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.
52
datasets available to search
ShareScore release 0.9.0
Dataset results
52 results for “training and test dataset”
Training and test dataset of STED images of microtubules in fixed cells
<p>Training and test dataset of microtubule used in the manuscript "Denoising diffusion models for high-resolution microscopy image restoration".</p>
Cu dataset – A copper ore labeled images dataset for segmentation training and testing
<p>This dataset is composed of 121 pairs of correlated images. Each pair contains one image of a copper ore sample acquired through reflected light microscopy (RGB, 24-bit), and the corresponding binary reference image (8-bit), in which the pixels are labeled as belonging to one of two classes: ore (0) or embedding resin (255).</p> <p>The sample came from a copper ore from Yauri Cusco (Peru) with a complex mineralogy, mainly composed of sulfides, oxides, silicates, and native copper. It was classified by size. The fraction +74-100 μm was cold mounted with epoxy resin and subsequently ground and polished.</p> <p>Correlative microscopy was employed for image acquisition. Thus, 121 fields were imaged on a reflected light microscope with a 20× (NA 0.40) objective lens and on a scanning electron microscope (SEM). In sequence, they were registered, resulting in images of 1017×753 pixels with a resolution of 0.53 µm/pixel. As matter of fact, some images (the images No. 2, 3, 24, 25, 46, 47, 69, 91, and 113) have slightly smaller sizes because they were cropped during the registration procedure to correct co-localization errors of the order of a few pixels. Finally, the images from SEM were thresholded to generate the reference images.</p> <p>Further description of this sample and its imaging procedure can be found in the work by Gomes and Paciornik (2012).</p> <p>This dataset was created for developing and testing deep learning models on semantic segmentation tasks. The paper of Filippo et al. (2021) presented a variant of the DeepLabv3+ model (Chen et al., 2018) that reached mean values of 90.56% and 92.12% for overall accuracy and F1 score, respectively, for 5 rounds of experiments (training and testing), each with a different, random initialization of network weights.</p> <p>For further questions and suggestions, please do not hesitate to contact us.</p> <p> </p> <p><strong>Contact email</strong>: ogomes@gmail.com</p> <p> </p> <p>If you use this dataset in your own work, please cite this DOI: 10.5281/zenodo.5020566</p> <p> </p> <p>Please also cite this paper, which provides additional details about the dataset:</p> <p>Michel Pedro Filippo, Otávio da Fonseca Martins Gomes, Gilson Alexandre Ostwald Pedro da Costa, Guilherme Lucio Abelha Mota. <em>Deep learning semantic segmentation of opaque and non-opaque minerals from epoxy resin in reflected light microscopy images</em>. <strong>Minerals Engineering</strong>, Volume 170, 2021, 107007, https://doi.org/10.1016/j.mineng.2021.107007.</p>
Training and test datasets for the PredictONCO tool
<p>This dataset was used for training and validating the <a href="https://loschmidt.chemi.muni.cz/predictonco/">PredictONCO </a>web tool, supporting decision-making in precision oncology by extending the bioinformatics predictions with advanced computing and machine learning. The dataset consists of 1073 single-point mutants of 42 proteins, whose effect was classified as Oncogenic (509 data points) and Benign (564 data points). All mutations were annotated with a clinically verified effect and were compiled from the ClinVar and OncoKB databases. The dataset was manually curated based on the available information in other precision oncology databases (The Clinical Knowledgebase by The Jackson Laboratory, Personalized Cancer Therapy Knowledge Base by MD Anderson Cancer Center, cBioPortal, DoCM database) or in the primary literature. To create the dataset, we also removed any possible overlaps with the data points used in the PredictSNP consensus predictor and its constituents. This was implemented to avoid any test set data leakage due to using the PredictSNP score as one of the features (see below).</p> <p>The entire dataset (<strong>SEQ</strong>) was further annotated by the pipeline of PredictONCO. Briefly, the following six features were calculated regardless of the structural information available: essentiality of the mutated residue (yes/no), the conservation of the position (the conservation grade and score), the domain where the mutation is located (cytoplasmic, extracellular, transmembrane, other), the PredictSNP score, and the number of essential residues in the protein. For approximately half of the data (<strong>STR</strong>: 377 and 76 oncogenic and benign data points, respectively), the structural information was available, and six more features were calculated: FoldX and Rosetta ddg_monomer scores, whether the residue is in the catalytic pocket (identification of residues forming the ligand-binding pocket was obtained from P2Rank), and the pKa changes (the minimum and maximum changes as well as the number of essential residues whose pKa was changed – all values obtained from PROPKA3). For both <strong>STR </strong>and <strong>SEQ </strong>datasets, 20% of the data was held out for testing. The data split was implemented at the position level to ensure that no position from the test data subset appears in the training data subset. </p> <p>For more details about the tool, please visit the <a href="https://loschmidt.chemi.muni.cz/predictonco/help">help page</a> or <a href="https://loschmidt.chemi.muni.cz/peg/contact/">get in touch with us</a>.</p> <p>14-Dec-2023 update: the file with features<em> PredictONCO-features.txt</em> now includes UniProt IDs, transcripts, PDB codes, and mutations.</p>
FeM dataset – An iron ore labeled images dataset for segmentation training and testing
<p>This dataset is composed of 81 pairs of correlated images. Each pair contains one image of an iron ore sample acquired through reflected light microscopy (RGB, 24-bit), and the corresponding binary reference image (8-bit), in which the pixels are labeled as belonging to one of two classes: ore (0) or embedding resin (255).</p> <p>The sample came from an itabiritic iron ore concentrate from Quadrilátero Ferrífero (Brazil) mainly composed of hematite and quartz, with little magnetite and goethite. It was classified by size and concentrated with a dense liquid. Then, the fraction -149+105 μm with density greater than 3.2 was cold mounted with epoxy resin and subsequently ground and polished.</p> <p>Correlative microscopy was employed for image acquisition. Thus, 81 fields were imaged on a reflected light microscope with a 10× (NA 0.20) objective lens and on a scanning electron microscope (SEM). In sequence, they were registered, resulting in images of 999×756 pixels with a resolution of 1.05 µm/pixel. Finally, the images from SEM were thresholded to generate the reference images.</p> <p>Further description of this sample and its imaging procedure can be found in the work by Gomes and Paciornik (2012).</p> <p>This dataset was created for developing and testing deep learning models on semantic segmentation tasks. The paper of Filippo et al. (2021) presented a variant of the DeepLabv3+ model that reached mean values of 91.43% and 93.13% for overall accuracy and F1 score, respectively, for 5 rounds of experiments (training and testing), each with a different, random initialization of network weights.</p> <p>For further questions and suggestions, please do not hesitate to contact us.</p> <p> </p> <p><strong>Contact email</strong>: ogomes@gmail.com</p> <p> </p> <p>If you use this dataset in your own work, please cite this DOI: 10.5281/zenodo.5014700</p> <p> </p> <p>Please also cite this paper, which provides additional details about the dataset:</p> <p>Michel Pedro Filippo, Otávio da Fonseca Martins Gomes, Gilson Alexandre Ostwald Pedro da Costa, Guilherme Lucio Abelha Mota. <em>Deep learning semantic segmentation of opaque and non-opaque minerals from epoxy resin in reflected light microscopy images</em>. <strong>Minerals Engineering</strong>, Volume 170, 2021, 107007, https://doi.org/10.1016/j.mineng.2021.107007.</p> <p> </p>
ZeroCostDL4Mic - CARE (2D) example training and test dataset
<p><strong>Name</strong>: ZeroCostDL4Mic - CARE (2D) example training and test dataset</p> <p>(see <a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">our Wiki</a> for details)</p> <p> </p> <p><strong>Data type</strong>: Paired microscopy images (fluorescence) of low and high signal-to-noise ratio</p> <p><strong>Microscopy data type</strong>: Fluorescence microscopy (Lifeact-RFP)</p> <p><strong>Microscope</strong>: Structured Illumination Microscopy (SIM) with a 60x 1.42 NA objective </p> <p><strong>Cell type</strong>: DCIS.COM Lifeact-RFP</p> <p><strong>File format</strong>: .tif (32-bit)</p> <p><strong>Image size</strong>: 1024x1024 (Pixel size: 40 nm)</p> <p> </p> <p><strong>Author(s)</strong>: Guillaume Jacquemet<sup>1,2</sup></p> <p><strong>Contact email</strong>: guillaume.jacquemet@abo.fi</p> <p><strong>Affiliation</strong>: </p> <p>1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland</p> <p>2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland</p> <p> </p> <p><strong>Associated publications</strong>: Unpublished</p> <p><strong>Funding bodies</strong>: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.</p>
ZeroCostDL4Mic - Noise2Void (2D) example training and test dataset
<p><strong>Name</strong>: ZeroCostDL4Mic - Noise2Void (2D) example training and test dataset</p> <p>(see <a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">our Wiki</a> for details)</p> <p> </p> <p><strong>Data type</strong>: Microscopy images (fluorescence)</p> <p><strong>Microscopy data type</strong>: Fluorescence microscopy (paxillin-GFP) </p> <p><strong>Microscope</strong>: Spinning disk confocal microscope with a 63x 1.4 NA objective </p> <p><strong>Cell type</strong>: U-251 glioma cells, endogenously expressing paxillin-GFP</p> <p><strong>File format</strong>: .tif (16-bit)</p> <p><strong>Image size</strong>: 512x512 (Pixel size: 248 nm)</p> <p> </p> <p><strong>Author(s)</strong>: Aki Stubb<sup>1</sup>, Guillaume Jacquemet<sup>1,2</sup> and Johanna Ivaska<sup>1</sup></p> <p><strong>Contact email</strong>: guillaume.jacquemet@abo.fi</p> <p><strong>Affiliation</strong>: </p> <p>1) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland</p> <p>2) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland</p> <p><br> </p> <p><strong>Associated publication</strong>: Stubb <em>et al.</em> 2020, Nano letters DOI: 10.1021/acs.nanolett.9b04083</p> <p><strong>Funding bodies</strong>: A.S. has been supported by the University of Turku Doctoral programme for Molecular Medicine (TuDMM).</p>
ZeroCostDL4Mic - Label-free prediction (fnet) example training and test dataset
<p><strong>Name</strong>: ZeroCostDL4Mic - Label-free prediction (fnet) example training and test dataset</p> <p>(see <a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">our Wiki</a> for details)</p> <p> </p> <p><strong>Data type</strong>: 3D paired microscopy images (fluorescence and transmitted light)</p> <p><strong>Microscopy data type</strong>: Confocal microscopy data (TOM20 labeled with Alexa Fluor 594)</p> <p><strong>Microscope</strong>: Leica SP8, HC PL APO 63x 1.40 NA oil objective </p> <p><strong>Cell type</strong>: HeLa (fixed using an organelle-preserving protocol)</p> <p><strong>File format</strong>: .tif (8-bit)</p> <p><strong>Image size</strong>: 512 x 512 x 32 (Pixel size: x and y: 90 nm pixel size, z: 150 nm)</p> <p> </p> <p><strong>Author(s)</strong>: Christoph Spahn</p> <p><strong>Contact email</strong>: c.spahn@chemie.uni-frankfurt.de, heilemann@chemie.uni-frankfurt.de</p> <p><strong>Affiliation</strong>: Institute of Physical and Theoretical Chemistry, Goethe-University Frankfurt, Frankfurt, Germany</p> <p> </p> <p><strong>Associated publications</strong>: Unpublished</p> <p><br> <strong>Funding body(ies)</strong>: M.H. and C.S.: German Science Foundation (grant nr. SFB1177). C.S.: European Molecular Biology Organization (short term fellowship 8589)</p>
Dataset for train and test BRITTANY (Biometric RecognITion Through gAit aNalYsis)
<p>This dataset can be used train and test the BRITTANY tool. Information contained in the dataset is especially suitable to be used as train and test data for neural network-based classifiers.</p> <p>This dataset contains 198 Rosbag files, of 5 seconds duration, recorded in different locations (kitchen, livingroom-window and livingroom-door) with Orbi-One robot stood still. Two sorts of Rosbag files have been recorded. In 90 Rosbag files (train*.bag), data recorded correspond to a person walking in a straight line in front of the robot. Data from five different people have been recorded. For each location and person, six Rosbag files have been recorded.</p> <p>In 108 Rosbag files (test*.bag), data recorded correspond to a person walking in a straight line in front of the robot. Data from six different people have been recorded. Five of those six people are the same as in the other rosbags and the other one is not registered in the system to evaluate the false-positive cases in the system.</p>
Train and test datasets used for the paper "Neural network time-series classifiers for gravitational-wave searches in single-detector periods"
<p>This repository contains the datasets used for training and testing during the work discussed in the paper "<a href="https://iopscience.iop.org/article/10.1088/1361-6382/ad40f0" target="_blank" rel="noopener">Neural network time-series classifiers for gravitational-wave searches in single-detector periods</a>". Please refer to this paper for more details on how the dataset was produced and cite it if you use these data:</p> <p><em>A. Trovato et al "Neural network time-series classifiers for gravitational-wave searches in single-detector periods", Class. Quant. Grav. 2024 DOI 10.1088/1361-6382/ad40f0.</em></p> <p>In this repository you will find six files in format npz, three of which refer to the test dataset and three to the train dataset. Each file name is of the type {label}_{train or test}.npz where "label" can be "glitch", "noise" or "signal", while the second part of the name indicates whether the file was used for training or testing.</p> <p>Each file is a collection of numpy arrays so it should be read with python. It contains 3 numpy arrays: 'X', 'Y' and 'metadata'. 'X' is a matrix containing 1-second segments of data sampled at 2048 Hz of the LIGO-Livingston detector, so it has shape: (number of samples, 2048). 'Y' contains the label for each segment, which is 0 for noise, 1 for signal and 2 for glitch, so it has shape: (number of samples,). In this case, the information on 'Y' is redundant since it's given directly by the filename. The 'metadata' matrix contains 17 metadata for each sample only for the case of signals, for glitch or noise it contains just 17 zeros for each sample. The shape of 'metadata' is thus: (number of samples, 17). For the signal files, for each sample the metadata is an array with these components:</p> <ol> <li>GPS start of the file from which this segment comes</li> <li>starting GPS time of this segment</li> <li>duration of the segment [s]</li> <li>mass1 [solar masses]</li> <li>mass2 [solar masses]</li> <li>spin1z</li> <li>spin2z</li> <li>inclination [radians]</li> <li>coalescence phase [radians]</li> <li>distance [Mpc]</li> <li>right_ascension [radians]</li> <li>declination [radians]</li> <li>polarization [radians]</li> <li>SNR (signal to noise ratio)</li> <li>shift of the signal w.r.t. the timeseries [s]</li> <li>length of the signal [s]</li> <li>fraction of the signal contained in the time window</li> </ol> <p>Number of samples:</p> <ul> <li>80000 for the file glitch_test.npz</li> <li>69998 for the file glitch_train.npz</li> <li>500000 for the file noise_test.npz</li> <li>250000 for the file noise_train.npz</li> <li>500000 for the file signal_test.npz</li> <li>250000 for the file signal_train.npz</li> </ul> <p>An example of few lines of python code to read each file is:</p> <pre><code>import numpy as np f = np.load("filename.npz") X = f['X'] Y = f['Y'] m = f['metadata'] </code></pre> <p>For the preparation of these data, we acknowledge the use of the following software packages: GWpy [1], PyCBC [2] and LALSuite [3]. </p> <p>This research has made use of data or software obtained from the Gravitational Wave Open Science Center (<a href="https://gwosc.org/" target="_blank" rel="noopener">gwosc.org</a>), a service of the LIGO Scientific Collaboration, the Virgo Collaboration, and KAGRA. This material is based upon work supported by NSF's LIGO Laboratory which is a major facility fully funded by the National Science Foundation, as well as the Science and Technology Facilities Council (STFC) of the United Kingdom, the Max-Planck-Society (MPS), and the State of Niedersachsen/Germany for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the Australian Research Council. Virgo is funded, through the European Gravitational Observatory (EGO), by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale di Fisica Nucleare (INFN) and the Dutch Nikhef, with contributions by institutions from Belgium, Germany, Greece, Hungary, Ireland, Japan, Monaco, Poland, Portugal, Spain. KAGRA is supported by Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan Society for the Promotion of Science (JSPS) in Japan; National Research Foundation (NRF) and Ministry of Science and ICT (MSIT) in Korea; Academia Sinica (AS) and National Science and Technology Council (NSTC) in Taiwan.</p> <p>[1] https://gwpy.github.io<br>[2] https://pycbc.org<br>[3] https://lscsoft.docs.ligo.org/lalsuite</p>
Datasets used to train and test the Cortical Spectro-Temporal Model (CSTM).
<p>ZIP files of folders containing all the datasets (audio file corpora) employed in our research to train the Encoder Layer (EL) and the SVMs and to test the complete CSTM. This folder includes a set of 840 corpora which are distributed in 2 corpora for each configuration organized by 2 sets of synthesized voices, 3 syllabic conditions (i.e. mono-, di- and tri-syllabic English words) and 10 completely different vocabularies all distributed in 6 acoustic variants, beyond the original version of the corpora.</p> <p>The 6 acoustic variants corresponds to: two levels of white noise (19.8 dB and 13.8 dB Signal to Noise Ratio (SNR) average Root Mean Square (RMS) power rate), two levels of reverberation (Reveberation-Time 60 dB (RT-60) value of 0.61 seconds and 1.78 seconds) and variations of pitch on both directions (from E to G and from E to C).</p>
Machine learning models, and training, validation and test datasets for: "Sequence determinants of human gene regulatory elements"
<p>This record contains the training, test and validation datasets used to train and evaluate the machine learning models in manuscript:</p> <p><strong>Sahu, Biswajyoti, et al. "Sequence determinants of human gene regulatory elements." (2021).</strong></p> <p><br> This record contains also the final hyperparameter-optimized models for each training dataset/task combination described in the manuscript. The README-files provided with the record describe the datasets and models in more detail. The datasets deposited here are derived from the original raw data (GEO accession: GSE180158) as described in the Methods of the manuscript.</p>
CNN models and training, validation and test datasets for "PlotMI: interpretation of pairwise interactions and positional preferences learned by a deep learning model from sequence data"
<p>Convolutional neural network (CNN) models and their respective training, validation and test datasets used in manuscript:</p> <p>Tuomo Hartonen, Teemu Kivioja and Jussi Taipale, "PlotMI: interpretation of pairwise interactions and positional preferences learned by a deep learning model from sequence data"</p>
Multichannel Displacement measurement via self mixing interferometry and neural network : training and test datasets
<p>Self mixing interferometry is a simple and robust sensing method which can be used (among other things) to measure the displacement of a target along the light propagation axis. While conceptually simple, the actual use of this method is less straightforward than originally envisioned because reconstructing the target displacement from the interferometric signal is often tricky. A small neural network can do this task very well after proper training, as described in [10.1364/OE.419844], with dataset [10.5281/zenodo.7303745]. </p> <p>Here, the dataset is composed by a training set and a test set, in a specific configuration in which 3 self-mixing sensors measure simultaneously the same target displacement. Both datasets contain the displacement itself and the 3 interferometric signals (1 per sensing channel)</p> <p><strong>The training set </strong>relies on two python/numpy data files corresponding to <strong>harmonic displacements</strong> for different frequencies ranging from 53 and 93 Hz and amplitudes from 3.5 to 7.5 µm : </p> <ul> <li>Training_set_2_lostchannel_displacement.npy : 93744-elements long numpy array containing the target's displacement in units of µm/ms with a 1.024 ms time step. </li> <li>Training_set_2_lostchannel_signal.npy : numpy array of shape (3, 93744, 256, 1) containing the interferometric signals. The first dimension refers to the channel (1, 2 or 3), the second dimension is the number of segments of 256 points. Each segment of 256 points correspond to a 1.024 ms window of signal, matching one element of the displacement. For instance, the displacement value in `displacement[416]` corresponds to the interferometric signal segment `signal[0,416,:,0]` for channel 1, `signal[1,416,:,0]` for channel 2 and `signal[2,416,:,0]` for channel 3. </li> </ul> <p><strong>The test </strong>set follows the same architecture and format as the training set, but contains only <strong>random displacements </strong>generated by a delta-correlated signal, which we Fourier filter with a fifth order Butterworth filter between 10 and 100 Hz :</p> <ul> <li>"Displacement_test.npy" : with shape (246078, 1)</li> <li>"Signal_test.npy" : with shape (3, 246078, 256, 1)</li> <li>Only the displacement type has changed from harmonic to random, from the training to the test datasets.</li> </ul> <p>These datasets have been used to train and test a 3 channel neural network (after data augmentation) in order to emphasize the high availability potential of multichannel schemes, against backscattered power fluctuations. </p>
Training and testing dataset for SNN
<p>Dataset used for training and testing the SNN model to predict value of partial computations of LAC application. Each even row represents a parametrization of a task, and each odd row represents a cache signature at the moment the task arrived.</p>
DELWAVE 1.0: Training and test datasets
<p>The training, test datasets, and trained static model corresponding to DELWAVE 1.0: Deep-learning surrogate model of surface wave<br> climate in the Adriatic Basin.</p>
Pre-rendered training and testing dataset for the paper Deep Convolutional Reconstruction for Gradient-Domain Rendering
<p>Pre-rendered data for training and evaluating the Neural Reconstruction for Gradient-Domain Path Tracing ("NGPT") algorithm described in the paper Deep Convolutional Reconstruction for Gradient-Domain Rendering.</p> <p>See https://github.com/mkettune/ngpt/.</p> <p>Download 'all_datasets.zip' for easy access to all of the data. Alternatively, you may download the datasets one by one.</p> <p>The displayed license (CC BY 4.0) covers the rendering and collection work. Copyrights of the individual scenes may need to be respected as well and are displayed in file 'LICENSE'.</p>
Dataset for testing and training map-matching methods
<p>We present a dataset for testing, bench-marking, and offline learning of map-matching algorithms. For the first time, a large enough dataset is available to prove or disprove map-matching hypotheses on a world-wide scale. There are several hundred map-matching algorithms published in literature, each tested only on a limited scale due to difficulties in collecting truly large scale data. Our contribution aims to provide a convenient gold standard to compare various map-matching algorithms between each other. Moreover, as many state-of-the-art map-matching algorithms are based on techniques that require offline learning, our dataset can be readily used as the training set. Because of the global coverage of our dataset, learning does not have to be be biased to the part of the world where the algorithm was tested.</p>
TTYY NCCT trainning and test dataset
Open the record for dataset details and reuse information.
GF2Hyper dataset for training and test
<p>GF2Hyper dataset involves Panchromatic/Multispectral imager carried on Gaofen-1 and Hyperion sensor on EO-1 satellite. Gaofen-1 is a Chinese satellite employed two imagers, which can obtain 8 m resolution multispectral images with four bands and a 2 m resolution PAN image that covered the full spectral ranges of corresponding multispectral images, from 450 to 900 nm. Images obtained by Hyperion are with a spatial resolution of 30 meters and obtain 242 bands from 357 to 2567 nm. In this dataset, we only select 148 useful bands. GF2Hyper dataset is used to verify the one situation of generalized spectral super-resolution, i.e., PansSR. PansSR is to achieve the joint enhancement of spatial resolution and spectral resolution using auxiliary higher-resolution spectral channels, usually, panchromatic images.</p> <p>The file named "GF2Hyper_train.h5" contains 1152 training samples where the high-resolution PAN images are with size of 128×128×1 while the size of multispectral images is 32×32×4. Moreover, we also give some test images in the "GF2Hyper_test.mat". Details about generalized spectral super-resolution can be found in the following paper.</p> <p>Paper: J. He, Q. Yuan, J. Li, and L. Zhang, "PoNet: A universal physical optimization-based spectral super-resolution network for arbitrary multispectral images," <em>Information Fusion</em>, vol. 80, pp. 205–225, 2022.</p> <p>More information about the author can be found at <a href="http://jianghe96.github.io/">https://jianghe96.github.io/</a></p> <p>If this dataset is helpful please cite as:</p> <pre>@article{He2022PoNet, title={PoNet: A universal physical optimization-based spectral super-resolution network for arbitrary multispectral images}, author={He, Jiang and Yuan, Qiangqiang and Li, Jie and Zhang, Liangpei}, journal={Information Fusion}, volume={80}, pages={205--225}, year={2022}, }</pre>
Sen2CHRIS dataset for training and test
<p>Compact High Resolution Imaging Spectrometer, namely CHRIS, is carried on PRoject for On Board Autonomy 1 (PROBA-1) satellite and provides hyperspectral images.However, CHRIS images are always accompanied by hybrid noise, stripe gap, and high cloud cover. Furthermore, all satellite passes are systematically acquired according to a fixed acquisition plan. Observation over a new specific area should be performed by submitting the request to add a new site to the acquisition plan, which costs much money and time.</p> <p>To verify the model performance of solving generalized spectral super-resolution, Sen2CHRIS data set is generated from three freely available data subsets, including Xiong'an, Washington DC Mall, and Chikusei, by downsampling the spectral channels of free hyperspectral data to the same of CHRIS and Sentinel-2 using Hysure.</p> <p>The file named "Chikusei_CHRIS.h5" contains 1016 training samples where the high-resolution multispectral images are with size of 128×128×4 while the size of low-resolution multispectral images is 64×64×4. Moreover, we also give some test images in the "Chikusei_test.mat".</p> <p>The file named "DCMall_CHRIS.h5" contains 2424 training samples where the high-resolution multispectral images are with size of 64×64×4 while the size of low-resolution multispectral images is 32×32×4. Moreover, we also give some test images in the "DCMall_test.mat".</p> <p>The file named "Xiongan_CHRIS.h5" contains 1216 training samples where the high-resolution multispectral images are with size of 128×128×4 while the size of low-resolution multispectral images is 64×64×4. Moreover, we also give some test images in the "Xiongan_test.mat". Details about generalized spectral super-resolution can be found in the following paper.</p> <p> </p> <p>Paper: J. He, Q. Yuan, J. Li, and L. Zhang, "PoNet: A universal physical optimization-based spectral super-resolution network for arbitrary multispectral images," <em>Information Fusion</em>, vol. 80, pp. 205–225, 2022.</p> <p>More information about the author can be found at <a href="http://jianghe96.github.io/">https://jianghe96.github.io/</a></p> <p>If this dataset is helpful please cite as:</p> <pre>@article{He2022PoNet, title={PoNet: A universal physical optimization-based spectral super-resolution network for arbitrary multispectral images}, author={He, Jiang and Yuan, Qiangqiang and Li, Jie and Zhang, Liangpei}, journal={Information Fusion}, volume={80}, pages={205--225}, year={2022}, }</pre>
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.