From 1e65f42de6b19515475eb1675cabac475b45f709 Mon Sep 17 00:00:00 2001 From: Copilot <198982749+Copilot@users.noreply.github.com> Date: Wed, 24 Jun 2026 20:04:58 +0100 Subject: [PATCH 1/5] Add Phantom maintainer spotlight (Daniel Price, Monash University) (#508) * Add phantom maintainer spotlight * Potential fix for pull request finding Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com> --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: samus-aran Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com> --- .../academia/phantom-maintainer-spotlight.md | 83 +++++++++++++++++++ 1 file changed, 83 insertions(+) create mode 100644 content/academia/phantom-maintainer-spotlight.md diff --git a/content/academia/phantom-maintainer-spotlight.md b/content/academia/phantom-maintainer-spotlight.md new file mode 100644 index 000000000..e6ac7e589 --- /dev/null +++ b/content/academia/phantom-maintainer-spotlight.md @@ -0,0 +1,83 @@ +--- +name: Daniel Price +institution: Monash University +department: School of Physics and Astronomy +projectName: Phantom +projectRepo: https://github.com/danieljprice/phantom +projectWebsite: https://phantomsph.github.io/ +maintainerProfiles: + - github: https://github.com/danieljprice + - orcid: https://orcid.org/0000-0002-4716-4235 +badges: ["Academic Maintainer", "Professor"] +description: "A fast, parallel code for simulating the Universe in 3D using the smoothed particle hydrodynamics (SPH) method, widely used in astrophysics to model phenomena such as star and planet formation, stellar collisions, and black hole interactions." +--- + +## What is Phantom, and what does it help people do? + +Phantom is a fast, parallel code for simulating the Universe in 3D using the smoothed particle hydrodynamics (SPH) method. SPH is a method where a fluid (gas, liquid, or solid) is discretised into a set of points that follow the fluid motion. + +Phantom is a "take the best and make it fast" code used widely in astrophysics. The goal is to make "experimenting on the Universe" easier. Astronomers want to understand how their observations can be explained from first principles. For example, when we observe a light curve that may be caused by a star being swallowed by a supermassive black hole, we can model it to test whether the theory fits the data. Similarly, we observe regions where planets are born, but do not fully understand the process, so we model the interactions between planets and their surrounding material to explain these observations. + +## What inspired you to start this project? + +Computer simulations play a unique role in astronomy. Since we cannot physically experiment with stars and black holes, we have to perform these experiments in a computer. Phantom enables this, allowing us to compare simulations with real observations made using telescopes. + +Our group focuses heavily on developing numerical algorithms, but these papers are often difficult to read and quite abstract. Around 2010, we realised there was a need for an open-source, public implementation of these algorithms that the scientific community could use. Phantom went public in 2018 and has since been widely adopted to simulate processes such as the birth of stars and planets, the spaghettification of stars by black holes, stellar collisions, and more. + +## How does this project connect to your academic work? + +This is a core research project. + +## Who contributes to the project? + +Contributors include collaborators from around the world, as well as PhD, Master's, and honours students, and undergraduate students working on research projects. + +## How are students involved in the project? + +Students contribute by writing code, developing tests, performing benchmarks, and helping to host workshops. + +## How is the project used in teaching or coursework? + +We use Phantom in one of our second-year "Introduction to Astrophysics" laboratory sessions, specifically focused on planet formation. + +## What impact has this project had on your students? + +The project supports key skill development in high-performance computing, supercomputing, and scientific modelling. + +This includes former PhD students such as Daniel Mentiplay, now employed at the Bureau of Meteorology, and David Liptai, now at the Swinburne Supercomputing Centre. + +## What impact has the project had beyond the classroom or research? + +There is a very active user and developer community around Phantom. This is reflected in the 25 co-authors on the code paper, 65 researchers who have contributed to the codebase, and over 200 forks of the repository. + +The code has been used in more than 471 published research studies across astronomy and astrophysics since 2018. We have also run six international, week-long Phantom user workshops and hackfests to actively support and grow the community. + +## What does it take to maintain the project? + +The project is maintained through automated GitHub workflows, extensive unit testing, and an annual major release cycle. + +## What have been the biggest challenges in maintaining the project, especially in an academic setting? + +The main challenges are maintaining a large codebase and ensuring it scales effectively to a growing number of users. + +## How do you ensure the project remains sustainable over time? + +We automate as many maintenance tasks as possible, write extensive unit tests, and encourage contributors to follow the same practices. This allows the codebase to be effectively regression tested and maintained over time. + +## How do you engage with your community? + +We engage through Read the Docs documentation, a Slack channel, GitHub Discussions, email, and contribution guidelines. + +We also run regular user workshops, which provide opportunities for in-person collaboration and hands-on engagement. + +## Have you taken part in any open source programs or events? + +We have run our own hackfests but have not participated in larger-scale open-source events, as the code is relatively specialised. + +## What would you love to achieve by showcasing your project? + +Showcasing the project helps encourage students to pursue further studies in computational astrophysics. + +## Is there anything else you'd like to share about your project or open source journey? + +We are particularly focused on encouraging women in computational science. For example, we award the Emmy Noether Prize for women in computational astrophysics at each workshop. From e737d047e00abb80353bc776898974ad288c3be5 Mon Sep 17 00:00:00 2001 From: Copilot <198982749+Copilot@users.noreply.github.com> Date: Wed, 24 Jun 2026 20:55:27 +0100 Subject: [PATCH 2/5] Add Oxford iHealth academia spotlight and remove unintended 2026 ICS diff (#509) * Add Oxford iHealth spotlight * Finalize Oxford iHealth spotlight * Revert maintainer-month-2026 calendar file --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> --- .../oxford-ihealth-maintainer-spotlight.md | 83 +++++++++++++++++++ 1 file changed, 83 insertions(+) create mode 100644 content/academia/oxford-ihealth-maintainer-spotlight.md diff --git a/content/academia/oxford-ihealth-maintainer-spotlight.md b/content/academia/oxford-ihealth-maintainer-spotlight.md new file mode 100644 index 000000000..218ab8565 --- /dev/null +++ b/content/academia/oxford-ihealth-maintainer-spotlight.md @@ -0,0 +1,83 @@ +--- +name: Ernest Guevarra +institution: University of Oxford +department: Nuffield Department of Medicine +projectName: Oxford iHealth +projectRepo: https://github.com/oxfordIHTM +projectWebsite: https://oxford-ihtm.io +maintainerProfiles: + - github: https://github.com/ernestguevarra + - orcid: https://orcid.org/0000-0002-4887-4415 +badges: ["Academic Maintainer", "Lecturer"] +description: "An open-source global health initiative that fosters innovation, research, and education in computational sciences, helping learners and practitioners build reproducible workflows and tools for global health." +--- + +## What is Oxford iHealth, and what does it help people do? + +Oxford iHealth fosters innovation, research, and education in computational sciences for global health. It provides a learning environment for open and reproducible science and collaborates with partners to apply this learning to solutions for global health challenges. + +## What inspired you to start this project? + +This project was initially meant to support the module on Open and Reproducible Science in R that I teach in the MSc in International Health and Tropical Medicine at the University of Oxford. + +Over the past four years, it has evolved into an initiative to foster innovation, research, and education in computational sciences for global health. The main aim is to equip students with the skills and tools to participate in open and reproducible science. + +## How does this project connect to your academic work? + +I teach students in the MSc in International Health and Tropical Medicine, where my main module focuses on Open and Reproducible Science using R. + +Through this project, I develop open-source educational modules for teaching, while also creating tools (primarily in R) that provide standard approaches for data extraction, processing, and analysis in global health. + +## Who contributes to the project? + +The project is contributed to initially and predominantly by colleagues from the global health sector. Now, through the module I teach, students also learn R and contribute to developing the tools. + +## How are students involved in the project? + +Students primarily learn how to use the tools. For those who are interested, we provide mentorship in open-source package development using R so they can later contribute. + +In the module, we run a three-day hackathon where students contribute code to a repository that addresses a real-world global health problem. + +## How is the project used in teaching or coursework? + +The main connection is that students use the open-source tools in their thesis or placement work in the field. + +## What impact has this project had on your students? + +Skills development is the main impact we are seeing at this stage. + +## What impact has the project had beyond the classroom or research? + +Our tooling is widely used by R users in the global health data analytics field. + +## What does it take to maintain the project? + +I lead most of the projects either as a lead developer or in a supervisory role. Contributors are predominantly students, alongside faculty and partners involved in related work. + +## What have been the biggest challenges in maintaining the project, especially in an academic setting? + +Within global health sciences, developing software as an academic activity is still not widely recognised. This makes it difficult to secure funding or resources, as there are limited opportunities that support this type of work. + +## How do you ensure the project remains sustainable over time? + +Integration into course curricula is our main approach to ensuring sustainability. + +## How do you engage with your community? + +The course acts as the entry point to the community. As students graduate, we provide discussion forums via the website and GitHub. + +We also offer documentation and mentorship for students who want to apply reproducible workflows and improve their data systems and analysis capabilities. + +## Have you taken part in any open source programs or events? + +We haven't. + +## What would you love to achieve by showcasing your project? + +We are growing Oxford iHealth in an organic way, with a strong focus on students from low- and middle-income countries. We are not aiming to grow the project too large beyond this group for now. + +A showcase would help demonstrate what we are building, connect with others who share similar approaches, and inspire others to adopt similar methods in their teaching. + +## Is there anything else you'd like to share about your project or open source journey? + +Our approach is centred on giving students the skills and tools to participate in open and reproducible science, rather than just consume it. We want them to feel empowered to create, build, and share, and through that process, contribute to meaningful change. From abebc253dbf05988f5540ba8fe5528dbeb366c22 Mon Sep 17 00:00:00 2001 From: Copilot <198982749+Copilot@users.noreply.github.com> Date: Wed, 24 Jun 2026 20:55:44 +0100 Subject: [PATCH 3/5] =?UTF-8?q?Add=20igraph=20maintainer=20spotlight=20for?= =?UTF-8?q?=20Szabolcs=20Horv=C3=A1t=20(#510)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> --- .../academia/igraph-maintainer-spotlight.md | 83 +++++++++++++++++++ 1 file changed, 83 insertions(+) create mode 100644 content/academia/igraph-maintainer-spotlight.md diff --git a/content/academia/igraph-maintainer-spotlight.md b/content/academia/igraph-maintainer-spotlight.md new file mode 100644 index 000000000..2c5460ca4 --- /dev/null +++ b/content/academia/igraph-maintainer-spotlight.md @@ -0,0 +1,83 @@ +--- +name: Szabolcs Horvát +institution: Reykjavik University +department: Computer Science +projectName: igraph +projectRepo: https://github.com/igraph/igraph/ +projectWebsite: https://igraph.org/ +maintainerProfiles: + - github: https://github.com/szhorvat/ + - orcid: https://orcid.org/0000-0002-3100-523X +badges: ["Academic Maintainer", "Assistant Professor"] +description: "A free and open-source collection of tools for the analysis of complex networks, with an emphasis on efficiency, portability, and ease of use." +--- + +## What is igraph, and what does it help people do? + +igraph is a free and open-source collection of tools for the analysis of complex networks, with an emphasis on efficiency, portability, and ease of use. It is used by researchers worldwide in network science, as well as in empirical fields where a network perspective is useful, from sociology through physics to biology. igraph can be used with R, Python, Mathematica, and C/C++. + +## What inspired this project? + +igraph was created by Gábor Csárdi and Tamás Nepusz around 2005 to support their doctoral research. At that time, there were no easy-to-use, free network analysis libraries available for high-level languages such as R and Python. + +Since then, many contributors have helped grow igraph into one of the most widely used tools of its kind. The current igraph team includes around ten people working across its different interfaces. + +## How does this project connect to your academic work? + +My work involves developing new mathematical and computational methods for analysing complex networks. Some of these methods are implemented in igraph, making them accessible to researchers around the world. + +## Who contributes to the project? + +The igraph team; students mentored, supervised, or taught by members of the team; and external volunteer contributors. + +## How are students involved in the project? + +Students contribute code, documentation, and testing, often by using igraph within their own projects. + +## How is the project used in teaching or coursework? + +igraph is the main computational analysis tool used in the network science course I teach. One possible class project is contributing directly to igraph. + +## What impact has this project had on your students? + +Several students have been introduced to open-source contributions through igraph. + +## What impact has the project had beyond the classroom or research? + +igraph is widely adopted. The original publication has received over 16,000 citations, and more than a thousand other software libraries depend on it. + +## What does it take to maintain the project? + +The project is maintained by the igraph team, with members based in multiple countries. Development is primarily managed through GitHub, which we use for issue tracking, pull requests, and releases. + +igraph is open source, and its development is fully transparent. The governance structure is documented publicly on GitHub. + +## What have been the biggest challenges in maintaining the project, especially in an academic setting? + +Securing funding and resources has been the biggest challenge, followed by balancing teaching and research responsibilities with development work. + +While academia allows us to involve students, mentoring is time-consuming. Retaining contributors, both local students and online contributors, is also difficult. Team members outside academia require funding to be able to dedicate time to the project. + +## How do you ensure the project remains sustainable over time? + +We apply for grants to support scientific software development and involve local students in ongoing work. + +We have also attracted volunteer contributors through GitHub who became regular team members. However, mentoring online contributors can be time-intensive, and many do not continue after their first contribution. + +## How do you engage with your community? + +We engage through a project forum, the GitHub issue tracker, and by maintaining detailed contribution guides and user documentation. + +## Have you taken part in any open source programs or events? + +Not yet. + +## What would you love to achieve by showcasing your project? + +To increase the visibility of igraph, and especially to raise awareness of the challenges faced by open-source scientific projects. + +Users often take open-source software for granted. Academic and research institutions typically do not allocate funding to tools that are free to use, even though their long-term sustainability depends on funding in the same way as commercial software. + +## Is there anything else you'd like to share about your project or open source journey? + +igraph is a team effort. If it is showcased, it should be presented that way, even though I submitted this form as an individual. From 237f9a41bea4afa9193d4579d53f9de5cccd8db0 Mon Sep 17 00:00:00 2001 From: samus-aran Date: Wed, 24 Jun 2026 20:56:11 +0100 Subject: [PATCH 4/5] Update maintainer information and project details (#511) * Update maintainer information and project details Updated maintainer's institution and title, and revised project description. * Potential fix for pull request finding Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com> --- content/academia/inti-maintainer-spotlight.md | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/content/academia/inti-maintainer-spotlight.md b/content/academia/inti-maintainer-spotlight.md index 71f0f6112..3916d732c 100644 --- a/content/academia/inti-maintainer-spotlight.md +++ b/content/academia/inti-maintainer-spotlight.md @@ -1,6 +1,6 @@ --- name: Flavio Lozano-Isla -institution: Universidad Nacional Toribio Rodriguez de Mendoza +institution: Universidad Nacional Toribio Rodriguez de Mendozas (UNTRM) department: Agronomy Faculty projectName: "inti: Tools and Statistical Procedures in Plant Science" projectRepo: https://github.com/Flavjack/inti @@ -9,14 +9,20 @@ maintainerProfiles: - github: https://github.com/Flavjack - orcid: https://orcid.org/0000-0002-0714-669X - linkedin: https://www.linkedin.com/in/flozanoisla/ -badges: ["Academic Maintainer", "Assistant Professor"] +badges: ["Academic Maintainer", "Professor"] description: "An R package providing tools and statistical procedures for plant science and experimental design, supporting researchers from experimental planning through data analysis and technical writing." --- +logo + + ## What is inti: Tools and Statistical Procedures in Plant Science, and what does it help people do? The inti package is part of the inkaverse project, which was developed for support with tools and statistical procedures for plant science and experimental design. Its main goal is to support researchers throughout the full workflow, from experimental planning and data collection with `tarpuy()`, to data analysis and visualisation with `yupana()`, and technical writing. The project aims to make scientific analysis more accessible, reproducible, and efficient for students, researchers, and professionals. +Inkaverse project overview + + ## What inspired you to start this project? During my PhD, I spent a significant amount of time analysing and organising data from field experiments. This experience inspired me to create a project that supports young scientists in learning statistics and plant science. I wanted to shorten their learning curve by providing an interactive approach that introduces them to the R programming language and helps them transition into data analysis and scientific programming. From 3825cfa7308d14bdb9d8f8dbcce8f30d747fffb1 Mon Sep 17 00:00:00 2001 From: Copilot <198982749+Copilot@users.noreply.github.com> Date: Wed, 24 Jun 2026 21:00:27 +0100 Subject: [PATCH 5/5] Remove unintended calendar artifact from spotlight PR (#512) * Add MPTRAC maintainer spotlight * Finalize MPTRAC spotlight * Revert generated ICS change * Remove ICS file change from PR * Update mptrac-maintainer-spotlight.md --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: samus-aran --- .../academia/mptrac-maintainer-spotlight.md | 140 ++++++++++++++++++ 1 file changed, 140 insertions(+) create mode 100644 content/academia/mptrac-maintainer-spotlight.md diff --git a/content/academia/mptrac-maintainer-spotlight.md b/content/academia/mptrac-maintainer-spotlight.md new file mode 100644 index 000000000..950415c92 --- /dev/null +++ b/content/academia/mptrac-maintainer-spotlight.md @@ -0,0 +1,140 @@ +--- +name: Lars Hoffmann +institution: Forschungszentrum Jülich +department: Jülich Supercomputing Centre +projectName: MPTRAC +projectRepo: https://github.com/slcs-jsc/mptrac +projectWebsite: https://helmholtz.software/software/mptrac +maintainerProfiles: + - github: https://github.com/lars2015 + - orcid: https://orcid.org/0000-0003-3773-4377 +badges: ["Academic Maintainer", "Division Head HPC"] +description: "An advanced atmospheric transport model for calculating air parcel trajectories from global reanalyses or forecasts, with stochastic diffusion, chemistry, and high-performance computing support." +--- + +## What is MPTRAC, and what does it help people do? + +MPTRAC is an advanced atmospheric transport model designed to calculate air parcel trajectories using wind and velocity fields from global reanalyses or forecasts. + +It accounts for mesoscale diffusion and subgrid-scale wind fluctuations by applying the Langevin equation, introducing stochastic perturbations to trajectories. It also includes an inter-parcel exchange module to represent air mixing and improve realism in simulations. + +The model supports a wide range of processes, including convection, sedimentation, chemical transformations, and deposition. It provides multiple output formats and visualization support, and is optimized for high-performance computing environments using hybrid MPI, OpenMP, and OpenACC parallelization. This allows it to run efficiently on systems ranging from single workstations to large supercomputers. + +## What inspired you to start this project? + +MPTRAC was developed to address the need for high-resolution, efficient, and flexible atmospheric transport modeling. There was growing demand for accurate simulations in areas such as air pollution modeling, stratospheric research, and climate studies. + +Existing models often lacked the efficiency needed for large-scale simulations or the flexibility to run on modern high-performance computing systems. We wanted to build a tool that could scale effectively while also integrating meteorological data and capturing complex physical processes. By combining stochastic modeling, advanced chemistry modules, and HPC capabilities, MPTRAC provides a more realistic and scalable approach to atmospheric simulations. + +## How does this project connect to your academic work? + +MPTRAC is a key tool in atmospheric science research, supporting studies on air pollution, stratospheric dynamics, and atmospheric transport processes. It is widely used in research institutions and universities for high-resolution simulations and scientific studies. + +It also supports interdisciplinary collaboration and contributes to peer-reviewed research, making it an important part of ongoing academic work in meteorology and climate science. + +## Who contributes to the project? + +The project is developed by researchers, postdocs, and PhD students from the Simulation and Data Laboratory Climate Science at Jülich Supercomputing Centre. It also benefits from international collaboration, with contributors from institutions such as the Institute of Atmospheric Physics in Beijing and Sun Yat-sen University in Guangzhou, as well as other researchers worldwide. + +## How are students involved in the project? + +Students are actively involved in many aspects of the project. They contribute code, optimize performance for HPC environments, and help with testing and debugging. They also work on documentation and sometimes design and implement new features. + +Through this work, students gain hands-on experience in scientific modeling, advanced coding techniques, and collaborative research, which are valuable for careers in academia, research, and industry. + +## How is the project used in teaching or coursework? + +MPTRAC is integrated into PhD projects focused on atmospheric science, where it is used for high-resolution simulations and to study complex atmospheric phenomena. + +It is also used in the guest student program at Jülich Supercomputing Centre, where students learn how to run simulations and optimize code for high-performance computing environments. This gives students practical experience in both atmospheric science and HPC. + +## What impact has this project had on your students? + +The project has had a strong impact on students by helping them develop skills in atmospheric science, data analysis, and high-performance computing. They gain practical experience with simulation techniques and learn how to optimize code for advanced computing systems. + +It also strengthens their understanding of open-source development and collaborative research, and often leads to deeper engagement in scientific projects and international collaborations. + +## What impact has the project had beyond the classroom or research? + +MPTRAC has contributed to high-quality research and supported the development of international scientific collaborations. It has been used in publications in well-established journals and in multiple PhD theses. + +The model has enabled research on topics such as wildfire smoke dispersion, volcanic ash transport, and atmospheric convection, making it a valuable tool for both academic and applied research in the global scientific community. + +## What does it take to maintain the project? + +Maintaining MPTRAC involves a structured team effort. Researchers, postdocs, PhD students, and collaborators contribute based on their expertise. The project uses regular code reviews, testing, and version control to maintain quality. + +Releases are made approximately every six months, and continuous integration and deployment workflows are used to test code automatically. Nightly builds are also run on HPC systems to ensure stability and performance. + +## What have been the biggest challenges in maintaining the project? + +One of the main challenges is balancing development work with teaching and research responsibilities. Securing funding and access to high-performance computing resources is also an ongoing effort. + +Another challenge is managing contributor turnover, as students graduate and new contributors need to be onboarded. Coordinating work across multiple institutions and international collaborators can also add complexity. + +## How do you ensure the project remains sustainable over time? + +Sustainability is supported by maintaining a diverse group of contributors, from early-career researchers to experienced scientists. Collaboration with other teams at Jülich Supercomputing Centre, especially those working on HPC technologies, also helps improve the model's performance and scalability. + +These partnerships and the mix of expertise help ensure continued development and innovation. + +## How do you engage with your community? + +We engage with the community through comprehensive documentation, regular user meetings, and clear contribution guidelines. We hold bi-weekly meetings to discuss progress and gather feedback. + +We also aim to respond quickly to user questions and provide a supportive environment for contributors, helping to build an active and engaged community. + +## Have you taken part in any open source programs or events? + +Yes, the team has participated in several hackathons focused on GPU porting and optimization, including work on systems such as LUMI and the upcoming JUPITER supercomputer. + +We also organized a code sprint in collaboration with the German national Earth System Model initiative to improve the model's user interface. + +## What would you love to achieve by showcasing your project? + +We would like to increase awareness of MPTRAC, attract new contributors, and build stronger collaborations with researchers and institutions. Showcasing the project also helps demonstrate its impact on research, education, and real-world challenges such as air pollution and climate modeling. + +It is also an opportunity to highlight the value of open-source development in scientific research. + +## Do you use AI tools in your day to day work on this project? If so, how? + +Yes, we have started using AI tools in several parts of the MPTRAC project. We have used AI to substantially improve our MkDocs-based user manual and our Doxygen-based developer manual. We have also used large language models, especially ChatGPT, to review and improve code design and performance. + +## Do you implement AI into your classroom or coursework (if applicable)? If so, what does that look like in practice? + +Not directly in formal coursework at this stage. However, PhD students and early-career researchers involved in the project are exposed to AI-supported workflows through documentation, code review, and software development activities. This gives them practical experience with how AI tools can support research software engineering while still requiring careful expert review. + +## Has AI changed how you maintain or manage your project? + +Yes, AI has started to change our maintenance workflows. It helps us improve documentation quality, review code more efficiently, explore design alternatives, and identify potential performance improvements. This can increase the speed and quality of development, but all AI-generated suggestions are still reviewed carefully by experienced developers. + +## Have you experimented with AI driven or automated workflows in your project? What has that looked like? + +Yes. We have used GitHub Copilot to support pull request reviews, for example by helping to spot possible issues, improve readability, and support the review process. More recently, we have started using Codex to bring code development, evaluation, and optimization to a new level. This is particularly interesting for an HPC code like MPTRAC, where code quality, numerical correctness, and performance are all critical. + + +## How do you see your contributors using AI when working on your project? + +We expect contributors to use AI mainly for documentation, code understanding, code review, testing, and implementation support. AI can be especially helpful for onboarding new contributors, explaining complex parts of the code base, and improving documentation. However, for scientific code contributions, human review remains essential to ensure correctness, reproducibility, and performance. + +## What concerns or challenges, if any, do you have about the use of AI in your project or field? + +Our main concerns are quality, trust, and scientific correctness. MPTRAC is used for atmospheric research, so suggested code changes must be carefully validated to avoid numerical errors or unintended changes in model behavior. + +There are also challenges around reproducibility, transparency, and making sure that students and contributors continue to develop a deep understanding of the scientific and technical foundations rather than relying too heavily on automated suggestions. + +## How has your approach to maintaining this project evolved over time? + +Our approach has become much more structured and collaborative over time. We use regular code reviews, testing, CI/CD workflows, documentation systems, and scheduled releases. More recently, AI tools have become part of this evolving workflow, helping with documentation, code review, design discussions, and performance optimization. + +## How do you see AI shaping the future of your project or field? + +We expect AI to become increasingly useful in scientific software development. In fields such as atmospheric modeling and high-performance computing, AI may help accelerate development and make complex software more accessible. + +At the same time, scientific expertise, careful validation, and transparent workflows will remain essential. + +## Is there anything else you'd like to share? + +The open-source journey of MPTRAC has enabled collaboration with a global community of researchers, developers, and students. This has supported continuous improvement and knowledge sharing across disciplines. + +We are excited to continue growing the community and advancing MPTRAC as a tool for atmospheric research, and we are grateful for the support provided by platforms like GitHub.