A new study published in Scientific Data by researchers from Yale University, Georgetown University, and a global coalition of academic and public health institutions proposes a long-awaited solution to one of wildlife disease ecology’s biggest barriers: fragmented, inconsistent data. The paper introduces a standardized framework for recording, formatting, and sharing wildlife disease data in ways that are transparent, flexible, and actionable.
The new data standard—developed in collaboration with experts from institutions across North America and Europe—marks a pivotal advance in aligning wildlife surveillance with global health security goals. It enables broader, more effective data aggregation across studies, bolstering our capacity to detect and respond to emerging infectious threats at the human-animal-environment interface.
A Flexible Core Data Standard with Global Applicability
At the heart of the proposal is a minimal yet comprehensive data and metadata standard encompassing 40 data fields (9 required) and 24 metadata fields (7 required). These fields are designed to document diagnostic outcomes, sampling context, and host characteristics at the finest taxonomic, spatial, and temporal resolutions possible. The structure is flexible enough to accommodate diverse methodologies—such as PCR, ELISA, or pooled testing—and is applicable across taxa and ecosystems.
Making Negative Results and Metadata Matter
The authors emphasize the importance of including negative results and contextual metadata—long neglected in wildlife pathogen surveillance. They show that most published datasets are limited to summary tables or only report positive detections, severely constraining secondary analysis. By mandating consistent documentation of negatives, the new standard enables more rigorous comparisons of disease prevalence across time, geography, and host species.
Prioritizing FAIR Principles in Surveillance Science
The standard aligns with the FAIR principles: Findable, Accessible, Interoperable, and Reusable. It is designed for use in platforms like the PHAROS database, Zenodo, and GBIF, and is compatible with global biodiversity data standards such as Darwin Core. Detailed project metadata and persistent identifiers (DOIs, ORCIDs) further promote discoverability and citation, enhancing recognition for data producers.
Protecting Public and Ecological Health in the National Interest
From COVID-19 to Ebola to mpox, emerging zoonoses have repeatedly underscored the urgent need for transparent, high-quality wildlife surveillance data. By empowering researchers to harmonize and share data more effectively, this standard strengthens the early warning systems critical to national and global biosecurity. U.S. agencies, international partners, and NGOs all benefit when ecological intelligence is timely, complete, and usable. This is not merely about academic rigor—it is about strengthening the public health firewall against the next pandemic threat.
Everyday Relevance: Why Data Standards Matter for Everyone
Imagine you’re a civil engineer overseeing bridge safety, but each contractor reports stress test results in different formats—or omits failed tests entirely. Now imagine trying to decide which bridges are at risk. That’s what wildlife disease researchers face. Without standard formats and full datasets, patterns are missed, risks are underestimated, and time is wasted. With a shared standard, it’s like all engineers speaking the same language—and lives may depend on it.
Best Practices for Ethical and Secure Data Sharing
Recognizing the sensitivity of high-resolution location data—particularly when it comes to threatened species or zoonotic pathogens—the standard includes detailed guidance for secure data obfuscation and context-aware sharing. These safeguards are essential to balance transparency with biosafety, and prevent misuse such as wildlife culling or bioterrorism.
The authors also stress the importance of using open, non-proprietary formats (e.g., .csv) and including readable documentation such as data dictionaries, test descriptions, and project metadata. This ensures datasets remain accessible to researchers worldwide, regardless of software access or institutional affiliation.
A Call to Action for the Scientific Community
The authors urge researchers to adopt the standard across future wildlife pathogen studies, and to deposit both positive and negative results in open-access repositories. Widespread adoption will not only improve reproducibility and ecological understanding, but also better equip global health actors to track, predict, and mitigate pathogen emergence.
As journal policies and funding mandates increasingly require open data, this standard provides a much-needed roadmap to meet those requirements without sacrificing flexibility or usability. It represents a foundational investment in the scientific infrastructure needed to make wildlife disease research both robust and globally interoperable.
Schwantes, C.J., Sánchez, C.A., Stevens, T. et al. (2025). A minimum data standard for wildlife disease research and surveillance. Scientific Data, 21 June 2025.