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105 results for “Ground truth”
Ground Truth Data of Falcata (Paraserianthes falcataria) Trees and Tree Farms/Plantations in Butuan City, Philippines for the Year 2020
<p>This dataset comprises location and extent data of<strong> </strong>selected/visited Falcata (<em>Paraserianthes falcataria</em>) trees and tree farms/plantations in Butuan City, Philippines. The CSV and GIS Shapefiles were generated from data collected through a series of ground truth (field) surveys conducted from October 20-29, 2020, in various parts of the city. More details can be found in the accompanying technical report. The field surveys were conducted as part of the <a href="../communities/nicer-itp-project-1" target="_blank" rel="noopener">CSU NICER ITP Center - Project 1. Development of a Geodatabase of Industrial Tree Plantations In Caraga Region Using Remote Sensing and GIS.</a></p> <p>Please refer to the Project 1 terminal report (<a href="../records/13735736" target="_blank" rel="noopener">https://zenodo.org/records/13735736</a>) for additional details.</p>
Handwritten Text Recognition Ground Truth Set: StABS Ratsbücher O10, Urfehdenbuch X
<p>Ground Truth for "Urfehdenbuch X der Stadt Basel (1563-1569)" at Staatsarchiv Basel-Stadt (StABS).</p> <p>Images and text aligned, using text-to-image (provided within Transkribus, <a href="https://www.readcoop.eu">www.readcoop.eu</a>).</p> <p>ALTO and Page XML are available for the text alignment.</p> <p>TEI to txt on page basis by Peter Dängeli.</p> <p>Derived from Transcription/TEI file: Urfehdenbuch X der Stadt Basel (1563-1569), in: Die Urfehdebücher der Stadt Basel – digitale Edition, hg. v. Susanna Burghartz, Sonia Calvi und Georg Vogeler Basel/Graz 2016. (zuletzt verändert am 31.1.2017): <a href="http://hdl.handle.net/11471/1010.2.1">hdl:11471/1010.2.1</a>.</p> <p>Image source: http://dokumente.stabs.ch/view/2010/Ratsbuecher_O_10/</p> <p>CC-BY-NC-SA: The license is inherited from the project "Urfehdebücher der Stadt Basel – digitale Edition": http://gams.uni-graz.at/o:ufbas.1563.</p>
Ground truth 3d F-object
<p>This dataset contains two 3D model files of a support test object in the shape of a 3D F, with a sharp point. The 3D model files differ in their scaling.</p> <p>The files in this dataset were altered from the Support Test Object by feilen available from <a href="https://www.thingiverse.com/thing:4360">https://www.thingiverse.com/thing:4360</a> .</p>
Ground truth 3d sphere models
<p>This dataset contains three 3D model files of a sphere, each at a different scale, in .stl format.</p> <p>These models were generated from the 318400-polygon 3D model file of a sphere authored by Kazzee at Thingiverse: <a href="https://www.thingiverse.com/thing:156207">https://www.thingiverse.com/thing:156207</a> .</p>
Ground truth 3d sphere dice models
<p>This dataset contains three 3D model files of sphere dice, with three, six, and twelve concavities, in the .stl format.</p> <p>These 3D model files were modified through scaling from the Sphere Dice dataset by anvil777, items d3.stl, d6.stl, and d12.stl, available from <a href="https://www.thingiverse.com/thing:676660">https://www.thingiverse.com/thing:676660</a> .</p>
Phase contrast images of bacteria and ground truth segmentations
<p><strong>Name</strong>: Phase contrast images of bacteria </p> <p><strong>Data type</strong>: Paired microscopy images and corresponding labels/masks used for model training, organized as recommended by the <a href="https://imagej.net/plugins/denoiseg">DenoiSeg documentation</a>.</p> <p><strong>Microscopy data type</strong>: Light microscopy (Phase Contrast)</p> <p><strong>Manual annotations</strong>: Labels/masks obtained via manual segmentation. For each region, all cells were annotated manually. Uncertain objects were left unannotated.</p> <p><strong>Microscope</strong>: Zeiss Axio Imager M2 epi-fluorescence microscope with a Zeiss Plan-Apochromat; 100x/1.4 oil DIC objective</p> <p><strong>File format</strong>: .tif (float 32-bits for phase contrast and 16-bit for mask images)</p> <p><strong>Image size</strong>: 256x256 pixels (Pixel size: 64.5 nm)</p> <p> </p> <p>Content: </p> <p>train - raw (33 files) </p> <p> - masks (33 files)</p> <p>test - raw (11 files) </p> <p> - masks (11 files)</p> <p> </p> <p>All images available in the raw folders were normalized by dividing the original images with a gaussian blurred version or the original image (200 pixels). A groovy code working within ImageJ/Fiji corresponding to this operation is as follow:</p> <pre><code class="language-java">ImagePlus normalize(ImagePlus input_image) { flatfield = (new Duplicator()).run(input_image) (new GaussianBlur()).blur(flatfield.getProcessor(), 200) return ImageCalculator.run(input_image, flatfield, "Divide create 32-bit") } import ij.ImagePlus import ij.plugin.Duplicator import ij.plugin.ImageCalculator import ij.plugin.filter.GaussianBlur</code></pre> <p> </p>
Padeřov-Bible-handwriting-ground-truth: Initial release
<p>This is ground truth based on the Padeřov Bible (Vienna, Austrian National Library, shelfmark Cod. 1175, 1432–1435), the bible of the third redaction of the Old Czech Bible translation. The transcription rules were based on semi-diplomatic transcription rules set by PERO OCR and <em>Směrnice pro vydávání starších českých textů </em>by Jiří Daňhelka (https://vokabular.ujc.cas.cz/moduly/edicnipoznamka.aspx?id=DanhelkaSmernice). Abbreviations were tagged and expanded.</p> <p>Ground truth was created specifically by Anna Michalcová (Czech Academy of Sciences, Czech Language Institute, final check of the transcribed text with the use of her model trained on the Cistercian Bible, New York, The Morgan Library & Museum, shelfmark MS M.752, a.michalcova@ujc.cas.cz), Kamil Bazelides (Comenius University in Bratislava, Faculty of Arts, 4r–8v), Jan Hajič (Czech Academy of Sciences, Masaryk Institute and Archives, 194v–199r), Eliška Pěnkavová (the University of South Bohemia in České Budějovice, Faculty of Arts, 199v–204r), Laura Maniaková (Masaryk University in Brno, Faculty of Arts, 204v–209r), Hana Kreisingerová (Czech Academy of Sciences, Czech Language Institute, 227v–229r), Jitka Filipová (Czech Academy of Sciences, Czech Language Institute, 355r–359v), Chi-hung Lu (Eötvös Loránd University, Faculty of Humanities, 366r–370v) and Martina Dvořáková (Moravian library in Brno, 373r–373v).</p> <p>Produced within the HTR Winter School 2022.</p>
Intersection Monitoring: A dataset for vehicle detection using an infrastructure camera including ground truth vehicle localization
<p><strong>Intersection Monitoring: A dataset for vehicle detection using an infrastructure camera including ground truth vehicle localization.</strong></p> <p>This dataset contains images from a infrastructure camera monitoring an intersection and the ground-truth information for a vehicle crossing it from different directions. The data is aimed to develop and improve image-based vehicle detection algorithms.</p> <p>The dataset includes two different recordings with the same structure. For each of them, the sequence of images is provided in PNG format, along with the ground truth of the vehicle. The ground truth data was captured using a high-precision GNSS receiver installed on the test vehicle, fused with in-vehicle sensors using an Extended Kalman Filter (EKF) .</p> <p><strong>Time considerations</strong></p> <p>The camera and GNSS receiver clocks were synchronized before each test to have the same time base.</p> <p>Each test last about 140 seconds.</p> <p><strong>Image files</strong></p> <p>Image files can be found on the <strong>img/</strong> folder within for each test. The camera was configured to record images at 25 fps:</p> <ul> <li>Test 1: 3704 files</li> <li>Test 2: 3426 files</li> </ul> <p><strong>Vehicle localization ground truth</strong></p> <p>The test vehicle is a prototype of Autonomous Vehicle developed by the <a href="https://autopia.car.upm-csic.es">AUTOPIA</a> research group at the <a href="https://car.upm-csic.es">Centre for Automation and Robotics</a> in Spain.</p> <p>Vehicle location mainly depends on a Trimble BX982 GNSS receiver using RTK inputs from a local station. However, the location algorithm applies an EKF for combining GNSS measurements with different onboard sensors providing yaw rate, longitudinal acceleration and speed, steering wheel position and speed, etc.</p> <p>For each test, the <strong>vehicle.csv</strong> file contains the vehicle information recorded at 20Hz. This file includes:</p> <ul> <li>UTM Time: Time using the format HHMMSSss: <ul> <li>HH: Hours</li> <li>MM: Minutes</li> <li>SS: Seconds</li> <li>ss: Fraction of seconds</li> </ul> </li> <li>UTM East: East coordinate of the GNSS antenna in the UTM frame, in meters.</li> <li>UTMNorth: North coordinate of the GNSS antenna in the UTM frame, in meters.</li> <li>Orientation: Yaw angle of the vehicle, measured from the East axis (x-axis).</li> <li>Speed: Vehicle speed in m/s</li> <li>Acceleration: Vehicle acceleration in m/s^2</li> </ul> <p><strong>Camera info</strong></p> <p>The file <strong>camera_parameters.json</strong> includes all the information about the intrinsic and extrinsic parameters for the camera. The configuration stored on this file appplies to all tests.</p> <p>The camera used is an AXIS M1125 with variable focal length. It was installed in a communication tower near the intersection.</p> <p><strong>Vehicle info</strong></p> <p>The file <strong>vehicle_parameters.json</strong> includes information about vehicle dimensions and antenna location. The GNSS antenna is installed near the rear axle of the vehicle, in the middle part of the vehicle. The configuration stored on this file appplies to all tests.</p> <p><strong>Tools</strong></p> <p>Some MATLAB tools can be found in <a href="https://github.com/autopia-car/datasets-tools-intersection-monitoring">https://github.com/autopia-car/datasets-tools-intersection-monitoring</a></p> <p><strong>Disclaimer</strong></p> <p>That the dataset comes "AS IS", without express or implied warranty and/or any liability exceeding mandatory statutory obligations. This especially applies to any obligations of care or indemnification in connection with the dataset. The dataset was created for our research purposes only and no quality assessment was done for the usage in products of any kind. We can therefore not guarantee for the correctness, completeness or reliability of the provided data set.</p>
Validating NISAR's cropland mapping approach and the USDA/NASS Cropland Data Layer against ground truth data in a fragmented urban agricultural region
<p>Field data used in manuscript:</p> <p>1 shapefile containing the ROI outline for which Sentinel-1 data was cropped</p> <p>1 shapefile containing the 93 fields investigated with their names, types and sizes as attributes </p> <p>8 annual csv data for active fields, consisting of 3 harvest dates and 5 planting dates. This list is after translating data from Farmlogic Report (not conducive to analysis in the format) and filtering for fields greater 1 acre. The study lateron further screened to use only fields greater than 2 acreas (0.81 hectares). </p> <p> </p>
Ground truth data used to train the synapse classifier used in Lillvis et al., 2022 for ExLLSM circuit reconstruction
Open the record for dataset details and reuse information.
Data-driven calibration of RAVEN-II surgical robot with ground truth joint positions
Open the record for dataset details and reuse information.
Ground truth annotations for top-view images of Arabidopsis thaliana
<ul> <li>ara_rosetteSet.tar.gz contains ground truth annotations for ordinary leaves (used to train model A of the <a href="https://doi.org/10.5281/zenodo.3946320">aradeepopsis pipeline</a>)</li> <li>ara_senescentSet.tar.gz contains ground truth annotations for ordinary and senescent leaves (used to train model B of the <a href="https://doi.org/10.5281/zenodo.3946320">aradeepopsis pipeline</a>)</li> <li>ara_anthoSet.tar.gz contains ground truth annotations for ordinary, senescent and anthocyanin-rich leaves (used to train model C of the <a href="https://doi.org/10.5281/zenodo.3946320">aradeepopsis pipeline</a>)</li> </ul>
2D bright field yeast cell images with ground truth annotations
<p>Dataset used to evaluate the method described in "Yeast cell detection and segmentation in bright field microscopy", ISBI 2014 (<strong>DOI: </strong>10.1109/ISBI.2014.6868107).</p> <p>Here, we provide the ground truth labels of: cell centers and segmentation, which are used in the publications:</p> <ul> <li>"Learning to Segment: Training Hierarchical Segmentation under a Topological Loss", MICCAI 2015. (DOI: 10.1007/978-3-319-24574-4_32)</li> <li>"Hierarchical Planar Correlation Clustering for Cell Segmentation", EMMCVPR 2015. (DOI: 10.1007/978-3-319-14612-6_36)</li> <li>"Cell Detection and Segmentation Using Correlation Clustering", MICCAI 2014. (DOI: 10.1007/978-3-319-10404-1_2)</li> </ul>
2D phase contrast HeLa cells images with ground truth annotations
<p>Original images are from http://www.robots.ox.ac.uk/~vgg/software/cell_detection/. This software is associated with the publication "Learning to Detect Cells Using Non-overlapping Extremal Regions", MICCAI 2012. (DOI: 10.1007/978-3-642-33415-3_43)</p> <p>Here, we provide the ground truth labels of: cell centers and segmentation, which are used in the publications:</p> <ul> <li>"Learning to Segment: Training Hierarchical Segmentation under a Topological Loss", MICCAI 2015. (DOI: 10.1007/978-3-319-24574-4_32)</li> <li>"Cell Detection and Segmentation Using Correlation Clustering", MICCAI 2014. (DOI: 10.1007/978-3-319-10404-1_2)</li> </ul>
2D bright field Fission yeast cell images with ground truth annotations
<p>Original images are from http://www-bcf.usc.edu/~forsburg/pombeX.html. This software is associated with the publication "PombeX: robust cell segmentation for fission yeast transillumination images", PLoS One 2013. (doi: 10.1371/journal.pone.0081434)</p> <p>Here, we provide the ground truth labels of: cell centers and segmentation, which are used in the publications:</p> <ul> <li>"Learning to Segment: Training Hierarchical Segmentation under a Topological Loss", MICCAI 2015. (DOI: 10.1007/978-3-319-24574-4_32)</li> <li>"Cell Detection and Segmentation Using Correlation Clustering", MICCAI 2014. (DOI: 10.1007/978-3-319-10404-1_2)</li> </ul>
"Spot the Difference" ground truth and video tracking data
<p>Ground truth and object tracking data required to reproduce the abstract submitted to DIS2017, at https://github.com/IDInteraction/DIS2017</p>
A coarse pixel scale ground "truth" dataset based on the global in situ site measurements from 2000 to 2021.
<p><em>In situ </em>measurements from sparsely distributed networks worldwide are a valuable source of data for validating satellite products. However, the significant differences in spatial scale between in situ and satellite measurements pose a challenge for validation. To address this challenge, we propose a new global coarse pixel-scale ground "truth" dataset which provides sequential pixel scale albedo on a 500 m resolution over the period 2000-2021. The creation of data set is done using ground measurements from 416 sparsely distributed networks worldwide and high-resolution long-term time series data corresponding to site-based data. Furthermore, we thoroughly assessed the effectiveness of the dataset at sites with different degrees of spatial representativeness. The results demonstrate that using this dataset in validation outperforms the direct comparison between satellite and <em>in situ</em> site measurements over heterogeneous surfaces when <em>in situ</em> measurement footprints are less than satellite pixel size. The dataset offers a unique collection of coarse pixel scale relative ground "truth" with the widest spatial distribution and longest time series. By merging temporal information from ground-based observations and spatial information from high-resolution data, the dataset we provided represents a valuable resource for validating and correcting worldwide surface albedo products over heterogeneous surfaces.</p>
Language Specific Event Recommendation Ground Truth
<p>This is a multilingual ground truth dataset for training, evaluating and testing the LaSER (Language-Specific Event Recommendation) model. It contains language-specific relevance scores for event-centric click-through pairs according to the publicly available Clickstream dataset in German, French and Russian as well as the user study annotations conducted for evaluating the language-specific recommendations by LaSER. For more details, refer to <a href="https://github.com/saraabdollahi/EventKG-Click">EventKG+Click</a> and <a href="https://github.com/saraabdollahi/LaSER">LaSER</a>.<br> <br> This dataset consists of two sets of files as follows:<br> 1. The ground truth dataset that is used for training the learning to rank (LTR) model in LaSER in three languages. The following files contain the language-specific relevance scores between a source and target entity based on EventKG+Click dataset:</p> <ul> <li>german_ground_truth.txt</li> <li>french_ground_truth.txt</li> <li>russian_ground_truth.txt</li> </ul> <p>In these files source and target represent the label of entities and events in the respective language. </p> <p>2. The second set contains the user study participants' annotations regarding different relevance criteria of recommended events by LaSER. The following three files contain the annotations of at least three participants per event:</p> <ul> <li>german_user_study_annotations.csv</li> <li>french_user_study_annotations.csv</li> <li>russian_user_study_annotations.csv</li> </ul> <p>In these files, "r1", "r2" and "r3" denote relevance to the topic, language community and general audience respectively. And topic and event represent the wikidata-id of entities and events. </p>
Ground truths for dataset search using similarity methods generated from a user evaluation
<p>The dataset contains ground truths for 6 different use cases and 10 levels of agreement among 10 users participating in a user evaluation of dataset search using similarity methods in data catalogs. The data contains collections linking to dataset metadata available in https://doi.org/10.5281/zenodo.4433464.</p>
Observed and Simulated Surface Wave and Roller Dissipation with Ground Truth Data at the West Coast of Sylt on Sep 27 - Oct 2, 2016
<p>This dataset contains post-processed Doppler marine radar observations and numerical simulations of surface wave and roller dissipation as well as wave energy flux across the surf zone of a double-barred sandy beach at the island of Sylt, Germany.</p> <p>The methodology to obtain dissipation from coherent marine radar data is described in the following article:</p> <p>Streßer, M., Horstmann J., Baschek, B. (2022): Surface Wave and Roller Dissipation Observed with Shore-based Doppler Marine Radar. Manuscript in preparation.</p> <p>The simulations were realized with with the <a href="https://github.com/mstresser/SimpleWaves1D">SimpleWaves1D</a> model using the <a href="https://linkinghub.elsevier.com/retrieve/pii/S0378383907000580">Janssen and Battjes (2007)</a> wave breaking parameterization. The wave buoy data used to force the model was recorded as part of the <a href="https://codm.hzg.de/codm/">COSYNA</a> observation system. The bathymetry transect was generated using the echosounder data of <a href="https://doi.pangaea.de/10.1594/PANGAEA.898407">Cysewski et al. (2019)</a></p> <p>Radar data and and simulation results are re-gridded to a common hourly time grid using nearest neighbor interpolation with the dimension [time x range].</p> <p>Structure of radar observations:</p> <pre><code>CMRgridded = struct with fields: t: [109×1 double] time as Matlab datenum r: [435×1 double] range, i.e. distance from radar antenna [m] cg: [435×109 double] wave group velocity [m/s] cp: [435×109 double] wave phase velocity [m/s] d: [435×109 double] local water depth [m] H_rms_roller: [435×109 double] energy wave height estimated using the roller concept [m] k: [435×109 double] local wave number [rad/m] w: [435×109 double] wave frequency [rad/s] Dr: [435×109 double] roller dissipation [W/m^2] Er: [435×109 double] roller energy [Nm/m^2] Fr: [435×109 double] flux of roller energy [W/m] Dw: [435×109 double] wave dissipation [W/m^2]</code></pre> <p>Structure of the simulation results:</p> <pre><code>SWgridded = struct with fields: t: [109×1 double] timestamp as Matlab datenum r: [435×1 double] range, i.e. distance from radar antenna [m] cg: [435×109 double] wave group velocity [m/s] cp: [435×109 double] wave phase velocity [m/s] d: [435×109 double] local water depth [m] H_rms: [435×109 double] energy wave height [m] E: [435×109 double] wave energy [Nm/m^2] Dr: [435×109 double] roller dissipation [W/m^2] Dw: [435×109 double] wave dissipation [W/m^2] Er: [435×109 double] roller energy [Nm/m^2] Qb: [435×109 double] fraction of breaking waves [-]</code></pre> <p> </p> <p>Ground truth data is available from two bottom mounted pressure wave gauges (PG) located at r=127.5 m (PG<sub>A</sub>) and r=180 m (PG<sub>B</sub>) and a wave rider buoy located at r=1100 m. </p> <p>Structure of the ground truth data;</p> <pre><code>PG = struct with fields: t: [209×1 double] timestamp as Matlab datenum Hs_pg_A: [209×1 double] significant wave height at the pressure gauge A Hs_pg_B: [209×1 double] significant wave height at the pressure gauge B WR = struct with fields: t: [1344×1 double] timestamp as Matlab datenum Hs: [1344×1 double] significant wave height [m] Dirp: [1344×1 double] wave direction at the peak frequency [°] Tp: [1344×1 double] peak period [s] Tmean: [1344×1 double] mean period, or Tm(0,1) [s] Tcross: [1344×1 double] zero-upcross period [s] Sprp: [1344×1 double] directional spread at the peak frequency [°] </code></pre> <p> </p> <p>The data is stored in Matlab<sup>®</sup> v7.3 format. Timestamps are given as Matlab<sup>®</sup> datenum, i.e. whole and fractional number of days from January 0, 0000.</p> <p> </p>
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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.