openCARP is a cardiac electrophysiology simulator freely available for academic purposes.
Status: September 2026. The project runs until December 2026.
Expected outcome: Complete documentation of the carputils framework.
The carputils code documentation is generated automatically through the continuous integration pipelines and published on the openCARP website. This connects the documentation to the evolving code and makes it accessible alongside the simulator documentation.
Expected outcome: Windows deployment of openCARP.
Windows deployment was streamlined using the Windows Subsystem for Linux (WSL). The installation instructions guide Windows users to the supported installation workflow and also describe Docker as an alternative.
Expected outcome: Unified user interface for bench and openCARP executables.
The command-line interfaces of bench and openCARP were aligned. Parameter handling was also modernised: openCARP now supports TOML and JSON files alongside the established parameter-file format, provides consistent long-form command-line options and offers help for individual parameters. Legacy inputs remain supported, with automated compatibility checks comparing modern and legacy behaviour. The changes are described in a blog post.
Expected outcome: Compile-on-demand web service.
The ionic-model compilation service allows users to upload an EasyML model and obtain a shared library without setting up a local compilation environment. It supports the platforms for which binary openCARP packages are provided and returns generated code and compilation logs. Instructions for converting between CellML and EasyML connect the service to established model formats.
Expected outcome: Automatic generator of carputils script from simulation inputs.
The tool parfile2carputils converts existing openCARP parameter files into carputils scripts. This helps users transfer established simulations into Python workflows that can incorporate preprocessing, postprocessing and experiment bundling.
Expected outcome: Maintained software with regular releases (at least 3 per year).
openCARP was maintained through successive public releases, with continued adaptation to compilers, dependencies and operating systems, alongside scientific extensions and bug fixes. Releases are persistently archived with metadata and DOIs. The latest release at this reporting date is openCARP v20.0, published on 1 September 2026.
Expected outcome: Automated regression tests included in the CI pipelines.
Automated regression testing was maintained and extended across the openCARP repositories. The obsolete autotester infrastructure was replaced by maintained pytest/GitLab workflows. Additional unit tests cover the new parameter parser and compatibility with existing inputs. Testing is integrated into development so that changes can be assessed before merge and release.
Expected outcome: Continuous software benchmarking and reporting.
Continuous benchmarking was established on KIT’s HoreKa supercomputer. The workflow builds the software revision under test, submits a representative experiment through the scheduler and preserves runtime, memory, filesystem and energy measurements as versioned CI artefacts. This links performance evidence to the corresponding software revision. The website provides an entry point to the benchmarking reports.
Expected outcome: Business plan for the openCARP virtual research environment.
A costed sustainability concept was published as part of the Software Management Plan. It defines three cumulative service levels: automated basic provision; coordinated maintenance, releases and online support; and an enhanced service including targeted development, personal training and community activities.
The concept makes the relationship between resources and service commitments explicit.
Expected outcome: Interactive elements on openCARP.org.
An interface was integrated into the website to display related entries from the Q&A system. This connects information on the website with community questions and answers while retaining the Q&A platform as the central knowledge base.
Authentication across GitLab, Q&A, JupyterLab and the ionic-model compilation service was also extended through Helmholtz ID, allowing sign-in using institutional accounts and ORCIDid.
Expected outcome: Online repository for sharing openCARP experiments.
The community experiment marketplace provides a public catalogue of shared simulation experiments. Building on the bundle feature developed during SuLMaSS, the publication workflow connects experiment scripts and inputs with software version information, structured metadata, licences and persistent archival records.
The sharing instructions describe how to create self-contained bundles, manage them in Git repositories and prepare them for publication. The marketplace includes experiments from both earlier studies and work published during CARPe-diem.
The generic metadata and publication infrastructure was also developed into the reusable software package FACILE-RS.
Expected outcome: Automatic deployment of experiments to openCARP.
The onboarding tutorials and the experiment marketplace includes links that open published experiments in the hosted openCARP Jupyter environment. This allows users to access the shared experiments without first installing openCARP locally. The service complements downloadable bundles and supports exploration and reuse of published workflows.
Expected outcome: At least 20 onboarding and training units.
Interactive onboarding tutorials were developed for single-cell simulations, tissue simulations and eikonal simulations. They introduce simulation control, model parameters and carputils through practical exercises. Besides the 10 onboarding tutorials, 50 didactic examples introduce users to specific simulator features.
Expected outcome: Unsupervised learning and training system.
The onboarding material is provided through JupyterLab for independent use, either on the hosted service or in a local Docker environment. It connects executable exercises with explanatory material. A knowledge self-evaluation complements the tutorials and helps users assess their preparation for working with openCARP.
Expected outcome: Comprehensive Getting Started web page.
The consolidated Getting Started area introduces openCARP, explains the available ways of accessing it and directs users to installation instructions, self-evaluation and onboarding tutorials. It provides a common entry point for researchers with different levels of computational and electrophysiological experience.
Expected outcome: Data management plan template.
Work in progress.
Expected outcome: Two physical user meetings.
Two physical user meetings were held, in Graz in 2024 and Bordeaux in 2026. Both were fully subscribed, with capacities of 50 and 65 places, respectively. The meetings combined practical training with scientific exchange and opportunities for experienced users to discuss developments with maintainers. Programmes and supporting information are available through the user-meeting pages.
Expected outcome: Advanced community session formats and concepts (such as intensive 30 minute online get-togethers with question and answer sessions).
Five thematic online user meetings were held:
These were complemented by monthly open developer calls, a maintainer workshop, visits by 11 external users and five further online interviews. Together, these formats provided recurring opportunities to discuss scientific needs, demonstrate functionality and involve users in development.
Expected outcome: Online real-time exchange and discussion platform for the user community.
We decided against establishing a separate real-time discussion platform to keep community communication focused in the existing Q&A forum. This avoids fragmentation across parallel channels and ensures that questions and solutions remain searchable and accessible to the wider community.
Expected outcome: Monthly news update via the mailing list and continuously via Twitter (@openCARP).
Communication continued through the newsletter and social media. The project moved its social-media presence from Twitter/X to LinkedIn. Newsletter recipients and LinkedIn followers together exceed 1,000. 20 newsletters and 35 social media posts were published.
Expected outcome: Material for disseminating openCARP (in oral presentations, on posters, on websites, etc.).
Expected outcome: User support provided in less than one week in the issue tracker and the Q&A system.
User support continued through the issue trackers and the public Q&A system. At the reporting date, the Q&A knowledge base contains more than 1,500 questions, answers and comments, with no unanswered questions.
Expected outcome: Project-internal evaluation via surveys during user meetings and online.
Systematic user-meeting surveys were used to assess the training and installation experience and improve subsequent meetings. The feedback also informed development priorities. Further input was collected through online interviews and direct exchanges with users.
Workshop surveys identified both improvements and remaining barriers. Installation satisfaction ranged from 5/5 for hosted Jupyter access to 2.8/5 for installation from source. Reported difficulties included meshalyzer paths, Python and package compatibility, Docker guidance, Binder reliability and initial instructions. These findings provide concrete priorities for further improvements to installation and onboarding.
Regular archived releases, automated regression testing and revision-linked performance measurements provide evidence of continued maintenance and quality control. The sustainability concept documents the resources and responsibilities needed for different levels of continued provision.
The public marketplace demonstrates the implemented connection between shared experiments, descriptive metadata, persistent publication and access through Jupyter. The workflow is operational, although publication still requires substantial expert curation. This experience identifies further automation of preparation and curation as an important opportunity to reduce maintenance effort.
Workshop survey responses showed an increase in self-rated understanding from 2.9 to 6.4 out of 10 following onboarding. Together with feedback on installation and tutorial use, this supports continued development of browser-based training.
The two fully subscribed physical meetings, five thematic online meetings and recurring developer calls demonstrate sustained opportunities for participation. The public Q&A platform preserves support exchanges for subsequent users.
The voluntary Users page records 24 groups, 107 users and 13 countries; GitLab records 565 active users. These figures describe the wider openCARP community at the reporting date, including participation established before CARPe-diem. They are not counts of new users attributable solely to this project.
openCARP is a cardiac electrophysiology simulator freely available for academic purposes.
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