| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Physics | 98 | 82 | 65 | 19 |
The aim of Computer Physics Communications is to expand the discussion of research in Mathematical analysis, Algorithm, Statistical physics, Monte Carlo method and Computational science. Most of the Mathematical analysis studies addressed also intersect with Nonlinear system. Computer Physics Communications links adjacent topics like Monte Carlo method with Particle physics.
The research on Computational science tackled can also make contributions to studies in the areas of Test data, Parallel computing and Fortran. The journal is mostly focused on Parallel computing, specifically CUDA.
The published papers mainly deal with areas of study such as Mathematical analysis, Particle physics, Algorithm, Fortran and Computational science. While the primary focus in the published papers is Particle physics, they also dissect topics surrounding Monte Carlo method and Statistical physics as a whole. While Computational science is the focus of the published articles, it also provides insights into the studies of Test data, Parallel computing, Code (cryptography) and Python (programming language).
The journal mainly tackles studies in Python (programming language), Computational science, Solver, Computation and Fortran. Python (programming language) research featured in it incorporates concerns from various other topics such as Computer program, Set (abstract data type), Expression (computer science) and Magnetic field. While the primary focus in it is Set (abstract data type), it also dissects topics surrounding Compiler and Algorithm as a whole.
The Computational science works featured in the journal incorporate elements from Chirality (electromagnetism), Propagator, Interface (Java), Source code and Modelica. It explores topics in Fortran which can be helpful for research in disciplines like Nanophotonics, Excited state, Statistical physics, Dissipation and Software. The presented research on Numerical analysis deals specifically with Finite element method but it also addresses topics in Monte Carlo method.
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 Computer Physics Communications (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 Computer Physics Communications (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 2022 edition, 18.42% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 9.68% were posted by at least one author from the top 10 institutions publishing in the journal. Another 12.90% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 22.58% of all publications and 54.84% 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.
Denghui Lu;Han Wang;Mohan Chen;Lin Lin;Lin Lin
(2021)Henning Bahl;Thomas Hahn;Sven Heinemeyer;Sven Heinemeyer;Wolfgang Hollik
(2020)Zdeněk Mašín;Jakub Benda;Jimena D. Gorfinkiel;Alex G. Harvey
(2020)Torbjörn Sjöstrand
(2020)Anne Reinarz;Dominic Etienne Charrier;Michael Bader;Luke Bovard
(2020)Tonatiuh Rangel;Tonatiuh Rangel;Mauro Del Ben;Daniele Varsano;Gabriel Antonius;Gabriel Antonius;Gabriel Antonius
(2020)Paul-Gerhard Reinhard;Bastian Schuetrumpf;Joachim A. Maruhn
(2021)Evgueni Ovtchinnikov;Richard Brown;Christoph Kolbitsch;Edoardo Pasca
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