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75 results for “behavior classification”
Accelerometer-Based Multivariate Time-Series Dataset for Calf Behavior Classification
<p><strong>AcTBeCalf Dataset Description</strong></p> <p>The AcTBeCalf dataset is a comprehensive dataset designed to support the classification of pre-weaned calf behaviors from accelerometer data. It contains detailed accelerometer readings aligned with annotated behaviors, providing a valuable resource for research in multivariate time-series classification and animal behavior analysis. The dataset includes accelerometer data collected from 30 pre-weaned Holstein Friesian and Jersey calves, housed in group pens at the Teagasc Moorepark Research Farm, Ireland. Each calf was equipped with a 3D accelerometer sensor (AX3, Axivity Ltd, Newcastle, UK) sampling at 25 Hz and attached to a neck collar from one week of birth over 13 weeks.</p> <p>This dataset encompasses 27.4 hours of accelerometer data aligned with calf behaviors, including both prominent behaviors like lying, standing, and running, as well as less frequent behaviors such as grooming, social interaction, and abnormal behaviors.</p> <p>The dataset consists of a single CSV file with the following columns:</p> <ul> <li><strong>dateTime</strong>: Timestamp of the accelerometer reading, sampled at 25 Hz.</li> <li><strong>calfid</strong>: Identification number of the calf (1-30).</li> <li><strong>accX</strong>: Accelerometer reading for the X axis (top-bottom direction)*.</li> <li><strong>accY</strong>: Accelerometer reading for the Y axis (backward-forward direction)*.</li> <li><strong>accZ</strong>: Accelerometer reading for the Z axis (left-right direction)*.</li> <li><strong>behavior</strong>: Annotated behavior based on an ethogram of 23 behaviors.</li> <li><strong>segId</strong>: Segment identification number associated with each accelerometer reading/row, representing all readings of the same behavior segment.</li> </ul> <p>* the directions are mentioned in relation to the position of the accelerometer sensor on the calf.</p> <p><strong>Code Files Description</strong></p> <p>The dataset is accompanied by several code files to facilitate the preprocessing and analysis of the accelerometer data and to support the development and evaluation of machine learning models. The main code files included in the dataset repository are:</p> <ol> <li><strong>accelerometer_time_correction.ipynb</strong>: This script corrects the accelerometer time drift, ensuring the alignment of the accelerometer data with the reference time.</li> <li><strong>shake_pattern_detector.py</strong>: This script includes an algorithm to detect shake patterns in the accelerometer signal for aligning the accelerometer time series with reference times.</li> <li><strong>aligning_accelerometer_data_with_annotations.ipynb</strong>: This notebook aligns the accelerometer time series with the annotated behaviors based on timestamps.</li> <li><strong>manual_inspection_ts_validation.ipynb</strong>: This notebook provides a manual inspection process for ensuring the accurate alignment of the accelerometer data with the annotated behaviors.</li> <li><strong>additional_ts_generation.ipynb</strong>: This notebook generates additional time-series data from the original X, Y, and Z accelerometer readings, including Magnitude, ODBA (Overall Dynamic Body Acceleration), VeDBA (Vectorial Dynamic Body Acceleration), pitch, and roll.</li> <li><strong>genSplit.py: </strong>This script provides the logic used for the generalized subject separation for machine learning model training, validation and testing.</li> <li><strong>active_inactive_classification.ipynb</strong>: This notebook details the process of classifying behaviors into active and inactive categories using a RandomForest model, achieving a balanced accuracy of 92%.</li> <li><strong>four_behv_classification.ipynb</strong>: This notebook employs the mini-ROCKET feature derivation mechanism and a RidgeClassifierCV to classify behaviors into four categories: drinking milk, lying, running, and other, achieving a balanced accuracy of 84%.</li> </ol> <p>Kindly cite one of the following papers when using this data:</p> <p>Dissanayake, O., McPherson, S. E., Allyndrée, J., Kennedy, E., Cunningham, P., & Riaboff, L. (2024). <em>Evaluating ROCKET and Catch22 features for calf behaviour classification from accelerometer data using Machine Learning models</em>. arXiv preprint arXiv:2404.18159.</p> <p>Dissanayake, O., McPherson, S. E., Allyndrée, J., Kennedy, E., Cunningham, P., & Riaboff, L. (2024). <em>Development of a digital tool for monitoring the behaviour of pre-weaned calves using accelerometer neck-collars</em>. arXiv preprint arXiv:2406.17352</p>
Japanese Black Beef Cow Behavior Classification Dataset
<p>Licensed under:<br> Attribution-NonCommercial-NoDerivatives 4.0 International<br> <a href="https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode">https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode</a></p> <p><strong>Japanese Black Beef Cow Behavior Classification Dataset</strong></p> <p>This dataset contains tri-axial accelerometer sensor data with thirteen different labeled cow behaviors. This data was gathered with a 16bit +/- 2g Kionix KX122-1037 accelerometer attached to the neck of six different Japanese Black Beef Cows (`cow1.csv`-`cow6.csv`) at a cow farm of Shinshu University in Nagano, Japan on the 12th of June, 2020.</p> <p>The data gathering took place over the course of one day in which the cows were allowed to roam freely in two different areas, namely, a grass field and farm pens, while being filmed with Sony FDR-X3000 4K video cameras.</p> <p>The timestamps of the video and accelerometer data were matched while human observers which included behavior experts and non-experts labeled the data from the video footage. The labeling and data gathering took a total of 69 person-hours.</p> <p>567 minutes of unlabeled data were parsed into 197 minutes of high-quality labeled data comprising thirteen behaviors by means of majority voting with three annotators. The time per behavior in number of samples (@25Hz) and their respective descriptions are shown in the following table:</p> <table> <thead> <tr> <th scope="col"> </th> <th scope="col">Cow 1</th> <th scope="col">Cow 2</th> <th scope="col">Cow 3</th> <th scope="col">Cow 4</th> <th scope="col">Cow 5</th> <th scope="col">Cow 6</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>RES</td> <td>35814</td> <td>47059</td> <td>20501</td> <td>15735</td> <td>11025</td> <td>19996</td> <td>Resting in standing position</td> </tr> <tr> <td>RUS</td> <td>1620</td> <td>25930</td> <td>11156</td> <td>14523</td> <td>0</td> <td>0</td> <td>Ruminating in standing position</td> </tr> <tr> <td>MOV</td> <td>6376</td> <td>8437</td> <td>7532</td> <td>17248</td> <td>4846</td> <td>5760</td> <td>Moving</td> </tr> <tr> <td>GRZ</td> <td>2416</td> <td>2199</td> <td>0</td> <td>2707</td> <td>2442</td> <td>7849</td> <td>Grazing</td> </tr> <tr> <td>SLT</td> <td>204</td> <td>0</td> <td>10654</td> <td>0</td> <td>0</td> <td>0</td> <td>Salt licking</td> </tr> <tr> <td>FES</td> <td>6809</td> <td>0</td> <td>0</td> <td>0</td> <td>1125</td> <td>0</td> <td>Feeding in stanchion</td> </tr> <tr> <td>DRN</td> <td>1176</td> <td>0</td> <td>1300</td> <td>0</td> <td>0</td> <td>0</td> <td>Drinking</td> </tr> <tr> <td>LCK</td> <td>0</td> <td>0</td> <td>649</td> <td>297</td> <td>0</td> <td>356</td> <td>Licking</td> </tr> <tr> <td>REL</td> <td>0 </td> <td>360</td> <td>0</td> <td>404</td> <td>0</td> <td>0</td> <td>Resting in lying position</td> </tr> <tr> <td>URI</td> <td>239</td> <td>0</td> <td>383</td> <td>0</td> <td>0</td> <td>0</td> <td>Urinating</td> </tr> <tr> <td>ATT</td> <td>57</td> <td>50</td> <td>0</td> <td>62</td> <td>0</td> <td>197</td> <td>Attacking</td> </tr> <tr> <td>ESC</td> <td>0</td> <td>0</td> <td>0</td> <td>128</td> <td>0</td> <td>0</td> <td>Escaping</td> </tr> <tr> <td>BMN</td> <td>0</td> <td>54</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>Being mounted</td> </tr> <tr> <td>ETC</td> <td>105917</td> <td>103084</td> <td>129297</td> <td>62064</td> <td>53922</td> <td>100571</td> <td>Other behaviors</td> </tr> <tr> <td>BLN</td> <td>151249</td> <td>82599</td> <td>88431</td> <td>111744</td> <td>61544</td> <td>45128</td> <td>Data without video, no label</td> </tr> <tr> <td>Sum</td> <td>311876</td> <td>269772</td> <td>269903</td> <td>224912</td> <td>134904</td> <td>179857</td> <td> </td> </tr> </tbody> </table> <p>Accelerometer sampling rate was set to 25Hz.</p> <p>The data is split into six .csv files which represents each of the 6 cows above. The columns of these files are defined as follows:</p> <table> <thead> <tr> <th scope="col">TimeStamp_UNIX [-]</th> <th scope="col">TimeStamp_JST [-]</th> <th scope="col">AccX [g]</th> <th scope="col">AccY [g]</th> <th scope="col">AccZ [g]</th> <th scope="col">Label [-]</th> </tr> </thead> <tbody> <tr> <td>GPS timestamp in UNIX</td> <td>GPS timestamp in JST</td> <td>X-axis acceleration</td> <td>Y-axis acceleration</td> <td>z-axis acceleration</td> <td>labeled behavior</td> </tr> </tbody> </table> <p>The gathering of this data with these cows was reviewed and approved by the Institutional Animal Care and Use Committee of Shinshu University.</p> <p><strong>Version History</strong></p> <p>v1.0.0: Release on 24th of September, 2021. First version.</p> <p>v2.0.0: This version. UNIX and Japan Standard Time (JST) time stamps are added for each .csv file of cow1-6. Added explanations of behaviors for ETC and BLN. More information on publications that use this dataset, data logger software that has been developed for this project.</p> <p><strong>Data logger open source software</strong></p> <p>Software developed for the data logger that was used to gather this dataset, Sony's IoT development board SPRESENSE, CXD5602PWBMAIN1. The function of this data logger is to write inertia sensor data along with timestamps. Timestamp data is corrected with GPS signal. Available in Arduino development environment.</p> <p><a href="https://zenodo.org/record/5848608#.YeFF9NHP3Z8">https://zenodo.org/record/5848608#.YeFF9NHP3Z8</a></p> <p><strong>Publications using this dataset</strong></p> <p><a href="https://ieeexplore.ieee.org/abstract/document/9566833">[1] Li, Chao, et al. "Data Augmentation for Inertial Sensor Data in CNNs for Cattle Behavior Classification." IEEE Sensors Letters 5.11 (2021): 1-4.</a></p> <p><a href="https://ieeexplore.ieee.org/abstract/document/9401342">[2] Bartels, Jim, et al. "A 216 microW, 87% Accurate Cow Behavior Classifying Decision Tree on FPGA With Interpolated Arctan2." 2021 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2021.</a></p>
FIGURE 48 in The Higher Classification of the Ant Subfamily Ponerinae (Hymenoptera: Formicidae), with a Review of Ponerine Ecology and Behavior
FIGURE 48. Worker caste of Ponera. Upper specimen: Ponera alpha: lateral and dorsal view of body and full-face view of head (CASENT0172423, April Nobile and www.antweb.org). Lower specimen: Ponera tenuis: lateral view of body and fullface view of head (ANIC32-001503 and CASENT0172429, April Nobile and www.antweb.org); world distribution of Ponera.
FIGURE 49 in The Higher Classification of the Ant Subfamily Ponerinae (Hymenoptera: Formicidae), with a Review of Ponerine Ecology and Behavior
FIGURE 49. Worker caste of Pseudoponera stigma: lateral and dorsal view of body and full-face view of head (CASENT0178180, April Nobile and www.antweb.org); world distribution of Pseudoponera.
FIGURE 46 in The Higher Classification of the Ant Subfamily Ponerinae (Hymenoptera: Formicidae), with a Review of Ponerine Ecology and Behavior
FIGURE 46. Worker caste of Iroponera odax: lateral and dorsal view of body and full-face view of head (holotype, MCZC); world distribution of Iroponera.
FIGURE 42 in The Higher Classification of the Ant Subfamily Ponerinae (Hymenoptera: Formicidae), with a Review of Ponerine Ecology and Behavior
FIGURE 42. Worker caste of Cryptopone rotundiceps: lateral and dorsal view of body and full-face view of head (CASENT0172100, April Nobile and www.antweb.org); world distribution of Cryptopone.
FIGURE 41 in The Higher Classification of the Ant Subfamily Ponerinae (Hymenoptera: Formicidae), with a Review of Ponerine Ecology and Behavior
FIGURE 41. Worker caste of Austroponera castanea: lateral and dorsal view of body and full-face view of head (CASENT0172341, April Nobile and www.antweb.org); world distribution of Austroponera.
FIGURE 40 in The Higher Classification of the Ant Subfamily Ponerinae (Hymenoptera: Formicidae), with a Review of Ponerine Ecology and Behavior
FIGURE 40. Worker caste of Psalidomyrmex procerus: lateral and dorsal view of body and full-face view of head (CASENT0003082, April Nobile and www.antweb.org); world distribution of Psalidomyrmex.
FIGURE 38 in The Higher Classification of the Ant Subfamily Ponerinae (Hymenoptera: Formicidae), with a Review of Ponerine Ecology and Behavior
FIGURE 38. Worker caste of Loboponera basalis: lateral and dorsal view of body and full-face view of head (Ivory Coast, G.Alpert, MCZC); world distribution of Loboponera.
FIGURE 37 in The Higher Classification of the Ant Subfamily Ponerinae (Hymenoptera: Formicidae), with a Review of Ponerine Ecology and Behavior
FIGURE 37. Worker caste of Feroponera ferox: lateral and dorsal view of body and full-face view of head (holotype, CASENT0900369, Will Ericson and www.antweb.org); world distribution of Feroponera.
FIGURE 35 in The Higher Classification of the Ant Subfamily Ponerinae (Hymenoptera: Formicidae), with a Review of Ponerine Ecology and Behavior
FIGURE 35. Worker caste of Centromyrmex brachycola: lateral and dorsal view of body and full-face view of head (CASENT0178344, April Nobile and www.antweb.org); world distribution of Centromyrmex.
FIGURE 34 in The Higher Classification of the Ant Subfamily Ponerinae (Hymenoptera: Formicidae), with a Review of Ponerine Ecology and Behavior
FIGURE 34. Worker caste of Boloponera vicans: lateral and dorsal view of body and full-face view of head (CASENT0401737, April Nobile and www.antweb.org); world distribution of Boloponera.
FIGURE 33 in The Higher Classification of the Ant Subfamily Ponerinae (Hymenoptera: Formicidae), with a Review of Ponerine Ecology and Behavior
FIGURE 33. Worker caste of Thaumatomyrmex ferox: lateral and dorsal view of body and full-face view of head (CASENT0249251, Shannon Hartman and www.antweb.org); world distribution of Thaumatomyrmex.
FIGURE 32 in The Higher Classification of the Ant Subfamily Ponerinae (Hymenoptera: Formicidae), with a Review of Ponerine Ecology and Behavior
FIGURE 32. Worker caste of Simopolta pergandei: lateral and dorsal view of body and full-face view of head (CASENT0178701, April Nobile and www.antweb.org); world distribution of Simopolta.
FIGURE 45 in The Higher Classification of the Ant Subfamily Ponerinae (Hymenoptera: Formicidae), with a Review of Ponerine Ecology and Behavior
FIGURE 45. Worker caste of Emeryopone buttelreepeni: lateral and dorsal view of body and full-face view of head (CASENT0104586, April Nobile and www.antweb.org); world distribution of Emeryopone.
FIGURE 31 in The Higher Classification of the Ant Subfamily Ponerinae (Hymenoptera: Formicidae), with a Review of Ponerine Ecology and Behavior
FIGURE 31. Worker caste of Pachycondyla crassinoda: lateral and dorsal view of body and full-face view of head (CASENT0249138, Ryan Perry and www.antweb.org); world distribution of Pachycondyla.
FIGURE 36 in The Higher Classification of the Ant Subfamily Ponerinae (Hymenoptera: Formicidae), with a Review of Ponerine Ecology and Behavior
FIGURE 36. Worker caste of Dolioponera fustigera: lateral and dorsal view of body and full-face view of head (CASENT0411307, April Nobile and www.antweb.org); world distribution of Dolioponera.
FIGURE 30 in The Higher Classification of the Ant Subfamily Ponerinae (Hymenoptera: Formicidae), with a Review of Ponerine Ecology and Behavior
FIGURE 30. Worker caste of Neoponera villosa: lateral and dorsal view of body and full-face view of head (CASENT0178188, April Nobile and www.antweb.org); world distribution of Neoponera.
FIGURE 29 in The Higher Classification of the Ant Subfamily Ponerinae (Hymenoptera: Formicidae), with a Review of Ponerine Ecology and Behavior
FIGURE 29. Worker caste of Mayaponera constricta: lateral and dorsal view of body and full-face view of head (CASENT0249137, Ryan Perry and www.antweb.org); world distribution of Mayaponera.
FIGURE 26 in The Higher Classification of the Ant Subfamily Ponerinae (Hymenoptera: Formicidae), with a Review of Ponerine Ecology and Behavior
FIGURE 26. Worker caste of Streblognathus peetersi: lateral and dorsal view of body and full-face view of head (CASENT0173636, April Nobile and www.antweb.org); world distribution of Streblognathus.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.