{
  "id": 355458,
  "title": "Leaderboard is Finalized - Congrats to our Winners, Recap",
  "url": "/competitions/hubmap-organ-segmentation/discussion/355458",
  "author_name": "Addison Howard",
  "post_date": "2022-09-26T20:09:00.193000",
  "votes": 14,
  "comment_count": 0,
  "views": 0,
  "content": "<p>Hey Kagglers,</p>\n<p>We’re happy to announce the conclusion of the HuBMAP + HPA - Hacking the Human Body competition! We were happy to have both HuBMAP and the HPA back for another round of competitions - this time to further the research being performed around the “next Human Genome Project.” These competitions have been an exciting way to bring the cutting edge in machine learning to this branch of science. We hope you were able to learn a lot, and use your machine learning skills on this unique challenge.</p>\n<p>This competition ended with 8,689 individuals joining the competition and 1,517 participants making a submission across 1,175 teams! We had 39,568 submissions from over 78 countries! For 286 users (including 36 users in the Top 100!), this was their first competition. An additional congratulations goes out to our 4 new Kaggle Competition Masters! Thank you all for your hard work in this competition and congratulations to our winners and to those who gained a new ranking!  </p>\n<p>Thank you once again to the team that helped put this competition together - specifically <a href=\"https://www.kaggle.com/leahscherschel\" target=\"_blank\">Leah</a>, <a href=\"https://www.kaggle.com/yashvrdnjain\" target=\"_blank\">Yash</a>, <a href=\"https://admwwwin.kaggle.com/katyborner\" target=\"_blank\">Katy</a>, <a href=\"https://www.kaggle.com/emmalumpan\" target=\"_blank\">Emma</a>, <a href=\"https://www.kaggle.com/lnhtrang\" target=\"_blank\">Trang</a>, and <a href=\"https://www.kaggle.com/cecilialindskog\" target=\"_blank\">Cecilia</a> along with the rest of the HuBMAP and HPA teams for bringing us this engaging problem to the Kaggle community!</p>\n<p>We're also continually pleased to see how Kaggle Competitions serve as a great medium and vehicle through which the greater data science community can learn and develop their machine learning abilities and skills. Beginners and experts can come together to start, grow, and succeed. Further, in this competition, many can use their machine learning skills to help a great cause like this one.</p>\n<p>We've cleaned the leaderboard and disqualified some teams that have violated the rules. If you think you were removed by mistake, or believe you have evidence that suggests another team cheated, please contact <a href=\"https://www.kaggle.com/compliance\" target=\"_blank\">compliance</a>. Please fill in all the fields honestly.</p>\n<p>The top potential winning teams have been contacted via email to provide their winning solutions for host review. Should we need to move down the leaderboard for any reason, we will do so and continue to make contact with subsequent teams. </p>\n<p>You should have already seen your points and medals awarded, however it may take a few hours to propagate through if yours are still missing.</p>\n<p>In the meantime, I've created this thread to be a curated list of your work. As you post your solutions, top notebooks, and general discussion posts, I'll update this thread to keep track. If you write a paper, thesis, or present at a conference, and would like to share your work, please let us know so we can share with the Kaggle community! Additionally, if you performed any data manipulation, or used a new technique in this competition that you'd like to share to further the industry, please let us know and we'll post it here! That way, should you be looking for takeaways in the future, or if you've just now stumbled across the competition, you have a place to see what surfaced from the work performed.</p>\n<p>Happy Modeling!<br>\nKaggle Team</p>\n<h3>Top Solution Write-ups</h3>\n<ul>\n<li><a href=\"https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/354857\" target=\"_blank\">2nd Place</a></li>\n<li><a href=\"https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/354683\" target=\"_blank\">3rd Place</a></li>\n<li><a href=\"https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/354851\" target=\"_blank\">4th Place</a></li>\n<li><a href=\"https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/354859\" target=\"_blank\">7th Place</a></li>\n<li><a href=\"https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/354701\" target=\"_blank\">11th Place</a></li>\n<li><a href=\"https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/354590\" target=\"_blank\">20th Place</a></li>\n<li><a href=\"https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/354744\" target=\"_blank\">25th Place</a></li>\n<li><a href=\"https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/354671\" target=\"_blank\">26th Place</a></li>\n<li><a href=\"https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/355213\" target=\"_blank\">27th Place</a></li>\n</ul>\n<h3>Other Top Discussions</h3>\n<ul>\n<li><a href=\"https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/332941\" target=\"_blank\">[ placeholder LB 0.81 single fold, coat-parallel-small at 1536 ] my experiment results</a></li>\n<li><a href=\"https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/332664\" target=\"_blank\">Previous HuBMAP - Solutions summary thread</a></li>\n<li><a href=\"https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/333886\" target=\"_blank\">External Data Sources</a></li>\n<li><a href=\"https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/333083\" target=\"_blank\">Good Visual Showing How Tissue Slice Thickness Impacts Staining Intensity</a></li>\n<li><a href=\"https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/332714\" target=\"_blank\">is there going to be a problem? HPA data and HuBMAP data</a></li>\n<li><a href=\"https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/333389\" target=\"_blank\">Some Insights</a></li>\n</ul>\n<h3>Top Notebooks</h3>\n<ul>\n<li><a href=\"https://www.kaggle.com/code/thedevastator/inference-fastai-baseline\" target=\"_blank\">[Inference] - FastAI Baseline</a></li>\n<li><a href=\"https://www.kaggle.com/code/alincijov/training-hubmap-lb-0-75-swin-transformer-v1\" target=\"_blank\">[Training] HuBMAP LB 0.75 Swin Transformer v1</a></li>\n<li><a href=\"https://www.kaggle.com/code/dschettler8845/eda-hubmap-hpa-organ-segmentation\" target=\"_blank\">🫁🫀 EDA – HuBMAP+HPA – Organ Segmentation 🫀🫁</a></li>\n<li><a href=\"https://www.kaggle.com/code/thedevastator/training-fastai-baseline\" target=\"_blank\">[Training] - FastAI Baseline</a></li>\n<li><a href=\"https://www.kaggle.com/code/ishandutta/hubmap-complete-understanding-and-eda-w-b\" target=\"_blank\">HuBMAP 🧠: Complete Understanding and EDA | W&amp;B 🪄</a></li>\n</ul>",
  "messages": [
    {
      "id": 1957244,
      "postDate": "2022-09-26T20:09:00.193Z",
      "content": "<p>Hey Kagglers,</p>\n<p>We’re happy to announce the conclusion of the HuBMAP + HPA - Hacking the Human Body competition! We were happy to have both HuBMAP and the HPA back for another round of competitions - this time to further the research being performed around the “next Human Genome Project.” These competitions have been an exciting way to bring the cutting edge in machine learning to this branch of science. We hope you were able to learn a lot, and use your machine learning skills on this unique challenge.</p>\n<p>This competition ended with 8,689 individuals joining the competition and 1,517 participants making a submission across 1,175 teams! We had 39,568 submissions from over 78 countries! For 286 users (including 36 users in the Top 100!), this was their first competition. An additional congratulations goes out to our 4 new Kaggle Competition Masters! Thank you all for your hard work in this competition and congratulations to our winners and to those who gained a new ranking!  </p>\n<p>Thank you once again to the team that helped put this competition together - specifically <a href=\"https://www.kaggle.com/leahscherschel\" target=\"_blank\">Leah</a>, <a href=\"https://www.kaggle.com/yashvrdnjain\" target=\"_blank\">Yash</a>, <a href=\"https://admwwwin.kaggle.com/katyborner\" target=\"_blank\">Katy</a>, <a href=\"https://www.kaggle.com/emmalumpan\" target=\"_blank\">Emma</a>, <a href=\"https://www.kaggle.com/lnhtrang\" target=\"_blank\">Trang</a>, and <a href=\"https://www.kaggle.com/cecilialindskog\" target=\"_blank\">Cecilia</a> along with the rest of the HuBMAP and HPA teams for bringing us this engaging problem to the Kaggle community!</p>\n<p>We're also continually pleased to see how Kaggle Competitions serve as a great medium and vehicle through which the greater data science community can learn and develop their machine learning abilities and skills. Beginners and experts can come together to start, grow, and succeed. Further, in this competition, many can use their machine learning skills to help a great cause like this one.</p>\n<p>We've cleaned the leaderboard and disqualified some teams that have violated the rules. If you think you were removed by mistake, or believe you have evidence that suggests another team cheated, please contact <a href=\"https://www.kaggle.com/compliance\" target=\"_blank\">compliance</a>. Please fill in all the fields honestly.</p>\n<p>The top potential winning teams have been contacted via email to provide their winning solutions for host review. Should we need to move down the leaderboard for any reason, we will do so and continue to make contact with subsequent teams. </p>\n<p>You should have already seen your points and medals awarded, however it may take a few hours to propagate through if yours are still missing.</p>\n<p>In the meantime, I've created this thread to be a curated list of your work. As you post your solutions, top notebooks, and general discussion posts, I'll update this thread to keep track. If you write a paper, thesis, or present at a conference, and would like to share your work, please let us know so we can share with the Kaggle community! Additionally, if you performed any data manipulation, or used a new technique in this competition that you'd like to share to further the industry, please let us know and we'll post it here! That way, should you be looking for takeaways in the future, or if you've just now stumbled across the competition, you have a place to see what surfaced from the work performed.</p>\n<p>Happy Modeling!<br>\nKaggle Team</p>\n<h3>Top Solution Write-ups</h3>\n<ul>\n<li><a href=\"https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/354857\" target=\"_blank\">2nd Place</a></li>\n<li><a href=\"https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/354683\" target=\"_blank\">3rd Place</a></li>\n<li><a href=\"https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/354851\" target=\"_blank\">4th Place</a></li>\n<li><a href=\"https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/354859\" target=\"_blank\">7th Place</a></li>\n<li><a href=\"https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/354701\" target=\"_blank\">11th Place</a></li>\n<li><a href=\"https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/354590\" target=\"_blank\">20th Place</a></li>\n<li><a href=\"https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/354744\" target=\"_blank\">25th Place</a></li>\n<li><a href=\"https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/354671\" target=\"_blank\">26th Place</a></li>\n<li><a href=\"https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/355213\" target=\"_blank\">27th Place</a></li>\n</ul>\n<h3>Other Top Discussions</h3>\n<ul>\n<li><a href=\"https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/332941\" target=\"_blank\">[ placeholder LB 0.81 single fold, coat-parallel-small at 1536 ] my experiment results</a></li>\n<li><a href=\"https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/332664\" target=\"_blank\">Previous HuBMAP - Solutions summary thread</a></li>\n<li><a href=\"https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/333886\" target=\"_blank\">External Data Sources</a></li>\n<li><a href=\"https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/333083\" target=\"_blank\">Good Visual Showing How Tissue Slice Thickness Impacts Staining Intensity</a></li>\n<li><a href=\"https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/332714\" target=\"_blank\">is there going to be a problem? HPA data and HuBMAP data</a></li>\n<li><a href=\"https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/333389\" target=\"_blank\">Some Insights</a></li>\n</ul>\n<h3>Top Notebooks</h3>\n<ul>\n<li><a href=\"https://www.kaggle.com/code/thedevastator/inference-fastai-baseline\" target=\"_blank\">[Inference] - FastAI Baseline</a></li>\n<li><a href=\"https://www.kaggle.com/code/alincijov/training-hubmap-lb-0-75-swin-transformer-v1\" target=\"_blank\">[Training] HuBMAP LB 0.75 Swin Transformer v1</a></li>\n<li><a href=\"https://www.kaggle.com/code/dschettler8845/eda-hubmap-hpa-organ-segmentation\" target=\"_blank\">🫁🫀 EDA – HuBMAP+HPA – Organ Segmentation 🫀🫁</a></li>\n<li><a href=\"https://www.kaggle.com/code/thedevastator/training-fastai-baseline\" target=\"_blank\">[Training] - FastAI Baseline</a></li>\n<li><a href=\"https://www.kaggle.com/code/ishandutta/hubmap-complete-understanding-and-eda-w-b\" target=\"_blank\">HuBMAP 🧠: Complete Understanding and EDA | W&amp;B 🪄</a></li>\n</ul>",
      "rawMarkdown": "Hey Kagglers,\n\nWe’re happy to announce the conclusion of the HuBMAP + HPA - Hacking the Human Body competition! We were happy to have both HuBMAP and the HPA back for another round of competitions - this time to further the research being performed around the “next Human Genome Project.” These competitions have been an exciting way to bring the cutting edge in machine learning to this branch of science. We hope you were able to learn a lot, and use your machine learning skills on this unique challenge.\n\nThis competition ended with 8,689 individuals joining the competition and 1,517 participants making a submission across 1,175 teams! We had 39,568 submissions from over 78 countries! For 286 users (including 36 users in the Top 100!), this was their first competition. An additional congratulations goes out to our 4 new Kaggle Competition Masters! Thank you all for your hard work in this competition and congratulations to our winners and to those who gained a new ranking!  \n\nThank you once again to the team that helped put this competition together - specifically [Leah](https://www.kaggle.com/leahscherschel), [Yash](https://www.kaggle.com/yashvrdnjain), [Katy](https://admwwwin.kaggle.com/katyborner), [Emma](https://www.kaggle.com/emmalumpan), [Trang](https://www.kaggle.com/lnhtrang), and [Cecilia](https://www.kaggle.com/cecilialindskog) along with the rest of the HuBMAP and HPA teams for bringing us this engaging problem to the Kaggle community!\n\nWe're also continually pleased to see how Kaggle Competitions serve as a great medium and vehicle through which the greater data science community can learn and develop their machine learning abilities and skills. Beginners and experts can come together to start, grow, and succeed. Further, in this competition, many can use their machine learning skills to help a great cause like this one.\n\nWe've cleaned the leaderboard and disqualified some teams that have violated the rules. If you think you were removed by mistake, or believe you have evidence that suggests another team cheated, please contact [compliance](https://www.kaggle.com/compliance). Please fill in all the fields honestly.\n\nThe top potential winning teams have been contacted via email to provide their winning solutions for host review. Should we need to move down the leaderboard for any reason, we will do so and continue to make contact with subsequent teams. \n\nYou should have already seen your points and medals awarded, however it may take a few hours to propagate through if yours are still missing.\n\nIn the meantime, I've created this thread to be a curated list of your work. As you post your solutions, top notebooks, and general discussion posts, I'll update this thread to keep track. If you write a paper, thesis, or present at a conference, and would like to share your work, please let us know so we can share with the Kaggle community! Additionally, if you performed any data manipulation, or used a new technique in this competition that you'd like to share to further the industry, please let us know and we'll post it here! That way, should you be looking for takeaways in the future, or if you've just now stumbled across the competition, you have a place to see what surfaced from the work performed.\n\nHappy Modeling!\nKaggle Team\n\n###Top Solution Write-ups\n\n- [2nd Place](https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/354857)\n- [3rd Place](https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/354683)\n- [4th Place](https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/354851)\n- [7th Place](https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/354859)\n- [11th Place](https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/354701)\n- [20th Place](https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/354590)\n- [25th Place](https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/354744)\n- [26th Place](https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/354671)\n- [27th Place](https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/355213)\n\n###Other Top Discussions\n- [[ placeholder LB 0.81 single fold, coat-parallel-small at 1536 ] my experiment results](https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/332941)\n- [Previous HuBMAP - Solutions summary thread](https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/332664)\n- [External Data Sources](https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/333886)\n- [Good Visual Showing How Tissue Slice Thickness Impacts Staining Intensity](https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/333083)\n- [is there going to be a problem? HPA data and HuBMAP data](https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/332714)\n- [Some Insights](https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/333389)\n\n###Top Notebooks\n- [[Inference] - FastAI Baseline](https://www.kaggle.com/code/thedevastator/inference-fastai-baseline)\n- [[Training] HuBMAP LB 0.75 Swin Transformer v1](https://www.kaggle.com/code/alincijov/training-hubmap-lb-0-75-swin-transformer-v1)\n- [🫁🫀 EDA – HuBMAP+HPA – Organ Segmentation 🫀🫁](https://www.kaggle.com/code/dschettler8845/eda-hubmap-hpa-organ-segmentation)\n- [[Training] - FastAI Baseline](https://www.kaggle.com/code/thedevastator/training-fastai-baseline)\n- [HuBMAP 🧠: Complete Understanding and EDA | W&B 🪄](https://www.kaggle.com/code/ishandutta/hubmap-complete-understanding-and-eda-w-b)\n\n",
      "votes": 14
    }
  ],
  "comments": [],
  "raw_markdown_by_id": {
    "1957244": "Hey Kagglers,\n\nWe’re happy to announce the conclusion of the HuBMAP + HPA - Hacking the Human Body competition! We were happy to have both HuBMAP and the HPA back for another round of competitions - this time to further the research being performed around the “next Human Genome Project.” These competitions have been an exciting way to bring the cutting edge in machine learning to this branch of science. We hope you were able to learn a lot, and use your machine learning skills on this unique challenge.\n\nThis competition ended with 8,689 individuals joining the competition and 1,517 participants making a submission across 1,175 teams! We had 39,568 submissions from over 78 countries! For 286 users (including 36 users in the Top 100!), this was their first competition. An additional congratulations goes out to our 4 new Kaggle Competition Masters! Thank you all for your hard work in this competition and congratulations to our winners and to those who gained a new ranking!  \n\nThank you once again to the team that helped put this competition together - specifically [Leah](https://www.kaggle.com/leahscherschel), [Yash](https://www.kaggle.com/yashvrdnjain), [Katy](https://admwwwin.kaggle.com/katyborner), [Emma](https://www.kaggle.com/emmalumpan), [Trang](https://www.kaggle.com/lnhtrang), and [Cecilia](https://www.kaggle.com/cecilialindskog) along with the rest of the HuBMAP and HPA teams for bringing us this engaging problem to the Kaggle community!\n\nWe're also continually pleased to see how Kaggle Competitions serve as a great medium and vehicle through which the greater data science community can learn and develop their machine learning abilities and skills. Beginners and experts can come together to start, grow, and succeed. Further, in this competition, many can use their machine learning skills to help a great cause like this one.\n\nWe've cleaned the leaderboard and disqualified some teams that have violated the rules. If you think you were removed by mistake, or believe you have evidence that suggests another team cheated, please contact [compliance](https://www.kaggle.com/compliance). Please fill in all the fields honestly.\n\nThe top potential winning teams have been contacted via email to provide their winning solutions for host review. Should we need to move down the leaderboard for any reason, we will do so and continue to make contact with subsequent teams. \n\nYou should have already seen your points and medals awarded, however it may take a few hours to propagate through if yours are still missing.\n\nIn the meantime, I've created this thread to be a curated list of your work. As you post your solutions, top notebooks, and general discussion posts, I'll update this thread to keep track. If you write a paper, thesis, or present at a conference, and would like to share your work, please let us know so we can share with the Kaggle community! Additionally, if you performed any data manipulation, or used a new technique in this competition that you'd like to share to further the industry, please let us know and we'll post it here! That way, should you be looking for takeaways in the future, or if you've just now stumbled across the competition, you have a place to see what surfaced from the work performed.\n\nHappy Modeling!\nKaggle Team\n\n###Top Solution Write-ups\n\n- [2nd Place](https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/354857)\n- [3rd Place](https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/354683)\n- [4th Place](https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/354851)\n- [7th Place](https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/354859)\n- [11th Place](https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/354701)\n- [20th Place](https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/354590)\n- [25th Place](https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/354744)\n- [26th Place](https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/354671)\n- [27th Place](https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/355213)\n\n###Other Top Discussions\n- [[ placeholder LB 0.81 single fold, coat-parallel-small at 1536 ] my experiment results](https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/332941)\n- [Previous HuBMAP - Solutions summary thread](https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/332664)\n- [External Data Sources](https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/333886)\n- [Good Visual Showing How Tissue Slice Thickness Impacts Staining Intensity](https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/333083)\n- [is there going to be a problem? HPA data and HuBMAP data](https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/332714)\n- [Some Insights](https://www.kaggle.com/competitions/hubmap-organ-segmentation/discussion/333389)\n\n###Top Notebooks\n- [[Inference] - FastAI Baseline](https://www.kaggle.com/code/thedevastator/inference-fastai-baseline)\n- [[Training] HuBMAP LB 0.75 Swin Transformer v1](https://www.kaggle.com/code/alincijov/training-hubmap-lb-0-75-swin-transformer-v1)\n- [🫁🫀 EDA – HuBMAP+HPA – Organ Segmentation 🫀🫁](https://www.kaggle.com/code/dschettler8845/eda-hubmap-hpa-organ-segmentation)\n- [[Training] - FastAI Baseline](https://www.kaggle.com/code/thedevastator/training-fastai-baseline)\n- [HuBMAP 🧠: Complete Understanding and EDA | W&B 🪄](https://www.kaggle.com/code/ishandutta/hubmap-complete-understanding-and-eda-w-b)\n\n"
  }
}