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236 results for “Honeybees”
Effects of Sinusoidal Vibrations on the Motion Response of Honeybees - datasets
<p>data sets on the effects of sinusoidal stimuli on the motion activity of honeybees. For more details please refer to </p> <p>Stefanec, M., Oberreiter, H., Becher, M. A., Haase, G., & Schmickl, T. (2021). Effects of Sinusoidal Vibrations on the Motion Response of Honeybees. <em>Frontiers in Physics</em>, <em>9</em>, 318.</p> <p>amplitude_experiments.csv contains the data of measured motion activity according to the pixel-based motion index in a certain region of interest in regards to different amplitudes at different frequencies.</p> <p>amplitude_experiments_with_velocity.csv contains the data of measured motion activity according to the pixel-based motion index in a certain region of interest in regards to different amplitudes at different frequencies as well as a post-hoc derived intensity measurement at a certain amplitude. This intensity measurement was detected by laser vibrometer on the surface of the honeycomb and represents the measurement at the point in the region of interest that had the highest intensity. This measurement could not be made during the experiments on the animals, but had to be made post-hoc, since a laser vibration measurement was only possible without animals passing through the laser point.<br> <br> frequency_experiments.csv contains the data of measured motion activity according to the pixel-based motion index in a certain region of interest in regards to different frequency stimuli.</p>
Autonomous tracking of honeybee behaviors over long-term periods with cooperating robots
<h1>Dataset and code description</h1> <p>This repository contains the codes and data for theScience Robotics paper <strong>Autonomous tracking of honeybee behaviors over long-term periods with cooperating robots</strong>.</p> <p>The codes are in <strong>rr_scirob_analyses</strong> and the datasets are in <strong>rr_scirob_data</strong>.<strong> </strong>If you want to rerun the data processing as presented in the paper, you need both <strong>rr_scirob_analyses</strong> and <strong>rr_scirob_data. </strong>You can copy the contents of <strong>rr_scirob_data </strong>into <strong>rr_scirob_analyses, </strong>as they have the same folder structure. Alternatively, you can run the <strong>download </strong>scripts to obtain the partial datasets relevant for certain subfigures. The file <strong>rr_scirob_data_readmes</strong> contains more detailed README files (rosbag info). You can copy its contents to <strong>rr_scirob_analyses </strong>after copying the contents of the <strong>rr_scirob_data</strong>.</p> <p>The individual datasets are organised into seven folders.</p> <h2>Three Figures with Key Behavioural Metrics </h2> <p>Three of the folders correspond to the Key Behavioural Measures, which are presented in three figures in the paper. These are:</p> <ul> <li>Figure-2-KBM-1-Queen Queen - related Key Behavioural Metrics</li> <li>Figure-3-KBM-2-Workers Worker Bee - related Key Behavioural Metrics</li> <li>Figure-4-KBM-3-Comb Comb and Brood -related Key Behavioural Metrics </li> </ul> <p>Each of these <em>Figure-X</em> folders contains the relevant figure from the paper and four subfolders corresponding to the panels of that figure. These are <strong>macro</strong>, <strong>micro</strong>, <strong>mezo</strong>, <strong>social</strong>, related to the four panels of that figure.<br>Each of these subfolders contains a README file, describing how to process the data and providing further details. <br>Furthermore, there are three additional folders located in each of the 'panel' folder:</p> <ul> <li><strong>data</strong>: this is used to store the data necessary to generate the graphs. You can either populate it with the data from Zenodo, i.e., https://zenodo.org/records/13801588 Alternatively, you can use the `download.sh` script wich will download and extract the necessary data from the RoboRoyale project cloud.</li> <li><strong>tmp</strong>: This folder is used to store intermediate results of the processing scripts</li> <li><strong> output</strong>: This folder is used to store all the generated outputs of the individual scripts. These should be identical with the panels of the figure in the paper. These figures are also provided in the relevant folders.</li> </ul> <p>Running the scripts contained in the micro, mezo, macro and social folders generates images and graphs in the output subfolders. These should be identical to the ones in the panels of Figures 2-4 in the paper.</p> <h2>One Resting Analysis Figure</h2> <p>One folder corresponds to the queen resting analysis figure</p> <ul> <li>Figure-5-Resting : Queen resting time analysis</li> </ul> <p>This folder has three subfolders named <strong>data</strong>, <strong>tmp</strong> and <strong>output</strong> similar to the previous folders. Again, running the scripts will generate the figures and/or run the statistical tests as in the previous case.</p> <h2>Three Performance Assessments: Queen Tracking, Workerbee Localisation and Oviposition Detection</h2> <p>Three other folders are related to performance analysis of the core methods required to calculate the KBMs.</p> <ul> <li>KBM-1-performance evaluation: Provides datasets and scripts to assess the performance of the queen marker detector</li> <li>KBM-2-performance evaluation: Provides datasets and scripts to assess the performance of the worker bee detector</li> <li>KBM-3-performance evaluation: Provides datasets and scripts to assess the performance of the oviposition detector </li> </ul> <p>Each of these folders contains a README file explaining what to run in order to evaluate the performance of the method and to replicate the paper's results.</p> <h2>Additional materials and data</h2> <p>The core data used here is the month-long queen tracking information, consisting of 28 million entries in a file <strong>2023-month-queenpos-short.txt.</strong> <br>A description of the file structure is provided in the README of the relevant KBM folder.</p> <p>Additional data are available in the dataset section of https://roboroyale.eu.</p> <h2>Rosbags</h2> <p>The work is based on the Robot Operating System (ROS) and thus, the raw data come in the form of rosbags. We provide a few of the rosbags to allow checking examples of video and other raw data as reported by the system:</p> <ul> <li>2023-10-25-08-42-20-Queen-Feeding.bag - queen feeding (KBM-1 Social)</li> <li>KPI1_2_mezo-queen_walk_sample.bag - queen walk as drawn in (KBM-1 Mezo)</li> <li>2023-10-10-00-04-10-trophylaxis.bag - worker bee trophylaxis (KBM-2 Social)</li> <li>2023-09-19-09-00-20-egg-removal.bag - worker bee removing egg (KBM-2 Social)</li> </ul> <h2>Licence </h2> <p>This data and code are under the Creative Commons Attribution-ShareAlike 4.0 International license. If you use these data in your work, please <strong>cite</strong> the relevant paper, i.e., Ulrich, Stefanec, Rekabi-bana et al.: <strong>Autonomous tracking of honeybee behaviors over long-term periods with cooperating robots</strong>. Science Robotics, 2024.</p> <p> </p>
Genome drafts of Lotmaria passim strains C2 and C3 isolated from honeybees in Spain
<p>Lotmaria passim is a highly prevalent parasite of honeybees. Herein is reported the draft genome sequences of L. passim C2 and C3 strains of 27.15 Mbp and 26.94 Mbp, respectively. The genomes were sequenced using Illumina MiSeq platform and will allow for further comparative and functional genomics studies.</p>
Raw data for Development of Germline Progenitors in Larval Queen Honeybee ovaries
<p>This repository contains raw files for images relating to a publication of honeybee ovary development. That work is Cullen, Delargy and Dearden 2024, <strong><span>Development of Germline Progenitors in Larval Queen Honeybee ovaries. </span></strong><span>The data is organised in folders relate to each figure, and is in .oir format, a raw data format produced by Olympus confocal systems. This data file format is able to be read by FIJI.</span></p>
TuringREGConnectionsWorkshop_HoneyBees
<p>Images of several different types of honeybee, some of which have pollen, and some of which don't. Can be used to train machine learning models to identify honey bee species and/or to identify "robber bees".</p>
Dataset for: Biohybrid superorganisms - on the design of a robotic system for thermal interactions with honeybee colonies
<p>Dataset containing electronic, mechical, firmware, and software design files associated with the article: </p> <p><br>"Biohybrid superorganisms - on the design of a robotic system for thermal interactions with honeybee colonies"<br>By R. Barmak, D. N. Hofstadler, M. Stefanec, L. Piotet, R. Cherfan, T. Schmickl, F. Mondada, and R. Mills. EPFL, Switzerland and Univeristy of Graz, Austria.<br>IEEE Access, 2024, Vol 12, pp 50849-50871.</p> <p>doi: 10.1109/ACCESS.2024.3385658</p> <p><a href="https://doi.org/10.1109/ACCESS.2024.3385658">https://doi.org/10.1109/ACCESS.2024.3385658</a></p> <p> </p> <table> <tbody> <tr> <td><strong>File name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>1_hw_pcb_schematics_rev2.pdf</td> <td>Electrical schematic of the robotic frame</td> </tr> <tr> <td>2_hw_pcb_stackup_rev2.pdf </td> <td>Technical specifications for the robotic frame PCB manufacturing</td> </tr> <tr> <td>3_hw_pcb_bom_rev2.pdf </td> <td>Electronics Bill of Materials (BoM)</td> </tr> <tr> <td>4_hw_pcb_gerber_rev2.zip </td> <td>Robotic frame PCB manufacturing files (gerbers)</td> </tr> <tr> <td>5_hw_pcb_altium_rev2.zip</td> <td>Altium Designer project files</td> </tr> <tr> <td>6_hw_mechanical_rev2.zip</td> <td>DXF and STEP files of the mechanical structure of the robotic frame</td> </tr> <tr> <td>7_sw_firmware_rev2.zip</td> <td>Firmware source code and compiled binaries for STM32 microcontroller</td> </tr> <tr> <td>8_sw_handlers-1.0.1.zip</td> <td>Software for high-level interface to robot from a host device </td> </tr> </tbody> </table>
Will Biomimetic Robots Be Able to Change a Hivemind to Guide Honeybees' Ecosystem Services? (dataset)
<p>Simulation data for different simulation runs from the publication "Will Biomimetic Robots Be Able to Change a Hivemind to Guide Honeybees’ Ecosystem Services?"<br> <br> Dataset for figure 4: The Model replicates Seeleys Choice Experiment (1991).<br> (Shown in the folder: 'Model_Validation_Choice_Empirical','Model_Validation_Choice_Model')<br> Aditionally the accumulated energy [J] (net gain) gathered threw the foraging targets is displayed.<br> <br> Dataset for figure 5: The Model replicates Seeleys Cross Inhibition Experiment (2009).<br> (Shown in the folder: 'Model_Validation_Equilib_Empirical','SModel_Validation_Equilib_Model')<br> Aditionally the accumulated energy [J] (net gain) gathered threw the foraging targets is displayed.<br> <br> Dataset for figure 6: Model data of Seeley's choice experiment (1991) under more natural conditions.<br> The effect of the single parameters and the effect of all parameters together is shown.<br> (Shown in the folder: 'Natural_Foraging_Forager_Size','Natural_Foraging_crop_Load',<br> 'Natural_Foraging_CFS','Natural_Foraging_All_Conditions',)<br> <br> Dataset for figure 7: Model data of Seeley's Cross Inhibition Experiment under more natural conditions.<br> The effect of the single parameters and the effect of all parameters together is shown.<br> (Shown in the folder: 'Natural_Foraging_Equilib_Forager_Size','Natural_Foraging_Equilib_crop_Load',<br> 'Natural_Foraging_Equilib_CFS','Natural_Foraging_Equilib_All_Conditions',)<br> <br> Dataset for figure 8: The influence of waggle dancing robots on the foraging behaviour of honeybees with two different qualities of the foraging targets A and B.<br> After 14400 second (12:00) a pesticide is sprayed on the foraging target B under natural conditions.<br> The waggle dancing robot starts advertising foraging target B when time > 14400 seconds.<br> (The folder shows: 'robo_influence_bad_vs_bad','robo_influence_good_vs_bad','robo_influence_good_vs_good',)</p> <p>Dataset for figure 9: The influence of 10 waggle dancing robots on the foraging behaviour of honeybees with two different qualities of the foraging targets A and B<br> and varrying colony fill status (CFS).<br> After 14400 second (12:00) a pesticide is sprayed on the foraging target B under natural conditions.<br> The 10 waggle dancing robots start advertising foraging target B when time > 14400 seconds. The CFS is varried (0.1, 0.3, 0.5, 0.7, 0.9).<br> (The folder shows: 'robo_influence_bad_vs_bad','robo_influence_good_vs_bad','robo_influence_good_vs_good',)<br> <br> Dataset for figure 10: The effects at the end of the waggle dancing robots on the accumulated energy (through the trips), accumulated pesticides (through trips on foraging target<br> B, where after time>14400 pesticide was sprayed on the foraging target), pollination flights to foraging target A.<br> After 14400 second (12:00) a pesticide is sprayed on the foraging target B under natural conditions.<br> The waggle dancing robot starts advertising foraging target A when time > 14400 seconds.<br> (The folder shows: 'robo_influence_acc_energy','robo_influence_acc_pesticide','robo_influence_pollination')<br> <br> The datasets are contained in the zipped file folder Figures_data.zip.</p>
Effects of wind on honeybee and bumblebee foraging behaviour on multiple plant species
<p>Dataset of results used for two publications. It shows the foraging behaviours of honeybees and bumblebees on multiple plant species in different wind speeds,</p>
SUPPLEMENTARY (For MD) An integrative pan-genome and subtractive proteomics approach for the identification of potential novel therapeutic drug target against antibiotic resistant honeybee pathogen Paenibacillus larvae
<p><strong>Parameters</strong></p><p>Force field: AMBER ff19SB</p><p>Water type: TIP3P</p><p>Ions: NaCl </p><p>Ligand topology force field: GAFF2</p><p>Temperature: 298k</p><p>Pressure: 1 bar</p><p>minimization step: 20000 on 5 nanoseconds</p><p>initial velocity is changed by changing "ntx" and "ig"</p><p>C2: ntx = 5 , ig = 8</p><p>C3: ntx = 2 , ig = 5</p><p> </p><p><strong>Uploads</strong>- </p><p>1. Zip file of all 3 main files</p><p>2. Unzip file of C1 (Trajectory, PDB complex after each 10 ns run, and Mp4 video of Complex)</p><p>3. Zip file of C1</p><p>4. Unzip file of C2 (Trajectory, PDB complex after each 10 ns run, and Mp4 video of Complex)</p><p>5. Zip file of C2</p><p>6. Unzip file of C3 (Trajectory, PDB complex after each 10 ns run, and Mp4 video of Complex)</p><p>7. Zip file of C3</p><p>8. Zip and unzip file of <strong>Initial</strong> PDB of complex prior to MD simulation with <strong>Post</strong> MD PDB (C1, C2, C3)</p><p>9. Zip file of <strong>topology</strong> files for C1, C2, and C3</p>
HoneyBee: Progressive Instruction Finetuning of Large Language Models for Materials Science
<p>We propose an instruction-based process for trustworthy data curation in materials science (MatSci-Instruct), which we then apply to finetune a LLaMa-based language model targeted for materials science (HoneyBee). MatSci-Instruct helps alleviate the scarcity of relevant, high-quality materials science textual data available in the open literature, and HoneyBee is the first billion-parameter language model specialized to materials science. In MatSci-Instruct we improve the trustworthiness of generated data by prompting multiple commercially available large language models for generation with an Instructor module (e.g. Chat-GPT) and verification from an independent Verifier module (e.g. Claude). Using MatSci-Instruct, we construct a dataset of multiple tasks and measure the quality of our dataset along multiple dimensions, including accuracy against known facts, relevance to materials science, as well as completeness and reasonableness of the data. Moreover, we iteratively generate more targeted instructions and instruction-data in a finetuning-evaluation-feedback loop leading to progressively better performance for our finetuned HoneyBee models. Our evaluation on the MatSci-NLP benchmark shows HoneyBee's outperformance of existing language models on materials science tasks and iterative improvement in successive stages of instruction-data refinement. We study the quality of HoneyBee's language modeling through automatic evaluation and analyze case studies to further understand the model's capabilities and limitations. Our code and relevant datasets are publicly available at https://github.com/BangLab-UdeM-Mila/NLP4MatSci-HoneyBee.</p>
Data from: Comparison of pooled semen insemination and single colony insemination as sustainable honeybee breeding strategies
<p>Instrumental insemination of honeybees allows for two opposing breeding strategies. In single colony insemination (SCI), all drones to inseminate a queen are taken from one colony. In pooled semen insemination (PSI), sperm of many genetically diverse drones is mixed and queens are fertilised from the resulting drone pool. While SCI allows for maximum pedigree control, proponents of PSI claim to reduce inbreeding and maintain genetic variance. Using stochastic simulation studies, we compared genetic progress and inbreeding rates in small honeybee populations under SCI and PSI. Four different selection criteria were covered: estimated breeding values (EBV), phenotypes, true breeding values (TBV), and random selection. Under EBV-based truncation selection, SCI yielded 9.0% to 44.4% higher genetic gain than PSI, but had vastly increased inbreeding rates. Under phenotypical or TBV selection, the gap between SCI and PSI in terms of genetic progress narrowed. Throughout, PSI yielded lower inbreeding rates than SCI, but the differences were only substantial under EBV truncation selection. As a result, PSI did not appear as a viable breeding strategy due to its incompatibility with modern methods of genetic evaluation. Instead, SCI is to be preferred but instead of strict truncation selection, strategies to avoid inbreeding need to be installed.</p>
Feeding with plant powders increases longevity and body weight of Western honeybee workers (Apis mellifera)
<p>Beekeepers routinely substitute honey from managed western honeybees, <em>Apis mellifera</em>, colonies with sugar water post-harvest, potentially leading to malnutrition. Although nutritional supplements have been created, a general consensus on proper colony nutrition for beekeeping has yet to be reached. Thus, finding easily obtainable fortified <em>A. mellifera</em> food alternatives is still of interest. Here, we test plant powder-enriched food supplements since <em>a priori</em> evidence suggests plant extracts can enhance dry body weight and longevity of workers. Freshly emerged workers were kept in hoarding cages (N=69 days) and fed either with 50 % (w/v) sucrose solution alone or additionally with one of 12 powders: <em>Laurus nobilis, Quercus </em>spp<em>., Curcuma longa, Hypericum </em>spp<em>., Spirulina platensis, Calendula officinalis, Chlorella vulgaris, Melissa officinalis, Moringa oleifera, Rosa canina, Trigonella foenum-graecum, </em>and<em> Urtica dioica </em>(N=2028 workers total). The dry body weight was significantly increased in <em>Quercus</em> spp., <em>Hypericum</em> spp., <em>Spirunlina platensis, Mellisa officinalis, Moringa oelifera</em>, and <em>Trigonella foenum-graecum</em> treatments. Further, the longevity was significantly increased in <em>Quercus </em>spp., <em>Curcuma longa, Calendulae officinalis, Chlorella vulgaris, Melissa officinalis, Rosa canina, Trigonella foenum-graecum, </em>and<em> Urtica diocia</em> treatments<em>.</em> Given that plant extracts can enhance <em>A. mellifera</em> health, plant powders possibly provide additional macro- (i.e. proteins, lipids, peptides) and micronutrients (minerals and vitamins) thereby enhancing nutrient availability. Further investigations into the mechanisms underlying these effects and field studies are recommended to validate these findings in real-hive scenarios.</p>
Honeybee optomotor behaviour is impaired by chronic exposure to insecticides
<p>Honeybees use wide&[ndash]field visual motion information to calculate the distance they have flown from the hive, and this information is communicated to conspecifics during the waggle dance. Seed treatment insecticides, including neonicotinoids and novel insecticides like sulfoxaflor, display detrimental effects on wild and managed bees, even when present at sublethal quantities. These effects include deficits in flight navigation and homing ability, resulting in decreased survival of exposed worker bees. Neonicotinoid insecticides disrupt visual motion detection in the locust, resulting in impaired escape behaviours, but it had not previously been shown whether seed treatment insecticides disrupt wide&[ndash]field motion detection in the honeybee. Here, we show that sublethal exposure to two commonly used insecticides, imidacloprid (a neonicotinoid) and sulfoxaflor, results in impaired optomotor behaviour in the honeybee. This behavioural effect correlates with altered stress and detoxification gene expression in the brain. Exposure to sulfoxaflor led to sparse increases in neuronal apoptosis, localized primarily in the optic lobes, however there was no effect of imidacloprid. We propose that exposure to cholinergic insecticides disrupts the honeybee&[nprime]s ability to accurately encode wide&[ndash]field visual motion, resulting in impaired optomotor behaviours. These findings provide a novel explanation for previously described effects of neonicotinoid insecticides on navigation and link these effects to sulfoxaflor for which there is a gap in scientific knowledge. --</p>
Fig. 2 in . Study of Nosema spp. in the Tomsk region, Siberia: co-infection is widespread in honeybee colonies
Fig. 2. Distribution of Nosema species in bee colonies (Apis mellifera) throughout the Tomsk region (dots A–I). Bee colonies not infected by Nosema are indicated in yellow; bee
Fig. 2 in Workflow of Lotmaria passim isolation: Experimental infection with a low-passage strain causes higher honeybee mortality rates than the PRA-403 reference strain
Fig. 2. Kaplan-Meier survival curves for the experimental groups (C1, control and PRA-403), showing the cumulative mortality over time. Vertical ticks indicate censored observations.
Fig. 1 in Workflow of Lotmaria passim isolation: Experimental infection with a low-passage strain causes higher honeybee mortality rates than the PRA-403 reference strain
Fig. 1. Workflow for the isolation of bee-infecting trypanosomatid parasites from honeybee guts. A. Dissection and tissue processing (steps 1–4), and trypanosomatid culture and expansion (step 5) in liquid or Solid Cultures. B. Growth curve of L. passim PRA-403 strain in decreasing concentrations of 5-Fluorocytosine (1 × 106 μg/ mL-100 μg/mL) to determine the maximum dose for parasite survival. C. Giemsa staining of L. passim C1 (CCP 1). D. Hoescht DNA staining of live L. passim C1 (CCP 1): N, Nucleus; K, Kinetoplast; E, Scanning Electron Microscopy of L. passim C1 (CCP 1) grown in Agar Solid cultures 20 days post-inoculation.
Nosemosis negatively affects honeybee survival: experimental and meta-analytic evidence
<p><span>Nosemosis, caused by microsporidian parasites of the genus <em>Nosema</em>, is considered a significant health concern for insect pollinators, including the economically important honeybee (<em>Apis mellifera</em>). Despite its acknowledged importance, the impact of this disease on honeybee survivorship remains unclear. Here, a standard laboratory cage trial was used to compare mortality rates between healthy and <em>Nosema</em>-infected honeybees. Additionally, a systematic review and meta-analysis of existing literature were conducted to explore how nosemosis contributes to increased mortality in honeybees tested under standard conditions. The review and meta-analysis included 50 studies that reported relevant experiments involving healthy and <em>Nosema</em>-infected individuals. Studies lacking survivorship curves or information on potential moderators, such as spore inoculation dose, age of inoculated bees, or factors that may impact energy expenditure, were excluded. Both the experimental results and meta-analysis revealed a consistent, robust effect of infection, indicating a threefold increase in mortality among the infected group of honeybee workers (hazard ratio for infected individuals = 3.16 [1.97, 5.07] and 2.99 [2.36, 3.79] in the experiment and meta-analysis, respectively). However, the meta-analysis also indicated high heterogeneity in the effect magnitude, which was not explained by our moderators. Furthermore, there was a serious risk of bias within studies and potential publication bias across studies. The findings underscore knowledge gaps in the literature. It is stressed that laboratory cage trials should be viewed as an initial step in evaluating the impact of <em>Nosema</em> on mortality and that complementary field and apiary studies are essential for identifying effective treatments to preserve honeybee populations.</span></p>
Larval nutrition impacts survival to adulthood, body size, and the allometric scaling of metabolic rate in adult honeybees
<p>Resting metabolic rate (RMR) is a fundamental physiological measure linked to numerous aspects of organismal function, including lifespan. Although dietary restriction in insects during larval growth/development affects adult RMR, the impact of larval diet <i>quality</i> on adult RMR has not been studied. Using <i>in vitro</i> rearing to control larval diet quality, we determined the effect of dietary protein and carbohydrate on honeybee survival-to-adulthood, time-to-eclosion, body mass/size and adult RMR. High carbohydrate larval diets increased survival-to-adulthood and time-to-eclosion compared to both low carbohydrate and high protein diets. Upon emergence, bees reared on the high protein diet were smaller and lighter than those reared on other diets, whilst those raised on the high carbohydrate diet varied more in body mass. Newly emerged adult bees' reared on the high carbohydrate diet showed a significantly steeper increase in allometric scaling of RMR compared to those reared on other diets. This suggests that diet quality influences survival-to-adulthood, time-to-eclosion, and the allometric scaling of RMR. Given that agricultural intensification and increasing urbanisation have led to a decrease in both forage availability and dietary diversity for bees, our results are critical to improving understanding of the impacts of poor developmental nutrition on bee growth/development and physiology.</p>
A robotic honeycomb for interaction with a honeybee colony
<p>This repository contains data for the results described in:</p> <p> Barmak R. and Stefanec M., Hofstadler D., Piotet L., Schnwetter-F-S S., Mondada F., Schmickl T., Mills R.,<br> A robotic honeycomb for interaction with a honeybee colony.<br> Science Robotics, 2023<br> DOI: 10.1126/scirobotics.add7385</p> <p> </p>
Insights into Varroa mite (Varroa destructor) infestation levels in local honeybee (Apis mellifera) colonies of Ethiopia
<p>These data were collected from three geographic regions of Ethiopia from September, 2020 to June 2022 in order to determine the prevalence of<em> varroa destructor </em>in Ethiopian honeybee colonies. The data was analyzed to compare the <em>Varroa destructor</em> mite among the honeybee development stages (brood Vs Adult), by the hive types (Local traditional Vs Frame modern) and across the geographic regions (Oromia, Amhara and SNNPR). </p> <p>Both the raw and the partially processed data were available here. </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.