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1,273
datasets available to search
ShareScore release 0.9.0
Dataset results
1,273 results for “Deep Learning”
Data from: Robust mosquito species identification from diverse body and wing images using deep learning
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Use of Deep Learning for structural analysis of CT-images of soil samples
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Supplement: Multicenter validated detection of focal cortical dysplasia using deep learning
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Data from: BubbleID: A deep learning framework for bubble interface dynamics analysis
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Data from: Deep learning reveals hidden diversity of <em>Synechococcus</em> in the coastal water of China: Novel clades and their ecological insights
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Data from: Physics-informed deep learning for plasmonic sensing of nanoscale protein dynamics in solution
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Data from: Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning
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Identification of free-ranging mugger crocodiles by applying deep learning methods on UAV imagery
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Data and code from: Learning a deep language model for microbiomes: The power of large scale unlabeled microbiome data
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Deep learning from phylogenies for diversification analyses
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Deep learning chronic wasting disease (CWD) immunohistochemistry (IHC) image dataset
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Data from: imageseg: An R package for deep learning-based image segmentation
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DEPP: Deep learning enables extending species trees using single genes
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DEPP: Deep learning enables extending species trees using single genes
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Benign samples used in article "DeepDetectNet vs RLAttackNet: An Adversarial Method to Improve Deep Learning-based Static Malware Detection Model"
<p>This repository contains all benign samples used in article "DeepDetectNet vs RLAttackNet: An Adversarial Method to Improve Deep Learning-based Static Malware Detection Model". It is safe to download these samples.</p>
2 Datasets of forests for Computer Vision and Deep Learning techniques
<p>Datasets:</p> <p>1) Coastal forest in Shonai area; contains an orthomosaic (processed by Metashape) of the coastal forest and annotated layers (processed in Gimp)</p> <p>Annotated layers: 0 = annotations of black locust; 1 = annotations of soil; 2 = annotations of man-made; 3 = annotations of other trees and the orthomosaic as JPEG file</p> <p>2) Mixed forest images of the Yamagata University Research Forest taken in the winter season; contains 7 orthomosaics (TIFF file), annotated layers (named wM1 to wM7; processed in Gimp)</p> <p>Orthomosaics: 7 orthomosaics of 5 different sites; for one of the sites we provide 3 orthomosaics of different days and illumination conditions</p> <p>Annotated layers: 0 = annotations of river class; 1 = annotations of deciduous class; 2 = annotations of uncovered class; 3 = annotations of evergreen class; 4 = annotations of man-made class and the orthomosaic as JPEG file</p> <p> </p> <p>The dataset was used for our transfer learning study in the field of forest applications. We used in one experiment the winter images to run deep learning algorithms. In a second experiment we used the coastal forest data for a similar approach.</p>
Data package from "Regional Mapping and Spatial Distribution Analysis of Canopy Palms in an Amazon Forest Using Deep Learning and VHR Images"
<p>This data package contains the very high resolution maps of canopy palms from the paper "Regional Mapping and Spatial Distribution Analysis of Canopy Palms in an Amazon Forest Using Deep Learning and VHR Images". These maps have been produced with two GeoEye-1 very high resolution images (0.5 m) and a Deep Learning method for image segmentation called U-net, methods and data are fully described in the article. The total size of the decompressed archive is 2.56 Go and is distributed in two shapefiles, one for each GeoEye-1 image. When using this dataset, please cite the original article https://doi.org/10.3390/rs12142225</p>
Smelling smells using Deep Learning
<p>We have two objectives to carry out this research. The first objective is to explore the feasibility of applying deep learning models to detect smells without extensive feature engineering, just by feeding the source code elements in tokenized form. Another goal of the work is to investigate the possibility of applying transfer-learning in the context of deep learning models for smell detection by investigating transferability of results between programming languages.</p> <p>This repository contains supporting material of the experiment.</p>
Supplementary material for 'A Deep Learning Approach to Geographical Candidate Selection through Toponym Matching'
<p>Supplementary material for the https://github.com/Living-with-machines/LwM_SIGSPATIAL2020_ToponymMatching repository, containing the underlying code and materials for the paper 'A Deep Learning Approach to Geographical Candidate Selection through Toponym Matching', accepted to SIGSPATIAL2020 as a poster paper.</p> <p> </p> <p>Coll Ardanuy, M., Hosseini, K., McDonough, K., Krause, A., van Strien, D. and Nanni, F. (2020): A Deep Learning Approach to Geographical Candidate Selection through Toponym Matching, SIGSPATIAL: Poster Paper.</p>
Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning: W+jet large test dataset
<p>W+jet events at generator and reconstruction level, used to train analysis-specific generative models.</p> <p>Events are represented as an array of relevant high-level features. Reco objects are matched to Gen objects and a minimal selection is applied to define the generator support in the N-dim space identified by the input features.</p> <p>About 2M events, used for large-scale testing</p> <p>Details in https://arxiv.org/abs/2010.01835</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.