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<p>The Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) was adopted in 1975 in an effort to manage the international biodiversity trade. Meetings regulating the implementation of CITES have since been held every 2-3 years with the involvement of diverse stakeholders representing country Party-signatories, non-Party states, international organizations, private sector interests, and NGOs. These meetings and their outcomes are of interest to environmental science scholars, social scientists, journalists, and advocacy organizations. Yet, no usable data on meeting attendees and their details exists. This limits researchers’ and advocates’ abilities to study or track CITES meeting attendance patterns, and their associated causes and effects. Applying NLP techniques to PDF attendance rosters, we build the first CITES attendee-level dataset, covering 20,987 attendee records for all meetings to date. The dataset contains rich information on attendee geo-locations, names, affiliations and genders, and variables associated with attendee delegations, among others. Summaries and validations underscore the promise of our data and suggest new avenues for research on international wildlife conservation.</p>

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Geo-located attendance data for CITES Conferences of the Parties

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月20日

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  • Daria Blinova,
  • Gayathri Emuru,
  • Rakesh Emuru,
  • Benjamin E. Bagozzi

摘要

The Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) was adopted in 1975 in an effort to manage the international biodiversity trade. Meetings regulating the implementation of CITES have since been held every 2-3 years with the involvement of diverse stakeholders representing country Party-signatories, non-Party states, international organizations, private sector interests, and NGOs. These meetings and their outcomes are of interest to environmental science scholars, social scientists, journalists, and advocacy organizations. Yet, no usable data on meeting attendees and their details exists. This limits researchers’ and advocates’ abilities to study or track CITES meeting attendance patterns, and their associated causes and effects. Applying NLP techniques to PDF attendance rosters, we build the first CITES attendee-level dataset, covering 20,987 attendee records for all meetings to date. The dataset contains rich information on attendee geo-locations, names, affiliations and genders, and variables associated with attendee delegations, among others. Summaries and validations underscore the promise of our data and suggest new avenues for research on international wildlife conservation.