Human–computer interaction. The featured works in Graph drawing and Data visualization, which all belong in the domain if Visualization, also overlaps with concepts under Scale (map) and Geological exploration. Visual Informatics facilitates the exploration of Visual analytics in relation to the field of Interface (Java).
The journal aims to bridge the gap between the study of Data science and disciplines such as Process (engineering), Factor (programming language), Geolocation and Movement (music). The studies on Algorithm discussed can also contribute to research in the domains of Peak signal-to-noise ratio, Image compression, Digital image and Discrete cosine transform. In Visual Informatics, Interactive visual analysis, Semantics and Behavioral pattern are investigated in conjunction with one another to address concerns in Use case research.
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 Visual Informatics (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 Visual Informatics (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, 4.76% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 55.00% were posted by at least one author from the top 10 institutions publishing in the journal. Another 0.00% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 5.00% of all publications and 40.00% 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.
Alongside the inherent passion for research and the exciting developments in the field, a career in Visual Informatics can provide numerous opportunities. For aspiring researchers or practitioners in this area, there are several potential career paths that can be pursued. For instance, you could consider becoming a Data Scientist, Visualization Engineer, or, with some additional training, a Special Education Teacher.
As a Data Scientist, you would have the opportunity to utilize machine learning algorithms, conduct data analytics, and create data-driven solutions. Being a Visualization Engineer could involve designing and implementing visual data representations, creating interactive visual tools, or improving user interface design. Furthermore, if you're interested in bridging the gap between informatics and education, becoming a Special Education Teacher could be a fascinating option.
As a Special Education Teacher, you would have the chance to use visual informatics tools to deliver more effective learning strategies accommodating students' unique learning needs. Although this could mean additional training, the benefit of potentially transforming educational outcomes for students with special needs can make this direction worthwhile. Therefore, if you think this might be the best fit for you, investigating the {anchor}special ed teacher requirements in Rhode Island would be an excellent place to start.
Remember, these are just a few examples of the potential career paths in the diverse and dynamic field of Visual Informatics. Whatever your career aspirations, the most important aspect is to find a role that aligns with your strengths, interests, and long-term career goals.
Natalia V. Andrienko;Natalia V. Andrienko;Gennady L. Andrienko;Gennady L. Andrienko;Silvia Miksch;Heidrun Schumann
(2021)Rongchen Guo;Takanori Fujiwara;Yiran Li;Kelly M. Lima
(2020)Honghui Mei;Huihua Guan;Chengye Xin;Xiao Wen
(2020)Yiran Li;Takanori Fujiwara;Yong K. Choi;Katherine K. Kim
(2020)Dongming Han;Jiacheng Pan;Xiaodong Zhao;Wei Chen
(2021)Christian Tominski;Gennady Andrienko;Natalia Andrienko;Susanne Bleisch
(2021)Pursuing a Computer Science degree online offers flexibility and a wide range of options to fit different educational needs and career goals. Many students explore easiest online degrees to balance work, life, and study commitments, making computer science an accessible choice for tech enthusiasts.
For those looking to enter the field quickly, best associate degrees can provide foundational knowledge and open doors to entry-level positions in IT and programming within just six months.
Students aiming for a bachelor's but constrained by time often consider bachelor degree online in 2 years programs. Accelerated options help learners complete their degrees faster without compromising quality, enabling quicker transitions into advanced roles.
Finally, understanding the value of your degree can be transformative. Exploring the best bachelor degrees helps prospective students identify programs that not only offer robust education but also lead to lucrative career opportunities in tech and beyond.
Fraunhofer Institute for Intelligent Analysis and Information Systems
Publications: 3
Fraunhofer Institute for Intelligent Analysis and Information Systems
Publications: 3
