Opportunities and Challenges in Democratizing Immunology Datasets
The field of immunology is rapidly progressing toward a systems-level understanding of immunity to tackle complex infectious diseases, autoimmune conditions, cancer, and beyond. In the last couple of decades, advancements in data acquisition techniques have presented opportunities to explore untappe...
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doaj-41d598425ecd42069225303e7c0478d42021-05-06T14:37:44ZengFrontiers Media S.A.Frontiers in Immunology1664-32242021-04-011210.3389/fimmu.2021.647536647536Opportunities and Challenges in Democratizing Immunology DatasetsSanchita Bhattacharya0Sanchita Bhattacharya1Zicheng Hu2Zicheng Hu3Atul J. Butte4Atul J. Butte5Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United StatesDepartment of Pediatrics, University of California, San Francisco, San Francisco, CA, United StatesBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United StatesDepartment of Pediatrics, University of California, San Francisco, San Francisco, CA, United StatesBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United StatesDepartment of Pediatrics, University of California, San Francisco, San Francisco, CA, United StatesThe field of immunology is rapidly progressing toward a systems-level understanding of immunity to tackle complex infectious diseases, autoimmune conditions, cancer, and beyond. In the last couple of decades, advancements in data acquisition techniques have presented opportunities to explore untapped areas of immunological research. Broad initiatives are launched to disseminate the datasets siloed in the global, federated, or private repositories, facilitating interoperability across various research domains. Concurrently, the application of computational methods, such as network analysis, meta-analysis, and machine learning have propelled the field forward by providing insight into salient features that influence the immunological response, which was otherwise left unexplored. Here, we review the opportunities and challenges in democratizing datasets, repositories, and community-wide knowledge sharing tools. We present use cases for repurposing open-access immunology datasets with advanced machine learning applications and more.https://www.frontiersin.org/articles/10.3389/fimmu.2021.647536/fullimmunologyopen-accessdemocratizationdata reusepublic repositories |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sanchita Bhattacharya Sanchita Bhattacharya Zicheng Hu Zicheng Hu Atul J. Butte Atul J. Butte |
spellingShingle |
Sanchita Bhattacharya Sanchita Bhattacharya Zicheng Hu Zicheng Hu Atul J. Butte Atul J. Butte Opportunities and Challenges in Democratizing Immunology Datasets Frontiers in Immunology immunology open-access democratization data reuse public repositories |
author_facet |
Sanchita Bhattacharya Sanchita Bhattacharya Zicheng Hu Zicheng Hu Atul J. Butte Atul J. Butte |
author_sort |
Sanchita Bhattacharya |
title |
Opportunities and Challenges in Democratizing Immunology Datasets |
title_short |
Opportunities and Challenges in Democratizing Immunology Datasets |
title_full |
Opportunities and Challenges in Democratizing Immunology Datasets |
title_fullStr |
Opportunities and Challenges in Democratizing Immunology Datasets |
title_full_unstemmed |
Opportunities and Challenges in Democratizing Immunology Datasets |
title_sort |
opportunities and challenges in democratizing immunology datasets |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Immunology |
issn |
1664-3224 |
publishDate |
2021-04-01 |
description |
The field of immunology is rapidly progressing toward a systems-level understanding of immunity to tackle complex infectious diseases, autoimmune conditions, cancer, and beyond. In the last couple of decades, advancements in data acquisition techniques have presented opportunities to explore untapped areas of immunological research. Broad initiatives are launched to disseminate the datasets siloed in the global, federated, or private repositories, facilitating interoperability across various research domains. Concurrently, the application of computational methods, such as network analysis, meta-analysis, and machine learning have propelled the field forward by providing insight into salient features that influence the immunological response, which was otherwise left unexplored. Here, we review the opportunities and challenges in democratizing datasets, repositories, and community-wide knowledge sharing tools. We present use cases for repurposing open-access immunology datasets with advanced machine learning applications and more. |
topic |
immunology open-access democratization data reuse public repositories |
url |
https://www.frontiersin.org/articles/10.3389/fimmu.2021.647536/full |
work_keys_str_mv |
AT sanchitabhattacharya opportunitiesandchallengesindemocratizingimmunologydatasets AT sanchitabhattacharya opportunitiesandchallengesindemocratizingimmunologydatasets AT zichenghu opportunitiesandchallengesindemocratizingimmunologydatasets AT zichenghu opportunitiesandchallengesindemocratizingimmunologydatasets AT atuljbutte opportunitiesandchallengesindemocratizingimmunologydatasets AT atuljbutte opportunitiesandchallengesindemocratizingimmunologydatasets |
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