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9,153 results for “behavior”
Figure 9 in Manatee behavioral response to boats
Figure 9. Model-estimated mean probability (6 SEM) of a manatee changing its heading (A) and mean number of heading changes per min (6 SEM) (C) according to presence of manatee chewing at the start of the boat pass. Model-estimated mean probability (6 SEM) of manatee making a change in depth (B) and mean number of depth changes per min (6 SEM) (D) according to seagrass presence at the manatee's location at the start of the boat pass. Means sharing any common letters are not significantly different (P> 0.05). The fixed factor values used to generate these estimates are listed in Table 3.
Figure 6 in Manatee behavioral response to boats
Figure 6. Model-estimated mean probability of a manatee making a change in roll (A), heading (B), depth (C), and fluking (D) (6 SEM) during a boat pass in relation to manatee-boat distance at CPA. Means sharing any common letters are not significantly different (P> 0.05). The fixed factor values used to generate these estimates are listed in Table 3.
FIGURE 6 in First Records for Cyclosomus inustus Andrewes (Coleoptera: Carabidae: Cyclosomini) for Taiwan, with Notes on Habitat and Behavior
FIGURE 6. Other carabid species active on coastal sand in northern Taiwan. A. Bembidion fusiforme Netolitzky. B. Mastax brittoni Quentin. C. Abroscelis anchoralis anchoralis (Chevrolat). D. Abroscelis anchoralis punctatissima (Schaum). E. Calomera angulata (Fabricius), F. Lophyra cancellata subtilesculpta (W. Horn). G. Cicindela batesi Fleutiaux. H. Cylindera kaleea angulimaculata (Mandl).
FIGURE 4 in First Records for Cyclosomus inustus Andrewes (Coleoptera: Carabidae: Cyclosomini) for Taiwan, with Notes on Habitat and Behavior
FIGURE 4. Distribution of habitats of Cyclosomus inustus Andrewes in Taiwan. A, Shihmen. B, Danshui. C, Kinshan. Areas outlined in yellow indicate the areas of the stabilized dunes where the beetles have been found (see also Fig. 3)..
Data and code for behavioral analysis of: Structural and Molecular Properties of Insect Type II Motor Axon Terminals.
<p>Data and code for behavioral analysis of: Structural and Molecular Properties of Insect Type II Motor Axon Terminals.</p> <p>v1.2: typos corrected and all files available in a single .zip file for download</p>
Figure 1. CNN architecture (adopted from Krizhevsky et al. '12)-Measuring Customer Behavior with Deep Convolutional Neural Networks
<p>The architecture of a CNN can be described as following. A small pixel region goes to input neurons and then connects to a first convolution hidden layer (Figure1). There we can see a set of learnable filters, which are activated during the presentation some particular type of feature in pixel region in the input. On this phase, CNN does shift invariance, which is carried by feature map. Subsampling layer goes next. There we have two processes: local averaging and sampling. As a result, we get declining resolution of feature map. To correspond this task CNN needs supervised learning. Before starting the experiment, we gave a set of labeled videos with different emotional experience. The system analyses images and finds similar features. Then the system creates a map, where it arranges videos in accordance with similar features. Thereby, images with similar emotions form certain class. To test the system, we add other videos and correct the system when it refers them improperly. The proposed model consists of four convolutional layers, followed by max-pooling layers, and three fully-connected layers with a final classificatory presented with MLP (with six basic outputs, corresponding to basic emotions for emotion classification and two outputs for motion classification for typical and non-typical behavior). The input data was presented as infrared camera output.</p>
BRAIN Journal-Two new software behavioral design patterns: Obligation Link and History Reminder-Figure 5. The Sequence Diagram of the "History Reminder" design pattern
<p>Analyzing the two main classes HistoryReminder and HistoryReminderOriginator of the “History Reminder” design pattern, we find that these two classes act as actors of the whole sequence of operations. The sequence diagram of the “History Reminder” design pattern is given below in Figure 5.</p>
BRAIN Journal-Two new software behavioral design patterns: Obligation Link and History Reminder-Figure 4. The Flow chart of the "History Reminder" design pattern
<p>The “History Reminder” design pattern has two main classes: HistoryReminder and HistoryReminderOriginator. The flow chart of the “History Reminder” design pattern is given in Figure 4.</p>
BRAIN Journal-Two new software behavioral design patterns: Obligation Link and History Reminder-Figure 3. The class diagram of the "History Reminder" design pattern
<p>The “History Reminder” design pattern consists of two basic classes. The first one is the HistoryReminder class and the second one is HistoryReminderOriginator class. Here, we have removed the Caretaker class which has been used to restore and save the memento state in the case of the traditional well-known Memento design pattern. The Caretaker object’s responsibility has been passed to the HistoryReminder object itself. It saves and restores the object within itself and returns it to the HistoryReminderOriginator object. The class diagram of the HistoryReminder design pattern is given below in Figure 3.</p>
BRAIN Journal-Two new software behavioral design patterns: Obligation Link and History Reminder-Figure 2. The class diagram of the "Obligation Link" design pattern
<p>Here is the structure of the “Obligation Link” design pattern described through another UML concept, called class diagrams. Contrary to the sequence diagrams which describe the behaviour, class diagrams specify static structural information about a program. The important information for a class, such as its attribute, operation, etc, along with relationship between classes can be specified using a class diagram</p>
BRAIN Journal-Two new software behavioral design patterns: Obligation Link and History Reminder-Figure 1. The sequence diagram of the "Obligation Link" design pattern
<p>A sequence diagram is used to model the flow of the logic of the system. A sequence diagram represents perhaps the most popular Unified Modelling Language (UML) diagram that describes the behavior of a scheme. Figure 1 shows the sequence diagram of “Obligation Link” design pattern.</p>
Influence of young age microbiome on adult sleep behavior in D. Melanogaster
<p>There is growing evidence for the interaction between the gut microbiome and the brain. Several studies report strong correlations between the composition of the gut microbiome and various neurological diseases. Moreover, gut bacteria are shown to influence levels of neurotransmitters, e.g GABA, which are unbalanced in stress related disorders, such as anxiety and depression but also in in sleep disorders.</p> <p><em>Drosophila Melanogaster</em> is a powerful model organism for investigating the interaction between the microbiome and the brain. In addition to available genetic techniques, yielding germ free (axenic) flies and establishing gnotobiotic cultures is faster and easier with fruit flies compared to other model organisms. Moreover, <em>Drosophila</em> microbiome is much simpler in complexity, in contrast to the vertebrate microbiome.</p> <p>We investigated the significance of the young age microbiome on adult sleep behaviour in <em>Drosophila</em>. Our hypothesis was that differences in microbiome composition might elucidate the reason for the behavioral variability in resilience/vulnerability to sleep deprivation, amongst individuals with same genetic background. However, our results suggest that there is no/ minor effect of the <em>Drosophila </em>microbiome on sleep behaviour. </p> <p> </p>
Deprecated Dataset for "Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior"
<p>This dataset is deprecated. <strong>The updated version of this Dataset is here:</strong> <a href="https://zenodo.org/record/3678559#.Xl9-Ji97FhE">https://zenodo.org/record/3678559#.Xl9-Ji97FhE</a></p> <p>Dataset for the publication "Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior". Antigoni-Maria Founta, Constantinos Djouvas, Despoina Chatzakou, Ilias Leontiadis, Jeremy Blackburn, Gianluca Stringhini, Athena Vakali, Michael Sirivianos and Nicolas Kourtellis. International AAAI Conference on Web and Social Media (ICWSM), 2018.</p> <p>The dataset provided here includes an updated version of the original dataset, with ~100k tweets annotated using the CrowdFlower platform:</p> <ul> <li>hatespeech_labels.csv: contains ~100k rows, where every row consists of a unique Tweet ID and its associated majority annotation</li> </ul> <p><em>UPDATE</em>: It has come to our understanding that a number of the tweets are not available anymore for download on Twitter. Therefore, <strong>upon request</strong>, we can provide one more file with the full ~100k tweet text and their associated majority labels. The tweets are shuffled so that there is no connection between tweet IDs and texts (in order to be aligned with the T&C of Twitter).</p> <p>To obtain the file contact a.m.founta at gmail dot com <strong>AND </strong>antonis26papa at gmail dot com.</p> <p><em>Please cite the paper in any published work that uses any of these resources.</em></p> <blockquote> <p>@inproceedings{founta2018large,<br> title={Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior},<br> author={Founta, Antigoni-Maria and Djouvas, Constantinos and Chatzakou, Despoina and Leontiadis, Ilias and Blackburn, Jeremy and Stringhini, Gianluca and Vakali, Athena and Sirivianos, Michael and Kourtellis, Nicolas},<br> booktitle={11th International Conference on Web and Social Media, ICWSM 2018},<br> year={2018},<br> organization={AAAI Press}<br> }</p> </blockquote> <p>For any further questions contact a.m.founta at gmail dot com.</p> <p> </p> <p>Publication DOI: <a href="https://doi.org/10.5281/zenodo.1443348">https://doi.org/10.5281/zenodo.1443348</a></p> <p>Github: <a href="https://github.com/ENCASEH2020/hatespeech-twitter">https://github.com/ENCASEH2020/hatespeech-twitter</a></p>
Is the Gaze Behavior During Stair Walking Affected by Pregnancy?-Figure 2. Eye-tracking glasses image showing the gaze location during stair ascent
<p>At each data collection, participants walked the same U-shaped staircase descending a 22- treads (riser: 0.16 m, run: 0.33 m, and width: 1.15 m), making a short U-turn downstairs and ascending back the staircase, one tread at a time (Figure 1). Only the data of stair walking were taken for further analysis. The staircase was equipped with a handrail on one side but none of the participants used it. To monitor the gaze a SensoMotoric Instruments (SMI) eye-tracking glasses (ETG) system (SMI, Inc.) at a frequency of 60 frames per second and 1280x960 pixel picture was used. Calibration was performed using a matrix of 3 points placed on a board in different highs and different horizontal placement. Mean gaze vectors of the right eye (x, y, z) for stair descent and stair ascent were obtained for each data collection session. Gaze vector x, y, z starts at the eye and heads off in mediolateral, up and down, and anterior-posterior direction, respectively (Figure 2) (Haffegee, Alexandrov, & Barrow, 2007; Scheel, & Staadt, 2015).</p>
Is the Gaze Behavior During Stair Walking Affected by Pregnancy?-Figure 1. A simplified representation of the analysed staircase path
<p>At each data collection, participants walked the same U-shaped staircase descending a 22- treads (riser: 0.16 m, run: 0.33 m, and width: 1.15 m), making a short U-turn downstairs and ascending back the staircase, one tread at a time (Figure 1). Only the data of stair walking were taken for further analysis. The staircase was equipped with a handrail on one side but none of the participants used it. To monitor the gaze a SensoMotoric Instruments (SMI) eye-tracking glasses (ETG) system (SMI, Inc.) at a frequency of 60 frames per second and 1280x960 pixel picture was used. Calibration was performed using a matrix of 3 points placed on a board in different highs and different horizontal placement. Mean gaze vectors of the right eye (x, y, z) for stair descent and stair ascent were obtained for each data collection session. Gaze vector x, y, z starts at the eye and heads off in mediolateral, up and down, and anterior-posterior direction, respectively (Figure 2) (Haffegee, Alexandrov, & Barrow, 2007; Scheel, & Staadt, 2015).</p>
DS6.SSSA-02. Human_Walking_Dataset_at_SSSA. Dataset for characterizing the walking behavior of subjects and identification of changes in the motion patterns, based on RGB-D cameras.
<p>This dataset is used for characterizing the wakling behavior of subjects. It is based on RGB-D camerasand obtained through data collection experiments at the premises of the Percro Labotory, TeCIP Intitute, Scuola Superiore Sant'Anna (Pisa, Italy). Data are collected for the gait patterns of 9 healthy participants.</p>
Pergola: boosting visualization and analysis of longitudinal data by unlocking genomic analysis tools - Mouse feeding behavior dataset
<p>Dataset contains feeding and drinking behavioral recordings of C57BL6/J male mice. Mice were distributed into 2 groups (9 control mice and 8 high-fat diet mice) and tracked individually on Phecomp cages for 9 weeks. During the first experimental week all animals were given <em>ad libitum</em> access to a standard chow (habituation phase). After this first week, control mice continued with the same diet regime while high-fat mice were exclusively given <em>ad libitum</em> access to a high-fat chow. Data was used originally in this publication <a href="http://onlinelibrary.wiley.com/doi/10.1111/adb.12595/abstract">10.1111/adb.12595.</a></p> <p>The data set consist in:</p> <p>- a "mouse_recordings" folder containing a CSV file containing mouse recordings and the files.</p> <p>- a "mappings" folder containing all the mappings used by the pergola in the pipeline to convert data.</p> <p>- a "phases" folder containing a CSV file containing experimental phases.</p> <p>- a "chromHMM_files" folder containing a cellmarkfiletable table used by chromHMM to learn a HMM model</p>
Auditory stream segregation and selective attention for cochlear implant listeners: Evidence from behavioral measures and event-related potentials
<p>Data set generated for the study "Auditory stream segregation and selective attention for cochlear implant listeners: Evidence from behavioral measures and event-related potentials" </p> <ol> <li><strong>behavioral.txt</strong>: d' scores obtained by the listeners on the deviant detection task. <ul> <li>subject: listener ID</li> <li>distractor: Electrode separation condition</li> <li>deviant: Deviant triplet</li> <li>d: d' scores</li> <li>exp: experimental session (BEH / ERP)</li> </ul> </li> <li><strong>ERP_by_condition.txt</strong>: <ul> <li>Subject: listener ID</li> <li>Type: Sound type (Target / Distractor)</li> <li>Dev: Deviant condition. Early = deviant triplets 1 or 2. Late = deviant triplet 3 or <em>none.</em></li> <li>rep: Triplet number</li> <li>sound: sound number within the triplet</li> <li>amplitude: amplitude difference between the active and the passive listening conditions.</li> </ul> </li> </ol> <p> </p>
Data for "Credit card risk behavior on college campuses: evidence from Brazil"
<p>This data set support the following research: College students frequently show they have little skill when it comes to using a credit card in a responsible manner. This article deals with this issue in an emerging market and in a pioneering manner. University students (<em>n</em> = 769) in São Paulo, Brazil's main financial center, replied to a questionnaire about their credit card use habits. Using Logit models, associations were discovered between personal characteristics and credit card use habits that involve financially risky behavior. The main results were: (a) a larger number of credit cards increases the probability of risky behavior; (b) students who alleged they knew what interest rates the card administrators were charging were less inclined to engage in risky behavior. The results are of interest to the financial industry, to university managers and to policy makers. This article points to the advisability, indeed necessity, of providing students with information about the use of financial products (notably credit cards) bearing in mind the high interest rates which their users are charged. The findings regarding student behavior in the use of credit cards in emerging economies are both significant and relevant. Furthermore, financial literature, while recognizing the importance of the topic, has not significantly examined the phenomenon in emerging economies.</p>
Avoiding sedentary behaviors requires more cortical resources than avoiding physical activity
<p><strong>Dataset related to the paper entitled "Avoiding sedentary behaviors requires more cortical resources than avoiding physical activity". </strong></p> <p>This dataset includes:</p> <p>1) A workbook</p> <p>2) Raw data ("raw_data_eprime_zen.csv") of the behavioral outcomes of the manikin task</p> <p>3) Self-reported data ("data_self_report_R_subset_zen.csv").</p> <p>4) Electroencephalography data ("ERP_by_subject_by_condition_data.zip").</p> <p>5) R script for the data management of the behavioral outcomes (i.e., from the raw data to data ready to be analyzed)</p> <p>6) Images used in the manikin task</p> <p>7) Eprime script for the manikin task</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.