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2,610 results for “TRACK”
InSAR stack of Fernandina volcano in Galápagos, Ecuador from Sentinel-1 descending track 128 processed with ISCE2/topsStack
<p>A stack of unwrapped interferograms on Fernandina volcano, Galápagos, Ecuador</p> <p>Sensor: Sentinel-1descending track 128</p> <p>Processor: ISCE/topsStack</p> <p>Tropospheric delay estimated from ERA-5 using PyAPS is attached.</p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MintPy/">MintPy</a>.</p> <p><strong>Version 1.x (~750 MB)</strong><br> Time: 2014.12.13 - 2018.06.19 (98 acquisitions, 288 interferograms)</p> <p><strong>Version 0.1 (~280 MB; for fast testing of code development)</strong><br> Time: 2014.12.13 - 2016.05..24 (36 acquisitions, 102 interferograms)</p>
Database of fitted spectra for: Changing-Look AGNs - I. Tracking the transition on the main sequence of quasars
<h3>Results from the spectral fitting for a sample of changing-look active galactic nuclei (AGNs) with SDSS spectroscopy using PyQSOFit.</h3>
Mediterranean Cyclone tracks between 1979-2018 (40 years) from a high-resolution perspective using ECMWF ERA5 dataset
<p>The present dataset presents the trajectories of the 13,157 cyclones identified within the Mediterranean Region (MR) between 1979 and 2018 (40 years). These cyclone tracks were obtained using the new Cyclone Detection and Tracking Method (CDTM) described in Aragão e Porcù (2021) to take advantage of the recent availability of a high-resolution reanalysis dataset of ECMWF ERA5. The CDTM uses hourly data of Geopotential Height at 1000 hPa with a spatial resolution of 0.25°x0.25°, and the analysis' domain covers the area within 15°W to 48° E and 21° N to 54°N. Additionally, trying to eliminate artificial low-pressure cores, short-living thermal-lows or too weak cyclones as much as possible, the present study only considered cyclones lasting more than 24h.<br> The dataset presents hourly information for all cyclones from the cyclogenesis time to the cyclolysis time. Each record presents: [1] Cyclone ID (integer, 8 digits), [2] Cyclone centre longitude position (°E, real, 8 digits, 3 decimal digits), [3] Cyclone centre latitude position (°N, real, 8 digits, 3 decimal digits), [4] Year (integer, 4 digits), [5] Month (integer, 2 digits), [6] Day (integer, 2 digits), [7] Hour (integer, 2 digits), [9] Cyclone centre Geopotential Height at 1000 hPa (m, real, 9 digits, 3 decimal digits).<br> The analyses presented in Aragão e Porcù (2021) revealed that the proposed CDTM is capable to capture almost the totality of the observed cyclones, as well as describing its respective area of cyclogenesis, trajectories, and durations. More than an adaptation to a high-resolution dataset, the method brings as its primary contribution a suitable set of parameters to systematically identify and track the cyclonic activities in the Mediterranean, where cyclones do not have sizeable horizontal pressure gradients and present a shorter lifetime compared to open-ocean cyclones.</p> <p>Cite this article</p> <p>Aragão, L., Porcù, F. Cyclonic activity in the Mediterranean region from a high-resolution perspective using ECMWF ERA5 dataset. <em>Clim Dyn</em> (2021). https://doi.org/10.1007/s00382-021-05963-x</p>
Dataset - Generating reliable estimates of tropical cyclone induced coastal hazards along the Bay of Bengal for current and future climates using synthetic tracks
<p>This data is complementary to the paper by Leijnse et al. 2022 "Generating reliable estimates of tropical cyclone induced coastal hazards along the Bay of Bengal for current and future climates using synthetic tracks" <br> https://doi.org/10.5194/nhess-2021-181</p> <p>This data is made available in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE</p> <p>For questions about the data ask: tim.leijnse@deltares.nl</p> <p>For more information about the tool to generate the used synthetic tracks TCWiSE see: <a href="https://www.deltares.nl/en/software/tcwise/">https://www.deltares.nl/en/software/tcwise/</a></p> <p> </p>
MAMEM Phase I Dataset - A dataset for multimodal human-computer interaction using biosignals and eye tracking information
<p>This dataset combines multimodal biosignals and eye tracking information gathered under a human-computer interaction framework. The dataset was developed in the vein of the MAMEM project that aims to endow people with motor disabilities with the ability to edit and author multimedia content through mental commands and gaze activity. The dataset includes EEG, eye-tracking, and physiological (GSR and Heart rate) signals along with demographic, clinical and behavioral data collected from 36 individuals (18 able-bodied and 18 motor-impaired). Data were collected during the interaction with specifically designed interface for web browsing and multimedia content manipulation and during imaginary movement tasks. Alongside these data we also include evaluation reports both from the subjects and the experimenters as far as the experimental procedure and collected dataset are concerned. We believe that the presented dataset will contribute towards the development and evaluation of modern human-computer interaction systems that would foster the integration of people with severe motor impairments back into society.</p>
Unverified GPS track of R/V Akademik Tryoshnikov during the Antarctic Circumnavigation Expedition (ACE) in the austral summer of 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>A Trimble Global Positioning System (GPS) recorded the route undertaken by the R/V Akademik Tryoshnikov during a circumnavigation of the Antarctic as part of the Antarctic Circumnavigation Expedition (ACE) in the austral summer of 2016/2017. The data provided in this dataset are raw NMEA strings containing date, time, latitude and longitude, with other NMEA variables allowing the accuracy of the location to be ascertained with one-second resolution.</p> <p>The data have not been quality checked or corrected.</p> <p>Data coverage is from 21st December 2016 until 11th April 2017.</p> <p><strong>Dataset contents </strong></p> <ul> <li>gpsdata_YYYYMMDD.log, data file, text</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p>Data files include the date (in UTC) on which the data were recorded in the format YYYYMMDD.</p> <p><strong>Dataset license</strong></p> <p>This unverified GPS track dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
THOR - people tracks
<p><strong>THÖR</strong> is a dataset with human motion trajectory and eye gaze data collected in an indoor environment with accurate ground truth for the position, head orientation, gaze direction, social grouping and goals. THÖR contains sensor data collected by a 3D lidar sensor and involves a mobile robot navigating the space. In comparison to other, our dataset has a larger variety in human motion behaviour, is less noisy, and contains annotations at higher frequencies.</p> <p>The dataset includes 13 separate recordings in 3 variations:</p> <ul> <li>``One obstacle" - features one obstacle in the environment and no robot</li> <li>``Moving robot" - features one obstacle in the environment and the moving robot</li> <li>``Three obstacles" - features three obstacles in the environment and no robot</li> </ul> <p><strong>THOR - people tracks </strong>is the part of THÖR data set containing ground truth position of people in the environment, including information about head orientation. The data are available in three formats:</p> <ol> <li>mat - Matlab binary file</li> <li>TSV - text file</li> <li>bag - ROS bag file</li> </ol> <p><strong>MAT files</strong></p> <ul> <li><strong>File </strong>- [char] Path to original QTM file</li> <li><strong>Timestamp </strong>- [string] Date and time of the startof the data collection</li> <li><strong>Start Fram </strong>- [char] 1</li> <li><strong>Frames </strong>- [double] Number of frames in the file</li> <li><strong>FrameRate</strong> - [double] Number of frames per second</li> <li><strong>Events</strong> - [struct] 0</li> <li><strong>Trajectories </strong>- [struct] 3D postion of observed reflective markers <ul> <li><strong>Labeled </strong>- [struct] Markers belonging to the tracked agents: <ul> <li><strong>Count </strong>- [double] Number of tracked markers</li> <li><strong>Labels </strong>- [cell] List of marker labels</li> <li><strong>Data </strong>- [double] Array of dimension {Count}x4x{Frames}, contains the 3D position of each marker and residue</li> </ul> </li> </ul> </li> <li><strong>RigidBodies </strong>- [struct] 6D pose of the helmet, corresponds to head poistion and orientation: <ul> <li><strong>Bodies </strong>- [double] Number of tracked bodies</li> <li><strong>Name </strong>- [cell] Bodies Names</li> <li><strong>Positions </strong>- [double] Array of dimension {Bodies}x3x{Frames} contains the position of the centre of the mass of the markers defining the rigid body</li> <li><strong>Rotations </strong>- [double] Array of dimension {Bodies}x9x{Frames} contains rotation matrix describing the orientation of the rigid body</li> <li><strong>RPYs </strong>- [double] Array of dimension {Bodies}x3x{Frames} contains orientation of the rigid body described as RPY angles</li> <li><strong>Residual </strong>- [double] Array of dimension {Bodies}x1x{Frames} contains residual for each rigid body</li> </ul> </li> </ul> <p><strong>TSV files</strong></p> <ol> <li><strong>3D data</strong> <ol> <li><strong>File Header</strong> <ul> <li>NO_OF_FRAMES - number of frames in the file </li> <li>NO_OF_CAMERAS - number of cameras tracking makers</li> <li>NO_OF_MARKERS - number of tracked markers</li> <li>FREQUENCY - tracking frequency [Hz] </li> <li>NO_OF_ANALOG - number of analog inputs </li> <li>ANALOG_FREQUENCY - frequency of analog input </li> <li>DESCRIPTION - --</li> <li>TIME_STAMP - the beginning of the data recording</li> <li>DATA_INCLUDED - the type of data included</li> <li>MARKER_NAMES - names of tracked makers</li> </ul> </li> <li><strong>Column names</strong> <ul> <li>Frame - frame ID</li> <li>Time - frame timestamp</li> <li>[marker name] [C] - coordinate of a [marker name] along [C] axis</li> </ul> </li> </ol> </li> <li><strong>6D data</strong> <ol> <li><strong>File Header</strong> <ul> <li>NO_OF_FRAMES - number of frames in the file </li> <li>NO_OF_CAMERAS - number of cameras tracking makers</li> <li>NO_OF_MARKERS - number of tracked markers</li> <li>FREQUENCY - tracking frequency [Hz] </li> <li>NO_OF_ANALOG - number of analog inputs </li> <li>ANALOG_FREQUENCY - frequency of analog input </li> <li>DESCRIPTION - --</li> <li>TIME_STAMP - the beginning of the data recording</li> <li>DATA_INCLUDED - the type of data included</li> <li>BODY_NAMES - names of tracked rigid bodies</li> </ul> </li> <li><strong>Colum Names</strong> <ul> <li>Frame - frame ID</li> <li>Time - frame timestamp</li> <li>The columns are grouped according to the rigid body. Each group starts with the name of the rigid body and then is followed by the position of the centre of the mas and the orientation expressed as RPY angles and rotation matrix</li> </ul> </li> </ol> </li> </ol> <p><strong>Reference:</strong></p> <p>For more details check project website <a href="http://thor.oru.se">thor.oru.se</a> or check our publications:</p> <pre><code>@article{thorDataset2019, title={TH\"OR: Human-Robot Indoor Navigation Experiment and Accurate Motion Trajectories Dataset}, author={Andrey Rudenko and Tomasz P. Kucner and Chittaranjan S. Swaminathan and Ravi T. Chadalavada and Kai O. Arras and Achim J. Lilienthal}, journal={arXiv preprint arXiv:1909.04403}, year={2019} }</code></pre> <p> </p>
Quality-checked, one-hour resolution cruise track of the Antarctic Circumnavigation Expedition (ACE) undertaken during the austral summer of 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>The Antarctic Circumnavigation Expedition (ACE), undertaken in the austral summer of 2016/2017 recorded the cruise track using two independent geo-location instruments: one using GLobal NAvigation Satellite Systems (GLONASS; hereafter referred to as GLONASS) and another primarily using the Global Positioning System (GPS; hereafter referred to as the Trimble GPS). Daily log files were recorded in real-time from both instruments during the expedition and added to MySQL database tables. Following the expedition, quality-checking work has been undertaken to provide a one-second resolution set of positions for the cruise track. Here we present the final quality-checked dataset aggregated to a resolution of one hour. This is of use for understanding the position of the vessel to a lower precision, such as for plotting the track throughout the voyage.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_cruise_track_1hour_YYYY-MM.csv, data file, comma-separated values</li> <li>README.txt, metadata, text file</li> <li>data_file_header.txt, metadata, text file</li> </ul> <p><strong>Dataset license</strong></p> <p>This quality-checked cruise track dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Depth of the sea floor along the 5-minute resolution cruise track of the Antarctic Circumnavigation Expedition (ACE) derived from GEBCO 2019 bathymetry data.
<p><strong>Dataset abstract</strong></p> <p>Depth of the seabed along the five-minute averaged cruise track (Landwehr et al., 2020; DOI: 10.5281/zenodo.3752691) of the Antarctic Circumnavigation Expedition (ACE) was calculated from the the General Bathymetric Chart of the Oceans (GEBCO; GEBCO Compilation Group, 2019) 2019 gridded 30-arc second bathymetry data. The nearest gridded value from the bathymetry dataset was used to find the depth at the averaged position.</p> <p>Provided within this dataset is the average position of the vessel during a five-minute time period (where the time given is the middle time of the averaging interval).</p> <p><strong>Dataset contents</strong></p> <ul> <li>cruise_track_gebco2019_depth_5min.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> <li>ace_cruise_track_gebco2019_depth_5min_change_log.txt, metadata, text</li> </ul> <p><strong>Change log</strong></p> <p><strong>v1.1</strong> - Added additional data coverage and therefore track coverage from 2016-11-17 - 2016-11-22 inclusive. Updated README.txt with information about data coverage. Added change_log file.</p> <p><strong>v1.0</strong> - Initial release of depth along cruise track data set.</p> <p><strong>Dataset license</strong></p> <p>This GEBCO sea floor depth along the cruise track dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
ERA-5 reanalysis results interpolated onto the five-minute average cruise track of the Antarctic Circumnavigation Expedition (ACE) during the austral summer of 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>ERA-5 fields at 1-hour temporal and grid size of 0.25° x 0.25° (0.5° x 0.5° for wave variables) have been downloaded from <a href="https://cds.climate.copernicus.eu/api/v2/resources/reanalysis-era5-single-levels">https://cds.climate.copernicus.eu/api/v2/resources/reanalysis-era5-single-levels</a>.</p> <p>The data are interpolated using two methods:</p> <p>'nearest': the value of the nearest ERA-5 grid cell is use;</p> <p>'linear': the values from the nearest grid cells in space and time are linearly interpolated to the [date_time, latitude, longitude] coordinate of the ship</p> <p>providing a number of atmospheric, land and oceanic climate variables interpolated along the five-minute cruise track.</p> <p>The data repository can be checked out at: <a href="https://renkulab.io/gitlab/ACE-ASAID/ecmwf-interpolation-to-cruise-track">https://renkulab.io/gitlab/ACE-ASAID/ecmwf-interpolation-to-cruise-track</a></p> <p><strong>Dataset contents</strong></p> <ul> <li>era5-on-cruise-track-5min-legs0-4-linear.csv, data file, comma-separated values</li> <li>era5-on-cruise-track-5min-legs0-4-nearest.csv, data file, comma-separated values</li> <li>interpolate-to-shiptrack.py, processing script, text/x-python</li> <li>download-ecmwf.ipynb, processing script, application/x-ipynb+json</li> <li>ecwmf_interpolate.zip, processing scripts, zip file</li> <li>data_file_header, metadata, text format</li> <li>README.txt, metadata, text format</li> </ul> <p><strong>Dataset license</strong></p> <p>This interpolation of the ERA-5 reanalysis output to the five-minute averaged cruise track and velocity dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></p>
Distance to the nearest land/coastline (including small subantarctic islands) for the five-minute average cruise track of the Antarctic Circumnavigation Expedition (ACE) during the austral summer of 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>This dataset is derived from:<br> - The GPS track of the R/V-Akademik Tryoshnikov (10.5281/zenodo.3772377)<br> - The shapefiles of the continents from NaturalEarth physical (@50m), version 4.1.0,<br> downloadable at [https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/50m/physical/ne_50m_land.zip](https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/50m/physical/ne_50m_land.zip)<br> - A manual entry of the smaller islands which may not be mapped on the NaturalEarth resource, namely:<br> - "Peter I": [-68.8282, -90.6157]<br> - "Scott": [-67.3783, -179.9117]<br> - "Young": [-66.2833, 162.4167]<br> - "Buckle": [-66.65, 163.05]<br> - "Sturge": [-67.416667, 164.733333]<br> - "Siple": [-73.65, -125]<br> - "Bouvetoya": [-54.4208, 3.3464]</p> <p>The calculation of the actual distance to land has been done in qGIS 3.2.3-Bonn https://qgis.org/downloads/, using the NNJoin plugin version 3.1.2 https://plugins.qgis.org/plugins/NNJoin/. After the point-to-closest polygon distance calculation, the python script in src/add_distance_to_small_islands.py replaces the distance calculation to the centerpoint of the islands (as reported above) if the boat is closer to the island centerpoint than any other coast.</p> <p>Data file: dist_to_land_incl_small_islands.csv</p> <p>The data repository can be checked out at: https://renkulab.io/gitlab/ACE-ASAID/cruise-track-distance-to-land<br> <br> <strong>Dataset contents</strong></p> <p>- dist_to_land_incl_small_islands.csv, data file, comma-separated values<br> - data_file_header, metadata, text format<br> - README.txt, metadata, text format<br> - add_distance_to_small_islands.py, python script, text format</p> <p><strong>Dataset license</strong></p> <p>This output to the five-minute averaged distance to land is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Historical Tropical Cyclone Along-track Potential Intensity (and Derived Quantities) for Six Ocean Basins from Reanalyses
<p>Supporting derived data for Shields et al. (2020, GRL).</p> <p>Derived tropical cyclone potential intensities and associated variables across the North Atlantic (NA), Eastern North Pacific (EP), North Indian (NI), South Indian (SI), South Pacific (SP), and Western North Pacific (WP) ocean basins, from MERRA2, ERA-I, and MERRA2 with SSTs replaced by HadISSTs. NA/WP basins also have potential and observed intensities calculated with NCEP/NCAR and ERA-20C reanalyses over 1950-2016 and 1950-2010, respectively.</p> <p>All files are netcdf format, organized by basin, with suffixes on data variables to indicate reanalysis:</p> <ul> <li>"_m": MERRA2 (Gelaro et al. 2017)</li> <li>"_h": MERRA2-HadISSTs (Rayner et al. 2003)</li> <li>"_e": ERA-I (Dee et al. 2011)</li> <li>"_n": NCEP/NCAR (Kalnay et al. 2016)</li> <li>"_c": ERA-20C (Stickler et al. 2014)</li> </ul> <p>When using this data, please include the citation:</p> <blockquote> <p><strong>Shannon Shields, Allison Wing, and Daniel M. Gilford, 2020: A Global Analysis of Interannual Variability of Potential and Actual Tropical Cyclone Intensities. Geophys. Res. Lett.</strong></p> </blockquote> <p>Potential intensities calculated with the Bister and Emanuel (2002) algorithm (<strong>pcmin.m</strong>) by Kerry Emanuel (revised by Daniel Gilford, Gilford et al. 2019), available freely at: ftp://texmex.mit.edu/pub/emanuel/TCMAX</p> <p>MERRA2, ERA-I, and MERRA2 with SSTs replaced by HadISSTs calculations were performed by Daniel Gilford; NCEP/NCAR and ERA-20C calculations were performed by Dr. Suzana Camargo (many thanks!).</p> <p>Please direct any questions or comments to daniel[dot]gilford[at]rutgers[dot]edu.</p>
LMD Inconel 718 V-tracks 2020-07-31
<p>Description of dataset 10.5281/zenodo.3980733</p> <p>Deposition of Inconel 718 single tracks with process parameters:<br> - Nominal power = 300 (W)<br> - Nominal velocities = 350, 600, 900 (mm/min)<br> - Angles = 20°, 45°, 90°<br> - Powder flux = 0.099 (g/s)<br> - Nr. nozzles = 4<br> - Argon carrier flux = 4 (l/min)<br> - Argon shielding gas flux = 15 (l/min)<br> - Substrate temperature = Ambient</p> <p>The dataset is constituted by:<br> - Melt pool images, in file Experiment_2020_7_31__16_19_31.zip, acquired at 200fps with 850 nm narrow band filter, 5ms exposure time. 400x400 px size<br> - trAll.csv containing:<br> - t: timestamp in ms. Synchronized with imAll.csv<br> - Xpos: laser spot X position in workspace<br> - Ypos: laser spot Y position in workspace<br> - Zpos: laser spot Z position in workspace<br> - G1: binary signal indicating active deposition (G1=1) or not<br> - D: track width measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - H: track heigth measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - A: track section area measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - sdres: roughness index of section profile (std. deviation w.r.t. smoothed profile)<br> - Vnom: laser spot translational speed in m/s (computed from Xpos, Ypos, Zpos and t data)<br> - Pnom: nominal power<br> - V: Vnom in mm/min<br> - imAll.csv containing:<br> - t: timestamp in ms. Synchronized with trAll.csv (some frames may have been lost)<br> - I_mean: mean image intensity (only on red channel)<br> - I_mean_crop: mean image intensity computed on central cropped image area (180x180 pixels)<br> - M_I_mean: I_mean after application of 8-sample moving average<br> - M_I_mean_crop: I_mean_crop after application of 8-sample moving average<br> - fileName: associated image file name<br> - beamON: laserON signal obtained from thresholding on images (background noise = off, minimal intensity level = on)</p>
INTERACT-II (INTERcomparison of Aerosol and Cloud Tracking - II)
<p>Following the previous efforts of INTERACT (INTERcomparison of Aerosol and Cloud Tracking), the INTERACT-II campaign used multi-wavelength Raman lidar measurements to assess the performance of an automatic compact micro-pulse lidar (MiniMPL) and two ceilometers (CL51 and CS135) in providing reliable information about optical and geometric atmospheric aerosol properties. The campaign took place at the CNR-IMAA Atmospheric Observatory (760 ma.s.l.; 40.60<sup>∘</sup> N, 15.72<sup>∘</sup> E) in the framework of ACTRIS-2 (Aerosol Clouds Trace gases Research InfraStructure) H2020 project. Co-located simultaneous measurements involving a MiniMPL, two ceilometers and two EARLINET multi-wavelength Raman lidars were performed from July to December 2016.</p> <p>All the data from the CIAO lidars, the MiniMPL and from theCHM15k, CS135 and the CT25K ceilometers, operating collocated and simultaneously during the INTERACT-II campaign, are provided here. Additional files for the correction of the MiniMPL incomplere overlap are also provided.</p> <p>The results of the campaign are described in detail in Madonna et al., 2018 (<a href="https://amt.copernicus.org/articles/11/2459/2018/">https://amt.copernicus.org/articles/11/2459/201</a>8/).</p>
Perceptions of Diversity in Electronic Music: the Impact of Listener, Artist, and Track Characteristics
<p>Data Release and facsimile of the survey, presented in the submission 3238 to the CSCW 2021 conference.</p> <p> </p>
TMY hourly generation profiles for Insolight hybrid Si/III-V planar micro-tracking modules in Madrid
<p>Hourly energy density (1 m<sup>2</sup>) generation profiles for Insolight hybrid Si/III-V planar micro-tracking modules installed in Madrid (40.5°N, -3.75°E), synthetically generated using <a href="https://github.com/isi-ies-group/cpvlib">CPVLIB library</a> (based on <a href="https://pvlib-python.readthedocs.io/en/stable/">PVLIB Python</a>) and ERA5 typical meteorological year. Performance model parameters were empirically fitted using several outdoor monitoring campaigns and indoor characterization at the <a href="https://www.ies.upm.es/Investigacion/Research_Lines/Concentrator_photovoltaics/CPV_characterization">collimated-light solar simulator</a> available at IES-UPM.</p> <p><strong>Location</strong>: 40.5°N, -3.75°E</p> <p><strong>Format</strong>: CSV (separator: semicolon); headers in first row.</p> <p><strong>Parameters </strong>(ordered from first column): </p> <ul> <li>Time: YYYY-MM-DD HH:MM:SS+TimeZoneOffset</li> <li>Latitude: latitude of the installation in °N</li> <li>Longitude: longitude of the installation in °E</li> <li>Wind speed [m/s]: average wind speed</li> <li>Tair [°C]: average ambient temperature</li> <li>precipitable_water [mm]: average precipitable water in the atmosphere</li> <li>GHI [Wh/m2]: global horizontal irradiation</li> <li>DHI [Wh/m2]: diffuse horizontal irradiation</li> <li>DNI [Wh/m2]: direct (beam) normal irradiation</li> <li>CPV submodule [kWh/m2]: energy generated per m<sup>2</sup> by the III-V CPV submodule</li> <li>Flat-plate submodule [kWh/m2]: energy generated per m<sup>2</sup> by the Si flat-plate submodule</li> <li>Hybrid [kWh/m2]: energy generated per m<sup>2</sup> by the whole Insolight hybrid module</li> </ul>
Pre-trained models for segmentation and tracking of Coronal Bright Fronts from SDO AIA Base Difference images
<p>Here we present pretrained U-NET-based models followed by SDO AIA Base Difference(BD) validation set after intensity tresholding [-50;150] with predicted feature masks samples. <br>We provide a command-line Python utility for image segmentation using our CNNs designed to process images of solar eruptive phenomena. The https://gitlab.com/iahelio/helios_cnn repository includes regularly updated and newly published models. </p> <p>First model we present is designed to predict the likelihood of each pixel belonging to a certain class or feature in the solar image. A probabilistic output allows for a more nuanced interpretation of ambiguous region. The output can be converted into binary masks through thresholding. The range of values also gives insights into the model's confidence</p> <p>We also present sample segmentation results and the second model designed to produce binary masks.</p>
# Replication code and data for: Tracking green space along streets of world cities
<p># Replication code and data for: Tracking green space along streets of world cities<br>Falchetta, G., & Hammad, A. T. (2025). Tracking green space along streets of world cities. Environmental Research: Infrastructure and Sustainability. https://doi.org/10.1088/2634-4505/add9c4 </p> <p>The file "gvi_358cities_2016_2023_yearly_falchetta_hammad.csv" contains<strong> output data</strong>, reporting sampling-point level data on the yearly (2016-2023) values of the Green View Index for the 190 cities covered in the paper AND an additional number of world cities (for a total of 358 cities). The "README_gvi_358cities_2016_2023_yearly_falchetta_hammad.txt" file contains a dictionary of each column name and units. </p> <p>____<br><br></p> <p>To replicate the analysis, the results, and the figures of the paper:</p> <ul> <li>Download input data from this Zenodo repository and code from Github https://github.com/giacfalk/urban_green_space_mapping_and_tracking</li> <li><em>*Optional data extraction steps* </em>(processed output data are already available in the Zenodo repository):<br> <ul> <li>Adjust your working directory</li> <li>Run [lines 4-11] of workflow/sourcer.R</li> <li>Run the Javascript scripts written by the string_generator_training.R and string_generator_prediction.R files in Google Earth Engine (https://code.earthengine.google.com) and complete the export to Drive tasks to generate the output .csv files</li> </ul> </li> <li>Run workflow/sourcer.R [lines 15-46] to train the ML model and make predictions (including figures and tables replication)</li> </ul> <div> <div> <div> </div> <div> <div> <div> </div> <div> <p dir="auto"> </p> <p dir="auto"> </p> </div> </div> </div> </div> </div> <div> <div> <div> </div> <div> <div> <div> </div> <div> <p dir="auto"> </p> <p dir="auto"> </p> </div> </div> </div> </div> </div>
InSAR stack of San Francisco Bay, California from Sentinel-1 descending track 42 processed with GMTSAR
<p>A stack of unwrapped interferograms in the San Francisco Bay area, California, USA</p> <p>Sensor: Sentinel-1 descending track 42</p> <p>Processor: <a href="https://github.com/gmtsar/gmtsar" target="_blank" rel="noopener">GMTSAR</a></p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MintPy/">MintPy</a>.</p> <p>The tropospheric delay estimated from ERA-5 using PyAPS is attached.</p> <p><strong>Version 1.x (~2.3 GB)</strong><br>Time: 2014.12.31 - 2024.06.05 (333 acquisitions, 1297 interferograms)</p> <p><strong>Version 0.x (~290 MB; for fast testing of code development)</strong><br>Time: 2020.01.04 - 2021.07.15 (70 acquisitions, 184 interferograms)</p>
Problems with nanoparticle tracking analysis (NTA) of urine extracellular vesicles (uEVs)
<p>Urinary extracellular vesicle (uEV) proteins may be used as specific markers of kidney damage in various pathophysiological conditions. The nanoparticle-tracking analysis (NTA) appears to be the most useful method for the analysis of uEVs due to its ability to analyze particles below 300 nm. The NTA method has been used to measure the size and concentration of uEVs and also allows for a deeper analysis of uEVs based on their protein composition using fluorescence measurements. However, despite much interest in the clinical application of uEVs, their analysis using the NTA method is poorly described and requires meticulous sample preparation, experimental adjustment of instrument settings, and above all, an understanding of the limitations of the method. We present the problems encountered during analysis with possible solutions: the choice of sample dilution, the method of the presentation and comparison of results, photobleaching, and the adjustment of instrument settings for a specific analysis.</p> <p> </p> <p>Figure 1. Expressions of specific markers CD63 in protein-standardized samples detected with Western blot analysis; anti-CD 63 (HPA010088, Sigma-Aldrich, Saint Louis, MO, USA, 1:1000); secondary antibodies conjugated to horseradish peroxidase (554021, BD Pharmingen (BD Biosciences, San Jose, CA, USA) 1:10000).</p> <p> </p> <p>Nanoparticle-Tracking Analysis of uEVs. A NanoSight NS300 instrument (Malvern Panalytical, Malvern, UK) was used to determine the concentrations and sizes of the uEVs in the samples. The total number of extracellular vesicles was measured during the continuous flow of samples delivered from a syringe pump.</p> <p>Figure 2. Determination of the size and concentration of uEVs: dilution factor—1:100; laser—405 nm.</p> <p>Figure 3. Effect of dilution on total number of particles per milliliter and size of uEVs in nanoparticle tracking analysis: sample dilutions—1:100, 1:500, and 1:1000; laser—488 nm.</p> <p>Figure 5. Fluorescence-based nanoparticle-tracking analysis of CD 63 expression in uEVs: without 500 nm long-pass filter; with 500 nm long-pass filter; comparison of sizes and concentrations of uEVs without and with 500 nm long-pass filter; dilution factor—1:100; laser—488 nm; anti-CD 63 (HPA010088, Sigma-Aldrich); secondary antibodies conjugated to Alexa Fluor 488 fluorescent dye (ab150073-500, Abcam, Cambridge, MA, USA).</p> <p>Figure 6. Fluorescence-based nanoparticle-tracking analysis of podocin expression in uEVs: without 500 nm long-pass filter; with 500 nm long-pass filter; comparison of sizes and concentrations of uEVs without and with 500 nm long-pass filter; dilution factor—1:100; laser—488 nm; anti-podocin (P0372, Sigma-Aldrich); secondary antibodies conjugated to Alexa Fluor 488 fluorescent dye (ab150073-500, Abcam, Cambridge, MA, USA).</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.