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87
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ShareScore release 0.9.0
Dataset results
87 results for “Learning Environment”
DeepREM: Deep-Learning-Based Radio Environment Map Estimation from Sparse Measurements
<p><strong>DeepREM: Deep-Learning-Based Radio Environment Map Estimation from Sparse Measurements</strong></p> <p>DeepREM combines two deep-learning models (U-Net and CGAN) that estimate Radio Environment Maps (REMs) from sparse measurements. In this repository, we present the dataset and the interactive app developed to use the resulting models derived from the research. </p> <p><strong>Urban REMs dataset: </strong>We present a (REM) dataset of urban scenarios. Each map provides coverage information in areas from 2290 x 3670 m<sup>2</sup> to 3810 x 5160 m<sup>2</sup> with a resolution of 10 m. Coverage areas are sampled from Colombian cities (Armenia, Bogota, Cali, Ibague, Manizales, Medellin, and Pasto) and U.S. cities (Columbus and Washington). The simulations include topographic and building vector database information and Intelligent Ray-Tracing as a propagation model.</p> <p>In our <strong>second version</strong>, 400 new REMs were added to the dataset, including 4 new city areas with different topographic and building features (100 REMs for each area). The new cities are Barranquilla, Bucaramanga, Popayan, and North Pasto, all in Colombia. In addition, we also present an interactive application developed in the Streamlit framework to test CGAN and UNET performance in RSRP and BS coverage predictions either with REMs from our dataset or with completely new user-supplied REMs.The repository of the app can be downloaded at the following link: <a href="https://gitlab.com/andreajohanacv/deeprem">DeepREMapp</a></p>
Embedded machine learning to promote detection of unsafe environments
<p>Datasets used to train and validate the devices and model described in the paper "<strong>Embedded machine learning of IoT streams to promote early detection of unsafe environments</strong>"</p>
Supplementary Videos for MEMTrack: A deep learning-based approach to micro- motor tracking in dense fibrous environments
<ul><li><strong>Video S1: </strong>Video shows the movement of representative bacterial biomotors from each of the four motility subpopulations in collagen. Video slowed down by 2x</li><li><strong>Video S2:</strong> Videos show ground truth annotation (red) by manual tracking and MEMTrack-enabled automated detection and tracking of bacteria in collagen. All scale bars are 20 µm and timestamp indicates s:ms. Video slowed down by 2x</li><li><strong>Video S3: </strong>Videos show ground truth annotation (red) by manual tracking and MEMTrack-enabled automated detection and tracking of bacteria in aqueous media. All scale bars are 20 µm and timestamp indicates s:ms. Video slowed down by 2x</li></ul>
Food discovery is associated with different reliance on social learning and lower cognitive flexibility across environments in a food caching bird
Open the record for dataset details and reuse information.
Use of Machine Learning in virtual learning environments: a bibliometric review
Open the record for dataset details and reuse information.
DeepCollision: Learning Configurations of Operating Environment of Autonomous Vehicles to Maximize their Collisions
<p>With the aim to test autonomous driving systems, we propose a novel reinforcement learning (RL)-based approach named <strong>DeepCollision </strong>to learn operating environment configurations of autonomous vehicles, including formalizing environment configuration learning as an MDP and adopting DQN algorithm as the RL solution; <strong>DeepCollision</strong> learns environment configurations to maximize collisions of an Autonomous Vehicle Under Test (AVUT).</p> <p>This dataset contains:</p> <ol> <li><strong>algorithms</strong> - The algorithm of DeepCollision, which includes the network architecture and the DQN hyperparameter settings;</li> <li><strong>pilot-study</strong> - All the raw data and plots for the pilot study;</li> <li><strong>formal-experiment</strong> - A dataset contains all the raw data for analysis and the scenarios with detailed demand values;</li> <li><strong>rest-api</strong> - The REST API endpoints for environment configuration and one <strong>example </strong>to show the usage of the APIs.</li> </ol>
Rapid Flood Simulation and Source Area Identification in Urban Environments via Interpretable Deep Learning
<p>Here is the data and original code for the paper titled <em>Rapid Flood Simulation and Source Area Identification in Urban Environments via Interpretable Deep Learning</em>. If you have any questions, please contact <a rel="noopener">202331470015@mail.bnu.edu.cn</a>.</p>
Rapid Flood Simulation and Source Area Identification in Urban Environments via Interpretable Deep Learning
<p>Here are the relevant data and preliminary code for the paper titled 'Rapid Flood Simulation and Source Area Identification in Urban Environments via Interpretable Deep Learning' for your reference. If you have any questions, please contact <a rel="noopener">202331470015@mail.bnu.edu.cn</a>.</p>
Data from: Visual environment, attention allocation, and learning in young children: when too much of a good thing may be bad
A large body of evidence supports the importance of focused attention for encoding and task performance. Yet young children with immature regulation of focused attention are often placed in elementary-school classrooms containing many displays that are not relevant to ongoing instruction. We investigated whether such displays can affect children's ability to maintain focused attention during instruction and to learn the lesson content. We placed kindergarten children in a laboratory classroom for six introductory science lessons, and we experimentally manipulated the visual environment in the classroom. Children were more distracted by the visual environment, spent more time off task, and demonstrated smaller learning gains when the walls were highly decorated than when the decorations were removed.
Adaptation and evolution of teaching method for university programming subject to the online learning environment - Commit Data
<p>This dataset contains commit UNIX timestamps for Copymaster assignment git repositories of students studying Operating Systems class at Technical University of Košice in the span of years 2017/2018 - 2020/2021.</p>
Adaptation and evolution of teaching method for university programming subject to the online learning environment - Student surveys dataset
<p>This dataset contains anonymized survey data of students studying Operating Systems class at the Technical University of Košice in the span of school years 2017/2018 to 2020/2021.</p>
Adaptation and evolution of teaching method for university programming subject to the online learning environment - Student point gain dataset
<p>This dataset contains anonymized study results of students studying Operating Systems class at the Technical University of Košice in the span of school years 2015/2016 to 2020/2021.</p>
ICT-enabled Social-emotional Learning: Development of Responsibility and Well-being in the Educational Environment
ClinicalTrials.gov study NCT04414449. IPD Sharing: NO. Countries: 1. Publications: 2.
Childcare Outdoor Learning Environments as Active Food Systems
ClinicalTrials.gov study NCT04864574. IPD Sharing: YES. Countries: 1. Publications: 1.
Diabetes Learning in Virtual Environments Just in Time for Community Reentry
ClinicalTrials.gov study NCT05286892. IPD Sharing: YES. Countries: 1. Publications: 42.
MIRA Clinical Learning Environment (MIRACLE): Lung
ClinicalTrials.gov study NCT05689437. IPD Sharing: YES. Countries: 1. Publications: 12.
Data from: Visual environment, attention allocation, and learning in young children: when too much of a good thing may be bad
Open the record for dataset details and reuse information.
Data from: Social learning in a high-risk environment: incomplete disregard for the 'minnow that cried pike' results in culturally transmitted neophobia
Many prey species rely on conspecifics to gather information about unknown predation threats, but little is known about the role of varying environmental conditions on the efficacy of social learning. We examined predator-naive minnows that had the opportunity to learn about predators from experienced models that were raised in either a low- or high-risk environment. There were striking differences in behaviour among models; high-risk models showed a weaker response to the predator cue and became neophobic in response to the control cue (a novel odour, NO). Observers that were previously paired with low-risk models acquired a strong antipredator response only to the predator cue. However, observers that interacted with high-risk models, displayed a much weaker response to the predator odour and a weak neophobic response to the NO. This is the first study reporting such different outcomes of social learning under different environmental conditions, and suggests high-risk environments promote the cultural transmission of neophobia more so than social learning. If such a transfer can be considered similar to secondary traumatization in humans, culturally transmitted neophobia in minnows may provide a good model system for understanding more about the social ecology of fear disorders.
Data from: Age and early social environment influence guppy social learning propensities
Social learning, learning from others, allows animals to quickly and adaptively adjust to changing environments, but only if social learning provides reliable, useful information in that environment. Early life conditions provide a potential cue to the reliability of social information later in life. Here, we addressed whether direct early life experience of the utility of social learning influences later social learning propensities. We reared guppy, Poecilia reticulata, fry for 45 days in three different social conditions which involved the presence of adult demonstrators providing cues about feeding locations in the tanks ('follow adults' and 'avoid adults' treatments), or their absence ('no adults' treatment). In the 'follow adults' treatment, juveniles that swam in the same direction as the adult demonstrators found food, whereas in the 'avoid adults' treatment, subjects that swam in the opposite direction to the demonstrators found food. We then tested the fish with a social learning task, to examine whether prior experience had influenced the social learning tendencies of the juveniles. After another 45 days of rearing under common-garden conditions with no adult fish present in the tanks, subjects were retested with the same social learning task, to investigate whether early experiences had effects persisting into adulthood. After 45 days of rearing we found no evidence for social learning in any of the experimental groups. However, after 90 days of rearing, we found evidence of social learning, but only in the 'follow adults' treatment. These results suggest that social learning propensities may develop over life, and that prior exposure to conspecifics providing useful foraging information during early life can shape the degree of reliance on social learning in adulthood.
Data from: Catecholaminergic regulation of learning rate in a dynamic environment
Adaptive behavior in a changing world requires flexibly adapting one's rate of learning to the rate of environmental change. Recent studies have examined the computational mechanisms by which various environmental factors determine the impact of new outcomes on existing beliefs (i.e., the 'learning rate'). However, the brain mechanisms, and in particular the neuromodulators, involved in this process are still largely unknown. The brain-wide neurophysiological effects of the catecholamines norepinephrine and dopamine on stimulus-evoked cortical responses suggest that the catecholamine systems are well positioned to regulate learning about environmental change, but more direct evidence for a role of this system is scant. Here, we report evidence from a study employing pharmacology, scalp electrophysiology and computational modeling (N = 32) that suggests an important role for catecholamines in learning rate regulation. We found that the P3 component of the EEG—an electrophysiological index of outcome-evoked phasic catecholamine release in the cortex—predicted learning rate, and formally mediated the effect of prediction-error magnitude on learning rate. P3 amplitude also mediated the effects of two computational variables—capturing the unexpectedness of an outcome and the uncertainty of a preexisting belief—on learning rate. Furthermore, a pharmacological manipulation of catecholamine activity affected learning rate following unanticipated task changes, in a way that depended on participants' baseline learning rate. Our findings provide converging evidence for a causal role of the human catecholamine systems in learning-rate regulation as a function of environmental change.
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.