地球资源数据云——数据资源详情
鸟类迁徙数据集（数据可视化/EDA）
该数据集《Bird Migration Dataset (Data Visualization / EDA)》主要用于监督学习任务，数据形态以时序/信号为主，应用场景偏向环保分类。 题目说明：A comprehensive catalogue for data science and visualization 任务类型：时序/信号监督学习。 建议流程：先做去噪和窗口化，再比较树模型与 1D CNN/LSTM 等时序基线。 评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。 可用文件：bird_migration_data.csv, Bird_Migration_Data_with_Origin.csv, bird_migration_with_origin_destination.csv。 This dataset contains 10,000 synthetic records simulating the migratory behavior of various bird species across global regions. Each entry represents a single bird tagged with a tracking device and includes detailed information such as flight distance, speed, altitude, weather conditions, tagging information, and migration outcomes. The data was entirely synthetically generated using randomized yet realistic values based on known ranges from ornithological studies. It is ideal for practicing data analysis and visualization techniques without privacy concerns or real - world data access restrictions. Because it’s artificial, the dataset can be freely used in education, portfolio projects, demo dashboards, machine learning pipelines, or business intelligence training. With over 40 columns, this dataset supports a wide array of analysis types. Analysts can explore questions like “Do certain species migrate in larger flocks?”, “How does weather impact nesting success?”, or “What conditions lead to migration interruptions?”. Users can also perform geospatial mapping of start and end locations, cluster birds by behavior, or build time series models based on migration months and environmental factors.
摘要概览
该数据集《Bird Migration Dataset (Data Visualization / EDA)》主要用于监督学习任务，数据形态以时序/信号为主，应用场景偏向环保分类。 题目说明：A comprehensive catalogue for data science and visualization
任务类型：时序/信号监督学习。
建议流程：先做去噪和窗口化，再比较树模型与 1D CNN/LSTM 等时序基线。
评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。
可用文件：bird_migration_data.csv, Bird_Migration_Data_with_Origin.csv, bird_migration_with_origin_destination.csv。
This dataset contains 10,000 synthetic records simulating the migratory behavior of various bird species across global regions. Each entry represents a single bird tagged with a tracking device and includes detailed information such as flight distance, speed, altitude, weather conditions, tagging information, and migration outcomes.
The data was entirely synthetically generated using randomized yet realistic values based on known ranges from ornithological studies. It is ideal for practicing data analysis and visualization techniques without privacy concerns or real - world data access restrictions.
Because it’s artificial, the dataset can be freely used in education, portfolio projects, demo dashboards, machine learning pipelines, or business intelligence training.
With over 40 columns, this dataset supports a wide array of analysis types. Analysts can explore questions like “Do certain species migrate in larger flocks?”, “How does weather impact nesting success?”, or “What conditions lead to migration interruptions?”.
Users can also perform geospatial mapping of start and end locations, cluster birds by behavior, or build time series models based on migration months and environmental factors.