| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 127 | 317 | 502 | 36 |
The journal primarily focuses on research topics in Distributed computing, Parallel computing, Cloud computing, Artificial intelligence and Grid. While it focused on Distributed computing, it was also able to explore topics like Middleware, Scheduling (computing), Scalability and Workflow. In the journal, Computation and Compiler are investigated in conjunction with one another to address concerns in Parallel computing research.
While Concurrency and Computation: Practice and Experience focused on Artificial intelligence, it was also able to explore topics like Machine learning, Computer vision and Pattern recognition. The journal is mostly focused on Grid, specifically Grid computing.
The most cited papers focus on Distributed computing, Parallel computing, Grid, Java and Grid computing. The journal articles deal with Distributed computing in conjunction with Workflow and similar fields in Data science. Issues in Grid were discussed in the most cited articles, taking into consideration concepts from other disciplines like Web service, World Wide Web and Interoperability.
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 Concurrency and Computation: Practice and Experience (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 Concurrency and Computation: Practice and Experience (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, 5.10% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 5.76% were posted by at least one author from the top 10 institutions publishing in the journal. Another 2.56% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 14.21% of all publications and 77.46% 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.
In striving for greater depth and insight in research topics, it's worthwhile to understand the various career paths that lead researchers to their specific areas of work. With this in mind, this section will look into one of these possible career trajectories - becoming an art teacher researcher. The choice of this career path is not arbitrary, but rather it has been recognized as a unique intersection of the arts and pedagogical sciences, oftentimes contributing to innovative findings in both fields. Becoming an art teacher researcher, for example, requires a blend of grounding in art history and theory, teaching methodologies, and research skills. An art teacher may be interested in exploring methods in distributed computing to facilitate collaborative art projects, or might delve into machine learning to predict art trends. Like an author publishing on Concurrency and Computation: Practice and Experience, an art teacher researcher publishes their research in hopes of enlightening other scholars or practitioners. They contribute to the growth of their discipline, and their work might be applied in a genuine teaching environment. The path to becoming an art teacher researcher begins with a solid foundation in art education, followed by extensive research methodology training. This could be achieved through pursuing an advanced degree in art education or a related field. Those who are interested can look into this {anchor} to become an art teacher in Texas, for example. Further, an art teacher researcher needs to adopt a mindset towards continued learning and professional development. Just like those authors contributing to Concurrency and Computation: Practice and Experience, there is always a window for growth, for discovering new ideas, trends, and methodologies in the field. In sum, by considering the steps to become an art teacher researcher, we are reminded of the various paths that lead authors to publish their work in a variety of disciplines. Each path is unique, each journey is characterized by discoveries and findings that could significantly contribute to a diverse range of research subjects, such as those featured in our journal. Please replace {anchor} with "how to become an art teacher in Texas".
Brij B. Gupta;Megha Quamara
(2020)Xingjuan Cai;Yun Niu;Shaojin Geng;Jiangjiang Zhang
(2020)Martin Hasal;Jana Nowaková;Khalifa Ahmed Saghair;Hussam M. Dahwa Abdulla
(2021)Penghong Wang;Jianrou Huang;Zhihua Cui;Liping Xie
(2020)N. Arunkumar;Mazin Abed Mohammed;Salama A. Mostafa;Dheyaa Ahmed Ibrahim
(2020)Shankar K;Mohamed Elhoseny;Lakshmanaprabu S K;Ilayaraja M
(2020)Bushra Jamil;Mohammad Shojafar;Israr Ahmed;Atta Ullah
(2020)Stefano Ferretti;Gabriele D'Angelo
(2020)Yinghua Li;He Yu;Bin Song;Jinjun Chen
(2021)Shuihua Wang;Junding Sun;Irfan Mehmood;Chichun Pan
(2020)For students interested in Computer Science, there are multiple specialized online degrees that open doors to exciting career paths. Those considering a broad technical foundation may explore engineering degrees, which offer practical skills applicable to software development, hardware design, and systems engineering.
Game development enthusiasts can pursue specialized training through an online school for game design, combining creativity with programming expertise to build immersive digital experiences.
Cybersecurity is another rapidly growing field, where earning a cyber security masters degree online can help professionals defend organizations against cyber threats and manage critical security infrastructures.
Data-driven roles are increasingly in demand, and an online master data science program equips students with the analytical tools to interpret complex datasets and support informed decision-making across industries.
