Schwantes Collin J, Sánchez Cecilia A, Stevens Tess, Zimmerman Ryan, Albery Greg, Becker Daniel J, Brookson Cole B, Kading Rebekah C, Keiser Carl N, Khandelwal Shashank, Kramer-Schadt Stephanie, Krut-Landau Raphael, McKee Clifton, Montecino-Latorre Diego, O'Donoghue Zoe, Olson Sarah H, O'Shea Mika, Poisot Timothée, Robertson Hailey, Ryan Sadie J, Seifert Stephanie N, Simons David, Vicente-Santos Amanda, Wood Chelsea L, Graeden Ellie, Carlson Colin J
Department of Epidemiology of Microbial Diseases, Yale University, New Haven, CT, USA.
Center for Global Health Science and Security, Georgetown University, Washington, DC, USA.
Sci Data. 2025 Jun 21;12(1):1054. doi: 10.1038/s41597-025-05332-x.
Rapid and comprehensive data sharing is vital to the transparency and actionability of wildlife infectious disease research and surveillance. Unfortunately, most best practices for publicly sharing these data are focused on pathogen determination and genetic sequence data. Other facets of wildlife disease data - particularly negative results - are often withheld or, at best, summarized in a descriptive table with limited metadata. Here, we propose a minimum data and metadata reporting standard for wildlife disease studies. Our data standard identifies a set of 40 data fields (9 required) and 24 metadata fields (7 required) sufficient to standardize and document a dataset consisting of records disaggregated to the finest possible spatial, temporal, and taxonomic scale. We illustrate how this standard is applied to an example study, which documented a novel alphacoronavirus found in bats in Belize. Finally, we outline best practices for how data should be formatted for optimal re-use, and how researchers can navigate potential safety concerns around data sharing.
快速而全面的数据共享对于野生动物传染病研究与监测的透明度及可操作性至关重要。不幸的是,公开共享这些数据的大多数最佳实践都集中在病原体鉴定和基因序列数据上。野生动物疾病数据的其他方面——尤其是阴性结果——往往被隐瞒,或者充其量只是在一个元数据有限的描述性表格中进行总结。在此,我们提出了野生动物疾病研究的最低数据和元数据报告标准。我们的数据标准确定了一组40个数据字段(9个为必填项)和24个元数据字段(7个为必填项),足以对一个由尽可能细化到空间、时间和分类学尺度的记录组成的数据集进行标准化和记录。我们举例说明了该标准如何应用于一项实例研究,该研究记录了在伯利兹蝙蝠中发现的一种新型甲型冠状病毒。最后,我们概述了数据应如何格式化以实现最佳再利用的最佳实践,以及研究人员如何应对数据共享方面潜在的安全问题。