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1,940 results for “fusion”
Circumpolar mid-winter thaw and refreeze based on fusion of Metop ASCAT and SMOS, 2011/2012 - 2021/2022
<p>Rain-on-Snow (ROS) events occur across many regions of the terrestrial Arctic in mid-winter. Snow pack properties are changing and in extreme cases ice layers form which affect wildlife, vegetation and soils beyond the duration of the event.</p> <p>Active and passive microwave data have been combined to identify events over land North of 65°N (Bartsch et al. 2023). In a first step Metop ASCAT (C-Band radar) was used to identify potential sudden snow structure change. In a second step, results have been masked for coincident observation of wet snow within +- 3 days based on SMOS (derived from Centre Aval de Traitement des Données SMOS (CATDS) level 3 product). Note that the SMOS retrievals can have data gaps due to radio frequency interferences (RFI) what leads to gaps in the event detection.</p> <p>The dataset is structured by centre points of the hexagonal grid of the used Metop ASCAT product (EUMETSAT, approximately 12.5 km nominal resolution). Attributes include point ID (GPI), latitude, longitude and</p> <ul> <li>aggregated number of events for the months November to February, 2011/12 to 2021/22, and their sum per winter (referred to as annual), or</li> <li>in case of daily results (date in file name) the magnitude of ASCAT backscatter change in dB (DSigma0; no data value is '0.0').</li> </ul> <p>The dataset extents Seawinds QuikScat (Ku-band) based results for 2000-2009 (Bartsch 2010, Freund and Bartsch 2020).</p>
Potential forest conservation value rasters for Denmark from Assmann et al. "LiDAR data fusion and machine learning identify temperate forests of high conservation value"
<p>Potential forest conservation value (high / low) rasters for Denmark based on a remote sensing data fusion approach. Please see manuscript (below) for a detailed description of the methods and data products. </p> <p><br>Jakob J. Assmann, Pil B. M. Pedersen, Jesper E. Moeslund, Cornelius Senf, Urs A. Treier, Derek Corcoran, Zsófia Koma, Thomas Nord-Larsen, Signe Normand. In prep. LiDAR data fusion and machine learning identify temperate forests of high conservation value.</p> <p><br>When using the data, please cite the above manuscript. </p> <p><br>Files description:</p> <ul> <li>Compressed and cloud optimised rasters of potential forest conservation value projections for Denmark (10 m res.) in EPSG:3857 <ul> <li>forest_quality_ranger_biowide_10m_cog_epsg3857.tif RandomForest model projections based on BIOWIDE stratification (!! best performing model !!)</li> <li>forest_quality_ranger_sustainscapes_10m_cog_epsg3857.tif RandomForest model projections based on SustainScapes stratification</li> <li>forest_quality_gbm_biowide_10m_cog_epsg3857.tif GBM model projections based on BIOWIDE stratification</li> <li>forest_quality_gbm_sustainscapes_10m_cog_epsg3857.tif GBM model projections based on SustainScapes stratification</li> </ul> </li> </ul> <p> </p> <ul> <li>Aggregated rasters of potential forest conservation value projections for Denmark (100 m res.) in EPSG:25832 <ul> <li>forest_quality_ranger_biowide_100m.tif RandomForest model projections based on BIOWIDE stratification (!! best performing model !!)</li> <li>forest_quality_ranger_sustainscapes_100m.tif RandomForest model projections based on SustainScapes stratification</li> <li>forest_quality_gbm_biowide_100m.tif GBM model projections based on BIOWIDE stratification</li> <li>forest_quality_gbm_sustainscapes_100m.tif GBM model projections based on SustainScapes stratification </li> </ul> </li> </ul> <p> </p> <ul> <li>Uncompressed and tiled rasters of potential forest conservation value projections for Denmark (10 m res.) in EPSG:25832<br>Please note: the archives contain approx. 42k tiles, each 10 x 10 km, as well as a VRT file for covenient loading. <ul> <li>forest_quality_ranger_biowide_10m.zip RandomForest model projections based on BIOWIDE stratification (!! best performing model !!)</li> <li>forest_quality_ranger_sustainscapes_10m.zip RandomForest model projections based on SustainScapes stratification</li> <li>forest_quality_gbm_biowide_10m.zip GBM model projections based on BIOWIDE stratification</li> <li>forest_quality_gbm_sustainscapes_10m.zip GBM model projections based on SustainScapes stratification</li> </ul> </li> </ul>
The Spitzer Spectroscopic Data Fusion - Merged Spectroscopic Redshift Catalogs in Spitzer Fields
<p>The Spitzer Spectroscopic Data Fusion merges miscellaneous spectroscopic information available within "popular" extragalactic survey fields.</p> <p>Last Updated on 20 March 2025 - <a href="https://zenodo.org/record/6368347">https://zenodo.org/record/6368347</a> - <a href="https://www.mattiavaccari.net/df/specz">https://www.mattiavaccari.net/df/specz</a></p> <p>Based on the Spitzer Data Fusion Project - <a href="https://doi.org/10.5281/zenodo.7850783">https://doi.org/10.5281/zenodo.7850783</a> - <a href="https://mattiavaccari.net/df">https://mattiavaccari.net/df</a></p> <p>Merged Spec-Z ("specz-merged") catalogs merge miscellaneous spec-z information available within a given field. Different spec-z catalogs available within a given field are merged (using a search radius of 1.0 arcsec), and for sources with multiple spec-z measurements the most reliable one is chosen following the (largely arbitrarily) assumed order of decreasing reliability indicated below for each field. If CAT1,...,CATN spec-z catalogs are available in a given field, Z_1 from CAT1 (i.e. NED) is adopted as "best" redshift (i.e. ZBEST), if available, otherwise Z_2 from CAT_2 is adopted if available, and so on up to Z_N and CAT_N. In using ZBEST it's thus important to bear in mind that this is not necessarily actually the "best" redshift for science purposes, and in particular that the choice of NED as CAT1 is often not ideal. However, Z_1,...,Z_N are included to allow users to define the "best" redshift based on their science needs when multiple redshift estimates are available for a given source. ZFLAG specifies which catalog is providing the ZBEST value according to the CATN number below. ZCLASS is meant to provide further info about the class/quality of the spectroscopic redshift measurement, but for the time being is not populated and is simply a copy of ZFLAG. ZWHERE is an additional binary/bit flag indicating in which of the N catalogs each source was given a spec-z estimate in. ZWHERE will e.g. be 2^0=1 if a redshift if available <em>only</em> from CAT_1, whereas it will be 2^0+2^1=3 if a redshift is available *only* from CAT1 and CAT2,<br>so that a source with a redshift available from all catalogues will have ZWHERE=2^0+...+2^n.</p> <p>See AAAREADME.SPECZ-MERGED within the ZIP archive for further information.</p>
ZHAW-ISC 3D ToF and RGB Fusion Dataset
<p>This dataset contains depth maps recorded by an ESPROS epc635 Time-of-Flight camera and RGB images from a Raspberry Pi camera module V2, for use in the fusion of these sensors to increase the resolution of the ToF camera.</p> <p>The dataset contains three scenes, one of a paper dodecahedron, a wooden grid with holes of various sizes, and a set of wooden bars with different distances between them. All scenes have a black background with low reflectivity at the 3D ToF camera’s illumination wavelength to reduce multi-path interference. An HDR image is created by combining two 3D ToF depth maps with different integration times based on the recorded amplitude of each pixel. The depth map is then transformed to the perspective of the RGB camera and the resulting holes are filled with the mean of their neighboring pixels.</p> <p>The content of the included files are:</p> <ul> <li>Recorded HDR ToF camera depth maps (160x60), containing the depth values in millimeters, stored as 16-bit PNG files. </li> <li>Recorded RGB images (2560x960)</li> </ul>
Quantification of Giant Unilamellar Vesicle Fusion Products by High-Throughput Image Analysis - Imaging Flow Citometry Data
<p>Imaging flow citometry (IFC) datasets analysed in "Quantification of Giant Unilamellar Vesicle Fusion Products by High-Throughput Image Analysis" (under revision).</p> <p>The folders contain acquisitions of giant unilamellar vesicles (GUVs) for lipid exchange and content exchange controls, with file naming convention DATE_SAMPLE_REPLICATE.rif, content exchange is indicated by CE samples in the 20230228_CE.zip folder, lipid exchange by LE samples in the 20221222_LE.zip folder. 24 samples per set are included, triplicates of isolated P1 (DOPE Af488 0.6% in LE; Dex-Af488 40 uM for CE), P2 (DOPE Cy50.6% in LE; Dex-Af647 10 uM for CE), NC (P1 + P2 1:1), PC (DOPE Af488 0.3% + DOPE Cy5 0.3 in LE; Dex-Af488 20 uM + Dex-Af647 5 uM for CE), and M samples numbered 1 to 4, prepared by mixing P1, P2 and PC in different ratios (M1= 1:1:1; M2= 1:1:0.5; M3= 1:1:0.1; M4= 1:1:0.05).</p> <p>Only .rif files are provided, they have to be elaborated via compensation and application of an analysis template using the Amnis IDEAS software. Compensation matrices for lipid exchange (20230217_LEcom.ctm) and content exchange (20230217_CEcomp.ctm) are included, as well as the analysis template (Lipid_exchange_analysis_6.2.ast). Gating in the latter may have to be adjusted to analyse LE and CE experiments.</p> <p>10000 objects in the GUV population or 50000 objects in total were acquired in each file. The files were elaborated in batch mode, outputting the statistic reports (Statistics report CE.txt for CE; Statistics report LE.txt for LE) that were elaborated using an R scirpt (included, IFC_analysis.R) </p>
Exploring Data Evaluation Strategies for Enhanced Identification of Host Cell Proteins in Drug Products of Therapeutic Antibodies and Fc-Fusion Proteins
<p>This data repository contains all previously unpublished raw data files for the manuscript “Exploring Data Evaluation Strategies for Enhanced Identification of Host Cell Proteins in Drug Products of Therapeutic Antibodies and Fc-Fusion Proteins” by Wolfgang Esser-Skala, Marius Segl, Therese Wohlschlager, Veronika Reisinger, Johann Holzmann, and Christian G. Huber. See <em>readme.md</em> for further information.</p>
Speciation through chromosomal fusion and fission in Lepidoptera
<p>28 Mai 2020<br> Phylogenetic trees, the chromoSSE script and the input data for the chromoSSE models belonging to the publication "<strong>Speciation through chromosomal fusion and fission in <em>Lepidoptera" </em></strong>doi 10.1098/rstb.2019.0539. For more information, contact jurriaan.devos@unibas.ch or kay.lucek@unibas.ch.</p> <p>The zipped folder "trees" contains three posterior distributions of chronograms for each of 16 genera, based on a sample of 100 trees each.<br> Each tree includes the outgroup taxon, and the ingroup-outgroup split was dated based on one of three strategies:<br> - For the files named GENUS_tmax_pl.tre based on the reported maximum (oldest) age of the reported interval;<br> - For the files named GENUS_tmed_pl.tre based on the reported median age;<br> - For the files named GENUS_tmax_pl.tre based on the reported minimum (youngest) age of the reported interval.<br> Note that the outgroups were pruned prior to diversification rate analysis.<br> The median age files were used as input for the ChromoSSE analysis; all files were used an input for the analyses based on Brownian Motion.</p> <p>The file "chromoSSE.Rev" contains a script that runs the cromoSSE models.<br> Inorder to use this script RevBayes needs to be installed. This can be done by using the link: https://revbayes.github.io/download.<br> It can be run with the command line:<br> $> rb chromoSSE.Rev --args 1<br> As a argument every number between 1 and 16 can be used. And represent a genera:<br> 1 = Colias, 2 = Erebia, 3 = Eunica, 4 = Eurema, 5 = Heliconius, 6 = Ithomia, 7 = Lycaena,<br> 8 = Lysandra, 9 = Memphis, 10 = Morpho, 11 = Oleria, 12 = Papilio, 13 = Pieris,<br> 14 = Polyommatus, 15 = Pteronymia, 16 = Taygetis.<br> The process runs automatically and generates MCMC outputfiles and stores them in the directory "output".<br> Each tree that is analyzed returns three files:<br> -The files named "GENUS.ChromoSSE_anc_statesX.log" logfile of the states;<br> -The files named "GENUS.ChromoSSE_finalX.tree" tree output of the analysis;<br> -The files named "GENUS.ChromoSSE_modelX.log" logfile of the model.<br> The files can be easily accessed by using the software Tracer: https://beast.community/tracer</p> <p>The zipped folder "data" contains input files needed for the chromoSSE analysis.<br> To run each analysis a tree "GENUS.pruned.trees" and a tsv-file "GENUS.pruned.states.tsv" with the number of chromosomes per species is needed.<br> In the trees all species without a chromosom number were excluded.</p>
Contour method and neutron diffraction dataset to determine the weld fusion zone shape on residual stress in submerged arc welding
<p>This is a dataset which formed the basis for "The effect of the weld fusion zone shape on residual stress in submerged arc welding" by A. Ishigami, M. J. Roy, J. N. Walsh and P. J. Withers appearing in the Journal of Advanced Manufacturing Technology.</p> <p>Two X-grade steel specimens with different high speed, submerged arc welds with very slight differences in fusion zone shape were compared with a novel contour method application as well as with neutron diffraction. Neutron diffraction was carried out with the SALSA instrument at the Institut Laue-Langevin in Grenoble, France with the assistance of T. Pirling. Data files with 441 in the descriptor refer to 'conventional' parameters (see publication), while 241 refers to 'new'.</p> <p>Provided in this dataset are four *.dat files, which contains data is in the form of a point cloud with one point per line, whitespace delimited in microns. Data was captured with a Nanofocus CF-4 laser profilometer sensor with point spacing 30 µm apart. Data with z coordinates below or above 500 µm are considered outside of the surface detection limits.</p> <p>Also included is an Excel worksheet, which contains the calculated residual stresses as found with LAMP (https://www.ill.eu/instruments-support/computing-for-science/cs-software/all-software/lamp/). Raw data is available here:</p> <p>P. J. Withers, A. Ishigami, T. Pirling, M. Roy, J. Walsh (2014). The effect of weld bead shape on residual stress in novel low heat input welding of steel [Data set]. ILL. http://doi.ill.fr/10.5291/ILL-DATA.1-02-145</p> <p>The authors would like to thank JFE Steel Corporation for both direct and in-direct support of this research. The authors would also like to thank the Institut Max von Laue-Paul Langevin for the allocation of beamtime at SALSA and gratefully acknowledge the help of Thilo Pirling for his assistance in performing the neutron diffraction experiments. A. Ishigami would like to thank Kenji Oi for his support of this research. M. J. Roy would like to thank Ian Winstanley for his assistance in performing the contour cuts. M. J. Roy acknowledges financial support from the EPSRC (EP/L01680X/1) through the Materials for Demanding Environments Centre for Doctoral Training.</p>
Aircraft Marshaling Signals Dataset of FMCW Radar and Event-Based Camera for Sensor Fusion
<p><strong>Dataset Introduction</strong></p><p>The advent of neural networks capable of learning salient features from variance in the radar data has expanded the breadth of radar applications, often as an alternative sensor or a complementary modality to camera vision. Gesture recognition for command control is arguably the most commonly explored application. Nevertheless, more suitable benchmarking datasets than currently available are needed to assess and compare the merits of the different proposed solutions and explore a broader range of scenarios than simple hand-gesturing a few centimeters away from a radar transmitter/receiver. Most current publicly available radar datasets used in gesture recognition provide limited diversity, do not provide access to raw ADC data, and are not significantly challenging. To address these shortcomings, we created and make available a new dataset that combines FMCW radar and dynamic vision camera of 10 aircraft marshalling signals (whole body) at several distances and angles from the sensors, recorded from 13 people. The two modalities are hardware synchronized using the radar's PRI signal. Moreover, in the supporting publication we propose a sparse encoding of the time domain (ADC) signals that achieve a dramatic data rate reduction (>76%) while retaining the efficacy of the downstream FFT processing (<2% accuracy loss on recognition tasks), and can be used to create an sparse event-based representation of the radar data. In this way the dataset can be used as a two-modality neuromorphic dataset.</p><p><strong>Synchronization of the two modalities</strong></p><p>The PRI pulses from the radar have been hard-wired to the event stream of the DVS sensor, and timestamped using the DVS clock. Based on this signal the DVS event stream has been segmented such that groups of events (time-bins) of the DVS are mapped with individual radar pulses (chirps).</p><p><strong>Data storage</strong></p><p>DVS events (x,y coords and timestamps) are stored in structured arrays, and one such structured array object is associated with the data of a radar transmission (pulse/chirp). A radar transmission is a vector of 512 ADC levels that correspond to sampling points of chirping signal (FMCW radar) that lasts about ~1.3ms. Every 192 radar transmissions are stacked in a matrix called a radar frame (each transmission is a row in that matrix). A data capture (recording) consisting of some thousands of continuous radar transmissions is therefore segmented in a number of radar frames. Finally radar frames and the corresponding DVS structured arrays are stored in separate containers in a custom-made multi-container file format (extension .rad). We provide a (rad file) parser for extracting the data out of these files. There is one file per capture of continuous gesture recording of about 10s.</p><p>Note the number of 192 transmissions per radar frame is an ad-hoc segmentation that suits the purpose of obtaining sufficient signal resolution in a 2D FFT typical in radar signal processing, for the range resolution of the specific radar. It also served the purpose of fast streaming storing of the data during capture. For extracting individual data points for the dataset however, one can pool together (concat) all the radar frames from a single capture file and re-segment them according to liking. The data loader that we provide offers this, with a default of re-segmenting every 769 transmissions (about 1s of gesturing).</p><p><strong>Data captures directory organization (</strong><a href="https://zenodo.org/api/records/10359770/draft/files/radar8Ghz-DVS-marshaling_signals_20220901_publication_anonymized.7z/content">radar8Ghz-DVS-marshaling_signals_20220901_publication_anonymized.7z</a><strong>)</strong></p><p>The dataset captures (recordings) are organized in a common directory structure which encompasses additional metadata information about the captures.</p><p>dataset_dir/<stage>/<room>/<person>-<gesture>-<distance>/ofxRadar8Ghz_yyyy-mm-dd_HH-MM-SS.rad</p><p>Identifiers</p><ul><li>stage [train, test].</li><li>room: [conference_room, foyer, open_space].</li><li>subject: [0-9]. Note that 0 stands for no person, and 1 for an unlabeled, random person (only present in test).</li><li>gesture: ['none', 'emergency_stop', 'move_ahead', 'move_back_v1', 'move_back_v2', 'slow_down' 'start_engines', 'stop_engines', 'straight_ahead', 'turn_left', 'turn_right'].</li><li>distance: ['xxx', '100', '150', '200', '250', '300', '350', '400', '450'] (in cm). Note that xxx is used for none gestures when there is no person present in front of the radar (i.e. background samples), or when a person is walking in front of the radar with varying distances but performing no gesture.</li></ul><p>The test data captures contain both subjects that appear in the train data as well as previously <i>unseen</i> subjects. Similarly the test data contain captures from the spaces that train data were recorded at, as well as from a new <i>unseen</i> open space.</p><p><strong>Files List</strong></p><p><a href="https://zenodo.org/api/records/10359770/draft/files/radar8Ghz-DVS-marshaling_signals_20220901_publication_anonymized.7z/content">radar8Ghz-DVS-marshaling_signals_20220901_publication_anonymized.7z</a></p><p>This is the actual archive bundle with the data captures (recordings).</p><p><a href="https://zenodo.org/api/records/10359770/draft/files/rad_file_parser_2.py/content">rad_file_parser_2.py</a></p><p>Parser for individual .rad files, which contain capture data.</p><p><a href="https://zenodo.org/api/records/10359770/draft/files/loader.py/content">loader.py</a></p><p>A convenience PyTorch Dataset loader (partly Tonic compatible). You practically only need this to quick-start if you don't want to delve too much into code reading. When you init a DvsRadarAircraftMarshallingSignals class object it automatically downloads the dataset archive and the .rad file parser, unpacks the archive, and imports the .rad parser to load the data. One can then <i>request from it </i>a training set, a validation set and a test set as torch.Datasets to work with<i>.</i> </p><p><a href="https://zenodo.org/api/records/10359770/draft/files/aircraft_marshalling_signals_howto.ipynb/content">aircraft_marshalling_signals_howto.ipynb</a></p><p>Jupyter notebook for exemplary basic use of loader.py</p><p><strong>Contact</strong></p><p>For further information or questions try contacting first M. Sifalakis or F. Corradi.</p><p> </p>
Fusion of Underwater Camera and Multibeam Sonar for Diver Detection and Tracking
<div><strong>Context</strong></div> <div> </div> <div>This dataset is related to previously published public dataset "Sonar-to-RGB Image Translation for Diver Monitoring in Poor Visibility Environments". <a href="../records/7728089">https://zenodo.org/records/7728089</a></div> <div>It contains ZED-right camera and sonar images collected from Hemmoor Lake and DFKI Maritime Exploration Hall.</div> <div> </div> <div>Sensors: Low Frq (1.2MHz) Blueprint Oculus M1200d Sonar and ZED Right Camera</div> <div> </div> <div><strong>Content</strong></div> <div> </div> <div>The dataset is created for Diver Detection and Diver Tracking applications.</div> <div> </div> <div>For the Diver Detection part, the dataset is prepared to train, validate and test YOLOv7 model.</div> <div>7095 images are used for training data, and 3095 images are used for validation data. These sets are augmented from originally captured and sampled ZED camera images. Augmentation methods are not applied to the Test data, which contains 822 images. Train and validation contain images from both the DFKI pool and Hemmor Lake, while the test data is only collected from the lake.</div> <div> </div> <div>To distinguish between the original image and the augmented image, check the name coding. </div> <div>Naming of object detection images:</div> <div>original_image_name.jpg</div> <div>if augmented:</div> <div>original_image_name_<augmentation_number_of_the_same_image>.jpg</div> <div> </div> <div>Object Detection Label Format: </div> <div>YOLO [(class), ((x_min + (x_max - x_min)/2) / image_width), ((y_min + (y_max - y_min)/2) / image_height), ((x_max - x_min) / image_width), ((y_max - y_min) / image_height)]</div> <div> </div> <div>Class: "diver", represented by "0" in object detection labels.</div> <div> </div> <div>Resolution of Object Detection Camera Images: 640x640</div> <div>Resolution of Object Tracking Camera Images: 1280x720</div> <div>Resolution of Object Tracking Low Frequency Sonar: 932x514</div> <div> </div> <div>About the Object Tracking on Sonar, the sampled data is the part where diver moves around the table and the platform. </div> <div>There are 4 cases shared in the dataset, which contain a sonar stream, and corresponding ZED-right camera images. </div> <div>Totally, 1193 points represent the diver on sonar images for the diver tracking application.</div> <div> </div> <div>For the tracking, "tracking_sonar_coordinates_<number>.csv" contains x,y coordinates of a point where the diver is in the sonar image. </div> <div>And "image_sonar_<number>.csv" file contains the matching between sonar and camera images.</div> <div> </div> <div><strong>Acknowledgements</strong></div> <div> </div> <div>The data in this repository were collected as a joint effort between the German Center for Artificial Intelligence (DFKI), the German Federal Agency for technical Relief (THW), and Kraken Robotics GmbH. This work is part of the project DeeperSense that received funding from the European Commission. Program H2020-ICT-2020-2 ICT-47-2020 Project Number: 101016958.</div> <p> </p>
Global Surface Ozone Concentration Dataset 1990-2017 Mapped at Fine Resolution through the Bayesian Maximum Entropy Data Fusion of Observations and Model Output
<p>This global surface ozone concentration dataset corresponds to the data developed in this paper:</p> <p>DeLang, M. N., J. S. Becker, K.-L. Chang, M. L. Serre, O. R. Cooper, M. G. Schultz, S. Schroder, X. Lu, L. Zhang, M. Deushi, B. Josse, C. A. Keller, J.-F. Lamarque, M. Lin, J. Liu, V. Marecal, S. A. Strode, K. Sudo, S. Tilmes, L. Zhang, S. Cleland, E. Collins, M. Brauer, and J. J. West (2021) Mapping yearly fine resolution global surface ozone through the Bayesian Maximum Entropy data fusion of observations and model output for 1990-2017, <em>Environmental Science & Technology</em>, 55, 4389-4398, doi: 10.1021/acs.est.0c07742.</p> <p>Ozone concentrations are estimated as described in the paper, with output shown for the Ozone Season Daily Maximum 8-hr metric (OSDMA8) for each year between 1990 and 2017, at 0.1 degree spatial resolution. Ozone is estimated through data fusion of output from several global models, with observations of ozone collected by TOAR. The data fusion involves application of the M3Fusion method to create a multi-model composite of several global models, followed by BME data fusion, as described in the paper. </p> <p>The *.nc file contains the latitude, longitude, ozone concentration estimate, and estimated variance for each 0.1 x 0.1 degree grid cell.</p> <p>Please contact Jason West (jasonwest@unc.edu) with questions about the dataset. We'd like to hear from you to know how you're using the data!</p> <p> </p> <p> </p>
Global Surface Ozone Concentration Dataset 1990-2017 Generated by Bayesian Maximum Entropy Data Fusion With RAMP Bias Correction
<p>This dataset reports estimates of surface ozone concentration at fine spatial resolution for 1990 to 2017, at 0.5 degree horizontal resolution. Also reported is the variance. Estimates correspond to this paper:</p> <p><span>Becker, J. S.</span><span>, DeLang, M. N., K.-L. Chang, M. L. Serre, O. R. Cooper, <u>H. Wang</u>, M. G. Schultz, S. Schroder, X. Lu, L. Zhang, M. Deushi, B. Josse, C. A. Keller, J.-F. Lamarque, M. Lin, J. Liu, V. Marecal, S. A. Strode, K. Sudo, S. Tilmes, L. Zhang, M. Brauer, and <span>J. J. West</span> (2023) Using Regionalized Air Quality Model Performance and Bayesian Maximum Entropy data fusion to map global surface ozone concentration, <em>Elementa Science of the Anthropocene</em>, 11: 1, doi: 10.1525/elementa.2022.00025.</span></p> <p>The dataset reports estimates of surface ozone for the OSDMA8 metric (the 6-month ozone-season average of the daily maximum 8-hr concentration), estimated through a data fusion of ozone observations from the Tropospheric Ozone Assessment Report (TOAR) database, and output from multiple global atmospheric models. Estimates are created in each year by a combination of M3Fusion to create a multi-model composite, Regional Air Quality Model Performance (RAMP) regional and nonlinear bias correction, and Bayesian Maximum Entropy (BME) data fusion in space and time. The estimates here are the final results using a weighted RAMP bias correction. </p>
Supplement to "Probabilistic load forecasting for the low voltage network: forecast fusion and daily peaks"
<p>This deposit contains the scripts and data used in the research article "Probabilistic load forecasting for the low voltage network: forecast fusion and daily peaks", which proposed a novel method for electricity demand forecasting in low voltage networks.</p> <p>The scripts are written in the form of R markdown and include additional commentary on the methodology. Both input data and the resulting forecast data and evaluation results are provided, though the latter two may be regenerated by running the scripts.</p>
Applying Sensor Fusion to Augment Hyperspectral Data with Depth Information
<p>The research data for the paper "Applying Sensor Fusion to Augment Hyperspectral Data with Depth Information"<br> <br> Data in the archive "hyperdepth.tar.gz" includes:</p> <p><br> <strong>calibration_images/</strong><br> includes preprocessed images for calibrating both cameras</p> <p><strong>pointclouds/</strong><br> Includes individual hyperspectral point clouds for each view (front, rightmost, right, leftmost, left with postfixes correspondingly: edesta, oikea, oikea2, vasen, vasen2)<br> <br> <strong>raw_images/</strong><br> Two directories "day5" and "day6" which include the raw hyperspectral images and kinect images<br> <br> Some extra images are included which were not used in the research paper.</p> <p> </p> <p><strong>2022-03-11_112336_stereocalibration.json</strong> includes calibration results (mainly the intrinsic camera matrix and extrinsic parameters) for the setup.</p>
[Data] Qualify-As-You-Go: Sensor Fusion of Optical and Acoustic Signatures with Contrastive Deep Learning for Multi-Material Composition Monitoring in Laser Powder Bed Fusion Process
<p><br>Growing demand for multi-material Laser Powder Bed Fusion (LPBF) faces process control and quality monitoring challenges, particularly in ensuring precise material composition. This study explores optical and acoustic emission signals during LPBF processes with multiple materials, addressing challenges in process control and ensuring accurate material composition. Experimental data from processing five powder compositions were collected using a custombuilt monitoring system in a commercial LPBF machine. The research categorised signals from LPBF processing various compositions, enhancing prediction accuracy by combining optical with acoustic data and training convolutional neural networks using contrastive learning. Latent spaces of trained models using two contrastive loss functions, clustered acoustic and optical<br>emissions based on similarities, aligning with five compositions. Contrastive learning and sensor fusion were found to be essential for monitoring LPBF processes involving multiple materials. This research advances the understanding of multi-material LPBF, highlighting sensor fusion strategies’ potential for improving quality control in additive manufacturing. Data set for this work is hosted here</p>
M3-OCTA:Leveraging Multimodal Fusion for Enhanced Diagnosis of Multiple Retinal Diseases in Ultra-wide OCTA
<p>Ultra-wide optical coherence tomography angiography (UW-OCTA) is an emerging imaging technique that offers significant advantages over traditional OCTA by providing an exceptionally wide scanning range of up to 24 x 20 mm^{2}, covering both the anterior and posterior regions of the retina. However, the currently accessible UW-OCTA datasets suffer from limited comprehensive hierarchical information and corresponding disease annotations. To address this limitation, we have curated the pioneering M3OCTA dataset, which is the first multimodal (i.e., multilayer), multi-disease, and widest field-of-view UW-OCTA dataset. Furthermore, the effective utilization of multi-layer ultra-wide ocular vasculature information from UW-OCTA remains underdeveloped. To tackle this challenge, we propose the first cross-modal fusion framework that leverages multi-modal information for diagnosing multiple diseases. Through extensive experiments conducted on our openly available M3OCTA dataset, we demonstrate the effectiveness and superior performance of our method, both in fixed and varying modalities settings. The construction of the M3OCTA dataset, the first multimodal OCTA dataset encompassing multiple diseases, aims to advance research in the ophthalmic image analysis community.</p> <p>Our proposed M3OCTA is the first multi-modal based ultra-wide retinal OCTA dataset, involving 1637 scans from 1046 eyes of 620 individuals imaged in Zigong First People’s Hospital through 24×20 scan mode. Specifically, 1067 scans contains choroid large vessel image; images of 1310 scans from 496 people are labeled as six classes in multi-label setting, including healthy, diabetic retinopathy (DR), diabetic macular edema (DME), Retinal Vein Occlusion (RVO), Hypertension (HBP) and Vitreous Hemorrhage (VH), and then split into train, validation and test set as 6:2:2. The remaining unlabeled data are only used in the pretraining step. Details of our M3OCTA and other public ones are listed in Table.1. Compared with others, M3OCTA dataset demonstrates superiorities in several aspects including the number of modalities, number of patients, image resolution, and FOV.</p> <p> </p> <p><strong>You can request this dataset through signing the attached agreement. The download link will send to you. </strong></p>
Proof-of-Concept Measurement for "Radar Band Fusion Using Frame-Based Compressed Sensing"
<p>This data set was created for a proof-of-concept test of the method described in "Radar Band Fusion Using Frame-Based Compressed Sensing". It consists of a measurment against a metal plate.</p> <p> </p>
Active Foam Dynamics of Tissue Spheroid Fusion (Datasets and scripts)
<p>The file attached supporting information for the paper "Active Foam Dynamics of Tissue Spheroid Fusion". Included are data from experiments, data from simulations, and a singularity image to execute a demo simulation similarly to the ones of the paper. </p>
Precipitation oxygen isoscape for mainland China from 1870 to 2017 generated based on data fusion and bias correction of iGCMs simulations
<p>The dataset includes the stable oxygen isotope of precipitation for the mainland of China over the 1870-2017 period, at a spatial resolution of 50-60 km and a monthly temporal resolution. In order to make full use of observations to integrate the advantages of various iGCMs, the combination of data fusion and bias correction methods are used. Some physical-based ancillary data are introduced in the fusion methods, including elevation and meteorological data, to enrich the climate and terrain information in the process of data fusion. Specifically,</p><p>(1) for the 1979-2001 period, nine simulations from six iGCMs (CAM2, GISS E, HadAM3, IsoGSM2, LMDZ4, and MIROC32) and ancillary data are fused with observations by using the CNN fusion method;</p><p>(2) for the 2002-2007 period, seven simulations from four iGCMs (GISS E, IsoGSM2, LMDZ4, and MIROC32) and ancillary data are fused by using the CNN fusion method;</p><p>(3) for the 1969-1978 period, four simulations from three iGCMs (CAM2, GISS E, and HadAM3) and ancillary data are fused by using the CNN fusion method;</p><p>(4) for the 1958-1968 and 2008-2017 periods, two iGCM simulations (CAM2 and HadAM3 for 1958-1968 and IsoGSM2 and LMDZ4 zoomed for 2008-2017) are corrected by using two BCMs, and ensemble mean (mean of four simulations) is then calculated;</p><p>(5) for the 1870-1957 period, one iGCM simulation (HadAM3) is corrected by using two BCMs, and the ensemble mean (mean of two simulations) is then calculated.</p><p>Compared with the existing iGCMs, the isoscape has high quality and stability for a large region in China at the monthly scale. However, it should be noted that the isoscape may be more reliable for the common periods of most iGCMs (1969-2007), but mediocre for other periods. </p>
Global gross primary production (GPP) product generated by data fusion based on random forest
<p>Improving the ability of gross primary production (GPP) estimates to capture extreme climate perturbations and reduce the uncertainty of GPP response processes to extreme climate is a new challenge. Based on the random forest algorithm, we integrated the multimodel GPP simulation results published by the Multiscale Synthesis and Terrestrial Model Intercomparison Project, the FLUXNET flux-site-observed GPP, the standardized precipitation index (SPI) and the standardized temperature index (STI) to generate a set of global GPP time-series data products from 2001 to 2010. The new GPP product was named DFRF-GPP, referring to the GPP generated by data fusion based on random forest. DFRF-GPP is highly reliable and can be used as a valuable data source for various applications, especially in high-temperature and drought-related studies.</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.