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1,273 results for “Deep Learning”
Deep learning from phylogenies for diversification analyses
<p>Birth-death models are widely used in combination with species phylogenies to study past diversification dynamics. Current inference approaches typically rely on likelihood-based methods. These methods are not generalizable, as a new likelihood formula must be established each time a new model is proposed; for some models, such a formula is not even tractable. Deep learning can bring solutions in such situations, as deep neural networks can be trained to learn the relation between simulations and parameter values as a regression problem. In this paper, we adapt a recently developed deep learning method from pathogen phylodynamics to the case of diversification inference, and we extend its applicability to the case of the inference of state-dependent diversification models from phylogenies associated with trait data. We demonstrate the accuracy and time efficiency of the approach for the time-constant homogeneous birth-death model and the Binary-State Speciation and Extinction model. Finally, we illustrate the use of the proposed inference machinery by reanalyzing a phylogeny of primates and their associated ecological role as seed dispersers. Deep learning inference provides at least the same accuracy as likelihood-based inference while being faster by several orders of magnitude, offering a promising new inference approach for deployment of future models in the field.</p>
GCNT-Plume: Long-term observation of global nuclear power plants thermal plumes using Landsat images and deep learning
<p>This repository contains the relevant code and data for the paper <strong>Long-term observation of global nuclear power plants thermal plumes using Landsat images and deep learning</strong><strong> </strong>(Wei et al, 2023, <em>Remote Sensing of Environment</em>).</p> <p>Specifically, the <strong>U-Net.zip</strong> file includes the associated codes for segmenting surface thermal plumes from nuclear power plants along the global coasts and the Great Lakes by using the U-Net model integrated with prior knowledge. The <strong>GCNT-Plume.zip</strong> file includes the occurrence footprints of core area plumes (the <strong>occurrence_all </strong>folder), raw water temperature increment (WST) images (the <strong>delta </strong>folder), mixed area plumes and annotations (the <strong>extractWithLocation </strong>folder), model-predicted core area plumes (the <strong>prediction*_*</strong> folders), the mixed/core area plumes and background areas in shapefile format (the <strong>sampleAnnotation* </strong>folders), and location information (the <strong>location.xlsx </strong>table). Please refer to the <strong>README.md </strong>file in the <strong>U-Net.zip</strong> file for more detailed information.</p>
SinoLC-1: the first 1-meter resolution national-scale land-cover map of China created with the deep learning framework and open-access data (User guide V2.4)
<p>The<strong> User Guide V2.4 </strong>of the SinoLC-1 land-cover product. The SinoLC-1 was created by the Low-to-High Network (L2HNet), which can be found at: <strong><a href="https://doi.org/10.1016/j.isprsjprs.2022.08.008">L2HNet</a></strong>. A more detailed description of the data can be found in the<strong> <a href="https://doi.org/10.5194/essd-15-4749-2023">paper</a>.</strong> More related work can be found at my <strong><a href="https://lizhuohong.github.io/lzh/">homepage</a>.</strong></p> <p><a href="https://zenodo.org/search?q=parent.id%3A7707461&f=allversions%3Atrue&l=list&p=1&s=10&sort=version"><strong>Click to check all the data versions and download the data (点击查看/下载所有数据版本)</strong></a></p> <p><strong>NOTE: If you have any data needs, questions, or technical issues, contact us at </strong><a href="http://ashelee@whu.edu.cn"><strong>ashelee@whu.edu.cn</strong></a><strong> (Zhuohong Li, 李卓鸿).</strong></p> <p>The land-cover mapping method with Python code is open-access at <a href="https://github.com/LiZhuoHong/Paraformer/"><strong>Code link</strong></a>. You can now update the high-resolution land-cover map by yourself with the code! The updated method is accepted by CVPR 2024 (<strong><a href="https://arxiv.org/abs/2403.02746">Paper link</a></strong>).</p> <p><strong>我们的最新制图算法被计算机视觉顶会CVPR2024接收(<a href="https://arxiv.org/abs/2403.02746">Paper link</a>),代码开源在:<a href="https://github.com/LiZhuoHong/Paraformer/">Code link</a>,您可以利用该代码高效地更新自己数据集的高分土地覆盖图。</strong></p> <p><strong>Citation format of the paper:</strong><br>Li, Z., He, W., Cheng, M., Hu, J., Yang, G., and Zhang, H.: SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data, Earth Syst. Sci. Data, 15, 4749–4780, 2023. </p> <p>Li, Z., Zhang, H., Lu, F., Xue, R., Yang, G. and Zhang, L.: Breaking the resolution barrier: A low-to-high network for large-scale high-resolution land-cover mapping using low-resolution labels, <em>ISPRS Journal of Photogrammetry and Remote Sensing</em>. <em>192</em>, pp.244-267, 2022.</p> <p><strong>BibTex format of the paper:</strong></p> <blockquote> <pre>@article{li2023sinolc, title={SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data}, author={Li, Zhuohong and He, Wei and Cheng, Mofan and Hu, Jingxin and Yang, Guangyi and Zhang, Hongyan}, journal={Earth System Science Data}, volume={15}, number={11}, pages={4749--4780}, year={2023}, publisher={Copernicus Publications G{\"o}ttingen, Germany} }</pre> <pre>@article{li2022breaking, title={Breaking the resolution barrier: A low-to-high network for large-scale high-resolution land-cover mapping using low-resolution labels}, author={Li, Zhuohong and Zhang, Hongyan and Lu, Fangxiao and Xue, Ruoyao and Yang, Guangyi and Zhang, Liangpei}, journal={ISPRS Journal of Photogrammetry and Remote Sensing}, volume={192}, pages={244--267}, year={2022}, publisher={Elsevier} }</pre> </blockquote>
Dataset for Deep learning solutions for mapping contour levee rice production systems from very high resolution imagery
<p>This dataset contains the two datasets detailed in "Deep learning solutions for mapping contour levee rice production systems from very high resolution imagery" by D.S. Dale Et al. (2023). </p> <p>The file "LonokeComplete.zip" file contains 16 .lif files that were used in the training and testing phase of the study. </p> <p>The "55tilesComplete.zip" file contains 110 .tif files (55 image and 55 label). These images were used to assess the models spatial transferability. </p> <p>Both file configurations are processed by the code linked in the paper. </p>
Data for "ChickenSense: A low-cost deep learning-based solution for real-time poultry feed intake monitoring using sound technology"
<p>The dataset includes two types of data: scale and audio. The scale data is recorded using a hanging scale and measures the feeders' weight with a 5 Hz frequency. The audio data contains 1824 hours of recorded audio using a piezo-electric sensor attached to a bucket feeder to capture vibrations as audio signals with a sampling rate of 48 kHz. Each .wav file contains 15 minutes of audio recording.<br> Along with the raw data, a part of the dataset containing 36 30-second audio recordings was sampled and labeled in four classes, including pecking, singing, anomaly, and silence, resulting in 3319 labeled events. </p>
Outputs of the Jupyter Notebook - Deep learning and variational inversion to quantify and attribute climate change (CIRC23)
<p>The dataset contains the outputs of the notebook "Deep learning and variational inversion to quantify and attribute climate change (CIRC23)" published in The Environmental Data Science Book.</p>
Replication Package for "Why Do Deep Learning Projects Differ in Compatible Framework Versions? An Exploratory Study"
<p>This dataset contains scripts and data used to generate relevant results for this paper. Detailed information are described in README.md. </p> <p>code</p> <p>This folder contains all the scripts used for the experiment. The upgrade.py and downgrade.py are used to perform upgrade and downgrade runs. The pairing.py is used to generate the DFVC pairs. The main.py is used to identify root causes of DFVC pairs.</p> <p>result</p> <p>This folder contains all the results of the experiments, including the runtime output (e.g., a_1.0.0.txt), the runtime environment (e.g., condalist_1.0.0.txt), and the project's runtime commands (e.g., pytorch-cifar.xlsx) of all tested 90 PyTorch and 50 TensorFlow projects.</p> <p><br> Distribution of dfvc pairs.xlsx</p> <p>This file includes 6,926 DFVC pairs and their root causes.</p> <p>Tested framework versions.xlsx</p> <p>This file includes the framework versions tested and the Python versions that the framework versions are compatible with.</p> <p>Tested projects.xlsx</p> <p>This file includes the tested 90 PyTorch projects and 50 TensorFlow projects. We provide the following main information: (a) project name, (b) stars, (c) link, (d) the starting version, (e) python version, (f) incompatible upgrade/downgrade version, and (g) compatible versions.</p>
Out-of-the-box calving front detection method using deep learning
<p>The calving front predictions of the <a href="https://www.nature.com/articles/s41592-020-01008-z">nnU-Net</a> on a benchmark <a href="https://doi.pangaea.de/10.1594/PANGAEA.940950">dataset</a>.<br> GitHub: https://github.com/ho11laqe/nnUNet_calvingfront_detection<br> Publication: https://tc.copernicus.org/preprints/tc-2023-34/</p>
Interpretation results used in the paper "Real-Time Dual-Para FWI-Inv of GPR Data Based on Robust Deep Learning"
<p>We provide the updated synthetic data and inversion results used for all interpretations presented in the paper "Real-Time Dual-Parameter FWI Inversion of GPR Data Based on Robust Deep Learning."</p>
Focal-TSMP: Deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation
<p>This is the preprocessed remote sensing dataset used in the paper<strong> "Focal-TSMP: Deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation"</strong>. It contains the preprocessed NOAA data along with the additional files necessary for the TSMP simulation.</p>
A Deep Learning-Based Low-Frequency Earthquake Catalog in Southern Vancouver Island
<p>A deep learning-based LFE catalog in southern Vancouver Island. Refer to the paper "<strong>Detection of Hidden Low-Frequency Earthquakes in Southern Vancouver Island with Deep Learning</strong>" by J.-T. Lin, A. M. Thomas, L. Bachelot, D. R. Toomey, J. Searcy, D. Melgar for details. The columns in the data are as follows: starttime: start of the detection window; OT: event origin time; lon: longitude; lat: latitude; depth: source depth in km; residual: averaged travel time residual; dt: shifted time from starttime to OT; N: number of stations used. </p>
Data related to the publication "Efficient molecular dynamics simulations of deep eutectic solvents with first-principles accuracy using machine learning interatomic potentials"
<p>The training data sets, the trained machine learning models, and input scripts for the training and molecular dynamics simulations.</p>
Deep learning models challenge the prevailing assumption that face-like effects for objects of expertise support domain-general mechanisms
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Data and code from: Deep learning-based autonomous retinal vein cannulation in ex vivo porcine eyes
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Data from: Data fusion for integrative species identification using deep learning
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phyddle: Software for exploring phylogenetic models with deep learning
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DeepInMiniscope: Deep-learning-powered physics-informed integrated miniscope
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High-content high-resolution microscopy and deep learning assisted analysis reveals host and bacterial heterogeneity during Shigella infection
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An intracochlear electrocochleography dataset: From raw data to objective analysis using deep learning
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Data from: Integrating deep learning derived morphological traits and molecular data for total-evidence phylogenetics: lessons from digitized collections
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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.