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Journal of Open Source Software
H-index 40

Journal of Open Source Software

2475-9066

Published by: Open Journals

https://joss.theoj.org/

Ranking & Metrics

Discipline name Position Best Scientists Publications D-Index
Computer Science 354 81 85 16

Additional Metrics

Number of Best Scientists*: 488
Documents by Best Scientists*: 457
Top 100 Ranked Scientists*: 21
SCIMAGO H-index:
SCIMAGO SJR:
Impact Factor: N/A

Overview

Top Research Topics at The Journal of Open Source Software?

The aim of the journal is to expand the discussion of research in Python (programming language), Programming language, Artificial intelligence, Computer graphics (images) and Computational science. Python (programming language) research featured in the journal incorporates concerns from various other topics such as Visualization, Software, JavaScript and Fortran. It holds forums on Artificial intelligence that merges themes from other disciplines such as Machine learning and Pattern recognition.

  • Python (programming language) (44.89%)
  • Programming language (17.58%)
  • Artificial intelligence (9.83%)

What are the most cited papers published in the journal?

  • Welcome to the Tidyverse (2169 citations)
  • corner.py: Scatterplot matrices in Python (982 citations)
  • hdbscan: Hierarchical density based clustering (466 citations)

Research areas of the most cited articles at The Journal of Open Source Software:

The published articles tackle a plethora of topics, such as Python (programming language), Artificial intelligence, Programming language, Computer graphics (images) and Computational science. While work presented in the journal papers provide substantial information on Python (programming language), it also covers topics in Information theory, Open source, Information retrieval and Visualization, Data visualization. Issues in Artificial intelligence were discussed in the most cited publications, taking into consideration concepts from other disciplines like Natural language processing, Machine learning, Object-oriented programming and Pattern recognition.

What topics the last edition of the journal is best known for?

  • Statistics
  • Quantum mechanics
  • Artificial intelligence

The previous edition focused in particular on these issues:

The main points discussed in The Journal of Open Source Software deals with Python (programming language), Programming language, Computational science, Artificial intelligence and Computer graphics (images). While work presented in it provided substantial information on Python (programming language), it also covered topics in Neutrino, Astronomy, Data visualization, Modular design and Software. The Journal of Open Source Software focused on Programming language research but expanded to cover Statistical model.

Aside from discussions in Computational science, it also deals with the subject of Supercomputer which intersects with Workflow disciplines. The studies on Artificial intelligence discussed can also contribute to research in the domains of Machine learning and Pattern recognition. Discussions in the journal are anchored in the subject of Computer graphics (images) and the similar topic of Visualization.

The most cited articles from the last journal are:

  • seaborn: statistical data visualization (239 citations)
  • performance: An R Package for Assessment, Comparison and Testing of Statistical Models (39 citations)
  • The Pencil Code, a modular MPI code for partial differential equations and particles: multipurpose and multiuser-maintained (27 citations)

Papers citation over time

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 The Journal of Open Source Software (based on the number of publications) are:

  • Vanessa Sochat (8 papers) absent at the last edition,
  • Dominique Makowski (7 papers) published 1 paper at the last edition, 2 less than at the previous edition,
  • Mikkel Meyer Andersen (6 papers) published 1 paper at the last edition the same number as at the previous edition,
  • Daniel Foreman-Mackey (5 papers) absent at the last edition,
  • Nima S. Hejazi (5 papers) absent at the last edition.

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 The Journal of Open Source Software (based on the number of publications) are:

  • Max Planck Society (21 papers) published 5 papers at the last edition, 1 less than at the previous edition,
  • University of Washington (6 papers) published 1 paper at the last edition, 2 less than at the previous edition,
  • Engineering and Physical Sciences Research Council (5 papers) absent at the last edition,
  • Fred Hutchinson Cancer Research Center (3 papers) published 1 paper at the last edition the same number as at the previous edition,
  • University of Oregon (2 papers) absent at the last edition.

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.

Publication chance based on affiliation

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, 91.21% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 75.00% were posted by at least one author from the top 10 institutions publishing in the journal. Another 12.50% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 12.50% of all publications and 0.00% were from other institutions.

Returning Authors Index

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.

Returning Institution Index

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.

The experience to innovation index

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:

  • Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
  • Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
  • Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
  • Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
  • Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

Employment Opportunities and Salary Prospects for Researchers

A career in research, particularly within topics featured in the Journal of Open Source Software, such as Python (programming language), Programming language, Artificial intelligence, Computer graphics (images), and Computational science, provides numerous opportunities. Often, researchers may transition into roles as faculty members in universities or become valued members of technological companies focusing on areas such as Artificial Intelligence, data visualization, machine learning, and pattern recognition. Historically, those transitioning from a research role to teaching have particularly chosen roles such as high school teachers. For those considering the transition to teaching in Arizona, a credible resource to support this career move can be found here, detailing the high school history teacher salary in Arizona. The guide provides information about the necessary qualifications, steps to enter the field, and potential salary prospects. For those researchers who prefer to remain within the industry, many corporations are in desperate need of the skills and expertise they possess. National corporations and technology start-ups alike value the perspective and innovative thinking that researchers bring to their teams. Despite the industry's competitive nature, researchers with a strong foundation in the topics discussed within the Journal of Open Source Software are often highly sought after, making this an attractive career choice for many.

Top Publications

  • sbi: A toolkit for simulation-based inference

    Álvaro Tejero-Cantero;Jan Boelts;Michael Deistler;Jan-Matthis Lueckmann

    (2020)
    175 Citations
  • Foolbox Native: Fast adversarial attacks to benchmark the robustness of machine learning models in PyTorch, TensorFlow, and JAX

    Jonas Rauber;Roland Zimmermann;Matthias Bethge;Wieland Brendel

    (2020)
    168 Citations
  • DataLad: distributed system for joint management of code, data, and their relationship

    Yaroslav O. Halchenko;Kyle Meyer;Benjamin Poldrack;Debanjum Singh Solanky

    (2021)
    113 Citations
  • 3dfier: automatic reconstruction of 3D city models

    Hugo Ledoux;Filip Biljecki;Balázs Dukai;Kavisha Kumar

    (2021)
    50 Citations
  • normflows: A PyTorch Package for Normalizing Flows

    (2023)
    44 Citations
  • DeepReg: a deep learning toolkit for medical image registration

    Yunguan Fu;Nina Montaña Brown;Shaheer U. Saeed;Adrià Casamitjana

    (2020)
    30 Citations
  • Sync Toolbox: A Python Package for Efficient, Robust, and Accurate Music Synchronization

    Meinard Müller;Yigitcan Özer;Michael Krause;Thomas Prätzlich

    (2021)
    30 Citations
  • FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems

    (2020)
    27 Citations
  • Minerva: a light-weight, narrative image browser for multiplexed tissue images.

    John Hoffer;Rumana Rashid;Jeremy L. Muhlich;Yu-An Chen

    (2020)
    27 Citations
  • FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems

    Kacper Sokol;Alexander Hepburn;Rafael Poyiadzi;Matthew Clifford

    (2020)
    26 Citations

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