food-ai-nexus/global-food-safety-incidents

--- license: cc-by-4.0 task_categories: - tabular-classification - time-series-forecasting tags: - food-safety - food-recalls - agriculture - time-series language: - en size_categories: - 10K<n<100K pretty_name: Global Food Safety Incidents (FOODAKAI / RASFF) --- **Global Food Safety Incidents (FOODAKAI / RASFF)** is a tabular time-series dataset containing food safety incident records — border rejections, alerts, recalls, and related notifications — for eight globally traded commodities sourced from the FOODAKAI platform (which aggregates RASFF and other regulatory feeds). With this dataset, researchers can train machine learning models to forecast the frequency or likelihood of food safety incidents for a given commodity, origin country, and time period. # Content - The dataset contains **30,184 incident records** spanning multiple years up to April 2021. - It covers **8 commodities**: almonds, cereals, chicken, cinnamon, cocoa, sesame seeds, shrimps, and strawberries. - Each record describes a single food safety incident with its type, hazard, country of origin, and date. - The `incident_type` column retains the original values from the FOODAKAI/RASFF source. Only formatting artifacts (leading hyphens from data extraction) have been corrected. - Column names have been standardized to `snake_case` for programmatic access. # Data Fields | Column | Type | Description | | --- | --- | --- | | `commodity` | string | The food commodity the incident relates to (e.g., `almonds`, `chicken`) | | `incident_type` | string | The regulatory classification of the incident (see values below) | | `hazard` | string | The specific hazard identified (e.g., `salmonella`, `aflatoxins`) | | `hazard_path` | string | The hazard subcategory path in the RASFF taxonomy | | `origin` | string | Country of origin of the implicated product | | `date` | string | Date of the incident record in ISO 8601 format (YYYY-MM-DD) | **Incident Type Values** The `incident_type` column contains the following values as they appear in the original FOODAKAI/RASFF source. Only leading-hyphen formatting artifacts have been corrected (e.g., `- food` → `food`). | Value | Global Count | % of Total | | --- | --- | --- | | `border rejection` | 19,470 | 64.50% | | `alert` | 6,487 | 21.49% | | `food` | 4,138 | 13.71% | | `feed` | 16 | 0.05% | | `major food product recall` | 16 | 0.05% | | `alert notification` | 4 | 0.01% | | `adulterated food` | 3 | 0.01% | | `food contact material` | 2 | 0.01% | | `company's own check` | 2 | 0.01% | | `poor quality food` | 1 | < 0.01% | | `unsafe food` | 1 | < 0.01% | # Uses The dataset was originally used in the Cornell Food Safety ML Repository to train time-series forecasting models for food safety incidents. It can also be used for anomaly detection, supply chain risk modeling, and food safety surveillance research. ```python from datasets import load_dataset # Load all commodities ds = load_dataset("food-ai-nexus/global-food-safety-incidents") # Load a single commodity ds = load_dataset("food-ai-nexus/global-food-safety-incidents", "chicken") ``` # License This dataset is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License. # Reference ```bibtex @misc{cornell_food_safety_ml_2023, title = {Forecasting Food Safety Incidents}, author = {{Cornell Food Safety ML Repository}}, year = {2023}, howpublished = {\url{https://github.com/Cornell-Food-Safety-ML-Repository/forecasting-food-safety-incidents}}, note = {Data sourced from the FOODAKAI platform (RASFF and other regulatory feeds)} } ```许可协议：CC BY 4.0 任务类别： - 表格分类 - 时间序列预测 标签： - 食品安全 - 食品召回 - 农业 - 时间序列 语言： - 英语 样本量区间： - 10000 < 样本量 < 100000 友好名称：全球食品安全事件数据集（FOODAKAI / RASFF） --- # 全球食品安全事件数据集（FOODAKAI / RASFF） **全球食品安全事件数据集（FOODAKAI / RASFF）** 是一款结构化表格时间序列数据集，包含源自FOODAKAI平台（整合了RASFF及其他监管数据源）的8种全球贸易大宗商品的食品安全事件记录——涵盖边境退运、预警、召回及相关通知。 研究人员可借助该数据集训练机器学习模型，针对指定大宗商品、原产国及时间周期，预测食品安全事件的发生频率或可能性。 ## 数据内容 - 该数据集包含截至2021年4月的多年间共计**30184条事件记录**。 - 覆盖**8类大宗商品**：杏仁、谷物、鸡肉、肉桂、可可、芝麻、虾及草莓。 - 每条记录描述单一起食品安全事件，包含事件类型、危害源、原产国及日期信息。 - `incident_type`字段保留了FOODAKAI/RASFF源数据的原始取值，仅修正了数据提取过程中产生的格式瑕疵（如提取出的前置连字符）。 - 字段名已统一采用蛇形命名法（snake_case），便于程序化访问。 ## 数据字段 | 字段名 | 数据类型 | 字段说明 | | --- | --- | --- | | `commodity` | 字符串 | 事件关联的食品大宗商品（例如：`almonds`、`chicken`） | | `incident_type` | 字符串 | 事件的监管分类（取值详见下文） | | `hazard` | 字符串 | 已识别的具体危害源（例如：`salmonella`沙门氏菌、`aflatoxins`黄曲霉毒素） | | `hazard_path` | 字符串 | RASFF分类体系中的危害子类别路径 | | `origin` | 字符串 | 涉事产品的原产国 | | `date` | 字符串 | 事件记录的日期，采用ISO 8601格式（YYYY-MM-DD） | ### 事件类型取值 `incident_type`字段保留了FOODAKAI/RASFF源数据的原始取值，仅修正了前置连字符的格式瑕疵（例如将`- food`修正为`food`）。 | 取值 | 全局计数 | 占总样本比例 | | --- | --- | --- | | `border rejection` | 19470 | 64.50% | | `alert` | 6487 | 21.49% | | `food` | 4138 | 13.71% | | `feed` | 16 | 0.05% | | `major food product recall` | 16 | 0.05% | | `alert notification` | 4 | 0.01% | | `adulterated food` | 3 | 0.01% | | `food contact material` | 2 | 0.01% | | `company's own check` | 2 | 0.01% | | `poor quality food` | 1 | < 0.01% | | `unsafe food` | 1 | < 0.01% | ## 数据集用途 该数据集最初被用于康奈尔大学食品安全机器学习仓库，以训练食品安全事件的时间序列预测模型。此外，还可应用于异常检测、供应链风险建模及食品安全监测相关研究。 python from datasets import load_dataset # 加载全品类数据集 ds = load_dataset("food-ai-nexus/global-food-safety-incidents") # 加载单品类（如鸡肉）数据集 ds = load_dataset("food-ai-nexus/global-food-safety-incidents", "chicken") ## 许可协议 本数据集采用知识共享署名4.0国际（CC BY 4.0）许可协议进行授权。 ## 参考文献 bibtex @misc{cornell_food_safety_ml_2023, title = {Forecasting Food Safety Incidents}, author = {{康奈尔大学食品安全机器学习仓库}}, year = {2023}, howpublished = {url{https://github.com/Cornell-Food-Safety-ML-Repository/forecasting-food-safety-incidents}}, note = {数据源自FOODAKAI平台（整合RASFF及其他监管数据源）} }