DOI: 10.5281/zenodo.7928120
title: Data used in Machine learning reveals the waggle drift's role in the honey bee dance communication system

description: <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