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Dataset results
199 results for “active learning”
Photovoice and case based learning activities in higher education (University of Zaragoza - Social Work degree)
<p>The collected variables are: students data (gender, age, university admittance marks from 0 to 14), academic perforrnace of photovoice (PV) activity (from 0 to 10), academic performance of Case based learning (CBL) activity (form 0 to 10), order of the exercises, satisfaction with the photovoice activity, satisfaction with the CBL activity, items and overall self-efficacy scale score</p>
Primary Care Pediatrics Learning Activity and Nutrition With Families
ClinicalTrials.gov study NCT02873715. IPD Sharing: NO. Countries: 1. Publications: 8.
Robot-Assisted Therapy and Motor Learning: An Active Learning Program for Stroke
ClinicalTrials.gov study NCT02747433. IPD Sharing: NO. Countries: 1. Publications: 1.
Developing and Testing an Implementation Strategy for Active Learning to Promote Physical Activity in Children
ClinicalTrials.gov study NCT05048433. IPD Sharing: NO. Countries: 1. Publications: 2.
Motor Learning in Health and Movement Disorders: Role of Physical Activity and Advanced Devices
ClinicalTrials.gov study NCT07066137. IPD Sharing: YES. Countries: 1. Publications: 1.
Dual loop active learning of hydrophobicity of patterned SAMs
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Neural ensemble reactivation in REM and SWS coordinate with muscle activity to promote rapid motor skill learning
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Data from: Optimizing gelation time for cell shape control through active learning
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Electrophysiological recordings of prefrontal activity over learning in non-human primates
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Data from: Active learning design: Modeling force output for axisymmetric soft pneumatic actuators
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Alarm cues and alarmed conspecifics: Neural activity during social learning from different cues in Trinidadian guppies
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Tables of Emission Line Spectra discovered by Active Deep Learning in LAMOST DR2
<p>Tables of emission line spectra discovered by Active Deep Learning in LAMOST DR2 spectra survey. These are referred to in an article Škoda,Podsztavek&Tvrdík "Active deep learning method for the discovery of objects of interest in large spectroscopic surveys" submitted to Astronomy and Astrophysics.</p> <p>The zipped files contain both CSV and VOTable formats, the HTML version may be opened directly in a browser. A link to an interactive spectrum plot at Strasbourg datacenter (CDS) Vizier archive may be activated from it as well in most common browsers.</p> <p>The detailed description of tables is in attached README file.</p>
Data from: Developmental changes in hippocampal CA1 single neuron firing and theta activity during associative learning
Hippocampal development is thought to play a crucial role in the emergence of many forms of learning and memory, but ontogenetic changes in hippocampal activity during learning have not been examined thoroughly. We examined the ontogeny of hippocampal function by recording theta and single neuron activity from the dorsal hippocampal CA1 area while rat pups were trained in associative learning. Three different age groups [postnatal days (P)17-19, P21-23, and P24-26] were trained over six sessions using a tone conditioned stimulus (CS) and a periorbital stimulation unconditioned stimulus (US). Learning increased as a function of age, with the P21-23 and P24-26 groups learning faster than the P17-19 group. Age- and learning-related changes in both theta and single neuron activity were observed. CA1 pyramidal cells in the older age groups showed greater task-related activity than the P17-19 group during CS-US paired sessions. The proportion of trials with a significant theta (4–10 Hz) power change, the theta/delta ratio, and theta peak frequency also increased in an age-dependent manner. Finally, spike/theta phase-locking during the CS showed an age-related increase. The findings indicate substantial developmental changes in dorsal hippocampal function that may play a role in the ontogeny of learning and memory.
Small Test Suites for Active Automata Learning: Supplemental Material
<p>Supplemental material for the paper: Small Test Suites for Active Automata Learning.</p> <p>Submitted to TACAS 2024.</p>
Appendix B and C of "The Galaxy Activity, Torus, and Outflow Survey (GATOS). Black hole mass estimation using machine learning"
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DeepCanola: Phenotyping Brassica Pods Using Semi-Synthetic Data and Active Learning
<p>Dataset accomapnying the publication: <em>DeepCanola: Phenotyping Brassica Pods Using Semi-Synthetic Data and Active Learning </em>by Van Vliet, Atkins et al.</p> <p>We provide model weights, training and vlidation datasets, as well as phenotype outputs. Each file/folder is outlined below:</p> <ul> <li><em>deepcanola.pth - </em>Model weights for the final Model 4, named DeepCanola</li> <li><em>generated_datasets</em> - Datasets generated at each stage of the active learning process, datasets 1-4 <ul> <li>Each folder is an iteration of the active learning process, inside each folder are the generated images and associated annotations stored in the COCO format.</li> </ul> </li> <li><em>real_world_datasets - </em>Real-world datasets used for either creation of the pod pools or validation. Datasets include: <ul> <li><em>br9</em> - Ordered and disordered dataset of images with generated pod length data of the ordered images stored in the `br9_gt_lengths.csv` file</li> <li><em>br11</em> - Ordered and disordered dataset of images only</li> <li><em>br17</em> - Ordered dataset with ground-truth of images with length annotations collected in ImageJ and stored in the `BR017 POD SCAN DATA.csv` file.</li> <li><em>misc</em> - Dataset of m<span>iscellaneous images including Brassica napus from Rothamstead and brassica relatives</span></li> </ul> </li> <li><em>data_generation_pools</em> - Pools used to generate semi-synthetic data at each step of the active learning process. Pools include: <ul> <li><em>background_pool</em> - Created background images to be selected at random by the semi-synthetic data generation script</li> <li><em>pod_pools - </em>Pools of pods used in the semi-synthetic data generation process. Pod pools include: <ul> <li><em>br9 - </em>673 pods with associated masks</li> <li><em>br9 and br17 - </em>673 + 332 pods with associated masks</li> </ul> </li> </ul> </li> <li><em>deepcanola_outputs - </em>Phenotype data outputs generated by DeepCanola. Each output is stored as a .csv file of both length measurements of each pod (with <em>_objects.csv</em> suffix), and average length measurements per image (with <em>_averages.csv</em> suffix). Outputs include: <ul> <li><em>br9_ordered</em></li> <li><em>br9_disordered</em></li> <li><em>br17</em></li> </ul> </li> </ul>
Neuronal activity in sensory cortex predicts the specificity of learning
<p>Learning to avoid dangerous signals while preserving normal responses to safe stimuli is essential for everyday behavior and survival. Following identical experiences, subjects exhibit fear specificity ranging from high (specializing fear to only the dangerous stimulus) to low (generalizing fear to safe stimuli), yet the neuronal basis of fear specificity remains unknown. Here, we identified the neuronal code that underlies inter-subject variability in fear specificity using longitudinal imaging of neuronal activity before and after differential fear conditioning in the auditory cortex of mice. Neuronal activity prior to, but not after learning predicted the level of specificity following fear conditioning across subjects. Stimulus representation in auditory cortex was reorganized following conditioning. However, the reorganized neuronal activity did not relate to the specificity of learning. These results present a novel neuronal code that determines individual patterns in learning. Keywords: fear conditioning, auditory cortex, sensory systems, learning, computational model, imaging, sensory cortex, tuning curve, neurobiology, population coding.</p>
Uncertainty-aware molecular dynamics from Bayesian active learning: Phase Transformations and Thermal Transport in SiC
<p>Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomic level processes. Active learning methods have been recently developed to train force fields efficiently and automatically. Among them, Bayesian active learning utilizes principled uncertainty quantification to make data acquisition decisions. In this work, we present an efficient Bayesian active learning workflow, where the force field is constructed from a sparse Gaussian process regression model based on atomic cluster expansion descriptors. To circumvent the high computational cost of the sparse Gaussian process uncertainty calculation, we formulate a high-performance approximate mapping of the uncertainty and demonstrate a speedup of several orders of magnitude. As an application, we train a model for silicon carbide (SiC), a wide-gap semiconductor with complex polymorphic structure and diverse technological applications in power electronics, nuclear physics and astronomy. We show that the high pressure phase transformation is accurately captured by the autonomous active learning workflow. The trained force field shows excellent agreement with both \textit{ab initio} calculations and experimental measurements, and outperforms existing empirical models on vibrational and thermal properties. The active learning workflow is readily generalized to a wide range of systems, accelerates computational understanding and design.</p>
Dataset from Research: Effect of active lecture and webquest on learning outcomes in higher education
<p>Dataset in a xlsx file used by Remedios Aguilar-Moya & collaborators in the research “Effect of direct instruction and webquest on learning outcomes in higher education”.</p>
Raw dataset for "Multi-Objective Bayesian Active Learning for MeV-ultrafast electron diffraction"
<p>this dataset contains raw data collected at the SLAC MeV-UED facility, the data was saved in .npy format. The name of each file starts with a number referring to the time stamp when it was recorded.</p> <p>“xxxxxxxxxx_Andor1.npy” contains the beam images recorded at the diffraction detector plane associated with the q-resolution</p> <p>“xxxxxxxxxx_qm.npy” contains the beam images recorded at the sample plane associated with the spot size</p> <p>“xxxxxxxxxx_scalars.npy” contains the machine settings and readouts from the EPICs system, scalar names are listed in “scalars.txt”</p> <p>“xxxxxxxxxx_vcc.npy” contains the images recorded at a virtual cathode camera</p> <p>“xxxxxxxxxx_THzon_img.npy” contains the THz streaked beam images associated with the temporal length</p> <p>“xxxxxxxxxx_THzoff_img.npy” contains the unstreaked beam images for subtracting intrinsic broadening without THz pulses</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.