Why you should share your data during a pandemic.
Why you should share your data during a pandemic.
复制标题
为什么你应该在大流行期间分享你的数据。
DOI：
10.1136/bmjgh-2021-004940
复制
发表时间：
2021-03
影响因子：
8.1
通讯作者：
Flaherman VJ
中科院分区：
文献类型：
--
作者：
Smith ER;Flaherman VJ
COVID-19大流行的破坏性影响部分可归因于缺乏有效预防和治疗的证据。全球科学界一直在与时间赛跑，以迅速获得此类证据。越来越多地使用前瞻性荟萃分析（PMA）和其他用于汇集多项研究数据的创新方法，并有可能加快知识产生的速度，特别是对于观察或监督数据。然而，从事这些创新项目的研究人员必须抛弃一些传统的学术研究做法。1因此，使用PMA或其他实时汇集工作可能会遇到个别研究人员的阻力，他们对这种创新的专业，道德和实用性影响持怀疑态度。我们以前曾提出，顺序PMA提供了一种有用的方法来快速生成政策和实践相关的指导;我们的团队目前正在与21个国家的研究人员合作，以汇集与妊娠期间SARS-CoV-2感染相关的数据。2虽然PMA过程需要研究者的承诺和协调数据收集要素的一些努力，但它也提供了与快速传播信息相关的实质性潜在利益。通过合作，连续更新的PMA允许在有足够的样本量用于个别研究之前就共享结果，因此可以快速为公共卫生政策决策提供信息，例如管理大流行所需的决策。在汇集已发表和未发表数据的工作中，我们遇到了习惯于更传统方法的科学家对数据共享的抵制。虽然许多研究人员，特别是那些在低收入和中等收入国家工作或具有国际合作经验的研究人员，已经欣然同意参加，但一些在学术环境中工作的研究人员，
The devastating impact of the COVID-19 pandemic can be partly attributed to a lack of evidence to inform effective prevention and treatment. The global scientific community has been racing against time to rapidly generate such evidence. Prospective meta-analysis (PMA) and other innovative approaches for pooling data from multiple studies are increasingly used and have the potential to expedite the pace at which knowledge is produced, especially for observational or surveillance data. However, investigators engaged in these innovative projects must leave behind some traditional practices of academic research. 1 For that reason, the use of PMA or other real-time pooling efforts may meet resistance from individual investigators dubious about the professional, ethical and utilitarian implications of such innovation. We have previously proposed that a sequential PMA offers a useful approach to rapidly generate policy and practice-relevant guidance; our group is currently engaged with investigators working in 21 countries to pool data related to SARS-CoV-2 infection during pregnancy. 2 While the PMA process requires commitment from investigators and some effort to harmonise data collection elements, it also provides substantial potential benefits related to rapid dissemination of information. Through collaboration, serially updated PMAs allow results to be shared well before adequate sample sizes are available for individual studies and can therefore rapidly inform public health policy decisions such as those needed for the management of the pandemic.In working to pool published and unpublished data, we have encountered resistance to data sharing from scientists accustomed to a more traditional approach. Although many investigators, especially those working in low-income and middle-income countries or with prior international collaboration experience, have readily agreed to participate, some investigators working in academic settings in
影响因子：
8.1
作者：
Khan MS;Dar O;Erondu NA;Rahman-Shepherd A;Hollmann L;Ihekweazu C;Ukandu O;Agogo E;Ikram A;Rathore TR;Okereke E;Squires N
通讯作者：
Squires N