Data used in Machine learning reveals the waggle drift's role in the honey bee dance communication system

Data and metadata used in "Machine learning reveals the waggle drift’s role in the honey bee dance communication system" All timestamps are given in ISO 8601 format. The following files are included: Berlin2019_waggle_phases.csv, Berlin2021_waggle_phases.csv Automatic individual detections of waggle phases during our recording periods in 2019 and 2021. timestamp: Date and time of the detection. cam_id: Camera ID (0: left side of the hive, 1: right side of the hive). x_median, y_median: Median position of the bee during the waggle phase (for 2019 given in millimeters after applying a homography, for 2021 in the original image coordinates). waggle_angle: Body orientation of the bee during the waggle phase in radians (0: oriented to the right, PI / 4: oriented upwards). Berlin2019_dances.csv Automatic detections of dance behavior during our recording period in 2019. dancer_id: Unique ID of the individual bee. dance_id: Unique ID of the dance. ts_from, ts_to: Date and time of the beginning and end of the dance. cam_id: Camera ID (0: left side of the hive, 1: right side of the hive). median_x, median_y: Median position of the individual during the dance. feeder_cam_id: ID of the feeder that the bee was detected at prior to the dance. Berlin2019_followers.csv Automatic detections of attendance and following behavior, corresponding to the dances in Berlin2019_dances.csv. dance_id: Unique ID of the dance being attended or followed. follower_id: Unique ID of the individual attending or following the dance. ts_from, ts_to: Date and time of the beginning and end of the interaction. label: “attendance” or “follower” cam_id: Camera ID (0: left side of the hive, 1: right side of the hive). Berlin2019_dances_with_manually_verified_times.csv A sample of dances from Berlin2019_dances.csv where the exact timestamps have been manually verified to correspond to the beginning of the first and last waggle phase down to a precision of ca. 166 ms (video material was recorded at 6 FPS). dance_id: Unique ID of the dance. dancer_id: Unique ID of the dancing individual. cam_id: Camera ID (0: left side of the hive, 1: right side of the hive). feeder_cam_id: ID of the feeder that the bee was detected at prior to the dance. dance_start, dance_end: Manually verified date and times of the beginning and end of the dance. Berlin2019_dance_classifier_labels.csv Manually annotated waggle phases or following behavior for our recording season in 2019 that was used to train the dancing and following classifier. Can be merged with the supplied individual detections. timestamp: Timestamp of the individual frame the behavior was observed in. frame_id: Unique ID of the video frame the behavior was observed in. bee_id: Unique ID of the individual bee. label: One of “nothing”, “waggle”, “follower” Berlin2019_dance_classifier_unlabeled.csv Additional unlabeled samples of timestamp and individual ID with the same format as Berlin2019_dance_classifier_labels.csv, but without a label. The data points have been sampled close to detections of our waggle phase classifier, so behaviors related to the waggle dance are likely overrepresented in that sample. Berlin2021_waggle_phase_classifier_labels.csv Manually annotated detections of our waggle phase detector (bb_wdd2) that were used to train the neural network filter (bb_wdd_filter) for the 2021 data. detection_id: Unique ID of the waggle phase. label: One of “waggle”, “activating”, “ventilating”, “trembling”, “other”. Where “waggle” denoted a waggle phase, “activating” is the shaking signal, “ventilating” is a bee fanning her wings. “trembling” denotes a tremble dance, but the distinction from the “other” class was often not clear, so “trembling” was merged into “other” for training. orientation: The body orientation of the bee that triggered the detection in radians (0: facing to the right, PI /4: facing up). metadata_path: Path to the individual detection in the same directory structure as created by the waggle dance detector. Berlin2021_waggle_phase_classifier_ground_truth.zip The output of the waggle dance detector (bb_wdd2) that corresponds to Berlin2021_waggle_phase_classifier_labels.csv and is used for training. The archive includes a directory structure as output by the bb_wdd2 and each directory includes the original image sequence that triggered the detection in an archive and the corresponding metadata. The training code supplied in bb_wdd_filter directly works with this directory structure. Berlin2019_tracks.zip Detections and tracks from the recording season in 2019 as produced by our tracking system. As the full data is several terabytes in size, we include the subset of our data here that is relevant for our publication which comprises over 46 million detections. We included tracks for all detected behaviors (dancing, following, attending) including one minute before and after the behavior. We also included all tracks that correspond to the labeled and unlabeled data that was used to train the dance classifier including 30 seconds before and after the data used for training. We grouped the exported data by date to make the handling easier, but to efficiently work with the data, we recommend importing it into an indexable database. The individual files contain the following columns: cam_id: Camera ID (0: left side of the hive, 1: right side of the hive). timestamp: Date and time of the detection. frame_id: Unique ID of the video frame of the recording from which the detection was extracted. track_id: Unique ID of an individual track (short motion path from one individual). For longer tracks, the detections can be linked based on the bee_id. bee_id: Unique ID of the individual bee. bee_id_confidence: Confidence between 0 and 1 that the bee_id is correct as output by our tracking system. x_pos_hive, y_pos_hive: Spatial position of the bee in the hive on the side indicated by cam_id. Given in millimeters after applying a homography on the video material. orientation_hive: Orientation of the bees’ thorax in the hive in radians (0: oriented to the right, PI / 4: oriented upwards). Berlin2019_feeder_experiment_log.csv Experiment log for our feeder experiments in 2019. date: Date given in the format year-month-day. feeder_cam_id: Numeric ID of the feeder. coordinates: Longitude and latitude of the feeder. For feeders 1 and 2 this is only given once and held constant. Feeder 3 had varying locations. time_opened, time_closed: Date and time when the feeder was set up or closed again. sucrose_solution: Concentration of the sucrose solution given as sugar:water (in terms of weight). On days where feeder 3 was open, the other two feeders offered water without sugar. Software used to acquire and analyze the data: bb_pipeline: Tag localization and decoding pipeline bb_pipeline_models: Pretrained localizer and decoder models for bb_pipeline bb_binary: Raw detection data storage format bb_irflash: IR flash system schematics and arduino code bb_imgacquisition: Recording and network storage bb_behavior: Database interaction and data (pre)processing, feature extraction bb_tracking: Tracking of bee detections over time bb_wdd2: Automatic detection and decoding of honey bee waggle dances bb_wdd_filter: Machine learning model to improve the accuracy of the waggle dance detector bb_dance_networks: Detection of dancing and following behavior from trajectories本数据集为论文《机器学习揭示摆尾漂移在蜜蜂舞蹈通讯系统中的作用》中使用的数据与元数据。所有时间戳均采用ISO 8601格式。本次包含以下文件： 1. Berlin2019_waggle_phases.csv、Berlin2021_waggle_phases.csv：2019年与2021年录制期间的蜜蜂摆尾相位自动个体检测结果。各字段说明如下： timestamp：检测发生的日期与时间。 cam_id：摄像头ID（0代表蜂箱左侧，1代表蜂箱右侧）。 x_median、y_median：摆尾阶段蜜蜂的中位位置（2019年数据经单应性变换（homography）后以毫米为单位，2021年数据为原始图像坐标）。 waggle_angle：摆尾阶段蜜蜂的身体朝向，单位为弧度（0代表朝向右侧，π/4代表朝向上方）。 2. Berlin2019_dances.csv：2019年录制期间的舞蜂行为自动检测结果。各字段说明如下： dancer_id：个体蜜蜂的唯一标识符。 dance_id：单次舞蹈的唯一标识符。 ts_from、ts_to：舞蹈开始与结束的日期与时间。 cam_id：摄像头ID（0代表蜂箱左侧，1代表蜂箱右侧）。 median_x、median_y：舞蹈期间个体的中位位置。 feeder_cam_id：舞蹈前被检测到的喂食器ID。 3. Berlin2019_followers.csv：对应Berlin2019_dances.csv中舞蹈行为的跟随者与出席行为自动检测结果。各字段说明如下： dance_id：被出席或跟随的舞蹈的唯一标识符。 follower_id：出席或跟随该舞蹈的个体蜜蜂的唯一标识符。 ts_from、ts_to：互动开始与结束的日期与时间。 label：标签类型，为“attendance”（出席）或“follower”（跟随者）。 cam_id：摄像头ID（0代表蜂箱左侧，1代表蜂箱右侧）。 4. Berlin2019_dances_with_manually_verified_times.csv：取自Berlin2019_dances.csv的舞蹈样本集，其精确时间戳已通过人工验证，首次与末次摆尾阶段的时间精度可达约166毫秒（视频录制帧率为6 FPS）。各字段说明如下： dance_id：单次舞蹈的唯一标识符。 dancer_id：舞蜂个体的唯一标识符。 cam_id：摄像头ID（0代表蜂箱左侧，1代表蜂箱右侧）。 feeder_cam_id：舞蹈前被检测到的喂食器ID。 dance_start、dance_end：人工验证的舞蹈开始与结束日期与时间。 5. Berlin2019_dance_classifier_labels.csv：2019年录制季中用于训练舞蜂与跟随行为分类器的人工标注摆尾相位或跟随行为数据，可与提供的个体检测结果合并。各字段说明如下： timestamp：观测到该行为的单帧图像时间戳。 frame_id：观测到该行为的视频帧唯一标识符。 bee_id：个体蜜蜂的唯一标识符。 label：标签类型，为“nothing”（无行为）、“waggle”（摆尾）或“follower”（跟随者）。 6. Berlin2019_dance_classifier_unlabeled.csv：与Berlin2019_dance_classifier_labels.csv格式相同的额外无标注样本，仅缺失标签字段。该数据样本采自接近摆尾舞（waggle dance）分类器检测结果的区域，因此摆尾舞相关行为在样本中占比偏高。 7. Berlin2021_waggle_phase_classifier_labels.csv：2021年数据中用于训练神经网络过滤器（bb_wdd_filter）的人工标注摆尾相位检测器（bb_wdd2）检测结果。各字段说明如下： detection_id：摆尾相位的唯一标识符。 label：标签类型，为“waggle”（摆尾相位）、“activating”（振翅信号）、“ventilating”（蜜蜂扇翅行为）、“trembling”（颤抖舞）或“other”（其他行为）。其中“trembling”与“other”类别的区分往往不明确，因此训练时将“trembling”合并至“other”类别。 orientation：触发检测的蜜蜂身体朝向，单位为弧度（0代表朝向右侧，π/4代表朝向上方）。 metadata_path：对应个体检测结果的文件路径，与摆尾舞检测器生成的目录结构一致。 8. Berlin2021_waggle_phase_classifier_ground_truth.zip：对应Berlin2021_waggle_phase_classifier_labels.csv的摆尾舞检测器（bb_wdd2）输出结果，用于模型训练。该压缩包包含bb_wdd2生成的目录结构，每个目录下均存放触发检测的原始图像序列压缩包与对应元数据。bb_wdd_filter中提供的训练代码可直接兼容该目录结构。 9. Berlin2019_tracks.zip：2019年录制季中由我们的跟踪系统生成的蜜蜂检测结果与轨迹数据。由于完整数据集规模达数TB，本次仅提供与本论文相关的子集数据，包含超过4600万条检测结果。我们导出了所有检测到的行为（舞蜂、跟随、出席）对应的轨迹，并包含行为发生前后各1分钟的数据；同时也包含所有用于训练舞蜂分类器的标注与未标注数据对应的轨迹，并包含训练数据前后各30秒的数据。为便于处理，我们按日期对导出数据进行了分组，但为高效使用该数据，建议将其导入可索引的数据库中。单个文件包含以下字段： cam_id：摄像头ID（0代表蜂箱左侧，1代表蜂箱右侧）。 timestamp：检测发生的日期与时间。 frame_id：提取该检测结果的录制视频帧唯一标识符。 track_id：单个轨迹（单个个体的短运动路径）的唯一标识符。对于更长的轨迹，可通过bee_id关联各检测结果。 bee_id：个体蜜蜂的唯一标识符。 bee_id_confidence：跟踪系统输出的bee_id正确性置信度，取值范围为0到1。 x_pos_hive、y_pos_hive：蜂箱内蜜蜂的空间位置，对应cam_id指定的蜂箱侧，经单应性变换后以毫米为单位。 orientation_hive：蜂箱内蜜蜂胸部的朝向，单位为弧度（0代表朝向右侧，π/4代表朝向上方）。 10. Berlin2019_feeder_experiment_log.csv：2019年喂食器实验的实验日志。各字段说明如下： date：日期，格式为年-月-日。 feeder_cam_id：喂食器的数字ID。 coordinates：喂食器的经纬度坐标。喂食器1与2的坐标仅设定一次并保持恒定，喂食器3的位置会发生变化。 time_opened、time_closed：喂食器开启与关闭的日期与时间。 sucrose_solution：蔗糖溶液的浓度，以糖:水（重量比）表示。在喂食器3开启的日期，其余两个喂食器仅提供无糖清水。 本数据集采集与分析所用软件如下： - bb_pipeline：标签定位与解码流程工具 - bb_pipeline_models：bb_pipeline所用的预训练定位器与解码器模型 - bb_binary：原始检测数据存储格式 - bb_irflash：红外闪光系统原理图与Arduino代码 - bb_imgacquisition：视频录制与网络存储工具 - bb_behavior：数据库交互与数据（预）处理、特征提取工具 - bb_tracking：蜜蜂检测结果的跨帧跟踪工具 - bb_wdd2：蜜蜂摆尾舞的自动检测与解码工具 - bb_wdd_filter：用于提升摆尾舞检测器准确率的机器学习模型 - bb_dance_networks：基于轨迹检测舞蜂与跟随行为的工具