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

<strong>Data and metadata used in "Machine learning reveals the waggle drift’s role in the honey bee dance communication system" </strong> All timestamps are given in ISO 8601 format. <strong>The following files are included:</strong> <strong>Berlin2019_waggle_phases.csv, Berlin2021_waggle_phases.csv</strong> 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). <strong>Berlin2019_dances.csv</strong> 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. <strong>Berlin2019_followers.csv</strong> 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). <strong>Berlin2019_dances_with_manually_verified_times.csv</strong> 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. <strong>Berlin2019_dance_classifier_labels.csv</strong> 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” <strong>Berlin2019_dance_classifier_unlabeled.csv</strong> 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. <strong>Berlin2021_waggle_phase_classifier_labels.csv</strong> 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. <strong>Berlin2021_waggle_phase_classifier_ground_truth.zip</strong> 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. <strong>Berlin2019_tracks.zip</strong> 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.<br> 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). <strong>Berlin2019_feeder_experiment_log.csv</strong> 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.<br> 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. <strong>Software used to acquire and analyze the data:</strong> 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**《机器学习揭示摆尾漂移在蜜蜂舞蹈通讯系统中的作用》所用数据与元数据** 所有时间戳均采用ISO 8601格式。 **包含以下文件：** `Berlin2019_waggle_phases.csv`、`Berlin2021_waggle_phases.csv`：2019年与2021年录制期间，自动检测得到的蜜蜂摆尾阶段（waggle phase）个体数据。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：蜂箱左侧，1：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表示朝向右侧，π/4表示朝向上方）。 `Berlin2019_dances.csv`：2019年录制期间，自动检测得到的舞蹈行为数据。各字段说明如下： - dancer_id：采集蜂个体的唯一标识ID。 - dance_id：单次舞蹈行为的唯一标识ID。 - ts_from、ts_to：本次舞蹈开始与结束的日期与时间。 - cam_id：摄像头ID（0：蜂箱左侧，1：蜂箱右侧）。 - median_x、median_y：舞蹈期间蜜蜂个体的中位位置。 - feeder_cam_id：舞蹈发生前，采集蜂被观测到的喂食器摄像头ID。 `Berlin2019_followers.csv`：对应`Berlin2019_dances.csv`中舞蹈行为的自动检测得到的跟随、参与行为数据。各字段说明如下： - dance_id：被跟随或参与的舞蹈行为的唯一标识ID。 - follower_id：参与或跟随该舞蹈的蜜蜂个体的唯一标识ID。 - ts_from、ts_to：本次互动开始与结束的日期与时间。 - label：行为标签，取值为"参与（attendance）"或"跟随（follower）"。 - cam_id：摄像头ID（0：蜂箱左侧，1：蜂箱右侧）。 `Berlin2019_dances_with_manually_verified_times.csv`：从`Berlin2019_dances.csv`中抽取的舞蹈样本，其精确时间戳已通过人工验证，首次摆尾阶段与末次摆尾阶段的时间精度约为166 ms（视频录制帧率为6帧每秒）。各字段说明如下： - dance_id：单次舞蹈行为的唯一标识ID。 - dancer_id：跳舞蜜蜂个体的唯一标识ID。 - cam_id：摄像头ID（0：蜂箱左侧，1：蜂箱右侧）。 - feeder_cam_id：舞蹈发生前，采集蜂被观测到的喂食器摄像头ID。 - dance_start、dance_end：人工验证得到的本次舞蹈开始与结束的日期与时间。 `Berlin2019_dance_classifier_labels.csv`：2019年录制季中，用于训练舞蹈与跟随行为分类器的人工标注摆尾阶段或跟随行为数据，可与提供的个体检测数据合并。各字段说明如下： - timestamp：观测到该行为的单帧图像的时间戳。 - frame_id：该行为所在视频帧的唯一标识ID。 - bee_id：蜜蜂个体的唯一标识ID。 - label：行为标签，取值为"无行为（nothing）"、"摆尾（waggle）"、"跟随（follower）"。 `Berlin2019_dance_classifier_unlabeled.csv`：与`Berlin2019_dance_classifier_labels.csv`格式一致的额外无标注样本，仅包含时间戳与个体ID，未标注行为标签。该数据样本采自摆尾阶段分类器的检测结果附近，因此摆尾舞相关行为在该样本中占比偏高。 `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）的摆尾阶段检测器（bb_wdd2）的人工标注检测结果。各字段说明如下： - detection_id：摆尾阶段的唯一标识ID。 - label：行为标签，取值为"摆尾（waggle）"、"振翅信号（activating）"、"扇翅（ventilating）"、"抖舞（trembling）"、"其他（other）"。其中"摆尾（waggle）"表示摆尾阶段，"振翅信号（activating）"为蜜蜂的振翅信号，"扇翅（ventilating）"指蜜蜂扇动翅膀的行为，"抖舞（trembling）"指抖舞，但该类别与"其他（other）"类的边界往往模糊，因此训练时将"抖舞（trembling）"合并至"其他（other）"类。 - orientation：触发检测的蜜蜂的身体朝向，单位为弧度（0表示朝向右侧，π/4表示朝向上方）。 - metadata_path：与摆尾舞检测器输出目录结构一致的个体检测元数据路径。 `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输出的标准目录结构，每个子目录中包含触发检测的原始图像序列压缩包与对应元数据。配套提供的bb_wdd_filter训练代码可直接适配该目录结构。 `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太字节，本次仅提供与本研究相关的子集，包含超过4600万条检测数据。我们导出了所有检测到的行为（舞蹈、跟随、参与）对应的轨迹，并包含行为发生前后各1分钟的数据；同时也导出了用于训练舞蹈分类器的标注与无标注数据对应的所有轨迹，并包含训练数据前后各30秒的数据。 我们按日期对导出数据进行了分组以方便处理，但为高效使用该数据，建议将其导入可索引数据库中。单个数据文件包含以下字段： - cam_id：摄像头ID（0：蜂箱左侧，1：蜂箱右侧）。 - timestamp：检测发生的日期与时间。 - frame_id：提取该检测结果的视频帧的唯一标识ID。 - track_id：单条轨迹的唯一标识ID（单一个体的短时运动路径）。对于长轨迹，可通过bee_id关联各检测结果。 - bee_id：蜜蜂个体的唯一标识ID。 - bee_id_confidence：跟踪系统输出的bee_id正确性置信度，取值范围为0至1。 - x_pos_hive、y_pos_hive：蜂箱内蜜蜂的空间位置，对应cam_id指定的蜂箱一侧，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表示朝向右侧，π/4表示朝向上方）。 `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的坐标固定不变，仅记录一次。喂食器3的位置随实验调整。 - time_opened、time_closed：喂食器开启与关闭的日期与时间。 - sucrose_solution：蔗糖溶液浓度，以糖:水（重量比）表示。在喂食器3开启的实验日，其余两个喂食器仅提供无糖清水。 **数据采集与分析所用软件：** - bb_pipeline：标签定位与解码流水线 - 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模块 - bb_behavior：数据库交互与数据（预）处理、特征提取模块 - bb_tracking：跨时间蜜蜂检测结果的跟踪模块 - bb_wdd2：蜜蜂摆尾舞的自动检测与解码模块 - bb_wdd_filter：用于提升摆尾舞检测器精度的机器学习模型 - bb_dance_networks：基于轨迹检测舞蹈与跟随行为的模块