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95 results for “Interactive Learning”
Enzyme-Substrate Interaction Dataset and Trained MEI Model for Deep Learning-Driven Insights
<p>This dataset and trained model are provided as part of our research on <em>Deep Learning-Driven Insights into Enzyme-Substrate Interaction Discovery</em>.</p>
Higher-order and distributed synergistic functional interactions encode information gain in goal-directed learning
<p>Dataset used in "Higher-order and distributed synergistic functional interactions encode information gain in goal-directed learning"</p> <p>https://www.biorxiv.org/content/10.1101/2024.09.23.614484v1</p>
Supporting data and code for the published paper: Machine Learning Nonadiabatic Dynamics: Eliminating Phase Freedom of Nonadiabatic Couplings with the State-Interaction State-Averaged Spin-Restricted Ensemble-Referenced Kohn–Sham Approach
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Chimney Swift processed dataset from "Unraveling hidden interactions in complex systems with deep learning"
<p>Pre-processed dataset of chimney swift trajectory from the paper "<a href="https://www.nature.com/articles/s41598-021-91878-w">Unraveling hidden interactions in complex systems with deep learning</a>"(https://www.nature.com/articles/s41598-021-91878-w). The original data is provided by "Three-dimensional trajectories and network analyses of group behaviour within chimney swift flocks during approaches to the roost" (<a href="https://doi.org/10.1098/rspb.2016.2602">https://doi.org/10.1098/rspb.2016.2602</a>).</p> <p>Needs to be placed at "./data/Flock/system" in order to properly generate the dataset (See https://github.com/nokpil/AgentNet).</p>
FIG. 1 in A novel food preference in the greater short-nosed fruit bat, Cynopterus sphinx: mother-pup interaction a strategy for learning
FIG. 1. Out-flying activity of different groups of C. sphinx pups during novel food preference test. The recorded number of out-flying activity did not significantly vary between the groups of pups. Data were shown as 0 ± SEM
FIG. 2 in A novel food preference in the greater short-nosed fruit bat, Cynopterus sphinx: mother-pup interaction a strategy for learning
FIG. 2. Behavioural responses of C. sphinx pups to the novel food during preference test. Pups feeding attempts (A), and feeding bouts (B) to the known and novel fruits, pups trained with mother showed more number of feeding attempts and bouts towards the novel fruits than other group pups. Data were shown as 0 ± SEM; *** — P <0.001
Evidence for maintenance of key components of vocal learning in aging budgerigars despite diminished affiliative social interaction
<p>In some species, the ability to acquire new vocalizations persists into adulthood and may be an important mediator of social interactions. While it is generally assumed that vocal learning persists undiminished throughout the lifespan of these open-ended learners, the stability of this trait remains largely unexplored. We hypothesize that vocal learning exhibits senescence, as is typical of complex cognitive traits, and that this decline may relate to age-dependent changes in sociality. The budgerigar (<em>Melopsittacus undulatus</em>), an open-ended learner which develops new contact call types that are shared with social associates upon joining new flocks, provides a robust assay for measuring the effects of aging on vocal learning ability. We formed captive flocks of 4 previously unfamiliar adult males of the same age class, either "young adults" (6 mo.-1 yr.) or "older adults" (≥ 3 yr.), and concurrently tracked changes in contact call structure and social interactions over time. Older adults exhibited decreased vocal diversity, which may be related to the sparser and weaker affiliative bonds observed in older adults. Older adults, however, displayed equivalent levels of vocal plasticity and vocal convergence compared to young adults, suggesting vocal learning ability is largely maintained into later adulthood in an open-ended learner.</p>
Data for the study: "Variational principle to regularize machine-learned density functionals: the non-interacting kinetic-energy functional"
<p>This set of files contains the raw data generated by the study titled:</p> <p>"Variational principle to regularize machine-learned density functionals: the non-interacting kinetic-energy functional"</p> <p>Contains:</p> <p>- A set of Jupyter Notebooks to analyzed the data and produce the figures presented in the paper.</p> <p>- runs: Contains the training and validation scripts for each of the systems studied in this work. Also holds the model weights and validation data.</p> <p>Three folders are found: Hchain, noninteracting and Atoms.</p> <p>- datasets: Holds all the datasets generated and employed in this work.</p>
Data for: Contributions of deep learning to automated numerical modelling of the interaction of electric fields and cartilage tissue based on 3D images
<p>Replication data for: Contributions of deep learning to automated numerical modelling of the interaction of electric fields and cartilage tissue based on 3D images</p> <p> </p> <p> </p>
Improving Visual Perception and Visuo-motor Learning With Neurofeedback of Brain Network Interaction.
ClinicalTrials.gov study NCT05732649. IPD Sharing: NO. Countries: 1. Publications: 9.
VHA Clinicians and Bioterror Events: Interactive Web-based Learning
ClinicalTrials.gov study NCT00123396. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Interactive Learning Method in Nursing Education
ClinicalTrials.gov study NCT05868278. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Interactive Ren'Py Learning in Physiotherapy Education
ClinicalTrials.gov study NCT07274839. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Data from: Costs of deception and learned resistance in deceptive interactions
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Data from: Interspecific interactions and learning variability jointly drive geographic differences in mate preferences
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Evidence for maintenance of key components of vocal learning in aging budgerigars despite diminished affiliative social interaction
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Dataset for interactive course on Deep Learning for Imaging
<p>This is a companion dataset for the course available at <a href="https://github.com/guiwitz/DLImaging">https://github.com/guiwitz/DLImaging</a> on Deep Learning for Imaging. It should be downloaded, decompressed, and placed at the same level as the "notebooks" folder of the course.</p> <p>The list of the original location of the data as well as their licenses can be found in the LICENSE file.</p>
Data from: Learned parasite avoidance is driven by host personality and resistance to infection in a fish–trematode interaction
Cognitive abilities related to the assessment of risk improve survival. While earlier studies have examined the ability of animals to learn to avoid predators, learned parasite avoidance has received little interest. In a series of behavioural trials with the trematode parasite Diplostomum pseudospathaceum, we asked whether sea trout (Salmo trutta trutta) hosts show associative learning in the context of parasitism and if so, whether learning capacity is related to the likelihood of infection mediated through host personality and resistance. We show that animals are capable of learning to avoid visual cues associated with the presence of parasites. However, avoidance behaviour ceased after the likely activation of host resistance following consecutive exposures during learning, suggesting that resistance to infection outweighs avoidance. Further, we found a positive relationship between learning ability and boldness, suggesting a compensation of risky lifestyles through increased investment in cognitive abilities. By contrast, an increased risk of infection due to low resistance was not balanced by learning ability. Instead, these traits were positively related, which may be explained by inherent physiological qualities controlling both traits. Overall, the results demonstrate that parasitism, in addition to other biological interactions such as predation, is an important selective factor in the evolution of animal cognition.
Data from: Building a responsive teacher: how temporal contingency of gaze interaction influences word learning with virtual tutors
Animated pedagogical agents are lifelike virtual characters designed to augment learning. A review of developmental psychology literature led to the hypothesis that the temporal contingency of such agents would promote human learning. We developed a Pedagogical Agent with Gaze Interaction (PAGI), an experimental animated pedagogical agent that engages in gaze interaction with students. In this study, university students learned words of a foreign language, with temporally contingent PAGI (live group) or recorded version of PAGI (recorded group), which played pre-recorded sequences from live sessions. The result revealed that students in the live group scored considerably better than those in the recorded group. The finding indicates that incorporating temporal contingency of gaze interaction from a pedagogical agent has positive effect on learning.
SpatPPI: a geometric deep learning model for predicting protein-protein interactions involving intrinsically disordered regions
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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.