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.
40
datasets available to search
ShareScore release 0.9.0
Dataset results
40 results for “Deep learning framework”
Dataset - DeepWealth: A Generalizable Open-Source Deep Learning Framework using Satellite Images for Well-Being Estimation
<p>This dataset encapsulates the Checkpoints obtained during the training process of the Deep Learning model, which can be used for new estimations.</p> <p>The aim of the DeepWealth package is to provide a generalizable Deep Learning framework for the use of remote sensing in poverty estimation. The combination of Deep Learning and Earth Observation data is increasingly being used to estimate socioeconomic conditions at regional and global scales. The proposed framework aligns with the Sustainable Development Goal SDG1 of ending poverty. The framework provides open-source data, code, and training models (checkpoints) for reproducibility and replicability.</p> <ul> <li>The source code can be found in <a href="https://github.com/PARSECworld/DeepWealth" target="_blank" rel="noopener">https://github.com/PARSECworld/DeepWealth</a></li> <li>The metadata from source code can be found in <a href="https://github.com/PARSECworld/DeepWealth/blob/main/metadata.pdf" target="_blank" rel="noopener">https://github.com/PARSECworld/DeepWealth/blob/main/metadata.pdf</a></li> <li>The paper describing the development of this framework can be found at: Ben Abbes, A., Machicao, J., Corrêa, P. L. P., Specht, A., Devillers, R., Ometto, J. P., Kondo, Y., & Mouillot, D. (2024). DeepWealth: A generalizable open-source deep learning framework using satellite images for well-being estimation. <em>SoftwareX</em>, 27, 101785. <a href="https://doi.org/10.1016/j.softx.2024.101785">https://doi.org/10.1016/j.softx.2024.101785</a> </li> </ul>
Advancing Vanadium Redox Flow Battery Analysis: A Deep Learning Framework for High-Throughput 3D Visualization and Bubble Quantification via Synchrotron X-ray Tomography
<p>Dataset and model of UTILE-Redox - Deep Learning based Tool for Autonomous 3D Bubble Analysis of Vanadium Flow Batteries from Synchrotron X-ray Imaging. This project focuses on the deep learning-based automatic analysis of Vanadium Redox Flow Batteries (VRFB) Synchrotron X-ray tomographies. This repository contains the Python implementation of the UTILE-Redox software for automatic volume analysis, feature extraction, and visualization of the results.</p>
Inverse design of metal-organic frameworks for direct air capture of CO2 via deep reinforcement learning
<p>The combination of several interesting characteristics makes metal-organic frameworks (MOFs) a highly sought-after class of nanomaterials for a broad range of applications like gas storage and separation, catalysis, drug delivery, and so on. However, the ever-expanding and nearly infinite chemical space of MOFs makes it extremely challenging to identify the most optimal materials for a given application. In this work, we present a novel approach using deep reinforcement learning for the inverse design of MOFs, our motivation being designing promising materials for the important environmental application of direct air capture of CO2 (DAC). We demonstrate that the reinforcement learning framework can successfully design MOFs with critical characteristics important for DAC. Our top-performing structures populate two separate subspaces of the MOF chemical space: the subspace with high CO2 heat of adsorption and the subspace with preferential adsorption of CO2 from humid air, with few structures having both characteristics. Our model can thus serve as an essential tool for the rational design and discovery of materials for different target properties and applications.</p>
Companion data artifacts: Technical framework demonstration for deep learning-based wood species classification with advanced sub-μ-CT imaging
<p>This is the companion data artifact collection for the IWAWA paper manuscript by Jannik Stebani, Tim Lewandrowski, Kilian Dremel, Simon Zabler and Volker Haag.It is generally to be used with the visualization and prediction showcases implemented in the Binder notebooks launched from this <a href="https://github.com/stebix/woodnet-showcase" target="_blank" rel="noopener">woodnet-showcase</a> GitHub repository.</p> <p>The artifacts amount to the following:</p> <ol> <li><code>acer-artifact.hdf5</code> : Exemplary <code>(256, 256, 256)</code> subvolume from a <em>Acer pseudoplatanus</em> sub-μ-CT scan</li> <li><code>pinus-artifact.hdf5</code> : Exemplary <code>(256, 256, 256)</code> subvolume from a <em>Pinus sylvestris </em>sub-μ-CT scan</li> <li><code>weights-artifact.pth</code> : Exemplary PyTorch trained weights for a woodnet/deep neural network to demonstrate classification of the above samples</li> </ol>
Complete results of Procedure section in manuscript "PathML: A unified framework for whole-slide image analysis with deep learning"
<p>The results from a complete run of the Procedure section of the paper "PathML: A unified framework for whole-slide image analysis with deep learning".</p>
Dataset for the paper "An end-to-end deep learning framework for wideband signal recognition"
<p>This dataset comprises a collection of synthetically generated wideband signals, which were used in experiments conducted for the paper "An end-to-end deep learning framework for wideband signal recognition," submitted for publication to IEEE <em>Access</em>. In this work, the proposed learning-based approach for signal detection, localization, and classification was evaluated on the public wideband signal recognition dataset introduced by West <em>et al.</em> (<a href="https://ieeexplore.ieee.org/document/9593265">https://ieeexplore.ieee.org/document/9593265</a>). We note that the dataset provided here contains only additional auxiliary data that were generated to complement the public benchmark dataset in experiments that employ data mixing and transfer learning techniques. As such, the synthetic wideband signals in our dataset are designed to mimic those in the benchmark dataset.<br> <br> Specifically, in our simulations, a wideband signal is modeled as a superposition of several narrowband emissions and receiver noise. Similar to the benchmark dataset, all wideband signals have a normalized sampling frequency of <span class="math-tex">\(F_\text{s} = 1\)</span> sample per second (sps), duration of <span class="math-tex">\(L = 1 000 000\)</span> samples, and a center frequency of <span class="math-tex">\(F_\text{c} = 0\)</span> Hz. The narrowband signals have varying center frequencies, bandwidths, start times, and durations, and are modulated with a range of modulation classes, including:</p> <ol> <li>Amplitude Modulation - Double Sideband (AM-DSB)</li> <li>Amplitude Modulation - Single Sideband (AM-SSB)</li> <li>Frequency Modulation (FM)</li> <li><span class="math-tex">\(M\)</span>-Frequency-Shift Keying (<span class="math-tex">\(M\)</span>-FSK) for <span class="math-tex">\(M \in \{2, 4\}\)</span></li> <li><span class="math-tex">\(M\)</span>-Continuous Phase Frequency-Shift Keying (<span class="math-tex">\(M\)</span>-CPFSK) for <span class="math-tex">\(M \in \{2, 4\}\)</span></li> <li>Gaussian Minimum Shift Keying (GMSK)</li> <li>On-Off Keying (OOK)</li> <li><span class="math-tex">\(M\)</span>-Phase-Shift Keying (<span class="math-tex">\(M\)</span>-PSK) for <span class="math-tex">\(M \in \{2, 4, 8\}\)</span></li> <li><span class="math-tex">\(M\)</span>-Quadrature Amplitude Modulation (<span class="math-tex">\(M\)</span>-QAM) for <span class="math-tex">\(M \in \{16, 64, 256\}\)</span></li> </ol> <p>The modulation parameters for each narrowband signal are randomly selected from predefined typical ranges. For all wideband signals, the value of the power spectral density of the added white Gaussian noise has been initially set to -174 dBm/Hz. Further noise addition according to the desired signal-to-noise ratio (SNR) level can be applied by the user. <br> <br> The data are stored and documented according to the Signal Metadata Format (SigMF) standard (<a href="https://github.com/sigmf/SigMF">https://github.com/sigmf/SigMF</a>). Each wideband signal in the dataset is represented by a data file (a binary file containing digital I/Q samples of the signal) and a metadata file (in JSON format) that annotates the signal. The annotations include the general properties of the wideband recording (sampling rate, center frequency, signal duration, and noise power spectral density), as well as specific information for each narrowband signal (start time, duration, lowest and highest frequency, modulation class label, and power). For every signal in the dataset, the data file and the annotations file share the same name. For example, the first training wideband signal is stored in the file "train_1.sigmf-data" and is annotated by the file "train_1.sigmf-meta".</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>
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>
Data from: BubbleID: A deep learning framework for bubble interface dynamics analysis
Open the record for dataset details and reuse information.
[Data] Real-time monitoring and quality assurance for laser-based directed energy deposition: integrating co-axial imaging and self-supervised deep learning framework
<p>The experimental setup utilized a co-axial color Charged Couple Device (CCD) camera, integrated into the laser deposition head. This camera operates at a frame rate of 30 frames per second and captures the morphology of the process area. The captured images consist of three RGB channels with a 640 × 480 pixels resolution. To enable the camera to capture the radiation from the process zone, a beam splitter is installed on Precitec's laser applicator head. An optical notch filter within the 650–675 nm range also blocks the laser wavelengths.</p> <p>The dataset consists of four categories that covers the process map of DED process [.rar file].<br>The dataset consist of around 48,000 images that are labelled into 4 categories [P1-P2-P3-P4]. The images correspond to DED process zone captured co-axially<br>The categories are function of linear laser energy deposited. The folder is already split into Train and Test.</p>
PathoEye: a deep learning framework for histopathological image analysis of skin tissue
<p>This dataset comprises two parts: one consisting of skin pathology slices near the epidermal layer of elderly individuals under sunlight exposure conditions, and the other comprising skin pathology slices near the epidermal layer of young individuals under the same conditions. Each type of image comes in two sizes: one is 128×128 pixels, and the other is 512×512 pixels.</p>
Data for "Physically Based Deep Learning Framework to Model Intense Precipitation Events at Engineering Scales"
<p>The dataset consists of high resolution (250 m) and low resolution (0.025 degree) climate model outputs in netCDF format. Each file contains data for one variable and one month.</p> <p>Low resolution files follow the naming scheme: montrealC_0025deg_200x200_ERA5_1m_YYYYMM_VAR.nc</p> <p>High resolution files follow the naming scheme: montrealC_250m_324x324_ERA5_TEB_100_noconv_YYYYMM_VAR.nc</p> <p>YYYYMM stands for the year (first 4 digits) and month (last 2 digits).</p> <p>_VAR indicates the variable contained in the file:</p> <ul> <li>_UU700 stands for the east-west component of wind at a pressure level of 700 hPa (hourly frequency)</li> <li>_VV700 stands for the north-south component of wind at a pressure level of 700 hPa (hourly frequency)</li> <li>When _VAR is omitted, the variable is precipitation at 1-minute temporal resolution</li> </ul>
Deep Gradient Reinforcement learning for Music Improvisation in cloud computing framework
<p><span>The improvised music is further rendered in the MIDI format. The Bach Chorales dataset with six different attributes relevant to musical compositions is employed in implementing the present research. The model was set up in a containerised cloud environment and controlled for smooth load distribution. Five different parameters, such as pitch frequency (PF), standard pitch delay (SPD), average distance between peaks (ADP), note duration gradient (NDG) and pitch class gradient (PCG) are leveraged to assess the quality of the improvised music.</span></p>
An unsupervised deep learning framework with variational autoencoders for genome-wide DNA methylation analysis and biologic feature extraction applied to breast cancer
<p>Supplemental data for the paper titled "An unsupervised deep learning framework with variational autoencoders for genome-wide DNA methylation analysis and biologic feature extraction applied to breast cancer"</p>
"Toy Data Set" referenced in the article "A deep-learning based analysis framework for ultra-high throughput screening time-series data" (https://doi.org/10.1101/2024.08.22.609110)
<p>This data set, referenced as "toy data set" in the article "A deep-learning based analysis framework for ultra-high throughput screening time-series data" (<a href="Lint-to-article">https://doi.org/10.1101/2024.08.22.609110</a>), mimics a high-throughput screening data set. To demonstrate the application of our analysis framework described in the main article this toy data set was generated. It contains in total 1536000 individual transient signals, splitted in 5 batches of each 200 plates in 1536-well plate format. Five distinct signal classes were used to resemble typical shapes encountered in biological experiments. Fequency of occurrences for each class is reported in the main article.</p>
Hybrid deep learning framework for evaluating field evapotranspiration considering the impact of soil salinity
<p>Entitled “A novel hybrid deep learning framework for evaluating field evapotranspiration considering the impact of soil salinity” for possible publication in Water Resources Research.</p> <p>Data:</p> <p>The Salinized Farmland Flux Station sites are located in the typical irrigated agricultural area of the arid continental monsoon region in northwest China.In this study, we used data from four flux tower located in saline farmland: two for maize (MZ1, MZ2) and two for sunflower (SF1, SF2).</p> <p>For each site, we collected the following variables at half-hourly temporal resolution: (i) latent heat (<em>LE</em>, W m<sup>-2</sup>) fluxes, serving as a direct measure representing the energy component of <em>ET</em> (mm h<sup>-1</sup>), (ii) net radiation (<em>R<sub>n</sub></em>, W m<sup>-2</sup>), (iii) ground heat flux (<em>G</em>, W m<sup>-2</sup>), (iv) solar irradiance (<em>R<sub>s</sub></em>, W m<sup>-2</sup>), (v) air temperature (<em>T</em><sub>a</sub>, °C), (vi) vapor pressure deficit (<em>VPD</em>, KP<sub>a</sub>), (vii) wind speed (<em>U<sub>s</sub></em>, m s<sup>-1</sup>), (viii) relative humidity (<em>RH,</em> %), and (ix) atmospheric carbon dioxide concentration (<em>C<sub>a</sub></em>, mg m<sup>-3</sup>). During the crop growth period, field in-situ measurements of soil and vegetation data from these farmlands are conducted approximately every 10 days. </p> <p>Code:</p> <p>All the codes were executed in Python. The provided hybrid deep learning model code can be run on Jupyter Notebook.</p>
Deep Learning Regional Climate Model Emulators: a comparison of two downscaling training frameworks [datasets]
<p>Outputs used in:</p> <p><em>van der Meer, M., de Roda Husman, S., Lhermitte, S.: </em>Deep Learning Regional Climate Model Emulators: a comparison of two downscaling training frameworks</p> <ul> <li>MAR(ACCESS1-3)_monthly_SMB.nc: MAR outputs with monthly values of SMB and components over the Antarctic ice sheet (1980--2100)</li> <li>MAR(ACCESS1-3)-stereographic_monthly_GCM_like.nc: MAR outputs upscaled to GCM resolution (1980--2100)</li> <li>ACCESS1-3-stereographic_monthly_cleaned.nc: GCM monthly outputs over the Antarctic ice sheet (1980--2100)</li> </ul> <p>The up-to-date working versions of our experiments and source code can be found and are available on our GitHub: <a href="https://github.com/marvande/RCM-Emulator">https://github.com/marvande/RCM-Emulator</a> and at this link: <a href="https://doi.org/10.5281/zenodo.7875967">https://doi.org/10.5281/zenodo.7875967</a></p> <p>Data usage notice:</p> <p>If you use any of these results, please acknowledge the work of the people involved in producing them. You should also refer to and cite the following paper:</p> <p><strong>Cite as: </strong>Marijn van der Meer, Sophie de Roda Husman, S Lhermitte. Deep Learning Regional Climate Model Emulators: a comparison of two downscaling training frameworks. <em>Authorea.</em> December 27, 2022 <br> DOI: <a href="https://doi.org/10.22541/essoar.167214210.02213149/v1">10.22541/essoar.167214210.02213149/v1</a> </p>
DeepCV: A Deep Learning Framework for Blind Search of Collective Variables in Expanded Configurational Space
<p>We present <em>Deep learning for Collective Variables</em> (DeepCV), a computer code that provides an efficient and customizable implementation of the deep autoencoder neural network (DAENN) algorithm that has been developed in our group for computing collective variables (CVs) and can be used with enhanced sampling methods to reconstruct free energy surfaces of chemical reactions. DeepCV can be used to conveniently calculate molecular features, train models, generate CVs, validate rare events from sampling, and analyze a trajectory for chemical reactions of interest. We use DeepCV in an example study of the conformational transition of cyclohexene, where metadynamics simulations are performed using DAENN-generated CVs. The results show that the adopted CVs give free energies in line with those obtained by previously developed CVs and experimental results. DeepCV is open-source software written in Python/C++ object-oriented languages, based on the TensorFlow framework and distributed free of charge for noncommercial purposes, which can be incorporated into general molecular dynamics software. DeepCV also comes with several additional tools, i.e., an application program interface (API), documentation, and tutorials.</p>
An Interpretable Framework to Characterize Compound Treatments on Filamentous Fungi using Cell Painting and Deep Metric Learning
<p>This deposit contains:</p> <ul> <li>{train,test,val}.csv: Meta-data files</li> <li>images.tar.gz: Archive of images</li> <li>checkpoint.pth.tar: Model weights</li> <li>code.tar.gz: Code necessary to reproduce our experiments.</li> </ul>
A Comparison framework for deep learning RFI detection algorithms in radio astronomy
<p>These are the datasets used for the study titled: A Comparison framework for deep learning RFI detection algorithms in radio astronomy. These files are made publicly available as an additional resource to the submission of the author's Masters degree at Stellenbosch University. The detection is done in the field of radio astronomy. Each dataset consists of images/spectrograms/waterfall plots for baselines, and the corresponding binary mask for each image. The datasets can be used to train machine learning models, or for the case of this study, supervised fully convolutional neural networks.</p> <p>The LOFAR datasets consists of real observations and was slightly modified from https://zenodo.org/record/6724065. See this resource regarding the observational parameters used to retrieve the data from the LOFAR Long Term Archive.The HERA dataset consists of simulated observations generated with hera_sim (https://readthedocs.org/projects/hera-sim/). The 28 March dataset consists of accurate pixel-perfect binary masks for each image. The 20 July dataset is identical to the first, except the binary masks are generated with AOFlagger. All three datasets have a test set stored with pixel-perfected simulation masks (HERA) or expert hand labeled masks (LOFAR).</p> <p>The csv file contains the results of all trained models and and has fields for: model class, #filters, #FLOPS, #weights, preprocessing methods, train, validation and test accuracy scores as well as list of (threshold, FPR, TPR) values to generate receiver operating characteristic curves. See https://github.com/CharlDuToit/RFI-NLN to visualize the results, to train new models.</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.