A practical guide to FAIR data management in the age of multi-OMICS and AI
Multi-cellular biological systems, including the immune system, are highly complex, dynamic, and adaptable. Systems biologists aim to understand such complexity at a quantitative level. However, these ambitious efforts are often limited by access to a variety of high-density intra-, extra- and multi...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1439434/full |
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| author | Douaa Mugahid Jared Lyon Charlie Demurjian Nathan Eolin Charlie Whittaker Mark Godek Douglas Lauffenburger Sarah Fortune Stuart Levine |
| author_facet | Douaa Mugahid Jared Lyon Charlie Demurjian Nathan Eolin Charlie Whittaker Mark Godek Douglas Lauffenburger Sarah Fortune Stuart Levine |
| author_sort | Douaa Mugahid |
| collection | DOAJ |
| description | Multi-cellular biological systems, including the immune system, are highly complex, dynamic, and adaptable. Systems biologists aim to understand such complexity at a quantitative level. However, these ambitious efforts are often limited by access to a variety of high-density intra-, extra- and multi-cellular measurements resolved in time and space and across a variety of perturbations. The advent of automation, OMICs and single-cell technologies now allows high dimensional multi-modal data acquisition from the same biological samples multiplexed at scale (multi-OMICs). As a result, systems biologists -theoretically- have access to more data than ever. However, the mathematical frameworks and computational tools needed to analyze and interpret such data are often still nascent, limiting the biological insights that can be obtained without years of computational method development and validation. More pressingly, much of the data sits in silos in formats that are incomprehensible to other scientists or machines limiting its value to the vaster scientific community, especially the computational biologists tasked with analyzing these vast amounts of data in more nuanced ways. With the rapid development and increasing interest in using artificial intelligence (AI) for the life sciences, improving how biologic data is organized and shared is more pressing than ever for scientific progress. Here, we outline a practical approach to multi-modal data management and FAIR sharing, which are in line with the latest US and EU funders’ data sharing policies. This framework can help extend the longevity and utility of data by allowing facile use and reuse, accelerating scientific discovery in the biomedical sciences. |
| format | Article |
| id | doaj-art-aefa0c3c25074498ac1c15b3ade2ed1d |
| institution | Kabale University |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj-art-aefa0c3c25074498ac1c15b3ade2ed1d2025-01-20T07:19:50ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-01-011510.3389/fimmu.2024.14394341439434A practical guide to FAIR data management in the age of multi-OMICS and AIDouaa Mugahid0Jared Lyon1Charlie Demurjian2Nathan Eolin3Charlie Whittaker4Mark Godek5Douglas Lauffenburger6Sarah Fortune7Stuart Levine8Department of Immunology and Infectious Diseases, T.H. Chan School of Public Health, Harvard University, Boston, MA, United StatesBioMicro Center, Massachusetts Institute of Technology, Cambridge, MA, United StatesBioMicro Center, Massachusetts Institute of Technology, Cambridge, MA, United StatesBioMicro Center, Massachusetts Institute of Technology, Cambridge, MA, United StatesBioMicro Center, Massachusetts Institute of Technology, Cambridge, MA, United StatesRagon Institute of Massachusetts General Hospital (MGH), Massachusetts Institute of Technology (MIT), and Harvard, Cambridge, MA, United StatesDepartment of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, United StatesDepartment of Immunology and Infectious Diseases, T.H. Chan School of Public Health, Harvard University, Boston, MA, United StatesBioMicro Center, Massachusetts Institute of Technology, Cambridge, MA, United StatesMulti-cellular biological systems, including the immune system, are highly complex, dynamic, and adaptable. Systems biologists aim to understand such complexity at a quantitative level. However, these ambitious efforts are often limited by access to a variety of high-density intra-, extra- and multi-cellular measurements resolved in time and space and across a variety of perturbations. The advent of automation, OMICs and single-cell technologies now allows high dimensional multi-modal data acquisition from the same biological samples multiplexed at scale (multi-OMICs). As a result, systems biologists -theoretically- have access to more data than ever. However, the mathematical frameworks and computational tools needed to analyze and interpret such data are often still nascent, limiting the biological insights that can be obtained without years of computational method development and validation. More pressingly, much of the data sits in silos in formats that are incomprehensible to other scientists or machines limiting its value to the vaster scientific community, especially the computational biologists tasked with analyzing these vast amounts of data in more nuanced ways. With the rapid development and increasing interest in using artificial intelligence (AI) for the life sciences, improving how biologic data is organized and shared is more pressing than ever for scientific progress. Here, we outline a practical approach to multi-modal data management and FAIR sharing, which are in line with the latest US and EU funders’ data sharing policies. This framework can help extend the longevity and utility of data by allowing facile use and reuse, accelerating scientific discovery in the biomedical sciences.https://www.frontiersin.org/articles/10.3389/fimmu.2024.1439434/fullFAIR datasystems biologyimmunologyOMICsmulti-modal dataartificial intelligence |
| spellingShingle | Douaa Mugahid Jared Lyon Charlie Demurjian Nathan Eolin Charlie Whittaker Mark Godek Douglas Lauffenburger Sarah Fortune Stuart Levine A practical guide to FAIR data management in the age of multi-OMICS and AI Frontiers in Immunology FAIR data systems biology immunology OMICs multi-modal data artificial intelligence |
| title | A practical guide to FAIR data management in the age of multi-OMICS and AI |
| title_full | A practical guide to FAIR data management in the age of multi-OMICS and AI |
| title_fullStr | A practical guide to FAIR data management in the age of multi-OMICS and AI |
| title_full_unstemmed | A practical guide to FAIR data management in the age of multi-OMICS and AI |
| title_short | A practical guide to FAIR data management in the age of multi-OMICS and AI |
| title_sort | practical guide to fair data management in the age of multi omics and ai |
| topic | FAIR data systems biology immunology OMICs multi-modal data artificial intelligence |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1439434/full |
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