The Journal of Open Source Software
The Journal of Open Source Software (JOSS) is a developer friendly, open access journal for research software packages.
What exactly do you mean by 'journal'
The Journal of Open Source Software (JOSS) is an academic journal (ISSN 2475-9066) with a formal
peer review process that is designed to improve the quality of the software submitted.
Upon acceptance into JOSS, a CrossRef DOI is minted and we list your paper on the JOSS website.
Don't we have enough journals already?
Perhaps, and in a perfect world we'd rather papers about software weren't necessary but we
recognize that for most researchers, papers and not software are the currency of academic research
and that citations are required for a good career.
We built this journal because we believe that after you've done the hard work of writing great
software, it shouldn't take weeks and months to write a paper about your work.
You said developer friendly, what do you mean?
We have a simple submission workflow and extensive documentation to help you prepare your
submission. If your software is already well documented then paper preparation should take no
more than an hour.
You can read more about our motivations to build JOSS in our
announcement blog post.
Code of Conduct
Although spaces may feel informal at times, we want to remind authors and reviewers (and anyone else) that this is a professional space. As such, the JOSS community adheres to a code of conduct adapted from the Contributor Covenant code of conduct.
Authors and reviewers will be required to confirm they have read our code of conduct, and are expected to adhere to it in all JOSS spaces and associated interactions.
Open Source Initiative
JOSS is a proud affiliate of the
Open Source Initiative.
As such we are committed to public support for open source software and the role OSI plays
therein. You can read more about the OSI's affilate program
The Journal of Open Source Software is a NumFOCUS-sponsored project.
Lorena A Barba (@labarba),
: Computational Science and Engineering, High-performance Computing
Associate Professor of Mechanical and Aerospace Engineering at the George Washington University,
leading a research group in computational fluid dynamics, computational physics and
high-performance computing. Member of the Board for NumFOCUS, a non-profit in support of
open-source scientific software.
A lapsed academic with a passion for new models of scientific collaboration, he's used big telescopes
to study dust in space, built
sequencing pipelines in Cambridge,
engaged millions of people in online citizen science by co-founding the
Zooniverse, and led science at GitHub. These days he's running the Data Science Mission Office at STScI in Baltimore, the science operations center for the Hubble Space Telescope, James Webb Space Telescope, Kepler, TESS, and more.
Tania Allard (@trallard),
: Biomechanics, Reproducible research, Open Science, Data Engineering
Research Software Engineer and data engineer, contributor and maintainer of a number of open source projects, mentor and community builder. She is particularly interested in reproducibility, sustainability, openness and FAIR principles in research software and data as well as bias an ethics in data science.
Jed Brown (@jedbrown),
: Computational Science and Engineering, Geophysics, High-performance Computing
Assistant Professor of Computer Science at the University of Colorado Boulder leading a research
group developing scalable algorithms and sustainable software for prediction, inference, and
design via high-fidelity and multiscale physically-based models. He is a core developer of
Associate Professor, Librarian, and Head of Archival Informatics and Special Collections at
Montana State University (MSU) Library, specializing in software development, metadata and data
modeling, linked and structured data, search engine optimization, and interface design. You can
find him on ORCID at
and as @jaclark on Twitter.
Research software engineer working at the UMCCR in
Melbourne, Australia. Likes to tap into many fields of science and computing including
deployable and reproducible scientific software in both HPC and Cloud computing environments for
scientific workflows and data analysis. In previous gigs enacted
NeuroStars a Q&A site for its growing neuroscience
community and also mentored different students via the Google Summer of Code program. More
recently, self taught embedded systems design and RF engineering among other hobbies.
Assistant professor in Computer Science and Engineering and Medicine at UC San Diego with a
research background in population genetics and bioinformatics. Interested in best practices for
reproducible and open computational science and in how to take advantage of online media to
change the face of scientific publishing.
Kathryn Huff (@katyhuff),
: Nuclear Engineering, Energy Engineering
Kathryn Huff is an Assistant Professor in Nuclear, Plasma, and Radiological Engineering at
the University of Illinois at Urbana-Champaign. Her research
focuses on modeling and simulation of advanced nuclear reactors and fuel cycles. She also
advocates for best practices in open, reproducible scientific computing.
George K. Thiruvathukal (@gkthiruvathukal),
: High performance & distributed computing, cyber-physical systems, software engineering, programming languages and systems, computational science, digital humanities
George K. Thiruvathukal is full professor of computer science at Loyola University Chicago and visiting faculty at Argonne National Laboratory in the Argonne Leadership Computing Facility. His research interests include high performance & distributed computing, cyber-physical systems, software engineering, programming languages and systems, history of computing, computational and data science, computing education, and ethical/legal/social issues in computing. George is the past editor-in-chief of IEEE Computing in Science and Engineering.
Assistant Professor in the
Psychology at the University of Nottingham.
Studying human memory and decision making using cognitive psychology and neuroimaging
approaches, and developing novel computational methods along the way.
Kevin M. Moerman (@Kevin-Mattheus-Moerman),
: Computational mechanics, Biomechanics, Finite Element Analysis, Meshing, Design Optimization, Inverse Analysis, Image-based Modelling
Computational mechanics and design engineer at NUI Galway, research affiliate MIT Media's lab. Developer for the GIBBON project.
Lorena Pantano (@lpantano),
: Small RNAseq, RNAseq, miRNA, isomiRs, visualization, genomics, transcriptomic, non-codingRNA, data integration
Research Scientist at Harvard T.H. Chan School of Public Health. She is focused on genomic regulation and data integration. 12 years of experience in biological data analysis using the most well-established tools and contributing to novel algorithms to improve the quantification and visualization of genomic data. She approaches scientific challenges with passion and believes that collaboration and not an individual alone can successfully conquer them.
Associate professor at Université Paris-Sud. Mathematician, computer scientist and strong defender of open-source and open science in general. Contributor and user of the SageMath software. Member of the OpenDreamKit European project for open-source development in Mathematics.
Jack Poulson (@poulson),
: Numerical optimization, numerical linear algebra, PDEs, high-performance computing, lattice reduction
An independent computational scientist currently running Hodge Star Scientific Computing and serving as Head of Engineering at Disaster Intelligence. He was previously a research scientist at Google and an assistant professor of mathematics at Stanford. His research interests range from the software engineering of high-performance mathematical libraries (e.g., conic optimization, lattice reduction, determinantal point processes, numerical PDEs) to their connections to pure mathematics (e.g., differential geometry, conic analysis, and representation theory). He is passionate about the accessibility of scientific publications, and, more generally, the ethical implications of technical work.
Elizabeth Ramirez (@eramirem),
: Computational Science and Engineering, Machine Learning, Digital Signal Processing
Applied Scientist at Descartes Labs, working on Nonlinear and Stochastic Dynamical Systems Modeling for Commodities. Applied Mathematician focused on Numerical Methods and Linear Algebra at Large Scale. Electrical Engineer.
Ariel Rokem (@arokem),
: Neuroscience, machine learning, computational social science
Trained in cognitive neuroscience (PhD: UC Berkeley, 2010) and computational neuroimaging
(Postdoc, Stanford, 2011-2015), Ariel Rokem is now a data scientist at the University of
Washington eScience Institute, where he continues to develop software for the analysis of human
neuroimaging data, develops tools for reproducible and open research practices, and collaborates
with researchers from a variety of fields to advance data-intensive research.
Kristen Thyng (@kthyng),
: computational fluid dynamics, oceanography, geosciences
Physical oceanographer at Texas A&M University. Researches coastal ocean dynamics, transport of material in the ocean, and tidal turbines as a renewable energy source.
Yo Yehudi (@yochannah),
: Web and browser technologies, data visualization, application programming interfaces
Software engineer at InterMine, an open source biological data warehouse based at the Department of Genetics in the University of Cambridge. Founder of Code Is Science, fellow of the Software Sustainability Institute, board member of the Open Bioinformatics Foundation, and an enthusiastic cohort host for the Mozilla Open Leaders program.
Bioinformatician and researcher at the
KEMRI-Wellcome Trust Research Programme one
of the Major Wellcome-Trust Overseas Programmes. George works with the
Virus Epidemiology and Control group and develops
bioinformatics methods for understanding virus transmission patterns and evolution. He undertook
his education in Kenya and is one of East-Africa's open source software developers with an keen
interest in bioinformatics and reproducible research.
A survey and experimental methodologist currently working as Associate Professor in Political
Behaviour at the London School of Economics and
Political Science. His research focuses on the effects of information on public opinion, as
well as techniques and tools for analyzing quantitative survey and experimental data. He has
published more than thirty R packages on CRAN, and has authored and contributed to numerous
other open source projects.
Lead Developer at the Mozilla Science Lab. Abby has
led development on various open source projects for science including Contributorship Badges for
Science and WormBase. With a background in bioinformatics and computer science, she builds tools
that use the web to move science forward.
Executive Director of Data Carpentry and Adjunct Professor in the BEACON Center for the Study of
Evolution in Action at Michigan State University. Her research background in is microbial
metagenomics and bioinformatics, and she has been a developer and contributor to several open
source bioinformatics projects. She also focuses on best practices in data analysis software
Cost and Sustainability Model
The Journal of Open Source Software is an open access journal committed to running at minimal costs, with zero publication fees (article processing charges) or subscription fees.
Under the NumFOCUS nonprofit umbrella, JOSS is now eligible to seek grants for sustaining its future. With an entirely volunteer team, JOSS is seeking to sustain its operations via donations and grants, keeping its low cost of operation and free service for authors.
In the spirit of transparency, below is an outline of our current running costs:
- Annual Crossref membership: $275 / year
- JOSS paper DOIs: $1 / accepted paper
- JOSS website hosting (Heroku): $19 / month
Assuming a publication rate of 200 papers per year this works out at ~$3.50 per paper ((19*12) + 200 + 275) / 200 .
Content Licensing & Open Access
JOSS is an open access journal. Copyright of JOSS papers is retained by submitting authors and accepted papers are subject to a Creative Commons Attribution 4.0 International License.
Any code snippets included in JOSS papers are subject to the MIT license regardless of the license of the submitted software package under review.
Any use of the JOSS logo is licensed CC BY 4.0.