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26 results for “Depth Map”
Mapping of a Mid-depth Salinity Maximum Intrusion south of New England in June 2021
<div> <div> <p>This dataset contains data from a process-oriented research cruise aboard the R/V Neil Armstrong from June 18th to July 2nd. The goal of this cruise was to map the three-dimensional structure of a mid-depth salinity maximum intrusion of warm salinity slope water extending onto the continental shelf south of New England. This was done through the use of Autonomous Underwater Vehicles (two REMUS 100 vehicles and one Tethys class AUV (Long Range AUV or LRAUV)), a towed Rockland Scientific Vertical Microstructure Profiler (VMP 250), and ship-board CTD and ADCP measurements. More details about the processing, data coverage, and usage can be found in the accompanying manuscript. This cruise took place on the shelf waters south of Cape Cod, MA, extending to the shelf break, with all of the data collected between 40°N to 41°N and 71.5°W to 70°W. Attached is a data map showing the location of all data included within this dataset. </p> </div> <div> <p> </p> </div> <div> <p>File Descriptions: </p> </div> <div> <p><strong>Datamap.jpg </strong></p> </div> <div> <p>A map of the locations of all data included within this dataset. </p> </div> <div> <p> </p> </div> <div> <p><strong>CTD_summer2021.mat </strong></p> </div> <div> <p>This file contains profiles from the ship-board CTD (SeaBird 911+). Raw data was processed and gridded into 1 decibar bins using standard procedures in Seasave V 7.26.7.121 (Look at cnv file header for details about processing). This file is organized as a structure, with each variable in the data being a different field called by dot notation and each row with the structure being a different CTD profile. Biooptical variables are not quality-controlled. </p> </div> <div> <ul> <li> <p>CTD.time: the time of each profile in the MATLAB datetime format (from the processed SeaBird header file) in GMT </p> </li> <li> <p>CTD.lon: degrees longitude of the profile (from the processed SeaBird header file) </p> </li> <li> <p>CTD.lat: degrees latitude of the profile (from the processed SeaBird header file) </p> </li> <li> <p>CTD.pres: the pressure in decibar at each location of the profile </p> </li> <li> <p>CTD.sal: the seawater practical salinity in psu </p> </li> <li> <p>CTD.temp: the seawater in-situ temperature in °C </p> </li> <li> <p>CTD.flor: seawater fluorescence in mg/ m3 </p> </li> <li> <p>CTD.depth: depth at each location within the profile in meters </p> </li> <li> <p>CTD.density: sigmatheta (the potential seawater density with respect to a reference pressure of 0 db) in kg.m3 minus 1,000kg/m3 </p> </li> </ul> </div> <div> <p> </p> </div> <div> <p><strong>CTD_Darter_MMMdd.mat and CTD_Edgar_MMMdd.mat </strong></p> </div> </div> <div> <div> <p>These files contain the data from the REMUS 100 missions, with Darter and Edgar being the two different REMUS 100 vehicles. </p> </div> <div> <ul> <li> <p>Conductivity: conductivity in mS/cm </p> </li> <li> <p>Depth: depth in meters </p> </li> <li> <p>Latitude: degrees latitude </p> </li> <li> <p>Longitude: degrees longitude </p> </li> <li> <p>Mission_number: the number of the REMUS mission </p> </li> <li> <p>Mission_time: time during the mission in seconds since midnight in GMT </p> </li> <li> <p>Salinity: the seawater practical salinity in psu </p> </li> <li> <p>Sound_speed: the sound speed in m/s </p> </li> <li> <p>Temperature: the seawater temperature in °C </p> </li> </ul> </div> <div> <p> </p> </div> <div> <p> </p> </div> <div> <p><strong>LRAUV_20210623T194917.mat and LRAUV_20210624T145829.mat </strong></p> </div> <div> <p>These files contain data from the Tethys Class LRAUV (Long Range AUV) missions. Each file contains 10 structure variables. </p> </div> </div> <div> <div> <ul> <li> <p>CTD_Seabird: structure containing the bin median temperature in °C and salinity in PSU. </p> </li> <li> <p>depth: the depth at each data point in meters. </p> </li> <li> <p>fix_residual_percent_distance_traveled: underwater dead-reckoned navigation error (based on GPS fix when on surface) as a percentage of distance traveled </p> </li> <li> <p>latitude: Latitude at each data point (not corrected for vehicle drift in underwater current) </p> </li> <li> <p>latitude_fix: latitude of GPS fix (vehicle surfaced) </p> </li> <li> <p>longitude: Longitude at each data point (not corrected for vehicle drift in underwater current) </p> </li> <li> <p>longitude_fix: longitude of GPS fix (vehicle surfaced) </p> </li> <li> <p>platform_battery_charge: The battery charge in ampere-hour </p> </li> <li> <p>time_fix: time in seconds since January 1, 1970 (epoch time) </p> </li> </ul> </div> <div> <p> </p> </div> <div> <p> </p> </div> <div> <p><strong>VMPtransact_YYYYMMdd.mat</strong></p> <p>Vertical Microstructure Profiler (Rockland Scientific VMP 250) </p> </div> <div> <p>These files contain the processed data for each Vertical Microstructure Profiler (Rockland Scientific VMP 250) transect, consisting of multiple profiles. Data has been gridded on a 1 decibar equidistant grid using standard procedures in Rockland Scientific’s processing software. Note: Bio-optical variables and dissipation rates have not been quality-controlled. </p> </div> <div> <ul> <li> <p>Time: Time in MATLAB datenum format (days since 0000-00-00 00:00:00) in GMT </p> </li> <li> <p>z: Pressure in decibar </p> </li> <li> <p>T: in-situ temperature in degC </p> </li> <li> <p>cnd: conductivity in mS/cm </p> </li> <li> <p>Chl: Chlorophyll from fluorescence in mg/ m3 </p> </li> <li> <p>turb: Turbidity in NTU </p> </li> <li> <p>eps: dissipation rate inferred from microstructure shear in m^2/s^3. (Note: Dissipation estimates come from standard fitting of microstructure data within a 1 decibar bin to a turbulence spectrum within Rockland Scientific’s standard processing. The dissipation data in the provided files has not been quality-controlled. </p> </li> </ul> </div> <div> <p> VMP-data was georeferenced by comparing the time stamps of VMP and processed ADCP files. </p> </div> <div> <p> </p> </div> <div> <p><strong>ADCP_ar50_wh300.mat </strong></p> </div> <div> <p>This file contains the data from the shipboard ADCP (Teledyne WH300 kHz). ADCP data was processed aboard using standard procedures in UHDAS/CODAS (University of Hawaii Technical Services Program, servicing UNOLS vessels (<a href="https://currents.soest.hawaii.edu/docs/adcp_doc/index.html" target="_blank" rel="noreferrer noopener">https://currents.soest.hawaii.edu/docs/adcp_doc/index.html). Vertical bin size is 2 m. </a>u: zonal (positive towards east) velocity component in m/s </p> <ul> <li> <p>v: meridional (positive towards north) component in m/s </p> </li> </ul> </div> </div> <div> <div> <ul> <li> <p>txy: time, longitude, and latitude of the velocity profiles. Time is in decimal days, with noon of Jan 1 being 0.5 decimal days and noon of January 20th being 19.5 decimal days of the reference year. For another example, 6am on June 18, 2021, is decimal day 168.25. All times are in GMT. </p> </li> <li> <p>refyear: The reference year from which the decimal days are calculated. </p> </li> <li> <p>depth: vertical coordinate of the velocity bin center </p> </li> <li> <p>pgood: percent good, a quality parameter showing the fraction of good pings within an ensemble average. </p> </li> <li> <p>spd_u: zonal ship speed in m/s </p> </li> <li> <p>spd_v: meridional ship speed in m/s </p> </li> <li> <p>tr_temp: ADCP transducer temperature in deg C </p> </li> <li> <p>amp: backscatter amplitude in relative units </p> </li> </ul> </div> <div> <p> </p> </div> <div> <p> </p> </div> <div> <p> </p> </div> <div> <p> </p> </div> <div> <p> </p> </div> <div> <p> </p> </div> <div> <p> </p> </div> </div>
Dataset Publication for "A Comprehensive Stress Drop Map from Trench to Depth in the Northern Chilean Subduction Zone"
<p><strong>Abstract</strong>: Stress drop catalog data publication supplement for "A Comprehensive Stress Drop Map from Trench to Depth in the Northern Chilean Subduction Zone" (Folesky, J., Pennington, CN., Kummerow J., Hofman LR. (JGR: Solid Earth, 2023) <a href="https://doi.org/10.1029/2023JB027549">https://doi.org/10.1029/2023JB027549</a>), obtained from wave form analysis using the spectral decomposition technique. Time duration is 2007 to 2021. Seismic events were taken from the IPOC catalog (Sippl, C., Schurr, B., Münchmeyer, J., Barrientos, S., Oncken, O. (2023): Catalogue of Earthquake Hypocenters for Northern Chile from 2007-2021 using IPOC (plus auxiliary) seismic stations.<br><a title="Follow link" href="https://doi.org/10.5880/GFZ.4.1.2023.004" target="_blank" rel="nofollow noopener">https://doi.org/10.5880/GFZ.4.1.2023.004</a>). Wave forms were obtained from the EIDA/GEOPHONE web page (eida.gfz-potsdam.de/webdc3/ or geofon.gfz-potsdam.de/waveform/)</p> <p><strong>File descriptions</strong>: table columns <br>ID, cls, Lon, Lat, Depth, Magntiude, vssource, fc1, fcbound1, fcbound2, sd<br>------------------<br>explanation<br>ID : origin time<br>cls : event class<br>Lon : longitude <br>Lat : latitude<br>Depth : depth in km<br>Magnitude : magnitude (MA)<br>vssource. : s- wave velocity at the event location<br>fc1. : corner frequency in Hz<br>fcbound1 : lower bound for fc1 from 5% variance reduction test in Hz<br>fcbound2. : upper bound for fc2 from 5% variance reduction test in Hz<br>sd : stress drop estimate in MPa</p>
Predicted soil organic carbon stock at 30 m in t/ha for 0-100 cm depth global / update of the map of mangrove forest soil carbon
<p>This is the 2nd update of maps produced by <a href="https://doi.org/10.1088/1748-9326/aabe1c">Sanderman et al (2018)</a>. The improvements to the <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-prediction-of-soil-organic-carbon.html">3D spatial predictions</a> include:</p> <ul> <li> <p>new updated global mangrove coverage map (contact Thomas Worthington),</p> </li> <li> <p>spatiotemporal predictions to account for differences in spectral reflectance at the time of field work,</p> </li> <li> <p>additional SOC points <a href="https://static-content.springer.com/esm/art%3A10.1038%2Fs41558-018-0162-5/MediaObjects/41558_2018_162_MOESM2_ESM.xlsx">published in Rovai et al. (2018)</a> used in model training (see gpkg file).</p> </li> </ul> <p>To open map in QGIS or similar, drag and drop the *.tif files. You can than add also the gpkg file contain the training points.</p> <p>Production steps (ensemble predictions using SuperLearner) are explained in detail at: </p> <ul> <li>R code: <a href="https://github.com/whrc/Mangrove-Soil-Carbon/">https://github.com/whrc/Mangrove-Soil-Carbon/</a> (see "R_code/GMW_mangroves_SOC_30m.R")</li> <li>Tutorial: <a href="https://envirometrix.github.io/PredictiveSoilMapping/soilmapping-using-mla.html#ensemble-predictions-using-superlearner-package">"Predictive Soil Mapping with R"</a></li> </ul> <p>Produced for the purpose of Mangrove Restoration Potential Map funded by The Nature Conservancy and IUCN. Contact TNC: Emily Landis <<a href="mailto:elandis@TNC.ORG">elandis@TNC.ORG</a>>. Contact IUCN / University of Cambridge: Thomas Worthington <<a href="mailto:taw52@cam.ac.uk">taw52@cam.ac.uk</a>>.</p> <ul> <li>The mangrove restoration potential map is available at: <a href="https://www.researchgate.net/deref/http%3A%2F%2Fmaps.oceanwealth.org%2Fmangrove-restoration%2F">http://maps.oceanwealth.org/mangrove-restoration/</a></li> </ul>
Interpolated depth to water table (groundwater) maps for the continental United States
<p><strong>DATA:</strong></p> <p>This is a collection of depth to water table maps with uncertainty estimates for the continental United States for years 1989 and 2019. Data used to create these maps were obtained from the National Ground-Water Monitoring Network (NGWMN). Data included 14,351 sites and 17,632,047 observations for the years 1989-2019. To improve our inference a set of auxiliary variables proven to have a relation with depth to water table were included. We paired point estimates of depth to water table data with environmental data, as well as terrain variables derived out of a base digital elevation model (DEM) created by NASA at a 1x1km resolution. Climatic layers (temperature, precipitation, and snow melt equivalent) for 1989-2019 were obtained from Daymet (Version 4), which provides a continuous grid of historical monthly and annual weather data, with a 1x1km spatial resolution (Thornton et al., 2020). Out of the DEM, primary (slope, aspect) and secondary terrain attributes (curvatures, upslope contributing areas) were used to calculate a compound topographic index (CTI). </p> <p> </p> <p><strong>MODELING FRAMEWORK:</strong></p> <p>Water table depth analyses were conducted using a three-step interpolation approach: 1) we utilized gradient boosted regression trees (GBRT) to make predictions, 2) we used kriging interpolation on GBRT residuals to reduce bias from spatial autocorrelation, to incorporate a spatial correlation structure and to create uncertainty maps, and 3) we then combined the GBRT and kriging predictions for the final map. This method is equivalent to a Universal Kriging, where in our case, we evaluated the trend using GBRT. Model metrics were calculated for the training (80% of the data) and validation (20% of the data) datasets to evaluate overall performance.</p> <p>*** Uncertainty is greater surrounding the 1989 interpolations due to a lower number of observations.</p>
Maps of depths are created for the site of 50 m length. Flow types are turbulent, broken standing waves, unbroken standing waves, and rippled. The average width was 8 m and varied from 5.5 to 12 m. Bed elements included bars, rocks, and step/pools. The average depth was 0.35 m, with a maximum of 0.6 m. The average velocity was 0.4 m/s, with a maximum of 1.2 m/s (figs 10). Distribution of bottom habitats at the locations with the crayfish are as follows: megalital — 5 %, macrolithal — 30 %, mesolithal — 25 %, microlithal — 15 %, psammal — 15 %, CPOM — 10 %. Assessment by hydrobiological parameters showed that the presence of Lyngbya and Oscillatoria, as well as the increase of the number of Oligochae- in New Findings Of White Clawed Crayfish, Austropotamobius Pallipes (Decapoda, Astacidae), And Peculiarities Of Its Spatial Distribution In Neretvica (Bosnia And Herzegovina)
Maps of depths are created for the site of 50 m length. Flow types are turbulent, broken standing waves, unbroken standing waves, and rippled. The average width was 8 m and varied from 5.5 to 12 m. Bed elements included bars, rocks, and step/pools. The average depth was 0.35 m, with a maximum of 0.6 m. The average velocity was 0.4 m/s, with a maximum of 1.2 m/s (figs 10). Distribution of bottom habitats at the locations with the crayfish are as follows: megalital — 5 %, macrolithal — 30 %, mesolithal — 25 %, microlithal — 15 %, psammal — 15 %, CPOM — 10 %. Assessment by hydrobiological parameters showed that the presence of Lyngbya and Oscillatoria, as well as the increase of the number of Oligochae-
Human-robot co-manipulation of soft materials: enable a robot manual guidance using a depth map feedback [Dataset]
<p>Dataset used for the paper submitted to RO-MAN 2022</p> <p>Human-robot co-manipulation of soft materials: enable a robot manual guidance using a depth map feedback<br> Giorgio Nicola, Enrico Villagrossi, Nicola Pedrocchi</p>
SOIL-WATERGRIDS v1, mapping dynamic changes in soil moisture and depth of water table from 1970 to 2014, dataset and modelling
<p>SOIL-WATERGRIDS is a comprehensive data product of the monthly estimates of volumetric soil water content at three depths within the root zone and the depth of the water table globally gridded at a resolution of 0.25x025 degree per grid cell from 1970 to 2014. The SOIL-WATERGRIDS data product also provides the full-scale global model (BRTSim, https://sites.google.com/site/thebrtsimproject/home) that allows third party users to assess the entire volumetric soil water content and water table dynamics from land surface to 50 m depth. </p> <p>This package includes a Technical Documentation with the details about the use of the data product.</p>
Human-robot co-manipulation of soft materials: enable a robot manual guidance using a depth map feedback [Video]
<p>Video of the paper submitted at RO-MAN 2022 </p> <p>Human-robot co-manipulation of soft materials: enable a robot manual guidance using a depth map feedback<br> Giorgio Nicola, Enrico Villagrossi, Nicola Pedrocchi</p> <p>Code for trainings and test available at:</p> <p>https://github.com/giorgionicola/SMAHRCO</p>
Dataset for image segmentation of tree trunks from depth maps captured with a an Android app using Google ARCore
<p>This dataset consists of pairs of depth maps created with a custom-built Android app using Google's ARCore for depth estimates, and tree trunk segments obtained from depth maps captured by Huawei''s AREngine and processed with a tree diameter estimation algorithm. This dataset was used for a machine learning segmentation task that aimed to improve the inputs from ARCore tree trunk depths to the tree diameter estimation algorithm. For each pair of depth maps and segments an RGB image of the scene where samples were captured is also included.</p>
UNICITY: A depth maps database for people detection in security airlocks
<p><strong>UNICITY: A depth maps database for people detection in security airlocks.</strong></p> <p>UNICITY consists of 58k images collected from 65 recorded sequences with one or two people performing different behaviors including attacks and trickeries, like for instance tailgating (when a person walks very close to another to get into a restricted area). It also provides full annotation of people such as the location of head and shoulders. As as result, UNICITY is perfectly suited for training and adapting machine learning algorithms for video surveillance applications.</p> <p><strong>Main Features:</strong></p> <ul> <li>UNICITY consists of 58k images using two depth sensors.</li> <li>65 recorded sequences with one or two people performing different behaviors such as attacks and tailgating.</li> <li>UNICITY also provides code for evaluation and visualization, and full annotation of people such as the location of head and shoulders.</li> <li>This new dataset is perfectly suited for training and adapting machine learning algorithms for video surveillance applications.</li> </ul> <p><strong>Citation</strong>:</p> <p>Please cite the following paper if you use the UNICITY dataset in your work (papers, articles, reports, books, software, etc):</p> <ul> <li>UNICITY: A depth maps database for people detection in security airlocks. J. Dumoulin, O. Canevet, M. Villamizar, H. Nunes, O.A. Khaled, E. Mugellini, F. Moscheni, and J.M Odobez. International Conference on Advanced Video and Signal-based Surveillance Workshop (AVSSW). November 2018.</li> </ul> <p><strong>Contributors:</strong></p> <ul> <li>Joël Dumoulin, HumanTech Institute, HES-SO Fribourg, Switzerland.</li> <li>Olivier Canévet, Idiap Research Institute, Martigny, Switzerland.</li> <li>Michael Villamizar, Idiap Research Institute, Martigny, Switzerland.</li> <li>Hugo Nunes, Fastcom Technology SA, Lausanne, Switzerland.</li> <li>Omar Abou Khaled, HumanTech Institute, HES-SO Fribourg, Switzerland.</li> <li>Elena Mugellini, HumanTech Institute, HES-SO Fribourg, Switzerland.</li> <li>Fabrice Moscheni, Fastcom Technology SA, Lausanne, Switzerland.</li> <li>Jean-Marc Odobez, Idiap Research Institute, Martigny, Switzerland.</li> </ul> <p><strong>Acknowledgement:</strong></p> <p>The work was supported by Innosuisse, the Swiss innovation agency, through the UNICITY (3D scene understanding through machine learning to secure entrance zones) project.</p> <p><strong>Links:</strong></p> <p>Next links contain additional information about the dataset:</p> <ul> <li>Innosuisse UNICITY project: <a href="https://www.idiap.ch/en/scientific-research/projects/UNICITY">[link]</a></li> <li>Paper describing the dataset: <a href="http://publications.idiap.ch/index.php/publications/show/3939">[link]</a></li> <li>Video presenting the dataset: <a href="https://www.youtube.com/watch?time_continue=2&v=pGrnI12OhmA">[link]</a></li> <li>Paper using the dataset for counting people and detecting intrusions: <a href="http://michael-villamizar.com/avss18.html">[link]</a> <ul> <li>WatchNet: Efficient and Depth-based Network for People Detection in Video Surveillance Systems.<br> M. Villamizar, A. Martinez-Gonzalez, O. Canevet and J-M. Odobez.<br> International Conference on Advanced Video and Signal-based Surveillance (AVSS) - 2018.</li> </ul> </li> </ul> <p><strong>Contact</strong>:</p> <p>For any questions, please contact:</p> <ul> <li>Michael Villamizar, Idiap Research Institute, Martigny -Switzerland</li> </ul>
Snow depth map and land cover map from satellite photogrammetry (Pleiades) in Tuolumne, California.
<p><strong>snow_depth_20170501_pleiades.tif</strong></p> <p>Snow depth from the difference of digital elevation models (DEMs) calculated from Pléiades images. The snow-on DEMs were acquired on 2017-05-01. The snow-off DEMs were acquired on 2017-08-08 and 2017-08-13.</p> <p> </p> <p><strong>land_cover_20170813-08_pleiades.tif land_cover_20170501_pleiades.tif</strong></p> <p>Land cover calculated from multi-spectral Pléiades images acquired on 2017-05-01 (snow-on) and 2017-08-08 and -13 (snow-off). Classes are snow (1), forest (2), bare rock/low vegetation (3), lake (4).</p>
Weekly depth to groundwater space-time maps for a shallow aquifer in rural Colombia
<p>The following dataset is related to the paper: Integrating community science research and space-time mapping to determine depth to groundwater in a remote rural region. It contains the results of the modeling approach that combines space-time depth to groundwater values with probabilistic depth to groundwater data. </p> <p> </p> <p><strong>Abstract [From the paper]:</strong></p> <p>We show how Community Science Research, an approach in which research projects are built with active community participation, added essential descriptive information to a statistical model to represent groundwater levels of a shallow aquifer located in Colombia. The community collected depth to groundwater during an extreme wet year and an average year in the watershed's middle-low-elevation areas. We used the data to map depth to groundwater using three statistical models, each with different configurations. Depth to groundwater is better represented by the model that incorporates qualitative data into quantitative observations. We map the probability of shallow depth to groundwater to identify how the aquifer responds to precipitation and show that in the wet year, the area of shallow depth to groundwater considerably increases compared to the average year. This difference implies that after the watershed receives an excess in precipitation, its flow regulation capacity can decrease, which is further threatened by local land-use activities. </p>
Procedurally generated simulation/animation of liquids in transparent containers with depth map/ segmentation map (part of transproteus dataset)
<p>Procedurally generated simulation/animation of liquids in transparent containers with depth map/ segmentation map (part of transproteus dataset)</p> <p>https://arxiv.org/ftp/arxiv/papers/2109/2109.07577.pdf</p>
LiDAR and thermal data for camera pose estimation using the depth-map correspondence algorithm
<p>Folder and file structure:</p> <ul> <li>lidar_roi.ply : ~360 MB mesh file which is a sub-part of the whole Orlova Chuka scan collected in [1]</li> <li>yyyy-mm-dd total of ~17 GB. All video data including raw data, exported video, digitised xy points and calibration results <ul> <li>2018-08-19</li> <li>2018-08-17</li> <li>2018-08-14</li> <li>2018-07-28</li> <li>2018-07-25</li> <li>2018-07-21</li> </ul> </li> </ul> <p><em>Thermal camera YYYY-MM-DD folder substructure</em>: Each of the yyyy-mm-dd dates is one recording session. Each session folder has the following structure:</p> <ul> <li>avi_files (present on some nights)</li> <li>cave_photos: (present on some nights)</li> <li>mic_and_wall_points</li> <li>tmc_files: (present on some nights) The TMC files is a proprietary format to store thermal camera video data (TeAx GmbH, Germany). on 2018-08-17, only P0000000 is provided as it doesnt' have humans blocking the scene. Each frame can be exported to csv using the ThermoViewer tool, downloadable at: https://thermalcapture.com/thermoviewer-download/</li> <li>video_calibration: results and associated data to get DLT coefficients estimated using the easyWand [2] workflow. <ul> <li>image : csv file with pixel values of the images used for annotations</li> <li>mics : 2D point locations of mics placed on the cave walls</li> <li>other_cave_surface : other points on the cave surface that were pointed at <ul> <li>calibration_output: results from easyWand runs. Choose the highest round number <ul> <li>yyyy-mm-dd_roundX_<wandscore>_cam1Tforms.mat (undistortion files)</li> <li>yyyy-mm-dd_roundX_<wandscore>_cam2Tforms.mat</li> <li>yyyy-mm-dd_roundX_<wandscore>_cam3Tforms.mat</li> <li>yyyy-mm-dd_roundX_<wandscore>_dltCoefs.csv (each column is one camera's DLT coefficients)</li> <li>yyyy-mm-dd_roundX_<wandscore>_easyWandData.mat (easyWand session file)</li> </ul> </li> <li>gravity: (mostly there) video and xy points for a falling object to align the calbiration to gravity. Output from DLTdv7 clicking session.</li> <li>wand: video and xy points of the 'wand' calibration object. Output from DLTdv7 clicking session.</li> <li>camera_intrinsic.txt or thermalcam_camprofiles_profile.txt : the camera instrinsics</li> </ul> </li> </ul> </li> </ul> <ul> <li>alignment_results: ~887 MB zipped folder. <ul> <li>dmcp_experiments: the results of DMCP alignment <ul> <li>round_01 : <em>ignore this folder</em></li> <li>round_03 : <em>ignore this folder</em></li> <li>round_05: here yyyy-mm-dd is short for all other nights. Each yyyy-mm-dd folder has multiple csv files. The 'transform.csv' is the most relevant file, as it holds the transformation matrix to move 3D points from camera triangulations into the LiDAR coordinate system. <ul> <li>2018-07-21--cam0</li> <li>2018-07-21--cam1</li> <li>2018-07-21--cam2</li> <li>yyyy-mm-dd--cam0</li> <li>yyyy-mm-dd--cam1</li> <li>yyyy-mm-dd--cam2</li> <li>...</li> <li>...</li> <li>...</li> <li>2018-08-19--cam0</li> <li>2018-08-19--cam1</li> <li>2018-08-19--cam2</li> </ul> </li> </ul> </li> </ul> </li> </ul> <p> </p> <p>CITATION: If you use this dataset for your research please cite this Zenodo dataset and the accompanying paper.</p> <p>This uploaded dataset is part of the <em>Ushichka</em> dataset [3]. The audio-video system was designed by Holger R. Goerlitz. The LiDAR data was collected by Asparuh Kamburov. Video data collected by Thejasvi Beleyur.</p> <p>References</p> <p>[1] : Kamburov, A., Goerlitz, H. R., Beleyur, T 2018, Geospatial modelling inside the "Orlova Chuka" cave in Bulgaria, <em>non-peer reviewed conference contribution</em>, XXVIII International Symposium on Modern Technologies and Professional Practise in Geodesy and related fields</p> <p>[2]: Theriault, D. H., Fuller, N. W., Jackson, B. E., Bluhm, E., Evangelista, D., Wu, Z., M., Betke & Hedrick, T. L. (2014). A protocol and calibration method for accurate multi-camera field videography. <em>Journal of Experimental Biology</em>, <em>217</em>(11), 1843-1848.</p> <p>[3]: Beleyur Thejasvi, 2021. Theoretical and empirical investigations of echolocation in bat groups, PhD dissertation, University of Konstanz (<a href="http://nbn-resolving.de/urn:nbn:de:bsz:352-2-q41u3qlu1em03">http://nbn-resolving.de/urn:nbn:de:bsz:352-2-q41u3qlu1em03</a>)</p>
Data From: An App for Tree Trunk Diameter Estimation from Coarse Optical Depth Maps
<p><strong>Overview</strong></p> <p>This dataset contains RGB and depth images (raw images and text files) of tree trunks collected using Google ARCore. It has 378 samples from four countries, with DBH ranging from 4.8-155.3cm. We annotated aspects like trunk shapes, leaning, lighting conditions when captured, etc. The data was originally intended for evaluating our designed tree diameter measurement app (https://github.com/MingyueX/GreenLens).</p> <p><em>Note: treedata.zip is deprecated, please download treedata_version_2.zip instead.</em></p> <p><strong>Data introduction</strong></p> <ul> <li>treedata_version_2.zip contains two folders - <em>rgb </em>and <em>depthtxt </em>- representing raw RGB images and depth text files respectively.</li> <li>data_label.xlsx includes the labels and corresponding descriptions.</li> </ul> <p><strong>Usage Notes</strong></p> <p>The <em>rgb </em>folder contains jpg images with 480*360 resolution.</p> <p>The files with no extension in the <em>depthtxt</em> folder are stored in the format as x-coordinate of pixel, y-coordinate of pixel, depth measurement of pixel and confidence value.</p>
Event catalog, Sp phase picks, and mapped Los Angels basin depth
Open the record for dataset details and reuse information.
Borre Viking - wooden carving (from depth maps)
this is a re-upload from one of my earliest models, but this time I calculated the mesh not from a dense point-cloud but from depth maps - got a very nice surface ... Source: Objaverse 1.0 / Sketchfab
Data from: Mapping coral and sponge habitats on a shelf-depth environment using multibeam sonar and ROV video observations: Learmonth Bank, northern British Columbia, Canada
Open the record for dataset details and reuse information.
Lidar-derived snow depth maps along the Chilean Extratropical Andes, winter 2018
<p>All the files included here contain the data produced for the manuscript "<strong>Spatial distribution and scaling properties of lidar-derived snow depth in the extratropical Andes</strong>", submitted for possible publication in Water Resources Research. Lidar measurements for snow-covered conditions were conducted on September 4th, August 9th, and October 25th 2018 in Tascadero, Las Bayas and Valle Hermoso, respectively. Each data acquisition was conducted using a Riegl VZ6000 long range scanner on dates with and without snow, using an angular resolution of 0.01°.</p> <p>Lidar-derived maps are contained in the following files:</p> <p>SiteName_SD_TPI.txt (SiteName: Tascadero, Las Bayas, VH East or VH West).</p> <p>Where the file structure is as follows:</p> <p>X,Y,Z,SD,SLP,NOR,TPI04,TPI07,TPI15,TPI20,TPI30,TPI40,TPI50</p> <p>where Z is bare earth elevation (m a.s.l.), SD is snow depth, SLP is slope (°), NOR is northness (°) whereby 180 = north facing and 0 = south facing, TPIXX is the Topographic Position Index (TPI) computed for a search distance of XX m, whereby positive differences are convex landforms, and negative is concave. The magnitude of TPI indicates the scale of the relative concavity/convexity.</p> <p>Variogram results are contained in files .rsav with the following nomenclature:</p> <p>SiteName_X_TypeOfVariogram.rsav</p> <p>Where X can be SD (snow depth) or Z (bare earth topography), and the type of variogram can be omnidirectional or directional.</p>
Maps of northern peatland extent, depth, carbon storage and nitrogen storage
<p>This dataset is grids of peatland extent, peat depth, peatland organic carbon storage, peatland total nitrogen storage and approximate extent of ombrotrophic/minerotrophic peatlands. </p> <p>The grids are geotiff files in 10 km pixel resolution projected in the World Azimuthal Equidistant projection. Note that the peat depth grid shows potential peat depth everywhere,also where there is no peatland cover. For files on peatland organic carbon, total nitrogen extent and extent of ombrotrophic/minerotrophic peatlands, there are separate files for Histosols (non-frozen peatlands) and Histels (frozen peatlands).</p> <p>For further details on how the data was created we refer to the paper by Hugelius et al (2020) in the journal Proceedings of the National Academy of Sciences of the United States of America: "Large stocks of peatland carbon and nitrogen are vulnerable to permafrost thaw" (https://www.pnas.org/cgi/doi/10.1073/pnas.1916387117)</p>
ScienceDex guides
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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.