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251
datasets available to search
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Dataset results
251 results for “deep learning models”
Deep Learning Based Models for Preimplantation Mouse and Human Embryos Based on Single Cell RNA Sequencing
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Trained Surface Layer Models and Metrics for "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications"
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Enzyme-Substrate Interaction Dataset and Trained MEI Model for Deep Learning-Driven Insights
<p>This dataset and trained model are provided as part of our research on <em>Deep Learning-Driven Insights into Enzyme-Substrate Interaction Discovery</em>.</p>
Data of Spatio-Temporal deep learning model for regional EPB irregularities short-term Prediction
<p>Using the dense ground-based GNSS receiver network and ionosonde data from East and Southeast Asia during 2010-2021, a novel Spatio-Temporal deep learning model for regional EPB irregularities short-term Prediction (STEP) was developed. The model integrates the convolutional neural network (CNN) and long short-term memory (LSTM) network, together with attention mechanisms, to capture both spatial and temporal features of regional ionospheric irregularities.<br>This dataset includes both the model and the results generated by STEP. The parameters provided are: UT (hours), Latitude (°), Longitude (°), Date, Y_pred (TECU/min), and Y_true (TECU/min). The dimensions of Y_pred and Y_true are 10812 x 610, where 10812 represents the product of the number of date and the number of UT (minus 18), and 610 corresponds to the product of the number of Latitude and Longitude. The model with a .pth extension can be loaded using PyTorch.</p>
Evaluating the method reproducibility of deep learning models in the biodiversity research
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Model 4 dataset for the manuscript "Improving trajectory calculations by FLEXPART 10.4+ using deep learning inspired single image superresolution"
<p>Model 4 dataset for the manuscript "Improving trajectory calculations by FLEXPART 10.4+ using deep learning inspired single image superresolution"</p>
Model 2 dataset for the manuscript "Improving trajectory calculations by FLEXPART 10.4+ using deep learning inspired single image superresolution"
<p>Dataset produced by the model 2 neural network for the manuscript "Improving trajectory calculations by FLEXPART 10.4+ using deep learning inspired single image superresolution"</p>
Predicting GPR40 Agonists with A Deep Learning-Based Ensemble Model
<p>This dataset includes the calculation steps and optimization process of an ensemble model, along with various results and related intermediate files</p>
Explainable Deep Learning for Automatic Rock Classification: High Accuracy Does Not Mean Great Model Performance <Dataset>
<p>This is the dataset of manuscript entitled "Explainable Deep Learning for Automatic Rock Classification: High Accuracy Does Not Mean Great Model Performance". The manuscript is currently under review. Full access of this dataset will be released once the manuscript is accepted.</p>
Comprehensive benchmark and architectural analysis of deep learning models for Nanopore sequencing basecalling
<p>Placeholder data for the Lambda phage data used in: Comprehensive benchmark and architectural analysis of deep learning models for Nanopore sequencing basecalling.</p> <p>For the complete dataset see the Sequence Read Archive under the PRJNA926802 bioproject ID.</p>
Supplementary Video 1: Training deep learning models for cell image segmentation with sparse annotations
<p>Supplementary Video 1: Training deep learning models for cell image segmentation with sparse annotations</p> <p><strong>Acknowledgements</strong></p> <p>I am grateful to Michalis Averof (IGFL, CNRS) in whose lab this work was initiated and carried out, and to Shuichi Onami (RIKEN, BDR) in whose lab part of this work was carried out.</p> <p>Applications used in this movie:</p> <p>StarDist: <a href="https://github.com/stardist/stardist">https://github.com/stardist/stardist</a></p> <p>QuPath: <a href="https://qupath.github.io/">https://qupath.github.io/</a></p>
Developing a Physics-informed Deep Learning Model to Simulate Runoff Response to Climate Change in Alpine Catchments
<p>This data archive includes the source code of EXP-HYDRO, standard DL, hybrid-J, and hybrid-Z models, as well as simulated daily runoff (mm/d) of all five models in the paper at the three subbasins in the source region of the Yellow River. For more details please see the publication.</p> <p>Please cite the paper as follows:</p> <p>Zhong, L., Lei, H., & Gao, B. (2023). Developing a physics-informed deep learning model to simulate runoff response to climate change in Alpine catchments. Water Resources Research, 59, e2022WR034118. https://doi. org/10.1029/2022WR034118</p> <p> </p>
Object detection for graphical user interface: old fashioned or deep learning or a combination? - Model&Datasets
<p>This repo contains the datasets, trained models, and data splitting in ESEC/FSE 2020 "Object detection for graphical user interface: old fashioned or deep learning or a combination?" paper.</p>
Datasets and Trained Models for "Unblind Your Apps: Predicting Natural-Language Labels for Mobile GUI Components by Deep Learning"
<p>Datasets and Trained models for ICSE 2020 "Unblind Your Apps: Predicting Natural-Language Labels for Mobile GUI Components by Deep Learning"</p>
Data for: Contributions of deep learning to automated numerical modelling of the interaction of electric fields and cartilage tissue based on 3D images
<p>Replication data for: Contributions of deep learning to automated numerical modelling of the interaction of electric fields and cartilage tissue based on 3D images</p> <p> </p> <p> </p>
A Hybrid Deep Learning-Based Forecasting Model for the Peak Height of Ionospheric F2 Layer
<p>The data for paper.</p>
Blast Furnace Raw Material Granularity Recognition Model Based on Deep Learning and Multimodal Fusion of 3D Point Cloud
<p>Provide data code</p>
Dataset for An efficient multivariate deep learning model for monitoring mooring line tension of floating wind turbine
<p>Reference data needed for mooring line tensions prediction of a 15 MW wind turbine.<br> <br> This dataset contains OpenFAST outputfiles for different design load cases used in the paper.</p> <p>These data files are designed to be used together with the python code, which is available publicly on https://github.com/ramisetti/ 3SDLMooringPrediction</p>
Survey2Survey: A deep learning generative model approach for cross-survey image mapping
<p>During the last decade, there has been an explosive growth in survey data and deep learning techniques, both of which have enabled great advances for astronomy. The amount of data from various surveys from multiple epochs with a wide range of wavelengths, albeit with varying brightness and quality, is overwhelming, and leveraging information from overlapping observations from different surveys has limitless potential in understanding galaxy formation and evolution. Synthetic galaxy image generation using physical models has been an important tool for survey data analysis, while deep learning generative models show great promise. In this paper, we present a novel approach for robustly expanding and improving survey data through cross survey feature translation. We trained two types of neural networks to map images from the Sloan Digital Sky Survey (SDSS) to corresponding images from the Dark Energy Survey (DES). This map was used to generate false DES representations of SDSS images, increasing the brightness and S/N while retaining important morphological information. We substantiate the robustness of our method by generating DES representations of SDSS images from outside the overlapping region, showing that the brightness and quality are improved even when the source images are of lower quality than the training images. Finally, we highlight several images in which the reconstruction process appears to have removed large artifacts from SDSS images. While only an initial application, our method shows promise as a method for robustly expanding and improving the quality of optical survey data and provides a potential avenue for cross-band reconstruction.</p><p>------------</p><p>This repository contains the image files from Survey2Survey: a deep learning generative model approach for cross-survey image mapping. Please cite https://arxiv.org/abs/2011.07124 if you use this data in a publication. For more information, contact Brandon Buncher at buncher2(at)illinois.edu</p><p><strong>--- Directory structure ---</strong></p><p>tutorial.ipynb demonstrates how to load the image files (uploaded here as tarballs). Images were obtained from the SDSS DR16 cutout server (https://skyserver.sdss.org/dr16/en/help/docs/api.aspx) and DES DR1 cutout server (https://des.ncsa.illinois.edu/desaccess/</p><ul><li>./sdss_train/ and ./des_train/ contain the original SDSS and DES images used to train the neural network (Stripe82)</li><li>./sdss_test/ and ./des_test/ contain the original SDSS and DES images used for the validation dataset (Stripe82)</li><li>./sdss_ext/ contain images from the external SDSS dataset (SDSS images without a DES counterpart, outside Stripe82)</li><li>./cae and ./cyclegan contain images generated by the CAE and CycleGAN, respectively. train_decoded/ and test_decoded/ contain the reconstructions of the images from the training dataset and test dataset, respectively. external_decoded/ contain the DES-like image reconstructions of SDSS objects from the external dataset (outside Stripe82).</li></ul>
Dataset, models and code for "Automating global landslide detection with heterogeneous ensemble deep-learning classification"
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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.