The Journal of Open Source Education is an educator friendly journal for publishing open-source educational materials and software.
JOSE, pronounced [hoe-zay], is a sibling journal to the Journal of Open Source Software (JOSS), which publishes open-source research software. JOSE relies on the journal management infrastructure and tools developed for JOSS.
JOSE publishes two types of articles that describe:
Currently, academia lacks a mechanism for crediting efforts to develop software for assisting teaching and learning or open-source educational content and materials. As a result, beyond personal motivation, there is little incentive to develop and share such material.
The Journal of Open Source Education (JOSE) is a scholarly journal with a formal peer review process designed to improve the quality of the software or content submitted. Upon acceptance into JOSE, a CrossRef DOI is minted and we list your paper on the JOSE website.
This is an initiative led directly by the Editorial Board on a purely volunteer basis. There is no publisher seeking revenue through the journal. JOSE runs on the efforts of the editors, authors, and reviewers, to communicate scholarly work to the open-source community without intermediaries.
"Open-source" implies that there are source files that are converted and rendered into a final learner-presentable form. Examples include Jupyter notebooks or plaintext/markup language documents like LaTeX, Markdown, ReStructuredText, AsciiDoc, and R Markdown. These documents should contain the lesson content and be designed to be reusable by other instructors. Lastly, the course content should make use of computation for learning with embedded or associated code snippets/programs.
We do not mean openly available slides, lecture notes, or YouTube videos, though these may be acceptable as supplementary materials. In addition, course syllabi by themselves are not suitable for submission (Syllabus may be more appropriate).
tl;dr: your course or lesson content must contain or use code to teach. We are not focused exclusively on learning to code, but coding to learn.
Open-source software that serves as educational technology; examples include (but are not limited to) alternatives to learning management systems, autograders, cloud systems for lesson delivery, student collaboration tools. For these tools, peer review will follow a similar process as JOSS.
We consider submissions from all areas of academia, although our computational focus may result in more natural submissions from STEM fields—but all are welcome!
Submissions must be "feature complete" to the extent that another educator could adopt, reuse, and/or extend for their purposes.
The ideal submission size is a course module, although entire courses are also acceptable.
JOSE submissions must:
Authors wishing to make a pre-submission enquiry should open an issue on the JOSE repository.
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. Past member of the Board (2014-2021) for NumFOCUS, a non-profit in support of open-source scientific software. Jupyter Distinguished Contributor 2020.
Design assistant at the University of Michigan and former Library as Research Lab Design Fellow working on projects related to digitization of cultural heritage materials and education (Dig4E). He is also an adjunct librarian at Oakland Community College.
rOpenSci Community Manager. R-Ladies Project Lead. Assistant Professor at Universidad Nacional Guillermo Brown (Argentina). Co-founder of MetaDocencia and LatinR. Member, The Carpentries Executive Council, R Consortium Infrastructure Steering Committee, and Sociedad Argentina de Informática (SADIO). Instructor and trainer for The Carpentries and RStudio Certified Instructor. INTA 's Researcher (since 1998; on leave). Former MetaDocencia Core Team (2020-2022) and RForwards Core Team (2021-2022). She teaches and develops open educational materials about R, remote sensing, and data science. She participates in and leads several translations effort of educational material, including the book Teaching Tech Together, The Carpentries' lessons, and the book R for Data Science.
Postdoctoral Research Associate at The Alan Turing Institute. His research focuses on modeling earth systems and environmental phenomena using artificial intelligence and data science. He contributes to open science education through software, tutorials, content and events in multiple global community-driven initiatives including the Turing Way (core member), Open Life Science (mentee/mentor) and Pangeo (member). He is also the lead of the Environmental Data Science Book, a computational notebook community promoting open source software, reproducibility and collaborative research in Environmental science.
Renata is a postdoctoral researcher at the School of Biology and Ecology at the University of Maine. She uses data- and- computationally-intensive methods to study biodiversity change across levels of organization. She develops R packages and teaching materials to help researchers and students develop computational skills and implement new theories and statistical approaches in ecology and evolution. Previously, she completed her PhD in Interdisciplinary Ecology at the University of Florida.
Associate Teaching Professor in the Department of Cognitive Science and The Halıcıoğlu Data Science Institute at The University of California San Diego. She develops curriculum and teaches undergraduate courses in programming (Python, R), data science, and genetics, with a particular focus on responsible and ethical approaches to data analysis. Co-author of Tidyverse Skills for Data Science in R (book and course sequence) and Cloud-Based Data Science (course sequence).
Associate professor in the Dept. of Chemical & Biological Engineering at the University at Buffalo, The State University of New York. Her research focuses on mathematical modeling for diseases and pharmaceutical treatments. Her teaching interests blend computational science, kinetics, transport, and applied mathematics for engineering education.
Assistant Professor of Statistics at Cornell College. He teaches undergraduate courses and collaborates with students on summer research in statistics and data science. His courses use collaborative learning and emphasize communication, reproducibility, and ethics, by integrating them throughout a class. He has started contributing to open source education by utilizing, maintaining, and collaborating, on existing resources, and creating introductory statistics materials.
Assistant Professor, Universidad EAFIT, Medellin, Colombia. BSc in Physics Engineering, MSc in Engineering and PhD in Computational Engineering. Research interests and experience in simulation-based engineering, numerical methods, biomimetics, mechanics of solids, design of materials, and wave propagation.
Assistant Teaching Professor at the Bren School of Environmental Science and Management, UC Santa Barbara. She teaches math, environmental data science, and statistics courses in R, emphasizing tools and best practices for reproducibility and collaboration. She contributes to open source education through software, open online tutorials and courses, and a library of original artwork for use in data science teaching materials.
Research Fellow at the School of Data Science and Computational Thinking at the University of Stellenbosch. Attending surgeon at Groote Schuur Hospital, was Head of Postgradute Surgical Research. and Head of Surgical Education in the Department of Surgery at The University of Cape Town. He served as the Health Sciences Faculty member on the University Senate Committee on online education. His research and educational passions include machine learning and data science, open science, open source software, and open educational resource development.
Stefani is an Assistant Professor of the Practice in the Business Information & Analytics Department at the University of Denver's Daniels College of Business, where she is also the co-director of the Center for Analytics and Innovation with Data (CAID). She teaches courses on Python programming, data mining and visualization, frequentist statistics, and project management. A political scientist by training, Stefani has worked as a data scientist in a public policy consulting firm in London, as well as a teacher in the Texas public school system. She is currently developing an open source textbook on survey methodologies in R for social scientists.
Instrument Data Scientist at the European Spallation Source. He is passionate about teaching physical scientists programming and data handling skills. He previously held a Visiting Lectureship at the University of Bath where he taught Python for chemistry. Now, he focuses on training users of ESS to reduce and analyse data.
Erin McKiernan is a professor in the Department of Physics, Biomedical Physics Program at the National Autonomous University of Mexico, specializing in experimental and theoretical neuroscience and cellular biophysics. She teaches several courses, including biochemistry, cellular and molecular biology, and human physiology. She is currently developing electrophysiology laboratory practicals as open educational resources.
Jason K. Moore is an assistant professor of BioMechanical Engineering at Delft University of Technology, where he leads the Bicycle Lab and teaches multibody dynamics and engineering computation. He is active in the Scientific Python community where he is a core developer of the SymPy and PyDy projects.
Mechanical engineer 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.
Assistant Professor at the University of Georgia, jointly appointed in the Departments of Computer Science and Cellular Biology. His research focuses on modeling diseases at the cellular level using a variety of imaging and machine learning techniques. He also advocates for best practices in open and reproducible research and education.
Clinical Associate Professor in the School of Information at the University of Michigan. He also works on developing standards for teaching and learning technology. Previously, he was the Executive Director of the Sakai Foundation and the Chief Architect of the Sakai Project. He has written several books including Using the Google App Engine, Python for Informatics, High Performance Computing, and Sakai: Free as in Freedom.
Assistant Professor, Teaching Stream in the Department of Physical and Environmental Sciences at the University of Toronto Scarborough. She teaches climate science and modeling and environmental data analysis through the development and use of a variety of open-source educational tools, labs and workshops.
Assistant Professor of Teaching in the Department of Statistics and Co-Director for the Master of Data Science program (Vancouver Option) at the University of British Columbia. She develops curriculum and teaches on responsible application of data science, collaborative software development and creating R and Python packages using modern tools and workflows. Co-author of two open textbooks: Data Science: A First Introduction and Python Packages.
Assistant Director of External Collaborations at Cold Spring Harbor Laboratory’s DNA Learning Center. He has been the Education, Outreach, and Training lead for CyVerse (a US national life science cyberinfrastructure funded by NSF), and he organizes, instructs, and speaks at a variety of bioinformatics-related events. He is an active Carpentries instructor, and a former Chair of the Software Carpentry foundation (an international organization of researchers that promotes training and education in software development, scientific data management, and open science).
To suggest a feature, report a bug, or enquire about a possible submission in JOSE, 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 JOSE 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 JOSE 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 JOSE staff. See Contacting JOSE.
The JOSE 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 Education 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, JOSE is now eligible to seek grants for sustaining its future. With an entirely volunteer team, JOSE 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.
JOSE is a diamond/platinum open access journal. Copyright of JOSE 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 JOSE 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.