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97 results for “behavioral patterns”
The Contributionsof Eye Gaze Fixations and Target-Lure Similarity to Behavioral and fMRI Indices of Pattern Separation and Pattern Completion
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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>
Dataset for article: Jaber-Lopez, T., Garcia-Gallego, A., Perakakis, P., Georgantzis, N. (2014). Physiological and behavioral patterns of corruption. Frontiers in Behavioral Neuroscience
<p>Dataset and matlab analysis scripts for article: Dataset for article: Jaber-Lopez, T., Garcia-Gallego, A., Perakakis, P., Georgantzis, N. (2014). Physiological and behavioral patterns of corruption. Frontiers in Behavioral Neuroscience</p>
Fig. 1 in Effects of the antennal sensilla distribution pattern on the behavioral responses of Tribolium castaneum (Coleoptera: Tenebrionidae)
Fig. 1. Tribolium castaneum antennal ultrastructure observations. Dorsal view of the whole antenna with 11 antennal segments with a black line indicating where antennae were cut. Sensilla basiconica were found only on the last 3 segments of the antennae and sensilla trichodea on all antennal segments.
Fig. 3 in Effects of the antennal sensilla distribution pattern on the behavioral responses of Tribolium castaneum (Coleoptera: Tenebrionidae)
Fig. 3. SEM photomicrographs of 6 types (A1, B1, C1, D1, E1, F1) of sensilla basiconica (SB) showing lateral view of the antennae of Tribolium castaneum. TEM photomicrographs 6 types (A2, B2, C2, D2, E2, F2) of sensilla basiconica (SB) showing the thin wall and continuous pores and dendrites. CW = cuticle wall, P = pores, D = dendrites, SBI = sensilla basiconica type 1, SBII = sensilla basiconica type II, SBIII = sensilla basiconica type III, SB IV = sensilla basiconica type IV, SBV = sensilla basiconica type V, SBVI = sensilla basiconica type VI.
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>
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>
Behavioral strategies and the spatial pattern formation of nesting
<p>This dataset contains data from a combined field and simulation study regarding spatial pattern formation of nesting, described in the paper: "Batsleer, F., Maes, D., Bonte, D. (2021) Behavioral strategies and the spatial pattern formation of nesting. The American Naturalist".</p> <p>The study investigates the relative importance of environmental and behavioral mechanisms in nest aggregations of the ground-nesting digger wasp <em>Bembix rostrata</em>. A field study was combined with an individual-based model that simulated the possible behaviors of spatial organisation of nesting.</p> <p>In the first analysis, a microhabitat model was built based on the location of the nests from a capture-mark-recapture study (CMR) and environmental variables NDVI (vegetation) and insolation (sun irradiance), derived from detailed remote sensing data from a drone flight.</p> <p>In the second analysis, an IBM was built that combined three possible mechanisms of nest choice to simulate the emerging spatial and network patterns found in the field. This was done by combining an environmental cue (based on the microhabitat model) and two relevant behavioral mechanisms related to local site fidelity and conspecific attraction. Strengths and combinations of the mechanisms could vary. Simulations were compared to the field data to find which combinations and strengths of mechanisms can best explain the emerging spatial and network patterns.</p> <p>The main results are that 1) the observed pattern in nature is best predicted by the simultaneous effect of a weak environmental cue and strong behavioral mechanisms. 2) individuals differ in their combination of mechanisms used and will either use local site fidelity (personal information) or conspecific attraction (inadvertent social information), but not both simultaneously. 3) We demonstrate that the nest pattern formation of a central place foraging insect cannot be considered as the sum of environmental and behavioral mechanisms.</p>
FIGURE 8 in A trilobite cluster from the Silurian Rochester Shale of New York: predation patterns and possible defensive behavior
FIGURE 8. Plot of regressed PC coordinates against log-centroid size and histogram of maximum specimen length, coded for injured and noninjured specimens. A, Regressed PC coordinates against log-centroid size that shows no obvious pattern in injured and noninjured specimens, although many of the larger specimens are injured. B, Histogram of specimen length has an approximately normal distribution with the two largest specimens showing an injury.
FIGURE 1. Slab preserving a in A trilobite cluster from the Silurian Rochester Shale of New York: predation patterns and possible defensive behavior
FIGURE 1. Slab preserving a cluster of 18 fully articulated individuals of Arctinurus boltoni (AMNH- FI-101514–101531) from the mid-Silurian (Wenlock) Rochester Shale, New York state. Stars indicate injured specimens. Scale bar = 10 cm.
FIGURE 5 in A trilobite cluster from the Silurian Rochester Shale of New York: predation patterns and possible defensive behavior
FIGURE 5. Specimens of Arctinurus boltoni with injuries to the thorax (A, B) and with reconstruction that mimics an injury (C, D), under plain and UV light. Arrows point to injuries described in the text. Scale bar = 1 mm. A–B, AMNH-FI-101518. C–D, AMNH-FI-101516.
FIGURE 4 in A trilobite cluster from the Silurian Rochester Shale of New York: predation patterns and possible defensive behavior
FIGURE 4. Further specimens of Arctinurus boltoni with injuries to the pygidium, under plain and UV light. Arrows point to injuries described in the text. Scale bar = 1 mm. A–B, AMNH-FI-101529. C–D, AMNH- FI-101530. E–F, AMNH-FI-101531.
FIGURE 3 in A trilobite cluster from the Silurian Rochester Shale of New York: predation patterns and possible defensive behavior
FIGURE 3. Specimens of Arctinurus boltoni with injuries to the pygidium, under plain and UV light (with brighter areas indicating parts of reconstructed exoskeleton). Arrows point to injuries described in the text. Scale bar = 1 mm. A–B, AMNH-FI-101521. C–D, AMNH-FI-101527.
FIGURE 2 in A trilobite cluster from the Silurian Rochester Shale of New York: predation patterns and possible defensive behavior
FIGURE 2. Diagram of 12 landmarks selected to describe the overall shape of the exoskeleton of Arctinurus boltoni.
FIGURE 7 in A trilobite cluster from the Silurian Rochester Shale of New York: predation patterns and possible defensive behavior
FIGURE 7. Principal components analysis of landmark data, with 49.5% variance in the data explained by the first two PCs (PC1=29.7%, PC2=19.8%). PC1 describes the variation in the intersection of the occipital furrow and anterior-posterior axis and junction points between posterior margin of the 11th tergite. PC2 mostly describes variation in cephalic width.
FIGURE 6 in A trilobite cluster from the Silurian Rochester Shale of New York: predation patterns and possible defensive behavior
FIGURE 6. Arctinurus boltoni specimen AMNH-FI-101520 with injuries to the thorax and pygidium, under A, plain and B, UV light. Arrows point to injuries described in the text. Scale bar = 1 mm.
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.