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236 results for “Honeybees”
Simple Physical Interactions Yield Social Self-Organization in Honeybees - datasets
<p>Empirical data of the location of the bees in complex thermal environments in specific time intervals. For more details please refer to <br> <br> Szopek M, Stokanic V, Radspieler G and Schmickl T (2021) Simple Physical Interactions Yield Social Self-Organization in Honeybees. <em>Front. Phys.</em> 9:670317. doi: 10.3389/fphy.2021.670317</p> <p> </p> <p>exp_1.csv contains the percentage of bees in the left, the center and the right evaluation zone in 1-minute intervals (runtime 30 min) for each of the 9 repetitions of Experiment 1 (static thermal environment with one global optimum at 36°C and a pessimum at 30°C).</p> <p>exp_2.csv contains the percentage of bees in the left, the center and the right evaluation zone in 1-minute intervals (runtime 30 min) for each of the 8 repetitions of Experiment 2 (static thermal environment with one global optimum at 36°C and one local optimum at 32°C).</p> <p>exp_3.csv contains the percentage of bees in the left, the center and the right evaluation zone in 1-minute intervals (runtime 30 min) for each of the 6 repetitions of Experiment 3 (static thermal environment with two equal optima of 36°C).</p> <p>exp_4.csv contains the percentage of bees in the left, the center and the right evaluation zone in 1-minute intervals (runtime 105 min) for each of the 17 repetitions of Experiment 4 (dynamic thermal environment).</p> <p>exp_5.csv contains the percentage of bees in the left, the center and the right evaluation zone at minute 30 for i) each of the 10 repetitions of Experiment 5 (static thermal environment with social stimulus) and ii) for each of the 8 repetitions of an experiment with the same thermal environment but without a social stimulus for comparison.</p>
Honeybee lifetime tracking data 2018
<p>Data from barcode-based tracking of honeybees during summer of 2018, taken in an observation hive located at University of Konstanz, Germany.</p> <p>Associated GitHub repository: <a href="https://github.com/jacobdavidson/bees_lifetimetracking_2018data">https://github.com/jacobdavidson/bees_lifetimetracking_2018data</a></p> <p>Data usage example is on <a href="https://github.com/jacobdavidson/bees_lifetimetracking_2018data/blob/main/Data%20usage%20example.ipynb">Github</a>, or can be run in a browser with <a href="https://mybinder.org/v2/gh/jacobdavidson/bees_lifetimetracking_2018data/HEAD?labpath=Data%20usage%20example.ipynb">Binder</a></p> <p><strong>File contents: </strong></p> <p><strong><a href="https://zenodo.org/record/6045860/files/2018_Quantity_Descriptions.xlsx?download=1">2018_Quantity_Descriptions.xlsx</a></strong></p> <p>List and description of quantities contained in the single-day summary metrics, and the metrics calculated at shorter intervals (hour, 5 minute, 1 minute). Also lists day number definitions, and cohort birthdates.</p> <p><strong><a href="https://zenodo.org/record/6045860/files/beetrajectories_days_000_to_049.zip?download=1">beetrajectories_days_000_to_049.zip</a>, <a href="https://zenodo.org/record/6045860/files/beetrajectories_days_050_to_085.zip?download=1">beetrajectories_days_050_to_085.zip</a></strong></p> <p>Single-day hdf files, each containing x-y trajectory data in the form:</p> <table> <thead> <tr> <th>daynum</th> <th>framenum</th> <th>uid</th> <th>x</th> <th>y</th> <th>camera</th> <th>theta</th> </tr> </thead> </table> <ul> <li>daynum: the day number of the observation period. See 'Day numbers and cohorts' sheet in 2018_Quantity_Descriptions.xlsx</li> <li>framenum: using the camera frame rate of 3fps, the frame number with respect to that particular day</li> <li>uid: the Unique ID of each bee</li> <li>x,y: pixel coordinate values of each bee. Conversion is 80 pixels/cm</li> <li>camera: which camera the bee was detected on: 0=exit side (with dance floor - shown at right in comb map images), 1=back side (show at left in comb map images)</li> <li>theta: orientation of the bee in the hive </li> </ul> <p><strong><a href="https://zenodo.org/record/6045860/files/comb-contents2018.zip?download=1">comb-contents2018.zip</a></strong></p> <p>Comb content color-coded png image files, as well as pkl files for using the comb data type (see <a href="https://github.com/jacobdavidson/bees_lifetimetracking_2018data/blob/main/Data%20usage%20example.ipynb">Data Usage Example</a>)</p> <p><strong><a href="https://zenodo.org/record/6045860/files/daydatamat.csv?download=1">daydatamat.csv</a></strong></p> <p>Summary behavioral metrics calculated for each tracked bee on each day of the experiment. See 2018_Quantity_Descriptions.xlsx for full list and detail of quantities. Data is in the form:</p> <table> <thead> <tr> <th>Age</th> <th>Day number</th> <th>Bee unique ID</th> <th>Cohort ID</th> <th><metric1></th> <th><metric2></th> <th>...</th> </tr> </thead> </table> <p><strong><a href="https://zenodo.org/record/6045860/files/df_day1min_alldays.zip?download=1">df_day1min_alldays.zip</a>, <a href="https://zenodo.org/record/6045860/files/df_day5min_alldays.zip?download=1">df_day5min_alldays.zip</a>, <a href="https://zenodo.org/record/6045860/files/df_dayhour_alldays.zip?download=1">df_dayhour_alldays.zip</a> </strong></p> <p>Behavioral metrics calculated over different time intervals: 1 minute, 5 minute, or 1 hour divisions. See 2018_Quantity_Descriptions.xlsx for full list and detail of quantities. Files are organized as a single .hdf file for each day. Each file contains data in the form:</p> <p>Per-hour data:</p> <table> <thead> <tr> <th>Age</th> <th>Day number</th> <th>Bee unique ID</th> <th>Cohort ID</th> <th>Hour</th> <th><metric1></th> <th><metric2></th> <th>...</th> </tr> </thead> </table> <p>5-minute or 1-minute data: </p> <table> <thead> <tr> <th>Age</th> <th>Day number</th> <th>Bee unique ID</th> <th>Cohort ID</th> <th>timedivision</th> <th><metric1></th> <th><metric2></th> <th>...</th> </tr> </thead> </table> <p><strong> <a href="https://zenodo.org/record/6045860/files/dfxy_dayhour_alldays.zip?download=1">dfxy_dayhour_alldays.zip</a></strong></p> <p>x-y histogram data, calculated by using a 2cmx2cm grid and binning for each hour of each day. One file per day, and each file contains data in the form:</p> <table> <thead> <tr> <th>Day number</th> <th>Bee unique ID</th> <th>Cohort ID</th> <th>Hour</th> <th>hist0</th> <th>hist1</th> <th>...</th> <th>hist1434</th> </tr> </thead> </table> <p>Convert the flattened histogram structure into a shape of [41,35] in order to view as shown in the paper or in the <a href="https://github.com/jacobdavidson/bees_lifetimetracking_2018data/blob/main/Data%20usage%20example.ipynb">Data Usage Example</a></p>
Micro-CT abdominal reconstruction of a worker honeybee abdomen
<p><strong>Micro-CT abdominal reconstruction of worker honeybees.</strong><em> </em></p> <p>The samples were obtained from a honeybee colony located in Cortijo Tinajas (Lecrín, Granada, Spain; coordinates: 36°55'25.1"N 3°31'42.9"W; 36.923645,-3.528575) and kindly provided by Apinevada S. L. Micro-CT scans were carried out at the Centro de Instrumentación Científica (University of Granada, Granada, UGR), using a Xradia 510 VERSA (ZEISS) (Xradia 510 VERSA (ZEISS). The following settings were established to get the same resolution of all samples: 4X magnification, 6.1515 μm pixel size, 200.021 mm mm source-sample distance, 20 mm detector-sample distance, and 2034 images.</p>
Video 2. Whole honeybee abdomen Micro-CT
<p>Videos of the whole bee abdomen using a collection of<strong> </strong> 2D images for 3D stacking. Micro-CT scans were carried out at the Centro de Instrumentación Científica (University of Granada, Granada, UGR), using a Xradia 510 VERSA (ZEISS) (Xradia 510 VERSA (ZEISS). The following settings were established to get the same resolution of all samples: 4X magnification, 6.1515 μm voxel size, 200.021 mm mm source-sample distance, 20 mm detector-sample distance, BIN 1 (2048x2048 pixels on CCD), and 2034 projections for whole bee abdomen. Voltage, current, filter and exposure time were adjusted according to features of the samples as follows: bee whole abdomen: 60 kV accelerating voltage (a.v.), 83 μA beam current (b.c.), 40 s exposure time (e.t.) and LE2 source filter (s.f.).</p>
Fig. 3 in . Study of Nosema spp. in the Tomsk region, Siberia: co-infection is widespread in honeybee colonies
Fig. 3. Distribution of Nosema in bee colonies (Apis mellifera) throughout the Tomsk
Fig. 1 in . Study of Nosema spp. in the Tomsk region, Siberia: co-infection is widespread in honeybee colonies
Fig. 1. Dynamics of infestation of bee colonies and apiaries with Nosema spp. in 2012–
Fig. 4 in . Study of Nosema spp. in the Tomsk region, Siberia: co-infection is widespread in honeybee colonies
Fig. 4. Long-term dynamics of infestation of apiaries with different Nosema species in
Data from: High foraging fidelity and plant-pollinator network dominance of non-native honeybees (Apis mellifera) in the Ecuadorian Andes
<p>These data reflect the floral visitor survey and mark-recapture efforts used in the 2022 study, "High Foraging Fidelity and Plant‑Pollinator Network Dominance of Non‑native Honeybees (<em>Apis mellifera</em>) in the Ecuadorian Andes"</p>
Collective flow of circadian clock information in honeybee colonies (Data)
<p>This repository contains the data used in the paper "Collective flow of circadian clock information in honeybee colonies".</p> <p><strong> </strong></p> <p>Paper: Collective flow of circadian clock information in honeybee colonies</p> <p><strong> </strong></p> <p>Code and more details are provided in the README file of<a href="https://github.com/BioroboticsLab/speedtransfer.git"> speedtransfer</a> repository.</p> <p><strong> </strong></p> <h2>Description of included files</h2> <p>All data sets exist for the period 01.08.-25.08.2016 and 20.08-14.09.2019.</p> <p><strong> </strong></p> <h3><strong>mean_velocity_2016.csv and mean_velocity_2019.csv</strong></h3> <p>The mean velocity for each bee and age is averaged over 10-minute time windows.</p> <p><strong>Keys:</strong></p> <ul> <li> <p>velocity: Mean euclidean distance of two consecutive points of a bee's hive position.</p> </li> <li> <p>datetime: Date in year-month-day hour:minute:seconds+ms:ns format.</p> </li> <li> <p>age: Age in days. Can be NaN if the bee has no associated death_date.</p> </li> </ul> <p> </p> <h3><strong>velocity_2088_2019.csv and velocity_5101_2019.csv</strong></h3> <p>The movement speed [mm/s] of two individual bees with the bee id 2088 and the bee id 5101 for the period 2019.</p> <p><strong>Keys:</strong></p> <ul> <li> <p>velocity: Euclidean distance of two consecutive points of a bee's hive position.</p> </li> <li>time_passed: Time [s] in between the datetime of that current and the last previous detection.</li> <li> <p>datetime: Date in year-month-day hour:minute:seconds+ms:ns format.</p> </li> </ul> <p> </p> <h3><strong>cosinor_2016.csv and cosinor_2019.csv</strong></h3> <p>A cosinor fit of the velocity per bee for a time window of 3 consecutive days according to the method proposed by <a href="https://doi.org/10.1186/1742-4682-11-16">Cornelissen</a>.</p> <p><strong>Keys</strong>:</p> <ul> <li> <p>mesor: Rhythm-adjusted mean of a cosine with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>amplitude: Amplitude of a cosine with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>phase: Acrophase of a cosine with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>p_value: P-value of an F-test for overall significance of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>p_mesor: P-value of the mesor coefficient of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>p_amplitude: P-value of the amplitude coefficient of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>p_acrophase: P-value of the phase coefficient of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>p_reject: P-value of a F-test for model validity.</p> </li> <li> <p>r_squared: R² value of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>r_squared_adj: Adjusted R² value of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>p_ks: P-value of a Kalgomorov-Smirnoff test of residual normality.</p> </li> <li> <p>p_hom: P-Value of F-test of variance homogeneity of cosinor fit.</p> </li> <li> <p>dw: Durbin-Watson statistic for the independence of the residuals.</p> </li> <li> <p>p_runs: P-value of runs test of independence of residuals.</p> </li> <li> <p>RSS: Residual sum of squared - the sum of squared differences between the data and the estimated values from the fitted model</p> </li> <li> <p>SSPE: Pure error sum of squares of cosinor fit.</p> </li> <li> <p>bee_id: Numeric unique identifier per individual bee.</p> </li> <li> <p>age: Age in days. Can be NaN if the bee has no associated death_date.</p> </li> <li> <p>date: Date and time in year-month-day hour:minute:seconds+ms:ns format. The hour is always 12.</p> </li> <li> <p>n_data_points: Number of data points per cosinor fit.</p> </li> <li> <p>data_point_dist_max: Maximum temporal distance between two consecutive timepoints of velocity data.</p> </li> <li> <p>data_point_dist_min: Minimum temporal distance between two consecutive timepoints of velocity data.</p> </li> <li> <p>data_point_dist_mean: Mean temporal distance between two consecutive timepoints of velocity data.</p> </li> <li> <p>data_point_dist_median: Median temporal distance between two consecutive timepoints of velocity data.</p> </li> <li> <p>day_mean: Mean velocity during the daytime defined as the time between 9 and 18 o'clock.</p> </li> <li> <p>day_std: Standard deviation of velocity during the daytime defined as the time between 9 and 18 o'clock.</p> </li> <li> <p>night_mean: Mean velocity during the nighttime defined as the time between 21 and 6 o'clock.</p> </li> <li> <p>night_std: Standard deviation of velocity during the nighttime defined as time between 21 and 6 o'clock.</p> </li> <li> <p>ad_fuller: P-value of augmented Dickey-Fuller test for testing whether the velocity data is stationary.</p> </li> <li> <p>fit_type: Median time bin in seconds used for fit, e.g. 3600 means that the median over a time window of 3600s is used for the fit.</p> </li> <li> <p>ci_acrophase_lower: Confidence interval lower bound for the acrophase fit value.</p> </li> <li> <p>ci_acrophase_upper: Confidence interval upper bound for the acrophase fit value.</p> </li> <li> <p>ci_mesor_lower: Confidence interval lower bound for the mesor fit value.</p> </li> <li> <p>ci_mesor_upper: Confidence interval upper bound for the mesor fit value.</p> </li> <li> <p>ci_amplitude_lower: Confidence interval lower bound for the amplitude fit value.</p> </li> <li> <p>ci_amplitude_upper: Confidence interval upper bound for the amplitude fit value.</p> </li> </ul> <p><strong> </strong></p> <h3><strong>interactions_side0_2016.csv, interactions_side0_2019.csv and interactions_side1_2016.csv, interactions_side1_2019.csv</strong></h3> <p>The bee interactions and their post-interaction velocity change. An interaction between two bees (bee0 and bee1) is defined when two bees are detected simultaneously in the hive with a confidence threshold of 0.25, the distance between the markings on their thorax bodies is no more than 14 mm. These interactions are combined into one interaction if the same detections occur within a time interval of 1 second or less between them. The resulting interaction data frames per bee are concatenated with the estimates of the cosinor dataframe.</p> <p><strong>Keys:</strong></p> <ul> <li> <p>bee_id0: Numeric unique identifier per individual bee.</p> </li> <li> <p>bee_id1: Numeric unique identifier per individual bee.</p> </li> <li> <p>interaction_start: Timestamp indicating interaction start time point.</p> </li> <li> <p>interaction_end: Timestamp indicating interaction end time point.</p> </li> <li> <p>x_pos_start_bee0: Numeric x-position of bee relative to hive at interaction start.</p> </li> <li> <p>y_pos_start_bee0: Numeric y-position of bee relative to hive at interaction start.</p> </li> <li> <p>theta_start_bee0: Numeric angle of bee relative to hive at interaction start.</p> </li> <li> <p>x_pos_start_bee1: Numeric x-position of bee relative to hive at interaction start.</p> </li> <li> <p>y_pos_start_bee1: Numeric y-position of bee relative to hive at interaction start.</p> </li> <li> <p>theta_start_bee1: Numeric angle of bee relative to hive at interaction start.</p> </li> <li> <p>x_pos_end_bee0: Numeric x-position of bee relative to hive at interaction end.</p> </li> <li> <p>y_pos_end_bee0: Numeric y-position of bee relative to hive at interaction end.</p> </li> <li> <p>theta_end_bee0: Numeric angle of bee relative to hive at interaction end.</p> </li> <li> <p>x_pos_end_bee1: Numeric x-position of bee relative to hive at interaction end.</p> </li> <li> <p>y_pos_end_bee1: Numeric y-position of bee relative to hive at interaction end.</p> </li> <li> <p>theta_end_bee1: Numeric angle of bee relative to hive at interaction end.</p> </li> <li> <p>vel_change_bee0: Numeric post-interaction absolute change of velocity: abs = vafter-vbefore with vbefore and vafter are calculated as the mean velocity 30s before and after the interaction.</p> </li> <li> <p>rel_change_bee0: Numeric post-interaction relative change of velocity: rel = (vafter-vbefore)/vbefore with vbefore and vafter are calculated as the mean velocity 30s before and after the interaction.</p> </li> <li> <p>vel_change_bee1: Numeric post-interaction absolute change of velocity: abs = vafter-vbefore with vbefore and vafter are calculated as the mean velocity 30s before and after the interaction.</p> </li> <li> <p>rel_change_bee1: Numeric post-interaction relative change of velocity:rel = (vafter-vbefore)/vbefore with vbefore and vafter are calculated as the mean velocity 30s before and after the interaction.</p> </li> </ul> <p>Modeling bees as rectangular mask to determine if bees body overlap when interacting - see more details in <a href="https://github.com/BioroboticsLab/speedtransfer.git">speedtransfer repository</a>:</p> <ul> <li> <p>x_trans_focal_bee0: Translated and rotated x-position relative to hive.</p> </li> <li> <p>y_trans_focal_bee0: Translated and rotated y-position relative to hive.</p> </li> <li> <p>theta_trans_focal_bee0: Translated and rotated theta relative to hive.</p> </li> <li> <p>x_trans_focal_bee1: Translated and rotated x-position relative to hive.</p> </li> <li> <p>y_trans_focal_bee1: Translated and rotated y-position relative to hive.</p> </li> <li> <p>theta_trans_focal_bee1: Translated and rotated theta relative to hive.</p> </li> <li> <p>overlapping: Bool indicating whether rectangular masks modeling the body of bees overlap.</p> </li> </ul> <p>Cosinor fit parameters - see more detailed in <a href="https://docs.google.com/document/d/1PHJWc9HqbYgndjX9WjkfSQoks81AR-E9cHC25xZgP84/edit#heading=h.wjr1wzfvmipm">Cosinor</a> data frame:</p> <ul> <li> <p>amplitude_bee0: Amplitude of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p>phase_bee0: Phase of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p>p_value_bee0: P-value of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p>r_squared_bee0: R² value of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p>amplitude_bee1: Amplitude of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p>phase_bee1: Phase of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p>p_value_bee1: P-value of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p>r_squared_bee1: R² of the cosinor fit per bee of the day of the interaction.</p> </li> </ul> <p> </p> <h3><strong>interactions_side0_null_model_2016.csv and interactions_side0_null_model_2019.csv</strong></h3> <p>A null model for bee interactions and their post-interaction velocity change. The interaction null model is created by taking the distribution of the start and end times of a given interaction dataframe and selecting two random bees at those times that the bees were detected in the hive at that time. These pairs of bees are considered as "interacting" and their post-interaction speed change is calculated. The resulting interaction data frames per bee are concatenated with the estimates of the cosinor dataframe. The position data is relative to pixels not to the hive coordinates.</p> <p>As this is a null model the following keys are the same as described in the <a href="https://docs.google.com/document/d/1PHJWc9HqbYgndjX9WjkfSQoks81AR-E9cHC25xZgP84/edit#heading=h.728ge538e768">Interaction</a> data frame.</p> <p><strong>Keys:</strong></p> <ul> <li> <p>bee_id0</p> </li> <li> <p>bee_id1</p> </li> <li> <p>interaction_start</p> </li> <li> <p>interaction_end</p> </li> <li> <p>x_pos_start_bee0</p> </li> <li> <p>y_pos_start_bee0</p> </li> <li> <p>theta_start_bee0</p> </li> <li> <p>x_pos_start_bee1</p> </li> <li> <p>y_pos_start_bee1</p> </li> <li> <p>theta_start_bee1</p> </li> <li> <p>x_pos_end_bee0</p> </li> <li> <p>y_pos_end_bee0</p> </li> <li> <p>theta_end_bee0</p> </li> <li> <p>x_pos_end_bee1</p> </li> <li> <p>y_pos_end_bee1</p> </li> <li> <p>theta_end_bee1</p> </li> <li> <p>vel_change_bee0</p> </li> <li> <p>rel_change_bee0</p> </li> <li> <p>vel_change_bee1</p> </li> <li> <p>rel_change_bee1</p> </li> <li> <p>age_bee0</p> </li> <li> <p>phase_bee0</p> </li> <li> <p>amplitude_bee0</p> </li> <li> <p>r_squared_bee0</p> </li> <li> <p>p_value_bee0</p> </li> <li> <p>age_bee1</p> </li> <li> <p>amplitude_bee1</p> </li> <li> <p>r_squared_bee1</p> </li> <li> <p>p_value_bee1</p> </li> <li> <p>phase_bee1</p> </li> </ul> <p> </p> <h3><strong>interaction_tree_paths_2016.csv and interactions_tree_paths_2019.csv</strong></h3> <p>Graph-theoretic interaction tree paths. By tracing back interactions that positively influenced the speed of a focal bee, we constructed a graph-theoretic tree structure starting from a young rhythmic bee and recursively adding activating (velocity change parent > 0) individuals. We examined the impact of sequential interactions among bees occurring between 10 am and 3 pm, focusing on a subgroup of n = 1000 bees that are significantly rhythmic, younger than 5 days old, and peak in activity after 12 pm. We limited the time window between interactions to 30 minutes and capped the cascade duration at 2 hours to ensure causal relevance. The resulting interaction trees are collected and each node of all paths in the interaction trees are concatenated to this dataframe.</p> <p><strong>Keys</strong>:</p> <ul> <li> <p>bee_id: Numeric unique identifier per individual bee which is a node in the tree.</p> </li> <li> <p>datetime: Date in year-month-day hour:minute:seconds+ms:ns format when the interaction takes place.</p> </li> <li> <p>x_pos: Numeric x-position of bee relative to hive at interaction start.</p> </li> <li> <p>y_pos: Numeric y-position of bee relative to hive at interaction start.</p> </li> <li> <p>vel_change_parent: <a href="https://docs.google.com/document/d/1PHJWc9HqbYgndjX9WjkfSQoks81AR-E9cHC25xZgP84/edit#heading=h.728ge538e768">Absolute velocity change</a> of bee of parent node.</p> </li> <li> <p>age: Age in days of node bee. Can be NaN if the bee has no associated death date.</p> </li> <li> <p>is_root: Bool indicating if node is root of tree.</p> </li> <li> <p>depth: Depth of node in tree. E.g. depth of root is 0.</p> </li> <li> <p>is_leaf: Bool indicating if node is leaf of tree.</p> </li> <li> <p>n_children: Number of children of the subtree of the node.</p> </li> <li> <p>parent: Numeric bee_id of parent node.</p> </li> <li> <p>tree_id: Numeric unique identifier of tree.</p> </li> <li> <p>time_gap: Python datetime.timedelta object of time delta in between the parent and child node interaction.</p> </li> <li> <p>path_id: Numeric unique identifier of path.</p> </li> </ul> <p>Cosinor fit parameters - see more detailed in <a href="https://docs.google.com/document/d/1PHJWc9HqbYgndjX9WjkfSQoks81AR-E9cHC25xZgP84/edit#heading=h.wjr1wzfvmipm">Cosinor</a> data frame:</p> <ul> <li> <p> r_squared: R² value of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p> phase: Phase of the cosinor fit per bee of the day of the interaction.</p> </li> </ul> <p> </p> <h2>Software used to acquire and analyze the data:</h2> <p><a href="https://github.com/BioroboticsLab/speedtransfer.git">speedtransfer: Cosinor fit and interaction calculation and further analyses.</a></p> <p><a href="https://github.com/BioroboticsLab/bb_rhythm">bb_rhythm: Cosinor fit and interaction calculation and further analyses.</a></p> <p><a href="https://github.com/BioroboticsLab/bb_behavior">bb_behavior: Database interaction and data (pre)processing, velocity calculation.</a></p> <p><a href="https://github.com/BioroboticsLab/bb_utils">bb_utils: Database settings and interaction.</a></p> <p><a href="https://github.com/walachey/slurmhelper">slurmhelper: A package for slurm script handling.</a></p> <p> </p>
Pheromone relay networks in the honeybee: messenger workers distribute the queen's fertility signal throughout the hive
<p>This resource contains two items:</p><p>1. Dataset; Within-hive trajectories of individually-tagged honeybee workers.</p><p>2. Model; A C++ script for simulating queen pheromone transmission via physical contacts between bees. </p>
Data for: Visual guidance of honeybees approaching a vertical landing surface
<p>Landing is a critical phase for flying animals, whereby many rely on visual cues to perform controlled touchdown. Foraging honeybees rely on regular landings on flowers to collect food, crucial for colony survival and reproduction. Here, we explore how honeybees utilize optical-expansion cues to regulate approach flight speed when landing on vertical surfaces. Three sensory-motor control models have been proposed for landings of natural flyers. Landing honeybees maintain a constant optical-expansion-rate set-point, resulting in a gradual decrease in approach velocity and gentile touchdown. Bumblebees exhibit a similar strategy, but they regularly switch to a new constant optic-expansion-rate set-point. Meanwhile, landing birds fly at a constant time-to-contact to achieve faster landings. Here, we re-examined the landing strategy of honeybee by fitting the three models to individual approach flights of honeybees landing on platforms with varying optic-expansion cues. Surprisingly, the landing model identified in bumblebees proves to be the most suitable for these honeybees. This reveals that honeybees adjust their optic-expansion-rate in a stepwise manner. Bees flying at low optic-expansion-rates tended to stepwise increase their set-point, while those flying at high optic-expansion-rates tend to stepwise decrease it. This modular landing control system enables honeybees to land rapidly and reliably under a wide range of initial flight conditions and visual landing platform patterns. The remarkable similarity between the landing strategies of honeybees and bumblebees suggests that this may also be prevalent among other flying insects. Furthermore, these findings hold promising potential for bioinspired guidance systems in flying robots.</p>
Honeybee lifetime tracking data 2019
<p>Data from barcode-based tracking of honeybees during summer of 2019, taken in an observation hive located at University of Konstanz, Germany.</p> <p>See <a href="https://github.com/jacobdavidson/bees_lifetimetracking_2019data">table of experiments and manipulations performed</a></p> <p>Associated GitHub repositories:<br> <a href="https://github.com/jacobdavidson/bees_lifetimetracking_2019data">https://github.com/jacobdavidson/bees_lifetimetracking_2019data</a><br> <a href="https://github.com/jacobdavidson/bees_drones_2019data">https://github.com/jacobdavidson/bees_drones_2019data</a></p> <p>Data usage example is on <a href="https://github.com/jacobdavidson/bees_lifetimetracking_2019data/blob/main/Data%20usage%20example%20-%20updated.ipynb">Github</a></p> <p><strong>File contents: </strong></p> <p><strong><a href="https://zenodo.org/record/7298798/files/2019_Quantity_Dates_Cohorts_DaysLived.xlsx?download=1">2019_Quantity_Dates_Cohorts_DaysLived.xlsx</a></strong></p> <p>List and description of quantities contained in the single-day summary metrics, and the metrics calculated at shorter intervals (hour, 5 minute, 1 minute). Also lists day number definitions, cohort birthdates, and estimated days lived per bee.</p> <p><strong><a href="https://zenodo.org/record/7298798/files/trajectories_000-019.zip?download=1">trajectories_000-019.zip</a>, <a href="https://zenodo.org/record/7298798/files/trajectories_020-039.zip?download=1">trajectories_020-039.zip</a>, <a href="https://zenodo.org/record/7298798/files/trajectories_040-059.zip?download=1">trajectories_040-059.zip</a>, <a href="https://zenodo.org/record/7298798/files/trajectories_060-079.zip?download=1">trajectories_060-079.zip</a>, <a href="https://zenodo.org/record/7298798/files/trajectories_080-099.zip?download=1">trajectories_080-099.zip</a>, <a href="https://zenodo.org/record/7298798/files/trajectories_100-114.zip?download=1">trajectories_100-114.zip</a></strong></p> <p>Single-day hdf files, each containing x-y trajectory data in the form:</p> <table> <thead> <tr> <th>daynum</th> <th>framenum</th> <th>uid</th> <th>x</th> <th>y</th> <th>camera</th> <th>theta</th> </tr> </thead> </table> <ul> <li>daynum: the day number of the observation period. See 'Day numbers' sheet in <a href="https://zenodo.org/record/7298798/files/2019_Quantity_Dates_Cohorts_DaysLived.xlsx?download=1">2019_Quantity_Dates_Cohorts_DaysLived.xlsx</a></li> <li>framenum: using the camera frame rate of 3fps, the frame number with respect to that particular day</li> <li>uid: the Unique ID of each bee</li> <li>x,y: pixel coordinate values of each bee. Conversion is 80 pixels/cm</li> <li>camera: which camera the bee was detected on: 0=exit side (with dance floor - shown at right in comb map images), 1=back side (show at left in comb map images)</li> <li>theta: orientation of the bee in the hive </li> </ul> <p><strong><a href="https://zenodo.org/record/7298798/files/comb-contents-images2019.zip?download=1">comb-contents-images2019.zip</a></strong></p> <p>Comb content color-coded png image files, as well as pkl files for using the comb data type (see <a href="https://github.com/jacobdavidson/bees_lifetimetracking_2019data/blob/main/Data%20usage%20example%20-%20updated.ipynb">Data Usage Example</a>)</p> <p><strong><a href="https://zenodo.org/record/7298798/files/daydatamat.csv?download=1">daydatamat.csv</a></strong></p> <p>Summary behavioral metrics calculated for each tracked bee on each day of the experiment. See 2018_Quantity_Descriptions.xlsx for full list and detail of quantities. Data is in the form:</p> <table> <thead> <tr> <th>Age</th> <th>Day number</th> <th>Bee unique ID</th> <th>Cohort ID</th> <th><metric1></th> <th><metric2></th> <th>...</th> </tr> </thead> </table> <p><strong><a href="https://zenodo.org/record/7298798/files/df_day1min.zip?download=1">df_day1min.zip</a>, <a href="https://zenodo.org/record/7298798/files/df_day5min.zip?download=1">df_day5min.zip</a>, <a href="https://zenodo.org/record/7298798/files/df_dayhour.zip?download=1">df_dayhour.zip</a></strong></p> <p>Behavioral metrics calculated over different time intervals: 1 minute, 5 minute, or 1 hour divisions. See 2018_Quantity_Descriptions.xlsx for full list and detail of quantities. Files are organized as a single .hdf file for each day. Each file contains data in the form:</p> <p>Per-hour data:</p> <table> <thead> <tr> <th>Age</th> <th>Day number</th> <th>Bee unique ID</th> <th>Cohort ID</th> <th>Hour</th> <th><metric1></th> <th><metric2></th> <th>...</th> </tr> </thead> </table> <p>5-minute or 1-minute data: </p> <table> <thead> <tr> <th>Age</th> <th>Day number</th> <th>Bee unique ID</th> <th>Cohort ID</th> <th>timedivision</th> <th><metric1></th> <th><metric2></th> <th>...</th> </tr> </thead> </table> <p><strong><a href="https://zenodo.org/record/7298798/files/dfxy_dayhour.zip?download=1">dfxy_dayhour.zip</a></strong></p> <p>x-y histogram data, calculated by using a 2cmx2cm grid and binning for each hour of each day. One file per day, and each file contains data in the form:</p> <table> <thead> <tr> <th>Day number</th> <th>Bee unique ID</th> <th>Cohort ID</th> <th>Hour</th> <th>hist0</th> <th>hist1</th> <th>...</th> <th>hist1434</th> </tr> </thead> </table> <p>Convert the flattened histogram structure into a shape of [41,35] in order to view as shown in the paper or in the <a href="https://github.com/jacobdavidson/bees_lifetimetracking_2019data/blob/main/Data%20usage%20example%20-%20updated.ipynb">Data Usage Example</a></p>
waggle run phase sound during honeybee waggle dance
<p>Acoustic files of waggle run phase sound produce during the honeybee waggle dance</p>
GeoDanceHive: An Operational Hive for Honeybees Dances Recording
<p>Honeybees are known for their ability to communicate about resources in their environment.<br> They inform the other foragers by performing specific dance sequences according to the spatial<br> characteristics of the resource. The purpose of our study is to provide a new tool for honeybees<br> dances recording, usable in the field, in a practical and fully automated way, without condemning<br> the harvest of honey. We designed and equipped an outdoor prototype of a production hive, later<br> called “GeoDanceHive”, allowing the continuous recording of honeybees’ behavior such as dances<br> and their analysis. The GeoDanceHive is divided into two sections, one for the colony and the other<br> serving as a recording studio. The time record of dances can be set up from minutes to several months.<br> To validate the encoding and sampling quality, we used an artificial feeder and visual decoding<br> to generate maps with the vector endpoints deduced from the dance information. The use of the<br> GeoDanceHive is designed for a wide range of users, who can meet different objectives, such as<br> researchers or professional beekeepers. Thus, our hive is a powerful tool for honeybees studies in the<br> field and could highly contribute to facilitating new research approaches and a better understanding<br> landscape ecology of key pollinators.</p>
Phylogeography of cavity-nesting honeybees (Apis)
<p>We examine phylogenetic relationships among species and populations of Asian cavity-nesting honeybees, emphasizing detection of potential unrecognized species in the geographically widespread <em>Apis cerana</em> Fabricius (Hymenoptera, Apidae). We carried out a phylogenetic analysis of genome-wide single nucleotide polymorphisms (SNPs) using BEASTv1.8.4 and IQ-TREE 2. Our samples cover the largest geographic area and number of populations of Asian cavity-nesting honeybees sampled to date. Nodes in the tree were calibrated using the mid-Miocene giant honeybee <em>Apis lithohermaea</em> Engel. We used STRUCTURE, Bayes Factor Delimitation, and discriminant analysis of principal components to infer probable species among populations of cavity-nesting honeybees currently recognized as <em>Apis cerana</em>. Our results support four species within <em>A. cerana</em>: the yellow "plains" honeybee of India and Sri Lanka; the lineage inhabiting the oceanic Philippine islands; the Sundaland lineage found in Indonesia, Malaysia and parts of southeast Asia; and a Mainland lineage, which we provisionally consider <em>A. cerana</em> in a narrow sense.</p>
Reduced parasite burden in feral honeybee colonies
<p>Bee parasites are the main threat to apiculture and since many parasite taxa can spill over from honeybees (Apis mellifera) to other bee species, honeybee disease management is important for pollinator conservation in general. It is unknown whether honeybees that escaped from apiaries (i.e. feral colonies) benefit from natural parasite‐reducing mechanisms like swarming or suffer from high parasite pressure due to the lack of medical treatment. In the latter case, they could function as parasite reservoirs and pose a risk to the health of managed honeybees (spillback) and wild bees (spillover). We compared the occurrence of 18 microparasites among managed (N = 74) and feral (N = 64) honeybee colony samples from four regions in Germany using qPCR. We distinguished five colony types representing differences in colony age and management histories, two variables potentially modulating parasite prevalence. Besides strong regional variation in parasite communities, parasite burden was consistently lower in feral than in managed colonies. The overall number of detected parasite taxa per colony was 15% lower and Trypanosomatidae, chronic bee paralysis virus, and deformed wing viruses A and B were less prevalent and abundant in feral colonies than in managed colonies. Parasite burden was lowest in newly founded feral colonies, intermediate in overwintered feral colonies and managed nucleus colonies, and highest in overwintered managed colonies and hived swarms. Our study confirms the hypothesis that the natural mode of colony reproduction and dispersal by swarming temporally reduces parasite pressure in honeybees. We conclude that feral colonies are unlikely to contribute significantly to the spread of bee diseases. There is no conflict between the conservation of wild‐living honeybees and the management of diseases in apiculture.</p>
Data from: Parasites, depredators, and limited resources as potential drivers of winter mortality of feral honeybee colonies in German forests
<p>Wild honeybees (<em>Apis mellifera</em>) are considered extinct in most parts of Europe. The likely causes of their decline include increased parasite burden, lack of high-quality nesting sites and associated depredation pressure, and food scarcity. In Germany, feral honeybees still colonize managed forests, but their survival rate is too low to maintain viable populations. Based on colony observations collected during a monitoring study, data on parasite prevalence, experiments on nest depredation, and analyses of land cover maps, we explored whether parasite pressure, depredation or expected landscape-level food availability explain feral colony winter mortality. Considering the colony-level occurrence of 18 microparasites in the previous summer, colonies that died did not have a higher parasite burden than colonies that survived. Camera traps installed at cavity trees revealed that four woodpecker species, great tits, and pine martens act as nest depredators. In a depredator exclusion experiment, the winter survival rate of colonies in cavities with protected entrances was 50% higher than that of colonies with unmanipulated entrances. Landscapes surrounding surviving colonies contained on average 6.4 percentage points more cropland than landscapes surrounding dying colonies, with cropland being known to disproportionately provide forage for bees in our study system. We conclude that the lack of spacious but well-protected nesting cavities and the shortage of food are currently more important than parasites in limiting populations of wild-living honeybees in German forests. Increasing the density and diversity of large tree cavities and promoting bee forage plants in forests will probably promote wild-living honeybees despite parasite pressure.</p>
Data from: Inducible versus constitutive social immunity: examining effects of colony infection on glucose oxidase and defensin-1 production in honeybees
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Spatiotemporal variation of small hive beetle infestation levels in honeybee host colonies
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Data from: Stress-induced loss of social resilience in honeybee colonies and its implications on fitness
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.