Deep Learning for Systemic Food Safety Analytics: Forecasting RASFF Notifications and Interpreting Risk DriversDeep Learning for Systemic Food Safety Analytics: Forecasting RASFF Notifications and Interpreting Risk Driversdatabase_Deep Learning for Systemic Food Safety Analytics: Forecasting RASFF Notifications and Interpreting Risk Drivers.xlsx

This dataset contains the raw monthly RASFF notification counts related to dairy products between 2001 and 2024, along with selected economic, environmental, social, and technological indicators for five EU countries.<br>The data were compiled to support time-series classification and systemic food safety risk analysis.<br>Each sheet in the Excel file corresponds to a different indicator category or RASFF alert grouping.<br>This dataset is intended to accompany the article submitted to <i>Food Control</i> titled <i>"Deep Learning for Systemic Food Safety Analytics: Forecasting RASFF Notifications and Interpreting Risk Drivers"</i>.<br>For academic, non-commercial use only.This dataset supports the study <em>"Deep Learning for Systemic Food Safety Analytics: Forecasting RASFF Notifications and Interpreting Risk Drivers"</em>, which presents an end-to-end deep learning framework to forecast monthly dairy-related food safety notifications from the EU’s Rapid Alert System for Food and Feed (RASFF).
The dataset integrates 15 systemic indicators across economic, environmental, social, and technological domains, covering five EU countries from 2001 to 2024. Indicators include variables such as antibiotic usage, temperature, agricultural income, trade volumes, and other factors influencing systemic food safety risks. 
By combining RASFF notification data with multi-domain systemic indicators, this dataset enables replication of the study’s results and facilitates further research on predictive food safety analytics, early warning systems, and explainable AI (XAI) applications in agri-food contexts. It can be applied in policy development, industry monitoring, and methodological innovation for time-series classification and risk interpretation.
<strong>Data type:</strong> Time-series, monthly resolution (2001–2024)<br>
<strong>File format:</strong> [XLSX]<br>

本数据集收录了2001年至2024年间与乳制品相关的欧盟食品和饲料快速预警系统（Rapid Alert System for Food and Feed，RASFF）月度原始通报数量，同时涵盖了5个欧盟国家的部分经济、环境、社会及技术领域指标。
本数据集旨在为时间序列分类与系统性食品安全风险分析提供支撑。
Excel文件中的每个工作表分别对应不同的指标类别或RASFF预警分组。
本数据集随投稿至《食品控制（Food Control）》期刊、题为《面向系统性食品安全分析的深度学习：欧盟RASFF通报预测与风险驱动因素可解释性》的文章一同发布。
本数据集仅可用于学术非商业用途。
本数据集支撑题为《面向系统性食品安全分析的深度学习：欧盟RASFF通报预测与风险驱动因素可解释性》的研究，该研究提出了一套端到端深度学习框架，用于预测欧盟食品和饲料快速预警系统（RASFF）月度乳制品相关食品安全通报。
本数据集整合了覆盖2001年至2024年5个欧盟国家的15项跨经济、环境、社会及技术领域的系统性指标，所涉变量包括抗生素使用量、气温、农业收入、贸易额及其他影响系统性食品安全风险的因素。
通过将RASFF通报数据与多领域系统性指标相结合，本数据集可复现该研究的结果，并推动食品安全预测分析、早期预警系统及农业食品领域可解释人工智能（XAI）应用等方向的进一步研究。其可应用于政策制定、行业监测以及时间序列分类与风险解释相关的方法学创新。
**数据类型：** 时间序列，月度分辨率（2001年—2024年）
**文件格式：** [XLSX]