| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 47 | 479 | 1410 | 62 |
IEEE Transactions on Visualization and Computer Graphics explores disciplines such as Visualization, Data visualization, Artificial intelligence, Computer vision and Rendering (computer graphics). The work on Visualization tackled in the journal brings together disciplines like Theoretical computer science, Computer graphics, Human–computer interaction, Information retrieval and Graphics. The Human–computer interaction works featured in it incorporate elements from User interface and Task analysis.
The discussions emphasized the topic of Data visualization in an attempt to further explore the field of Data mining. Some problems in Data mining that were presented in IEEE Transactions on Visualization and Computer Graphics overlapped with concepts under Data modeling and Cluster analysis. While it focused on Artificial intelligence, it was also able to explore topics like Machine learning, Computer graphics (images) and Pattern recognition.
Algorithm, Computational geometry and Animation are some topics wherein Computer vision research discussed in IEEE Transactions on Visualization and Computer Graphics have an impact. The presented research on Computational geometry deals specifically with Mesh generation but it also addresses topics in Polygon mesh. The main emphasis of the journal is the research on Rendering (computer graphics), emphasizing the topic of Volume rendering.
The most cited papers aim to foster the development of research in Data visualization, Visualization, Artificial intelligence, Computer vision and Information visualization. Issues in Data visualization were discussed in the most cited articles, taking into consideration concepts from other disciplines like Visual analytics, Theoretical computer science, Computer graphics and Human–computer interaction. While Artificial intelligence is the focus of the published articles, it also provides insights into the studies of Algorithm and Machine learning.
The journal investigates areas of study like Visualization, Artificial intelligence, Data visualization, Human–computer interaction and Visual analytics. IEEE Transactions on Visualization and Computer Graphics focuses on Visualization research as part of the broader topic of Data mining. IEEE Transactions on Visualization and Computer Graphics facilitates discussions on Artificial intelligence that incorporate concepts from other fields like Machine learning, Computer vision and Pattern recognition.
Rendering (computer graphics) and Augmented reality are all areas of Computer vision tackled in IEEE Transactions on Visualization and Computer Graphics. The journal explores topics in Data visualization which can be helpful for research in disciplines like Data modeling, Information visualization, Information retrieval and Domain (software engineering). Most of the works presented in the journal deals with Visual analytics but it intersects with the subject of Text mining.
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 IEEE Transactions on Visualization and Computer Graphics (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 IEEE Transactions on Visualization and Computer Graphics (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, 19.67% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 26.72% were posted by at least one author from the top 10 institutions publishing in the journal. Another 12.55% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 20.85% of all publications and 39.88% 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.
HTML Format: Government grants and funding can play a significant role in influencing the direction of research within computer graphics and visualization. Field advancements rely on substantial funding to spearhead innovative projects and explore new territories in technology.
In a recent analysis of the U.S. Department of Education's grants, it was found that a significant amount of funding was directed towards the application of computer graphics and visual analytics in Special Education. These projects focus on creating interactive learning platforms and tools to enhance the accessibility and quality of education for students with special needs. By leveraging technology, special educators can better cater to varied learning styles and make education more inclusive.
If you are a special educator interested in integrating these graphical technologies into your teaching methods, it may require understanding specific guidelines and honing new skills. Find more about the required expertise and qualifications in our article about special ed teacher requirements in New Hampshire.
To stay updated on recent government grants and their effect on research and development in computer graphics, visualization, and special education, subscribe to our newsletter and follow our updates.
Yongcheng Jing;Yezhou Yang;Zunlei Feng;Jingwen Ye
(2020)James Wexler;Mahima Pushkarna;Tolga Bolukbasi;Martin Wattenberg
(2020)Mateus Espadoto;Rafael M. Martins;Andreas Kerren;Nina S. T. Hirata
(2021)Unknown
(2022)Zijie J. Wang;Robert Turko;Omar Shaikh;Haekyu Park
(2021)Fred Hohman;Haekyu Park;Caleb Robinson;Duen Horng Polo Chau
(2020)Mickael Sereno;Xiyao Wang;Lonni Besancon;Michael J Mcguffin
(2020)Yun Wang;Zhida Sun;Haidong Zhang;Weiwei Cui
(2020)For those interested in studying Computer Science in the USA, exploring online degree options can provide flexibility and convenience. Many students seek the cheapest easiest online degree paths to balance education with work or personal commitments without compromising quality.
Accelerated learning is another popular choice. Programs offering a degree in 6 months online enable students to quickly gain foundational knowledge, especially useful for those looking to enter the workforce promptly or pivot careers.
For a more comprehensive education, accelerated degree programs online provide intensive coursework that can shorten the time to graduation while maintaining a rigorous curriculum. These programs often appeal to highly motivated learners eager to advance their careers rapidly.
It’s also important to consider the financial payoff of your studies. Computer Science is among the highest earning degrees, offering strong career prospects and salary potential. Choosing the right online program can maximize both educational value and long-term career growth.
