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9,153 results for “Behavior”
The influence of boating noise on the parental care behaviors of smallmouth bass (Micropterus dolomieu) during the summer of 2024 at Douglas Lake, Michigan, USA.
Anthropogenic noise is on the increase and in aquatic systems one of the major sources of noise is boat traffic. For organisms in lakes, rivers, and oceans that are capable of hearing, anthropogenic noise may alter behavior in a number of different ways. Here we did a combination of field and experimental work by locating smallmouth bass nests that were actively being guarded by males. Using an underwater drone, we monitored nest guarding behavior before and after a boat ran by the nest. In addition, we monitored behavior during this period while simultaneously recording boat motor noise. The results showed that the sequence of behavior performed by bass was altered during and after the boat ran by the nest.
Boldness and Congener Impact on the Behavior of Faxonius rusticus and Faxonius virilis
Competition is an important ecological interaction that drives a number of processes from evolution to behavioral and physiological mechanisms. Competition between congeners is often intense given the significant overlap of niche structures for the two species. An interesting aspect of competition that is understudied is the mechanisms by which organisms know that they are in competition with another species. Thus, the sensory cues or signals that are being detected by competitors is the initial mechanism for changes in behavior or physiology. Crayfish are the most invasive aquatic species and often replace existing crayfish species through superior competition. This study was designed to investigate how chemical cues my be used by overlapping species of crayfish to determine the degree and intensity of competition and how that recognition changes resource use. These studies were performed in flow through mesocosms at the University of Michigan Biological Station. The results indicate that internal factors (size and personality) play a role in determining resource use for some resources, whereas external (chemical cues) and internal (personality) play a role in determining resource use for other resources.
The effects the mode of delivery of microcystin-LR has on rusty crayfish (Faxonius rusticus) behavior and physiology
Microcystin is a deadly toxin produced during algal blooms. The cells release MCLR which can cause significant neurological and behavioral damage to organisms exposed to the toxin. MCLR can exist in the water column and in the sediment and through normal ecological processes move between those two states or location. Given the location of the toxin, it is possible that MCLR in the sediment could have different adverse effects on organisms as opposed to when the toxin is in the water column. We tested that idea using crayfish that were exposed to either nothing, a vehicle to carry the mclr, and mclr. In addition, the toxin and vehicle were dosed either in the water column or in the sediment. Behavioral and physiological measures were taken after 4 days of exposure, The results indicate that at both the behavioral and physiological level, the location of the toxin has different adverse effects.
Laboratory study on microplastic fiber size and concentration effects on leopard frog (Lithobates pipiens) tadpole survival, development, behavior, and parasite susceptibility
This dataset contains comprehensive raw data from a completed laboratory experiment conducted from May 24 to June 30, 2021 (with additional analysis performed in 2025), investigating the effects of polyester microplastic (MP) fiber exposure on northern leopard frog (Lithobates pipiens) tadpoles and their interactions with echinostome trematodes (Echinostoma sp.). Tadpole egg masses were collected from a wetland in Indiana, USA, and ramshorn snails (Helisoma trivolvis), serving as trematode hosts, were collected from Tioga County, New York, USA. The experiment was conducted under controlled laboratory conditions using a static-renewal design, exposing tadpoles to short (~0.24 mm) or long (~1.50 mm) polyester MP fibers at concentrations of 0, 10, or 40 µg L⁻¹ for 32 days, followed by controlled exposure to echinostome cercariae. The dataset includes measurements of tadpole mortality, developmental traits (mass, snout-to-vent length, Gosner stage), behavioral activity (number of moving pre- and post-parasite exposure), MP fiber ingestion, and susceptibility to trematode infection (metacercarial cyst counts in kidneys). These data provide a resource for studying the ecological and toxicological impacts of microplastics on amphibian health, and host-parasite dynamics in freshwater ecosystems, making the dataset suitable for researchers in ecotoxicology, and disease ecology. The dataset is complete, with no ongoing data collection, and is designed to support analyses of microplastic-mediated effects on aquatic organisms.
The impact of the presence of the crayfish Faxonius virilis on the excavating behavior of Faxonius propinquus, Michigan, 2025
Faxonius propinquus are native crayfish to the Upper Midwest. They are burrowing crayfish that work the sediment and make small burrows with numerous openings. These crayfish are imperiled by Faxonius virilis which overlap in niche structure. We were interested in the impact of the chemical cues emanating from F. virilis on the sediment and burrowing nature of F. propinquus. We conducted a study at the University of Michigan's Biological Station using F. propinquus caught in nearby Douglas Lake. In addition, we collected marl substrate from where these animals are found. We created flow through mesocosms and placed F. propinquus in the mesocosm with marl substrate. Then upstream of this, we placed a F. virilis in a flow through container to allow chemical cues from F. virilis to reach F. propinquus. We did a 48 hour study where 24 hours were with a competitor chemical cue and 24 were without. This 48 cycle was repeated to provide discrete 48 hour replicates. We monitored sediment working behavior at night and during the day.
Behavioral, physiological, and neural signatures of surprise during naturalistic sports viewing
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Dataset of Concurrent EEG, ECG, and Behavior with Multiple Doses of transcranial Electrical Stimulation - BIDS
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The behavioral phenotype of early life adversity
<p>In this dataset, we categorized studies investigating the effects of early life adversity on behavior in mice and rats. The dataset is ideal for meta-analyses. For more information about the dataset and the project, see https://osf.io/ra947/</p>
Replication Data for: "Copularity of French and Dutch (semi-)copular constructions: a behavioral profile analysis"
<p>This data package contains all the data relevant to reproduce the results presented in the publication "Copularity of French and Dutch (semi-)copular constructions: a behavioral profile analysis".</p>
Data for: Segmentation and Holocene Behavior of the Middle Strand of the North Anatolian Fault (NW Turkey)
<p>This dataset is associated to the article "Segmentation and Holocene Behavior of the Middle Strand of the North Anatolian Fault (NW Turkey)" published in Tectonics (<a href="https://doi.org/10.1029/2021TC006870">https://doi.org/10.1029/2021TC006870</a>).</p> <p>It includes the following:</p> <ul> <li>A description file, including a list of data files, and a description of how the marker quality score was determined in this study ("Supporting Information.docx")</li> <li>A table summarizing the historical earthquakes in the region of interest ("TableS1.xlsx")</li> <li>A table summarizing the paleoseismic investigations in the region of interest ("TableS2.xlsx")</li> <li>The full horizontal offset retrodeformations ("offsets_X.tif")</li> <li>A table of the offset values measured along the MNAF (TableS3.xlsx")</li> <li>The georeferenced fault map ("MNAF_2021.gml" and "MNAF_2021.xsd")</li> <li>A figure showing examples of vertical slip markers along the MNAF south of Iznik Lake ("FigS1.tif")</li> <li>A figure showing field examples of Late Quaternary faulting along the MNAF ("FigS2.png")</li> <li>A figure showing the results of the automatic fault discretization procedure ("FigS3.png")</li> </ul>
When my wrongs are worse than yours: behavioral and neural asymmetries in first-person and third-person perspectives of accidents
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Brain mechanisms underlying episodic future thinking of sustainable behaviors
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EEG: Electrophysiological biomarkers of behavioral dimensions from cross-species paradigms
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Replication package of "Search-based Crash Reproduction using Behavioral Model Seeding"
<p>Search-based crash reproduction approaches assist developers during debugging by generating a test case which reproduces a crash given its stack trace. One of the fundamental steps of this approach is creating objects needed to trigger the crash. One way to overcome this limitation is seeding: using information about the application during the search process. With seeding, the existing usages of classes can be used in the<br> search process to produce realistic sequences of method calls which create the required objects. In this study, we introduce behavioral model seeding: a new seeding method which learns class usages from both<br> the system under test and existing test cases. Learned usages are then synthesized in a behavioral model (state machine). Then, this model serves to guide the evolutionary process. To assess behavioral model-seeding, we evaluate it against test-seeding (the state-of-the-art technique for seeding realistic objects) and no-seeding (without seeding any class usage). For this evaluation, we use a benchmark of 122 hard-to-reproduce crashes stemming from six open-source projects. Our results indicate that behavioral model-seeding outperforms both test seeding and no-seeding by a minimum of 6% without any notable negative impact on efficiency.</p>
Water restriction induces behavioral fight but impairs thermoregulation in a dry-skinned ectotherm
<p>Raw data of the article "Water restriction induces behavioral fight but impairs thermoregulation in a dry‐skinned ectotherm" by Rozen-Rechels D. et al., published in Oikos in 2020 (https://doi.org/10.1111/oik.06910). These data are freely available and are provided as csv files. Check the readme file for information on metadata.</p> <p>Data were formatted by David Rozen-Rechels and produced according to standards and protocols described in the companion paper.</p>
MoTiV: a Dataset of European User Mobility for Behavioral-Data
<p>Mobility is a system involving several stakeholders. Therefore, it is relevant to characterize mobility behavior and preferences in a detailed way, to enable nuanced decisions. Current paradigms rely mostly on time saving, proposing to users solutions that include the shortest path. Even though the value of travel time can be extended beyond travel duration, no dataset to characterize mobility and value of travel time from different perspectives exists. This creates a gap between novel mobility paradigms and the characterization of user mobility. To enable the mining of user mobility under these new paradigms, in this paper, we present the MoTiV (Mobility and Time Value) dataset, which contains data about travelers and their journeys, collected from a mobile application, called Woorti. Each trip contains multi-faceted information: from the transport mode, through its evaluation, to the positive/negative experience factors. We also present a use case, which compares corresponding legs with different transport modes, studying experience factors that negatively impact users. We conclude by discussing other application domains and research opportunities enabled by the dataset.</p>
Short Fatigue Crack Behavior under various Level of Mixed-Mode
<p>This is dataset to paper: Short Fatigue Crack Behavior under various Level of Mixed-Mode</p>
AIM aggregated dataset of normal driving behavior at intersections
<p>As part of the Application Platform for Intelligent Mobility (AIM), the traffic situation of an intersection in Braunschweig (Germany) was recorded in order to better understand the behavior of road users. In the project L3pilot, the normal driving behavior at the intersection was analyzed. We focused on kinematic and interaction behaviour of a vehicle (turning left or right, going straight) with oncoming road users (VRU and motorised vehicles) and with lead vehicle. Altogether, 30 days of trajectory data of different months of 2018 and 2019 of the relevant scenarios were analyzed. The datasets include the aggregated parameters in the following scenarios: </p> <ul> <li>Scenario L1: left turning following vehicle from West to North interacting with the lead vehicle from West to North.</li> <li>Scenario L2: left turning vehicle from West to North interacting with oncoming vehicle from East to West.</li> <li>Scenario L3: left turning vehicle from West to North interacting with oncoming bicycle.</li> <li>Scenario R1: right turning following vehicle from East to North interacting with the lead vehicle from East to North.</li> <li>Scenario R2: right turning vehicle from East to North interacting with bicycle from East to West.</li> <li>Scenario S: straight driving following vehicle from East to West interacting with the lead vehicle from East to West.</li> </ul> <p> </p> <table> <caption>meta table of car following</caption> <tbody> <tr> <td>col</td> <td>colname</td> <td>unit</td> <td>description</td> </tr> <tr> <td>1</td> <td>id</td> <td>-</td> <td>trial id</td> </tr> <tr> <td>2</td> <td>subscenario</td> <td>-</td> <td>the following vehicle stopped (stop) or not (non-stop)</td> </tr> <tr> <td>3</td> <td>m(v)</td> <td>m/s</td> <td>average velocity of following vehicle</td> </tr> <tr> <td>4</td> <td>max(v)</td> <td>m/s</td> <td>maximum velocity of following vehicle</td> </tr> <tr> <td>5</td> <td>sd(v)</td> <td>m/s</td> <td>standard deviation of velocity of following vehicle</td> </tr> <tr> <td>6</td> <td>mdn(v)</td> <td>m/s</td> <td>median velocity of following vehicle</td> </tr> <tr> <td>7</td> <td>m(ax)</td> <td>m/s²</td> <td>average longitudinal acceleration of following vehicle</td> </tr> <tr> <td>8</td> <td>max(ax)</td> <td>m/s²</td> <td>maximum longitudinal acceleration of following vehicle</td> </tr> <tr> <td>9</td> <td>min(ax)</td> <td>m/s²</td> <td>minimum longitudinal acceleration of following vehicle</td> </tr> <tr> <td>10</td> <td>sd(ax)</td> <td>m/s²</td> <td>standard deviation of longitudinal acceleration of following vehicle</td> </tr> <tr> <td>11</td> <td>mdn(ax)</td> <td>m/s²</td> <td>median longitudinal acceleration of following vehicle</td> </tr> <tr> <td>12</td> <td>duration</td> <td>s</td> <td>duration</td> </tr> <tr> <td>13</td> <td>m(d)</td> <td>m</td> <td>average distance to lead vehicle</td> </tr> <tr> <td>14</td> <td>min(d)</td> <td>m</td> <td>minimum distance to lead vehicle</td> </tr> <tr> <td>15</td> <td>m(THW)</td> <td>s</td> <td>average time headway</td> </tr> <tr> <td>16</td> <td>min(THW)</td> <td>s</td> <td>minimum time headway</td> </tr> <tr> <td>17</td> <td>m(TTC)</td> <td>s</td> <td>average time to collision</td> </tr> <tr> <td>18</td> <td>min(TTC)</td> <td>s</td> <td>minimum time to collision</td> </tr> <tr> <td>19</td> <td>v_min(TTC)</td> <td>m/s</td> <td>velocity of following vehicle at the minimum time to collision</td> </tr> <tr> <td>20</td> <td>a_min(TTC)</td> <td>m/s²</td> <td>acceleration of following vehicle at the minimum time to collision</td> </tr> <tr> <td>21</td> <td>THW_min(TTC)</td> <td>s</td> <td>time headway at the minimum time to collision</td> </tr> <tr> <td>22</td> <td>d_min(TTC)</td> <td>m</td> <td>distance to lead vehicle at the minimum time to collision</td> </tr> </tbody> </table> <p> </p> <table> <caption>meta table of crossing</caption> <tbody> <tr> <td>col</td> <td>colname</td> <td>unit</td> <td>description</td> </tr> <tr> <td>1</td> <td>id</td> <td>-</td> <td>trial id</td> </tr> <tr> <td>2</td> <td>subscenario</td> <td>-</td> <td>vehicle yielded (yielding) or didn't yield (non-yielding) to the oncoming road user</td> </tr> <tr> <td>3</td> <td>m(v)</td> <td>m/s</td> <td>average velocity of following vehicle</td> </tr> <tr> <td>4</td> <td>max(v)</td> <td>m/s</td> <td>maximum velocity of following vehicle</td> </tr> <tr> <td>5</td> <td>sd(v)</td> <td>m/s</td> <td>standard deviation of velocity of following vehicle</td> </tr> <tr> <td>6</td> <td>mdn(v)</td> <td>m/s</td> <td>median velocity of following vehicle</td> </tr> <tr> <td>7</td> <td>m(ax)</td> <td>m/s²</td> <td>average longitudinal acceleration of following vehicle</td> </tr> <tr> <td>8</td> <td>max(ax)</td> <td>m/s²</td> <td>maximum longitudinal acceleration of following vehicle</td> </tr> <tr> <td>9</td> <td>min(ax)</td> <td>m/s²</td> <td>minimum longitudinal acceleration of following vehicle</td> </tr> <tr> <td>10</td> <td>sd(ax)</td> <td>m/s²</td> <td>standard deviation of longitudinal acceleration of following vehicle</td> </tr> <tr> <td>11</td> <td>mdn(ax)</td> <td>m/s²</td> <td>median longitudinal acceleration of following vehicle</td> </tr> <tr> <td>12</td> <td>duration</td> <td>s</td> <td>duration</td> </tr> <tr> <td>13</td> <td>PET</td> <td>s</td> <td>post encroachment time</td> </tr> <tr> <td>14</td> <td>m(TAdv)</td> <td>s</td> <td>average time advantage</td> </tr> <tr> <td>15</td> <td>min(TAdv)</td> <td>s</td> <td>minimum time advantage</td> </tr> <tr> <td>16</td> <td>v_min(TAdv)_id1</td> <td>m/s</td> <td>Oncoming object’s velocity the moment of minimum time advantage</td> </tr> <tr> <td>17</td> <td>a_min(TAdv)_id1</td> <td>m/s²</td> <td>Oncoming object’s acceleration in heading the moment of minimum time advantage</td> </tr> <tr> <td>18</td> <td>v_min(TAdv)_id2</td> <td>m/s</td> <td>vehicle’s velocity the moment of minimum time advantage</td> </tr> <tr> <td>19</td> <td>a_min(TAdv)_id2</td> <td>m/s²</td> <td>vehicle’s acceleration in heading the minimum time advantage</td> </tr> <tr> <td>20</td> <td>THW_min(TAdv)_id1</td> <td>s</td> <td>Timeheadway oncoming object to the crossing area’s entering part the moment of minimum time advantage</td> </tr> <tr> <td>21</td> <td>THW_min(TAdv)_id2</td> <td>s</td> <td>Timeheadway vehicle to the crossing area’s entering part the moment of minimum time advantage</td> </tr> <tr> <td>22</td> <td>d_min(TAdv)_id1</td> <td>m</td> <td>Distance oncoming object to the crossing area’s entering part the moment of minimum time advantage</td> </tr> <tr> <td>23</td> <td>d_min(TAdv)_id2</td> <td>m</td> <td>Distance vehicle to the crossing area’s entering part the moment of minimum time advantage</td> </tr> </tbody> </table> <p> </p>
Dataset - paper: Child eating behaviors, parental feeding practices and food shopping motivations during the COVID-19 lockdown in France
<p>Dataset corresponding to a paper that has been published in Appetite (Philippe K, Chabanet C, Issanchou S, Monnery-Patris S. <em>Child eating behaviors, parental feeding practices and food shopping motivations during the COVID-19 lockdown in France: (How) did they change? </em>Appetite. 2021 Jun 1;161:105132. doi: <strong>10.1016/j.appet.2021.105132</strong>. Epub 2021 Jan 23. PMID: 33493611; PMCID: PMC7825985).</p> <p>The objective of the study was to evaluate possible changes in eating behaviors in children aged 3–12 years, in parental eating and cooking behaviors, in parental feeding practices, and also in parental motivations when shopping for food during the lockdown, compared to the period before the lockdown.</p> <p>Information about the dataset and the corresponding documents can be found in the document "Metadata-paper-COVID.docx".</p>
What does it take to generate new growth - Survey data on company perceptions on innovative behavior
<p>This data includes raw survey data, a codebook and the survey form for the survey <em>what does it take to generate new growth? </em>The survey focused on comprehensively mapping the Finnish companies growth outlooks and their underlying management practices and principles. The study creates an overview of top managers’ views on Finnish companies’ growth, innovativeness, and the ability for renewal. It allows us to identify what sets high-growing companies apart from others. The Codebook is associated with an SPSS and CSV file including the data.</p>
ScienceDex guides
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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.