Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
125
datasets available to search
ShareScore release 0.9.0
Dataset results
125 results for “Structure learning”
Evaluating the Effects of a Structured Training Program on the Learning Curve of Residents in Laparoscopic Surgery
ClinicalTrials.gov study NCT02665000. IPD Sharing: UNDECIDED. Countries: 1. Publications: 7.
Efficacy of Near-Assisted Learning (NAL) in Improving Students' Objective Structured Clinical Examination (OSCE) Grades
ClinicalTrials.gov study NCT04177979. IPD Sharing: NO. Countries: 1. Publications: 13.
Structure Learning Training and Cognitive Flexibility
ClinicalTrials.gov study NCT05611788. IPD Sharing: YES. Countries: 1. Publications: 14.
Data from: Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning
Open the record for dataset details and reuse information.
Adjacency matrices and nodal attributes for prestige and homophily predict network structure for social learning of medicinal plant knowledge
Open the record for dataset details and reuse information.
Early life learning ability predicts adult social structure, with potential implications for fitness outcomes in the wild
Open the record for dataset details and reuse information.
Predicting Hydrophobicity by Learning Spatiotemporal Features of Interfacial Water Structure: Combining Molecular Dynamics Simulations with Convolutional Neural Networks
<p>Files for reproducing results from Kelkar et al. (JPCB 2020) - Predicting Hydrophobicity by Learning Spatiotemporal Features of Interfacial Water Structure: Combining Molecular Dynamics Simulations with Convolutional Neural Networks</p> <p> </p> <p>This folder contains simulations starter files and also plug-and-play datasets to test ML algorithms on molecular dynamics (MD) simulation data.</p> <p> </p> <p>All analysis scripts can also be found on GitLab on this link: https://gitlab.com/atharva-kelkar/kelkar_et_al_jpcb_2020</p>
Data from: Reproductive isolation with a learned trait in a structured population
Assortative mating displays and/or preferences can be affected by learning across a wide range of animal taxa, but the specifics of how this learning affects speciation with gene flow are not well understood. We use population genetic models with trait learning to investigate how the identity of the tutor affects the divergence of a self-referent phenotype-matching trait. We find that oblique learning (learning from unrelated individual of the previous generation) and maternal learning mask sexual selection and therefore do not allow the maintenance of divergence. In contrast, by enhancing positive frequency-dependent sexual selection, paternal learning can maintain more divergence than genetic inheritance, but leads to the loss of polymorphism more easily. Furthermore, paternal learning inhibits the invasion of a novel self-referent phenotype-matching trait, especially in a large population.
Data from: Neural structure mapping in human probabilistic reward learning
Humans can learn abstract concepts that describe invariances over relational patterns in data. One such concept, known as magnitude, allows stimuli to be compactly represented on a single dimension (i.e. on a mental line). Here, we measured representations of magnitude in humans by recording neural signals whilst they viewed symbolic numbers. During a subsequent reward-guided learning task, the neural patterns elicited by novel complex visual images reflected their payout probability in a way that suggested they were encoded onto the same mental number line, with 'bad' bandits sharing neural representation with 'small' numbers and 'good' bandits with 'large' numbers. Using neural network simulations, we provide a mechanistic model that explains our findings and shows how structural alignment can promote transfer learning. Our findings suggest that in humans, learning about reward probability is accompanied by structural alignment of value representations with neural codes for the abstract concept of magnitude.
MOTIVATION IN THE STRUCTURE OF LEARNING ACTIVITY OF A HIGHER EDUCATIONAL INSTITUTION STUDENT
Open the record for dataset details and reuse information.
Left atrial polygenic scores for "Deep Learning of Left Atrial Structure and Function Provides Link to Atrial Fibrillation Risk"
Open the record for dataset details and reuse information.
Graph Contrastive Learning with Adversarial Structure Refinement (GCL-ASR)
Open the record for dataset details and reuse information.
Predicting pore structure and oil-bearing characteristics in saline-alkaline lacustrine shale strata via transfer learning
<p>玛湖凹陷凤城组样品的原始数据和吉木萨凹陷芦草沟组样品的原始数据</p>
Predicting pore structure and oil-bearing characteristics in saline-alkaline lacustrine shale strata via transfer learning
<p>玛湖凹陷凤城组和吉木萨尔凹陷芦草沟组样本的原始数据,以及核心 Python 代码。</p>
Dual-Transformer Architecture for Cross-Modal Learning on Tree-Structured Data in Regression Tasks
Open the record for dataset details and reuse information.
Models from "Applying Machine Learning to Characterize and Extrapolate the Relationship Between Seismic Structure and Surface Heat Flow"
Open the record for dataset details and reuse information.
Conformation Database for Publication: Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction
<p><strong>Conformation database</strong> for 2022 Publication "Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction"</p> <ul> <li>DOI of Physica A publication: <a href="https://doi.org/10.1016/j.physa.2022.128395">https://doi.org/10.1016/j.physa.2022.128395</a></li> <li>GitHub source code: <a href="https://github.com/CompSoftMatterBiophysics-CityU-HK/Applying-DRL-to-HP-Model-for-Protein-Structure-Prediction">https://github.com/CompSoftMatterBiophysics-CityU-HK/Applying-DRL-to-HP-Model-for-Protein-Structure-Prediction</a></li> </ul> <p>This conformation database shows the distinct conformations of best-known and next best energies:</p> <p>├── <strong>20merA</strong><br> │ ├── <strong>20merA_E8_set</strong><br> │ ├── <strong>20merA_E9_set</strong><br> │ ├── confs_20merA_E8.txt<br> │ └── confs_20merA_E9.txt<br> ├── <strong>20merB</strong><br> │ ├── <strong>20merB_E10_set</strong><br> │ ├── <strong>20merB_E9_set</strong><br> │ ├── confs_20merB_E10.txt<br> │ └── confs_20merB_E9.txt<br> ├── <strong>24mer</strong><br> │ ├── <strong>24mer_E8_set</strong><br> │ ├── <strong>24mer_E9_set</strong><br> │ ├── confs_24mer_E8.txt<br> │ └── confs_24mer_E9.txt<br> ├── <strong>25mer</strong><br> │ ├── <strong>25mer_E7_set</strong><br> │ ├── <strong>25mer_E8_set</strong><br> │ ├── confs_25mer_E7.txt<br> │ └── confs_25mer_E8.txt<br> ├── <strong>36mer</strong><br> │ ├── <strong>36mer_E13_set</strong><br> │ ├── <strong>36mer_E14_set</strong><br> │ ├── confs_36mer_E13.txt<br> │ └── confs_36mer_E14.txt<br> ├── <strong>48mer</strong><br> │ ├── <strong>48mer_E22_set</strong><br> │ ├── <strong>48mer_E23_set</strong><br> │ ├── confs_48mer_E22.txt<br> │ └── confs_48mer_E23.txt<br> └── <strong>50mer</strong><br> ├── <strong>50mer_E20_set</strong><br> ├── <strong>50mer_E21_set</strong><br> ├── confs_50mer_E20.txt<br> └── confs_50mer_E21.txt</p>
PYTHIA6 Dataset: Machine learning-based jet and event classification at the Electron-Ion Collider with applications to hadron structure and spin physics
<p>Dataset corresponding to: https://inspirehep.net/literature/2164495</p> <p>The data set contains PYTHIA6 jets in ep collisions, with separate files for LO DIS and photoproduction samples. For further details about the data set, see https://inspirehep.net/literature/2164495. Examples of how to analyze the data set can be found at: https://github.com/jdmulligan/ml-eic-flavor.</p> <p>Please contact james.mulligan@berkeley.edu with any questions.</p>
Data from: Neural structure mapping in human probabilistic reward learning
Open the record for dataset details and reuse information.
Data from: Reproductive isolation with a learned trait in a structured population
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
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.