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Dataset results
199 results for “Active Learning”
Figs 7–10 in The description of a new species of gall-inducing wasp: a learning activity for primary school students
Figs 7–10. (7,8) Tanaostigmodes shrek: (7) habitus; (8) face. (9,10) Galls: (9) three normal Bossiaea seeds with elaiosomes, and one seed with gall induced in elaiosome; (10) close up of gall induced in elaiosome.
Figs 1–6 in The description of a new species of gall-inducing wasp: a learning activity for primary school students
Figs 1–6. Students at work on the project. (1) Students, from left to right: Tilly Harper, Sam Hardwick, Sam La Salle, Gareth Houghton, Matthew Mullaney, Alex La Salle; (2,3) Students using the digital camera attached to microscope. (4–6) Students at work using the scanning electron microscope.
Figs 17–22 in The description of a new species of gall-inducing wasp: a learning activity for primary school students
Figs 17–22. Tanaostigmodes shrek: (17) forewing; (18) base of forewing; (19) antenna (dry mount); (20) antenna (slide mount); (21) antenna (dry mount); (22) antenna (slide mount).
PyTAIL Benchmark of Active Learning on Social Media Text Classification
<p>PyTAIL Benchmark of Active Learning on Social Media Text Classification</p><p>Read our paper for details: https://arxiv.org/abs/2211.13786</p><ul><li>ArXiv: https://arxiv.org/abs/2211.13786</li><li>Dataset: https://doi.org/10.5281/zenodo.7236430</li><li>Code: https://github.com/socialmediaie/pytail</li><li>Video: https://www.youtube.com/watch?v=AwDu64gN8t4 </li></ul>
Behavioural Activation and Severe Learning Disabilities
ClinicalTrials.gov study NCT06851741. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Move, Play, Learn! Creating Active Classrooms in Early Care and Education Centers
ClinicalTrials.gov study NCT02851030. IPD Sharing: NO. Countries: 0. Publications: 0.
IMPROVING CAUSE DETECTION SYSTEMS WITH ACTIVE LEARNING
IMPROVING CAUSE DETECTION SYSTEMS WITH ACTIVE LEARNING ISAAC PERSING AND VINCENT NG Abstract. Active learning has been successfully applied to many natural language processing tasks for obtaining annotated data in a cost-effective manner. We propose several extensions to an active learner that adopts the margin-based uncertainty sampling framework. Experimental results on a cause detection problem involving the classification of aviation safety reports demonstrate the effectiveness of our extensions.
Multi-omics and machine learning reveal context-specific gene regulatory activities of PML-RARA in Acute Promyelocytic Leukemia [RNA-seq]
GEO Series GSE173754. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Multi-omics and machine learning reveal context-specific gene regulatory activities of PML-RARA in Acute Promyelocytic Leukemia [Cut&Run]
GEO Series GSE173753. Homo sapiens. 8 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Multi-omics and machine learning reveal context-specific gene regulatory activities of PML-RARA in Acute Promyelocytic Leukemia [Capture Hi-C]
GEO Series GSE173752. Homo sapiens. 6 samples. Type: Other.
Prediction of on-target and off-target activity of CRISPR-Cas13dguide RNAs using deep learning
GEO Series GSE232228. synthetic construct; Homo sapiens. 32 samples. Type: Other.
Empowering Sexuality Education: Active learning through University-School-Health-Community partnerships
<p>Data set of the article: Empowering Sexuality Education: Active learning through University-School-Health-Community partnerships </p>
Multi-omics and machine learning reveal context-specific gene regulatory activities of PML-RARA in Acute Promyelocytic Leukemia [Cut&Tag]
GEO Series GSE209833. Homo sapiens. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Activity-Dependent Remodeling of Corticostriatal Axonal Boutons During Motor Learning
<p>Source Data</p>
Association of Perfectionism with the academic performance of medical students and its interaction with self-efficacy in an active learning curriculum
Open the record for dataset details and reuse information.
Multi-omics and machine learning reveal context-specific gene regulatory activities of PML-RARA in Acute Promyelocytic Leukemia [APL PBMCs ATAC-seq]
GEO Series GSE215101. Homo sapiens. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints. Datasets, Benchmark Results, and Torch Files.
<div> <div># ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints. Datasets, Benchmark Results, and Torch Files.</div> <br> <div>## Table of Contents</div> <br> <div>- [Overview](#overview)</div> <div>- [Folder Structure](#folder-structure)</div> <div>- [Contents](#contents)</div> <div>- [Licenses](#licenses)</div> <br> <div>## Overview</div> <div>This project contains three datasets along with stored results from the conducted benchmark analysis and torch files for running or reproducing active learning experiments.</div> <br> <div>## Folder Structure</div> <br> <div>```plaintext</div> <div>conBatchBAL_datasets/</div> <div>├── benchmark_results/</div> <div>├── benchmark_torch_files/</div> <div>├── build6k/</div> <div>├── mnist6k/</div> <div>└── nieman17k/</div> <div>```</div> <br> <div>## Contents:</div> <div>- benchmark_results/: This directory contains the results and config files for reproducing the experiments presented in the paper.</div> <br> <div>- benchmark_torch_files/: This folder contains the required torch (and json) files to run/reproduce active learning experiments.</div> <br> <div>- build6k/: This folder contains approximately 6000 aerial images of buildings in Rotterdam with their corresponding energy efficiency class and geolocation.</div> <br> <div>- mnist6k/: This folder contains approximately 6000 images of digits *artificially* geolocated in Rotterdam. The geolocations correspond to the buildings contained on the *build6k* dataset.</div> <br> <div>- nieman17k/: This folder contains approximately 17000 aerial images of buildings in Rotterdam with their corresponding typology class and geolocation.</div> <br> <div>**Additional readme files are included in each directory.**</div> <br> <div>## Licenses</div> <br> <div>- build6k/</div> <div>The build6k dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) [LICENSE](https://creativecommons.org/licenses/by/4.0/), allowing for use, distribution, and modifications with proper attribution.</div> <br> <div>- mnist6k/</div> <div>The mnist6k dataset is released under the CC BY-SA 3.0 [LICENSE](https://creativecommons.org/licenses/by-sa/3.0/).</div> <br> <div>- nieman17k/</div> <div>The nieman17k dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) [LICENSE](https://creativecommons.org/licenses/by/4.0/), allowing for use, distribution, and modifications with proper attribution.</div> <br> <div>- Benchmark Results and Torch Files</div> <div>The benchmark results and Torch files generated as part of this project are released under the Creative Commons Attribution 4.0 International (CC BY 4.0) [LICENSE](https://creativecommons.org/licenses/by/4.0/), allowing for use, distribution, and modifications with proper attribution.</div> <br> <div>**License details are included separately in each directory**</div> </div>
Bacteria-Specific Features Selection for Enhanced Antimicrobial Peptide Activity Predictions Using Machine-Learning Methods
<p>We developed a new computational approach that allowed us to train several supervised machine-learning models using a specific set of data associated with peptides targeting E. coli bacteria. LASSO regression and Support Vector Machine techniques have been utilized to select, among more than 1500 physio-chemical descriptors, the most important features that can be used to classify a peptide as antimicrobial or ineffective against E. coli. We then performed the classification of active versus inactive AMPs using the Support Vector classifiers, Logistic Regression, and Random Forest methods. This computational study allows us to make recommendations of how to design more efficient anti-bacterial drug therapies.</p>
Dataset for User Preference Optimization for Control of Ankle Exoskeletons using Sample Efficient Active Learning
<p>Dataset for User Preference Optimization for Control of Ankle Exoskeletons using Sample Efficient Active Learning</p>
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.