{
  "id": 666779,
  "title": "Open-Sourcing Your ECG Digitization Solutions",
  "url": "/competitions/physionet-ecg-image-digitization/discussion/666779",
  "author_name": "Matthew Reyna",
  "post_date": "2026-01-09T03:22:24.237000",
  "votes": 13,
  "comment_count": 3,
  "views": 0,
  "content": "<h1>Open-Sourcing Your ECG Digitization Solutions</h1>\n<p>Dear Kagglers,</p>\n<p>As we enter the final two weeks of the <strong>PhysioNet/Kaggle - Digitization of ECG Images</strong> competition, we have been incredibly impressed by the ingenuity displayed on the leaderboard. Your solutions have the potential to make decades of clinical ECG history and cardiovascular care more accessible.</p>\n<p>As organizers, our goal is to ensure this competition leaves a lasting legacy in the medical AI community. To that end, beyond pretrained models, <strong>we are strongly encouraging all participating teams to open-source their source code in a reusable and retrainable way.</strong></p>\n<p>Beyond the immediate prize pool, we are offering two significant incentives for teams that choose to share their work:</p>\n<h2>1. Co-Authorship and Publication Opportunities</h2>\n<p>We will be preparing a high-impact publication summarizing the findings, methodologies, and state-of-the-art benchmarks established during this competition.</p>\n<ul>\n<li>Teams that open-source their code in a standard and reusable format, and provide a substantive description of their methodology, will be invited to contribute to the publication and be considered for co-authorship of this publication.</li>\n<li>This is a unique opportunity to have your work recognized in the clinical and signal-processing communities and to document the “lessons learned” from your team’s data preprocessing and architecture pipelines.</li>\n</ul>\n<h2>2. Access to Extended Datasets for Further Research</h2>\n<p>We recognize that the data provided during the competition is only the beginning.</p>\n<ul>\n<li>Teams that commit to a meaningful (reusable) open-source release (e.g., via a public GitHub repository or a public Kaggle Notebook) will be granted <strong>priority access to a larger, extended repository of ECG images and ground-truth waveforms currently held by the PhysioNet Challenge team.</strong></li>\n<li>This additional data will allow you to further refine, evaluate, and validate your models for future academic papers or real-world clinical applications.</li>\n</ul>\n<h2>Why Open Source?</h2>\n<p>Open-sourcing your solution ensures that your tools can be used by researchers in resource-limited settings where digital ECG records are scarce. By sharing your code, you help bridge the gap between \"scanned paper\" and \"actionable data,\" directly contributing to the advancement of global health. </p>\n<h2>Why Not Just Use the Winning Entry?</h2>\n<p>In our experience, many of the non-winning teams provide useful and interesting solutions to the problem, which can sometimes generalize beyond the training and test data. By sharing interesting approaches, you enhance the scientific commons. In addition, we can build meta-models that aggregate or borrow from the most diverse approaches to form a super-algorithm.  </p>\n<h2>How to Indicate your Interest</h2>\n<p>When the competition concludes, we ask that teams complete <a href=\"https://forms.gle/3c4Mw62aQi5W3nA98\" target=\"_blank\">this form</a> to submit their repository links and methodology summaries. In the meantime, we encourage you to start documenting your code and considering an open-source license (such as BSD, MIT, or Apache 2.0). If you want to reach out to us contact <a href=\"challenge@dbmi.emory.edu\" target=\"_blank\">challenge@dbmi.emory.edu</a>.</p>\n<h2>Codebase Requirements</h2>\n<p>Codebases considered for the subsequent publication and official open-source releases are expected to follow best practices in biomedical signal processing and software engineering. The goal is to ensure that released solutions are readable, reusable, reproducible, and technically robust. Specifically, the codebases we select from should meet the following minimal requirements:</p>\n<ol>\n<li><strong>Documentation:</strong> Provide a clear and concise <code>README.md</code> file. The README should be sufficient for peers with a computer science or biomedical engineering background to understand, install, and run the code. Over-documentation is not required; clarity and long-term maintainability are the priority (e.g., readability and reproducibility for yourself and peers both now and six months from now).</li>\n<li><strong>Modularity and Design:</strong> Organize your codes in a modular and parameterized manner. Common pipeline stages (e.g., image segmentation, grid detection, scale estimation, waveform reconstruction, post-processing) should be separated into logically independent modules or functions. Reusable and dataset-agnostic components should be encapsulated in functions or classes to facilitate reuse and integration with other approaches. This is essential to enable method comparison, fusion, and future extensions.</li>\n<li><strong>Environment and Reproducibility:</strong> Use isolated virtual environments for your code and document the installation procedure. Provide a clear dependency specification (e.g., requirements.txt or equivalent). The code should be buildable and runnable on unseen data in a clean, Docker-like environment without manual changes. Retraining and evaluation should be possible without modifying source code.</li>\n<li><strong>Parameter Management:</strong> Avoid hardcoded parameters. All configurable parameters must be passed explicitly through function arguments or configuration files. Global constants should be defined once, preferably at the top level of scripts or notebooks. This includes (but is not limited to): sampling frequency, signal length, number of leads, lead names, image resolution, page size, orientation, and scaling factors. The design should allow users to modify and optimize parameters without altering core logic. It is understandable that certain features and modules may not generalize beyond the current dataset. Clearly indicate those functions in the readme file.</li>\n<li><strong>Repository Structure:</strong> Share code through a single public GitHub repository or equivalent. The repository should not rely on external private repositories or undocumented assets outside the virtual environment. Dependencies must be limited to those listed in the provided requirements file. For quality control and traceability, the final selected solutions will be shared publicly and cited in publications and on a dedicated webpage using a DOI generated by Zotero or similar services.</li>\n<li><strong>Selection Considerations:</strong> Several teams have already generously shared code and models that have been reused by others; this will be taken into account. When selecting codebases for publication or official releases, priority will be given to: 1) well-engineered and generalizable solutions, 2) well-documented source codes, 3) complementary methodological approaches that could be potentially fused together for improved performance, and 4) designs that support extension to unseen datasets. As a result, the selection will not be solely based on leaderboard performance.</li>\n<li><strong>Licensing and Intellectual Property:</strong> Publicly released versions of the code must not include proprietary software, libraries, models, or assets that the authors do not own or have the explicit right to redistribute. All shared repositories must be released under a recognized open-source license that permits free use for research purposes at a minimum (e.g., BSD, MIT, or Apache 2.0), ensuring that the code can be legally reused, modified, and built upon by the research community.</li>\n</ol>\n<p>Thank you for your hard work and for helping us bring legacy ECG data into the AI age. We look forward to seeing your innovative solutions!</p>\n<p>Best regards,</p>\n<p>The Kaggle/PhysioNet Challenge Organizing Team</p>",
  "messages": [
    {
      "id": 3388524,
      "postDate": "2026-01-09T03:22:24.237Z",
      "content": "<h1>Open-Sourcing Your ECG Digitization Solutions</h1>\n<p>Dear Kagglers,</p>\n<p>As we enter the final two weeks of the <strong>PhysioNet/Kaggle - Digitization of ECG Images</strong> competition, we have been incredibly impressed by the ingenuity displayed on the leaderboard. Your solutions have the potential to make decades of clinical ECG history and cardiovascular care more accessible.</p>\n<p>As organizers, our goal is to ensure this competition leaves a lasting legacy in the medical AI community. To that end, beyond pretrained models, <strong>we are strongly encouraging all participating teams to open-source their source code in a reusable and retrainable way.</strong></p>\n<p>Beyond the immediate prize pool, we are offering two significant incentives for teams that choose to share their work:</p>\n<h2>1. Co-Authorship and Publication Opportunities</h2>\n<p>We will be preparing a high-impact publication summarizing the findings, methodologies, and state-of-the-art benchmarks established during this competition.</p>\n<ul>\n<li>Teams that open-source their code in a standard and reusable format, and provide a substantive description of their methodology, will be invited to contribute to the publication and be considered for co-authorship of this publication.</li>\n<li>This is a unique opportunity to have your work recognized in the clinical and signal-processing communities and to document the “lessons learned” from your team’s data preprocessing and architecture pipelines.</li>\n</ul>\n<h2>2. Access to Extended Datasets for Further Research</h2>\n<p>We recognize that the data provided during the competition is only the beginning.</p>\n<ul>\n<li>Teams that commit to a meaningful (reusable) open-source release (e.g., via a public GitHub repository or a public Kaggle Notebook) will be granted <strong>priority access to a larger, extended repository of ECG images and ground-truth waveforms currently held by the PhysioNet Challenge team.</strong></li>\n<li>This additional data will allow you to further refine, evaluate, and validate your models for future academic papers or real-world clinical applications.</li>\n</ul>\n<h2>Why Open Source?</h2>\n<p>Open-sourcing your solution ensures that your tools can be used by researchers in resource-limited settings where digital ECG records are scarce. By sharing your code, you help bridge the gap between \"scanned paper\" and \"actionable data,\" directly contributing to the advancement of global health. </p>\n<h2>Why Not Just Use the Winning Entry?</h2>\n<p>In our experience, many of the non-winning teams provide useful and interesting solutions to the problem, which can sometimes generalize beyond the training and test data. By sharing interesting approaches, you enhance the scientific commons. In addition, we can build meta-models that aggregate or borrow from the most diverse approaches to form a super-algorithm.  </p>\n<h2>How to Indicate your Interest</h2>\n<p>When the competition concludes, we ask that teams complete <a href=\"https://forms.gle/3c4Mw62aQi5W3nA98\" target=\"_blank\">this form</a> to submit their repository links and methodology summaries. In the meantime, we encourage you to start documenting your code and considering an open-source license (such as BSD, MIT, or Apache 2.0). If you want to reach out to us contact <a href=\"challenge@dbmi.emory.edu\" target=\"_blank\">challenge@dbmi.emory.edu</a>.</p>\n<h2>Codebase Requirements</h2>\n<p>Codebases considered for the subsequent publication and official open-source releases are expected to follow best practices in biomedical signal processing and software engineering. The goal is to ensure that released solutions are readable, reusable, reproducible, and technically robust. Specifically, the codebases we select from should meet the following minimal requirements:</p>\n<ol>\n<li><strong>Documentation:</strong> Provide a clear and concise <code>README.md</code> file. The README should be sufficient for peers with a computer science or biomedical engineering background to understand, install, and run the code. Over-documentation is not required; clarity and long-term maintainability are the priority (e.g., readability and reproducibility for yourself and peers both now and six months from now).</li>\n<li><strong>Modularity and Design:</strong> Organize your codes in a modular and parameterized manner. Common pipeline stages (e.g., image segmentation, grid detection, scale estimation, waveform reconstruction, post-processing) should be separated into logically independent modules or functions. Reusable and dataset-agnostic components should be encapsulated in functions or classes to facilitate reuse and integration with other approaches. This is essential to enable method comparison, fusion, and future extensions.</li>\n<li><strong>Environment and Reproducibility:</strong> Use isolated virtual environments for your code and document the installation procedure. Provide a clear dependency specification (e.g., requirements.txt or equivalent). The code should be buildable and runnable on unseen data in a clean, Docker-like environment without manual changes. Retraining and evaluation should be possible without modifying source code.</li>\n<li><strong>Parameter Management:</strong> Avoid hardcoded parameters. All configurable parameters must be passed explicitly through function arguments or configuration files. Global constants should be defined once, preferably at the top level of scripts or notebooks. This includes (but is not limited to): sampling frequency, signal length, number of leads, lead names, image resolution, page size, orientation, and scaling factors. The design should allow users to modify and optimize parameters without altering core logic. It is understandable that certain features and modules may not generalize beyond the current dataset. Clearly indicate those functions in the readme file.</li>\n<li><strong>Repository Structure:</strong> Share code through a single public GitHub repository or equivalent. The repository should not rely on external private repositories or undocumented assets outside the virtual environment. Dependencies must be limited to those listed in the provided requirements file. For quality control and traceability, the final selected solutions will be shared publicly and cited in publications and on a dedicated webpage using a DOI generated by Zotero or similar services.</li>\n<li><strong>Selection Considerations:</strong> Several teams have already generously shared code and models that have been reused by others; this will be taken into account. When selecting codebases for publication or official releases, priority will be given to: 1) well-engineered and generalizable solutions, 2) well-documented source codes, 3) complementary methodological approaches that could be potentially fused together for improved performance, and 4) designs that support extension to unseen datasets. As a result, the selection will not be solely based on leaderboard performance.</li>\n<li><strong>Licensing and Intellectual Property:</strong> Publicly released versions of the code must not include proprietary software, libraries, models, or assets that the authors do not own or have the explicit right to redistribute. All shared repositories must be released under a recognized open-source license that permits free use for research purposes at a minimum (e.g., BSD, MIT, or Apache 2.0), ensuring that the code can be legally reused, modified, and built upon by the research community.</li>\n</ol>\n<p>Thank you for your hard work and for helping us bring legacy ECG data into the AI age. We look forward to seeing your innovative solutions!</p>\n<p>Best regards,</p>\n<p>The Kaggle/PhysioNet Challenge Organizing Team</p>",
      "rawMarkdown": "# Open-Sourcing Your ECG Digitization Solutions\n\nDear Kagglers,\n\nAs we enter the final two weeks of the **PhysioNet/Kaggle - Digitization of ECG Images** competition, we have been incredibly impressed by the ingenuity displayed on the leaderboard. Your solutions have the potential to make decades of clinical ECG history and cardiovascular care more accessible.\n\nAs organizers, our goal is to ensure this competition leaves a lasting legacy in the medical AI community. To that end, beyond pretrained models, **we are strongly encouraging all participating teams to open-source their source code in a reusable and retrainable way.**\n\nBeyond the immediate prize pool, we are offering two significant incentives for teams that choose to share their work:\n\n## 1. Co-Authorship and Publication Opportunities\n\nWe will be preparing a high-impact publication summarizing the findings, methodologies, and state-of-the-art benchmarks established during this competition.\n\n- Teams that open-source their code in a standard and reusable format, and provide a substantive description of their methodology, will be invited to contribute to the publication and be considered for co-authorship of this publication.\n- This is a unique opportunity to have your work recognized in the clinical and signal-processing communities and to document the “lessons learned” from your team’s data preprocessing and architecture pipelines.\n\n## 2. Access to Extended Datasets for Further Research\n\nWe recognize that the data provided during the competition is only the beginning.\n\n- Teams that commit to a meaningful (reusable) open-source release (e.g., via a public GitHub repository or a public Kaggle Notebook) will be granted **priority access to a larger, extended repository of ECG images and ground-truth waveforms currently held by the PhysioNet Challenge team.**\n- This additional data will allow you to further refine, evaluate, and validate your models for future academic papers or real-world clinical applications.\n\n## Why Open Source?\n\nOpen-sourcing your solution ensures that your tools can be used by researchers in resource-limited settings where digital ECG records are scarce. By sharing your code, you help bridge the gap between \"scanned paper\" and \"actionable data,\" directly contributing to the advancement of global health. \n\n## Why Not Just Use the Winning Entry?\n\nIn our experience, many of the non-winning teams provide useful and interesting solutions to the problem, which can sometimes generalize beyond the training and test data. By sharing interesting approaches, you enhance the scientific commons. In addition, we can build meta-models that aggregate or borrow from the most diverse approaches to form a super-algorithm.  \n\n## How to Indicate your Interest\n\nWhen the competition concludes, we ask that teams complete [this form](https://forms.gle/3c4Mw62aQi5W3nA98) to submit their repository links and methodology summaries. In the meantime, we encourage you to start documenting your code and considering an open-source license (such as BSD, MIT, or Apache 2.0). If you want to reach out to us contact [challenge@dbmi.emory.edu](challenge@dbmi.emory.edu).\n\n## Codebase Requirements\nCodebases considered for the subsequent publication and official open-source releases are expected to follow best practices in biomedical signal processing and software engineering. The goal is to ensure that released solutions are readable, reusable, reproducible, and technically robust. Specifically, the codebases we select from should meet the following minimal requirements:\n\n1. **Documentation:** Provide a clear and concise `README.md` file. The README should be sufficient for peers with a computer science or biomedical engineering background to understand, install, and run the code. Over-documentation is not required; clarity and long-term maintainability are the priority (e.g., readability and reproducibility for yourself and peers both now and six months from now).\n2. **Modularity and Design:** Organize your codes in a modular and parameterized manner. Common pipeline stages (e.g., image segmentation, grid detection, scale estimation, waveform reconstruction, post-processing) should be separated into logically independent modules or functions. Reusable and dataset-agnostic components should be encapsulated in functions or classes to facilitate reuse and integration with other approaches. This is essential to enable method comparison, fusion, and future extensions.\n3. **Environment and Reproducibility:** Use isolated virtual environments for your code and document the installation procedure. Provide a clear dependency specification (e.g., requirements.txt or equivalent). The code should be buildable and runnable on unseen data in a clean, Docker-like environment without manual changes. Retraining and evaluation should be possible without modifying source code.\n4. **Parameter Management:** Avoid hardcoded parameters. All configurable parameters must be passed explicitly through function arguments or configuration files. Global constants should be defined once, preferably at the top level of scripts or notebooks. This includes (but is not limited to): sampling frequency, signal length, number of leads, lead names, image resolution, page size, orientation, and scaling factors. The design should allow users to modify and optimize parameters without altering core logic. It is understandable that certain features and modules may not generalize beyond the current dataset. Clearly indicate those functions in the readme file.\n5. **Repository Structure:** Share code through a single public GitHub repository or equivalent. The repository should not rely on external private repositories or undocumented assets outside the virtual environment. Dependencies must be limited to those listed in the provided requirements file. For quality control and traceability, the final selected solutions will be shared publicly and cited in publications and on a dedicated webpage using a DOI generated by Zotero or similar services.\n6. **Selection Considerations:** Several teams have already generously shared code and models that have been reused by others; this will be taken into account. When selecting codebases for publication or official releases, priority will be given to: 1) well-engineered and generalizable solutions, 2) well-documented source codes, 3) complementary methodological approaches that could be potentially fused together for improved performance, and 4) designs that support extension to unseen datasets. As a result, the selection will not be solely based on leaderboard performance.\n7. **Licensing and Intellectual Property:** Publicly released versions of the code must not include proprietary software, libraries, models, or assets that the authors do not own or have the explicit right to redistribute. All shared repositories must be released under a recognized open-source license that permits free use for research purposes at a minimum (e.g., BSD, MIT, or Apache 2.0), ensuring that the code can be legally reused, modified, and built upon by the research community.\n\nThank you for your hard work and for helping us bring legacy ECG data into the AI age. We look forward to seeing your innovative solutions!\n\nBest regards,\n\nThe Kaggle/PhysioNet Challenge Organizing Team",
      "votes": 13
    },
    {
      "id": 3396472,
      "postDate": "2026-01-25T06:08:08.450Z",
      "content": "<p><a href=\"https://www.kaggle.com/matthewreyna\" target=\"_blank\">@matthewreyna</a> Hi Matt, I’ve participated in the 2023 and 2025 PhysioNet Challenges, though I missed the current one. I'm curious about your thoughts on the shift to Kaggle versus the traditional Google Form submission process. Do you think the 2026 Challenge will return to Kaggle? Personally, I found the Kaggle integration much more efficient and hope you stick with it.</p>",
      "rawMarkdown": "@matthewreyna Hi Matt, I’ve participated in the 2023 and 2025 PhysioNet Challenges, though I missed the current one. I'm curious about your thoughts on the shift to Kaggle versus the traditional Google Form submission process. Do you think the 2026 Challenge will return to Kaggle? Personally, I found the Kaggle integration much more efficient and hope you stick with it."
    },
    {
      "id": 3395309,
      "postDate": "2026-01-22T17:39:08.833Z",
      "rawMarkdown": "",
      "isDeleted": true,
      "replies": [
        {
          "id": 3395358,
          "postDate": "2026-01-22T19:56:11.140Z",
          "content": "<p>yes do it </p>",
          "rawMarkdown": "yes do it \n"
        }
      ]
    }
  ],
  "comments": [
    {
      "id": 3396472,
      "author_name": "Waylon Wu",
      "author_url": "",
      "post_date": "2026-01-25T06:08:08.450000",
      "content": "<p><a href=\"https://www.kaggle.com/matthewreyna\" target=\"_blank\">@matthewreyna</a> Hi Matt, I’ve participated in the 2023 and 2025 PhysioNet Challenges, though I missed the current one. I'm curious about your thoughts on the shift to Kaggle versus the traditional Google Form submission process. Do you think the 2026 Challenge will return to Kaggle? Personally, I found the Kaggle integration much more efficient and hope you stick with it.</p>",
      "votes": 0,
      "replies": []
    },
    {
      "id": 3395309,
      "author_name": "",
      "author_url": "",
      "post_date": "2026-01-22T17:39:08.833000",
      "content": "",
      "votes": 0,
      "replies": [
        {
          "id": 3395358,
          "author_name": "Yash Yadav",
          "author_url": "",
          "post_date": "2026-01-22T19:56:11.140000",
          "content": "<p>yes do it </p>",
          "votes": 0,
          "replies": []
        }
      ]
    }
  ],
  "raw_markdown_by_id": {
    "3388524": "# Open-Sourcing Your ECG Digitization Solutions\n\nDear Kagglers,\n\nAs we enter the final two weeks of the **PhysioNet/Kaggle - Digitization of ECG Images** competition, we have been incredibly impressed by the ingenuity displayed on the leaderboard. Your solutions have the potential to make decades of clinical ECG history and cardiovascular care more accessible.\n\nAs organizers, our goal is to ensure this competition leaves a lasting legacy in the medical AI community. To that end, beyond pretrained models, **we are strongly encouraging all participating teams to open-source their source code in a reusable and retrainable way.**\n\nBeyond the immediate prize pool, we are offering two significant incentives for teams that choose to share their work:\n\n## 1. Co-Authorship and Publication Opportunities\n\nWe will be preparing a high-impact publication summarizing the findings, methodologies, and state-of-the-art benchmarks established during this competition.\n\n- Teams that open-source their code in a standard and reusable format, and provide a substantive description of their methodology, will be invited to contribute to the publication and be considered for co-authorship of this publication.\n- This is a unique opportunity to have your work recognized in the clinical and signal-processing communities and to document the “lessons learned” from your team’s data preprocessing and architecture pipelines.\n\n## 2. Access to Extended Datasets for Further Research\n\nWe recognize that the data provided during the competition is only the beginning.\n\n- Teams that commit to a meaningful (reusable) open-source release (e.g., via a public GitHub repository or a public Kaggle Notebook) will be granted **priority access to a larger, extended repository of ECG images and ground-truth waveforms currently held by the PhysioNet Challenge team.**\n- This additional data will allow you to further refine, evaluate, and validate your models for future academic papers or real-world clinical applications.\n\n## Why Open Source?\n\nOpen-sourcing your solution ensures that your tools can be used by researchers in resource-limited settings where digital ECG records are scarce. By sharing your code, you help bridge the gap between \"scanned paper\" and \"actionable data,\" directly contributing to the advancement of global health. \n\n## Why Not Just Use the Winning Entry?\n\nIn our experience, many of the non-winning teams provide useful and interesting solutions to the problem, which can sometimes generalize beyond the training and test data. By sharing interesting approaches, you enhance the scientific commons. In addition, we can build meta-models that aggregate or borrow from the most diverse approaches to form a super-algorithm.  \n\n## How to Indicate your Interest\n\nWhen the competition concludes, we ask that teams complete [this form](https://forms.gle/3c4Mw62aQi5W3nA98) to submit their repository links and methodology summaries. In the meantime, we encourage you to start documenting your code and considering an open-source license (such as BSD, MIT, or Apache 2.0). If you want to reach out to us contact [challenge@dbmi.emory.edu](challenge@dbmi.emory.edu).\n\n## Codebase Requirements\nCodebases considered for the subsequent publication and official open-source releases are expected to follow best practices in biomedical signal processing and software engineering. The goal is to ensure that released solutions are readable, reusable, reproducible, and technically robust. Specifically, the codebases we select from should meet the following minimal requirements:\n\n1. **Documentation:** Provide a clear and concise `README.md` file. The README should be sufficient for peers with a computer science or biomedical engineering background to understand, install, and run the code. Over-documentation is not required; clarity and long-term maintainability are the priority (e.g., readability and reproducibility for yourself and peers both now and six months from now).\n2. **Modularity and Design:** Organize your codes in a modular and parameterized manner. Common pipeline stages (e.g., image segmentation, grid detection, scale estimation, waveform reconstruction, post-processing) should be separated into logically independent modules or functions. Reusable and dataset-agnostic components should be encapsulated in functions or classes to facilitate reuse and integration with other approaches. This is essential to enable method comparison, fusion, and future extensions.\n3. **Environment and Reproducibility:** Use isolated virtual environments for your code and document the installation procedure. Provide a clear dependency specification (e.g., requirements.txt or equivalent). The code should be buildable and runnable on unseen data in a clean, Docker-like environment without manual changes. Retraining and evaluation should be possible without modifying source code.\n4. **Parameter Management:** Avoid hardcoded parameters. All configurable parameters must be passed explicitly through function arguments or configuration files. Global constants should be defined once, preferably at the top level of scripts or notebooks. This includes (but is not limited to): sampling frequency, signal length, number of leads, lead names, image resolution, page size, orientation, and scaling factors. The design should allow users to modify and optimize parameters without altering core logic. It is understandable that certain features and modules may not generalize beyond the current dataset. Clearly indicate those functions in the readme file.\n5. **Repository Structure:** Share code through a single public GitHub repository or equivalent. The repository should not rely on external private repositories or undocumented assets outside the virtual environment. Dependencies must be limited to those listed in the provided requirements file. For quality control and traceability, the final selected solutions will be shared publicly and cited in publications and on a dedicated webpage using a DOI generated by Zotero or similar services.\n6. **Selection Considerations:** Several teams have already generously shared code and models that have been reused by others; this will be taken into account. When selecting codebases for publication or official releases, priority will be given to: 1) well-engineered and generalizable solutions, 2) well-documented source codes, 3) complementary methodological approaches that could be potentially fused together for improved performance, and 4) designs that support extension to unseen datasets. As a result, the selection will not be solely based on leaderboard performance.\n7. **Licensing and Intellectual Property:** Publicly released versions of the code must not include proprietary software, libraries, models, or assets that the authors do not own or have the explicit right to redistribute. All shared repositories must be released under a recognized open-source license that permits free use for research purposes at a minimum (e.g., BSD, MIT, or Apache 2.0), ensuring that the code can be legally reused, modified, and built upon by the research community.\n\nThank you for your hard work and for helping us bring legacy ECG data into the AI age. We look forward to seeing your innovative solutions!\n\nBest regards,\n\nThe Kaggle/PhysioNet Challenge Organizing Team",
    "3396472": "@matthewreyna Hi Matt, I’ve participated in the 2023 and 2025 PhysioNet Challenges, though I missed the current one. I'm curious about your thoughts on the shift to Kaggle versus the traditional Google Form submission process. Do you think the 2026 Challenge will return to Kaggle? Personally, I found the Kaggle integration much more efficient and hope you stick with it.",
    "3395309": ""
  }
}