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9,153 results for “behavior”
Dataset for "Large scale patterns and drivers of the diving behavior of gill-breathing large pelagic predators"
<p>This dataset includes all supporting data and scritps to generate figure panels in the paper "Large scale patterns and drivers of the diving behavior of gill-breathing large pelagic predators" (A. Nuno, J. Guiet, B. Baranek and D. Bianchi)</p>
MINIATURA 6 Housing decisions, behavioral aspects of choices, price expectations and anchoring effect - Polsh case study
<p>The data was created as a result of a survey conducted in accordance with the guidelines: - the survey questionnaire consisted of approximately 30 questions and a form, - the surveyed population was defined as 1,000 households living in a large Polish city (over 450,000 inhabitants), quota selection based on the number of city inhabitants, - CAWI method (online), - completion date: 1 week. The survey was parameterized. Part of the sample is a control trial, part is an experimental trial.</p><p>Dane powstały w wyniku przeprowadzonej ankiety zgodnie z wytycznymi: - kwestionariusz badania składał się z ok. 30 pytań oraz metryczki, - badana zbiorowość określono na 1000 gospodarstw domowych zamieszkałych w dużym mieście Polski (powyżej 450 tys. ludności), dobór kwotowy na podstawie liczby mieszkańców miast, - badanie metodą CAWI (on-line), - termin realizacji 1 tydzień. Ankieta byłą sparametryzowana. Część próby stanowi próba kontrolna, część próba eksperymentalna. </p>
CBS - Charging Behavior Survey
<p>Two online surveys, one for BEV, and one for truck drivers, were set up and yielded a total of 348 responses, which are all included in this data set. For the BEV survey, stickers with QR codes linking to the respective online survey were attached to 56 charging stations in Munich and its surroundings. The truck survey was sent to professional truck drivers known to the Chair of Automotive Technology of the Technical University of Munich. </p><p>The data repository available contains three data file types, all in CSV format:</p><ol><li>The surveys' complete results are provided in both German (original) and English (translated) language (<i>bev_survey_ger.csv</i>, <i>bev_survey_eng.csv</i>, <i>truck_survey_ger.csv</i>, <i>truck_survey_eng.csv</i>).</li><li>Question encodings are given by <i>bev_question_encoding.csv</i> and <i>truck_question_encoding.csv</i>. These encoding files contain the original question texts, their English translations, the corresponding column name mapping to column names in the survey data CSV, and the data type per question.</li><li><i>bev_response_translation.csv </i>and <i>truck_response_translation.csv </i>comprise all original response options and their English translations.</li></ol><p>Additionally, we provide two Python files for an easy import of the data sets. </p><p>The data was collected from December 2022 to February 2023. </p>
Dataset and supplementary files - Behavioral response of chub (Squalius cephalus), barbel (Barbus barbus) and brown trout (Salmo trutta) to pulsed direct current electric fields and resulting optimal waveform for use at electrified bar racks
<p><strong>Behavior Library.zip: </strong>For each species and behavior observed during the experiments an exemplary video is provided. </p><p><strong>Behavior_all.pdf: </strong>Additional plots showing the thresholds for the first time each individual behavior was observed for all fish species and tested waveforms</p><p><strong>Species.pdf: </strong>Additional plot allowing direct comparison of observed thresholds for the tested species when subjected to different waveforms. </p><p><strong>data.csv:</strong> All data necessary to reevaluate the conducted experiments. The dataset consists of</p><ul><li>Experiment ID</li><li>waveform - indicating the set of electrical parameters used</li><li>fish species and fish id </li><li>behavior - observed behavior</li><li>time from and time to - time in s after the start of the experiment that a behavior was started and ended respectively</li><li>type - point or interval referring to whether a behavior is considered instantaneous or continuous</li><li>voltage - applied voltage at the start of the given behavior</li><li>experiment_timestamp - date and time of the start of the experiment</li><li>breathing rate start - breathing rate at the start of the experiment</li><li>water conductivity - water conductivity at a reference temperature of 25°C [muS/cm]</li><li>water temperature [°C]</li><li>breathing rate end - breathing rate at the end of the experiment</li><li>meta behavior - assigned category of meta behavior based on the observe behavior category</li><li>standard length, total length and height - standard length, total length and height of the tested fish in [mm]</li><li>volume - calculated fish volume based on the measured length and height and an assumed elliptical form of the fish</li><li>Fangdatum - Date of catch</li><li>t.Pulse - pulse length of the tested waveform [ms]</li><li>Frequency - Frequency of the tested waveform</li><li>N.Pulses.Group - Number of pulses per group of pulses for the waveform pattern</li><li>t.Gap - time between two pulses within a group of pulses [ms]</li><li>DutyCycle - Percentage of time current is flowing for a given waveform. Calculated based on the waveform parameters</li><li>usage - first, second or third time a fish was used in the experiments. </li><li>field strength - field strength at the time of this behavior calculated based on the applied voltage</li><li>c_w ambient water conductivity [muS/cm]</li><li>p_d - power density calculated based on the field strength and the ambient water conductivity</li><li>p_t - power transferred to the fish calculated based on the field strength, the ambient water conductivity and an assumed conductivity of the fish of 115 muS/cm</li></ul><p> </p><p> </p>
Reward perseveration is shaped by GABAA-mediated dopamine pauses: Behavior Data
<p>Behavior assay data used to generate the following figures in the paper "Reward perseveration is shaped by GABAA-mediated dopamine pauses":</p> <ul> <li>Figure 2, all panels</li> <li>Figure 4, all panels</li> <li>Ext. Fig 3, panels a-c, e, f</li> <li>Ext. Fig 5, all panels</li> </ul> <p>Specifics of the data:</p> <ul> <li>ddHTP.zip contains the control raw data in MATLAB files</li> <li>HTP.zip contains the experimental raw data in MATLAB files</li> <li>The various Behavior_summary_data.csv files contain the subject details and extracted analyses, as detailed in each title.</li> <li>ddHTP_other_DART_pilots.zip and the corresponding .csv file hold the raw and analyzed data for the ddHTP controls of ongoing experiments shown in Ext. Fig 5c. </li> </ul>
Reward perseveration is shaped by GABAA-mediated dopamine pauses: Histology from Behavior Data Mice
<p>Histology images for the experimental +HTP behavior mice in the dataset 10.5281/zenodo.10903566. This histology was used to generate the following figures in the paper "Reward perseveration is shaped by GABAA-mediated dopamine pauses":</p> <ul> <li>Ext. Fig 3, panel d</li> </ul> <p>Specifics of the data:</p> <ul> <li>HTP_Histo_Cohort_VX.zip contains the raw images (Olympus .vsi files) and drawn ROIs and fluorescence analysis (MATLAB files) for each mouse/tissue section in cohort VX</li> <li>HTP_Histo_Cohort_VZ.zip contains the raw images (Olympus .vsi files) and drawn ROIs and fluorescence analysis (MATLAB files) for each mouse/tissue section in cohort VZ</li> <li>HTP_Histo_Cohort_VAJ.zip contains the raw images (Olympus .vsi files) and drawn ROIs and fluorescence analysis (MATLAB files) for each mouse/tissue section in cohort VAJ</li> <li>HTP_Histo_Cohort_VAK.zip contains the raw images (Olympus .vsi files) and drawn ROIs and fluorescence analysis (MATLAB files) for each mouse/tissue section in cohort VAK</li> <li> Behavior_summary_data_HTP_Histology.csv contains the summarized fluorescence quantification per animal</li> </ul>
A Case-Control Study to Measure Behavioral Risks of Malware Encounters in Organizations
<p>The behavior of enterprise users (e.g. browsing at night or visiting gambling sites) is a potential factor that might increase the chances of malware encounters (e.g. coinminers vs ransomware) on the field. This dataset report the aggregated results of a case-control study on telemetry data collected by Trend Micro, a global cybersecurity vendor, to identify users’ behavioral characteristics that can be used to differentiate cybersecurity risks profiles.</p>
Bio-logger Ethogram Benchmark: A benchmark for computational analysis of animal behavior, using animal-borne tags
<p>This repository contains the datasets and experiment results presented in our <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a>:</p> <blockquote> <p>B. Hoffman, M. Cusimano, V. Baglione, D. Canestrari, D. Chevallier, D. DeSantis, L. Jeantet, M. Ladds, T. Maekawa, V. Mata-Silva, V. Moreno-González, A. Pagano, E. Trapote, O. Vainio, A. Vehkaoja, K. Yoda, K. Zacarian, A. Friedlaender, "A benchmark for computational analysis of animal behavior, using animal-borne tags," 2023.</p> </blockquote> <p>Standardized code to implement, train, and evaluate models can be found at <a href="https://github.com/earthspecies/BEBE/">https://github.com/earthspecies/BEBE/</a>. </p> <p>Please note the licenses in each dataset folder.</p> <p><strong>Zip folders beginning with "formatted":</strong> These are the datasets we used to run the experiments reported in the benchmark paper. </p> <p><strong>Zip folders beginning with "raw": </strong>These are the unprocessed datasets used in BEBE. Code to process these raw datasets into the formatted ones used by BEBE can be found at <a href="https://github.com/earthspecies/BEBE-datasets/">https://github.com/earthspecies/BEBE-datasets/</a>.</p> <p><strong>Zip folders beginning with "experiments": </strong>Results of the cross-validation experiments reported in the paper, as well as hyperparameter optimization. Confusion matrices for all experiments can also be found here. Note that dt, rf, and svm refer to the feature set from Nathan et al., 2012.</p> <p><em>Results used in Fig. 4 of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a> (deep neural networks vs. classical models)</em><br>{dataset}_ harnet_nogyr<br>{dataset}_CRNN<br>{dataset}_CNN<br>{dataset}_dt<br>{dataset}_rf<br>{dataset}_svm<br>{dataset}_wavelet_dt<br>{dataset}_wavelet_rf<br>{dataset}_wavelet_svm</p> <p><em>Results used in Fig. 5D of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a> (full data setting)<br></em>If dataset contains gyroscope (HAR, jeantet_turtles, vehkaoja_dogs):<br>{dataset}_harnet_nogyr<br>{dataset}_harnet_random_nogyr<br>{dataset}_harnet_unfrozen_nogyr<br>{dataset}_RNN_nogyr<br>{dataset}_CRNN_nogyr<br>{dataset}_rf_nogyr<br><br>Otherwise:<br>{dataset}_harnet_nogyr<br>{dataset}_harnet_unfrozen_nogyr<br>{dataset}_harnet_random_nogyr<br>{dataset}_RNN_nogyr<br>{dataset}_CRNN<br>{dataset}_rf</p> <p><em>Results used in Fig. 5E of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a> (reduced data setting)<br></em>If dataset contains gyroscope (HAR, jeantet_turtles, vehkaoja_dogs):<br>{dataset}_harnet_low_data_nogyr<br>{dataset}_harnet_random_low_data_nogyr<br>{dataset}_harnet_unfrozen_low_data_nogyr<br>{dataset}_RNN_low_data_nogyr<br>{dataset}_wavelet_RNN_low_data_nogyr<br>{dataset}_CRNN_low_data_nogyr<br>{dataset}_rf_low_data_nogyr</p> <p>Otherwise:<br>{dataset}_harnet_low_data_nogyr<br>{dataset}_harnet_random_low_data_nogyr<br>{dataset}_harnet_unfrozen_low_data_nogyr<br>{dataset}_RNN_low_data_nogyr<br>{dataset}_wavelet_RNN_low_data_nogyr<br>{dataset}_CRNN_low_data<br>{dataset}_rf_low_data<br><br></p> <p><strong>CSV files</strong>: we also include summaries of the experimental results in experiments_summary.csv, experiments_by_fold_individual.csv, experiments_by_fold_behavior.csv. </p> <p><em>experiments_summary.csv - results averaged over individuals and behavior classes<br></em>dataset (str): name of dataset<br>experiment (str): name of model with experiment setting <br>fig4 (bool): True if dataset+experiment was used in figure 4 of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fig5d (bool): True if dataset+experiment was used in figure 5d of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fig5e (bool): True if dataset+experiment was used in figure 5e of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>f1_mean (float): mean of macro-averaged F1 score, averaged over individuals in test folds<br>f1_std (float): standard deviation of macro-averaged F1 score, computed over individuals in test folds<br>prec_mean, prec_std (float): analogous for precision<br>rec_mean, rec_std (float): analogous for recall<em><br><br>experiments_by_fold_individual.csv - results per individual in the test folds<br></em>dataset (str): name of dataset<br>experiment (str): name of model with experiment setting <br>fig4 (bool): True if dataset+experiment was used in figure 4 of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fig5d (bool): True if dataset+experiment was used in figure 5d of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fig5e (bool): True if dataset+experiment was used in figure 5e of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fold (int): test fold index<br>individual (int): individuals are numbered zero-indexed, starting from fold 1<br>f1 (float): macro-averaged f1 score for this individual<br>precision (float): macro-averaged precision for this individual<br>recall (float): macro-averaged recall for this individual<em><br></em></p> <p><em>experiments_by_fold_behavior.csv - results per behavior class, for each test fold<br></em>dataset (str): name of dataset<br>experiment (str): name of model with experiment setting <br>fig4 (bool): True if dataset+experiment was used in figure 4 of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fig5d (bool): True if dataset+experiment was used in figure 5d of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fig5e (bool): True if dataset+experiment was used in figure 5e of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fold (int): test fold index<br>behavior_class (str): name of behavior class<br>f1 (float): f1 score for this behavior, averaged over individuals in the test fold<br>precision (float): precision for this behavior, averaged over individuals in the test fold<br>recall (float): recall for this behavior, averaged over individuals in the test fold<br>train_ground_truth_label_counts (int): number of timepoints labeled with this behavior class, in the training set<em><br></em></p>
CREATTIVE3D multimodal dataset of user behavior in virtual reality
<p>In the context of the <a href="https://project.inria.fr/creattive3d/">ANR CREATTIVE3D</a> project, we join the expertise of computer science, neuroscience, and clinical practitioners, with the aim to analyze the impact that a simulated low-vision condition has on user navigation behavior in complex road crossing scenes: a common daily situation where the difficulty to access and process visual information (e.g., traffic lights, approaching cars) in a timely fashion can lead to serious consequences on a person's safety and well-being. As a secondary objective, we also aim to investigate the potential role virtual reality could play in rehabilitation and training protocols for low-vision patients.</p> <p>This dataset contains the data as part of the study described in <a href="https://hal.science/hal-04102737">An Integrated Framework for Understanding Multimodal Embodied Experiences in Interactive Virtual Reality</a>.</p> <p>The dataset is metadata for the pre-print <a href="https://inria.hal.science/hal-04429351">Exploring, walking, and interacting in virtual reality with simulated low vision: a living contextual dataset</a></p> <p>To use this dataset, please cite:</p> <blockquote> <pre>@unpublished{wu:hal-04429351, TITLE = {{Exploring, walking, and interacting in virtual reality with simulated low vision: a living contextual dataset}}, AUTHOR = {Wu, Hui-Yin and Robert, Florent Alain Sauveur and Gallo, Franz Franco and <br> Pirkovets, Kateryna and Quere, Cl{\'e}ment and Delachambre, Johanna and <br> Ramano{\"e}l, Stephen and Gros, Auriane and Winckler, Marco and Sassatelli, Lucile and <br> Hayotte, Meggy and Menin, Aline and Kornprobst, Pierre}, URL = {https://inria.hal.science/hal-04429351}, NOTE = {working paper or preprint}, YEAR = {2023}, MONTH = Dec, KEYWORDS = {Virtual reality ; Dataset ; Context ; Low vision ; 3D environments ; User study}, PDF = {https://inria.hal.science/hal-04429351/file/2023_CREATTIVE3D_dataset_arxiv_.pdf}, HAL_ID = {hal-04429351}, HAL_VERSION = {v1}, }<br><br>@inproceedings{robert2023integrated, title={An integrated framework for understanding multimodal embodied experiences in interactive virtual reality}, author={Robert, Florent and Wu, Hui-Yin and Sassatelli, Lucile and Ramanoel, Stephen and <br> Gros, Auriane and Winckler, Marco}, booktitle={Proceedings of the 2023 ACM International Conference on Interactive Media Experiences}, pages={14--26}, year={2023} }</pre> </blockquote> <h3> </h3> <h3>Versions</h3> <p>2024-12-18: Updated readme with description of labels, columns, and suggestions on how to start exploring the dataset. We also provide the questionnaire responses and observation notes in English (questionnaire_translation_EN.csv).</p>
Open data repository, Knab et al., Prediction of stroke outcome in mice based on non-invasive MRI and behavioral testing
<p><strong>Open data repository, Knab et al., Prediction of stroke outcome in mice based on non-invasive MRI and behavioral testing</strong></p> <p><strong>Latest version of files: repository_v2.0.zip, Behavior Data_v2.0.xlsx and MRI IDs Testing&Replication Cohort.xlsx (please ignore repository.zip)</strong></p> <p>Open data repository Knab et al. Prediction of stroke outcome in mice based on non-invasvive MRI and behavioral testing</p> <p>Open code and documentation of prediction models available via <a href="https://github.com/major-s/mouse-mcao-outcome-predictor">https://github.com/major-s/mouse-mcao-outcome-predictor</a></p> <p><strong>Content:</strong></p> <p>README.txt</p> <p>This information</p> <p><strong>dat</strong></p> <p>Contains MRI data in NIFTI format and secondary data from atlas registration. For documentation of atlas registration files see https://pubmed.ncbi.nlm.nih.gov/28829217/<br>Files used for the manuscript:<br>t2.nii: t2 weighted image acquired 24 h post stroke<br>masklesion.nii: manually delineated lesion<br>x_masklesion.nii: lesion in atlas space<br>ix_ANO.nii: Allen brain atlas in native space (i.e. matching t2.nii)<br>Lesion volume was calculated by volume of voxels unequal 0 in x_masklesion.nii<br>Overlap of regions defined by ix_ANO.nii with masklesion.nii were used for calculating percent damage in each atlas region</p> <p><strong>prediction_models</strong></p> <p>Contains separated training and test data as xlsx and csv files with lesion volumes in cubic mm of the Allen brain atlas space, percent damage per atlas region and behavioral data. The training data was used as input for training prediction models in MATLAB, the results were created using the test data.<br>The files have following sturcture:<br>Column 1: animal ID<br>Columns 2-537: MRI regions (column title corresponds to the region number as used in the Allen common coordinate framework)<br>Column 538: lesion volume<br>Column 539: initial performance (subacute deficit) = mean performance/deficit on days 2-6<br>Column 540: mean performance/deficit on days 2-6 = initial performance (subacute deficit) - this column equals column 539 but has different header which was used to train the residual from initial deficit<br>Column 541: residual performance/deficit<br>Column 542: test or training group<br>Consecutive rows contain data for each animal specified by the animal id</p> <p>The repository also contains all trained models, prediction results for the test data and tables with resulting median absolute error (MedAE) and 5th, 25th, 75th and 95 absolute error quantiles for each model.<br>The model files end with '_models.mat' and contain 50 independently trained models each. Each model version is specified by number 1-50.<br>The result files end with '_test_results.mat' or '_test_results.xlsx', files with MedAE and quantiles end with '_test_errors.xlsx' or '_test_errors.csv. The common part of filenames specifies the used paradigm<br>Folder 'subacute deficit prediction' contains:<br> - initial_performance_from_lesion_volume: prediction of subacute deficit using lesion volume<br> - initial_performance_from_segmented_mri: prediction of subacute deficit using segmented mri<br>Folder 'long-term outcome prediction' contains:<br> - lesion_volume: prediction of residual deficit using lesion volume<br> - segmented_mri: prediction of residual deficit using segmented_mri<br> - initial_performance: prediction of residual deficit using subacute deficit<br>Folder 'mri_inc_oob_imp' contains models trained using increasing number of mri segments sorted according to the out-of-bag importance. The number of used segments is given in the file name. The models, results and errors are separated in subfolders.</p> <p>Files with equal file name and different extension always contain the same data</p> <p><strong>templates</strong><br>Allen atlas, template, brain mask, hemisphere masks, tissue probability masks in NIFTI format including annotations of region IDs and parameter.m file for use in MATLAB toolbox ANTx2<br> </p>
Origin of relaxor behavior in barium titanate based lead-free relaxors
<p>It is well known that disordered relaxor ferroelectrics exhibit local polar correlations. The origin of localized fields that disrupt long range polar order for different substitution types, however, is unclear. Currently, it is known that substituents of the same valence as Ti4+ at the B-site of barium titanate lattice produce random disruption of Ti-O-Ti chains that induces relaxor behavior. On the other hand, investigating lattice disruption and relaxor behavior resulting from substituents of different valence at the B-site is more complex due to the simultaneous occurrence of charge imbalances and displacements of the substituent cation. The existence of an effective charge mediated mechanism for relaxor behavior appearing at low (<10%) substituent contents in heterovalent modified barium titanate ceramics is evinced from this data, which underpin the publication by the same name currently in press by Advanced Electronic Materials. These results will add credits to the current understanding of relaxor behavior in chemically modified ferroelectric materials and also acknowledge the critical role of defects (such as cation vacancies) in lattice disruption, paving the way for chemistry-based materials design in the field of dielectric and energy storage applications</p>
Bark Beetle Behavioral Response to 4-Allylanisole
<p><strong>Experiment 1: </strong>Experiment 1 was established in May 2018 and was a dose response study that evaluated the behavioral response of southern pine beetle (<em>Dendroctonus frontalis</em>), black turpentine beetle (<em>Dendroctonus terebrans</em>), and clerid predator beetles (<em>Thanasimus dubius</em>) to 4-allylanisole when combined with bark beetle pheromone components and a demonstrated host-produced synergist (<em>alpha</em>-pinene). </p> <p>This experiment contained four different collection dates, four sites in Oconee National Forest in Georgia, four traps per site, and four different treatments/lure combinations.</p> <p>Treatments: 1) Control with pheromone components (frontalin and brevicomin) + <em>alpha</em>-pinene; 2) pheromone components + <em>alpha</em>-pinene + low release rate of 4-allylanisole (4.8 mg/day) (LOW4AA); 3) pheromone components + <em>alpha</em>-pinene + medium release rate of 4-allylanisole (48 mg/day) (MED4AA); and 4) pheromone components + <em>alpha</em>-pinene + high release rate of 4-allylanisole (500 mg/day) (HIGH4AA).</p> <p>Variables in data include date of collection (Date), date of collection with dummy codes for each of the four collection times (Time), collection site (Site), trap number (Trap), lure combination (Treatment), number of southern pine beetle (SPB), number of black turpentine beetles (BTB), and number of clerid predator beetles (Clerids).</p> <p> </p> <p><strong>Experiment 2: </strong>Experiment 2 was established in April 2019 and was a dose response study that assessed the capacity of 4-allylanisole to influence beetle response when combined with attractive bark beetle pheromone components in the absence of other host-produced odors.</p> <p>This experiment contained four different collection dates, four sites in Oconee National Forest in Georgia, four traps per site, and four different treatments/lure combinations.</p> <p>Treatments: 1) Control with only pheromone components (frontalin and brevicomin); 2) pheromone components + low release rate of 4-allylanisole (4.8 mg/day) (LOW4AA); 3) pheromone components + medium release rate of 4-allylanisole (48 mg/day) (MED4AA); and 4) pheromone components + high release rate of 4-allylanisole (500 mg/day) (HIGH4AA).</p> <p>Variables in data include date of collection (Date), date of collection with dummy codes for each of the four collection times (Time), collection site (Site), trap number (Trap), lure combination (Treatment), number of southern pine beetle (SPB), number of black turpentine beetles (BTB), and number of clerid predator beetles (Clerids).</p> <p> </p> <p><strong>Experiment 3: </strong>Experiment 3 assessed the efficacy of 4-allylanisole to enhance the standard lure for <em>D. frontalis </em>and whether the presence of <em>alpha</em>- and <em>beta-</em>pinene and 4-allylanisole simultaneously enhances attraction over either host odor component when present singly<em>. </em></p> <p>Treatments: 1) pheromone components (frontaline and <em>endo</em>-brevicomin (A); 2) pheromone components + 4-allylanisole (B); 3) pheromone components + <em>alpha-/beta-</em>pinene (C); 4) pheromone components + <em>alpha-/beta-</em>pinene + 4-allylanisole (D); 5) pheromone components + turpentine sock (E); and 6) pheromone components + turpentine + 4-allylanisole (F).</p> <p>Variable in the data include date of collection (Date), time of collection which is a dummy code for each collection date (Time), number of days before collection (Days), site (Block), trap number (Trap), lure combination (Treatment), number of male southern pine beetles (Male), number of female southern pine beetles (Female), number of southern pine beetles (SPB), and number of clerid predator beetles.</p> <p> </p> <p>Questions regarding this data can be e-mailed to hmunro@uga.edu.</p>
Dataset of behavioral and neurophysiological data of a virtual sailing task published in: "Providing task instructions during motor training enhances performance and modulates attentional brain networks"
<p>Dataset belonging to the behavioral and neurophysiological data of the publication: "Providing task instructions during motor training enhances performance and modulates attentional brain networks". The two uploaded Zip files contain kinematic and electroencephalographic data of 36 participants for the Obstacle and HorizonTask.</p>
Dataset: An Empirical Analysis of Pool Hopping Behavior in the Bitcoin Blockchain
<p>We provide the first empirical analysis of pool hopping behavior among 15 mining pools throughout Bitcoin's history. Bitcoin mining is a critical activity that keeps the Bitcoin system secure, valid, and stable. Mining pools have emerged as major players that ensure that the Bitcoin system stays secure, valid, and stable. Individual miners join mining pools to benefit from a more stable and predictable income. Many questions remain open regarding how mining pools have evolved throughout Bitcoin's history and when and why miners join or leave mining pools. We propose a heuristic algorithm to extract the payout flow from mining pools and detect the pools' migration of miners. Our results showed that reward rules and pool fees influence miners' decisions to join, change, or exit from a mining pool, thus affecting the dynamics of mining pool market shares. Our analysis provides evidence that mining activity becomes an industry as miners' decisions follow classical economic rationale. </p>
Raw data for: "CalDAG-GEFI mediates striatal cholinergic modulation of dendritic excitability, synaptic plasticity and psychomotor behaviors"
<p>Figure 2. CDGI mediates the M1R modulation of dendritic excitability but not the M1R</p> <p>modulation of somatic excitability.</p> <p>(A and B) Sagittal sections through the brains of CDGI knockout mice in which the direct</p> <p>pathway was visualized (red) in D1-tdTomato mice (A) and the indirect pathway was visualized</p> <p>(green) in D2-GFP mice.</p> <p>(C) Sample somatic voltage changes evoked by 120pA current injections in iSPNs from WT</p> <p>(black) and CDGI-KO (red) before and after bath application of oxo-M (10 µM).</p> <p>(C-D) Current-response curves of iSPNs from WT (B, n=5 cells) and CDGI-KO mice (C, n=7</p> <p>cells). Somatic excitability of iSPNs was similarly enhanced by oxo-M in WT and CDGI-KO.</p> <p>(E) Sample somatic recordings in response to 140pA current injections in dSPNs from WT</p> <p>(black) and CDGI KO (red) before and after bath application of oxo-M (10 µM).</p> <p>(F-H) Current-response curves of dSPNs from WT (E) and CDGI-KO (F) mice (n=4-6).</p> <p>(I) Trains of five EPSPs were evoked by stimulation of glutamatergic afferent fibers at 40 Hz.</p> <p>Oxo-M (10 µM) increased EPSP summation in iSPNs of WT, but not in CDGI-KO or when</p> <p>M1Rs were blocked by M1R antagonist VU0255035 in WT (5 M).</p> <p>(J) Box plot showing the effect of oxoM on synaptic summation. The EPSP5/EPSP1 ratio was</p> <p>increased by oxoM in iSPNs of WT (p = 0.002, Wilcoxon test; n = 10), but not in iSPNs of 27</p> <p>CDGI-KO mice (p = 0.25, n = 9) or in iSPNs of WT mice in the presence of VU0255035 (p =</p> <p>0.69, n = 6).</p> <p>(K) Box plot showing the effect of oxoM on the kinetics of synaptic response. The decay time</p> <p>constant of EPSP5 was significantly increased by oxoM in iSPNs of WT (p = 0.002); but not</p> <p>when CDGI was genetically deleted (p = 0.65) or when M1R was pharmacologically blocked (p</p> <p>= 0.84).</p>
Learning to embed lifetime social behavior from interaction dynamics - Data
<p><strong>Interaction matrices and metadata used in "Learning to embed lifetime social behavior from interaction dynamics"</strong></p> <p>The following files are included:</p> <ul> <li>interactions_bn16_sparse.npz and interactions_bn19_sparse.npz: These are the interaction affinity matrices for the BN16 and BN19 datasets as described in the publication. The data is stored as compressed sparse tensors with time on the first, and the individuals on the second and third dimensions. The data was stored using the <a href="http://sparse.pydata.org">pydata/sparse</a> library 0.9.1</li> <li> <p>alive_bn16.csv and alive_bn19.csv: These files contain the dates of emergence (also corresponding to the dates they were introduced into the colonies) and heuristically determined number days alive for all individuals in the interaction matrices. Death dates were determined using a bayesian changepoint model and the number of daily detections of each individual</p> </li> <li> <p>rhythmicity_bn16.csv and rhythmicity_bn19.csv: These files contain the circadian rhythmicity values used in the evaluation of the method. The circadian rhythmicity is the <span class="math-tex">\(R^2\)</span> value of a sine with a 24 hour period fitted to the individuals' movement velocities over a three day window</p> </li> <li> <p>indices_bn16.csv and indices_bn19.csv: These files contain the mapping between the original marker IDs used during the recording of the data (which has gaps, because not all markers were used) and the sequential indices used in the interaction matrices. These files can therefore be used to look up the original ID of an individual based on it's index in the interaction matrix and vice versa</p> </li> <li> <p>time_spent_on_substrates.csv: This data was used for the mapping from factors to the proportion of time spent on various cell substrates (Figure 5). The positions of the individuals were accumulated by minute, and the column "location_descriptor_count" contains the total number of minutes on the respective day that the individual was detected</p> </li> </ul> <p>See <a href="https://doi.org/10.1101/2020.05.06.076943">10.1101/2020.05.06.076943</a> for more details about the bayesian changepoint model, circadian rhythmicity calculation, and location mapping.</p>
Stimulation of medial amygdala GABA neurons with kinetically different channelrhodopsins yields opposite behavioral outcomes
<p>This dataset continues the dataset accessible by doi 10.5281/zenodo.4311847. The latter also contains all the relevant metadata description.</p>
Intrinsic Temporal Behavior of Titanium Oxide Memristors for Neuromorphic Systems: Raw dataset
<p><strong>Methodology</strong></p> <p>Experiments were conducted on in-house fabricated Pt/TiO<sub>2</sub>/Au structure with thickness of 15/25/20<em>nm</em> respectively. The fabricated structures were characterized using a 16 x 16 probe card mounted on a Cascade Microtech Summit 12000 Prober controlled via a ARC ONE DC I-V control system.</p> <p>Stimulation was conducted over five repeating cycles to demonstrate the behaviour of the resistance over time. Potentiation and depression were triggered by pulses of opposite polarity. The bias could potentially be altered to achieve a desired starting resistance.</p> <p>A total of five repeating cycles were conducted. Each experiment was conducted on a pristine device with 4V programming pulse width of 10ms and inter-pulse of 100ms. A total of 100 pulses were applied per direction. Each identical programming pulse was followed by a 0.1V non-invasive reading pulse for a fixed duration.</p> <p>Attached csv file include resistive values for three devices along with their corresponding pulse train and time.</p>
Fire behavior dataset of Campos Amazônicos Fire Experiment (Amazonas, Brazil)
<p>This dataset presents fire behavior data related to the Campos Amazônicos Fire Experiment (CAFE) (Amazonas, Brazil) project. Located within a protected area within the largest enclave of tropical savanna in the Southern Amazon, CAFE comprises a careful and systematic experimental design that was conceived from the start to evaluate satellite observations and their potential and limitations in studying spatial and temporal fire dynamics in tropical savannas.</p> <p>More information about the experimental design can be found in the following publication:</p> <p>Alves, D. B.; Fidelis, A.; Pérez-Cabello, F.; Alvarado, S. T.; Conciani, D. E.; Cambraia, B. C.; Silveira, A. L. P.; Silva, T. S. F. Impact of Image Acquisition Lag-Time on Monitoring Short-Term Postfire Spectral Dynamics in Tropical Savannas: the Campos Amazônicos Fire Experiment. Journal of Applied Remote Sensing v. 16, n. 3 (2022) - <a href="https://doi.org/10.1117/1.JRS.16.034507">https://doi.org/10.1117/1.JRS.16.034507</a></p> <p>_________________________________________________________________________________________________________</p> <p>August 08, 2022 - Version 1.0 includes data from 24 experimental fires carried out in 2019 (12 in Early-Dry Season - EDS; 12 in Middle-Dry Season - MDS), each corresponding to a plot of 1 hectare (100x100 meters). Controlled burning occurred during two separate field campaigns in 2019, the first between May 19th and 25th, and the second between August 22nd and 26th. The files available include: i) a table (Fire_behavior_dataset.csv) that contains the fire parameters calculated for each experimental fire performed; ii) a text file (List_of_variables.rtf), which details each variable available in the table.</p> <p>_________________________________________________________________________________________________________</p> <p>We thank the management team of the Campos Amazônicos National Park, and in particular to its Fire Brigade (squad leaders José Furtado Neto, Genaldo Júnior, Ademilton Carvalho, Simei Limoeiro, José Alexandre Medeiros, Antonio Machado and Leandro Lacerda, and on behalf of them to all other members of the brigade), who ensure safe burning of all fire experiments (SISBIO license number 67210-5). This work was supported by the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP, grant numbers 2019/07357-8; 2015/06743-0); the Conselho Nacional de Pesquisa e Desenvolvimento (CNPq, grant numbers 154660/2018-3; 441968/2018-0; 303988/2018-5)</p>
Data set for "Reward-based learning drives rapid sensory signals in medial prefrontal cortex and dorsal hippocampus necessary for goal-directed behavior"
<p>Data set for: Le Merre P, Esmaeili V, Charrière E, Galan K, Salin P-A, Petersen CCH, Crochet S (2018) Reward-based learning drives rapid sensory signals in medial prefrontal cortex and dorsal hippocampus necessary for goal-directed behavior. Neuron, https://doi.org/10.1016/j.neuron.2017.11.031</p> <p>There are 44 files in this data upload:<br> 1. '2018_LeMerre_Neuron.pdf' - this is a pdf version of the online publication.<br> 2. 'Chronic_LFP_data.mat' - this is a Matlab data structure, which contains all the chronic LFP data for the publication.<br> 3. 'Silicon_Probe_data.mat' - this is a Matlab data structure, which contains all the mPFC silicon probe recording data for the publication.<br> 4. 'Opto_Inactivation_data.mat' - this is a Matlab data structure, which contains all the optogenetic inactivation data for the publication.<br> 5. 'Mus_Inactivation_data.mat' - this is a Matlab data structure, which contains all the pharmacological (Muscimol) inactivation data for the publication.<br> 6. 'Learning_Days_Mtrx.mat' - this is a Matlab data file, which contains the selected training days analyzed for the Trained condition in the Detection Task.<br> 7. 'Exposed_Days_Mtrx.mat' - this is a Matlab data file, which contains the selected days analyzed for the Exposed condition in the Neutral Exposure.<br> 8. 'p_value_colormap.mat' - this is a Matlab data file, which contains the color map used to display the p value in the Matlab codes 'plot_fig2A_SEP_D1_vs_Trained.m'; 'plot_fig2B_Amplitude_D1_vs_Trained.m'; 'plot_fig3A_SEP_D1_vs_Exposed.m'; 'plot_fig4A_SEP_H_vs_M.m’.<br> 9. 'p_value_colormap2.mat' - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code 'plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m’; ’plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m’.<br> 10. 'scatterplot_colormap.mat' - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code 'plot_fig2C_Scatterplot_Amplitude_vs_dprime.m'.<br> 11. 'SEP_colormtrx.mat' - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code 'plot_fig1B_Sensory_Evoked_Potentials.m'; 'plot_figS3A_SEP_EMG_amplitude_ReactionTime.m'.<br> 12. 'zscore_colormap.mat' - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code 'plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m'; 'plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m’.<br> 13. 'Chronic_LFP_dataViewer.fig' - this is a Matlab Figure file, which is the GUI layout for 'Chronic_LFP_dataViewer.m'.<br> 14. 'Chronic_LFP_dataViewer.m' - this is a Matlab code, which displays the data contained in 'Chronic_LFP_data.mat'.<br> 15. 'Silicon_Probe_dataViewer.fig' - this is a Matlab Figure file, which is the GUI layout for 'Silicon_Probe_dataViewer.m'.<br> 16. 'Silicon_Probe_dataViewer.m' - this is a Matlab code, which displays the data contained in 'Silicon_Probe_data.mat'.<br> 17. 'plot_fig1B_Sensory_Evoked_Potentials.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results published in figure 1, panel B (Le Merre et al., 2018).<br> 18. 'plot_fig1C_Silicon_Probe_Hit_trials.m' - this is a Matlab code, which analyses the data in 'Silicon_Probe_data.mat', and displays the results in the same way as the published figure 1, panel C (Le Merre et al., 2018).<br> 19. 'plot_fig2A_SEP_D1_vs_Trained.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure 2, panel A (Le Merre et al., 2018).<br> 20. 'plot_fig2B_Amplitude_D1_vs_Trained.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure 2, panel B (Le Merre et al., 2018).<br> 21. 'plot_fig2C_Scatterplot_Amplitude_vs_dprime.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure 2, panel C (Le Merre et al., 2018).<br> 22. 'plot_fig3A_SEP_D1_vs_Exposed.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure 3, panel A (Le Merre et al., 2018).<br> 23. 'plot_fig3B_Amplitude_D1_vs_Exposed.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure 3, panel B (Le Merre et al., 2018).<br> 24. 'plot_fig3C_ROC_Trained_vs_Exposed.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the ROCs in the same way as the published figure 3, panel C (Le Merre et al., 2018).<br> 25. 'plot_fig3C_ROC_Randomization.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the label shuffled ROCs in the same way as the published figure 3, panel C (Le Merre et al., 2018).<br> 26. 'plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m' - this is a Matlab code, which analyses the data in 'Silicon_Probe_data.mat', and displays the results in the same way as the published figure 3, panel D (Le Merre et al., 2018).<br> 27. 'plot_fig4A_SEP_H_vs_M.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure 4, panel A (Le Merre et al., 2018).<br> 28. 'plot_fig4B_Amplitude_ H_vs_M.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure 4, panel B (Le Merre et al., 2018).<br> 29. 'plot_fig4C_mPFC_PSTH_Hit_vs_Miss.m' - this is a Matlab code, which analyses the data in 'Silicon_Probe_data.mat', and displays the results in the same way as the published figure 4, panel C, left panel (Le Merre et al., 2018).<br> 30. 'plot_fig4C_Scatterplot_modulation_Hit_vs_Miss.m' - this is a Matlab code, which analyses the data in 'Silicon_Probe_data.mat', and displays the results in the same way as the published figure 4, panel C, right panel (Le Merre et al., 2018).<br> 31. 'plot_fig4D_Photoinhibitions.m' - this is a Matlab code, which analyses the data in 'Opto_Inactivation_data.mat', and displays the results published in figure 4, panel D (Le Merre et al., 2018).<br> 32. 'plot_figS2D_Performance_DetectionTask_NeutralExposition.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure S2, panel D (Le Merre et al., 2018).<br> 33. 'plot_figS3A_SEP_EMG_amplitude_ReactionTime.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure S3, panel A (Le Merre et al., 2018).<br> 34. 'plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m' - this is a Matlab code, which analyses the data in 'Silicon_Probe_data.mat', and displays the results in the same way as the published figure S3, panel B (Le Merre et al., 2018).<br> 35. 'plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m' - this is a Matlab code, which analyses the data in 'Silicon_Probe_data.mat', and displays the results in the same way as the published figure S4, panel A (Le Merre et al., 2018).<br> 36. 'plot_figS4B_Pharmacological_Inactivations.m' - this is a Matlab code, which analyses the data in 'Mus_Inactivation_data.mat', and displays the results published in figure S4 (Le Merre et al., 2018).<br> 37. 'Load_LFP_Multisite_database.m' - this is a Matlab code, which is called in the Matlab codes that analyze the data in 'Chronic_LFP_data.mat'.<br> 38. 'Load_Silicon_Probe_database.m' - this is a Matlab code, which is called in the Matlab codes that analyze the data in 'Silicon_Probe_data.mat'.<br> 39. 'Load_Optogenetic_Inactivation_database.m' - this is a Matlab code, which is called in the Matlab code that analyzes the data in 'Opto_Inactivation_data.mat'.<br> 40. 'Load_Pharmacological_Inactivation_database.m' - this is a Matlab code, which is called in the Matlab code that analyzes the data in 'Mus_Inactivation_data.mat'.<br> 41. 'bonf_holm.m' - this is a Matlab code developed by D. M. Groppe, which is called in the Matlab code 'plot_figS4B_Pharmacological_Inactivations.m':<br> https://ch.mathworks.com/matlabcentral/fileexchange/28303-bonferroni-holm-correction-for-multiple-comparisons<br> 42. 'boundedline.m' - this is a Matlab code developed by K. Kearney, which is called in the Matlab codes 'plot_fig1C_Silicon_Probe_Hit_trials.m'; 'plot_fig2A_SEP_D1_vs_Trained.m'; 'plot_fig3A_SEP_D1_vs_Exposed.m’; 'plot_fig3C_ROC_Trained_vs_Exposed.m'; 'plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m'; 'plot_fig4A_SEP_H_vs_M.m'; 'plot_fig4C_mPFC_PSTH_Hit_vs_Miss.m'; 'plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m'; 'plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m':<br> https://ch.mathworks.com/matlabcentral/fileexchange/27485-boundedline-m<br> 43. 'inpaint_nans.m' - this is a Matlab code, which is called in the Matlab code 'boundedline.m'.<br> 44. 'PSTH_Simple.m' - this is a Matlab code developed by V. Esmaeili, which is called in the Matlab codes 'plot_fig1C_Silicon_Probe_Hit_trials.m'; 'plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m'; 'plot_fig4C_mPFC_PSTH_Hit_vs_Miss.m'; 'plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m'; 'plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m’.</p>
ScienceDex guides
Understand access before you commit
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.