Wild meat refers to terrestrial wildlife used for food worldwide, and despite a substantial body of site-level research on its hunting, trade, and consumption, previous studies have highlighted the need for a centralized public repository to improve evidence-based decision-making. The WILDMEAT Use Database addresses this gap by compiling published and unpublished data within a relational PostgreSQL database built on open data and FAIR principles, and structured using international metadata standards such as DDI, Dublin Core, and Darwin Core. The database accommodates three main data types—hunting, consumption, and market data—sourced from diverse formats including journal articles, raw spreadsheets, and field survey forms, and integrates both quantitative and qualitative information using standardized codes to facilitate data validation. For each study, curated flat datasets are generated in MS Excel and made accessible to registered users via the WILDMEAT Data Explorer hosted on CIFOR-Dataverse, while the underlying relational database remains non-public. Data inclusion is subject to strict criteria, requiring quantitative measurement of wild meat use, systematic sampling methods, species-level carcass identification where possible, and records of carcass counts or biomass, with additional information solicited from original authors when data are incomplete.
This work is licensed under CC-BY 4.0
This work is licensed under CC-BY 4.0
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TI - WILDMEAT Use Database: Database user guidelines
AU - CIFOR-ICRAF
AB - Wild meat refers to terrestrial wildlife used for food worldwide, and despite a substantial body of site-level research on its hunting, trade, and consumption, previous studies have highlighted the need for a centralized public repository to improve evidence-based decision-making. The WILDMEAT Use Database addresses this gap by compiling published and unpublished data within a relational PostgreSQL database built on open data and FAIR principles, and structured using international metadata standards such as DDI, Dublin Core, and Darwin Core. The database accommodates three main data types—hunting, consumption, and market data—sourced from diverse formats including journal articles, raw spreadsheets, and field survey forms, and integrates both quantitative and qualitative information using standardized codes to facilitate data validation. For each study, curated flat datasets are generated in MS Excel and made accessible to registered users via the WILDMEAT Data Explorer hosted on CIFOR-Dataverse, while the underlying relational database remains non-public. Data inclusion is subject to strict criteria, requiring quantitative measurement of wild meat use, systematic sampling methods, species-level carcass identification where possible, and records of carcass counts or biomass, with additional information solicited from original authors when data are incomplete.
PY - 2026
PB - CIFOR-ICRAF
PP - Bogor, Indonesia and Nairobi, Kenya
UR - https://www.cifor-icraf.org/knowledge/publication/46552/
KW - bushmeat, data analysis, data collection, data management, databases, decision making, hunting, meat, meat consumption, trade, wildlife, wildlife conservation
ER -Endnote (.ciw)
%T WILDMEAT Use Database: Database user guidelines
%A CIFOR-ICRAF
%D 2026
%I CIFOR-ICRAF
%C Bogor, Indonesia and Nairobi, Kenya
%U https://www.cifor-icraf.org/knowledge/publication/46552/
%X Wild meat refers to terrestrial wildlife used for food worldwide, and despite a substantial body of site-level research on its hunting, trade, and consumption, previous studies have highlighted the need for a centralized public repository to improve evidence-based decision-making. The WILDMEAT Use Database addresses this gap by compiling published and unpublished data within a relational PostgreSQL database built on open data and FAIR principles, and structured using international metadata standards such as DDI, Dublin Core, and Darwin Core. The database accommodates three main data types—hunting, consumption, and market data—sourced from diverse formats including journal articles, raw spreadsheets, and field survey forms, and integrates both quantitative and qualitative information using standardized codes to facilitate data validation. For each study, curated flat datasets are generated in MS Excel and made accessible to registered users via the WILDMEAT Data Explorer hosted on CIFOR-Dataverse, while the underlying relational database remains non-public. Data inclusion is subject to strict criteria, requiring quantitative measurement of wild meat use, systematic sampling methods, species-level carcass identification where possible, and records of carcass counts or biomass, with additional information solicited from original authors when data are incomplete.
%K bushmeat
%K data analysis
%K data collection
%K data management
%K databases
%K decision making
%K hunting
%K meat
%K meat consumption
%K trade
%K wildlife
%K wildlife conservation
Publisher
CIFOR-ICRAF: Bogor, Indonesia and Nairobi, Kenya
Año de publicación
2026
Autores
Idioma
English
Palabras clave
bushmeat, data analysis, data collection, data management, databases, decision making, hunting, meat, meat consumption, trade, wildlife, wildlife conservation
