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4,612 results for “labelled”
Labeled 17 Hardwood Species and 55 Genotypes of Populus Stomatal Images Datasets
<p>Research has indicated the potential of using machine learning algorithms to detect and measure stomata automatically. However, the current limitation for further improving and fine-tuning machine learning-based stomatal study methods is due to the small, inconsistent, and monotypic nature of stomatal datasets, which are also not easily accessible. To address this issue, our collection comprises about 11,000 unique images of hardwood leaf stomata gathered from projects conducted between 2015 and 2020-2022. The dataset includes over 7,000 images of 17 frequently encountered hardwood species, including oak, maple, ash, elm, and hickory, as well as over 3,000 images of 55 genotypes from seven Populus taxa (as detailed in Table 1). Each image has been labeled as either <em>inner guard cell walls</em> or <em>whole_stomata</em> (stomatal aperture and guard cells) and has a corresponding YOLO label file that can be transformed to other annotation formats. These images and labels are publicly available, making it easier to train machine-learning models and examine leaf stomatal traits. By utilizing our dataset, users can (1) use state-of-the-art machine learning models to identify, count, and quantify leaf stomata; (2) investigate the diverse range of stomatal characteristics across different types of hardwood trees; and (3) create new indices for measuring stomata.</p>
SPASS dataset: A synthetic polyphonic dataset with spatiotemporal labels of sound sources
<p>SPASS is a synthetic dataset that consists of 10-seconds audio segments from 5 acoustic scenes:</p> <ul> <li>Park</li> <li>Square</li> <li>Street</li> <li>Waterfront</li> <li>Market</li> </ul> <p>Each acoustic scene has 5,000 audio recordings and its corresponding metadata.</p> <p>The audio recordings were created using a 3D acoustic simulation environment (RAVEN, <a href="https://www.virtualacoustics.org/RAVEN/">https://www.virtualacoustics.org/RAVEN/</a>).</p> <p>SPASS was made as a training dataset for the FuSA system (<a href="https://www.acusticauach.cl/fusa/">https://www.acusticauach.cl/fusa/</a>). This is a polyphonic dataset for Sound Event Detection (SED) tasks.</p> <p>The metadata files includes the class of each sound event, their onset and offset in time, the position in the space (cartesian) and their final position if the class was moving.</p> <p>This research was funded by ANID FONDEF grant number ID20I10333.</p>
Human Inner Ear Anatomy: Labeled Volume CT Data of Inner Ear Fluid Space and Anatomical Landmarks
<p>The provided dataset comprises 43 instances of temporal bone volume CT scans. The scans were performed on human cadaveric specimen with a resulting isotropic voxel size of <span class="math-tex">\(99 \times 99 \times 99 \, \, \mathrm{\mu m}^3\)</span>. Voxel-wise image labels of the fluid space of the bony labyrinth, subdivided in the three semantic classes cochlear volume, vestibular volume and semicircular canal volume are provided. In addition, each dataset contains JSON-like descriptor data defining the voxel coordinates of the anatomical landmarks: (1) apex of the cochlea, (2) oval window and (3) round window. The dataset can be used to train and evaluate algorithmic machine learning models for automated innear ear analysis in the context of the supervised learning paradigm.</p> <p> </p> <p><strong>Usage Notes</strong></p> <p>The datasets are formatted in the HDF5 format developed by the <a href="https://www.hdfgroup.org/solutions/hdf5/">HDF5 Group</a>. We utilized and thus recommend the usage of Python bindings <a href="https://www.h5py.org/">pyHDF</a> to handle the datasets.</p> <p>The flat-panel volume CT raw data, labels and landmarks are saved in the HDF5-internal file structure using the respective group and datasets:</p> <pre><code>raw/raw-0 label/label-0 landmark/landmark-0 landmark/landmark-1 landmark/landmark-2</code></pre> <p>Array raw and label data can be read from the file by indexing into an opened h5py file handle, for example as numpy.ndarray. Further metadata is contained in the attribute dictionaries of the raw and label datasets.</p> <p>Landmark coordinate data is available as an attribute dict and contains the coordinate system (LPS or RAS), IJK voxel coordinates and label information. The helicotrema or cochlea top is globally saved in landmark 0, the oval window in landmark 1 and the round window in landmark 2. Read as a Python dictionary, exemplary landmark information for a dataset may reads as follows:</p> <pre><code class="language-python">{'coordsys': 'LPS', 'id': 1, 'ijk_position': array([181, 188, 100]), 'label': 'CochleaTop', 'orientation': array([-1., -0., -0., -0., -1., -0., 0., 0., 1.]), 'xyz_position': array([ 44.21109689, -139.38058589, -183.48249736])}</code></pre> <p> </p> <pre><code class="language-python">{'coordsys': 'LPS', 'id': 2, 'ijk_position': array([222, 182, 145]), 'label': 'OvalWindow', 'orientation': array([-1., -0., -0., -0., -1., -0., 0., 0., 1.]), 'xyz_position': array([ 48.27890112, -139.95991131, -179.04103763])}</code></pre> <p> </p> <pre><code class="language-python">{'coordsys': 'LPS', 'id': 3, 'ijk_position': array([223, 209, 147]), 'label': 'RoundWindow', 'orientation': array([-1., -0., -0., -0., -1., -0., 0., 0., 1.]), 'xyz_position': array([ 48.33120126, -137.27135678, -178.8665465 ])}</code></pre> <p> </p>
Dataset for demonstration of quantitative label-free imaging with phase and polarization
<p>The QLIPP_Reconstruction_Resources_20x.zip file contains raw images of mouse brain slice and anisotropic glass target acquired with QLIPP. The file also contains the configuration files to reconstruct the phase, retardance, and orientation from this data with the recOrder pipeline. The tutorial slides for using this dataset for reconstruction can be found here (10.5281/zenodo.5135889).</p> <p> </p> <p>v1.1.0: Upload two zip files for automated testing of recOrder and waveOrder repositories.</p> <p>v1.2.0: add pycromanager dataset for testing the reader and converter in waveOrder.</p> <p>v1.3.0: reduce the size of recOrder test dataset</p> <p>v 1.4.0: add datasets for new data schema defined for recOrder 0.4.0 </p> <p>v1.5.0: add a dataset that shows images of an embryo </p>
A multi-scale labeled dataset for boulder segmentation and navigation on small bodies
<p>The capability to detect boulders on the surface of small bodies is beneficial for vision-based applications such as hazard detection during critical operations, safety quantification, autonomous planning of scientific operations, and autonomous navigation. This task, however, is challenging due to the wide assortment of irregular shapes, the characteristics of the boulders population, and the rapid variability in the illumination conditions. Moreover, the lack of publicly available labeled datasets damps the research about data-driven algorithms. The following dataset has been designed and made publicly available to tackle these challenges. Its purpose is twofold. First, from the lessons learned from previous datasets, to develop a multi-purpose, high-fidelity dataset with boulders scattered across the surface of a small body. Second, to exploit domain randomization, artificial noise addition, scaling, and post-processing, enabling the design of data-driven pipelines. </p> <p>The methodology used to generate the dataset is illustrated in the work "A multi-scale labeled dataset for boulder segmentation and navigation on small bodies" by Mattia Pugliatti and Michele Maestrini, presented at the 74th IAC (International Astronautical Congress), 2024, Baku, Azerbaijan.</p> <p>The dataset contains the image-label pairs of 47502 samples, organized with the following structure: </p> <p>Dataset_PugliattiMaestrini_2023IAC<br> --img<br> --labels<br> --masks</p> <p>The dataset is comprised of 47502 samples. The "img" folder contains the input, 512x 512 grayscale images. The "labels" folder includes the .txt segmentation labels of the 15 most prominent boulders for each image detected with the methodology illustrated in the IAC paper. The "masks" dataset contains the segmentation masks for all image layers, with the values being encoded between 0 and 17 as uint8. The samples are named as XXXXXX_YYY. XXXXXX stands for the image's original ID during rendering. YYY corresponds to the sub-splits of the original image obtained at rendering: </p> <p> 001 - Top-Left crop<br> 002 - Top-Right crop<br> 003 - Bottom-Left crop<br> 004 - Bottom-right crop<br> 005 - Whole, resized</p> <p>The file "10000_ub_2023-01-18 00.09.43.txt" contains all the values of the rendering inputs detailed in the IAC paper.</p>
Respiration, isotope composition, and carbon source partitioning from incubations of soil amended with litter and isotope-labeled lignin
We incubated 10 forest soils (collected from sites across North America, including the Luquillo LTER/CZO) in the laboratory for over two years to quantify the decomposition of carbon derived from added litter and lignin, as well as from extant soil organic matter. Each soil was subjected to two substrate addition treatments: a) litter derived from a C4 grass precipitated with 13C-enriched lignin, or the same C4 grass litter was precipitated with natural-abundance lignin. The concentrations and delta13C composition of carbon dioxide produced from each soil were measured periodically over time and partitioned into sources (soil organic matter, litter, and added lignin) using isotope mixing models. The methods and results are described in detail by a manuscript in Ecology (Hall et al., 2020).
Table S3. List of Locustella sound recordings included in bioacoustic analysis surrounding description of the Taliabu Grasshopper-Warbler. The table provides information on sound library sources and sampling localities of recordings as well as raw data on all 11 bioacoustic parameters measured (see Supplementary Materials section SM3 for more details on parameters). Recordings whose source is labeled as "private recording" were obtained by colleagues and are available upon demand from the corresponding author.
<p>supplement to Rheindt, Frank E., Prawiradilaga, Dewi M., Ashari, Hidayat, Suparno, Gwee, Chyi Yin, Lee, Geraldine W. X., Wu, Meng Yue, Ng, Nathaniel S. R. (2020): A lost world in Wallacea: Description of a montane archipelagic avifauna. Science 367: 167-170, DOI: 10.1126/science.aax2146</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>
Fig. 10. Hemisphaeroides spp. A–E. Hemisphaeroides aeneoniger Melichar, 1903, syntype. A. Habitus, dorsal view. B. Habitus, lateral view. C. Labels. D. Habitus, ventral view. E in Nomenclatural changes in the planthopper tribe Hemisphaeriini (Hemiptera: Fulgoromorpha: Issidae), with the description of a new genus and a new species
Fig. 10. Hemisphaeroides spp. A–E. Hemisphaeroides aeneoniger Melichar, 1903, syntype. A. Habitus, dorsal view. B. Habitus, lateral view. C. Labels. D. Habitus, ventral view. E. Habitus, caudal view. — F–I. Hemisphaeroides atromaculatus Distant, 1916, syntype. F. Habitus, dorsal view. G. Habitus, lateral view. H. Metope and clypeus, facial view. I. Labels. Scale bar = 1 mm.
Mass spectrometry output SILAC labelled (F/Y) biological replicate 1 - anti-HLA-A29 antibody DK1G8
<p>Mass spectrometry output from SILAC labelled (F/Y) ERAP2 wildetype versus (CRISPR Cas9-mediated) ERAP2-KO lymphoblastoid cell line from a Birdshot Uveitis patient (ERAP1 hap10/10 ERAP2 hapA/A). Peptides were eluted from immuno-purifications with anti-HLA-A29 antibody DK1G8. The dataset was used for subsequent filtering and differential expression analysis by <em>limma</em>. </p>
Togo Cropland Map and Labeled Dataset
<p>This dataset provides a 10 m resolution map of cropland in Togo (togo_cropland_2019.zip). Each pixel represents a posterior probability (ranging 0 to 1) that the pixel contains crops, predicted using an LSTM classifier and multi-spectral time series of Sentinel-2 satellite observations. For more details on the method, please see Kerner and Tseng, et al. (full reference below).</p> <p>This dataset also provides the hand-labeled polygons used for training (crop_merged_v2.zi, noncrop_merged_v2.zip) and testing (togo_test_majority.zip) the model, which were created by experts based on photointerpretation of high-resolution imagery (primarily SkySat and PlanetScope) in QGIS and Google Earth Pro.</p> <p>If you use any part of this dataset, please cite the following paper: Hannah Kerner, Gabriel Tseng, Inbal Becker-Reshef, Catherine Nakalembe, Brian Barker, Blake Munshell, Madhava Paliyam, and Mehdi Hosseini. 2020. Rapid Response Crop Maps in Data Sparse Regions. In review for <em>KDD ’20: ACMSIGKDD Conference on Knowledge Discovery and Data Mining Workshops</em>, August 22–27, 2020, San Diego, CA. </p>
Plasmonic Nanosensors for the Label-Free Imaging of Dynamic Protein Patterns
<p>Additional data to support our work on "Plasmonic Nanosensors for the Label-Free Imaging of Dynamic Protein Patterns" published in the Journal of Physical Chemistry Letters (DOI: 10.1021/acs.jpclett.0c01400)</p> <p>Movies:<br> - S1: MinVideo_EColi.mp4<br> - S2: MinVideo_DOPC_DOPG_CL.mp4<br> - S3: MinVideo_DOPC_DOPG.mp4<br> Audio Files:<br> - S1: MinSound_EColi.mp4<br> - S2: MinSound_DOPC_DOPG_CL.mp4<br> - S3: MinSound_DOPC_DOPG.mp4</p>
Kenya Cropland Map and Non-Crop Labelled dataset
<p>This dataset provides a 10m resolution map of cropland in Kenya for the 2019-2020 growing season (kenya_cropland_binary_2019.tif.zip) and a 10m resolution map of cropland in Busia County, Kenya for the 2020-2021 growing season (busia_cropland_binary_2020.tif.zip). Each pixel has a binary value, 0 if it does not contain crops and 1 if it does. These values were obtained by thresholding the predictions of an LSTM classifier trained on multi-spectral time series of Sentinel-2 satellite observations. A thresholding value of 0.5 was used.</p> <p>This dataset also provides the hand-labelled non-crop points used for training, which were created by labelling high-resolution satellite imagery in QGIS and Google Earth Pro.</p> <p>For more information, or if you use any part of this dataset, please refer to / cite the following paper: Gabriel Tseng, Hannah Kerner, Catherine Nakalembe and Inbal Becker-Reshef. 2020. Annual and in-season mapping of cropland at field scale with sparse labels. Tackling Climate Change with Machine Learning workshop at NeurIPS ’20: December 11th, 2020</p>
cigKast: A data of 3D synthetic seismic volumes with labeled paleokarsts for deep-learning-based paleokarst interpretation
<p>cigKarst is a dataset created by the <a href="http://cig.ustc.edu.cn/">Computational Interpretation Group (CIG)</a> for the deep-learning-based peleokarst interpretation in 3D seismic images, <a href="http://cig.ustc.edu.cn/xinming/list.htm" target="_blank" rel="noopener">Xinming Wu</a> is the main contributor to the dataset.</p> <p>This dataset contains 120 pairs of synthetic 3D seismic images and the corresponding label images with the ground truth of the paleokarst systems simulated in the seismic images. More detail of building this dataset is discussed in the paper published at the journal of JGR Solid Earth:</p> <p><strong>Wu, X.</strong>, S. Yan, J. Qi, and H. Zeng, 2020, Deep learning for characterizing paleokarst collapse features in 3D seismic images. <strong>JGR, Solid Earth</strong>, Vol. 125(9), 1-23, e2020JB019685. <a href="http://cig.ustc.edu.cn/_upload/tpl/05/cd/1485/template1485/papers/wu2020karst.pdf">[PDF]</a>. doi: 10.1029/2020JB019685</p> <p>Below are some brief description of the dataset:</p> <p>1) The "seismic.zip" contains 120 3D seismic images, each image is with the dimension of 256X256X256;</p> <p> 2) The "karst.zip" contains 120 3D label images of the karsts. Each label image is with the same dimension of 256X256X256. The values in a label image are set with ones in the karst areas while zeros elsewhere, which is why the compressed label images in the karst.zip is much smaller than the seismic images compressed in the seismic.zip</p>
Cataract101-Extended labels
<p>Extended labels for cataract101 dataset. CSV label file per video with copied surgical step, surgeon id, surgeon's experience per frame from original labels. Additionally, we added the elapsed time from the start of the surgery, the remaining surgical time (rsd) to the end of the surgery, and a valid flag that is true from the start to the end of the surgery. This flag is used to cut out video content that does not show the actual surgery.</p>
SDSS Photometric Measurements with Labels
<p>The Sloan Digital Sky Survey (SDSS) is a comprehensive survey of the northern sky. This dataset contains a subset of this survey, namely the photometric measurements and spectroscopic labels of around 2.8 million objects. The dataset was generated by submitting an SQL query to the DR12 catalog on the SDSS CasJobs site.</p> <p>Each row in the dataset corresponds to an object in the sky. There are 14 columns:</p> <ul> <li>The first two columns contain the right ascension (<strong>ra</strong>) and declination (<strong>dec</strong>) of an object. These two coordinates uniquely determine its position.</li> <li>The third column (<strong>class</strong>) is the spectroscopic class (Star, Galaxy, and Quasar) as determined by expert opinion. This can be the target vector in a classification model.</li> <li>There are 11 columns that we can use as feature vectors. These are the different PSF and Petrosian magnitude and colour measurements: <ul> <li><strong>psfMag_r_w14</strong>: The PSF magnitude measurement in r-band, assuming the object is a point source.</li> <li><strong>psf_u_g_14</strong>: The difference between the PSF magnitude measurement in u-band and the g-band (i.e. the u-g colour), assuming the object is a point source.</li> <li><strong>psf_g_r_14</strong>: The difference between the PSF magnitude measurement in g-band and the r-band (i.e. the g-r colour), assuming the object is a point source.</li> <li><strong>psf_r_i_14</strong>: The difference between the PSF magnitude measurement in r-band and the i-band (i.e. the r-i colour), assuming the object is a point source.</li> <li><strong>psf_i_z_14</strong>: The difference between the PSF magnitude measurement in i-band and the z-band (i.e. the i-z colour), assuming the object is a point source.</li> <li><strong>petroMag_r_w14</strong>: The Petrosian magnitude measurement in r-band, assuming the object is an extended source.</li> <li><strong>petro_u_g_w14</strong>: The difference between the Petrosian magnitude measurement in the u-band and the g-band (i.e. the u-g colour), assuming the object is an extended source.</li> <li><strong>petro_g_r_w14</strong>: The difference between the Petrosian magnitude measurement in the g-band and the r-band (i.e. the g-r colour), assuming the object is an extended source.</li> <li><strong>petro_r_i_w14</strong>: The difference between the Petrosian magnitude measurement in the r-band and the i-band (i.e. the r-i colour), assuming the object is an extended source.</li> <li><strong>petro_i_z_w14</strong>: The difference between the Petrosian magnitude measurement in the i-band and the z-band (i.e. the i-z colour), assuming the object is an extended source.</li> <li><strong>petroRad_r</strong>: The size measurement of the object in r-band in arc seconds.</li> </ul> </li> </ul> <p>The measurements have been corrected for dust extinction (the scattering of light by the galactic dust) using the correction set provided by Wolf (2014). Each feature has also been standardised to have zero mean and unit variance.</p> <p>For the code to generate this dataset, please go to the Github repo: https://github.com/chengsoonong/mclass-sky</p> <p>If you use the SDSS data in your papers, please see here for instructions on how to cite: http://www.sdss.org/collaboration/citing-sdss/</p> <p>Please also cite this upload if you have used this particular pre-processed dataset.</p>
1151 commits with software maintenance activity labels (corrective,perfective,adaptive)
<p>Data format: CSV</p> <p>Separator character: '#'</p> <p><strong>This dataset contains 1151 commits manually labeled with maintenance activities ("c" for corrective, "p" for perfective, "a" for adaptive)</strong> according to the definition by Mockus et al. in <em>"Mockus, A. and Votta, L.G., 2000, October. Identifying Reasons for Software Changes using Historic Databases. In icsm (pp. 120-130)"</em>.</p> <p>In addition, this dataset also contains <strong>further information (features) extracted from the commits</strong>:</p> <ol> <li>The <strong>source code changes</strong> performed by the commit author as part of a given commit (statement added, statement removed, etc.) <ul> <li>The source code change taxonomy is detailed in <em>"Fluri, B. and Gall, H.C., 2006, June. Classifying change types for qualifying change couplings. In Program Comprehension, 2006. ICPC 2006. 14th IEEE International Conference on (pp. 35-45). IEEE."</em></li> </ul> </li> <li>A binary indication (1/0) whether a given commit contains any of the <strong>keywords from a pre-computed </strong>(according to a word frequency analysis)<strong> set of keywords</strong> <strong>indicative of each maintenance activity</strong>.</li> </ol> <p>The dataset consists of commits sampled from the following open source projects:</p> <ol> <li>RxJava</li> <li>hbase</li> <li>elasticsearch</li> <li>intellij-community</li> <li>hadoop</li> <li>drools</li> <li>kotlin</li> <li>restlet-framework-java</li> <li>orientdb</li> <li>camel</li> <li>spring-framework </li> </ol> <p>This dataset is a supporting material for the paper <strong>"Boosting Automatic Commit Classification Into Maintenance Activities By Utilizing Source Code Changes", to appear in PROMISE 2017.</strong></p>
Training data for MaxQuant and Msstats label-free analysis in Galaxy
<p>The files serve as input and intermediate results for a MaxQuant and Msstats training on skin cancer tissues (<a href="https://doi.org/10.1016/j.matbio.2017.11.004">https://doi.org/10.1016/j.matbio.2017.11.004</a>) in the Galaxy training network (https://training.galaxyproject.org).</p> <p>Input files: human FASTA database for Maxquant. Annotation file and comparison matrix file for Msstats.</p> <p>Intermediate result files: MaxQuant protein groups, evidence and PTXQC.</p>
LSD4WSD : An Open Dataset for Wet Snow Detection with SAR Data and Physical Labelling
<p><strong>LSD4WSD V2.0</strong></p><p><strong>L</strong>earning <strong>S</strong>AR <strong>D</strong>ataset for <strong>W</strong>et <strong>S</strong>now <strong>D</strong>etection - Full Analysis Version. </p><p>The aim of this dataset is to provide a basis for automatic learning to detect wet snow. It is based on Sentinel-1 SAR GRD satellite images acquired between August 2020 and August 2021 over the French Alps. The new version of this dataset is no longer simply restricted to a classification task, and provides a set of metadata for each sample.</p><p>Modification and improvements of the version 2.0.0 :</p><ul><li><i>Number of massif:</i> add 7 new massif to cover the all Sentinel-1 images (cf `info.pdf`).</li><li><i>Acquisition:</i> add images of the descending pass in addition to those originally used in the ascending pass.</li><li><i>Sample: </i>reduction in the size of the samples considered to 15 by 15 to facilitate evaluation at the central pixel.</li><li><i>Sample: </i>increased density of extracted windows, with a distance of approximately 500 meters between the centers of the windows.</li><li><i>Sample:</i> removal of the pre-processing involving the use of logarithms.</li><li><i>Sample:</i> removal of the pre-processing involving the normalisation.</li><li><i>Labels:</i> new structure for the labels part: dictionary with keys: `topography`, `metadata` and `physics`.</li><li><i>Labels:</i> `physics`: addition of direct information from the CROCUS model for 3 simulations: Liquid Water Content, snow height and minimum snowpack temperature.</li><li><i>Labels:</i> `topography`: information on the slope, altitude and average orientation of the sample.</li><li><i>Labels:</i> `metadata` : information on the date of the sample, the mountain massif and the run (ascending or descending).</li><li><i>Dataset</i>: removal of the train/test split*</li></ul><p>*We leave it up to the user to use the Group Kfold method to validate the models using the alpine massif information.</p><p>Finally, it consists of 2467516 samples of size 15 by 15 by 9. For each sample, the 9 metadata are provided, using in particular the <a href="https://www.umr-cnrm.fr/spip.php?article265&lang=en">Crocus</a> physical model:</p><ul><li>topography:<ul><li>elevation (meters) (average),</li><li>orientation (degrees) (average),</li><li>slope (degrees) (average),</li></ul></li><li>metadata:<ul><li>name of the alpine massif,</li><li>date of acquisition,</li><li>type of acquisition (ascending/descending),</li></ul></li><li>physics<ul><li>Liquid Water Content (km/m2),</li><li>snow height (m),</li><li>minimum snowpack temperature (Celsius degree).</li></ul></li></ul><p>The 9 channels are in the following order:</p><ul><li>Sentinel-1 polarimetric channels: VV, VH and the combination C: VV/VH in linear,</li><li>Topographical features: altitude, orientation, slope</li><li>Polarimetric ratio with a reference summer image: VV/VVref, VH/VHref, C/Cref**</li></ul><p>** The reference image selected is that of August 9th 2020, as a reference image without snow (cf. <a href="https://ieeexplore.ieee.org/document/842004">Nagler&al</a>)</p><p>An overview of the distribution and a summary of the sample statistics can be found in the file info.pdf.</p><p>The data is stored in .hdf5 format with gzip compression. We provide a python script to read and request the data. The script is dataset_load.py. It is based on the h5py, numpy and pandas libraries. It allows to select a part or the whole dataset using requests on the metadata. The script is documented and can be used as described in the README.md file</p><p>The processing chain is available at the following <a href="https://github.com/Matthieu-Gallet/LSD4WSD-dataset"><strong>Github</strong></a> address.</p><p>The authors would like to acknowledge the support from the National Centre for Space Studies (CNES) in providing computing facilities and access to SAR images via the PEPS platform.</p><p>The authors would like to deeply thank Mathieu Fructus for running the Crocus simulations.</p><p><strong>Erratum :</strong></p><p>In the dataloader file, the name of the "aquisition" column must be added twice, see the correction below.:</p><blockquote><p>dtst_ld = Dataset_loader(path_dataset,shuffle=False,descrp=["date","massif","aquisition","aquisition","elevation","slope","orientation","tmin","hsnow","tel",],) </p></blockquote><p>If you have any comments, questions or suggestions, please contact the authors: </p><ul><li>matthieu.gallet@univ-smb.fr</li><li>fatima.karbou@meteo.fr</li><li>abdourrahmane.atto@univ-smb.fr</li><li>emmanuel.trouve@univ-smb.fr</li></ul>
Data from: Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning
<div> <div> <p>Pulse timing is an important topic in nuclear instrumentation, with far-reaching applications from high energy physics to radiation imaging. While high-speed analog-to-digital converters become more and more developed and accessible, their potential uses and merits in nuclear detector signal processing are still uncertain, partially due to associated timing algorithms which are not fully understood and utilized.</p> <p>In the paper "Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning", we propose a novel method based on deep learning for timing analysis of modularized detectors without explicit needs of labelling event data. By taking advantage of the intrinsic time correlations, a label-free loss function with a specially designed regularizer is formed to supervise the training of neural networks towards a meaningful and accurate mapping function. We mathematically demonstrate the existence of the optimal function desired by the method, and give a systematic algorithm for training and calibration of the model. The proposed method is validated on <strong>two experimental datasets</strong> based on silicon photomultipliers (SiPM) as main transducers:</p> <ol> <li>In the toy experiment, we collect data from a pair of SiPM sensors from a common laser source. The neural network model achieves the single-channel time resolution of 8.8 ps and exhibits robustness against concept drift in the dataset. </li> <li>In the electromagnetic calorimeter experiment, we collect data from an eight-channel calorimeter module. Several neural network models (Fully-Connected, Convolutional Neural Network and Long Short Term Memory) are tested to show their conformance to the underlying physical constraint and to judge their performance against traditional methods. </li> </ol> <p>In total, the proposed method works well in either ideal or noisy experimental condition and recovers the time information from waveform samples successfully and precisely. <strong>The dataset in this repository serves as a basis for similar researches on timing performance of SiPM-based nuclear detectors, and on application of neural networks to typical signals of nuclear radiation detectors.</strong></p> </div> </div>
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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)
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DANDI Archive for NWB datasets
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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.