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Data from: United States wildlife and wildlife product imports from 2000–2014

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agdatacommons.nal.usda.gov2024-02-13 更新2025-03-23 收录
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The global wildlife trade network is a massive system that has been shown to threaten biodiversity, introduce non-native species and pathogens, and cause chronic animal welfare concerns. Despite its scale and impact, comprehensive characterization of the global wildlife trade is hampered by data that are limited in their temporal or taxonomic scope and detail. To help fill this gap, we present data on 15 years of the importation of wildlife and their derived products into the United States (2000–2014), originally collected by the United States Fish and Wildlife Service. We curated and cleaned the data and added taxonomic information to improve data usability. These data include >2 million wildlife or wildlife product shipments, representing >60 biological classes and >3.2 billion live organisms. Further, the majority of species in the dataset are not currently reported on by CITES parties. These data will be broadly useful to both scientists and policymakers seeking to better understand the volume, sources, biological composition, and potential risks of the global wildlife trade. Resources in this dataset:Resource Title: United States LEMIS wildlife trade data curated by EcoHealth Alliance (Version 1.1.0) - Zenodo. File Name: Web Page, url: https://doi.org/10.5281/zenodo.3565869 Over 5.5 million USFWS LEMIS wildlife or wildlife product records spanning 15 years and 28 data fields. These records were derived from >2 million unique shipments processed by USFWS during the time period and represent >3.2 billion live organisms. We provide the final cleaned data as a single comma-separated value file. Original raw data as provided by the USFWS are also available. Although relatively large (~1 gigabyte), the cleaned data file can be imported into a software environment of choice for data analysis. Alternatively, the assocated R package provides access to a release of the same cleaned dataset but with a data download and manipulation framework that is designed to work well with this large dataset. Both the Zenodo data repository and the R package contain a metadata file describing each of the data fields as well as a lookup table to retrieve full values for the abbreviated codes used throughout the dataset. Contents: lemis_2000_2014_cleaned.csv: This file represents the compiled, cleaned LEMIS data from 2000-2014. This data is identical to the version 1.1.0 dataset available through the lemis R package. lemis_codes.csv: Full values for all coded values used in the LEMIS data. Identical to the output from the lemis R package function "lemis_codes()". lemis_metadata.csv: Data fields and field descriptions for all variables in the LEMIS data. Identical to the output from the lemis R package function "lemis_metadata()". raw_data.zip: This archive contains all of the raw LEMIS data files that are processed and cleaned with the code contained in the 'data-raw' subdirectory of the lemis R package repository.Resource Software Recommended: R package,url: https://github.com/ecohealthalliance/lemis

全球野生动物贸易网络是一项庞大的体系,业已证明其威胁生物多样性、引入非原生物种及病原体,并引发动物福利的长期担忧。尽管其规模与影响巨大,但由于数据在时间范围或分类学上的局限性,对全球野生动物贸易的全面描述受到阻碍。为填补这一空白,我们提供了关于2000年至2014年期间美国(2000-2014年)野生动物及其衍生产品进口15年数据,这些数据最初由美国鱼类和野生动物管理局收集。我们对数据进行整理和清洗,并添加了分类学信息以提升数据的使用性。这些数据包括超过200万份野生动物或野生动物产品运输,涵盖了超过60个生物类群和超过320亿个活体生物。此外,数据集中的大多数物种尚未被CITES成员国报告。这些数据对科学家和政策制定者了解全球野生动物贸易的规模、来源、生物组成和潜在风险将具有广泛的应用价值。本数据集包含以下资源: 资源标题:由EcoHealth Alliance整理的美国LEMIS野生动物贸易数据(版本1.1.0)- Zenodo 文件名:网页,url:https://doi.org/10.5281/zenodo.3565869 包含超过550万份美国鱼类和野生动物管理局LEMIS野生动物或野生动物产品记录,涵盖15年和28个数据字段。这些记录来源于美国鱼类和野生动物管理局在此期间处理的超过200万个独特运输,代表了超过320亿个活体生物。我们提供了最终清洗后的数据作为一个单一的逗号分隔值文件。原始的未处理数据由美国鱼类和野生动物管理局提供。尽管清洗后的数据文件相对较大(约1GB),但可以导入所选的软件环境进行分析。此外,关联的R包提供了对同一清洗数据集的访问,但具有一个旨在与大型数据集良好工作的数据下载和处理框架。Zenodo数据存储库和R包都包含一个描述每个数据字段的元数据文件以及一个查找表,用于检索数据集中使用的缩写代码的完整值。 内容: lemis_2000_2014_cleaned.csv:此文件代表2000-2014年编译后的清洁LEMIS数据。此数据与通过lemis R包可获得的版本1.1.0数据集相同。 lemis_codes.csv:LEMIS数据中使用的所有编码值的完整值。与lemis R包函数"lemis_codes()
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