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9,153 results for “behavior”
Home & Community Social Behavior Scales / Escalas de Conducta Social en Casa y en la Comunidad // MERRELL-HOME_Data_CC-CS_T2-Post
<p>Answers given by parents of children in the second grade of Primary Education to the 64 items of the Home & Community Social Behavior Scales by Merrell (2002; adapted by Salazar and Caballo, 2006). Data collected in public schools in Castellón and Seville (Spain) in the spring of the school year 2010-11. The mother and father answer the questionnaire separately. The calculation of the 4 subscales is included: Peer Relations, Self-Management – Compliance, Antisocial – Aggressive and Defiant – Disruptive, as well as the 2 general scales: Social Competence and Antisocial Behavior. To learn more about our research and access to other datasets of this or other measures, follow the link <a href="https://www.uji.es/departaments/psi/base/opengrei/">GREI Longitudinal Project</a>.</p> <p>Respuestas que los padres de niños/as de segundo curso de Educación Primaria dan a los 64 ítems de las Escalas de Conducta Social en Casa y en la Comunidad de Merrell (2002; adaptadas por Salazar y Caballo, 2006). Datos recogidos en centros públicos de Castellón y Sevilla (España) en primavera del curso escolar 2010-11. La madre y el padre responden el cuestionario por separado. Se incluye el cálculo de las 4 subescalas: Relaciones con los compañeros, Autocontrol – obediencia, Comportamiento antisocial - agresivo y Comportamiento desafiante-disruptivo, y de las 2 escalas generales: Competencia Social y Comportamiento Antisocial. Para saber más sobre nuestra investigación y acceder a otros ficheros de datos de esta medida o de otras, siga el enlace <a href="https://www.uji.es/departaments/psi/base/opengrei/">Proyecto Longitudinal GREI</a>.</p>
Home & Community Social Behavior Scales / Escalas de Conducta Social en Casa y en la Comunidad // MERRELL-HOME_Data_CC_T6-Post
<p>Answers given by parents of children in the sixth grade of Primary Education to the 64 items of the Home & Community Social Behavior Scales by Merrell (2002; adapted by Salazar and Caballo, 2006). Data collected in public schools in Castellón (Spain) in the spring of the school year 2014-15. The mother and father answer the questionnaire separately. The calculation of the 4 subscales is included: Peer Relations, Self-Management – Compliance, Antisocial – Aggressive and Defiant – Disruptive, as well as the 2 general scales: Social Competence and Antisocial Behavior. To learn more about our research and access to other datasets of this or other measures, follow the link <a href="https://www.uji.es/departaments/psi/base/opengrei/">GREI Longitudinal Project</a>.</p> <p>Respuestas que los padres de niños/as de sexto curso de Educación Primaria dan a los 64 ítems de las Escalas de Conducta Social en Casa y en la Comunidad de Merrell (2002; adaptadas por Salazar y Caballo, 2006). Datos recogidos en centros públicos de Castellón (España) en primavera del curso escolar 2014-15. La madre y el padre responden el cuestionario por separado. Se incluye el cálculo de las 4 subescalas: Relaciones con los compañeros, Autocontrol – obediencia, Comportamiento antisocial - agresivo y Comportamiento desafiante-disruptivo, y de las 2 escalas generales: Competencia Social y Comportamiento Antisocial. Para saber más sobre nuestra investigación y acceder a otros ficheros de datos de esta medida o de otras, siga el enlace <a href="https://www.uji.es/departaments/psi/base/opengrei/">Proyecto Longitudinal GREI</a>.</p>
Home & Community Social Behavior Scales / Escalas de Conducta Social en Casa y en la Comunidad // MERRELL-HOME_Data_CC-CS_T1-Pre
<p>Answers given by parents of children in the first grade of Primary Education to the 64 items of the Home & Community Social Behavior Scales by Merrell (2002; adapted by Salazar and Caballo, 2006). Data collected in public schools in Castellón and Seville (Spain) in the autumn of the school year 2009-10. The mother and father answer the questionnaire separately. The calculation of the 4 subscales is included: Peer Relations, Self-Management – Compliance, Antisocial – Aggressive and Defiant – Disruptive, as well as the 2 general scales: Social Competence and Antisocial Behavior. To learn more about our research and access to other datasets of this or other measures, follow the link <a href="https://www.uji.es/departaments/psi/base/opengrei/">GREI Longitudinal Project</a>.</p> <p>Respuestas que los padres de niños/as de primer curso de Educación Primaria dan a los 64 ítems de las Escalas de Conducta Social en Casa y en la Comunidad de Merrell (2002; adaptadas por Salazar y Caballo, 2006). Datos recogidos en centros públicos de Castellón y Sevilla (España) en otoño del curso escolar 2009-10. La madre y el padre responden el cuestionario por separado. Se incluye el cálculo de las 4 subescalas: Relaciones con los compañeros, Autocontrol – obediencia, Comportamiento antisocial - agresivo y Comportamiento desafiante-disruptivo, y de las 2 escalas generales: Competencia Social y Comportamiento Antisocial. Para saber más sobre nuestra investigación y acceder a otros ficheros de datos de esta medida o de otras, siga el enlace <a href="https://www.uji.es/departaments/psi/base/opengrei/">Proyecto Longitudinal GREI</a>.</p>
Home & Community Social Behavior Scales / Escalas de Conducta Social en Casa y en la Comunidad // MERRELL-HOME_Data_CC_T4-Post
<p>Answers given by parents of children in the fourth grade of Primary Education to the 64 items of the Home & Community Social Behavior Scales by Merrell (2002; adapted by Salazar and Caballo, 2006). Data collected in public schools in Castellón (Spain) in the spring of the school year 2012-13. The mother and father answer the questionnaire separately. The calculation of the 4 subscales is included: Peer Relations, Self-Management – Compliance, Antisocial – Aggressive and Defiant – Disruptive, as well as the 2 general scales: Social Competence and Antisocial Behavior. To learn more about our research and access to other datasets of this or other measures, follow the link <a href="https://www.uji.es/departaments/psi/base/opengrei/">GREI Longitudinal Project</a>.</p> <p>Respuestas que los padres de niños/as de cuarto curso de Educación Primaria dan a los 64 ítems de las Escalas de Conducta Social en Casa y en la Comunidad de Merrell (2002; adaptadas por Salazar y Caballo, 2006). Datos recogidos en centros públicos de Castellón (España) en primavera del curso escolar 2012-13. La madre y el padre responden el cuestionario por separado. Se incluye el cálculo de las 4 subescalas: Relaciones con los compañeros, Autocontrol – obediencia, Comportamiento antisocial - agresivo y Comportamiento desafiante-disruptivo, y de las 2 escalas generales: Competencia Social y Comportamiento Antisocial. Para saber más sobre nuestra investigación y acceder a otros ficheros de datos de esta medida o de otras, siga el enlace <a href="https://www.uji.es/departaments/psi/base/opengrei/">Proyecto Longitudinal GREI</a>.</p>
Investigating Online Art Search through Quantitative Behavioral Data and Machine Learning Techniques - Dataset
<p>This dataset includes the detailed values and scripts used to study behavioral aspects of users searching online for Art and Culture by analyzing quantitative data collected by the Art Boulevard search engine using machine learning techniques. This dataset is part of the core methodology, results and discussion sections of the research paper entitled "<strong>Investigating Online Art Search through Quantitative Behavioral Data and Machine Learning Techniques</strong>"</p>
Preliminary biohybrid experiments with the Behavioral Observation & Biohybrid Interaction framework (featuring the LureBot)
<p>This is the dataset that corresponds to preliminary biohybrid interaction experiments conducted with the Behavioral Observation & Biohybrid Interaction (BOBI) framework, featuring the LureBot.</p> <p> </p> <p>The directory contains:</p> <ol> <li>Trajectories of fish and/or robot agents for different scenarios (<filename>.dat).</li> <li>A robot index file (<filename>_ridx.dat) that corresponds to each trajectory file, that contains the robot's ID (starting from 0) in the experiment, or a negative ID in the case of fish-only experiments.</li> </ol> <p> </p> <p>A brief explanation of the directory's contents:</p> <ul> <li>[Open-loop dynamics] <strong>Circular</strong> trajectories <ul> <li><strong>Disc-shaped</strong>: 1-hour-long trajectories of the LureBot with a disc-shaped lure performing a circular trajectory while a single H. rhodostomus is in the tank.</li> <li><strong>Biomimetic</strong>: 1-hour-long trajectories of the LureBot with a biomimetic lure performing a circular trajectory while a single H. rhodostomus is in the tank.</li> </ul> </li> </ul> <p> </p> <ul> <li>[Open-loop dynamics] <strong>Eightfold rose</strong> trajectories <ul> <li><strong>Disc-shaped</strong>: 1-hour-long trajectories of the LureBot with a disc-shaped lure performing an eightfold rose trajectory while a single H. rhodostomus is in the tank.</li> <li><strong>Biomimetic</strong>: 1-hour-long trajectories of the LureBot with a biomimetic lure performing an eightfold rose trajectory while a single H. rhodostomus is in the tank.</li> </ul> </li> </ul> <p> </p> <ul> <li>[Closed-loop dynamics] <strong>Biomimetic Interaction Model</strong> trajectories <ul> <li><strong>single_agent</strong>: <ul> <li><strong>Fish</strong>: 1-hour-long trajectories of a single H. rhodostomus interacting with the wall alone.</li> <li><strong>Biomimetic</strong>: 1-hour-long trajectories of a single robot following a biomimetic model.</li> </ul> </li> <li><strong>pair_agents</strong>: <ul> <li><strong>Fish-only</strong>: 1-hour-long trajectories of a pair of H. rhodostomus interacting with the wall and with each other.</li> <li><strong>Disc-shaped</strong>: 1-hour-long trajectories of the LureBot with a disc-shaped lure biomimetically interacting with a single H. rhodostomus.</li> <li><strong>Biomimetic</strong>: 1-hour-long trajectories of the LureBot with a biomimetic lure biomimetically interacting with a single H. rhodostomus.</li> </ul> </li> <li><strong>group5_agents</strong>: <ul> <li><strong>Fish-only</strong>: 1-hour-long trajectories of 5 H. rhodostomus interacting with the wall and with each other.</li> <li><strong>Biomimetic</strong>: 1-hour-long trajectories of the LureBot with a biomimetic lure biomimetically interacting with 4 H. rhodostomus.</li> </ul> </li> </ul> </li> </ul>
Fig. 3 in Description Of The Agonistic Behavior Of Aegla Longirostri (Decapoda: Aeglidae)
Fig. 3. Mean duration (in seconds) of time spent by males of Aegla longirostri, in each of the levels of aggression intensity (-2 to 5, see definition in Table 3). *Represents a significant difference in the Mann-Whitney Test (P, 0.05) (Calculation with overall values for each level of intensity).
Fig. 2 in Description Of The Agonistic Behavior Of Aegla Longirostri (Decapoda: Aeglidae)
Fig. 2. Sum of intensities (-2 to 5) of combats between similar-sized males of Aegla longirostri. *Represents significant difference in the x2 Test (P, 0.05). (Size – cephalothorax length in mm of winners ''W'' and losers ''L.'' Pair 1- W = 15.18 X L = 14.27; Pair 2- W = 16.54 X L = 15.78; Pair 3- W = 18.65 X L = 17.71; Pair 4- W = 18.31 X L = 18.73; Pair 5- W = 18.70 X L = 18.64; Pair 6- W = 18.86 X L = 19.27; Pair 7- W = 19.26 X L = 19.09; Pair 8 - W = 19.45 X L = 19.50; Pair 9- W = 20.19 X L = 19.94; and Pair 10- W = 20.84 X L = 20.64).
Fig. 1 in Description Of The Agonistic Behavior Of Aegla Longirostri (Decapoda: Aeglidae)
Fig. 1. Selected frames from the films documenting the agonistic behavior of A. longirostri: A-B: Hitting with chelipeds; C-E-F: Fight and D: Going up the opponent.
Fig. 4 in Description Of The Agonistic Behavior Of Aegla Longirostri (Decapoda: Aeglidae)
Fig. 4. Progression in mean intensity of aggressive combats related to time in encounters, of males of Aegla longirostri.
Dataset for: Effect of developmental temperatures on Aphidius colemani host-foraging behavior at high temperature
<p>This is the dataset for the following study; <strong>Effect of developmental temperatures on <em>Aphidius colemani</em> host-foraging behavior at high temperature.</strong></p> <p>We explored how three rearing temperatures (10, 20, and 28°C) affected host-foraging behaviors and associated traits under warm conditions in the insect parasitoid <em>Aphidius colemani.</em></p>
Data from: Behavior shapes retinal motion statistics during natural locomotion
<p>Walking through an environment generates retinal motion, which humans rely on to perform a variety of visual tasks. Retinal motion patterns are determined by an interconnected set of factors, including gaze location, gaze stabilization, the structure of the environment, and the walker's goals. The characteristics of these motion signals have important consequences for neural organization and behavior. However, to date, there are no empirical <em>in situ</em> measurements of how combined eye and body movements interact with real 3D environments to shape the statistics of retinal motion signals. Here, we collect measurements of the eyes, the body, and the 3D environment during locomotion. We describe properties of the resulting retinal motion patterns. We explain how these patterns are shaped by gaze location in the world, as well as by behavior, and how they may provide a template for the way motion sensitivity and receptive field properties vary across the visual field.</p>
Data and code for the research work "Disentangling material, social, and cognitive determinants of human behavior and belief".
<p>This repository contains data files and Matlab and R code for the research work "Disentangling material, social, and cognitive determinants of human behavior and belief".</p>
Dataset and RScript - Effect of insecticide on termite alarm behavior
<p>Dataset and RScript of the MS "How to perceive the insecticide? The Neotropical termite <em>Nasutitermes corniger</em> (Termitidae: Nasutitermitinae) triggers alert behavior after exposure to imidacloprid".</p>
Code, scripts and data for: Seasonality and competition select for variable germination behavior in perennials
<p class="MsoNoSpacing"><span>The occurrence of within-population variation in germination behavior and associated traits such as seed size has long fascinated evolutionary ecologists. In annuals, unpredictable environments are known to select for bet-hedging strategies causing variation in dormancy duration and germination strategies. Variation in germination timing and associated traits is also commonly observed in perennials and often tracks gradients of environmental predictability. Although bet-hedging is thought to occur less frequently in long-lived organisms, these observations suggest a role of bet-hedging strategies in perennials occupying unpredictable environments. We use complementary analytical and evolutionary simulation models of within-individual variation in germination behavior in seasonal environments to show how bet-hedging interacts with fluctuating selection, life-history traits, and competitive asymmetries among germination strategies. We reveal substantial scope for bet-hedging to produce variation in germination behavior in long-lived plants, when "false starts" to the growing season results in either competitive advantages or increased mortality risk for alternative germination strategies. Additionally, we find that lowering adult survival may, in contrast to classic bet-hedging theory, result in less spreading of germination by decreasing density-dependent competition. These models extend insights from bet-hedging theory to perennials and explore how competitive communities may be affected by ongoing changes in climate and seasonality patterns.</span></p>
Figures 5–12 in Nesting behavior of the spider wasp Calopompilus pyrrhomelas (Walker) (Hymenoptera: Pompilidae)
Figures 5–12. Calopompilus pyrrhomelas (Pompilidae) and Antrodiaetus montanus (Antrodiaetidae). Photographs © Marshal Hedin. 5) Immobilized host spider, as initially found, about one meter from its burrow entrance. 6) Wasp dragging spider backwards across sand some distance from its burrow entrance, grasping the end of its right foreleg with her mandibles. 7) Wasp entering and exiting the spider's open burrow—a form of reconnaissance. 8) Wasp dragging spider backwards near its open burrow, grasping its spinnerets with her mandibles. 9) Wasp backing into entrance with spider in tow, retaining grasp of its spinnerets with her mandibles. 10) Spider gradually disappearing as it is being pulled down the burrow by wasp. 11) Partly closed collapsible collar door with wasp and spider inside burrow. 12) Fully closed collapsible collar door with wasp and spider inside burrow.
Figures 1–4 in Nesting behavior of the spider wasp Calopompilus pyrrhomelas (Walker) (Hymenoptera: Pompilidae)
Figures 1–4. Calopompilus pyrrhomelas (Pompilidae) and Calisoga longitarsis (Nemesiidae). Photographs © Kerry Blackwell. 1) Wasp extracted host spider from its open burrow and is searching for it on the ground surface. 2) Wasp caught the escaped spider, stung and paralyzed it, and is examining it as it lies ventral side upward on the ground. 3) Wasp dragged the immobilized spider next to its burrow entrance and is repositioning it with its abdomen beside the opening for ready entry. 4) Wasp walked away from the repositioned spider to groom and rest before returning to pull it inside and down its burrow.
Distribution, population density and behavior of dwarf galagos (Paragalago sp.) in Taita Hills, Kenya
<p>We studied habitat preferences and behavior of dwarf galagos (<em>Paragalago </em>sp.), recently rediscovered from the Taita Hills, Kenya. Small populations of Taita dwarf galagos survive in the two largest remnants of moist montane forest. Inspection of several smaller forest fragments failed to provide evidence of additional survivors. Acoustic data on the two remaining populations were obtained with AudioMoths, and analyzed in relation to forest structure data obtained by airborne lidar and by ground-level observations. A Zero-inflated negative binomial GLMM was implemented with calls per hour as the response variable and indicator of relative population density. Our results demonstrate that Taita dwarf galagos prefer dense canopy coverage and avoid forest edges. Regarding forest height, they prefer lower 20–30 m tall forest. Forest size also significantly affects Taita dwarf galago population size. Mbololo forest (185 ha) has a relatively viable population, whereas in Ngangao forest (120 ha) dwarf galagos are nearly extinct. The calls of Taita dwarf galagos resemble calls of Kenya coast dwarf galagos (<em>Paragalago cocos</em>). However, some differences exist between the Taita animals and those recently recorded by us at the Kenyan coast, and even between the two remaining populations in the Taita Hills. In addition to other data, we present the first ever photographs of the Taita dwarf galagos from the Mbololo forest and compare them to those from Ngangao forest and the Kenya coast from Diani and Shimba Hills. We conclude that DNA studies are urgently needed to resolve the taxonomic status of both surviving populations of dwarf galagos in the Taita Hills.</p>
Cortex-wide neural dynamics predict behavioral states and provide a neural basis for resting-state dynamic functional connectivity
<p><strong>GENERAL INFORMATION</strong></p> <p>This data is described in the following publication: </p> <p><strong>Cortex-wide neural dynamics predict behavioral states and provide a neural basis for resting-state dynamic functional connectivity</strong>, Somayeh Shahsavarani<sup>1,2,5</sup>, David N. Thibodeaux<sup>1,5</sup>, Weihao Xu<sup>1</sup>, Sharon H. Kim<sup>1</sup>, Fatema Lodgher<sup>1</sup>, Chinwendu Nwokeabia<sup>1</sup>, Morgan Cambareri<sup>1</sup>, Alexis J. Yagielski<sup>1</sup>, Hanzhi T. Zhao<sup>1</sup>, Daniel A. Handwerker<sup>2</sup>, Javier Gonzalez-Castillo<sup>2</sup>, Peter A. Bandettini<sup>2,3</sup>, Elizabeth M. C. Hillman<sup>1,4,6,*</sup> Cell Reports (2023): <a href="https://doi.org/10.1016/j.celrep.2023.112527">https://doi.org/10.1016/j.celrep.2023.112527</a></p> <p><br> 1. Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Biomedical Engineering, Columbia University, New York, NY, USA<br> 2. Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA<br> 3. Functional MRI Core Facility, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA<br> 4. Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA<br> 5. These authors contributed equally<br> 6. Lead contact<br> *Correspondence: elizabeth.hillman@columbia.edu</p> <p>Preprocessing and analysis code that generated / can be used with this data is posted at: <br> GitHub: <a href="https://doi.org/10.5281/zenodo.7860561">https://doi.org/10.5281/zenodo.7860561</a></p> <p><strong>DATA OVERVIEW </strong></p> <p>This dataset comprises simultaneous neuronal and hemodynamic data collected using wide-field optical mapping (WFOM) techniques. The data were obtained from head-fixed mice that were allowed to behave spontaneously without any external stimulation. For more detail, please refer to the Readme file.</p>
Multifaceted Online Coordinated Behavior in the 2020 US Presidential Election
<p>This dataset contains ~140M tweets related to the 2020 United States Presidential Election, published and collected between October 2, 2020, and December 2, 2020. In addition, we provide nodes and edges of the superspreader user similarity network, as described in the paper below.</p> <p><strong>Tardelli, S., Nizzoli, L., Avvenuti, M., Cresci, S., & Tesconi, M. Multifaceted Online Coordinated Behavior in the 2020 US Presidential Election.</strong></p> <p>In detail, the dataset consists of:</p> <ul> <li><em>tweet-ids.csv.zip</em></li> <li><em>user_similarity_nework_nodes.csv</em>: a CSV file with the columns "id" and "cluster," relating to the nodes of the superspreader user similarity network mentioned in the paper.</li> <li><em>user_similarity_nework_edges.csv</em>: a CSV file with the columns "source," "target," "weight," and "alpha" relating to the edges of the superspreader user similarity network mentioned in the paper.</li> </ul>
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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.