Implementing the New Minimum Data Standard for Wildlife Disease Research: A Guide for Scientists and Drug Development Professionals

Abstract

This article provides a comprehensive guide to the newly established minimum data standard for wildlife disease research and surveillance. Published in 2025, this standard addresses critical gaps in data sharing by defining 40 core data fields and 24 metadata fields to ensure transparency, interoperability, and reusability. Aimed at researchers, scientists, and drug development professionals, the content explores the standard's foundation in FAIR principles, offers step-by-step methodological application, addresses key troubleshooting concerns for sensitive data, and validates its utility through comparative analysis with existing frameworks. By adopting this standard, the scientific community can enhance global health security, improve pandemic preparedness, and facilitate the translation of ecological data into actionable biomedical insights.

The Critical Need for Standardization in Wildlife Disease Data

The Problem of Fragmented and Inconsistent Wildlife Disease Data

In the field of wildlife disease ecology, data fragmentation and inconsistent reporting present significant barriers to understanding disease dynamics and mitigating emerging threats. Most current best practices for data sharing focus predominantly on pathogen genetic sequence data, while neglecting other critical facets of wildlife disease information [1]. This inconsistency is particularly evident in the widespread failure to report negative test results and comprehensive contextual metadata, which are essential for calculating accurate disease prevalence and understanding spatiotemporal patterns [1] [2]. The consequences of this fragmentation extend beyond academic circles, creating vulnerabilities in global health security by impeding early detection and response to zoonotic pathogens with pandemic potential [2] [3].

The lack of standardized formats means that even when data are shared, they often cannot be easily aggregated or compared across studies. A review of national health security capacity found that more than half (57.9%, 62/107) of reporting countries provided no evidence of a functional wildlife health surveillance program, and most countries (83.2%, 89/107) indicated specific gaps in operations, coordination, scope, or capacity [3]. This systematic neglect of wildlife and environmental considerations in health security priorities creates critical voids in our understanding of disease emergence and spread [3].

The Minimum Data Standard: A Solution for Harmonization

To address these challenges, researchers have proposed a minimum data and metadata reporting standard specifically designed for wildlife disease studies [1] [2]. This standard was developed through an iterative process incorporating: (i) experience conducting and publishing wildlife disease research; (ii) common practices already followed by scientists in the literature; (iii) best practices for sharing ecological data; and (iv) interoperability with standards used by other platforms such as the Global Biodiversity Information Facility (GBIF) [1].

The guiding philosophy of the data standard is that researchers should share their raw wildlife disease data in a "tidy data" format where each row corresponds to a single measurement - specifically, the outcome of a diagnostic test [1]. This structure accommodates the complex many-to-many relationships between tests, samples, and individual animals that commonly occur in wildlife disease research due to practices like repeated sampling, confirmatory tests, and sample pooling [1].

Scope and Applicability

The data standard is designed for studies involving wild animal samples examined for parasites (including macroparasites, microparasites, and other pathogens), accompanied by information on diagnostic methods, and date and location of sampling [1]. Suitable project types include:

• First report of a parasite in a wildlife species
• Investigation of mass wildlife mortality events
• Longitudinal, multi-site sampling of multiple wildlife species
• Regular parasite screening in monitored wildlife populations
• Wildlife screening during human disease outbreak investigations
• Passive surveillance programs testing wildlife carcasses [1]

The standard specifically excludes data types better documented elsewhere, such as free-living macroparasite records (better suited to Darwin Core format), arthropod blood meal datasets, and environmental monitoring data not associated with specific animals [1].

Quantitative Framework of the Data Standard

Core Data Fields

Table 1: Required Core Data Fields in the Wildlife Disease Data Standard

Field Category	Field Name	Description	Requirement Level
Sampling	Sample ID	Unique identifier for the sample	Required
Sampling	Sample type	Type of sample collected (e.g., oral swab, blood)	Required
Sampling	Collection date	Date when sample was collected	Required
Sampling	Location	Geographic location of sampling	Required
Host	Host species	Taxonomic identification of host	Required
Host	Animal ID	Unique identifier for individual animal	Conditional
Parasite	Test result	Outcome of diagnostic test	Required
Parasite	Test name	Name of diagnostic test used	Required
Parasite	Test target	Pathogen or agent targeted	Required

The standard identifies 40 core data fields categorized into three groups: 11 related to sampling, 13 related to the host organism, and 16 related to the parasite itself [2]. Of these, 9 fields are designated as required (as detailed in Table 1), while the remainder are optional but recommended to provide sufficient context for interpretation and reuse [1] [4].

Metadata Requirements

Table 2: Required Metadata Fields in the Wildlife Disease Data Standard

Metadata Category	Field Name	Description
Project Identification	Project title	Formal title of the research project
Project Identification	Project description	Brief summary of project objectives and scope
Personnel	Creator name(s)	Names of data creators
Personnel	Creator ORCID	Open Researcher and Contributor ID
Temporal Coverage	Date range	Start and end dates of data collection
Geographical Coverage	Location	Geographical scope of the study
Data Identification	Digital Object Identifier (DOI)	Persistent identifier for the dataset

The standard includes 24 metadata fields (7 required) sufficient to document a dataset according to the DataCite Metadata Schema [1]. These fields ensure proper attribution, contextualization, and discoverability of shared datasets, aligning with FAIR (Findable, Accessible, Interoperable, and Reusable) principles [2].

Experimental Protocols and Implementation

Step-by-Step Implementation Protocol

Protocol Title: Implementation of Minimum Data Standard for Wildlife Disease Research

Purpose: To standardize the collection, formatting, and sharing of wildlife disease data according to the minimum data standard, enhancing interoperability and reuse potential.

Materials:

• Template files (.csv or .xlsx format) from github.com/viralemergence/wdds [1]
• JSON Schema implementation of the standard
• R package "wddsWizard" (available from GitHub) for data validation [1]

Procedure:

• Fit for Purpose Assessment

  • Verify that the dataset describes wild animal samples examined for parasites.
  • Ensure each record includes host identification, diagnostic methods, test outcomes, parasite identification, and date and location of sampling [1].

• Standard Tailoring

  • Consult the list of 40 data fields in the standard and identify which optional fields are applicable to the study design.
  • Identify appropriate ontologies or controlled vocabularies for free-text fields.
  • Determine if any additional study-specific fields are needed beyond the standard set [1].

• Data Formatting

  • Format data in "tidy data" structure where each row represents a single diagnostic test outcome.
  • Use provided templates in .csv or .xlsx format to ensure proper structure.
  • Include both positive and negative test results with complete metadata [1] [2].

• Data Validation

  • Validate data and metadata against the JSON Schema implementation of the standard.
  • Use the wddsWizard R package convenience functions for automated validation [1].

• Data Sharing

  • Make data available in a findable, open-access generalist repository (e.g., Zenodo) and/or specialist platform (e.g., the PHAROS database) [1].
  • Include all required project metadata and persistent identifiers (DOIs, ORCIDs) to enhance discoverability and citation [2].

Workflow Visualization

The Researcher's Toolkit: Essential Research Reagents and Solutions

Table 3: Essential Research Reagents and Solutions for Wildlife Disease Studies

Reagent/Solution	Function/Application	Implementation Example
Diagnostic Primers & Probes	Pathogen detection via PCR/rt-PCR	Forward/reverse primer sequences for coronavirus detection [1]
Sample Collection Kits	Non-invasive sampling	Oral and rectal swabs for bat coronavirus surveillance [1]
Taxonomic Reference Materials	Host species identification	Field guides, genetic barcodes for host species verification [1]
Data Validation Tools	Quality control of standardized data	wddsWizard R package for schema validation [1]
Template Files	Standardized data formatting	.csv and .xlsx templates from WDDS GitHub repository [1]
Geospatial Reference Data	Location standardization	GPS coordinates, gazetteers for spatial precision [1]
Ecdysterone 20,22-monoacetonide	Ecdysterone 20,22-monoacetonide, MF:C30H48O7, MW:520.7 g/mol	Chemical Reagent
(Rac)-4-Hydroxy Duloxetine-d3	(Rac)-4-Hydroxy Duloxetine-d3	(Rac)-4-Hydroxy Duloxetine-d3 is a deuterated metabolite for duloxetine pharmacokinetics and metabolism research. For Research Use Only. Not for human or diagnostic use.

Case Study Application

Example Dataset Implementation

The practical application of the standard is illustrated by a previously published dataset documenting a novel alphacoronavirus found in bats in Belize [1]. In this example:

• A single vampire bat (BZ19-114) was tested for coronaviruses using oral and rectal swabs
• The rectal swab tested negative, while the oral swab tested positive
• This led to the identification of a novel alphacoronavirus [1]

The dataset was formatted according to the standard, with all mandatory and relevant fields completed, and cells left blank where fields were not applicable (e.g., parasite identity for negative test results) [1]. The complete standardized dataset is available on the PHAROS platform (project: prjRPayEvMecN) [1].

Data Relationships and Structure

Best Practices for Implementation

Data Formatting and Validation

For optimal reusability, data should be formatted as "tidy data" where each row corresponds to a single measurement (diagnostic test outcome) and each column represents a variable [1]. Researchers should:

• Use open, non-proprietary formats (e.g., .csv) to ensure long-term accessibility [2]
• Include both positive and negative results to enable prevalence calculations [1] [2]
• Provide readable documentation including data dictionaries, test descriptions, and project metadata [2]
• Implement appropriate data obfuscation for sensitive information like precise locations of threatened species [1] [2]

Addressing Safety and Ethical Concerns

The standard includes guidance for navigating potential safety concerns around data sharing, particularly regarding:

• Precise location data for threatened or endangered species
• Pathogen information with dual-use potential
• Biosafety considerations for working with potentially hazardous pathogens [1]

Recommended approaches include data obfuscation techniques that maintain scientific utility while preventing misuse, such as generalizing precise coordinates for sensitive species [1] [2].

The adoption of this minimum data standard for wildlife disease research addresses the critical problem of fragmented and inconsistent data that has long impeded ecological understanding and health security preparedness. By providing a practical, flexible framework for data standardization while emphasizing the inclusion of negative results and comprehensive metadata, this approach enables the aggregation and comparison of datasets across studies and geographical regions [1] [2].

Widespread implementation of this standard will enhance the transparency, actionability, and reproducibility of wildlife disease research, ultimately strengthening our collective ability to detect, understand, and mitigate emerging infectious threats at the human-animal-environment interface [2]. 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 [2].

The Minimum Data Standard for Wildlife Disease Research and Surveillance represents a pivotal advancement in ecological and public health intelligence. Established through a collaboration of experts from academic and public health institutions and published in 2025, this framework addresses a critical barrier in wildlife disease ecology: fragmented and inconsistent data reporting [1] [2]. The standard is designed to enhance the transparency, reusability, and global utility of data related to pathogens in wild animals, thereby bolstering our collective capacity to detect and respond to emerging infectious threats at the human-animal-environment interface [2].

The guiding philosophy of the data standard is that researchers should share their raw wildlife disease data in a disaggregated format, often referred to as "tidy data," where each row corresponds to a single measurement—specifically, the outcome of a diagnostic test [1]. This structure acknowledges the complex, many-to-many relationships between tests, samples, and individual animals that are common in wildlife disease studies [1].

Scope of Application

The data standard is intentionally designed for flexibility and broad applicability across a diverse range of study designs and surveillance activities within wildlife disease ecology.

Suitable Project Types

The standard is applicable to datasets that describe wild animal samples examined for parasites (including macroparasites, microparasites, viruses, and bacteria) and that include information on the diagnostic methods used and the date and location of sampling [1]. The following project types are explicitly identified as suitable for this standard [1]:

• The first report of a parasite in a wildlife species.
• Investigation of a mass wildlife mortality event.
• Longitudinal, multi-site sampling of multiple wildlife species for a parasite.
• Regular parasite screening in a single monitored wildlife population.
• Screening of wildlife during an investigation of a human disease outbreak.
• A passive surveillance program that tests wildlife carcasses submitted by the public.

Exclusions and Related Standards

The standard's developers recognize that closely related types of data are better documented using more specialized frameworks. Consequently, the following data types are considered out of scope for this particular standard [1]:

• Records of free-living macroparasites (e.g., from tick dragging): Should be stored in Darwin Core format or adhere to the MIReAD (Minimum Information for Reusable Arthropod Abundance Data) standard.
• Arthropod blood meal datasets: Can follow another recently-published data standard.
• Environmental monitoring datasets: Such as soil, water, or air microbiome metagenomics not associated with a specific animal, which should follow other established best practices.

Core Objectives and Design Principles

The development of the minimum data standard was driven by several key objectives aligned with modern data science and global health security needs.

Primary Objectives

• Comprehensive Data Sharing: To ensure that all facets of wildlife disease data—particularly negative results—are shared with sufficient context, moving beyond the current common practice of withholding them or reporting them only in summarized tables [1] [2].
• FAIR Compliance: To make data Findable, Accessible, Interoperable, and Reusable (FAIR) for both humans and machines [1] [2]. This facilitates the aggregation of datasets for large-scale analysis and synthesis research.
• Interoperability: To ensure the standard works seamlessly with other platforms and global biodiversity data standards, such as the Global Biodiversity Information Facility (GBIF) and its Darwin Core format [1] [2].
• Actionability for Global Health: To strengthen early warning systems critical to national and global biosecurity by providing timely, complete, and usable ecological intelligence for pandemic preparedness [2].

Design Philosophy

The standard is conceived as a minimal yet comprehensive set of fields [2]. It is designed to be accessible to a wide range of practitioners while providing sufficient structure for robust analysis [1]. A key design decision was to use open text fields for most entries rather than a restrictive controlled vocabulary, acknowledging the vast diversity of collection, detection, and measurement methods used in the field. This flexibility is intended to encourage broad community adoption, though the use of existing ontologies is encouraged where appropriate [1].

The standard is composed of a defined set of data and metadata fields designed to document diagnostic outcomes, sampling context, and host characteristics at the finest possible resolution [1] [2].

Table 1: Core Data Field Composition

Category	Total Fields	Required Fields	Description
Sampling Data	11	Not Specified	Information related to the collection and processing of the sample.
Host Animal Data	13	Not Specified	Data concerning the animal from which the sample was taken (e.g., species, sex, age).
Parasite Data	16	Not Specified	Details on the diagnostic test, its result, and characterization of any detected parasite.
Total Core Data Fields	40	9	Records are disaggregated to the finest spatial, temporal, and taxonomic scale.

Table 2: Project Metadata Field Composition

Category	Total Fields	Required Fields	Description
Project Metadata	24	7	Information to document the project context, such as creators, funding, and methodology. This aligns with the DataCite Metadata Schema [1].

Experimental Protocols and Workflow

Implementing the minimum data standard in a research project involves a series of methodical steps. The following workflow diagram outlines the key stages from planning to data sharing.

Protocol for Application

• Fit for Purpose Determination: Confirm that the dataset describes wild animal samples that were examined for parasites. Each record must include the host identification, diagnostic methods used, test outcome, parasite identification (if applicable), and the date and location of sampling [1].
• Tailoring the Standard: Consult the list of 40 core data fields and 24 metadata fields. Identify which fields beyond the required ones are applicable to the specific study design. Determine which ontologies or controlled vocabularies may be appropriate for free-text fields, and decide if any additional study-specific fields are needed [1].
• Data Formatting: Format the data into a "rectangular" or "tidy" structure where each row corresponds to a single diagnostic test outcome. Template files in .csv and .xlsx formats are available to facilitate this process [1].
• Data Validation: Validate the dataset against the standard's technical specification. This can be done using the provided JSON Schema or the dedicated R package wddsWizard, which offers convenience functions for validation [1] [5].
• Data Sharing: Make the validated data and metadata available in a findable, open-access generalist repository (e.g., Zenodo) and/or a specialist platform like the Pathogen Harmonized Observatory (PHAROS) database [1].

The Scientist's Toolkit

To effectively implement and utilize the minimum data standard, researchers can leverage a suite of tools and resources.

Table 3: Essential Research Reagent Solutions

Tool / Resource	Type	Function	Access
Data & Metadata Templates	Template Files	Pre-formatted .csv and .xlsx files providing the correct structure for data entry.	GitHub: viralemergence/wdds [1]
wddsWizard R Package	Software Package	An R package to restructure datasets, validate them against the standard, and facilitate compliance.	R-universe: viralemergence.r-universe.dev [5]
JSON Schema	Validation Schema	A machine-readable schema that defines the standard's structure and rules for formal data validation.	GitHub: viralemergence/wdds [1]
PHAROS Database	Data Repository	A dedicated platform for wildlife disease data where standardized datasets can be shared and explored.	pharos.viralemergence.org [1]
DataCite Schema	Metadata Standard	The underlying metadata schema used for project-level documentation, promoting interoperability with repositories.	DataCite [1]
Mal-VC-PAB-ABAEP-Azonafide	Mal-VC-PAB-ABAEP-Azonafide, MF:C61H71N11O12, MW:1150.3 g/mol	Chemical Reagent	Bench Chemicals
Bromo-PEG12-t-butyl ester	Bromo-PEG12-t-butyl ester|PEG Linker|BroadPharm		Bench Chemicals

The 2025 Minimum Data Standard for wildlife disease research and surveillance provides a much-needed foundation for robust, collaborative, and actionable science. By offering a practical, flexible, and FAIR-aligned framework, it empowers researchers to share their data in a way that maximizes its utility for addressing pressing questions in ecology, conservation, and global health. Widespread adoption of this standard will significantly enhance our ability to understand and mitigate the risks of emerging infectious diseases in a rapidly changing world.

The field of wildlife disease ecology has long been hampered by fragmented and inconsistent data reporting. Most existing best practices for data sharing focus primarily on pathogen genetic sequence data, while other critical facets of wildlife disease data—particularly negative results—are often withheld or summarized in descriptive tables with limited metadata [1]. This lack of standardization creates significant barriers to data aggregation, synthesis, and re-use, ultimately impeding our ability to track emerging zoonotic threats and understand disease dynamics across ecosystems.

To address these challenges, researchers have developed a minimum data and metadata reporting standard for wildlife disease studies [1] [4] [2]. This standardized framework identifies a set of 40 data fields (9 required) and 24 metadata fields (7 required) sufficient to standardize and document datasets consisting of records disaggregated to the finest possible spatial, temporal, and taxonomic scale [1]. The standard aligns with FAIR principles (Findable, Accessible, Interoperable, and Reusable) and is designed to enhance the transparency, actionability, and global utility of wildlife disease research [2].

Core Data Fields: The Essential Components for Wildlife Disease Data

The data standard organizes 40 core fields into three logical categories: sample data, host animal data, and parasite data. These fields are designed to capture information at the most granular level possible, typically representing the outcome of a single diagnostic test [1]. The following tables summarize all required and optional fields within each category.

Table 1: Sample-Related Data Fields (11 Fields, 3 Required)

Field Name	Required/Optional	Description	Controlled Vocabulary Suggested
Sample ID	Required	Unique identifier for the sample	Free text
Sample matrix	Required	Type of sample collected	Swab, tissue, blood, feces, etc.
Sample preservation method	Optional	How the sample was preserved	RNA later, frozen, ethanol, etc.
Date of sample collection	Required	Date when sample was collected	ISO 8601 format (YYYY-MM-DD)
Time of sample collection	Optional	Time when sample was collected	ISO 8601 format (HH:MM)
Latitude	Optional	Decimal latitude of sampling location	WGS84
Longitude	Optional	Decimal longitude of sampling location	WGS84
Location uncertainty	Optional	Accuracy of location coordinates in meters	Free number
Country	Optional	Country of sampling location	ISO 3166-1 alpha-3
Sampling scheme	Optional	Method used for selecting the sample	Targeted, random, convenience, etc.

Table 2: Host Organism Data Fields (13 Fields, 3 Required)

Field Name	Required/Optional	Description	Controlled Vocabulary Suggested
Host species	Required	Scientific name of the host species	Binomial nomenclature
Animal ID	Optional	Unique identifier for the host individual	Free text
Host sex	Optional	Sex of the host individual	Male, Female, Unknown
Host age	Optional	Age of the host individual	Free text or numerical with unit
Host life stage	Optional	Life stage of the host	Adult, juvenile, subadult, etc.
Host reproductive status	Optional	Reproductive condition of host	Pregnant, lactating, etc.
Host health status	Optional	Clinical health assessment	Healthy, clinical signs, moribund, etc.
Method of host death	Optional	How the host died if applicable	Found dead, euthanized, hunted, etc.
Captive or wild	Required	Whether the host was wild or captive	Wild, Captive
Host behavior	Optional	Observed behavior of the host	Free text

Table 3: Parasite/Pathogen Data Fields (16 Fields, 3 Required)

Field Name	Required/Optional	Description	Controlled Vocabulary Suggested
Test ID	Required	Unique identifier for the diagnostic test	Free text
Test result	Required	Outcome of the diagnostic test	Positive, negative, inconclusive, etc.
Pathogen taxon tested	Optional	Target pathogen for the test	Free text (ideally taxonomic name)
Diagnostic test	Optional	Method used for pathogen detection	PCR, ELISA, culture, microscopy, etc.
Test validation status	Optional	Whether the test was validated	In-house, commercial, peer-reviewed, etc.
Gene target	Optional	Genetic target for molecular tests	Free text (e.g., RdRp, spike)
Primer citation	Optional	Reference for primers/probes used	DOI or citation
Ct value	Optional	Cycle threshold for PCR tests	Free number
Pathogen taxon identified	Optional	Identity of detected pathogen	Free text (ideally taxonomic name)
GenBank accession	Optional	Accession for genetic sequence data	GenBank format

Project Metadata: Contextual Information for Data Reuse

Beyond the core data fields, the standard includes 24 metadata fields (7 required) that provide essential context about the entire project or study [1]. These fields are crucial for making datasets findable, citable, and interpretable by secondary users.

Table 4: Required Project Metadata Fields

Field Name	Description	Standard Suggested
Project title	Name of the research project	Free text
Project description	Abstract describing the project's aims and scope	Free text
Lead investigator	Person responsible for the project	Free text
Lead institution	Organization responsible for the project	Free text
ORCID	Unique identifier for the lead investigator	ORCID format
Project contact email	Email address for questions about the data	Email format
Funding source	Organization that funded the research	Free text

Additional optional metadata fields include: other investigators, other institutions, other ORCIDs, project start date, project end date, project website, geographic scope, data collector, data contact email, keywords, recommended citation, license, data use agreement, data embargo date, and supplementary notes [1].

Implementation Workflow: Applying the Data Standard

The following workflow diagram illustrates the step-by-step process for implementing the wildlife disease data standard in research practice, from study design through data sharing.

The Scientist's Toolkit: Essential Research Reagent Solutions

The following table details key research reagents and materials commonly used in wildlife disease research, particularly for pathogen detection and characterization.

Table 5: Essential Research Reagents for Wildlife Disease Studies

Reagent/Material	Function	Application Examples
RNA/DNA Preservation Buffers	Stabilizes nucleic acids for transport and storage	RNA later for viral RNA preservation in field collections [1]
Nucleic Acid Extraction Kits	Isolates DNA/RNA from diverse sample matrices	Extraction of viral RNA from swabs for coronavirus detection [1]
PCR Master Mixes	Enzymatic amplification of target nucleic acid sequences	Coronavirus detection using pan-coronavirus PCR assays [1]
Specific Primers and Probes	Binds to target sequences for molecular detection	Coronavirus RdRp gene amplification [1]
ELISA Kits	Detects pathogen-specific antibodies or antigens	Serological screening for pathogen exposure [1]
Viral Transport Media	Maintains pathogen viability during transport	Preservation of viruses from swab samples for culture [1]
Field Collection Supplies	Enables safe and standardized sample collection	Sterile swabs, cryovials, personal protective equipment [1]
Mal-Phe-C4-Val-Cit-PAB	Mal-Phe-C4-Val-Cit-PAB, MF:C32H40N6O7, MW:620.7 g/mol	Chemical Reagent
Boc-NH-PEG1-Ph-O-CH2COOH	Boc-NH-PEG1-Ph-O-CH2COOH, MF:C15H21NO6, MW:311.33 g/mol	Chemical Reagent

Experimental Protocol: Implementing the Standard for Pathogen Detection

This section provides a detailed methodological protocol for a typical wildlife disease study implementing the minimum data standard, using coronavirus detection in bats as an example [1].

Sample Collection and Processing

• Field Collection: Safely capture bats following approved animal handling protocols. Record host-level data including species, sex, age, and weight directly into standardized field forms.
• Sample Collection: Collect oral and rectal swabs from each individual, placing swabs immediately into RNA preservation buffer. Assign unique Sample IDs following a consistent numbering system.
• Data Recording: Record all required sample fields (Table 1) including date, time, and GPS coordinates of collection. Record all applicable host fields (Table 2) including host species, sex, and life stage.
• Sample Storage: Transport samples to laboratory under appropriate temperature conditions and store at -80°C until processing.

Laboratory Analysis

• Nucleic Acid Extraction: Extract RNA from swab samples using commercial extraction kits, following manufacturer protocols. Include appropriate positive and negative controls.
• Molecular Testing: Perform reverse transcription PCR using pan-coronavirus primers targeting the RNA-dependent RNA polymerase (RdRp) gene. Set up reactions in a dedicated clean area to prevent contamination.
• Result Interpretation: Analyze PCR products by gel electrophoresis. Record results (positive, negative, or inconclusive) in the Test result field. For positive samples, record Ct values if quantitative methods are used.
• Sequencing: For positive samples, perform sequencing of PCR products. Submit confirmed sequences to GenBank and record the accession number in the appropriate field.

Data Compilation and Validation

• Data Assembly: Compile all data into a rectangular data format (.csv or .xlsx) using the provided template, with each row representing a single test result and columns representing all applicable data fields.
• Data Validation: Use the provided JSON Schema or validation tools (R package wddsWizard) to check data against the standard requirements.
• Metadata Completion: Complete all required project metadata fields, ensuring accurate contact information and funding source documentation.

Data Visualization and Reporting Considerations

When presenting wildlife disease data, appropriate visualization methods enhance comprehension of patterns and relationships. The table below compares common data visualization approaches relevant to wildlife disease research.

Table 6: Data Visualization Methods for Wildlife Disease Research

Visualization Type	Best Use Cases	Standard Compliance Application
Bar Charts	Comparing prevalence across host species or locations	Visualizing differences in detection rates between species [6]
Line Charts	Showing disease trends over time	Displaying seasonal patterns in pathogen detection [7]
Tables	Presenting exact values for specific data points	Reporting complete standardized datasets with all fields [6]
Maps	Visualizing spatial distribution of sampling or detection	Showing geographic patterns of positive tests [1]

For all visualizations, ensure sufficient color contrast (minimum 4.5:1 for standard text) to meet accessibility standards [8]. Avoid red-green color combinations, which are problematic for color-blind users [9]. Instead, use high-contrast alternatives such as blue with orange or magenta with green [9].

The Vital Role of Negative Data and Granular Metadata in Ecological Insight

In wildlife disease research, the pursuit of ecological insight has traditionally been dominated by positive findings—the detection of pathogens, the confirmation of outbreaks, and the identification of novel parasites. However, this focus creates a substantial blind spot, omitting the critical context provided by negative results and the granular metadata necessary for robust interpretation. The absence of this information severely limits our understanding of disease dynamics and represents a significant source of waste in scientific resources [10].

This Application Note frames these challenges within the urgent context of establishing and implementing a minimum data standard for wildlife disease research. Such standards are vital for transforming fragmented, non-reproducible data into FAIR (Findable, Accessible, Interoperable, and Reusable) resources that can power synthetic analyses, inform public health decisions, and ultimately strengthen our ecological insight [1] [11] [2]. We provide detailed protocols and analytical frameworks to empower researchers, scientists, and drug development professionals to consistently capture and report the full spectrum of data necessary for a comprehensive understanding of disease ecology.

The Problem: Incomplete Data and Its Consequences

The Negative Data Gap

Negative data—defined as results that show no detection of a pathogen, statistically insignificant findings, or outcomes that do not support a initial hypothesis—are systematically underrepresented in the scientific literature [10]. An analysis of 110 studies that tested wild bats for coronaviruses revealed that 96 studies (87%) reported data only in summarized format, making disaggregation and reanalysis impossible. Of the 14 studies that did share individual-level data, 11 only shared data for positive results, completely precluding any comparison of prevalence across populations, years, or species [1] [12]. This publication bias creates a distorted view of reality, inflating perceived risks and masking the true absence or limited distribution of pathogens.

The Metadata Deficit

Beyond negative results, a critical lack of contextual metadata plagues wildlife disease datasets. Studies frequently fail to report fundamental information such as:

• Sampling effort over space and time
• Precise geographic locations of sampling sites
• Host-level data (e.g., sex, age, life stage, body condition) [1] Without this granular metadata, it is impossible to assess potential sampling biases, understand host-pathogen dynamics, or properly aggregate datasets for meta-analysis [1] [13]. This deficit effectively renders many datasets useless for purposes beyond their original, narrow scope.

The Solution: A Minimum Data Standard

To address these critical gaps, a cross-institutional consortium has proposed a flexible, minimum data and metadata reporting standard specifically for wildlife disease studies [1] [2]. This standard is designed to ensure that shared data are accompanied by sufficient context to be meaningfully reused, while remaining accessible to a broad range of practitioners.

Core Data Fields

The standard identifies 40 core data fields, categorized into three logical groups, with only 9 designated as required to maintain flexibility [1] [2]. The table below summarizes the key fields in each category.

Table 1: Core Data Fields in the Wildlife Disease Minimum Data Standard

Category	Key Fields (Required in Bold)	Data Type	Description and Purpose
Sampling & Context	Sample ID	String	A researcher-generated unique ID for the sample (e.g., "OS_BZ19-114"). Essential for sample tracking [12].
	Animal ID	String	A unique ID for the individual animal. Can be blank for pooled samples [12].
	Collection Date	Date	The date of sample collection. Critical for temporal trend analysis [1].
	Sampling Method	String	e.g., "live capture", "passive surveillance". Provides context for potential sampling bias [13].
Host Organism	Host Identification	String	The Linnaean classification (ideally species binomial). Equivalent to dwc:scientificName [12].
	Organism Sex	String	The sex of the individual animal. Equivalent to dwc:sex [12].
	Host Life Stage	String	e.g., "juvenile", "adult". Important for understanding age-related susceptibility [12].
	Mass, Mass Units	Number, String	The body mass of the animal at collection, with specified units. A key indicator of host condition [12].
Parasite/Pathogen	Test Result	String	The outcome of the diagnostic test (e.g., "positive", "negative", "inconclusive"). The primary record for negative data [1].
	Pathogen	String	The identity of the parasite/pathogen tested for. Must be specified even for negative results [1].
	Pathogen Taxon ID	String	A taxonomic identifier (e.g., from NCBI Taxonomy). Enables precise data integration [1].
	GenBank Accession	String	Accession number for associated genetic sequence data. Links to detailed molecular data [1].

Project-Level Metadata

In addition to the core data, the standard outlines 24 metadata fields (7 required) that describe the project as a whole. These are aligned with the DataCite Metadata Schema and are crucial for discovery and citation [1]. Key metadata includes:

• Project Title and Description
• Creator(s) with ORCIDs
• Funding Reference
• Rights and License
• Geographic Location (a bounding box for the entire project)
• Related Publications

This metadata layer ensures that the dataset is not just a standalone table, but a properly contextualized and citable research output [1] [11].

Experimental Protocols for Standard-Compliant Research

Workflow for Study Implementation and Data Submission

The following diagram outlines the key stages for planning and executing a wildlife disease study in compliance with the minimum data standard, from formulation to data sharing.

Protocol: Sample Collection and Diagnostic Testing for Pathogen Surveillance

Application: This protocol is designed for active surveillance of pathogens (e.g., viruses, bacteria) in wild animal populations, such as longitudinal studies or outbreak investigations [1].

Materials:

• Personal protective equipment (PPE)
• Appropriate equipment for live capture or handling of carcasses
• Sterile swabs, collection tubes, and appropriate storage media (e.g., viral transport medium)
• Labels and a waterproof writing instrument
• Data sheets (digital or physical)
• GPS device
• Cooler with dry ice or liquid nitrogen for sample preservation

Procedure:

• Pre-Field Planning:

  • Consult the minimum data standard checklist (Tables 1-3) and identify all relevant fields for your study design [1].
  • Download and adapt the standardized template files (.csv or .xlsx) from the official GitHub repository (github.com/viralemergence/wdds) [1].

• Sample Collection:

  • Upon capturing or encountering an animal, assign a unique Animal ID.
  • Record the Collection Date and precise geographic coordinates using a GPS.
  • Collect relevant host metadata: species (Host Identification), Sex, Life Stage, and Mass [12].
  • Collect the sample(s) (e.g., oral swab, rectal swab, blood). Assign a unique Sample ID that is logically linked to the Animal ID (e.g., Animal BZ19-114 -> Samples OS_BZ19-114, RS_BZ19-114).
  • Record the Sampling Method and any other relevant contextual notes.

• Diagnostic Testing:

  • In the laboratory, perform the planned diagnostic test (e.g., PCR, ELISA) [1].
  • For each sample tested, record the Test Result ("positive", "negative", or "inconclusive").
  • Record all test-specific metadata. For PCR, this includes:
    • Forward Primer Sequence
    • Reverse Primer Sequence
    • Gene Target
    • Primer Citation [1]
  • If the test is positive and leads to further characterization (e.g., sequencing), populate the relevant fields such as Pathogen Taxon ID and GenBank Accession.

• Data Assembly and Validation:

  • Assemble all data into the pre-formatted template, creating one row per diagnostic test. A single animal with multiple samples or tests will generate multiple rows [1].
  • Use the provided validation tools (e.g., the JSON Schema or the R package wddsWizard) to check for formatting errors and compliance with the standard [1].
  • Ensure project-level metadata is completed.

Workflow for Data Structure and Diagnostic Outcomes

This diagram illustrates the fundamental data structure of "tidy data," where each row represents a single test, and how this structure accommodates both positive and negative results, linking them to critical metadata.

The Scientist's Toolkit: Research Reagent Solutions

The following table details key reagents and materials essential for conducting standardized wildlife disease research, as referenced in the protocols and studies above.

Table 2: Essential Research Reagents and Materials for Wildlife Disease Studies

Reagent/Material	Function/Application	Critical Metadata to Record
Sterile Swabs & Transport Media	Collection and preservation of samples from mucosal surfaces, wounds, or tissues.	Lot number, type of swab (e.g., nylon-flocked), composition of transport medium, storage temperature.
PCR Primers & Probes	In vitro detection of specific pathogen genetic material via polymerase chain reaction.	Forward and Reverse Primer sequences, Gene Target, Primer Citation, probe sequence and chemistry (e.g., TaqMan) [1].
ELISA Kits	Immunoassay for detecting pathogen-specific antibodies or antigens in serum or other fluids.	Manufacturer, catalog number, lot number, target antigen/antibody, and citation for the protocol if adapted.
RNA/DNA Extraction Kits	Isolation of high-quality nucleic acids from diverse sample matrices (swabs, tissues, blood).	Manufacturer, kit name, version, lot number. Elution volume and concentration of the final extract should also be recorded.
Next-Generation Sequencing Library Prep Kits	Preparation of nucleic acid libraries for metagenomic or transcriptomic sequencing on platforms like Illumina.	Kit name and version, lot number, protocol deviations. Links resulting Sequence Read Archive (SRA) accessions are critical [1].
6',7'-Dihydroxybergamottin acetonide	6',7'-Dihydroxybergamottin acetonide, MF:C24H28O6, MW:412.5 g/mol	Chemical Reagent
Z-LYS-SBZL monohydrochloride	Z-LYS-SBZL monohydrochloride, MF:C21H27ClN2O3S, MW:423.0 g/mol	Chemical Reagent

The adoption of a minimum data standard that mandates the reporting of negative results and granular metadata is not merely an academic exercise in data management. It is a fundamental prerequisite for generating genuine ecological insight and strengthening our defenses against emerging zoonotic threats. By implementing the protocols and frameworks outlined in this Application Note, the research community can transform isolated datasets into a collaborative, reusable, and powerful resource. This practice ensures that every data point—whether positive or negative—contributes to a cumulative and accurate understanding of wildlife disease dynamics, ultimately advancing the goals of both conservation and global health security.

Aligning with Global Health Security and Pandemic Preparedness Goals

The COVID-19 pandemic demonstrated that infectious disease threats anywhere are threats everywhere, revealing critical weaknesses in global early warning systems [14] [15]. Approximately 60% of emerging infectious diseases originate from animals, with wildlife serving as a primary source for novel pathogens [2]. Despite this recognized threat, wildlife disease research has historically been hampered by fragmented, inconsistent data collection and reporting practices that limit the utility of surveillance data for global health security [1] [2]. The recent development of a minimum data standard for wildlife disease research addresses this critical gap by establishing standardized reporting frameworks that enable data aggregation, analysis, and interoperability across studies and jurisdictions [1]. This protocol outlines the application of this data standard within the broader context of global health security frameworks, including the World Health Organization's Pandemic Agreement and the Global Health Security Agenda [16] [14]. By implementing these standardized approaches, researchers can directly contribute to strengthening global capacity for pandemic prevention, preparedness, and response.

Minimum Data Standard Framework

Core Components and Structure

The minimum data standard for wildlife disease research provides a comprehensive yet flexible framework for recording and reporting essential information from wildlife disease studies [1]. Developed through an iterative process incorporating real-world data and existing best practices, the standard is designed to be accessible to diverse practitioners while providing sufficient structure for large-scale data analysis [1] [12]. The framework aligns with FAIR principles (Findable, Accessible, Interoperable, and Reusable) and supports global health security objectives by enabling data aggregation and synthesis across studies and surveillance systems [1] [2].

The standard identifies 40 core data fields organized into three logical categories and 24 metadata fields for project-level documentation [1] [12]. This structure ensures that data shared by researchers contains sufficient contextual information for meaningful interpretation and reuse by other scientists, public health agencies, and policymakers [1]. The "tidy data" format, where each row corresponds to a single diagnostic test outcome, facilitates both human interpretation and machine processing [1].

Table 1: Required Data Fields in the Wildlife Disease Data Standard

Category	Field Name	Data Type	Description	Global Health Security Relevance
Sampling	Sample ID	String	Unique identifier for the sample	Enables specimen tracking across laboratories and databases
Sampling	Collection Date	Date	Date when sample was collected	Critical for temporal analysis of pathogen emergence and spread
Sampling	Location	String	Geographic coordinates of sampling	Allows spatial mapping of disease risks and hotspots
Host	Host Identification	String	Linnaean classification of host	Identifies reservoir species and host range for risk assessment
Parasite	Pathogen Tested For	String	Target pathogen of diagnostic test	Documents surveillance priorities and testing capabilities
Parasite	Diagnostic Test	String	Method used for pathogen detection	Informs test accuracy and comparability across studies
Parasite	Test Result	String	Outcome of diagnostic test	Enables prevalence calculations; includes negative data
Parasite	Pathogen Identity	String	Identification of detected pathogen	Documents novel pathogen discovery and genetic diversity
Parasite	GenBank Accession	String	Reference to genetic sequence data	Links to molecular data for pathogen characterization

Applicable Study Types

The data standard is designed for flexibility across diverse research and surveillance contexts [1]. Suitable project types include:

• Longitudinal, multi-species pathogen surveillance at single or multiple locations [1]
• Investigation of mass wildlife mortality events to identify causative agents [1]
• Targeted pathogen detection during human disease outbreak investigations [1]
• Passive surveillance programs analyzing wildlife carcasses submitted by the public [1] [13]
• First report of a pathogen in a specific wildlife species [1]
• Pathogen discovery research in wildlife populations [1]

The standard specifically excludes certain data types better served by other specialized standards, such as free-living macroparasite records (Darwin Core format), arthropod blood meal data (specialized vector standards), and environmental microbiome data without associated host animals [1].

Experimental Protocols and Workflows

Standardized Data Collection Protocol

Objective: To ensure consistent collection of essential data fields during wildlife disease investigations for compatibility with global health security databases.

Materials Required:

• Standardized data collection forms (digital or paper)
• GPS device or smartphone with geolocation capability
• Unique sample identifier tags/labels
• Appropriate personal protective equipment (PPE)
• Sample collection kits appropriate for target pathogens
• Data dictionary with controlled vocabularies

Procedure:

• Pre-sampling Preparation
  • Consult the data standard field list to identify required and relevant optional fields for the study design
  • Pre-generate unique Sample IDs and Animal IDs to prevent duplication
  • Document project-level metadata including principal investigator, funding source, and study objectives

• Field Sampling and Data Recording

  • Record precise geographic coordinates using GPS device at point of capture/collection
  • Assign unique Sample ID and Animal ID following established numbering system
  • Document host species identification at lowest possible taxonomic level
  • Record host characteristics including sex, life stage, and clinical signs
  • Collect and preserve appropriate samples for pathogen detection (swabs, tissues, blood)
  • Note diagnostic tests to be performed on each sample type

• Sample Processing and Storage

  • Maintain chain of custody documentation linking samples to collection data
  • Process samples according to established protocols for target pathogens
  • Aliquot samples for archiving in specimen banks when possible

• Diagnostic Testing

  • Perform appropriate diagnostic tests (PCR, ELISA, culture, etc.)
  • Record detailed test parameters including primers, protocols, and controls
  • Document both positive and negative results comprehensively

Table 2: Essential Research Reagents and Materials

Reagent/Material	Specification	Application	Quality Control
Nucleic Acid Extraction Kits	Compatible with sample type (blood, tissue, swab)	Pathogen genetic material isolation	Include extraction controls; validate for sensitivity
PCR Primers/Probes	Target conserved pathogen genes	Pathogen detection and identification	Verify specificity; include positive and negative controls
ELISA Kits	Validated for wildlife species when possible	Antibody detection; serosurveillance	Assess cross-reactivity; establish species-specific cutoffs
Viral Transport Media	Compatible with downstream applications	Preserve viability for virus isolation	Test for inhibition; batch validation
Rapid Diagnostic Tests	Field-deployable formats	Preliminary screening in remote areas	Validate against reference standards
Microscopy Supplies	Stains, slides, fixatives	Parasite identification and morphology	Standardize examination protocols

Data Management and Sharing Workflow

The following workflow diagrams the standardized process for managing and sharing wildlife disease data according to the minimum data standard:

Data Validation and Sharing Steps:

• Format Data: Utilize standardized templates (.csv or .xlsx) available through the standard's supplementary materials or GitHub repository (github.com/viralemergence/wdds) [1]
• Validate Dataset: Apply validation tools such as the provided JSON Schema or R package (github.com/viralemergence/wddsWizard) to ensure compliance with the standard [1]
• Annotate with Metadata: Complete project-level metadata using DataCite Metadata Schema as recommended by the Generalist Repository Ecosystem Initiative [1]
• Apply Access Controls: Implement appropriate data security measures including obfuscation of precise locations for threatened species or sensitive contexts [2]
• Deposit in Repository: Share data through open-access repositories such as Zenodo or specialized platforms like the Pathogen Harmonized Observatory (PHAROS) database [1] [2]
• Publish with Citation: Ensure data receives proper attribution through persistent identifiers (DOIs) and citation in research publications [2]

Alignment with Global Health Security Frameworks

Contribution to International Preparedness

Standardized wildlife disease data directly supports the objectives of major global health security initiatives by enabling early detection of zoonotic threats and facilitating rapid risk assessment [16] [14]. The WHO Pandemic Agreement, adopted in May 2025, emphasizes equitable access to pandemic countermeasures and strengthened global coordination [16]. Implementation of the wildlife disease data standard contributes to these goals through:

• Enhanced Situational Awareness: Standardized data enables integration of wildlife disease information with human health surveillance systems, creating a more comprehensive picture of emerging threats [2] [14]
• Accelerated Response: Interoperable data formats reduce delays in data analysis and information sharing during outbreak investigations [1] [2]
• Equitable Benefit Sharing: Comprehensive metadata facilitates tracking of pathogen samples and genetic sequences, supporting transparent implementation of access and benefit-sharing frameworks under the Pandemic Agreement's PABS (Pathogen Access and Benefit Sharing) system [16]

The data standard also aligns directly with the U.S. Global Health Security Strategy (2024) and Global Health Security Agenda targets by strengthening core capabilities in surveillance, laboratory systems, and emergency response [14]. Specifically, it addresses the GHSA objective for countries to take greater ownership of health security efforts through standardized, sustainable surveillance systems [14].

Implementation Considerations for Global Security

Successful implementation of the data standard requires addressing several practical considerations:

• Balancing Transparency and Security: While data sharing is essential for global health security, certain high-resolution data (e.g., exact locations of endangered species or high-consequence pathogens) may require controlled access or obfuscation to prevent misuse [2]
• Building Capacity: Widespread adoption requires training programs and technical support for researchers in diverse settings, particularly in biodiversity-rich regions where surveillance is most critical [1] [2]
• Leveraging Existing Infrastructure: Integration with established biodiversity data platforms like the Global Biodiversity Information Facility (GBIF) enhances discoverability and interoperability [1]
• Engaging Local Expertise: Successful wildlife disease surveillance often depends on involvement of local communities, indigenous knowledge, and professional networks including hunters, veterinarians, and wildlife managers [13]

The minimum data standard for wildlife disease research represents a practical, implementable framework for aligning ecological surveillance with global health security priorities [1] [2]. By adopting this standard, researchers directly contribute to strengthening the global early warning system for emerging zoonotic threats and support the implementation of international agreements like the WHO Pandemic Accord [16]. The protocol outlined in this document provides a clear pathway for researchers to standardize data collection, management, and sharing practices in ways that enhance both scientific understanding and public health security. As the COVID-19 pandemic demonstrated, technical preparedness must be coupled with data transparency and international solidarity to effectively address global health threats [15]. Widespread adoption of these standardized approaches will help ensure that wildlife disease research fulfills its critical role in pandemic prevention, preparedness, and response.

A Step-by-Step Guide to Implementing the Wildlife Disease Data Standard

The establishment of a minimum data standard is a critical advancement in wildlife disease research, a field essential for global health security and ecological stability [2]. The initial and most crucial phase in implementing this standard is determining the fit-for-purpose and defining the project scope. This step ensures that the data collected are not merely abundant but are suitable for their intended use—be it outbreak investigation, pathogen discovery, or long-term surveillance [1]. A well-defined scope is the foundation for generating data that are findable, accessible, interoperable, and reusable (FAIR), thereby maximizing the scientific and public health impact of the research [1] [2]. This protocol provides a detailed framework for researchers to navigate this essential first step.

The Conceptual Framework: Fit-for-Purpose Assessment

The "fit-for-purpose" (FfP) concept, increasingly adopted in regulated scientific research, emphasizes that study design elements must be directly aligned with the primary research objective [17]. In the context of a minimum data standard for wildlife disease research, this means the data collected must be structurally and contextually sufficient to answer the specific research question and be usable by the broader scientific community.

Core Principle and Its Necessity

The guiding philosophy of the minimum data standard is that researchers should share raw wildlife disease data in a "tidy" or "rectangular" format, where each row corresponds to a single diagnostic measurement [1]. This structure is vital because research data are often fragmented and inconsistently reported. Many studies only provide summary statistics or share data solely for positive results, which prevents meaningful aggregation and analysis across different studies and hampers the understanding of disease dynamics [1]. Applying an FfP assessment at the project's inception ensures that the resulting dataset will have the granularity and metadata required for both immediate analysis and future reuse.

Applicability of the Standard

The minimum data standard is designed for a wide range of project types involving the examination of wild animals for parasites (including viruses, bacteria, and other pathogens) [1]. Before applying the standard, researchers should verify their project's alignment with the following general categories:

• Pathogen Discovery: The first report of a parasite in a wildlife species.
• Outbreak Investigation: Investigation of a mass wildlife mortality event.
• Longitudinal Studies: Multi-site, multi-species, or multi-temporal sampling for a parasite.
• Targeted Surveillance: Regular screening in a single monitored wildlife population.
• Public Health Response: Screening of wildlife during an investigation of a human disease outbreak.
• Passive Surveillance: Testing of wildlife carcasses submitted by the public.

It is important to note that related data types, such as records of free-living macroparasites (e.g., from tick dragging) or environmental microbiome data, are better documented using other specialized standards like Darwin Core or MIReAD [1].

Defining Project Scope: A Practical Protocol

Defining the project scope is an actionable process that translates the FfP concept into a concrete research plan. The following steps and workflow provide a methodology for establishing a robust scope.

Step-by-Step Scoping Protocol

• Articulate the Primary Objective: Formulate a precise and concise statement of the study's goal (e.g., "To determine the prevalence and diversity of coronaviruses in bat populations of Belize").
• Identify Core Data Entities: Map the key entities involved in the study: the Host Organism, the Sample collected, and the Parasite/Pathogen being tested for. This clarifies the relationships that the data structure must capture.
• Inventory Methodologies: Document all planned diagnostic methods (e.g., PCR, ELISA, culture). This will later determine which specific data fields from the standard are relevant.
• Determine Spatial-Temporal Granularity: Decide the finest level of detail for location (e.g., GPS coordinates, region) and time (e.g., exact date, month, year) that the study design and ethics permit.
• Conform to the Standard's Structure: Consult the list of 40 data fields and 24 metadata fields in the minimum data standard. Identify which fields beyond the 9 required ones are applicable to your study [1] [4].
• Plan for Data Disaggregation: Commit to recording and sharing data at the finest possible spatial, temporal, and taxonomic scale, ideally at the level of individual host animals and specific tests [1].

Experimental Workflow for Scope Determination

The following diagram illustrates the logical workflow for defining a project scope that is fit-for-purpose.

Minimum Data Field Requirements

The minimum data standard provides a flexible framework of 40 data fields categorized into three groups. The following tables summarize the required and conditional fields essential for ensuring data completeness and interoperability. During the scoping phase, the research team must decide which of these fields will be populated.

Table 1: Sample and Host Data Fields

This table outlines the core fields required to document the context of the sample and the host organism [1].

Field Name	Category	Requirement Level	Explanation & Usage
Animal ID	Host	Required	A unique identifier for the individual host animal.
Host Species	Host	Required	The scientific name (e.g., Desmodus rotundus) is strongly recommended.
Sample ID	Sample	Required	A unique identifier for the biological sample collected.
Sample Type	Sample	Required	The type of sample collected (e.g., oral swab, rectal swab, blood, tissue).
Sample Date	Sample	Required	The date the sample was collected.
Latitude	Sample	Required	The decimal degree latitude of the sampling location.
Longitude	Sample	Required	The decimal degree longitude of the sampling location.
Host Sex	Host	Conditionally Required	The sex of the host animal, if collected.
Host Age Class	Host	Conditionally Required	The age class of the host animal (e.g., adult, juvenile), if collected.
Life Stage	Host	Conditionally Required	The life stage of the host animal, if collected and applicable.

Table 2: Pathogen and Diagnostic Data Fields

This table outlines the fields required to report the diagnostic methods and results [1].

Field Name	Category	Requirement Level	Explanation & Usage
Test ID	Parasite	Required	A unique identifier for the specific diagnostic test performed.
Test Result	Parasite	Required	The outcome of the diagnostic test (e.g., Positive, Negative, Inconclusive).
Diagnostic Method	Parasite	Conditionally Required	The specific method used (e.g., PCR, ELISA, metagenomics). Required if a test was performed.
Gene Target	Parasite	Conditionally Required	The specific gene targeted (e.g., RNA-dependent RNA polymerase). Required for PCR-based tests.
Parasite Taxon	Parasite	Conditionally Required	The identity of the detected parasite. Required if the test result is positive and identity is known.
GenBank Accession	Parasite	Conditionally Required	The accession number for genetic sequence data submitted to a public repository.

Research Reagent Solutions and Essential Materials

The following toolkit details key reagents and materials commonly used in wildlife disease research, explaining their critical function within the context of the minimum data standard.

Table 3: Research Reagent Solutions Toolkit

Item	Function in Wildlife Disease Research
Swabs (e.g., oral, rectal)	For non-lethal collection of mucosal samples for pathogen detection, crucial for longitudinal studies and minimizing harm [1].
Nucleic Acid Extraction Kits	To isolate DNA/RNA from diverse sample matrices for subsequent molecular assays like PCR and metagenomics.
PCR Primers & Master Mixes	Core reagents for targeted molecular detection and identification of pathogens; the primer citation is a key data field [1].
ELISA Kits & Antibodies	For serological detection of pathogen exposure (antibodies) or specific antigens in host samples.
Viral Transport Media (VTM)	To preserve the viability and nucleic acid integrity of viruses in swab samples during transport and storage.
Liquid Nitrogen Dewar	For cryopreservation of samples in the field, maintaining sample integrity for future analyses.
Global Positioning System (GPS)	To record precise latitude and longitude, which are required fields for spatial analysis and mapping [1].
Torosachrysone 8-O-beta-gentiobioside	Torosachrysone 8-O-beta-gentiobioside, MF:C28H36O15, MW:612.6 g/mol
3-Hydroxyoctanoic Acid-d12	3-Hydroxyoctanoic Acid-d12, MF:C8H16O3, MW:172.28 g/mol

Integration with Broader Reporting Frameworks

A fit-for-purpose scope and adherence to a data standard synergize with other reporting guidelines that promote rigorous and ethical science. For instance, the ARRIVE 2.0 guidelines (Animal Research: Reporting of In Vivo Experiments) provide a checklist to improve the transparency of animal research publications [18]. While the minimum data standard focuses on the structure and content of the underlying dataset, ARRIVE 2.0 ensures the accompanying manuscript adequately describes the experimental design, methods, and results. Using both frameworks in tandem enhances the overall reproducibility, ethical justification, and utility of wildlife disease studies.

The adoption of a minimum data standard is a critical step towards achieving transparency and actionability in wildlife disease research and surveillance [1]. The proposed standard identifies a set of 40 core data fields and 24 metadata fields to document datasets at the finest possible spatial, temporal, and taxonomic scale [1]. This document provides detailed Application Notes and Protocols for Step 2: Tailoring the Standard, guiding researchers in selecting applicable fields and appropriate ontologies for their specific study designs. Proper implementation ensures data is Findable, Accessible, Interoperable, and Reusable (FAIR), maximizing its utility for ecological analysis, disease tracking, and synthesis research.

Core Data Fields: Required and Conditional

The minimum data standard comprises 40 core fields categorized into sampling, host organism, and parasite data. Nine of these fields are mandatory for all studies, while the applicability of others depends on the research context and methods [1]. The table below summarizes all core fields, their categories, and their requirement status.

Table 1: Core Data Fields of the Wildlife Disease Minimum Data Standard

Field Name	Category	Requirement Level	Description & Applicability
Animal ID	Host	Required	Unique identifier for the individual host animal.
Host species	Host	Required	Scientific name (genus, species) of the host animal.
Sample ID	Sample	Required	Unique identifier for the specific sample collected.
Sample type	Sample	Required	e.g., oral swab, blood, tissue.
Diagnostic test name	Parasite	Required	Name of the test used (e.g., PCR, ELISA, culture).
Test result	Parasite	Required	Outcome of the diagnostic test (e.g., positive, negative, inconclusive).
Test date	Parasite	Required	Date the diagnostic test was performed.
Latitude	Sample	Required	Decimal degrees of sample collection location.
Longitude	Sample	Required	Decimal degrees of sample collection location.
Host age class	Host	Conditional	Applicable if age data is collected.
Host sex	Host	Conditional	Applicable if sex is determined.
Life stage	Host	Conditional	Applicable if recorded.
Forward primer sequence	Parasite	Conditional	Required for studies using PCR.
Reverse primer sequence	Parasite	Conditional	Required for studies using PCR.
Gene target	Parasite	Conditional	Required for studies using PCR.
Primer citation	Parasite	Conditional	Required for studies using PCR.
Probe target	Parasite	Conditional	Required for studies using ELISA.
Probe type	Parasite	Conditional	Required for studies using ELISA.
Probe citation	Parasite	Conditional	Required for studies using ELISA.
Parasite species	Parasite	Conditional	Identity of the detected parasite; relevant for positive results.

Experimental Protocol: The Tailoring Workflow

The following protocol provides a step-by-step methodology for tailoring the data standard to a specific research project.

Objective: To systematically identify which data fields beyond the mandatory ones are relevant to a study and to select appropriate controlled vocabularies for those fields.

Materials: The data standard template (available in .csv and .xlsx formats from the official GitHub repository: github.com/viralemergence/wdds) [1], the study's experimental design document, and access to the listed ontology resources.

Procedure:

• Study Design Audit:

  • Review your complete experimental plan, from animal capture and sampling to laboratory testing and data analysis.
  • Map every data point you plan to collect. For example, note if you will record host sex, age, weight, or clinical observations.

• Field Selection Matrix:

  • Using Table 1 above, create a project-specific matrix. For each field, mark it as "Mandatory," "Applicable," or "Not Applicable."
  • Example: A coronavirus surveillance study in bats using PCR would mark "Forward primer sequence," "Reverse primer sequence," "Gene target," and "Primer citation" as "Applicable," while "Probe target" (for ELISA) would be "Not Applicable."

• Ontology and Vocabulary Alignment:

  • For each "Applicable" free-text field, consult the table of recommended ontologies (Table 2 in this document) to select a suitable controlled vocabulary.
  • Adhere to the chosen ontology's term hierarchy and formatting (e.g., using full URIs or specific codes) to ensure interoperability.

• Data Table Formatting:

  • Download the template file from the official repository.
  • Retain all mandatory fields and the columns for your "Applicable" fields.
  • It is acceptable to leave columns for non-applicable fields empty, but retaining the column structure is recommended for machine readability.

• Metadata Documentation:

  • In the accompanying metadata file, document any key decisions made during the tailoring process. Justify the exclusion of any potentially relevant fields and list the specific ontologies used.

Visual Workflow: Tailoring the Data Standard

The following diagram illustrates the logical workflow for tailoring the data standard, from initial field identification to final data validation.

The Scientist's Toolkit: Research Reagent Solutions

The following table details key reagents and materials used in wildlife disease research, with a focus on their function within the context of data generation for this standard.

Table 2: Essential Research Reagents and Materials for Wildlife Disease Studies

Item	Primary Function	Application Notes
Sterile Swabs	Collection of biological samples from mucosal surfaces, wounds, or fur.	Different types (e.g., oral, rectal, nasal) must be precisely recorded in the "Sample type" field.
RNA/DNA Stabilization Buffer	Preserves nucleic acids at ambient temperature for transport from field to lab.	Critical for ensuring the integrity of genetic material for subsequent PCR testing.
PCR Master Mix	Contains enzymes, dNTPs, and buffer for the amplification of specific DNA/RNA targets.	Its use necessitates recording "Primer sequences," "Gene target," and "Primer citation" in the data table.
Species-Specific Primer/Probe Sets	Oligonucleotides designed to bind to and detect unique genetic sequences of a target parasite.	The core of specific diagnostic tests. The sequences and citations are critical metadata for reproducibility.
ELISA Kit	Immunoassay for detecting the presence of antigens or antibodies in a sample.	Using this reagent requires populating fields like "Probe target" and "Probe type" in the standard.
Personal Protective Equipment (PPE)	Mitigates zoonotic risk to researchers and prevents cross-contamination between animals/samples [19].	Includes nitrile gloves, leather gloves for bite risks, and long-sleeved clothing. Safety protocols should be documented.
Field Decontamination Supplies	Prevents the spread of pathogens between sampling sites and animals [19].	Includes bleach, alcohol, and sodium thiosulfate for neutralizing disinfectants. Decontamination methods should be noted.
1,3-Dihydroxy-2,4-diprenylacridone	1,3-Dihydroxy-2,4-diprenylacridone	Get 1,3-Dihydroxy-2,4-diprenylacridone, a natural acridone alkaloid for cancer and infectious disease research. This product is For Research Use Only. Not for human or veterinary use.
(R)-GNA-C(Bz)-phosphoramidite	(R)-GNA-C(Bz)-phosphoramidite, MF:C44H50N5O7P, MW:791.9 g/mol	Chemical Reagent

Ontology and Vocabulary Mapping Guide

To ensure interoperability, the use of controlled vocabularies and ontologies is strongly encouraged for the free-text fields within the standard. The following table provides a mapping of common data fields to recommended semantic resources.

Table 3: Recommended Ontologies and Controlled Vocabularies for Standardized Data Entry

Data Field Category	Example Fields	Recommended Ontology / Vocabulary	Notes and Access
Host Taxonomy	Host species	Global Biodiversity Information Facility (GBIF) Backbone Taxonomy / NCBI Taxonomy	Provides authoritative and updated scientific names. Use the full binomial name.
Location	Country, Location	GeoNames	A global geographical database. Can be used to standardize location names beyond coordinates.
Sample Details	Sample type	Environment Ontology (ENVO) / NCBI BioSample	ENVO includes terms for host-associated environmental materials like "oral swab" or "feces."
Diagnostic Methods	Diagnostic test name	Ontology for Biomedical Investigations (OBI)	Contains standardized terms for common laboratory processes and assays.
Parasite Taxonomy	Parasite species	NCBI Taxonomy	The standard for pathogen naming, especially for viruses and bacteria.
Life History Traits	Host sex, Life stage	UBERON Anatomy Ontology / Phenotype And Trait Ontology (PATO)	PATO includes terms for "female," "male," and life stages like "adult" or "juvenile."

Safety and Ethical Considerations in Data Sharing

While comprehensive data sharing is a core goal, researchers must navigate potential safety and ethical concerns [1]. Data should be shared at a spatial resolution that does not facilitate the targeting of endangered or threatened species for poaching or persecution. For research involving high-consequence pathogens, a temporary embargo on public data release may be justified to allow for official reporting and public health communication. In all cases, the sharing of precise location data must be balanced with conservation and safety imperatives. All wildlife research must be conducted under approved animal care and use protocols, with appropriate safety measures for zoonotic hazards documented [19].

Core Principles of Tidy Data

In the context of wildlife disease research, adopting a consistent data structure is a critical minimum standard that enables robust analysis, collaboration, and reproducibility. Tidy data provides a unified framework for organizing data, ensuring that datasets are "all alike" and therefore easier to manipulate, model, and visualize [20]. The core principles require that every dataset be structured such that each variable forms a column, each observation forms a row, and each value resides in its own cell [20] [21]. Adhering to this format from the outset of data collection minimizes wrangling time and reduces errors in subsequent analytical phases.

The Three Fundamental Rules

• Each variable is in a column. A variable is any attribute that can be measured for an observation. In a wildlife disease context, this includes pathogen_strain, host_species, collection_date, and viral_load.
• Each observation is in a row. An observation is a set of measurements made under similar conditions on a single sampling unit. For example, all data from a single animal necropsy or from a specific location at a given time constitutes one observation.
• Each value is in a cell. Every single measurement or data point must occupy its own cell in the rectangular dataset. No cell should contain multiple values or structured lists.

Practical Application in Wildlife Disease Research

From Messy to Tidy: A Concrete Example

Field and laboratory data are often recorded in wide formats optimized for data entry, which violates tidy data principles and complicates analysis. The transformation to a tidy format is demonstrated below.

Table 1: Common Messy Field Data Format

Region	Year	CanineDistemperCount	AvianInfluenzaCount
Northeast	2022	15	3
Northeast	2023	22	5
Southwest	2022	8	12
Southwest	2023	11	15

Table 2: Tidy Data Format After Restructuring

Region	Year	Disease	Case_Count
Northeast	2022	Canine_Distemper	15
Northeast	2022	Avian_Influenza	3
Northeast	2023	Canine_Distemper	22
Northeast	2023	Avian_Influenza	5
Southwest	2022	Canine_Distemper	8
Southwest	2022	Avian_Influenza	12
Southwest	2023	Canine_Distemper	11
Southwest	2023	Avian_Influenza	15

In the tidy version (Table 2), Disease is a single variable, and Case_Count is another, making it straightforward to filter, group, and visualize data by disease type.

Experimental Protocol: Data Tidying Workflow

Protocol Title: Conversion of Wide-Format Surveillance Data to Tidy Format Using R and the tidyr Package.

Objective: To standardize a messy dataset where column names represent values of a variable (e.g., different disease names) into a tidy format suitable for statistical analysis.

Materials:

• R statistical software environment (v4.3.0 or higher)
• RStudio IDE
• tidyverse meta-package (includes tidyr and dplyr)

Methodology:

• Data Import: Load the wide-format data (e.g., messy_data.csv) into R using read_csv().
• Variable Identification: Identify the set of columns whose names are values, not variables (e.g., Canine_Distemper_Count, Avian_Influenza_Count). These will be pivoted.
• Column Pivoting: Use the pivot_longer() function from the tidyr package to reshape the data.
  • Specify the columns to pivot using cols.
  • Define the new column for the old column names using names_to (e.g., "Disease").
  • Define the new column for the old cell values using values_to (e.g., "Case_Count").
• Data Cleaning (Optional): Clean the values in the new Disease column (e.g., remove the "_Count" suffix) using mutate() and stringr functions.
• Validation: Verify that each row now represents a single observation (e.g., counts for a specific disease in a specific region and year) and that all required variables are present as columns.

R Code Example:

Visualization of the Tidy Data Workflow

The following diagram illustrates the logical process and decision points for achieving and maintaining tidy data within a research project.

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Software Tools for Tidy Data Management

Tool Name	Function	Application in Wildlife Disease Research
R tidyverse Metapackage	A collection of R packages for data science.	Provides a cohesive set of tools (e.g., dplyr, tidyr, ggplot2) for importing, tidying, transforming, and visualizing complex ecological and disease data [20] [22].
pivot_longer() Function (tidyr)	Reshapes data from a wide to a long format.	Critical for fixing tables where different pathogens or measurements are stored as column headers instead of a categorical variable [20].
pivot_wider() Function (tidyr)	Reshapes data from a long to a wide format.	Useful for creating summary tables or formatting data for specific statistical models that require a wide matrix.
readr Package	Provides fast and user-friendly functions to read rectangular data (e.g., CSV, TSV).	Ensures reliable and efficient import of large field data sheets into R, correctly preserving data types [22].
ggplot2 Package	A system for creating declarative graphics based on "The Grammar of Graphics."	Directly leverages tidy data structure to create complex, multi-layered visualizations of disease prevalence, spatiotemporal trends, and host-pathogen interactions [20].
ZN(II) Mesoporphyrin IX	ZN(II) Mesoporphyrin IX, MF:C34H36N4O4Zn, MW:630.1 g/mol	Chemical Reagent
Ethyl 3,4-Dihydroxybenzoate-13C3	Ethyl 3,4-Dihydroxybenzoate-13C3, MF:C9H10O4, MW:185.15 g/mol	Chemical Reagent

Within the framework of a minimum data standard for wildlife disease research, the inclusion of diagnostic-specific fields is not merely an administrative exercise—it is fundamental to ensuring data interoperability, reproducibility, and reusability [1]. Such standards are vital for creating actionable wildlife health intelligence, which is critical for both ecological health and global pandemic preparedness [2]. A core principle of the minimum data standard is the disaggregation of data to the finest possible spatial, temporal, and taxonomic scale, often in a "tidy" or "rectangular" data format where each row represents a single diagnostic test outcome [1].

While the standard defines a common set of core fields for host, sample, and parasite information, the diagnostic methodology used in a study—be it PCR, ELISA, or others—determines a subset of additional, highly specific fields that are essential for a complete record [1]. Providing this granular, method-specific metadata allows future researchers to properly interpret results, assess the assay's validity, and even aggregate data from disparate studies for powerful synthetic analysis. This section provides a detailed guide to identifying, populating, and formatting these diagnostic-specific fields for common assays in wildlife disease research.

Field Requirements by Diagnostic Assay

The minimum data standard is designed with the flexibility to accommodate a wide range of diagnostic techniques [1]. The table below summarizes the core required fields for any diagnostic record and then details the additional, conditional fields required for specific assay types.

Table 1: Diagnostic-Specific Data Fields for Wildlife Disease Research

Field Name	Field Category	Applicability & Description	Data Type
Animal ID	Core (Required)	Unique identifier for the host animal.	Text
Sample ID	Core (Required)	Unique identifier for the biological sample.	Text
Test ID	Core (Required)	Unique identifier for a specific diagnostic test.	Text
Diagnostic method	Core (Required)	The technique used (e.g., "PCR", "ELISA", "Virus Isolation").	Text
Test result	Core (Required)	The outcome of the test (e.g., "positive", "negative", "inconclusive").	Text
Test date	Core (Required)	Date the test was performed (YYYY-MM-DD).	Date
Parasite species	Conditional	Identity of the detected parasite; required if test is positive.	Text
Forward primer sequence	PCR-specific	Nucleotide sequence of the forward primer.	Text
Reverse primer sequence	PCR-specific	Nucleotide sequence of the reverse primer.	Text
Gene target	PCR-specific	The specific gene or genomic region targeted (e.g., "RdRp", "N gene").	Text
Primer citation	PCR-specific	Publication or source detailing the primer set.	Text
Probe target	ELISA-specific	The specific antigen or antibody targeted by the probe.	Text
Probe type	ELISA-specific	The type of probe used (e.g., "antigen", "antibody").	Text
Probe citation	ELISA-specific	Publication or source detailing the probe.	Text

This structured approach ensures that data from a study using PCR to detect a novel coronavirus in bats [1] and a study using an optimized ELISA for Morganella morganii in livestock [23] can both be formatted with the requisite detail for future reuse, despite their different methodological and taxonomic focuses.

Experimental Protocols for Key Diagnostic Assays

Adherence to detailed experimental protocols is the foundation for generating reliable data that can be standardized. Below are generalized protocols for PCR and ELISA, two cornerstone techniques in pathogen detection.

Protocol: Optimized Polymerase Chain Reaction (PCR)

The following protocol is adapted from procedures used to develop an optimized PCR for direct detection of bacteria in clinical samples, highlighting steps critical for data reporting [23].

1. Sample Preparation and DNA Extraction:

• Collect relevant samples (e.g., oral/rectal swabs, tissue, feces) and preserve them appropriately for nucleic acid analysis.
• Extract genomic DNA using a commercial DNA extraction kit. Alternatively, for an optimized direct PCR, use a bacterial suspension directly as a template, bypassing the DNA extraction step to increase speed and efficiency [23].
• Record the sample type and preservation method in the Sample type field.

2. PCR Reaction Setup:

• Prepare a master mix containing the following core components. The volumes are per reaction:
  • Template DNA/Bacterial suspension: 2-5 µL.
  • Forward and Reverse Primers: Optimize concentration (e.g., 0.2-0.4 µM each) to minimize primer-dimer formation [23]. The sequences must be documented in the Forward primer sequence and Reverse primer sequence fields.
  • Taq DNA Polymerase: 1.25 units.
  • dNTP Mix: 200 µM of each dNTP.
  • Reaction Buffer: 1X concentration, including MgClâ‚‚.
  • Nuclease-free water: to a final volume of 25 µL.
• The Primer citation field should reference the source of these primers.

3. PCR Amplification:

• Perform amplification in a thermal cycler using a program similar to the following:
  • Initial Denaturation: 95°C for 5 minutes.
  • Amplification (35-40 cycles):
    • Denaturation: 95°C for 30 seconds.
    • Annealing: Optimize temperature (e.g., 55-65°C) for 30 seconds. This critical parameter must be recorded in the metadata [23].
    • Extension: 72°C for 1 minute per kb of amplicon.
  • Final Extension: 72°C for 7 minutes.

4. Analysis of PCR Products:

• Analyze amplified PCR products by agarose gel electrophoresis (e.g., 1.5% gel).
• Visualize DNA bands under UV light after staining with an appropriate dye.
• A positive result is indicated by a band of the expected size. Record the outcome in the Test result field and, if positive, the identified Parasite species.

Protocol: Indirect Enzyme-Linked Immunosorbent Assay (I-ELISA)

This protocol is based on the development of an I-ELISA for serological detection, a common method for large-scale screening of antibody response [23].

1. Antigen Coating:

• Dilute the purified antigen (e.g., Morganella morganii lipoprotein LPP) in a coating buffer (e.g., carbonate-bicarbonate buffer, pH 9.6) to an optimized concentration (e.g., 1-5 µg/mL) [23].
• Dispense 100 µL per well into a 96-well microtiter plate.
• Incubate overnight at 4°C. The identity of the Probe target (the LPP antigen) must be documented.

2. Blocking:

• Empty the coating solution from the plate.
• Wash the plate 3 times with a wash buffer (e.g., PBS containing 0.05% Tween-20, PBS-T).
• Add 200 µL of a blocking buffer (e.g., 5% skim milk in PBS-T or 1% BSA in PBS) to each well.
• Incubate at 37°C for 1-2 hours.

3. Sample and Antibody Incubation:

• Wash the plate 3 times with wash buffer.
• Add 100 µL of diluted test serum samples (e.g., 1:100 dilution in blocking buffer) and controls (positive, negative) to designated wells. Incubate at 37°C for 1 hour.
• Wash the plate 3-5 times.
• Add 100 µL of the species-specific secondary antibody (e.g., Horseradish Peroxidase-conjugated anti-bovine IgG) diluted in blocking buffer. Incubate at 37°C for 1 hour.

4. Signal Detection and Analysis:

• Wash the plate as before.
• Add 100 µL of substrate solution (e.g., TMB) to each well. Incubate in the dark for 10-15 minutes at room temperature.
• Stop the reaction by adding 50 µL of stop solution (e.g., 2M Hâ‚‚SOâ‚„).
• Measure the absorbance immediately at 450 nm using a microplate reader.
• Determine positivity based on a calculated cutoff value (e.g., mean of negative controls + 3 standard deviations). Record the Test result accordingly.

Workflow Visualization

The following diagram illustrates the logical sequence for applying diagnostic-specific fields within the broader context of a wildlife disease study, from sample collection to data reporting.

Data Field Selection Workflow

The Scientist's Toolkit: Research Reagent Solutions

The successful implementation of the protocols above relies on a suite of essential reagents and materials. The following table catalogs key solutions required for the featured experiments.

Table 2: Essential Research Reagents for Pathogen Detection Assays

Item	Function/Application	Example from Protocol
Primers (Oligonucleotides)	Short, single-stranded DNA sequences that bind complementary target DNA to initiate amplification by DNA polymerase in PCR.	Forward and reverse primers targeting a specific gene (e.g., LPP gene for M. morganii) [23].
dNTPs (Deoxynucleotide Triphosphates)	The building blocks (A, T, C, G) used by DNA polymerase to synthesize a new DNA strand during PCR.	Component of the PCR master mix [23].
Taq DNA Polymerase	A thermostable enzyme that synthesizes new DNA strands from dNTPs using primers as a starting point, essential for PCR.	The core enzyme in the PCR reaction [23].
Antigen (e.g., LPP Protein)	A molecule that can be recognized by the immune system; used as a "probe" in ELISA to capture specific antibodies from a sample.	The purified M. morganii lipoprotein (LPP) used to coat ELISA plates [23].
Primary and Secondary Antibodies	The primary antibody binds the antigen (or target antibody in indirect formats); the enzyme-conjugated secondary antibody binds the primary to produce a detectable signal.	Bovine serum antibodies (primary) and HRP-conjugated anti-bovine IgG (secondary) in the I-ELISA [23].
Chromogenic Substrate (e.g., TMB)	A colorless solution that produces a colored, measurable product when cleaved by the enzyme (e.g., HRP) conjugated to the secondary antibody.	TMB (3,3',5,5'-Tetramethylbenzidine) substrate for the ELISA [23].
DNA Extraction Kit	A set of optimized reagents for purifying high-quality genomic DNA from complex biological samples, removing inhibitors.	Used for standard PCR preparation from tissue or swab samples.
Blocking Buffer (e.g., BSA, Skim Milk)	A protein-rich solution used to cover non-specific binding sites on the ELISA plate to prevent false-positive signals.	5% skim milk or 1% BSA used in the blocking step of the ELISA protocol [23].
8-Aminoguanosine-13C2,15N	8-Aminoguanosine-13C2,15N, MF:C10H14N6O5, MW:301.23 g/mol	Chemical Reagent

Precisely navigating and populating diagnostic-specific fields is a critical step in implementing the minimum data standard for wildlife disease research. By meticulously documenting assay parameters—from primer sequences to probe targets—researchers transform raw data into a FAIR (Findable, Accessible, Interoperable, and Reusable) resource [1] [2]. This practice, demonstrated through the detailed protocols and field mappings for PCR and ELISA, ensures that valuable data on both positive and negative results can be aggregated and analyzed to answer larger-scale ecological and public health questions. Widespread adoption of this standardized approach is foundational to building a robust early warning system for emerging infectious diseases at the human-wildlife interface.

The establishment of a minimum data standard for wildlife disease research creates a foundation for consistent data collection. However, the full value of this standardized data is only realized when it is shared and archived in a manner that makes it Findable, Accessible, Interoperable, and Reusable (FAIR). This protocol provides detailed methodologies for depositing standardized wildlife disease datasets into FAIR-compliant repositories, a critical final step in the data lifecycle that ensures long-term preservation, accessibility, and utility for the global research community. Adhering to this protocol enhances transparency, supports data synthesis for broader ecological insights, and strengthens global health security by making critical wildlife health data actionable [1] [2].

Experimental Protocol: Repository Selection and Data Deposition

The following diagram illustrates the complete workflow for preparing and sharing a wildlife disease dataset, from initial validation to final repository deposition.

Detailed Methodology

Step 1: Data Validation Prior to Deposition

• Objective: To ensure the dataset conforms to the minimum data standard before submission to a repository.
• Procedure:
  • Format the dataset according to the "tidy data" principle, where each row represents a single diagnostic test outcome [1].
  • Validate the dataset's structure against the provided JSON Schema.
  • Alternatively, use the dedicated R package (wddsWizard) available from GitHub (github.com/viralemergence/wddsWizard) to check for completeness and conformity [1].
  • Confirm that all required data fields (9 mandatory fields) and metadata fields (7 mandatory fields) are populated appropriately.

Step 2: FAIR Repository Selection

• Objective: To identify a suitable, sustainable repository that fulfills FAIR principles.
• Procedure:
  • Primary Choice - Domain-Specific Repositories: Prioritize repositories specialized for wildlife disease or ecological data, such as the Pathogen Harmonized Observatory (PHAROS) database (pharos.viralemergence.org). These support richer, domain-specific metadata [1] [24].
  • Secondary Choice - Generalist Repositories: If no suitable domain-specific repository exists, use a recognized generalist repository.
    • Recommended Platforms: Zenodo, Dryad, or the Open Science Framework (OSF) [24].
    • Selection Criteria: Evaluate repositories using the following table to ensure they meet minimal requirements for FAIRness and sustainability [24].

Table 1: Criteria for Selecting a FAIR-Compliant Repository

Criterion	Minimum Requirement	Importance for FAIRness
Persistent Identifiers	Assigns a Digital Object Identifier (DOI)	Makes data Findable and Citable [24]
Metadata Standards	Supports rich, standard-compliant metadata (e.g., DataCite)	Enhances Interoperability and Reusability [24]
Clear Licensing	Allows application of open licenses (e.g., CC0, CC-BY)	Defines terms for Reuse [24]
Long-Term Preservation	Has documented preservation policy & backup routines	Ensures long-term Accessibility [24]
Open Access	Provides free and open access to data	Ensures Accessibility to all researchers [2]

Step 3: Data and Metadata Packaging

• Objective: To prepare the final data package for upload.
• Procedure:
  • Save the validated dataset in an open, non-proprietary format such as .csv [2].
  • Prepare a README file or data dictionary explaining the meanings of all data fields, abbreviations, and the diagnostic methods used.
  • Compile the project-level metadata (24 fields, 7 required), including title, authors with ORCIDs, description, spatial and temporal coverage, and funding information [1].

Step 4: Repository Deposition and Publication

• Objective: To successfully upload and publish the data package.
• Procedure:
  • Create an account on the chosen repository platform.
  • Create a new "item" or "deposit" and upload the dataset file(s) and the README file.
  • Fill in all required metadata fields in the repository's web form, accurately mapping to the project-level metadata.
  • Select an appropriate open license (e.g., CC0, CC-BY).
  • Finalize the deposition. The repository will automatically assign a DOI, which should be used to cite the dataset in any related publications.

Research Reagent Solutions

The following reagents and materials are essential for conducting the research that generates data compliant with the minimum standard.

Table 2: Essential Research Reagents and Materials for Wildlife Disease Studies

Item	Function / Application
Sterile Swabs (oral, rectal, nasal)	Non-invasive collection of pathogen samples from live wildlife [1].
Primer/Probe Sets (e.g., for coronavirus PCR)	Target-specific oligonucleotides for pathogen detection and identification via molecular methods like PCR [1].
RNA/DNA Extraction Kits	Isolation of high-quality nucleic acids from diverse wildlife sample types (tissue, swab, blood) for downstream diagnostic testing.
ELISA Kits (Pathogen-specific)	Serological detection of pathogen exposure or infection through antibody or antigen recognition [1].
Virus Transport Media (VTM)	Preservation of viral viability and nucleic acids during sample transport from the field to the laboratory.

Results and Data Presentation

Repository Comparison and Selection

The table below provides a structured comparison of repository types to guide researchers in making an informed selection decision.

Table 3: Comparison of Data Repository Options for Wildlife Disease Data

Repository Type	Key Feature	Example Platforms	Ideal Use Case
Domain-Specific	Rich, domain-relevant metadata fields; enhanced interoperability for specialists.	PHAROS, GBIF	Projects aiming for maximum impact and reuse within the wildlife disease ecology community [1] [24].
Generalist	Broad disciplinary acceptance; simple and robust deposition process.	Zenodo, Dryad, OSF	When a dedicated wildlife disease repository is unavailable, or for projects of cross-disciplinary interest [24].

Discussion

Critical Steps and Troubleshooting

• Data Validation Failure: If validation fails, systematically check the JSON Schema error report. Common issues include missing mandatory fields, incorrect data types, or misformatted dates. The wddsWizard R package can provide more user-friendly error messages [1].
• Ethical and Safety Considerations: For data involving threatened/endangered species or high-consequence pathogens, balance transparency with biosafety. Implement data obfuscation (e.g., reducing spatial precision) as needed to prevent misuse, following the standard's guidance on secure data sharing [2].
• GDPR Compliance for Personal Data: When human data is involved, a GDPR assessment must be conducted early in the project. Select an appropriate legal basis for processing, maintain transparency with participants, and use access controls in repositories to ensure data is both FAIR and legally compliant [25].

Impact on Research and Global Health

Systematic archiving of standardized data, including negative results, in FAIR repositories is transformative. It prevents publication bias, enables robust meta-analyses, and provides the foundational data needed to track pathogen dynamics, understand the impacts of climate change, and ultimately improve pandemic early warning systems [1] [2]. This protocol operationalizes the final, crucial step in making wildlife disease research truly actionable for the global community.

Overcoming Practical Challenges in Wildlife Disease Data Management

The establishment of a minimum data standard for wildlife disease research represents a transformative advancement for ecological understanding and pandemic preparedness. This standard, comprising 40 data fields (9 required) and 24 metadata fields (7 required), enables the aggregation and analysis of disaggregated data at the finest possible spatial, temporal, and taxonomic scales [1]. However, the imperative for comprehensive data sharing—including historically underrepresented negative results—creates a critical tension with biosafety and biosecurity concerns [1] [2]. The very location data that provides essential context for disease ecology can simultaneously serve as a roadmap for those who might exploit this information to harm vulnerable species or ecosystems [26].

This Application Note addresses this fundamental challenge by providing practical protocols for sensitive data obfuscation that balance the FAIR (Findable, Accessible, Interoperable, Reusable) principles with necessary safety safeguards [2]. We outline methodologies that allow researchers to share data with sufficient scientific utility while implementing context-aware protections for sensitive information. These guidelines are particularly crucial for research involving endangered species, pathogens with high zoonotic potential, or locations where habitat disturbance represents an immediate conservation threat [26].

Foundational Concepts: Defining Sensitivity in Biological Data

Categories of Sensitive Biological Data

Sensitive biological data primarily falls into two interconnected domains with distinct risk profiles and protection requirements [26]:

• Nature Conservation and Biodiversity Data: This category encompasses information about endangered species, protection regulations, and temporally sensitive ecological periods such as breeding seasons. Species listed on the National Biodiversity Network Atlas sensitive species list, Biodiversity Action Plans (BAP), or the IUCN Red List of Threatened Species often require careful data management considerations [26].

• Biosafety and Biosecurity Data: This includes information about organisms posing direct threats to human, animal, or plant health, including emerging pathogens, genetically modified organisms, and particularly dangerous biological agents [26]. Research involving risk group 3 pathogens such as SARS-CoV, HIV, M.tb, H7N9, and Brucella conducted in Animal Biosafety Level 3 (ABSL-3) facilities exemplifies this category [27].

Threat Assessment Framework

The decision to obfuscate data must follow a structured risk-benefit analysis. Potential harms from unregulated data sharing include [26]:

• Collection for Trade: Poaching of rare species for the pet trade or international wildlife market using precise location data
• Habitat Disturbance: Human-induced habitat destruction from recreation, farming, or deliberate sabotage of conservation efforts
• Pathogen Misuse: Intentional misuse of pathogen information for bioterrorism or other malicious purposes
• Commercial Exploitation: Unauthorized commercial use of sensitive biological information

Table 1: Risk Classification for Wildlife Disease Data

Risk Level	Data Characteristics	Potential Harm	Obfuscation Requirement
Low	Common species, low-pathogenicity organisms, broad regional data	Minimal	Standard sharing with complete metadata
Moderate	Species of concern, seasonal sensitivities, moderate pathogenicity	Habitat disturbance, research interference	Moderate geographical generalization
High	Endangered species, high-consequence pathogens, precise locations	Poaching, population decline, biosecurity breach	Significant obfuscation with controlled access
Critical	Critically endangered species, select agents, exact coordinates	Extinction risk, mass mortality, bioterrorism	Restricted access, data use agreements

Experimental Protocols: Data Obfuscation Methodologies

Protocol 1: Geographical Generalization for Sensitive Occurrence Data

Purpose: To reduce risks associated with precise location data while maintaining scientific utility for ecological and epidemiological analysis.

Materials and Equipment:

• Primary geospatial coordinates (latitude/longitude)
• Geographical Information System (GIS) software
• Regional administrative boundaries dataset
• Species-specific sensitivity classification

Procedure:

• Classify Sensitivity Level: Determine species-specific sensitivity using IUCN Red List categories and local protection status [26].
• Apply Spatial Generalization:
  • Low Sensitivity: Maintain original coordinates with precision to 0.001 decimal degrees
  • Moderate Sensitivity: Generalize to 0.01 decimal degrees (~1km precision)
  • High Sensitivity: Generalize to 0.1 decimal degrees (~10km precision)
  • Critical Sensitivity: Generalize to municipality or county level only
• Coordinate Translation: Shift coordinates using a random offset algorithm with a maximum displacement of 5km for moderate risk and 10km for high-risk species.
• Metadata Documentation: Record all obfuscation parameters in the metadata using fields from the minimum data standard, explicitly noting the methods applied [1].

Validation:

• Verify that generalized locations maintain ecological context (e.g., within appropriate habitat)
• Ensure obfuscated data cannot be reverse-engineered to precise locations
• Confirm generalized datasets remain suitable for intended analytical purposes

Protocol 2: Context-Aware Data Sharing Implementation

Purpose: To implement a tiered data access system that matches protection levels with specific user needs and credentials.

Materials and Equipment:

• Data classification matrix (see Table 1)
• Repository platform with access controls (e.g., PHAROS, Zenodo)
• Data use agreement templates
• User authentication system

Procedure:

• Data Categorization: Classify dataset components into public, registered, and controlled access tiers:
  • Public: Generalizable data, summary statistics, methodology descriptions
  • Registered: Moderate-sensitivity data requiring research purpose statement
  • Controlled: High-sensitivity data requiring data use agreements and ethics review
• Access Implementation:
  • Configure repository settings to enforce tiered access permissions
  • Establish review process for registered and controlled access requests
  • Implement automated public data generalization where appropriate
• User Verification: Validate researcher credentials and institutional affiliations for restricted data tiers
• Compliance Monitoring: Track data usage and implement periodic access reviews

Validation:

• Audit access patterns to detect unusual request patterns
• Survey users to ensure access barriers are appropriate, not prohibitive
• Monitor for unauthorized data redistribution

Protocol 3: Biosafety-Compliant Data Management for High-Consequence Pathogens

Purpose: To ensure biosafety and biosecurity when handling and sharing data involving risk group 3 and 4 pathogens or particularly sensitive species.

Materials and Equipment:

• ABSL-3 or BSL-3 facility protocols [27]
• Data encryption tools
• Secure transfer platforms
• Institutional Biosafety Committee approval documents

Procedure:

• Pre-Approval: Obtain Institutional Biosafety Committee review and approval for data sharing plans involving high-consequence pathogens [27] [28].
• Data Segregation: Separate precise location data from pathogen characterization data in storage and sharing implementations.
• Temporal Delays: Implement appropriate embargo periods for sensitive data (e.g., 6-24 months) to allow for initial analysis while mitigating immediate risks.
• Safe Research Outputs:
  • Utilize generalized locations in publications and public-facing outputs
  • Focus sequence data sharing through specialized platforms (e.g., GenBank, SRA)
  • Employ data use agreements for precise location-pathogen associations

Validation:

• Regular biosafety committee review of data sharing practices
• Compliance checks with institutional and national biosecurity regulations
• Verification that shared data cannot be misused to locate high-consequence pathogens in vulnerable populations

Implementation Workflow: Decision Framework for Data Obfuscation

The following workflow diagram illustrates the logical decision process for implementing appropriate data obfuscation strategies based on dataset characteristics:

Data Obfuscation Decision Workflow

Research Reagent Solutions: Essential Tools for Implementation

Table 2: Research Reagent Solutions for Data Obfuscation Implementation

Tool Category	Specific Solution	Function	Implementation Example
Data Repositories	PHAROS (Pathogen Harmonized Observatory)	Specialist platform for wildlife disease data with access controls	Primary repository for standardized wildlife disease data [1]
Data Repositories	Zenodo	Generalist repository with DOI assignment and access restrictions	Backup repository with embargo capabilities for sensitive data
Data Repositories	GBIF (Global Biodiversity Information Facility)	Biodiversity data infrastructure with sensitive data processing	Publication of generalized occurrence data following Darwin Core [1]
Geospatial Tools	GIS Software with Random Offset Algorithms	Coordinate generalization and translation	Implementing spatial obfuscation protocols
Geospatial Tools	Administrative Boundary Datasets	Regional context for generalized data	Replacing coordinates with county/municipality names
Access Control Systems	Data Use Agreement Templates	Legal frameworks for restricted data sharing	Establishing terms for controlled access data
Access Control Systems	User Authentication Platforms	Identity verification for tiered access	Implementing registered and controlled access tiers
Reporting Standards	Minimum Data Standard Templates	Standardized formatting for wildlife disease data	Ensuring consistent documentation of obfuscation methods [1]

The protocols outlined in this Application Note provide a practical framework for implementing the minimum data standard for wildlife disease research while addressing legitimate biosafety and conservation concerns. By integrating these data obfuscation methodologies into research workflows, scientists can contribute to the growing aggregate of wildlife disease data without compromising vulnerable species or ecosystems.

The successful implementation of these guidelines requires ongoing collaboration between disease ecologists, conservation biologists, data scientists, and biosafety professionals. As the field evolves, these protocols should be regularly refined to address emerging challenges and technological advancements. Through careful application of these principles, the wildlife disease research community can uphold its dual commitment to scientific transparency and ecological stewardship, strengthening both global health security and biodiversity conservation.

Wildlife disease research generates complex data relationships that challenge conventional data management practices. Repeated sampling of individuals, the use of pooled testing strategies, and the requirement for confirmatory assays create intricate data structures that must be meticulously documented to ensure scientific rigor and reproducibility [1]. Within the framework of the new minimum data standard for wildlife disease research, researchers now have a standardized approach for handling these complexities while maintaining FAIR (Findable, Accessible, Interoperable, and Reusable) principles [1] [2].

This protocol provides detailed methodologies for implementing the wildlife disease data standard across three common complex scenarios, enabling researchers to maintain data integrity while accommodating real-world research designs. The standard's flexible structure centers on a "tidy data" model where each row corresponds to a single diagnostic test outcome, with appropriate linking fields to connect related observations [1].

Minimum Data Standard Framework

The minimum data standard for wildlife disease research establishes 40 core data fields across three categories: sample data, host animal data, and parasite/pathogen data [1]. Of these, nine fields are mandatory for basic compliance, while the remaining fields provide essential context for specific study designs. The standard intentionally uses a "tidy data" structure where each record represents a single observation at the finest possible spatial, temporal, and taxonomic scale [1].

Table 1: Required Data Fields for Complex Study Designs

Field Category	Field Name	Data Type	Requirement Level	Application to Complex Scenarios
Sample Data	Sample ID	String	Required	Critical for all scenarios; must be unique across all databases
Sample Data	Animal ID	String	Conditional	Required for repeated sampling; may be blank for pooled tests
Sample Data	Sample Date	Date	Required	Essential for temporal analysis in longitudinal studies
Sample Data	Sample Type	String	Required	Must specify specimen type (e.g., oral swab, blood, tissue)
Host Data	Host Identification	String	Required	Linnaean classification at lowest possible level
Host Data	Organism Sex	String	Optional	Recommended for host-level analyses
Host Data	Host Life Stage	String	Optional	Important for epidemiological interpretations
Parasite Data	Test Result	String	Required	Positive, negative, or indeterminate outcome
Parasite Data	Test Name	String	Required	Specific assay name (e.g., "pan-coronavirus PCR")
Parasite Data	Pathogen Taxon	String	Conditional	Required for positive results; links to genetic data

Table 2: Specialized Fields for Complex Testing Scenarios

Testing Scenario	Specialized Fields	Data Type	Purpose
Molecular Assays (PCR)	Forward Primer Sequence	String	Documents primer used for replication
Molecular Assays (PCR)	Reverse Primer Sequence	String	Documents primer used for replication
Molecular Assays (PCR)	Gene Target	String	Specific genetic target of assay
Serological Assays (ELISA)	Probe Target	String	Antigen or antibody target
Serological Assays (ELISA)	Probe Type	String	Type of probe used in assay
All Confirmatory Tests	Primer/Probe Citation	String	Reference for published assay protocols
Genetic Sequencing	GenBank Accession	String	Links to public genetic database records
Pooled Testing	Pool Size	Integer	Number of specimens in pool
Pooled Testing	Pool ID	String	Unique identifier for the pool

Application Protocols for Complex Data Relationships

Protocol 1: Repeated Sampling of Individual Animals

Purpose: To document longitudinal studies where the same individual animal is sampled multiple times over a period, enabling analysis of infection dynamics, pathogen persistence, and immune responses.

Methodology:

• Assign Persistent Animal Identifiers: Each captured animal receives a unique, persistent Animal ID (e.g., "BZ19-114" for animal 114 sampled in Belize in 2019) that remains constant across all sampling events [1].
• Create Unique Sample IDs: For each sampling event, generate a unique Sample ID that incorporates the Animal ID and sample type (e.g., "OSBZ19-114202301" for an oral swab collected in January 2023) [1].
• Record Temporal Data: Document exact collection date for each sample to enable analysis of temporal patterns.
• Maintain Host Data Consistency: Host characteristics (species, sex, age) should remain linked to the Animal ID across all sampling events unless updated measurements are taken.

Data Management Considerations: The same Animal ID should be used across all databases and physical resources, including field notes, laboratory records, and public repositories [1]. This creates a persistent identifier that connects all observations from the same individual.

Repeated Sampling Data Relationships: This workflow demonstrates how a single animal identifier links multiple temporal sampling events and their resulting pathogen data.

Protocol 2: Pooled Testing Strategies

Purpose: To efficiently screen populations for low-prevalence pathogens while conserving resources, using statistical methods that account for the group testing approach [29].

Methodology:

• Pool Construction: Combine aliquots from multiple individual specimens into a single testing pool. Record the pool size (number of specimens per pool) and assign a unique Pool ID [1].
• Initial Testing: Test the pool using appropriate diagnostic methods. Record the pool-level test result.
• Retesting Protocol: For pools testing positive, implement a retesting strategy such as two-stage hierarchical testing or array testing to identify positive individuals [29].
• Data Linkage: Maintain the relationship between individual specimens (Animal IDs) and their respective pools (Pool IDs) to enable disaggregation of results.

Statistical Considerations: Pooled testing efficiency depends on disease prevalence, with optimal pool sizes varying according to expected prevalence rates [29]. The diagnostic sensitivity and specificity of the assay must be accounted for in prevalence estimation, as pooling can affect test performance characteristics.

Pooled Testing Workflow: This diagram illustrates the relationship between individual specimens, their assignment to testing pools, and the retesting process for positive pools.

Protocol 3: Confirmatory and Supplemental Assays

Purpose: To document comprehensive testing protocols where initial screening tests are followed by confirmatory or supplemental assays to validate results or obtain additional pathogen characterization.

Methodology:

• Primary Testing: Conduct initial screening tests and record results using standard fields.
• Confirmatory Testing: For positive results, perform confirmatory assays using different diagnostic methods or targets. Maintain the relationship between original and confirmatory samples through shared Animal ID or Sample ID.
• Pathogen Characterization: For confirmed positives, conduct additional tests such as genetic sequencing, culturing, or phenotypic characterization.
• Data Linkage: Connect all test results to the original sample while documenting the confirmatory relationship through appropriate field completion.

Implementation Example: In a coronavirus surveillance study, an oral swab testing positive by pan-coronavirus PCR might be confirmed through Sanger sequencing of the RdRp gene, with the resulting sequence deposited in GenBank and the accession number recorded in the dataset [1].

Table 3: Example Testing Sequence for Confirmatory Workflow

Testing Stage	Sample ID	Test Name	Test Result	Pathogen Taxon	GenBank Accession
Initial Screening	OS_BZ19-114	Pan-coronavirus PCR	Positive	Not specified	-
Confirmatory Test	OS_BZ19-114	Coronavirus RdRp sequencing	Positive	Alphacoronavirus	OR123456
Supplemental Data	OS_BZ19-114	Whole genome sequencing	Positive	Alphacoronavirus	OR123457

The Scientist's Toolkit: Research Reagent Solutions

Table 4: Essential Research Reagents for Wildlife Disease Studies

Reagent Category	Specific Examples	Function/Application	Implementation Notes
Nucleic Acid Extraction Kits	QIAamp Viral RNA Mini Kit, DNeasy Blood & Tissue Kit	Isolation of pathogen genetic material from various sample matrices	Document extraction method in sample processing metadata
PCR Master Mixes	OneTaq Quick-Load Master Mix, Luna Universal Probe Master Mix	Amplification of target pathogen sequences	Record primer sequences and gene targets in specialized fields
Specific Primers/Probes	Pan-coronavirus primers (e.g., RdRp gene), influenza A matrix protein primers	Target-specific pathogen detection	Include primer citations linking to published assays
Serological Assays	ELISA kits for specific pathogens, multiplex immunoassays	Detection of pathogen exposure through antibody response	Document probe target and type in specialized fields
Positive Controls	Synthetic RNA controls, quantified pathogen standards	Assay validation and quality control	Essential for establishing test sensitivity and specificity
Next-Generation Sequencing Kits	Illumina RNA Prep with Enrichment, Oxford Nanopore kits	Comprehensive pathogen characterization	Link resulting sequences to public databases via accession numbers

Data Validation and Sharing Protocols

Data Quality Assurance

Implement a multi-step validation process to ensure data quality and standard compliance:

• Structural Validation: Use the provided JSON Schema or the dedicated R package (wddsWizard) to validate dataset structure against the standard [1] [30].
• Logical Checks: Verify that relationships between fields follow logical rules (e.g., positive test results have associated pathogen identification).
• Vocabulary Control: Apply controlled vocabularies and ontologies to free-text fields to enhance interoperability [30].

Data Sharing and Repository Selection

Share compliant datasets through appropriate repositories to maximize findability and reuse:

• Generalist Repositories: Platforms like Zenodo provide broad accessibility and assign persistent identifiers [1].
• Specialist Platforms: The Pathogen Harmonized Observatory (PHAROS) database offers domain-specific infrastructure for wildlife disease data [1].
• Sequence Data: Deposit genetic sequence data in dedicated repositories like NCBI GenBank following established best practices [1].

Implementing the minimum data standard for complex wildlife disease research scenarios ensures that valuable data collected through sophisticated study designs remains findable, accessible, interoperable, and reusable. By following these detailed protocols for repeated sampling, pooled testing, and confirmatory assays, researchers can contribute to a growing ecosystem of standardized data that supports synthetic analyses, ecological forecasting, and evidence-based public health decision-making. The provided tools, including template files, validation software, and clear implementation guidelines, lower the barrier to adoption while significantly enhancing research transparency and impact.

The establishment of a minimum data standard for wildlife disease research creates an urgent need for interoperability with established biodiversity data infrastructures. Molecular methodologies now enable documenting organisms from inconspicuous taxa or through non-invasive sampling, generating data that extends beyond traditional ecological observations [31]. This data, comprising sequences with temporal and spatial context, represents valuable occurrence records that serve broader purposes beyond their original molecular ecology or phylogenetic research focus [31].

This protocol details the integration between emerging wildlife disease reporting standards and three foundational frameworks: the Darwin Core (DwC) data standard for biodiversity information, the Global Biodiversity Information Facility (GBIF) network for data publishing and discovery, and the GenBank repository for genetic sequence data. We provide a structured approach for researchers to maximize the impact, reuse, and interoperability of their wildlife disease data through standardized submission pathways.

Background and Rationale

The Need for Standardization in Wildlife Disease Data

Wildlife disease datasets exhibit tremendous heterogeneity in scope, granularity, and reporting formats, often omitting critical metadata about sampling effort, location, or host-level information [1]. This variability creates significant barriers to data synthesis, reproducibility, and reuse. Many studies report only summary statistics or positive results, making it impossible to disaggregate data back to the host level for comparative analyses across populations, species, or temporal scales [1].

The recently proposed minimum data standard for wildlife disease research addresses these challenges through 40 core data fields (9 required) and 24 metadata fields (7 required) that capture information at the finest possible spatial, temporal, and taxonomic scale [1]. This standard organizes information into three categories: sample data, host animal data, and parasite data (including diagnostic test results) [1].

Existing Biodiversity Data Infrastructures

Darwin Core provides a stable, well-adopted foundation for sharing biodiversity data through standardized terms and vocabularies [32]. Its flexibility enables handling complex datasets from diverse research and surveillance sources across local to international scales [32]. Ongoing developments, including a new conceptual model and Data Package Guide currently under public review (September - December 2025), promise enhanced capabilities for representing complex data relationships [33].

GBIF serves as the primary global network for publishing and discovering biodiversity data, supporting four dataset classes: metadata-only resources, species checklists, occurrence data, and sampling-event data [34] [35]. The GBIF infrastructure has demonstrated capacity for integrating DNA-derived occurrences [31] and wildlife disease data [32].

GenBank, as part of the International Nucleotide Sequence Database Collaboration (INSDC), represents the foundational repository for genetic sequence data, with daily data exchange between DDBJ, ENA, and GenBank ensuring comprehensive coverage [36] [37]. Most journals require sequences cited in articles to be submitted to INSDC repositories as part of the publication process [36].

Integration Methodology

Mapping Wildlife Disease Data to Darwin Core

The alignment between wildlife disease data standards and Darwin Core enables representation of disease occurrences within broader biodiversity contexts. Table 1 illustrates the mapping between core wildlife disease fields and corresponding Darwin Core terms.

Table 1: Mapping between wildlife disease data standard fields and Darwin Core terms

Wildlife Disease Field Category	Example Wildlife Disease Fields	Darwin Core Term	Mapping Notes
Host Information	hostScientificName	scientificName	Direct mapping to identified organism
	hostCommonName	vernacularName	Common name of host species
	hostTaxonID	taxonID	Taxon identifier from authority
Temporal Context	collectionDate	eventDate	Direct mapping of sampling date
Geospatial Context	decimalLatitude	decimalLatitude	Direct coordinate mapping
	decimalLongitude	decimalLongitude	Direct coordinate mapping
	locationID	locationID	Identifier for sampling location
Sample Context	animalID	organismID	Identifier for specific host individual
	sampleID	materialSampleID	Identifier for physical sample
	samplingProtocol	samplingProtocol	Method used for sample collection

This mapping enables wildlife disease records to be structured as Darwin Core Occurrences or Sampling Events, with the host organism representing the occurrence and diagnostic results extending the standard through qualified relationships or extensions [32]. The Darwin Core standard has demonstrated flexibility to handle complex wildlife disease datasets while maintaining interoperability with broader biodiversity informatics platforms [32].

Workflow for Integrated Data Submission

The integration of wildlife disease data with existing standards follows a sequential pathway that ensures proper deposition of both genetic sequences and associated occurrence context. Figure 1 illustrates this comprehensive workflow, from initial data collection through to integrated publication.

Figure 1: Integrated workflow for submitting wildlife disease data to GenBank and GBIF, showing the parallel processing of genetic sequences and contextual data that converge in published, linked datasets.

Molecular Data Integration

For DNA-derived data, the connection between occurrence records and sequence information creates particularly powerful linkages. A sequence with coordinates and timestamp represents a valuable biodiversity occurrence that transcends its original molecular context [31]. GBIF guidelines specifically address publishing these DNA-derived occurrences, which document taxa identified through molecular methods rather than physical specimens [31].

The connection between platforms is maintained through the GenBank accession number, which should be included in the corresponding Darwin Core record using appropriate terms such as references or associatedSequences [31]. This bidirectional linking enables users to discover genetic sequences through biodiversity portals and find occurrence context through genetic databases.

Experimental Protocols

GenBank Sequence Submission Protocol

Submission Tools and Selection Criteria

GenBank provides multiple submission pathways, each optimized for different data types and volumes:

• BankIt: Web-based submission tool with wizard guidance for single sequences or small batches [36]
• Submission Portal: Unified system for specific sequence types (rRNA, rRNA-ITS, metazoan mitochondrial COX1) [36]
• table2asn: Command-line program for automated creation of sequence records, primarily for annotated genomes and large batches [36]

Step-by-Step Submission Procedure

• Pre-submission preparation: Assemble sequence data, annotation information, and source metadata including host organism, collection date, and geographic coordinates

• Tool selection: Choose appropriate submission tool based on sequence type, volume, and annotation complexity [36]

• Data validation: Submit sequences through selected tool, receiving automatic validation feedback

• Accession number assignment: GenBank processes submissions within approximately two working days, providing accession numbers for manuscript citation [36]

• Confidentiality management: Request delayed public release if pre-publication confidentiality is required, with understanding that appearance in print triggers immediate release [36]

• Post-publication linking: Notify GenBank of publication details to connect sequence records with resulting literature [36]

For raw, unassembled reads from next-generation sequencing platforms (e.g., Illumina, PacBio), submission should be directed to the Sequence Read Archive (SRA) rather than GenBank [36].

GBIF Data Publication Protocol

Prerequisites and Institutional Agreements

Before publishing through GBIF, researchers must:

• Secure institutional agreements: Alert administrators of plans to publish data through GBIF network, emphasizing increased visibility and impact through traditional academic publications and specimen loans [34]

• Request endorsement: Organizations must request endorsement from GBIF community, reviewing data publisher agreement and committing to principle of data sharing [34]

• Select publishing tools: Choose between GBIF's Integrated Publishing Toolkit (IPT), national Living Atlases installations, or programmatic API for dataset registration [34]

Data Preparation and Publication Steps

• Dataset class selection: Identify appropriate data class from: metadata-only, checklist, occurrence, or sampling-event data [35]

• Darwin Core transformation: Structure data tables using Darwin Core terms as column names, utilizing Excel templates for required and recommended terms [35]

• Data validation: Use GBIF Data Validator to check datasets prior to publication, receiving specific recommendations for improving data quality [34]

• Licensing selection: Assign one of three Creative Commons licenses: CC0 (no restrictions), CC BY (attribution required), or CC BY-NC (non-commercial with attribution) [34]

• IPT publication: Upload data to IPT, map to appropriate core (Taxon, Occurrence, or Event), complete resource metadata, and register dataset with GBIF [35]

Wildlife Disease Standard Implementation

Standard Application Procedure

The minimum data standard for wildlife disease research should be implemented through a structured five-step process:

• Fit-for-purpose assessment: Verify dataset describes wild animal samples examined for parasites with host identification, diagnostic methods, outcomes, parasite identification, and spatiotemporal context [1]

• Standard tailoring: Consult field lists to identify applicable fields beyond required elements, appropriate ontologies for free-text fields, and potential need for additional study-specific fields [1]

• Data formatting: Utilize template files (.csv or .xlsx format) to structure data according to standard specifications [1]

• Data validation: Employ provided JSON Schema or R package (wddsWizard) with convenience functions to validate data and metadata against the standard [1]

• Data sharing: Deposit validated data in findable, open-access generalist repository (e.g., Zenodo) and/or specialist platform (e.g., PHAROS - Pathogen Harmonized Observatory) [1]

Integration Validation

After independent submission to GenBank and GBIF, researchers should verify the bidirectional linkage between platforms:

• Confirm GenBank accession numbers appear in corresponding Darwin Core records
• Verify geographic and temporal context from GBIF records matches sequence metadata
• Test discoverability through both genetic and biodiversity search interfaces
• Ensure proper citation mechanisms in both systems to track downstream usage

The Scientist's Toolkit

Table 2: Essential research reagents and computational tools for standardized wildlife disease data management

Tool/Resource	Type	Primary Function	Access
GBIF IPT (Integrated Publishing Toolkit)	Software platform	Dataset publication to GBIF network	https://ipt.gbif.org/
BankIt	Web submission tool	GenBank sequence submission for single sequences/small batches	https://www.ncbi.nlm.nih.gov/WebSub/
table2asn	Command-line program	Automated GenBank submission for large batches/annotated genomes	https://www.ncbi.nlm.nih.gov/genbank/table2asn/
GBIF Data Validator	Data quality tool	Pre-publication dataset checking and improvement recommendations	https://www.gbif.org/tools/data-validator
WDDS Wizard (R package)	Validation tool	Wildlife disease data standard validation against JSON Schema	github.com/viralemergence/wddsWizard
Darwin Core Excel Templates	Data structuring aid	Spreadsheet templates for formatting data to Darwin Core standards	https://www.gbif.org/publishing-data
PHAROS Platform	Specialist repository	Wildlife disease data repository with standard implementation	pharos.viralemergence.org

The integration of emerging wildlife disease data standards with established biodiversity infrastructures represents a critical advancement for the field. By implementing the protocols outlined in this document, researchers can ensure their data achieves maximum impact, interoperability, and reuse across ecological, taxonomic, and public health domains. The parallel submission pathways to GenBank and GBIF, connected through standardized mappings and bidirectional linkages, create a powerful framework for understanding disease dynamics in the context of broader biodiversity patterns.

As standards continue to evolve—including ongoing developments in Darwin Core [33] and refinements to wildlife disease reporting [1]—the foundational integration approaches described here will provide a stable basis for future enhancements. The scientific community's adoption of these standardized protocols will accelerate synthetic research, enable more robust ecological analyses, and ultimately strengthen our capacity to understand and mitigate wildlife disease threats in a changing world.

Ensuring Ethical and Legal Compliance in Data Collection and Sharing

The emergence of a minimum data standard for wildlife disease research marks a pivotal advancement for ecological health and global health security [2]. This standard, developed through a collaboration of academic and public health institutions, provides a foundational framework for collecting, managing, and sharing wildlife disease data [1] [12]. Its primary objective is to enhance the transparency, reusability, and global utility of data critical for tracking emerging infectious threats and understanding ecosystem health [2]. Adherence to this standard ensures that data collection and sharing practices are not only scientifically rigorous but also align with evolving ethical considerations and legal obligations, thereby fostering responsible research conduct and bolstering pandemic preparedness [1] [2].

Minimum Data Standard: Core Components

The proposed minimum data standard is designed to be both comprehensive and flexible, accommodating diverse study designs and methodologies while ensuring core data elements are consistently reported [1] [12]. It encompasses 40 core data fields, of which 9 are mandatory, and 24 metadata fields, with 7 required to provide essential project-level context [1] [12] [2].

Table 1: Required Core Data Fields (n=9)

Variable Category	Field Name	Descriptor
Sampling	Sample ID	A researcher-generated unique ID for the sample (e.g., "OS BZ19-114") [12].
Host Organism	Host identification	The Linnaean classification of the animal, ideally to species level (e.g., "Odocoileus virginianus") [12].
Parasite/Pathogen	Diagnostic method	The technique used to identify the parasite (e.g., "PCR," "ELISA," "culture") [1].
	Test result	The outcome of the diagnostic test (e.g., "positive," "negative," "inconclusive") [1].
	Parasite identity	The identity of the detected parasite, reported at the lowest possible taxonomic level [1].
Spatio-temporal	Date of sample collection	The date the sample was taken from the host animal [1].
	Location name	A researcher-assigned name for the sampling location [1].
	Latitude	The latitude of the sampling location in decimal degrees (WGS84) [1].
	Longitude	The longitude of the sampling location in decimal degrees (WGS84) [1].

Table 2: Selected Supplementary Core Data Fields

Variable Category	Field Name	Descriptor
Host Organism	Organism sex	The sex of the individual animal [12].
	Host life stage	The life stage of the animal (e.g., "juvenile," "adult") [12].
	Mass & Mass units	The mass of the animal at sampling and corresponding units [12].
Parasite/Pathogen	Gene target	The gene targeted for amplification (for PCR tests) [1].
	Primer citation	A citation for the primer set used [1].
	GenBank accession	Accession number for pathogen genetic sequence data submitted to GenBank [1].

This standard explicitly requires the reporting of negative test results and data disaggregated to the finest possible spatial, temporal, and taxonomic scale, which are often omitted but are critical for robust prevalence estimates and meta-analyses [1] [2]. The standard's structure facilitates the creation of "tidy data," where each row represents a single diagnostic test measurement, ensuring optimal re-use [1] [12].

Ethical Compliance Framework

Navigating Data Sensitivity and Animal Privacy

Ethical compliance in wildlife disease research extends beyond institutional animal care protocols. The collection and sharing of high-resolution data, particularly location information for threatened species, introduces significant ethical responsibilities regarding animal privacy and well-being [2] [38].

• Mitigating Harms from Data Sharing: Publicly sharing precise location data can inadvertently cause harm, such as increasing stress to animals from repeated disturbances or enabling poaching and wildlife culling [2] [38]. Researchers must implement data obfuscation techniques (e.g., generalizing coordinates) and develop context-aware data sharing plans that balance transparency with biosafety [2].
• Recognizing Animal Privacy Interests: A growing body of research indicates that animals exhibit "privacy behaviours," such as seeking seclusion or controlling communications [38]. These behaviors suggest derivative interests in avoiding observation, which ground ethical obligations for researchers. The obligation is to limit others' ability to access information about wildlife when such access could cause harm or infringe upon these interests [38].

Engaging Indigenous Knowledge and Worldviews

True ethical compliance requires challenging structural barriers within research ethics policies that marginalize Indigenous voices and Knowledge systems [39]. Western research paradigms often prioritize quantifiable indicators of animal welfare and the production of knowledge as capital, which can conflict with Indigenous worldviews that emphasize relationality and reciprocal responsibilities to wildlife [39].

Researchers should:

• Re-examine Ethics Board Structures: Move beyond token Indigenous representation on ethics committees to structurally reform policies and procedures, creating space for different knowledge systems and justifications for research [39].
• Honor Agreements and Protocols: Adhere to existing frameworks, such as modern treaties, that stipulate equal participation of Indigenous nations in wildlife management and research conducted on their territories [39].

Legal and Regulatory Compliance Framework

Data Protection Laws and Sharing Obligations

While wildlife disease data itself may not constitute "personal data" in the traditional sense, the infrastructure and principles of data protection laws provide a critical compliance framework, especially when data involves location-based information or is managed by entities operating in regulated jurisdictions.

• Landscape of US State Laws: The United States lacks a comprehensive federal privacy law, creating a complex patchwork of state regulations (e.g., in California, Colorado, Virginia, and others effective in 2025 like Delaware, Nebraska, and New Hampshire) [40] [41]. Businesses and institutions involved in data processing must navigate varying requirements regarding data subject rights, data minimization, and opt-out mechanisms for targeted advertising [40].
• Core Compliance Obligations: Key requirements under these laws include:
  • Data Minimization: Maryland's law, for instance, requires that data collection be limited to what is "reasonably necessary and proportionate" to provide the requested service, a principle that can be applied to research data collection [40].
  • Data Protection Assessments: New Jersey's law mandates a documented data protection assessment before engaging in processing activities that present a heightened risk of harm to consumers [40]. This mirrors the need for risk assessments before sharing sensitive wildlife data.
  • Security Safeguards: Implementing reasonable data security measures to protect information from unauthorized access is a universal requirement and is essential for preventing the misuse of wildlife disease data [41].

International Reporting and the One Health Approach

The World Organisation for Animal Health (WOAH) plays a central role in the global legal and regulatory landscape for animal disease reporting [42]. Its WAHIS (World Animal Health Information System) platform provides a homogeneous tool for members to report listed diseases in both domestic and wild animals, facilitating a near real-time global picture of disease status [42]. Integrating national wildlife disease surveillance data into international reporting mechanisms like WAHIS is a critical step in legal compliance and fostering a One Health approach to managing risks at the human-animal-ecosystem interface [42].

Experimental Protocol: Implementing the Standard

This protocol outlines the steps for formatting, validating, and sharing a wildlife disease dataset according to the minimum data standard, using a hypothetical coronavirus surveillance study in bats as an example.

Pre-Collection Planning and Data Assembly

• Define Project Scope: Confirm the dataset describes wild animal samples tested for parasites and includes diagnostic methods, dates, and locations [1].
• Tailor the Standard: Review all 40 core fields and 24 metadata fields. Identify which optional fields are applicable (e.g., Host life stage, Mass) and which ontologies to use for free-text fields [1].
• Format the Data:
  • Use the provided .csv or .xlsx templates [1].
  • Structure the data in a "tidy" format where each row corresponds to a single diagnostic test on a single sample [1].
  • For the example: A single bat (Animal ID: BZ19-114) provides oral and rectal swabs (Sample IDs: OS BZ19-114, RS BZ19-114), which are tested via PCR. This generates two rows in the dataset—one for each test [1].
  • Include all required fields. For a positive test, populate parasite-specific fields like Parasite identity and GenBank accession. For a negative test, these fields are left blank, but the record is still included [1].

Data Validation and Security Review

• Technical Validation: Use the provided JSON Schema or the dedicated R package (wddsWizard) to validate the dataset's structure and required fields against the standard [1].
• Ethical and Security Review: Conduct a risk assessment on the finalized dataset.
  • Sensitive Data Identification: Flag precise coordinates of endangered species or locations with high poaching risk.
  • Risk Mitigation: For public repositories, apply data obfuscation (e.g., generalize latitude/longitude to a larger grid cell) to mitigate risks of wildlife disturbance or poaching [2].
  • Compliance Check: Ensure data handling aligns with institutional data protection policies and any relevant state privacy laws, particularly concerning data security and minimization [40] [41].

Data Sharing and Publication

• Select a Repository: Deposit the data in an open-access generalist repository (e.g., Zenodo) or a specialist platform like the Pathogen Harmonized Observatory (PHAROS) database [1] [2].
• Provide Rich Metadata: Complete all relevant project-level metadata fields, including project title, description, creator ORCIDs, and funding source, to enhance findability and interoperability [1].
• Obtain a Persistent Identifier: Secure a Digital Object Identifier (DOI) for the dataset to ensure permanent access and enable proper citation [2].

The following workflow diagram summarizes this data preparation and sharing process.

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Resources for Standard-Compliant Wildlife Disease Research

Tool / Resource	Type	Function in Compliance & Research
WDDS Templates	Data Template	Pre-formatted .csv/.xlsx files providing the correct structure for data entry, ensuring adherence to the 40-field standard [1].
WDDS JSON Schema / R Package	Validation Tool	Machine-readable schema and R package (wddsWizard) for validating dataset structure and fields against the standard [1].
PHAROS Database	Data Repository	A dedicated platform for archiving and sharing wildlife disease data that aligns with the minimum data standard, enhancing findability and reusability [1].
GBIF / Darwin Core	Data Standard	A biodiversity data standard; interoperability with this and other standards (e.g., MIReAD) is a core feature of the wildlife disease standard [1] [12].
GenBank / SRA	Data Repository	Specialized archives for pathogen genetic sequence data; the standard assumes sequence data is deposited here and linked via the GenBank accession field [1].
WOAH-WAHIS	Reporting System	The global official system for reporting listed animal diseases to international authorities, a key destination for standardized national surveillance data [42].

In the field of wildlife disease research, the lack of standardized data reporting has long hindered the ability to aggregate datasets, compare findings across studies, and conduct robust synthetic analyses. This limitation is particularly problematic for understanding emerging zoonotic threats and ecological health, where data fragmentation can obscure critical patterns in pathogen distribution and dynamics. To address this challenge, the community has developed a minimum data standard for wildlife disease research and surveillance [1] [12]. This standard establishes a common framework for reporting key elements of disease studies, enabling improved data sharing, reuse, and aggregation in alignment with FAIR principles (Findable, Accessible, Interoperable, and Reusable) [2].

The theoretical foundation of this standard centers on the use of "tidy data" principles, where each row corresponds to a single diagnostic test outcome, creating a disaggregated record structure that preserves the finest spatial, temporal, and taxonomic resolution [1] [12]. However, adopting any data standard requires practical tools for implementation and validation. This is where the wddsWizard R package and its associated JSON Schema provide critical infrastructure, offering researchers a streamlined pathway to standardize and validate their datasets against the community-defined requirements [30].

Technical Specifications of the Validation Framework

The wddsWizard R Package

The wddsWizard package serves as a bridge between researchers' native datasets and the formal requirements of the Wildlife Disease Data Standard. Developed to support the standardization of wildlife disease data, this package provides a suite of functions that enable researchers to validate their data structures programmatically [30]. The package is openly available through GitHub, reflecting its development as a community resource rather than a proprietary tool.

At its core, the package implements the validation logic through integration with the jsonvalidate package in R, using the AJV (Another JSON Schema Validator) engine to perform rigorous checks against the standard's formal specification [30]. This implementation ensures that validation occurs consistently and reproduces the same results across computing environments, a critical feature for collaborative research projects and data aggregation initiatives.

The JSON Schema Foundation

The Wildlife Disease Data Standard is formally defined through a JSON Schema, which provides a machine-readable specification of the required data structure, fields, and constraints [30] [1]. JSON Schema is a widely adopted standard for validating JSON documents, making it an interoperable choice for defining data structures that may be used across multiple programming languages and platforms.

The schema encapsulates all requirements of the data standard, including:

• The complete set of 40 data fields (9 required) and 24 metadata fields (7 required) that constitute the standard [1] [12]
• Data type specifications for each field (string, number, boolean)
• Validation rules for required fields and value constraints
• Structural requirements for the overall dataset organization

This schema serves as the single source of truth for what constitutes a valid dataset, ensuring that all tools and platforms implementing the standard do so consistently.

Comprehensive Validation Protocol

The following diagram illustrates the complete workflow for standardizing and validating wildlife disease data using the wddsWizard package and JSON Schema:

Step-by-Step Implementation

Prerequisite Installation and Setup

Begin by installing the necessary packages in R. The wddsWizard package is available via GitHub, while its dependency jsonvalidate is available from CRAN:

Data Formatting and Template Preparation

Before validation, datasets must be structured according to the Wildlife Disease Data Standard template. The standard requires data in a "tidy" format where each row represents a single diagnostic test outcome [1] [12]. Researchers should:

• Download the latest CSV templates from the official GitHub repository (github.com/viralemergence/wdds) [1]
• Map existing dataset fields to the standard's 40 data fields, ensuring all 9 required fields are included
• Ensure categorical data uses consistent terminology and that numeric fields have appropriate units specified

Table 1: Required Data Fields in the Wildlife Disease Data Standard

Field Name	Data Type	Description	Example
Sample ID	String	Unique identifier for the sample	"OS BZ19-114"
Host identification	String	Linnaean classification of host	"Desmodus rotundus"
Decimal latitude	Number	Geographic latitude in decimal degrees	17.2546
Decimal longitude	Number	Geographic longitude in decimal degrees	-88.7698
Event date	String	Date of sample collection	"2019-03-15"
Diagnostic test name	String	Name of diagnostic test used	"coronavirus PCR"
Test result	String	Outcome of diagnostic test	"positive"
Test target	String	Pathogen or marker targeted	"Alphacoronavirus"
Parasite taxon	String	Identity of detected parasite	"Alphacoronavirus"

Schema Retrieval and Validation Execution

With formatted data, researchers can proceed with the programmatic validation:

Interpretation of Validation Results

A successful validation generates a confirmation message, indicating the dataset complies with the standard. Failed validation returns detailed error messages that specify:

• Missing required fields that must be added to the dataset
• Data type mismatches where field values don't match expected types
• Structural issues with the overall dataset organization
• Value violations where content falls outside acceptable ranges or formats

Researchers should systematically address each identified issue and re-validate until the dataset passes all checks.

Successful implementation of the data standard requires specific computational tools and resources. The following table details key components of the validation toolkit:

Table 2: Essential Research Reagents and Computational Resources

Resource	Type	Function	Access Point
wddsWizard	R Package	Programmatic validation of data against WDDS	GitHub: viralemergence/wddsWizard
WDDS JSON Schema	Data Schema	Machine-readable specification of the standard	Included in wddsWizard package
JSON Validator (AJV)	Validation Engine	Core validation engine that checks data structure	Via jsonvalidate R package
CSV Templates	Data Template	Pre-formatted tables for data collection	GitHub: viralemergence/wdds
Controlled Vocabularies	Terminology	Standardized terms for specific fields	Ontology lookup services
PHAROS Database	Data Repository	Specialist platform for sharing validated data	pharos.viralemergence.org

Application to Diverse Research Contexts

The validation framework is designed for flexibility across multiple wildlife disease research scenarios. The following diagram illustrates how researchers with different project types can implement the standard:

The standard is particularly valuable for including negative test results and contextual metadata that are often omitted from published studies but are essential for calculating accurate prevalence rates and understanding disease dynamics [1] [2]. By capturing these elements in a standardized format, the framework addresses a critical gap in wildlife disease data representation.

Discussion and Best Practices

Implementation Considerations

Successful implementation of the validation framework requires attention to several practical considerations. Researchers working with sensitive data related to endangered species or high-consequence pathogens should implement appropriate safeguards, which may include data obfuscation techniques for precise location information [2]. The standard supports these considerations while maintaining scientific utility.

For free-text fields, researchers should utilize controlled vocabularies or ontologies where possible to enhance interoperability. Recommended resources for identifying appropriate terms include the OBO Foundry, the Ontology Lookup Service, and the NCBO BioPortal [30]. This practice maintains flexibility while promoting consistent terminology across datasets.

Data Sharing and Repository Integration

Once validated, datasets should be shared through appropriate repositories to maximize their utility and impact. Compatible platforms include:

• Generalist repositories: Zenodo, Dryad, or Figshare, which accept any research data type
• Specialist platforms: The Pathogen Harmonized Observatory (PHAROS) database, specifically designed for wildlife disease data [1]
• Biodiversity platforms: Global Biodiversity Information Facility (GBIF), which can accommodate disease occurrence data [1]

When sharing data, researchers should include comprehensive project-level metadata using the DataCite Metadata Schema as recommended by the Generalist Repository Ecosystem Initiative [1] [12]. This practice ensures proper citation and discoverability of shared datasets.

The wddsWizard R package and JSON Schema validation framework provide an essential toolkit for implementing the minimum data standard in wildlife disease research. By offering a standardized, programmatic approach to data validation, these tools lower the technical barriers to adopting community standards while ensuring rigorous quality control. As adoption grows, these validated datasets will form an increasingly powerful foundation for synthetic analyses, ecological forecasting, and evidence-based decision-making at the interface of wildlife health and global security.

Researchers are encouraged to integrate these validation protocols early in their research workflows, ideally during the data management planning phase, to maximize efficiency and compliance with emerging best practices in reproducible wildlife disease science.

Validating the Standard: Real-World Applications and Comparative Advantages

The surveillance of wildlife pathogens, particularly coronaviruses (CoVs) in bats, is a critical component of global One Health initiatives. Bats (Order: Chiroptera) are natural reservoirs for a vast diversity of CoVs, including both Alphacoronavirus and Betacoronavirus genera, and have been implicated in the emergence of several human diseases [43] [44]. Understanding the dynamics of bat-CoV interactions requires not only robust field and laboratory methodologies but also the consistent application of data reporting standards to ensure that findings are Findable, Accessible, Interoperable, and Reusable (FAIR) [12].

This application note details a comprehensive protocol for the detection and characterization of a novel alphacoronavirus in phyllostomid bats from Belize, framed within the context of a proposed minimum data standard for wildlife disease research [12]. We provide a detailed workflow—from field sampling and molecular diagnostics to data structuring—designed to facilitate data interoperability, reproducibility, and synthesis across studies.

Application Notes & Experimental Protocols

Field Sampling and Host Data Collection

The initial phase of the study involves the strategic collection of samples and associated host data from wild bat populations.

• Bat Capture and Identification: Bats are captured using mist nets or hand nets at selected sampling sites. Each individual is identified to the lowest possible taxonomic level (ideally species), following morphological taxonomic keys [43]. This host identification is a required field in the minimum data standard [12].
• Sample Collection: Two oral and two rectal swabs are collected from each bat. Swabs are placed in viral transport medium (VTM) and immediately stored in liquid nitrogen containers for transport to the laboratory [43].
• Host Metadata Recording: Critical host-level data is recorded for each individual, aligning with the standardized data fields. The table below summarizes the essential host-related data fields to be collected.

Table 1: Essential Host and Sample Metadata

Variable	Data Type	Required	Descriptor
Host Identification	String	✓	Linnaean classification (e.g., "Carollia sowelli") [12]
Sample ID	String	✓	Unique identifier for the sample (e.g., "OS BZ19-114") [12]
Animal ID	String		Unique identifier for the individual animal [12]
Organism Sex	String		Sex of the animal [12]
Host Life Stage	String		Life stage (e.g., "juvenile", "adult") [12]
Live Capture	Boolean		Whether the animal was alive at capture [12]
Mass	Number		Body mass of the animal [12]
Mass Units	String		Units for mass (e.g., "g") [12]

Molecular Detection of Alphacoronavirus

The following protocol outlines the steps for detecting alphacoronavirus RNA in collected swab samples.

• RNA Extraction: Total RNA is extracted from the oral and rectal swab samples pooled for each bat. Automated nucleic acid extraction systems, such as the MAGMAX FLEX with the MagMax Core Nucleic Acid Isolation Kit, are used following the manufacturer's protocol. The extracted RNA is quantified using a spectrophotometer (e.g., NanoDrop) and stored at -80 °C [43].
• Pan-Coronavirus RT-Nested-PCR: The presence of Orthocoronavirinae is determined by a nested PCR reaction targeting the conserved RNA-dependent RNA-polymerase (RdRp) gene.
  • First-Round RT-PCR: The One-Step RT-PCR is performed using primers CHU1F and CHU1R [43].
  • Second-Round Nested PCR: A portion of the first-round product is used as a template for a second PCR amplification with internal primers CHU2F and CHU2R [43].
  • Visualization: The PCR products are separated on a 1.5% agarose gel. Samples producing an amplicon of the expected size are considered positive.

Table 2: Primer Sequences for Pancoronavirus Nested RT-PCR

Primer Name	Sequence (5' to 3')	Round
CHU1F	GGKTGGGAYTAYCCKAARTG	First
CHU1R	TGYTGTSWRCARAAYTCRTG	First
CHU2F	GGTTGGGACTATCCTAAGTGTGA	Nested
CHU2R	CCATCATCAGATAGAATCATCAT	Nested

Proteomic Analysis of Host Serum (Optional)

To investigate the host immune response to CoV infection, serum proteomic profiling can be performed.

• Serum Sample Preparation: Blood samples are collected, and serum is separated. For safety, serum may be heat-inactivated (56°C for 30 minutes), though this should be documented as it can affect the proteome [45].
• Liquid Chromatography-Mass Spectrometry (LC-MS/MS): Proteins are digested into peptides and analyzed using Data-Independent Acquisition (DIA) mass spectrometry to achieve a comprehensive profile of the serum proteome [45].
• Bioinformatic Analysis:
  • Acquired spectra are searched against a custom protein database containing host (bat) and viral sequences.
  • Identified proteins are mapped to human orthologs using BLAST+ to facilitate functional annotation.
  • Differential abundance analysis is conducted between CoV-infected and uninfected bats using moderated t-tests.
  • Gene Ontology (GO) enrichment analysis is performed to identify biological processes associated with CoV infection [45].

Data Standardization and Reporting

Adhering to the minimum data standard is crucial for making research data FAIR. The following workflow ensures data is collected, formatted, and documented correctly.

Diagram 1: Data Standardization Workflow

• Data Structure: Researchers should share raw data in a "tidy data" format, where each row corresponds to a single diagnostic test outcome. This structure accommodates complex relationships between tests, samples, and individual animals [12].
• Core Data Fields: The minimum standard includes 40 core data fields across three categories: sample, host, and parasite. Of these, 9 fields are required, including Sample ID and Host identification [12].
• Project Metadata: The dataset must be accompanied by project-level metadata (7 of 24 fields are required), following schemas like the DataCite Metadata Schema, to provide essential context such as principal investigators, funding sources, and licensing [12].
• Safety and Sharing: Researchers must navigate potential safety concerns related to data sharing. Data can be made available under controlled access if necessary, and all data should be formatted to minimize room for error or loss of information [12].

The Scientist's Toolkit

This section lists key reagents and materials essential for executing the protocols described in this application note.

Table 3: Key Research Reagent Solutions

Item	Function / Application	Example / Source
Mist Nets	Passive capture of bats for field sampling.	-
Viral Transport Medium (VTM)	Preservation of viral viability and nucleic acids in swab samples during transport.	-
Automated Nucleic Acid Extractor	High-throughput, consistent isolation of DNA/RNA from swab samples.	MAGMAX FLEX [43]
Pancoronavirus Primers	Detection of a broad range of coronaviruses via RT-nested PCR.	Chu et al. primers targeting RdRp gene [43]
One-Step RT-PCR Kit	Combined reverse transcription and PCR amplification for detection of RNA viruses.	SuperScript III One-Step RT-PCR System [43]
LC-MS/MS System	High-sensitivity identification and quantification of proteins in serum samples.	-
Heat Inactivation Protocol	Safety measure to inactivate potential pathogens in serum prior to proteomic analysis.	56°C for 30 minutes [45]

Results and Data Presentation

The application of the above protocols in Belize revealed an Alphacoronavirus prevalence of 22.22% to 36.36% across three phyllostomid bat species: Desmodus rotundus (vampire bat), Carollia sowelli (Sowell's short-tailed bat), and Sturnira parvidens (little yellow-shouldered bat) [44]. Phylogenetic analysis of the partial RdRp gene sequences placed the novel viruses within the Alphacoronavirus genus and suggested evolutionary relationships with human CoVs 229E and NL63 [44].

Proteomic analysis, while not detecting viral proteins in serum, identified 32 candidate protein biomarkers of CoV infection in vampire bats. Gene Ontology analysis of these biomarkers indicated that infected bats exhibited downregulation of the complement system and humoral immunity, alongside upregulation of neutrophil-mediated immunity and glutathione processes [45].

Standardized Data Compilation

The following table demonstrates how the core findings from the Belize study can be structured according to the minimum data standard, ensuring interoperability.

Table 4: Example Data Record Structured per Minimum Standard

Field	Example Value	Category
Sample ID	OS_DR_BZ21_001	Sample
Animal ID	DR_BZ21_001	Sample
Host Identification	Desmodus rotundus	Host (Required)
Organism Sex	Female	Host
Host Life Stage	Adult	Host
Live Capture	TRUE	Host
Mass	35.5	Host
Mass Units	g	Host
Test Result	Positive	Parasite
Target Gene	RdRp	Parasite
Parasite Genus	Alphacoronavirus	Parasite

This application note provides a detailed protocol for documenting a novel alphacoronavirus in bats, integrating rigorous experimental methods with a standardized data reporting framework. By adhering to the described workflows and the accompanying minimum data standard, researchers can generate data that is not only scientifically robust but also readily available for future synthesis and analysis. This approach is fundamental for advancing the field of wildlife disease ecology and enhancing our preparedness for zoonotic emergence.

The emergence of a minimum data standard for wildlife disease research addresses a critical deficiency in ecological and public health surveillance: the pervasive reliance on summary-only reporting [1]. This practice, where data are aggregated into descriptive tables, obscures the granular host-level and spatial-temporal details essential for robust analysis [12]. Such summarization has historically constrained the utility of wildlife disease data for meta-analyses, the development of predictive models for emerging zoonoses, and the assessment of global change impacts on disease dynamics [1] [2]. The new standard provides a structured framework for sharing disaggregated data, fundamentally enhancing data reusability, analytical flexibility, and actionable insight generation [1]. This analysis quantitatively and qualitatively compares the capabilities of the minimum data standard against traditional summary-only reporting, demonstrating its transformative potential for the field.

The minimum data standard introduces a comprehensive set of data and metadata fields designed to capture the full context of wildlife disease investigations [1] [2]. The table below summarizes the core quantitative differences in data reporting between the two approaches.

Table 1: Quantitative Comparison of Data Reporting Approaches

Aspect	Minimum Data Standard	Summary-Only Reporting
Total Data Fields	40 fields (9 required) [1] [2]	Typically < 10 fields (e.g., species, location, prevalence) [1]
Total Metadata Fields	24 fields (7 required) [1]	Often minimal or absent
Reporting of Negative Results	Mandatory for each test [1] [2]	Often omitted or only positive results shared [1]
Data Disaggregation	Record-level (per test/sample), enabling host-level analysis [1]	Aggregated (e.g., prevalence per site/species), limiting analysis [1]
Spatial Granularity	Precise coordinates or detailed location descriptors [1]	Often broad regional descriptors [1]
Host-Level Detail	Sex, life stage, age, mass, etc. (13 fields) [12]	Rarely included; if so, only as summary statistics [1]

Key Experimental Protocols for Implementing the Standard

Adopting the minimum data standard involves a structured process from data collection to sharing. The following protocol outlines the key steps for researchers.

Protocol: Data Collection and Standardization

Background: This protocol describes the procedure for formatting a raw wildlife disease dataset according to the minimum data standard, ensuring it is Findable, Accessible, Interoperable, and Reusable (FAIR) [1] [2].

Key Features

• Transforms project-specific data into a community-standard format.
• Ensures inclusion of critical, often-missing data such as negative results and methodological metadata.
• Prepares data for deposition in public repositories.

Materials and Reagents

• Raw Data: The original dataset, ideally in a spreadsheet format (e.g., .csv, .xlsx).
• Data Standard Template: The official template file (.csv or .xlsx) available via the standard's GitHub repository (github.com/viralemergence/wdds) [1].
• Validation Tool: The provided JSON Schema or the accompanying R package (wddsWizard) for data validation [1].

Procedure

• Assess Fit for Purpose: Confirm the dataset describes wild animal samples examined for parasites and includes information on diagnostic methods, date, and location [1].
• Tailor the Standard: Review the list of 40 data fields (Tables 1-3 in the standard) and 24 metadata fields. Identify which optional fields are applicable to your study design and if any additional fields are necessary [1].
• Map and Format Data: a. Populate the template file, mapping your raw data to the corresponding standard fields. b. Critical Step: Ensure all nine required fields are populated for every record: Sample ID, Host identification, Analysis date, Analysis method, Analysis target, Result, Location, Location scale, and Country [1]. c. Critical Step: Include a record for every diagnostic test performed, including those with negative results [1] [2]. d. Use controlled vocabularies (e.g., from Darwin Core) for fields like Host life stage and Organism sex where possible to enhance interoperability [1] [12].
• Validate the Dataset: Use the provided R package or validate against the JSON Schema to check for formatting errors or missing required fields [1].
• Document with Metadata: Complete the project-level metadata table, including required fields such as title, creator, identifier, publisher, publication year, and license [1].

Data Analysis The output of this protocol is a validated, "tidy" dataset where each row represents a single diagnostic test outcome [1]. This structure is immediately usable for a wide range of analyses in statistical software (e.g., R, Python) without the need for manual extraction or restructuring.

Validation of Protocol This protocol is validated by its application to real-world datasets, such as a study of coronavirus in Belizean bats, which was successfully formatted and shared on the Pathogen Harmonized Observatory (PHAROS) platform [1].

General Notes and Troubleshooting

• Note: The standard is flexible. Leave non-applicable fields blank (e.g., Gene target for non-PCR tests) [1].
• Troubleshooting: If validation fails, carefully review error messages from the validation tool, which typically indicate missing required fields or data type mismatches.

Workflow Visualization: From Data Collection to Re-use

The following diagram illustrates the logical workflow and comparative outcomes of applying the standard versus summary-only reporting.

The Scientist's Toolkit: Essential Research Reagent Solutions

Implementing the minimum data standard requires both conceptual and practical tools. The following table details key resources and their functions in the standardization process.

Table 2: Essential Reagents and Resources for Standardized Wildlife Disease Research

Item / Resource	Function / Description	Critical Application in the Standard
Standard Template Files (.csv/.xlsx)	Pre-formatted files listing all 40 data fields [1].	Provides the foundational structure for data entry, ensuring consistency across studies.
JSON Schema / R Validation Package	Machine-readable rule set and software for checking data compliance [1].	Automates data quality control by verifying the presence of required fields and correct data types before repository submission.
Controlled Vocabularies & Ontologies	Standardized terminology (e.g., Darwin Core, NCBI Taxonomy) [1].	Ensures interoperability by providing common names for host species, life stages, and other variables, preventing ambiguity.
Generalist Data Repository (e.g., Zenodo)	Platform for publishing and preserving research datasets with a persistent DOI [1] [2].	Makes standardized data Findable and Accessible, fulfilling the FAIR principles and enabling citation.
Specialist Platform (e.g., PHAROS)	A database dedicated to wildlife disease data [1].	Allows for advanced querying, visualization, and aggregation of standardized datasets from multiple studies.
Persistent Identifier (e.g., ORCID iD)	A unique identifier for researchers [2].	Included in metadata to ensure unambiguous attribution for data creators, promoting a culture of data sharing.

Enhancing Interoperability with Biodiversity and Human Health Data Platforms

The convergence of biodiversity monitoring and human health surveillance represents a critical frontier in public health and ecological conservation. The emergence of zoonotic diseases underscores the intricate connections between ecosystem integrity and human health outcomes [46]. The Kunming-Montreal Global Biodiversity Framework and the Global Action Plan on Biodiversity and Health provide renewed impetus for developing integrated monitoring systems that can effectively track these complex relationships [46]. Despite this, a significant implementation gap persists, with limited adoption of standardized metrics that bridge these historically separate domains [46].

The absence of harmonized data standards severely hampers the ability to conduct secondary analyses, aggregate datasets across studies, and generate actionable insights for pandemic prevention and ecological health management [1] [2]. This application note addresses this critical gap by presenting a detailed protocol for implementing a minimum data standard specifically designed to enhance interoperability between wildlife disease research platforms and broader biodiversity data infrastructures. By adopting this standardized framework, researchers can significantly improve the findability, accessibility, interoperability, and reusability (FAIR) of wildlife disease data, thereby strengthening early warning systems for emerging health threats at the human-animal-environment interface [2].

Core Data Standards and Specifications

The foundation for enhanced interoperability lies in implementing a consistent minimum data standard that captures essential information across sampling, host organisms, and parasite detection. The standard presented here builds on recent scientific consensus regarding the core data elements required for meaningful data integration and reuse in wildlife disease studies [1] [2] [4].

Table 1: Minimum Data Standard for Wildlife Disease Research

Category	Field Name	Requirement Level	Description	Controlled Vocabulary Recommended
Sampling	Sampling Date	Required	Date of sample collection	ISO 8601 format
	Geographic Coordinates	Required	Latitude and longitude of sampling	Decimal degrees
	Diagnostic Method	Required	Test used for parasite detection	Open text
Host Organism	Host Species	Required	Taxonomic identification of host	GBIF Backbone Taxonomy
	Life Stage	Conditional	Host developmental stage	Open text
	Animal ID	Optional	Unique identifier for individual	Open text
Parasite/Pathogen	Test Result	Required	Outcome of diagnostic test	Positive/Negative/Inconclusive
	Parasite Species	Conditional	Taxonomic identification of parasite	GBIF Backbone Taxonomy
	GenBank Accession	Conditional	Identifier for genetic sequence data	GenBank format

This standardized framework encompasses 40 data fields (9 required) and 24 metadata fields (7 required) that collectively document diagnostic outcomes at the finest possible spatial, temporal, and taxonomic resolution [1] [2]. A critical innovation of this standard is its mandatory inclusion of negative test results, which have historically been underrepresented in wildlife disease data despite their essential value for understanding disease prevalence and distribution [1] [2]. The standard is designed to accommodate diverse diagnostic methodologies—including PCR, ELISA, and pooled testing approaches—while maintaining structural consistency for data aggregation and analysis [1].

The standard aligns with and extends existing biodiversity data frameworks, particularly the Darwin Core standard used by the Global Biodiversity Information Facility (GBIF), ensuring compatibility with broader biodiversity data infrastructures [1] [47]. This strategic alignment enables wildlife disease data to contribute to both health surveillance objectives and essential biodiversity variables (EBVs) tracking, effectively bridging the historical divide between public health and ecological monitoring frameworks [48] [46].

Experimental Protocol: Implementation Workflow

This protocol provides a step-by-step framework for implementing the minimum data standard in wildlife disease research and surveillance programs. The workflow ensures consistent data collection, formatting, and sharing practices that enhance interoperability between biodiversity and health data platforms.

Materials and Equipment

Table 2: Research Reagent Solutions and Essential Materials

Item	Function	Implementation Context
Template Files (.csv, .xlsx)	Standardized structure for data recording	Pre-formatted templates ensure consistent implementation across studies [1]
JSON Schema Validator	Automated validation of data structure	Checks compliance with standard before data publication [1]
GBIF Backbone Taxonomy	Taxonomic normalization service	Ensures consistent species identification across datasets [1] [47]
PHAROS Database	Specialized repository for wildlife disease data	Platform for sharing standardized datasets with the community [1]
Generalist Repository (Zenodo)	FAIR-compliant data archive	Provides persistent identifiers and long-term preservation [1] [2]

Step-by-Step Procedure

• Project Evaluation and Planning

  • Determine applicability: This standard is suitable for studies examining wild animal samples for parasites, with information on diagnostic methods, and date and location of sampling [1].
  • Define project-specific variables: Identify which optional fields beyond the 9 required ones are relevant to the study design and objectives [1].
  • Establish data sharing protocols: Develop plans for managing sensitive data, including location obfuscation for threatened species or precise coordinates that could enable wildlife persecution [1] [2].

• Data Collection and Standardization

  • Utilize provided templates: Download standardized template files (.csv or .xlsx format) from the official GitHub repository (github.com/viralemergence/wdds) [1].
  • Implement controlled vocabularies: Apply consistent terminology for fields such as diagnostic methods, life stage, and test results to enhance interoperability [1].
  • Record negative results: Systematically document all tests conducted, including those with negative outcomes, to enable robust prevalence calculations [1] [2].

• Data Validation and Quality Control

  • Execute validation checks: Use the provided JSON Schema or dedicated R package (github.com/viralemergence/wddsWizard) to validate data against the standard [1].
  • Verify taxonomic consistency: Cross-reference host and parasite species identifications with the GBIF Backbone Taxonomy to ensure nomenclature alignment [1] [47].
  • Conduct spatial validation: Verify that coordinate data falls within expected ranges and implement obfuscation where necessary for sensitive species [2].

• Data Publication and Integration

  • Select appropriate repositories: Deposit validated datasets in specialized platforms (e.g., PHAROS database) or generalist repositories (e.g., Zenodo) with appropriate metadata [1] [2].
  • Apply persistent identifiers: Include Digital Object Identifiers (DOIs) for datasets and ORCIDs for researchers to enhance findability and proper attribution [2].
  • Publish to biodiversity infrastructures: Where appropriate, share data through platforms like OBIS and GBIF to connect with broader biodiversity monitoring efforts [47].

Integration with Biodiversity Monitoring Frameworks

The implementation of wildlife disease data standards achieves maximum impact when strategically aligned with existing biodiversity monitoring infrastructures and policy frameworks. Europe's evolving biodiversity monitoring landscape, characterized by the development of Thematic Hubs and the future European Biodiversity Observation Coordination Centre (EBOCC), offers a strategic pathway for this integration [49]. These expert-driven platforms serve as coordination mechanisms for specific biodiversity domains, facilitating structured dialogue and methodological alignment across monitoring communities [49].

The Biodiversa+ partnership has identified wildlife diseases as one of twelve priority areas for enhanced monitoring capacity, recognizing their significance for both ecosystem health and human health security [48]. This prioritization creates a strategic entry point for integrating standardized disease surveillance data into broader biodiversity observation networks. Furthermore, initiatives such as the OBIS-GBIF Joint Strategy for Marine Biodiversity Data (2025–2030) demonstrate practical frameworks for making biodiversity data more interoperable, accessible, and actionable for science and decision-making [47].

For effective policy integration, standardized wildlife disease data should be incorporated into National Biodiversity Strategies and Action Plans (NBSAPs) as countries work to update these documents in alignment with the Kunming-Montreal Global Biodiversity Framework [46]. This integration ensures that wildlife health monitoring becomes an institutionalized component of national biodiversity assessments rather than remaining a separate public health activity. The recently proposed specifications for cross-scale inclusion of harmonized biodiversity monitoring protocols provide practical guidance for achieving this integration through common minimum requirements for monitoring objectives, variables, sampling units, and reporting formats [49].

Applications and Impact Assessment

The implementation of standardized data practices for wildlife disease research generates significant scientific and policy benefits across multiple domains. The enhanced interoperability enables more robust secondary analyses and ecological synthesis research, supporting the investigation of macroecological patterns of pathogen distribution and the impacts of global change on disease dynamics [1] [46].

From a public health perspective, standardized data dramatically improves early warning systems for emerging zoonotic threats. The COVID-19 pandemic has underscored the urgent need for transparent, high-quality wildlife surveillance data that can be rapidly aggregated and analyzed to assess spillover risk [2]. By mandating consistent documentation of sampling context, host characteristics, and detection methods, the standard enables more accurate risk assessments and targeted surveillance interventions [1] [2].

For conservation and ecosystem management, integrating disease surveillance with biodiversity monitoring provides crucial insights into wildlife population health and the impacts of diseases on species of conservation concern [48] [46]. This integrated perspective is essential for implementing the One Health approach, which recognizes the interconnected health of humans, animals, plants, and ecosystems [46]. The standardized data facilitates the development of integrated science-based metrics that can quantify the environmental burden of disease and track progress toward achieving both biodiversity conservation and public health objectives [46].

Adoption barriers may include technical capacity limitations, concerns about data sensitivity, and institutional resistance to changing established practices. These challenges can be addressed through targeted training programs, clear guidance on ethical data sharing, and demonstration of the tangible benefits achieved through earlier adopters [47] [2]. The growing requirements from funding agencies and scientific journals for FAIR data practices provide additional impetus for researchers to adopt these standardized approaches [2].

Application Note: The Minimum Data Standard as a Foundation for Synthesis

The Minimum Data Standard for wildlife disease research is specifically designed to transform disparate, non-comparable datasets into a harmonized and interoperable resource. Its structure directly addresses the major bottlenecks in synthesis research by ensuring data is shared at the finest spatial, temporal, and taxonomic scale, and that critical contextual metadata is consistently reported [1]. This enables two primary forms of synthesis: meta-analyses, which quantitatively combine results from multiple studies, and predictive modeling, which uses aggregated data to forecast disease dynamics and inform management decisions.

The standard's requirement to include negative data (non-detections) is particularly crucial. Most historical studies only report positive detections or provide summarized prevalence data, making it impossible to recalculate true prevalence or compare across studies for a meta-analysis [1]. By providing disaggregated data, the standard allows for the recalculation of effect sizes and the investigation of heterogeneity, which are the cornerstones of a robust meta-analysis [50].

For modeling, the key parameters required to initialize and parameterize frameworks—such as pathogenicity, host breadth, transmission pathways, and spatiotemporal location—are explicitly captured in the standard's fields [51]. This provides modelers with the foundational data needed to build predictive models early in an outbreak, even amidst uncertainty.

Protocol for Conducting a Meta-Analysis Using Standardized Wildlife Disease Data

This protocol outlines the steps for utilizing datasets adhering to the Minimum Data Standard to conduct a systematic review and meta-analysis of wildlife disease effects.

Phase 1: Systematic Review and Data Extraction

• Literature Search and Screening: Execute a comprehensive search across multiple online databases and repositories (e.g., Zenodo, PHAROS, GBIF) using predefined search strings. The search should be for studies on the specific host-parasite system of interest [1] [50].
• Study Selection and Appraisal: Screen studies based on inclusion/exclusion criteria (e.g., study design, diagnostic method). Critically appraise study quality to identify potential sources of bias [50].
• Data Extraction into a Harmonized Format: For studies sharing data according to the Minimum Data Standard, extract the following core data points for each record. The required fields in the standard ensure these data are available.
  • Host Species
  • Location (Decimal Latitude and Longitude)
  • Test Result and Test Date
  • Diagnostic Method
  • Host-level data (Sex, Age Class) where available [1]

Phase 2: Data Transformation and Effect Size Calculation

• Aggregate to Study Level: Calculate summary statistics (e.g., number of positive tests, total samples) for each unique combination of study, host species, and location from the individual-level data.
• Calculate Effect Estimates: For each study, compute an appropriate effect size. A common measure for prevalence data is the Standardized Mean Difference (SMD), which expresses the difference in outcomes on a uniform scale, making studies with different measurement units comparable [50].
• Construct a Forest Plot: Tabulate the raw data, calculated effect size, confidence interval, and weight for each study. Visually present this information in a forest plot to show the effect size of individual studies and the pooled effect [50].

Table 1: Data Structure for Meta-Analysis of Pathogen Prevalence

Study ID	Host Species	Positive Samples	Total Samples	Prevalence	Effect Size (SMD)	95% CI	Weight (%)
Smith et al. 2020	Myotis lucifugus	15	100	0.15	0.45	(0.21, 0.69)	15.2
Jones et al. 2021	Eptesicus fuscus	8	50	0.16	0.48	(0.15, 0.81)	10.1
Lee et al. 2022	Myotis lucifugus	22	110	0.20	0.60	(0.38, 0.82)	16.5
Overall Effect (Pooled)					0.51	(0.38, 0.64)	100

Phase 3: Analysis and Heterogeneity Assessment

• Pool Effect Sizes: Use a random-effects model to calculate a pooled effect estimate, which accounts for variability between studies beyond sampling error [50].
• Quantify Heterogeneity: Calculate the I² statistic to quantify the percentage of total variation across studies due to heterogeneity rather than chance. An I² value greater than 50% is typically considered to represent substantial heterogeneity [50].
• Explore Heterogeneity: Use subgroup analysis or meta-regression to investigate whether factors captured by the standard (e.g., Host Species, Age Class, Diagnostic Method) explain the heterogeneity in results.

Protocol for Informing Predictive Models with Standardized Data

This protocol describes how to use data formatted according to the Minimum Data Standard to parameterize and initialize predictive models for emerging wildlife diseases.

Phase 1: Model Framework Selection

• Define Management Objective: Clearly state the goal of the modeling exercise (e.g., predict spatial spread, evaluate intervention efficacy) [51].
• Select Appropriate Modeling Framework: Choose a model class based on the management objective, system knowledge, and data availability [51]:
  • Compartmental Models (SIR): For population-level dynamics when the host population is well-connected.
  • Occupancy/Patch Models: For diseases in hosts living in discrete, patchy habitats.
  • Ecological Diffusion Models: For predicting the rate and direction of spatial spread.
  • Agent-Based Models: For complex, spatially-explicit host behavior and transmission.

Phase 2: Parameterization from Standardized Data

The Minimum Data Standard provides direct inputs for the five key characteristics needed for predictive modeling of disease systems [51].

• Map Data Fields to Model Parameters: Extract and calculate the following from the standardized dataset:
  • Pathogenicity: Infer from Host Health Outcome and population-level effects.
  • Transmission Pathways: Inferred from Sample Type and Diagnostic Method.
  • Host Breadth: Compiled from unique Host Species entries.
  • Host Social/Movement Behavior: Inferred from Host Species ecology and Location data.
  • Environmental Niche: Derived from Location and associated environmental data.

Table 2: Mapping Data Standard Fields to Key Modeling Parameters

Key Model Characteristic	Relevant Data Standard Fields	Model Parameter Example
Pathogenicity	Host Health Outcome, Parasite Load	Disease-induced mortality rate; recovery rate
Transmission Pathways	Sample Type, Diagnostic Method	Transmission rate (β); transmission matrix
Taxonomic Host Breadth	Host Species, Host Taxonomy	Number of susceptible host species; reservoir competence
Host Social/Movement Behavior	Host Species, Location	Contact rate; diffusion coefficient
Environmental Niche	Location, Test Date	Environmental suitability layer; seasonality factor

Phase 3: Model Implementation and Iteration

• Initialize Model: Use the compiled parameters to set up the initial state of the model.
• Incorporate Uncertainty: Use bounds on parameter estimates to run models that reflect reducible parametric uncertainty [51].
• Validate and Update: As new surveillance data adhering to the standard becomes available, compare model predictions to observed outcomes and update the model structure or parameters accordingly.

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Resources for Implementing the Workflow

Tool / Resource	Function / Description	Access Information
WDDS Wizard (R package)	Convenience functions to validate a dataset and its metadata against the JSON Schema implementing the data standard.	GitHub: github.com/viralemergence/wddsWizard [1]
Template Files (.csv/.xlsx)	Pre-formatted files containing the required and optional data fields, ensuring correct structure from the start of a project.	GitHub: github.com/viralemergence/wdds [1]
PHAROS Database	A dedicated platform for wildlife disease data, accepting submissions formatted according to the Minimum Data Standard.	Web: pharos.viralemergence.org [1] [2]
GBIF (Global Biodiversity Information Facility)	A global data infrastructure that allows sharing of and access to biodiversity data, compatible with the Darwin Core standard which aligns with this data standard.	Web: gbif.org [1]
WebAIM Color Contrast Checker	An online tool to verify that color contrasts in visualizations (e.g., model output diagrams) meet WCAG accessibility guidelines (AA level: 4.5:1).	Web: webaim.org/resources/contrastchecker/ [52]

The minimum data standard for wildlife disease research establishes a unified framework for reporting data on pathogen detection in wild animals. Its core strength lies in a flexible, technology-agnostic structure that captures essential information regardless of the diagnostic method used, ensuring interoperability and long-term relevance [1] [2]. The standard comprises 40 core data fields (9 required) and 24 metadata fields (7 required), designed to document findings at the most granular spatial, temporal, and taxonomic levels possible [1]. This architecture allows researchers to maintain data consistency and reusability even as diagnostic technologies evolve, supporting the core FAIR principles (Findable, Accessible, Interoperable, and Reusable) that are critical for synthetic research and global health security [1] [2].

Core Data Structure and Adaptability

The "Tidy Data" Framework

The standard's foundation is a "tidy data" format, where each row represents a single diagnostic measurement [1]. This simple rectangular structure (.csv) is inherently adaptable, capable of accommodating complex many-to-many relationships between samples, hosts, and tests that arise from advanced methodologies like repeated sampling, sample pooling, or confirmatory testing [1].

The table below summarizes the standard's core data fields, demonstrating how its organization supports diverse data types.

Table 1: Core Data Fields of the Wildlife Disease Data Standard

Category	Field Example	Field Type	Required	Description
Sample Data	Sample ID	String	✓	Unique identifier for the sample [12].
	Animal ID	String		Unique identifier for the individual host [12].
Host Data	Host Identification	String	✓	Linnaean classification (e.g., Odocoileus virginianus) [12].
	Organism Sex	String		Sex of the host individual [12].
	Host Life Stage	String		Life stage (e.g., juvenile, adult) [12].
	Mass / Length	Number		Morphological data with relevant units [12].
Parasite/Pathogen Data	Diagnostic Test	String	✓	Name of the diagnostic test (e.g., PCR, ELISA) [1].
	Test Result	String	✓	Outcome of the test (e.g., positive, negative, Ct value) [1].
	Pathogen Identity	String		Taxonomy of the detected parasite/pathogen [1].
	Gene Target / Probe Target	String		Method-specific field (e.g., for PCR or ELISA) [1].

Mechanism for Technological Integration

The standard achieves flexibility through several key features:

• Technology-Agnostic Core Fields: Fundamental fields like Diagnostic Test and Test Result are open-text, capturing essential outcomes from any current or future technology [1].
• Modular Method-Specific Fields: The standard incorporates optional, modular fields for details specific to a technology family. For example, PCR tests utilize fields like Forward primer sequence and Gene target, while ELISA tests use fields like Probe target and Probe type [1].
• Emphasis on Contextual Metadata: Comprehensive metadata ensures that data generated by any diagnostic method remains interpretable. This includes detailed sampling protocols, diagnostic test performance characteristics, and data processing steps [1] [2].

Protocol for Applying the Standard to New Diagnostic Technologies

This protocol guides researchers in integrating data from emerging diagnostic platforms into the standard, ensuring consistency and reusability.

Protocol: Integrating a Novel Diagnostic Method

Objective: To completely and accurately report data from a novel diagnostic technology using the minimum data standard.

Pre-requisites:

• A dataset from a wildlife disease study where samples from wild animals have been tested for parasites/pathogens.
• The diagnostic method used should be clearly defined.

Step-by-Step Procedure:

• Determine Applicability

  • Confirm the dataset describes wild animal samples tested for parasites/pathogens, with information on diagnostic methods, date, and location [1].

• Map Data to Core Fields

  • Identify corresponding data for the 9 required fields: Sample ID, Host Identification, Diagnostic Test, Test Result, Pathogen Identity (if positive), and date/location fields [1] [2].
  • Populate other relevant core fields from Table 1 (e.g., Organism Sex, Host Life Stage).

• Define Technology-Specific Parameters

  • Diagnostic Test: Record the full, specific name of the new technology (e.g., "CRISPR-based lateral flow assay").
  • Test Result: Define the result format (e.g., "positive/negative", "nanopore read count", "digital PCR count"). The standard accommodates diverse quantitative and qualitative results [1].
  • Create a Test Citation: Provide a citation (publication or manufacturer's protocol) detailing the principle and procedure of the novel method.

• Capture Novel Metadata

  • In the project metadata, document the limit of detection, protocol version, and unique data interpretation rules associated with the new technology.
  • For AI-driven diagnostics, document the algorithm version and training data provenance to ensure future reproducibility [53] [54].

• Validate and Share Data

  • Use the provided JSON Schema or R package (wddsWizard) to validate the dataset's structure [1].
  • Format data using open, non-proprietary formats (e.g., .csv) and deposit in an open-access repository (e.g., Zenodo, PHAROS) with a rich data dictionary [1] [2].

Visualizing the Flexible Data Architecture

The following diagram illustrates the standard's core-periphery architecture, which allows it to remain stable while integrating new technologies.

Figure 1: The core-periphery architecture of the data standard enables its stability and extensibility. The central core of required fields remains constant, while technology-specific modules can be added or updated as diagnostics evolve.

Implementation Workflow for Researchers

The workflow for applying the standard, from study design to data sharing, is outlined below.

Figure 2: The five-step implementation workflow guides researchers from data collection to sharing, ensuring compliance with the standard.

The Scientist's Toolkit: Research Reagent Solutions

The table below lists essential materials and resources for implementing the data standard in wildlife disease research.

Table 2: Essential Research Reagent Solutions and Resources

Item	Function/Description	Example/Reference
Template Files	Pre-formatted .csv or .xlsx files ensuring correct data structure.	Available on GitHub: github.com/viralemergence/wdds [1].
JSON Schema	Machine-readable schema to validate dataset structure and formatting.	Provided with the standard for automated validation [1].
R Package wddsWizard	Convenience functions in R to validate data and metadata.	Available on GitHub: github.com/viralemergence/wddsWizard [1].
Controlled Vocabularies & Ontologies	Standardized terms (e.g., for species, units) to enhance interoperability.	Encouraged use of existing ontologies; supporting information provides guidance [1].
Data Repositories	Open-access platforms for sharing standardized data to ensure findability and reusability.	Generalist (e.g., Zenodo) or specialist (e.g., PHAROS, GBIF) repositories [1] [2].

Conclusion

The implementation of a minimum data standard for wildlife disease research marks a pivotal advancement for both ecological science and biomedical progress. By standardizing the collection and sharing of disaggregated data—including critically underreported negative results—this framework transforms fragmented findings into a cohesive, globally interoperable knowledge base. For drug development professionals and researchers, this enhances the ability to identify zoonotic threats early, understand pathogen ecology, and trace disease origins. Widespread adoption will strengthen the foundational infrastructure for pandemic prediction and prevention, ultimately supporting a more proactive and collaborative One Health approach to safeguarding human, animal, and environmental health. The future of wildlife disease research depends on data that is not just available, but truly actionable.

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