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9,153 results for “behavior”
Neurons for infant social behaviors in the mouse zona incerta
<p><strong>Neurons for infant social behaviors in the mouse zona incerta</strong></p> <p>Repository containing datasets supporting the study.</p> <p>Github link to related analysis code: https://github.com/yxl95/zona_incerta_infant_social_behavior</p>
Knowledge of Social Networks for Health is Associated with COVID-19 Health Protective Behaviors
<p>This is the dataset and stata code for the paper "Knowledge of Social Networks for Health is Associated with COVID-19 Health Protective Behaviors” submitted to Plos One May 1st, 2024.</p>
Data Set for "Alteration's control on frictional behavior and the depth of the ductile shear zone in geothermal reservoirs in volcanic arcs" I: Lesser Antilles Volcanic Arc
<p>Data set for the 48 friction experiments performed for gouge samples (altered andesitic rocks) from the Lesser Antilles used in the manuscript, "Alteration's control on frictional behavior and the depth of the ductile shear zone in geothermal reservoirs in volcanic arcs". This data set can be used in combination with the data set for the Cascades used in the same manuscript (doi:10.5281/zenodo.10964936). This large combined data set (of 108 frictional experiments) represents a unique opportunity to systematically study frictional behaviour in the framework of rate and state. All samples are tested in wet and dry conditions at 10, 30, and 50 MPa with velocity steps and slide-hold-slides. These two data sets have the further advantage of being performed with exactly the same protocol (same run in, same initial gouge thickness, same velocity steps, same hold periods), in the same machine, by the same operator (or by an operator who was trained and supervised by the original operator). </p>
Raw images from: Detecting life by behavior, the overlooked sensitivity of behavioral assays
<p>Raw images of the manuscript entitle: "Detecting life by behavior, the overlooked sensitivity of behavioral assays"</p> <p>Description: Using a magnetotactic bacterial species,<em> Magnetospirillum magneticum</em>, we conduct a lab sensitivity experiment comparing PCR with the hanging drop behavioral assay, using a dilution series.</p> <p>Data:</p> <p>1.-Gel image resulted from the <em>Magnetospirillum magneticum </em>PCR assays. </p> <p>2.-Microphotographs of <em>Magnetospirillum magneticum </em>obtained using the hanging drop technique and serial dilution. </p> <p>3.-Videos 1 to 4.Environmental samples were taken from Agmon Hula lake, (33° 10′ N 35° 60′ E). We used the HDT (see main MS) to morphologically identify magnetotactic bacterial species.</p>
Dautan et al 2024 " Gut-Initiated Alpha Synuclein Fibrils Drive Parkinson's Disease Phenotypes: Temporal Mapping of non-Motor Symptoms and REM Sleep Behavior Disorder"
<p><span>Parkinson’s disease (PD) is characterized by progressive motor as well as less recognized non-motor symptoms that arise often years before motor manifestation, including sleep and gastrointestinal disturbances. Despite the heavy burden on the patient’s quality of life, these non-motor manifestations are poorly understood. To elucidate the temporal dynamics of the disease, we employed a mice model involving injection of alpha-synuclein (αSyn) pre-formed fibrils (PFF) in the duodenum and antrum as a gut-brain model of Parkinsonism. Using anatomical mapping of αSyn PFF propagation and behavioral and physiological characterizations, we unveil a correlation between post-injection time the temporal dynamics of αSyn propagation and non-motor/motor manifestations of the disease. We highlight the concurrent presence of aggregates in key brain regions, expressing acetylcholine or dopamine and their functions in sleep duration, wakefulness, and particularly REM-associated atonia corresponging to REM behavioral disorder-like symptoms. This study presents a novel and in-depth exploration into the multifaceted nature of PD, unraveling the complex connections between α-synucleinopathies, gut-brain connectivity, and the emergence of non-motor phenotypes.</span></p>
Viewing behavior and vertical eye-level light for non-image-forming effects
<p>When considering non-image-forming (NIF) light effects on people, knowing the light vertically at eye-level is necessary. However, people are dynamic in their behavior and constantly change their viewing direction. This means that light measured vertically towards a constant direction might differ from the actual light that reaches people’s eyes. If the difference is large, viewing behavior might need to be included in lighting design measurements and simulations predicting the potential of the light to induce NIF light effects. This dataset was collected during an experiment on the difference between the actual dynamic eye-level light of office workers while seated at a desk (dynamic condition) and light measured statically towards a computer screen (static condition). The dataset was collected to test the hypothesis: "There is a significant and relevant difference between simultaneously measured static and dynamic light conditions in an office environment occupied by one user." It includes measured and simulated light quantities (illuminance, alpha-opic quantities according to CIE S026 and light-driven alertness according to the non-visual direct response model) together with participants' measured face orientation (horizontal and vertical) in an office environment with a single user.</p>
Behavioral economics approach to reduce injectable discontinuation rate in rural Ethiopia
<p>Data used for the study titled "Application of behavioral economics principles to reduce injectable contraceptive discontinuation rate in rural Ethiopia: A stratified-pair, cluster-randomized field study" is reposited here. Data was analyzed using Stata 15.1. The repository includes the data and the Stata do-files that replicates the study results. The study manuscript has been submitted to Gates Open Research. </p>
Behavioral economics approach to reduce injectable discontinuation rate in rural Ethiopia
<p>Data used for the study titled "Application of behavioral economics principles to reduce injectable contraceptive discontinuation rate in rural Ethiopia: A stratified-pair, cluster-randomized field study" is reposited here. Data was analyzed using Stata 15.1. The repository includes the data and the Stata do-files that replicates the study results. The study manuscript has been submitted to Gates Open Research. </p>
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>
GENEActiv accelerometer files collected during the project entitled "Cultures et comportements alimentaires de la jeunesse dans les pays francophones du Pacifique au XXIème siècle: exemple de la Nouvelle-Calédonie" [Eng: "Eating cultures and behaviors of young people in French-speaking Pacific countries in the 21st century: the example of New Caledonia"] (anonymized version - third part)
<p><a title="GENEActiv" href="https://activinsights.com/technology/geneactiv/" target="_blank" rel="noopener">GENEActiv</a> accelerometer .csv files converted with a 1 second epoch from raw GENEActiv .bin files recorded during the project entitled "<strong>Cultures et comportements alimentaires de la jeunesse dans les pays francophones du Pacifique au XXIème siècle: exemple de la Nouvelle-Calédonie</strong>" [en: "<strong>Eating cultures and behaviors of young people in French-speaking Pacific countries in the 21st century: the example of New Caledonia</strong>"]. Devices are 60-Hz triaxial accelerometers.</p> <p>This dataset also contains <strong>participantCharacteristics.csv</strong> that povides basic information about participants and <strong>read_a_binFile_share.R</strong> that is a short R code aiming at converting and saving accelerometer data from .bin files in 1 second epoch .csv files (consider the Methods section).</p> <p>Participant characteristics: 10 to 16 years old students and some parents.</p> <p>Number of participants: 231 (206 adolescents + 25 adults).</p> <p>Year of the study: 2018 - 2019.</p> <p>Place of the study: New Caledonia.</p> <p>The accelerometer .csv files with a 1 second epoch and extracted from raw .bin files are available in open datasets:</p> <ul> <li><a title="Open dataset - first part" href="https://doi.org/10.5281/zenodo.12615468" target="_blank" rel="noopener">anonymized version - first part</a></li> <li><a title="Open dataset - second part" href="https://doi.org/10.5281/zenodo.12638746" target="_blank" rel="noopener">anonymized version - second part</a></li> <li><a title="Open dataset - third part" href="https://doi.org/10.5281/zenodo.12682660" target="_blank" rel="noopener">anonymized version - third part</a></li> </ul> <p>The accelerometer raw .bin files are available in <strong>restricted datasets</strong>:</p> <ul> <li><a title="Restricted dataset - first part" href="https://doi.org/10.5281/zenodo.11594645" target="_blank" rel="noopener">non-anonymized version - first part</a></li> <li><a title="Restricted dataset - second part" href="https://doi.org/10.5281/zenodo.12638965" target="_blank" rel="noopener">non-anonymized version - second part</a></li> <li><a title="Restricted dataset - third part" href="https://doi.org/10.5281/zenodo.12661429" target="_blank" rel="noopener">non-anonymized version - third part</a></li> </ul> <p>Other participant characteristics (age, place of living, cultural community and socio-economic status) are available in a <a title="Information associated with GENEActiv accelerometer files collected during the project entitled "Cultures et comportements alimentaires de la jeunesse dans les pays francophones du Pacifique au XXIème siècle: exemple de la Nouvelle-Calédonie" [en: "Eating cultures and behaviors of young people in French-speaking Pacific countries in the 21st century: the example of New Caledonia"] (non-anonymized information version)" href="https://doi.org/10.5281/zenodo.12195186" target="_blank" rel="noopener">restricted non-anonymized dataset</a>.</p> <p>When using this dataset, please cite the following reference:<br><a title="Wattelez et al. 2025" href="https://doi.org/10.1016/j.dib.2024.111228" target="_blank" rel="noopener">G. Wattelez, S. Frayon, O. Galy, Assessing physical activity/behavior of adolescents living in the Pacific with accelerometer data: 231 GENEActiv records in New Caledonia, Data in Brief 58 (2025) 111228, doi: 10.1016/j.dib.2024.111228</a></p>
GENEActiv accelerometer files collected during the project entitled "Cultures et comportements alimentaires de la jeunesse dans les pays francophones du Pacifique au XXIème siècle: exemple de la Nouvelle-Calédonie" [Eng: "Eating cultures and behaviors of young people in French-speaking Pacific countries in the 21st century: the example of New Caledonia"] (anonymized version - second part)
<p><a title="GENEActiv" href="https://activinsights.com/technology/geneactiv/" target="_blank" rel="noopener">GENEActiv</a> accelerometer .csv files converted with a 1 second epoch from raw GENEActiv .bin files recorded during the project entitled "<strong>Cultures et comportements alimentaires de la jeunesse dans les pays francophones du Pacifique au XXIème siècle: exemple de la Nouvelle-Calédonie</strong>" [en: "<strong>Eating cultures and behaviors of young people in French-speaking Pacific countries in the 21st century: the example of New Caledonia</strong>"]. Devices are 60-Hz triaxial accelerometers.</p> <p>This dataset also contains <strong>participantCharacteristics.csv</strong> that povides basic information about participants and <strong>read_a_binFile_share.R</strong> that is a short R code aiming at converting and saving accelerometer data from .bin files in 1 second epoch .csv files (consider the Methods section).</p> <p>Participant characteristics: 10 to 16 years old students and some parents.</p> <p>Number of participants: 231 (206 adolescents + 25 adults).</p> <p>Year of the study: 2018 - 2019.</p> <p>Place of the study: New Caledonia.</p> <p>The accelerometer .csv files with a 1 second epoch and extracted from raw .bin files are available in open datasets:</p> <ul> <li><a title="Open dataset - first part" href="https://doi.org/10.5281/zenodo.12615468" target="_blank" rel="noopener">anonymized version - first part</a></li> <li><a title="Open dataset - second part" href="https://doi.org/10.5281/zenodo.12638746" target="_blank" rel="noopener">anonymized version - second part</a></li> <li><a title="Open dataset - third part" href="https://doi.org/10.5281/zenodo.12682660" target="_blank" rel="noopener">anonymized version - third part</a></li> </ul> <p>The accelerometer raw .bin files are available in <strong>restricted datasets</strong>:</p> <ul> <li><a title="Restricted dataset - first part" href="https://doi.org/10.5281/zenodo.11594645" target="_blank" rel="noopener">non-anonymized version - first part</a></li> <li><a title="Restricted dataset - second part" href="https://doi.org/10.5281/zenodo.12638965" target="_blank" rel="noopener">non-anonymized version - second part</a></li> <li><a title="Restricted dataset - third part" href="https://doi.org/10.5281/zenodo.12661429" target="_blank" rel="noopener">non-anonymized version - third part</a></li> </ul> <p>Other participant characteristics (age, place of living, cultural community and socio-economic status) are available in a <a title="Information associated with GENEActiv accelerometer files collected during the project entitled "Cultures et comportements alimentaires de la jeunesse dans les pays francophones du Pacifique au XXIème siècle: exemple de la Nouvelle-Calédonie" [en: "Eating cultures and behaviors of young people in French-speaking Pacific countries in the 21st century: the example of New Caledonia"] (non-anonymized information version)" href="https://doi.org/10.5281/zenodo.12195186" target="_blank" rel="noopener">restricted non-anonymized dataset</a>.</p> <p>When using this dataset, please cite the following reference:<br><a title="Wattelez et al. 2025" href="https://doi.org/10.1016/j.dib.2024.111228" target="_blank" rel="noopener">G. Wattelez, S. Frayon, O. Galy, Assessing physical activity/behavior of adolescents living in the Pacific with accelerometer data: 231 GENEActiv records in New Caledonia, Data in Brief 58 (2025) 111228, doi: 10.1016/j.dib.2024.111228</a></p>
Crystallization behavior and structural build-up of palm stearin - wax hybrid fat blends
<p>This dataset was used in the publication <em>"Crystallization behavior and structural build-up of palm stearin - wax hybrid fat blends"</em>. An overview of the abbreviations and the dataset can be found below.</p>
Data associated with "Microbiota-derived metabolites inhibit Salmonella virulent subpopulation development by acting on single-cell behaviors"
<p>Data used for the publication Microbiota-derived metabolites inihibit Salmonella virulent subpopulation development by acting on single-cell behaviors. </p> <p> </p> <p>all_hi_2307202.csv Single-cell quantifications of Salmonella SPI-1 reporter cells grown in the presence of SCFAs.</p> <p>all_no_2307202.csv Single-cell quantifications of Salmonella SPI-1 reporter cells grown in the absence of SCFAs.</p> <p>odmeasurements.csv OD measurements of plate-reader assays of Salmonella SPI-1 reporter cells and controls grown in a range of SCFA conditions. </p> <p>gfpmeasurements.csv GFP measurements of plate-reader assays of Salmonella SPI-1 reporter cells and controls grown in a range of SCFA conditions. </p>
Social networks and transformative behaviors in a grassland social-ecological system
<p>Dataframe for analysis presented in Nesbitt et al.'s <span>Social networks and transformative behaviors in a grassland social-ecological system published in People and Nature. Dataframe includes responses from an ego network survey administered to Nebraska (USA) ranchers in 2021. </span></p> <p><span>Metadata describes each variable in further detail including the question number from the survey.</span></p> <p> </p>
Human Behavioral Reaction Collection
<p>This dataset collects human behavioral reactions to robotic failures. This data was recorded from a user study and has been processed for anonymization. The dataset.csv contains human responses in terms of facial emotional values of the participants for the robot failures as they collaborated with a Baxter robot in a HRC task. The task details are defined in the papers:</p> <div> <h2>Citation</h2> </div> <p>If you use this dataset, please cite the following papers:</p> <ol> <li>P. Khanna, E. Yadollahi, M. Bj¨orkman, I. Leite, and C. Smith, “Effects of explanation strategies to resolve failures in human-robot collaboration,” in IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 2023, pp. 1829–1836</li> <li>P. Khanna, E. Yadollahi, M. Bj¨orkman, I. Leite, and C. Smith, “User study exploring the role of explanation of failures by<br>robots in human robot collaboration tasks,” in The Imperfectly Relatable Robot: An interdisciplinary workshop on the role of failure in HRI, Stockholm, Sweden, Mar. 2023. [Online]. Available: https://doi.org/10.48550/arXiv.2303.16010</li> </ol> <p>@misc{khanna2023userstudyexploringrole,<br> title={User Study Exploring the Role of Explanation of Failures by Robots in Human Robot Collaboration Tasks}, <br> author={Parag Khanna and Elmira Yadollahi and Mårten Björkman and Iolanda Leite and Christian Smith},<br> year={2023},<br> eprint={2303.16010},<br> archivePrefix={arXiv},<br> primaryClass={cs.RO},<br> url={https://arxiv.org/abs/2303.16010}, <br>}</p> <p> </p> <p>@misc{khanna2025reflexdatasetmultimodaldataset,</p> <p> title={REFLEX Dataset: A Multimodal Dataset of Human Reactions to Robot Failures and Explanations}, <br> author={Parag Khanna and Andreas Naoum and Elmira Yadollahi and Mårten Björkman and Christian Smith},<br> year={2025},<br> eprint={2502.14185},<br> archivePrefix={arXiv},<br> primaryClass={cs.RO},<br> url={https://arxiv.org/abs/2502.14185}, <br>}</p> <p> </p>
Dataset: Effect of decynium-22 on zebrafish anxiety-like behavior
<p>Data for the research project "Effect of decynium-22 on zebrafish anxiety-like behavior", collected at Laboratório de Neurociências e Comportamento "Frederico Guilherme Graeff", Faculdade de Psicologia, Universidade Federal do Sul e Sudeste do Pará.</p>
Supplemental data from: Nature or nurture: A genetic basis for the behavioral selection of depth in siscowet and lean lake charr (Salvelinus namaycush) ecomorphs
<p>These files contain the raw depth and temperature sensor data from siscowet and lean lake charr (<em>Salvelinus namaycush</em>) ecomorphs tagged with pop-up satellite archival tags (PSATs). These fish were produced from wild gametes taken from Lake Superior and reared in a common garden study for nine years and then tagged with PSATs and released in southern Lake Superior. The dataset is supplemental to:</p> <p>Goetz, F., Sitar, S., Seider, M., and Jasonowicz, A. 2022. Nature or nurture: A genetic basis for the behavioral selection of depth in siscowet and lean lake charr (<em>Salvelinus namaycush</em>) ecomorphs. Canadian Journal of Fisheries and Aquatic Sciences. (in press).</p> <p><strong>Data description for metadata.csv:</strong></p> <p>This file contains the metadata associated with each tag deployment. This includes biological data as well as key mission paramters.</p> <table> <thead> <tr> <td>Column</td> <td>Type</td> <td>Description</td> </tr> </thead> <tbody> <tr> <td>mission_id</td> <td>integer</td> <td>mission identifier</td> </tr> <tr> <td>tag_sn</td> <td>integer</td> <td>tag serial number</td> </tr> <tr> <td>ecotype</td> <td>string</td> <td>lake trout ecotype</td> </tr> <tr> <td>release_date</td> <td>string</td> <td>date of tag release</td> </tr> <tr> <td>length_mm</td> <td>float</td> <td>total length in mm</td> </tr> <tr> <td>weight_g</td> <td>float</td> <td>weight in g</td> </tr> <tr> <td>lipid</td> <td>float</td> <td>lipid level meadured by Distell fatmeter set in research mode</td> </tr> <tr> <td>release_site</td> <td>string</td> <td>release site (deep or shallow site)</td> </tr> <tr> <td>sampling_rate</td> <td>string</td> <td>sampling interval of tag (format=HH:MM:SS)</td> </tr> <tr> <td>mission_end_utc</td> <td>datetime</td> <td>programmed tag pop off date and time in UTC time (format=YYYY-MM-DD HH:MM:SS)</td> </tr> <tr> <td>notes</td> <td>string</td> <td>notes and comments</td> </tr> </tbody> </table> <p> </p> <p><strong>Data description for the raw sensor data files:</strong></p> <p>The raw sensor data is found in the files that are prefixed with "raw-sensor-data". The data for each tag is in contained in a seperate file and the files are named as follows "raw-sensor-data-{<em><strong>mission_identifier</strong></em>}-{<em><strong>tag_serial_number</strong></em>}.csv".</p> <table> <thead> <tr> <td>Column</td> <td>Type</td> <td>Description</td> </tr> </thead> <tbody> <tr> <td>mission_id</td> <td>integer</td> <td>mission identifier</td> </tr> <tr> <td>tag_sn</td> <td>integer</td> <td>tag serial number</td> </tr> <tr> <td>timestamp_utc</td> <td>datetime</td> <td>timestamp of sensor reading (format=YYYY-MM-DD HH:MM:SS)</td> </tr> <tr> <td>depth_m</td> <td>string</td> <td>depth in meters</td> </tr> <tr> <td>temperature_c</td> <td>string</td> <td>temperature in degrees celcius</td> </tr> </tbody> </table>
Data to support Whitney JL, Coleman RR, Deakos MH "Genomic evidence indicates small island-resident populations and sex-biased behaviors of Hawaiian Reef Manta Rays"
<p>Datasets supporting the manuscript: Whitney JL, Coleman RR, Deakos MH "Genomic evidence indicates small island-resident populations and sex-biased behaviors of Hawaiian Reef Manta Rays". <em>BMC Ecology and Evolution </em><strong>23</strong>, 31 (2023). https://doi.org/10.1186/s12862-023-02130-0</p> <p>Nuclear data:</p> <p>"Mobula-alfredi_nuclear_reference_RAD_contigs.fasta" is a fasta of 359,751 contigs that serve as the reference for nuclear alignment of genotypes to RAD loci. Contigs begin and end with GATC cut site.</p> <p>Mobula-alfredi_nuclear_all_2048snps_38genotypes.vcf is a VCF file with all 2048 nuclear SNPs in final filtered SNP dataset. 38 genotypes are included from Maui Nui and Hawaii Island. This 2048 SNPs includes both 2038 neutral and 10 outlier SNPs. </p> <p>Mobula-alfredi_nuclear_neutral_2038snps_38genotypes.vcf is a VCF file with 2038 neutral nuclear SNPs genotyped in 38 individuals from Maui Nui and Hawaii Island. </p> <p>Mobula-alfredi_nuclear_outliers_10snps_38genotypes.vcf is a VCF file with 10 outlier SNPs genotyped in 38 individuals from Maui Nui and Hawaii Island. </p> <p>Structure (.str) files are also provided in addition to VCFs. In all files Population prefixes M=Maui Nui and K=Hawaii Island. </p> <p>Mitochondrial data:</p> <p>Mobula-alfredi_mitogenome_34haplotypes_9sites_min4x.vcf is a VCF file with 9 variant sites across the mitogenome haplotyped in 34 individuals from Maui Nui and Hawaii Island. </p> <p>Mobula-alfredi_mitogenome_34haplotypes_allsites_min4x.fasta is a FASTA file with whole mitogenomes aligned to OP562409 [https://www.ncbi.nlm.nih.gov/nuccore/OP562409]. Sites with less than 4x coverage were masked with Ns. </p> <p>Mobula-alfredi_mitogenome_reference_OP562409.fasta is a FASTA file containing the <em>Mobula alfredi</em> reference mitogenome OP562409 [https://www.ncbi.nlm.nih.gov/nuccore/OP562409].</p> <p> </p>
Evolution of left-right asymmetry in the sensory system and foraging behavior during adaptation to food-sparse cave environments
<p>Laterality in relation to behavior and sensory systems is found commonly in a variety of animal taxa. Despite the advantages conferred by laterality (e.g., the startle response and complex motor activities), little is known about the evolution of laterality and its plasticity in response to ecological demands. In the present study, a comparative study model, the Mexican tetra (<em>Astyanax mexicanus</em>), composed of two morphotypes, i.e., riverine surface fish and cave-dwelling cavefish, was used to address the relationship between environment and laterality. The use of a machine learning-based fish posture detection system and sensory ablation revealed that the left cranial lateral line significantly supports one type of foraging behavior, i.e., vibration attraction behavior, in one cave population. Additionally, left-right asymmetric approaches toward a vibrating rod became symmetrical after fasting in one cave population but not in the other populations. Based on these findings, we propose a model explaining how the observed sensory laterality and behavioral shift could help adaptation in terms of the tradeoff in energy gain and loss during foraging according to differences in food availability among caves.</p> <p>This repository contains all of raw videos used in this study.</p> <p>Please let us know if you have any question on these videos</p>
Data for: Mercury contamination challenges the behavioral response of a keystone species to Arctic climate change
<p>Combined effects of multiple, climate change-associated stressors are of mounting concern, especially in Artic ecosystems. Elevated mercury (Hg) exposure in Arctic animals could affect behavioural responses to changes in foraging landscapes linked to climate change, generating interactive effects on behaviour and population resilience. We investigated this hypothesis in the little auk (<em>Alle alle</em>), a keystone Artic seabird. We compiled behavioural data using accelerometers, and quantified blood mercury and environmental conditions (sea surface temperature (SST), sea ice coverage (SIC)) across multiple years. These datasets contain the behavioral, blood Hg and environmental data (SST, SIC) used in our analyses. Details about the datasets are found in the accompanying word document.</p>
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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.