{"title":"评论:NIAID合作流感疫苗创新中心(CIVICs)内临床前和临床疫苗开发公共数据共享过程","authors":"Frans Ileana Cuevas","doi":"10.1016/j.vaccine.2025.127547","DOIUrl":null,"url":null,"abstract":"<div><div>The 2019 coronavirus disease (COVID-19) pandemic increased efforts for rapid data sharing and dissemination among researchers as well as to data repositories. Researchers and studies prioritized data sharing, which increased understanding of SARS-CoV-2's pathology. Eventually, this effort to maximize collaboration and data dissemination, led to the development of mRNA vaccines. This successful effort has highlighted the importance of data sharing and the implementation of data management policies, including the National Institutes of Health's (NIH) Data Sharing Policy of 2023. Moreover, programs such as the National Institute of Allergy and Infectious Diseases (NIAID) funded Collaborative Influenza Vaccine Innovation Centers (CIVICs), have beta-tested this policy, with the help of the Statistical, Data Management and Coordination Center (SDMCC) and its data standards, and deemed it useful. However, the process has also initiated pertinent discussion on potential improvements and optimizations for the <em>future</em> of data sharing. Here, I use the CIVICs data sharing reporting standards and process as a data sharing example, and suggest logistical improvements to propose a better-equipped model for the vaccinology community.</div></div>","PeriodicalId":23491,"journal":{"name":"Vaccine","volume":"62 ","pages":"Article 127547"},"PeriodicalIF":4.5000,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Commentary: Processes of pre-clinical and clinical vaccine development public data sharing within the NIAID collaborative influenza vaccine innovation centers (CIVICs)\",\"authors\":\"Frans Ileana Cuevas\",\"doi\":\"10.1016/j.vaccine.2025.127547\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The 2019 coronavirus disease (COVID-19) pandemic increased efforts for rapid data sharing and dissemination among researchers as well as to data repositories. Researchers and studies prioritized data sharing, which increased understanding of SARS-CoV-2's pathology. Eventually, this effort to maximize collaboration and data dissemination, led to the development of mRNA vaccines. This successful effort has highlighted the importance of data sharing and the implementation of data management policies, including the National Institutes of Health's (NIH) Data Sharing Policy of 2023. Moreover, programs such as the National Institute of Allergy and Infectious Diseases (NIAID) funded Collaborative Influenza Vaccine Innovation Centers (CIVICs), have beta-tested this policy, with the help of the Statistical, Data Management and Coordination Center (SDMCC) and its data standards, and deemed it useful. However, the process has also initiated pertinent discussion on potential improvements and optimizations for the <em>future</em> of data sharing. Here, I use the CIVICs data sharing reporting standards and process as a data sharing example, and suggest logistical improvements to propose a better-equipped model for the vaccinology community.</div></div>\",\"PeriodicalId\":23491,\"journal\":{\"name\":\"Vaccine\",\"volume\":\"62 \",\"pages\":\"Article 127547\"},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2025-07-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Vaccine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0264410X25008448\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"IMMUNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Vaccine","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0264410X25008448","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"IMMUNOLOGY","Score":null,"Total":0}
Commentary: Processes of pre-clinical and clinical vaccine development public data sharing within the NIAID collaborative influenza vaccine innovation centers (CIVICs)
The 2019 coronavirus disease (COVID-19) pandemic increased efforts for rapid data sharing and dissemination among researchers as well as to data repositories. Researchers and studies prioritized data sharing, which increased understanding of SARS-CoV-2's pathology. Eventually, this effort to maximize collaboration and data dissemination, led to the development of mRNA vaccines. This successful effort has highlighted the importance of data sharing and the implementation of data management policies, including the National Institutes of Health's (NIH) Data Sharing Policy of 2023. Moreover, programs such as the National Institute of Allergy and Infectious Diseases (NIAID) funded Collaborative Influenza Vaccine Innovation Centers (CIVICs), have beta-tested this policy, with the help of the Statistical, Data Management and Coordination Center (SDMCC) and its data standards, and deemed it useful. However, the process has also initiated pertinent discussion on potential improvements and optimizations for the future of data sharing. Here, I use the CIVICs data sharing reporting standards and process as a data sharing example, and suggest logistical improvements to propose a better-equipped model for the vaccinology community.
期刊介绍:
Vaccine is unique in publishing the highest quality science across all disciplines relevant to the field of vaccinology - all original article submissions across basic and clinical research, vaccine manufacturing, history, public policy, behavioral science and ethics, social sciences, safety, and many other related areas are welcomed. The submission categories as given in the Guide for Authors indicate where we receive the most papers. Papers outside these major areas are also welcome and authors are encouraged to contact us with specific questions.