The Journal of Open Source Software is a developer friendly, open access journal for research software packages.
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.
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.
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.
Not all software is eligible to be published in JOSS.
JOSS publishes articles about research software. This definition includes software that: solves complex modeling problems in a scientific context (physics, mathematics, biology, medicine, social science, neuroscience, engineering); supports the functioning of research instruments or the execution of research experiments; extracts knowledge from large data sets; offers a mathematical library; or similar.
JOSS submissions must:
Authors wishing to make a pre-submission enquiry should open an issue on the JOSS repository.
Works on computer, computational, and data research at NCSA, CS, ECE, and the iSchool at the University of Illinois at Urbana-Champaign, and has a strong interest in studying common elements of how research is done by people using software and data.
Associate Professor of Mechancial Engineering in the School of Mechanical, Industrial, and Manufacturing Engineering at Oregon State University. Computational researcher in combustion, fluid dynamics, and chemical kinetics, with an interest in numerical methods and GPU computing strategies.
MetOcean Data Scientist at Axiom Data Science. Researches coastal ocean dynamics, transport of material in the ocean, and tidal turbines as a renewable energy source.
Gabriela is a Data Scientist interested in building open, fair, and diverse Ecosystems on software platforms. She has worked towards this goal at several companies, including GitHub and Atlassian. Currently, she works on making localization of digital content more accessible and inclusive at Netflix. Throughout Gabriela’s career, she has helped product teams empower users by advocating for the inclusion of multicultural perspectives in technology and creating synergies across different groups with diverse resources.
Associate Professor at Department of Mathematical Sciences, Aalborg University, Denmark. Research interests include statistics for forensic genetics (mainly lineage markers like Y chromosomal DNA profiles) and open source research software. Teaches statistics and data science at BSc, MSc and PhD level. Involved in multiple software projects (e.g. for infrastructure and computer algebra).
Research scientist at the U.S. Geological Survey Landslide Hazards Program. Her research focuses on integrating observations, model development, and model analysis to understand the evolution of the Earth's surface. She is a core developer of the Landlab toolkit.
Astrophysics researcher at CIEMAT, mathematician and software engineer currently developing chemical evolution models for galaxies. Juanjo has worked as advisor on open source policies and contributed code to many popular libraries like Rails or Astropy. He is member of the founder team of Consul, the more widely used open sourced citizen participation software.
Sebastian Benthall is an interdisciplinary researcher working on the economics of privacy and security at NYU School of Law. He received his PhD in Information Management and Systems from the University of California, Berkeley. His research has focused on how privacy and security depends on data flow and digital supply chain networks. More recently, he has become a contributing Research Engineer on Econ-ARK, an open source toolkit for structural modeling of heterogeneous agents.
Computational physicist at IBM Research Europe, with interests in gravitational physics, high-energy physics, and fluid dynamics, as well as large-scale Scientific Computing in general.
Monica Bobra is a research scientist at Stanford University in the W. W. Hansen Experimental Physics Laboratory, where she studies the Sun and space weather as a member of the NASA Solar Dynamics Observatory science team.
Fred is a biostatistician with research interests in statistical genetics and quantitative trait locus mapping in model organisms. He maintains the qtl2pleio R package. As a postdoctoral researcher at the University of Massachusetts Medical School, he collaborates with microbiologists to study the genetics of tuberculosis susceptibility.
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 PETSc.
Pierre de Buyl works at the Royal Meteorological Institute of Belgium, where he processes remote sensing data for the earth radiation budget. He is a former researcher in statistical physics and one of the editors of the SciPy Lecture Notes.
Patrick is an applied mathematician at the Center of Computation and Technology at Louisiana State University. His research interests are non-local models, e.g. peridynamics; asynchronous many-task systems; and high performance computing. He is the co-host of the FLOSS for science podcast. Patrick received his PhD from the University of Bonn in Germany.
Elizabeth is a cognitive neuroscientist at McGill University working to model individual brain activity using high-dimensional, naturalistic data sets. She is a strong advocate for the role of community-driven science to improve the generalizability of inferences in human brain mapping.
Conducting research on high-dimensional geometric computing, statistics, optimization and their applications. Coordinator of the GeomScale project. Affiliated with the Department of Informatics and Telecommunications, NKUA. Scientific collaborator at Ouragan team, INRIA.
Martin is currently a Research Associate in the Geographic Data Science Lab at the University of Liverpool. Researcher in urban morphology and geographic data science focusing on quantitative analysis and classification of urban form, remote sensing, and AI. He is the author of momepy, the open-source urban morphology measuring toolkit for Python, and a member of the development teams of GeoPandas, the open-source Python package for geographic data, and PySAL, the Python library for spatial analysis.
Dan Foreman-Mackey is an Associate Research Scientist at the Flatiron Institute in the Center for Computational Astrophysics. His research program focuses on the development and application of probabilistic data analysis techniques to make novel discoveries and solve fundamental problems in astrophysics.
Expert knowledge in Julia, C, Python. Familiar with Fortran, Octave/Matlab. Strong domain knowledge in Physics (particularly solid-state, condensed matter, electronic structure theory), Chemistry (particularly quantum-chemistry, molecular-dynamics, cheminformatics) and Material science (particularly functional materials), and Machine-Learning (particularly as applied to the previous areas). Expertise in Photovoltaics and general device physics. Software that I'm deeply familiar with: Gaussian, GROMACS, LAMMPS, VASP, GPAW, NWCHEM, ASE, Pymol.
A game theorist, a research software developer and a postdoc at the research group Dynamics of Social Behavior at the Max Planck Institute for Evolutionary Biology. Nikoleta's primary research interest is mathematical modelling and its applications to biology, ecology and sociology. She is a fellow of the Software Sustainability Institute, a core developer of the Axelrod-Python library and an advocate for open source.
Associate Professor in Chemical Engineering at the University of Waterloo, runs the Porous Materials Engineering & Analysis Lab with a research focus on the transport phenomena in porous materials, especially electrodes. Lead developer of OpenPNM a pore network modeling package, PoreSpy a quantitative image analysis toolkit for tomograms.
Evolutionary Biologist turned Research Software Engineer in Epidemiology (at the London School of Tropical Hygiene). In love with open science, free software and collaborative projects. My current research interests are wide as today, I'm more interested in building tools for research practitioners.
Olivia Guest is a computational modeler and theoretician in cognitive science and neuroscience. She creates and evaluates computational accounts for categorization and conceptual representation using behavioural and neuroimaging data.
Experimental atmospheric chemist and software engineer at QuantAQ. He has spent much of his academic career building and maintaining a number of open source projects related to air quality and air quality sensors.
Jay is an engineer-turned-geomorphologist. He creates and applies numerical models and remote sensing workflows to understand how rivers and coastal environments evolve over time.
Director of Liberate Science GmbH, focused on repairing knowledge distribution. Research interests include modes of modular publishing, forensic statistics, and providing researchers with easier ways to do more rigorous science. PhD (2020) from Tilburg University in methodology and statistics.
Bita's research focus lies at the intersection of Machine Learning and HPC. She is interested in developing high performance infrastructures for accelerating large scale Deep Learning models.
Postdoc at the Lab for Data Intensive Biology at University of California Davis studying the formation and maintenance of online communities, and also working on indexing, searching and scaling data analysis in large public sequencing databases using data sketches, Python, Rust and decentralizing technologies.
Director of Data Management and Interoperability at the Frederick National Laboratory for Cancer Research, he leads efforts to design, build and maintain scientist-friendly research data systems that integrate clinical and multiomic data across thousands of cancer patient-donors. He is active in open source software development and a supporter of the FAIR (Findable, Accessible, Interoperable, Reusable) movement in scientific data management. A molecular evolutionary biologist by training, he served as an Associate Editor for the Journal of Molecular Evolution from 2008-2013.
I am currently a Research Associate at the Oden Institute, University of Texas at Austin and also affiliated to Department of Aerospace Engineering and Engineering Mechanics and Department of Biomedical Engineering at UT Austin as a adjunct faculty. My research is driven by the application of mathematics and computational science to present-day relevant and challenging problems. Specific areas of interest include mechanics of solids and granular media, computational oncology, and multiscale modeling.
Vince is a mathematician at Cardiff University. He is a maintainer and contributor to a number of open source software packages and a contributor to the UK python community. His research interests are in the field of game theory and stochastic processes and also has a keen interest in pedagogy. Vince is a fellow of the Software Sustainability Institute and is interested in reproducibility and sustainability of scientific/mathematical research.
Rachel is a postdoc at Carnegie Mellon, where she works on software development for science-informed machine learning in electrochemical systems. Previously, she did her PhD at MIT on novel photovoltaic materials using density functional theory and statistical inference approaches.
Associate-professor at the Delft University of Technology in the Netherlands. Particularly interested in combining the fields of geographical information systems (GIS) and computational geometry, with an emphasis on 3D modelling.
Assistant Professor in the School of 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.
Brian McFee is Assistant Professor of Music Technology and Data Science New York University. His work lies at the intersection of machine learning and audio analysis. He is an active open source software developer, and the principal maintainer of the librosa package for audio analysis.
Applied mathematician, I have worked with numerical optimization and numerical linear algebra at the Federal University of Santa Catarina, in Brazil. Now I am a software engineer at Quansight, working on NumPy and other open-source software. Interested in open science, open research, and teaching practices in mathematics and computer science.
Political scientist turned data engineer and data scientist, Lorena Mesa is a GitHub data engineer and a Director and Vice-Chair, Elect on the Python Software Foundation. Lorena's passion for ethical and accountable open source and reproducible science has led her to be a supporter of the FAIR movement and champion of FATE - fair, accountable, and transparent - algorithms. One part activist, one part space enthusiast, and another part Trekkie, Lorena abides by the motto to "live long and prosper".
Juan is a Senior Research Fellow at the Biomedicine Discovery Institute, Monash University, Australia. He spent his PhD in computational biology working on methods to study gene expression data, before moving on to develop image analysis tools to elucidate neuronal circuits. He now spends all his time developing open source software for imaging in biological research, and is a core developer for scikit-image and napari.
She is focused on genomic regulation and data integration, and has 12 years of experience in biological data analysis and contributing to novel algorithms to improve the quantification and visualization of genomic data.
Interdisciplinary researcher in renewable energy and environmental science. My group's research is on the global transition to a 100% clean and renewable energy system, and the technical, economic and policy barriers on the way to that goal. We develop software like Calliope to help us do so.
Associate professor at University Paris-Saclay. 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.
Independent computational scientist running Tech Inquiry and Hodge Star Scientific Computing. Previously, research scientist at Google and assistant professor of mathematics at Stanford. His research interests: software engineering of high-performance mathematical libraries (e.g., conic optimization, lattice reduction, determinantal point processes, numerical PDEs), their connections to pure mathematics (e.g., differential geometry, conic analysis, representation theory).
Scientific programmer in the DIgital Agriculture Group at the University of Arizona, Kristina maintains and promotes open software and datasets, provides computational training, and runs ecosystem models. She is interested in building open, inclusive, interdisciplinary scientific communities.
Amy Roberts is an Assistant Professor of Physics at the University of Colorado Denver, where she leads a research group focused on dark matter detection. She is particularly interested in building accessible analysis tools for the dark matter community and reproducible analysis infrastructure for large data sets.
Marie E. Rognes is Chief Research Scientist at Simula Research Laboratory, Oslo, Norway. Her research focuses on numerical methods for partial differential equations, software for scientific computing, with applications in biomechanics and neuroscience. She is a core member of the FEniCS and Dolfin-adjoint Projects.
Trained in cognitive neuroscience (PhD: UC Berkeley, 2010) and computational neuroimaging (Postdoc, Stanford, 2011-2015), Ariel Rokem is now a Research Assistant Professor in Psychology and 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.
Bioinformatician working at the University of Birmingham, UK. Research areas include data sketching for genomics, variation graphs and metagenome profiling. Currently working on long-read sequencing applications for real-time genomic epidemiology.
Kelly L. Rowland is a Computer Systems Engineer in the Data Science Engagement Group at NERSC at LBNL, supporting the work of the JGI as well as other program efforts. She obtained her Ph.D. in Nuclear Engineering with a Designated Emphasis in Computational Science and Engineering from the University of California, Berkeley.
Works in computational applied mathematics at the Faculty of Sciences, Universidad Nacional Autónoma de México (National University of Mexico, UNAM). Research and teaching interests in the areas of scientific computing, nonlinear dynamics, and interval analysis. Currently a visiting professor at MIT. Co-author of the JuliaIntervals suite of packages for interval arithmetic and applications.
Professor, working at Istanbul University, Turkey. Lectures on operations research, decision science, and computer programming. Research in computational statistics, optimization, algorithms, and data analysis. Developer and maintainer of many open source projects in several languages including R, Julia, Python, and Java. Author of three books on R programming and genetic algorithms.
Statistician, R developer. Expertise in supervised learning (statistical modelling, machine learning), Bayesian methods, dimension reduction/representation learning. Research focus on functional data.
Jacob Schreiber is a post-doctoral researcher at Stanford University. His research primarily involves answering questions in genomics by applying, or developing, machine learning methodologies to large amounts of data. In the course of this work, he has served as a core developer for scikit-learn and has written several machine learning packages for Python.
Adi is Senior Prototyping Architect for robotics at Amazon Web Services, where he creates very early iterations of products that break new technical grounds in cloud robotics. His core expertise lies in autonomous behavior design of UAVs (drones) and robot arms. Previously, Adi led the robotics development program at Canonical and the UAV research program at Ford. Adi holds two degrees from Stanford University.
Research Associate at the Friedrich Miescher Institute for Biomedical Research in Basel, Switzerland, with a research background mainly in development and evaluation of analysis methods for transcriptomics data. Developer and maintainer of several open-source R packages for analysis, quality assessment and interactive visualization of high-throughput biological data.
Associate Professor in Statistics at the Center for Lifespan Changes in Brain Cogntion, Department of Psychology, University of Oslo. Research interests include development of statistical and machine learning methods with applications in cognitive neuroscience. Developer and maintainer of several R packages.
Research scientist working on compilers and program transformation systems for high performance computing at the Lawrence Livermore National Laboratory, and adjunct faculty at the Washington State University Department of Mathematics and Statistics. His PhD work in computer engineering focused on measurement and analysis methods for performance analysis of high performance systems. He believes that open and reproducible research software artifacts are critical to the advancement of computational science.
Senior Lecturer in Experimental Psychology and Institutional Lead for Open and Reproducible Research at the University of Manchester. His research interests include open and reproducible research, eye-movements during written language comprehension, the processing of data visualisations, and conditional reasoning.
Research Scientist in Audio-ML. His main research interest is in audio processing for music and speech. He recently also became involved in ecoacoustics and cognitive sciences and is a strong advocate for open and reproducible research. Fabian received his Ph.D. in Electrical Engineering from the University of Erlangen-Nuremberg in Germany.
Senior software engineer at Ant Group, building AI infrastructure and AutoML platform. He's a PMC member of XGBoost and Apache MXNet, co-chair of Kubeflow, committer of TensorFlow, Couler, and ElasticDL. He's also the co-author of TensorFlow in Practice and Dive into Deep Learning.
Professor of computer science at Loyola University, Chicago, and visiting faculty at the Argonne National Laboratory Leadership Computing Facility. Research interests: programming languages, computer systems, computational science, digital humanities, parallel and distributed computing, software engineering, machine learning, computer vision, robotics, and interdisciplinary applications. Past editor-in-chief of IEEE Computing in Science and Engineering.
Interdisciplinary scientist analysing future energy systems and the hurdles of a transition towards decarbonised energy supply. I am a system scientist by training and have been working as a software and research software engineer in the past.
Lecturer in Geophysics at the University of Liverpool, UK. Researching methods for determining the inner structure of the Earth from geophysical observations, like disturbances in the Earth's gravity and magnetic fields. I develop open-source software for processing, modelling, and visualizing geophysical data.
Chris is a data scientist specializing in all things geospatial who spends a lot of time developing open-source software ecosystems and mentoring. His research at the US DOE's Pacific Northwest National Laboratory focuses on MultiSector Dynamics and multi-model integration.
Biologist, quantitative ecologist and open science enthusiast at Universidade Federal de Alagoas, Brazil, where he coordinates the Quantitative Ecology Lab. He is interested in a wide range of topics related to evolutionary ecology and conservation biology, but also in other fields such as active learning methods and digital games in education.
Research fellow at Northumbria University, using atomistic modelling to study materials for new renewable energy technologies. Developing software for pre- and post-processing large-scale electronic-structure calculations and spreading the "better software, better research" message as a fellow of the Software Sustainability Institute.
Associate professor for the transition of energy systems at the Europa-Universität Flensburg. Her junior research groups focuses on pathways to climate neutral and sustainable energy systems with a foucs on sufficiency, thus an absolute reduction of energy demand. Different energy and sector models support her research for reaching sustainable energy, building, industry and transport sectors.
To suggest a feature, report a bug, or enquire about a possible submission in JOSS, please open a GitHub Issue.
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.
We also want to remind authors and reviewers (and anyone else) that we expect and require ethical behavior. Some examples are:
Any potentially unethical behavior should be brought to the attention of the JOSS staff. See Contacting JOSS.
The JOSS Editors will track any concerns and respond to the submitter with a resolution, which will range from doing nothing if the editors disagree about the issue to withdrawing papers and notifying authors' institutions.
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:
Assuming a publication rate of 200 papers per year this works out at ~$4.75 per paper ((19*12) + 200 + 275 + 250) / 200.
A more detailed analysis of our running costs is available on our blog.
JOSS has an experimental collaboration with AAS publishing where authors submitting to one of the AAS journals can also publish a companion software paper in JOSS, thereby receiving a review of their software. For this service, JOSS receives a small donation from AAS publishing. In 2019, JOSS received $200 as a result of this collaboration.
JOSS is a diamond/platinum 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..
Public user content licensed CC BY 4.0 unless otherwise specified.