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23,670 results for “Site”
Malwa site survey : archaeological site distribution maps
<p>Malwa site survey : archaeological site distribution maps. (1) mosaic based on Survey of India maps; (2) (3) study area; (4) Vidisha Raisen area.</p>
Malwa site survey : Bhopal District, photographic documentation (version 1, TIFF files)
<p>Malwa site survey : Bhopal District, photographic documentation, based on surveys conducted by Department of Archaeology and Museums, Madhya Pradesh.</p>
Malwa site survey : Sehore सीहोर District, Budni Tahsīl, photographic documentation (part 2)
<p>Malwa site survey: <a href="https://en.wikipedia.org/wiki/Sehore_district">Sehore District</a>, <a href="https://en.wikipedia.org/w/index.php?title=Budni&redirect=no">Budni</a> Tahsīl, photographic documentation (part 2), based on surveys conducted by Department of Archaeology and Museums, Madhya Pradesh.</p>
Malwa site survey : Sehore सीहोर District, Budni Tahsīl, photographic documentation (part 1)
<p>Malwa site survey: <a href="https://en.wikipedia.org/wiki/Sehore_district">Sehore</a> <a href="https://en.wikipedia.org/wiki/Sehore_district">District</a>, <a href="https://en.wikipedia.org/w/index.php?title=Budni&redirect=no">Budni</a> Tahsīl, photographic documentation (part 1), based on surveys conducted by Department of Archaeology and Museums, Madhya Pradesh.</p>
Malwa site survey : Sehore सीहोर District, Budni Tahsīl, village finds.
<p>Malwa site survey: <a href="https://en.wikipedia.org/wiki/Sehore_district">Sehore District</a>, <a href="https://en.wikipedia.org/w/index.php?title=Budni&redirect=no">Budni</a> Tahsīl, village finds, as documented by the Department of Archaeology & Museums, Madhya Pradesh.</p>
Malwa site survey: Bhopal District, Berasia Tahsīl, photographic documentation.
<p>Malwa site survey: <a href="https://en.wikipedia.org/wiki/Bhopal_district">Bhopal District</a>, <a href="https://en.wikipedia.org/wiki/Berasia">Berasia</a> Tahsīl, photographic documentation.</p> <p> </p>
Malwa site survey : Bhopal District, photographic documentation (version 2, JPEG files)
<p>Malwa site survey : Bhopal District, photographic documentation (version 2, JPEG files), based on surveys conducted by Department of Archaeology and Museums, Madhya Pradesh.</p>
Malwa site survey: Bhopal District, Berasia Tahsīl, photographic documentation (with villages named)
<p>Malwa site survey: <a href="https://en.wikipedia.org/wiki/Bhopal_district">Bhopal District</a>, <a href="https://en.wikipedia.org/wiki/Berasia">Berasia</a> Tahsīl, photographic documentation (with villages named).</p>
Malwa site survey : Raisen District, Begamganj Tahsīl, photographic documentation
<p> Malwa site survey : <a href="https://en.wikipedia.org/wiki/Raisen_district">Raisen District</a>, <a href="https://en.wikipedia.org/wiki/Begamganj">Begamganj</a> Tahsīl, photographic documentation.</p>
Datasets and Supporting Materials for the IPIN 2017 Competition Track 3 (Smartphone-based, off-site)
<p>This package contains the datasets and supplementary materials used in the IPIN 2017 Competition (Sapporo, Japan).</p> <p><strong>Contents:</strong></p> <ol> <li>Track3_LogfileDescription_and_SupplementaryMaterial.pdf: Description of the logfiles and supplemental materials.</li> <li>Track3_TechnicalAnnex.pdf: Technical annex describing the competition </li> <li>01-Logfiles: This folder contains a subfolder with the 25 training logfiles, a subfolder with the 9 validation logfiles, and a subfolder with the 7 blind evaluation logfiles as provided to competitors.</li> <li>02-Supplementary_Materials: This folder contains the Matlab/Octave parser, the raster maps, the visualization of the training routes and the location of the BLE beacon (CAR) and some Wi-Fi APs (UJIUB).</li> <li>03-Evaluation: This folder contains the scripts used to calculate the competition metric, the 75th percentile on the 505 evaluation points. The ground truth is also provided in MatLab format and as a CSV file. Since the results must be provided with a 2Hz freq. starting from apptimestamp 0, the GT includes the closest timestamp matching the timing provided by competitors.</li> </ol> <p><strong>Please, cite the following works when using the datasets included in this package:</strong></p> <ul> <li>Torres-Sospedra, J.; Jiménez, A. R.; Moreira, A.; Lungenstrass, T.; Lu, W.-C.; Knauth, S.; Mendoza-Silva, G.M.; Seco, F.; Perez-Navarro, A.; Nicolau, M.J.; Costa, A.; Meneses, F.; Farina, J.; Morales, J.P.; Lu, W.-C.; Cheng, H.-T.; Yang, S.-S.; Fang, S.-H.; Chien, Y.-R. and Tsao, Y. Off-line evaluation of mobile-centric Indoor Positioning Systems: the experiences from the 2017 IPIN competition Sensors Vol. 18(2), 2018. <a href="http://dx.doi.org/10.3390/s18020487">http://dx.doi.org/10.3390/s18020487</a></li> <li>Jimenez, A.R.; Mendoza-Silva, G.M.; Seco, F.; Torres-Sospedra, J. Datasets and Supporting Materials for the IPIN 2017 Competition Track 3 (Smartphone-based, off-site). <a href="http://dx.doi.org/10.5281/zenodo.2823924">http://dx.doi.org/10.5281/zenodo.2823924</a> </li> </ul> <p><strong>Additional information can be found at:</strong></p> <ul> <li><a href="http://evaal.aaloa.org/2017/2017-competition-home">http://evaal.aaloa.org/2017/2017-competition-home</a></li> <li><a href="http://indoorloc.uji.es/ipin2017track3/">http://indoorloc.uji.es/ipin2017track3/</a></li> </ul> <p><strong>For any further questions about the database and this competition track, please contact: </strong></p> <ul> <li>Joaquín Torres (<a href="mailto:jtorres@uji.es?subject=IPIN%202016%20Competition%20Dataset%20(Zenodo)">jtorres@uji.es</a>) Institute of New Imaging Technologies, Universitat Jaume I, Spain. </li> <li>Antonio R. Jiménez (<a href="mailto:antonio.jimenez@csic.es?subject=IPIN%202016%20Competition%20Dataset%20(Zenodo)">antonio.jimenez@csic.es</a>) Center of Automation and Robotics (CAR)-CSIC/UPM, Spain. </li> </ul> <p><br> </p>
Datasets and Supporting Materials for the IPIN 2018 Competition Track 3 (Smartphone-based, off-site)
<p>This package contains the datasets and supplementary materials used in the IPIN 2018 Competition (Nantes, France).</p> <p><strong>Contents:</strong></p> <ol> <li>IPIN2018_CallForCompetition_v2.1: Call for competition including the technical annex describing the competition </li> <li>01-Logfiles: This folder contains a subfolder with the 22 training logfiles, a subfolder with the 15 (13 + 2) validation logfiles, and a subfolder with the 1 blind evaluation logfile as provided to competitors.</li> <li>02-Supplementary_Materials: This folder contains the Matlab/octave parser, the raster maps, the vector maps and the visualization of the training routes.</li> <li>03-Evaluation: This folder contains the scripts used to calculate the competition metric, the 75th percentile on the 99 evaluation points. The ground truth is also provided in MatLab format and as a CSV file. Since the results must be provided with a 2Hz freq. starting from apptimestamp 0, the GT includes the closest timestamp matching the timing provided by competitors.</li> <li>03-Evaluation_alternative: This folder contains the alternative scripts used to calculate the competition metric, the 75th percentile on the 99 evaluation points. This version is compatible with MatLab and Octave and does not require any toolbox. In some cases, the differences in the reported errors might be around 10 cm with respect to the script used in the competition. The ground truth is also provided in MatLab format and as a CSV file. Since the results must be provided with a 2Hz freq. starting from apptimestamp 0, the GT includes the closest timestamp matching the timing provided by competitors.</li> </ol> <p><strong>Please, cite the following works when using the datasets included in this package:</strong></p> <ul> <li>Jimenez, A.R.; Mendoza-Silva, G.M.; Ortiz, M.; Perez-Navarro, A.; Perul, J.; Seco, F.; Torres-Sospedra, J. Datasets and Supporting Materials for the IPIN 2018 Competition Track 3 (Smartphone-based, off-site). <a href="http://dx.doi.org/10.5281/zenodo.2823964">http://dx.doi.org/10.5281/zenodo.2823964</a></li> <li>Renaudin, V.; Ortiz, M.; Perul, J.; Torres-Sospedra, J.; Ramón Jimenez, A.; Pérez-Navarro, A.; Martín Mendoza-Silva, G.; Seco, F.; Landau, Y.; Marbel, R.; Ben-Moshe, B.; Zheng, X.; Ye, F.; Kuang, J.; Li, Y.; Niu, X.; Landa, V.; Hacohen, S.; Shvalb, N.; Lu, C.; Uchiyama, H.; Thomas, D.; Shimada, A.; Taniguchi, R.; Ding, Z.; Xu, F.; Kronenwett, N.; Vladimirov, B.; Lee, S.; Cho, E.; Jun, S.; Lee, C.; Park, S.; Lee, Y.; Rew, J.; Park, C.; Jeong, H.; Han, J.; Lee, K.; Zhang, W.; Li, X.; Wei, D.; Zhang, Y.; Park, S. Y.; Park, C. G.; Knauth, S.; Pipelidis, G.; Tsiamitros, N.; Lungenstrass, T.; Pablo Morales, J.; Trogh, J.; Plets, D.; Opiela, M.; Shih-Hau Fang Tsao, Y.; Chien, Y.-R.; Yang, S.-S.; Ye, S.-J.; Ali, M. U.; Hur, S.; and Park, Y. Evaluating Indoor Positioning Systems in a Shopping Mall: The Lessons Learned from the IPIN 2018 Competition IEEE Access Vol. 7, pp. 148594-148628, 2019. http://dx.doi.org/10.1109/ACCESS.2019.2944389</li> </ul> <p><strong>Additional information can be found at:</strong></p> <ul> <li><a href="http://evaal.aaloa.org/2018/call-for-competitions">http://evaal.aaloa.org/2018/call-for-competitions</a></li> <li><a href="http://ipin-conference.org/2018/ipincompetition/">http://ipin-conference.org/2018/ipincompetition/</a></li> </ul> <p><strong>For any further questions about the database and this competition track, please contact: </strong></p> <ul> <li>Joaquín Torres (<a href="mailto:jtorres@uji.es?subject=IPIN%202016%20Competition%20Dataset%20(Zenodo)">jtorres@uji.es</a>) Institute of New Imaging Technologies, Universitat Jaume I, Spain. </li> <li>Antonio R. Jiménez (<a href="mailto:antonio.jimenez@csic.es?subject=IPIN%202016%20Competition%20Dataset%20(Zenodo)">antonio.jimenez@csic.es</a>) Center of Automation and Robotics (CAR)-CSIC/UPM, Spain. </li> </ul>
Modified half-hourly FLUXNET dataset for 10 Boreal forest sites (CA-Obs,CA-Ojp,CA-Qfo,FI-Hyy,FI-Ken,FI-Let,FI-Sod,RU-Fyo,RU-Zot,US-Prr)
<p>This set contains half-hourly driving data and observations used in the simulations described in gmd-2018-313 (doi:10.5194/gmd-2018-313). Originally, this data is part of the FLUXNET2015 dataset (doi:10.17616/R36K9X). We have quality checked and gap-filled this data to suit the simulations.</p> <p>The upload contains site specific csv-files, a data header that is common to all files and a README. The actual data contains half-hourly values for:</p> <ul> <li>gross primary production (GPP, mol m<sup>-2 </sup>s<sup>-1</sup>)</li> <li>evapotranspiration (ET, kg m<sup>-2 </sup>s<sup>-1</sup>)</li> <li>air temperature (air_temp, degrees celcius)</li> <li>air pressure (air_pressure, Pa)</li> <li>precipitation (precip, kg m<sup>-2 </sup>s<sup>-1</sup>)</li> <li>specific humidity (qair, kg<sup> </sup>kg<sup>-1</sup>)</li> <li>wind speed (wspeed, m s<sup>-1</sup>)</li> <li>CO2 concentration (CO2, mol mol<sup>-1</sup>)</li> <li>shortwave radiation (shortwave, W m<sup>-2</sup>)</li> <li>longwave radiation (longwave, W m<sup>-2</sup>)</li> <li>potential shortwave radiation (mpot, W m<sup>-2</sup>)</li> </ul> <p>The sites (named by their FLUXNET identifier) and the years of data in this set are:</p> <ul> <li>CA-Obs (Saskatchewan) 1999-2006</li> <li>CA-Ojp (Saskatchewan) 2004-2006</li> <li>CA-Qfo (Quebec) 2003-2010</li> <li>FI-Hyy (Hyytiälä) 1999-2006</li> <li>FI-Ken (Kenttärova) 2003-2010</li> <li>FI-Let (Lettosuo) 2010-2012</li> <li>FI-Sod (Sodankylä) 2001-2008</li> <li>RU-Fyo (Fyodorkovskoye) 2002-2009</li> <li>RU-Zot (Zotino) 2002-2004</li> <li>US-Prr (Poker Flat) 2011-2013</li> </ul>
Site-Specific MCER Response Spectra for Los Angeles Region based on 3-D Numerical Simulations and the NGA West2 Equations
<p><strong>ABSTRACT</strong></p> <p>The Utilization of Ground Motion Simulation (UGMS) committee of the Southern California Earthquake Center (SCEC) developed site-specific, risk-targeted Maximum Considered Earthquake (MCER) response spectra for the Los Angeles region. The long period (T ≥ 2-sec) MCER response spectra were computed as the weighted average of MCER spectral accelerations derived from (1) 3-D numerical ground-motion simulations using the CyberShake computational platform, and (2) empirical ground-motion prediction equations (GMPEs) from the Pacific Earthquake Engineering Research (PEER) Center NGAWest2 project. The short period (T < 2- sec) MCER response spectra were computed exclusively from the NGAWest2 GMPEs. A web-based lookup tool was also developed so users can obtain the MCER response spectrum for a specified latitude and longitude and for a specified site class or 30-m average shear-wave velocity, VS30. The tool provides acceleration ordinates of the MCER response spectrum at 21 natural periods in the 0 to 10-sec band.</p> <p>This dataset includes a Java application to run queries. It serves as the backend data source for the web-based tool that can be found at: <a href="https://data2.scec.org/ugms-mcerGM-tool_v18.4/">https://data2.scec.org/ugms-mcerGM-tool_v18.4/</a>.</p> <p>For more information, please see <a href="https://www.scec.org/research/ugms">https://www.scec.org/research/ugms</a>.</p> <p><strong>DISCLAIMER</strong></p> <p>The UGMS MCER Tool is provided "as is" and without warranties of any kind. While SCEC and the UGMS Committee have made every effort to provide data from reliable sources or methodologies, SCEC and the UGMS Committee do not make any representations or warranties as to the accuracy, completeness, reliability, currency, or quality of any data provided herein. SCEC and the UGMS Committee do not intend the results provided by this tool to replace the sound judgment of a competent professional, who has knowledge and experience in the appropriate field(s) of practice. By using this tool, you accept to release SCEC and the UGMS Committee of any and all liability.</p> <p>Please note: The site-specific, design response spectral acceleration, Sa, returned by this tool for user-specified inputs, must be compared to the minimum Sa requirement described in Section 21.3 of ASCE 7-16 (second and third paragraphs). This minimum Sa is computed as 80% of the design response spectrum derived from the SDS, SD1, and TL values obtained from the ASCE tool at https://asce7hazardtool.online/. The larger of the site-specific Sa and the 80% minimum Sa at each period, T, is the final design response spectral acceleration. This final Sa x 1.5 is the final MCER response spectral acceleration.</p>
STORM Project: monitoring environment conditions at Baths of Diocletian site (Rome, Italy). Dataset 2018 - 2019
<p>This dataset was created by the Engineering Ingegneria Informatica S.p.A. through a set of prototypes based on Libelium Waspmote for collecting the following parameters: </p> <ul> <li>Climate parameters (Temperature, Relative Humidity, Barometric Pressure, Luminosity, Wind direction/speed and Rainfull) using a Libelim PlugAndSense Agricolture Pro;</li> <li>Environmental Parameters (Monoxide Carbon, Oxigen, Air Polluction, Volatile Organic Compounds VOC, Carbon Dioxide, Nitric Dioxide , Hydrogen Sulfide, Sulfure Dioxide and Particle Matter PM 1, 2.5 and 10) using two nodes: PlugAndSense Smart Cities Pro and Waspmote with gases sensor board;</li> <li>Acoustic Noise Sensor and Vibrations with accelerometer, using a prototype based on Libelium Waspmote.</li> </ul> <p>The data produced by the sensors were acquired and sent to the Meshlium (mini-pc linux based) which automatically saved and sent to the STORM Platform. The dataset is composed of the data obtained from February 2018 to March 2019.</p> <p>STORM (Safeguarding Cultural Heritage through Technical and Organisational Resources Management) is a HORIZON 2020 funded European Union Cultural Heritage project that aims at the protection of Cultural Heritage through a combination of technical and organizational resources (http://www.storm-project.eu).</p>
Pauni (पौनि Bhandārā district) Maharashtra. Stone head at the site of the stūpa.
<p>Pauni (पौनि Bhandārā district) Maharashtra. Stone head at the site of the <em>stūpa</em>, kept in the Jagannāth temple. Probably the head of a <em>yakṣa</em>.</p>
Pauni (पौनि Bhandārā district) Maharashtra. Stone head at the site of the stūpa.
<p>Pauni (पौनि Bhandārā district) Maharashtra. Stone head at the site of the <em>stūpa</em>, kept in the Jagannāth temple.</p>
AirHeritage Datalake: Multi-site, Multi-season, Multi Unit dataset including Fixed and Mobile Citizen science data from networked Air Quality Low-Cost Multi-Sensors devices and reference stations
<p>This datalake comprises several datasets from <strong>37 networked low cost air quality multisensors</strong> (<strong>30</strong> <strong>mobile</strong> ENEA MONICA(tm) + <strong>7</strong> <strong>fixed</strong>) along with <strong>3</strong> (fixed) + <strong>1</strong> (mobile) <strong>reference stations</strong> operated by Campania Regional Envronmental Protection Agency. The datalake is organized in 3 main directories respectively related to fixed nodes, mobile nodes and nearby reference stations including a mobile laboratory used for colocation campaigns; each subdirectory include its own metadata description file.</p> <p>Data, curated by Energy and Data Science Laboratory of ENEA, include multi-weeks colocation periods when low cost devices have been colocated with reference stations as well as operational periods during which sensors are deployed for fixed or mobile monitoring campaigns. Data have been recorded during 2021 and 2022 in a<strong> pervasive, multi-site, multi-seasonal deployment</strong> in Portici, a densely populated small area city (4km2, 55k + inhabitants) located 7km south of Naples, Italy.</p> <p>The datalake consists in actual sensors and reference intrumentations timeseries along with metadata description files with deployment dates and location data. The dataset files include high sampling frequency raw sensor data of quality-controlled sensor network along with co-located reference stations data sets. Sensor data include electrochemical sensors data (intended target pollutants: NO2, O3, CO), Optical sensor data (PM2.5, PM10, PM1) readings along with meteorological parameters. .</p> <p>Further description of sensors and reference instruments are reported in the accompanying paper (see citation request).</p> <p>The dataset can be used for </p> <ul> <li> <strong>advanced (remote/universal/in field) data driven calibration strategies</strong> test or development including <strong>machine learning </strong>models</li> <li><strong>mobile opportunistic data fusion</strong> methods development</li> <li><strong>geomatics and data assimilation</strong> models studies</li> </ul> <p>as well as low cost sensor characterization performance studies. </p>
Dataset for the publication "Superconducting gravimeter observations show that satellite-derived snow depth image improves the simulation of the snow water equivalent evolution in a high alpine site"
<p>This datasset contains data to reproduce the following figures of the paper <em>Superconducting gravimeter observations show that satellite-derived snow depth image improves the simulation of the snow water equivalent evolution in a high alpine site</em>:</p> <ul> <li> <p>Time series data of Figures 1c and 2</p> </li> <li> <p>Data (*.asc) used for plotting Figures 1d and 1e (as well as Figure S3 and S4)</p> </li> <li>Pléiades snow depth map (Figure S1)</li> <li> <p>Data used for plotting Figure S2</p> </li> </ul> <p> </p>
Plasmodesmata Act as Unconventional Membrane Contact Sites Regulating Inter-Cellular Molecular Exchange in Plants.
<p>This table contains peaks aera values from LC-MS for lipidomic quantification of PIP and PIP2. These data were used for Pérez-Sancho, Jessica and Smokvarska, Marija and Glavier, Marie and Sritharan, Sujith and Dubois, Gwennogan and Dietrich, Victor and Platre, Matthieu and Li, Ziqiang Patrick and Paterlini, Andrea and Moreau, Hortense and Fouillen, Laetitia and Grison, Magali S. and Cana-Quijada, Pepe and Moraes, Tatiana Sousa and Immel, Françoise and Wattelet, Valerie and Ducros, Mathieu and Brocard, Lysiane and Chambaud, Clément and Zabrady, Matej and Luo, Yongming and Busch, Wolfgang and Tilsner, Jens and Helariutta, Yrjö and Russinova, Jenny and Taly, Antoine and Jaillais, Yvon and Bayer, Emmanuelle, Plasmodesmata Act as Unconventional Membrane Contact Sites Regulating Inter-Cellular Molecular Exchange in Plants. </p>
Particle size and velocity distributions from a Thies Clima 3D Stereo disdrometer installed at the Casale Calore site in L'Aquila (Italy), monthly netCDF archive
<p>Disdrometric data from a Thies Clima 3D Stereo disdrometer, with 22 size classes and 20 velocity classes, located at the instrumented site of Casale Calore in L'Aquila (Italy, 42.3831 N, 13.3148 E, 683 m a.s.l.), managed by the University of L'Aquila and the Center of Excellence Telesensing of Environment and Model Prediction of Severe Events (CETEMPS). </p> <p>Mid values and widths of the classes and instrument ancillary data are provided. One-minute spectra are aggregated every 5 minutes and saved in monthly netCDF files.</p> <p>Metadata available at <a href="https://antarcticdatacenter.cnr.it/geonetwork/srv/eng/catalog.search#/metadata/27e2bd39-097e-4512-96f0-fb213cd59a00">https://antarcticdatacenter.cnr.it/geonetwork/srv/eng/catalog.search#/metadata/27e2bd39-097e-4512-96f0-fb213cd59a00</a></p> <p>--------------------------------------------------------------------</p> <p>Example of netCDF file structure:</p> <h2><strong>File "LAQ_3DS_202301_5min.nc"</strong></h2> <pre><strong> dimensions</strong>: <em>diameter </em>= 22; <em>velocity </em>= 20; <em>n_image </em>= 20; <em>y_image </em>= 12; <em>x_image </em>= 12; <em>time </em>= UNLIMITED; // (8741 currently) <strong>variables</strong>: long <em>time_UTC</em>(time=8741); :description = "Measurement time. Timestamp indicates the end of the observation interval, e.g. 01-Mar-2020 00:05:00 represents the particle counts registered between 01-Mar-2020 00:00:01 and 01-Mar-2020 00:05:00."; :time_zone = "UTC"; :units = "Seconds since 1970-01-01 00:00:00 (Unix time)."; :_ChunkSizes = 512U; // uint float <em>diameters</em>(diameter=22); :description = "Mid values of the size classes"; :units = "mm"; float <em>velocities</em>(velocity=20); :description = "Mid values of the velocity classes"; :units = "m s^-1"; float <em>diameters_width</em>(diameter=22); :description = "Width of the size classes"; :units = "mm"; float <em>velocities_width</em>(velocity=20); :description = "Width of the velocity classes"; :units = "m s^-1"; int <em>spectrum</em>(diameter=22, velocity=20, time=8741); :description = "Matrix of particle counts in each of the 22 diameter sizes and 20 velocity ranges over 5 minutes."; :units = "counts"; :_ChunkSizes = 22U, 20U, 1U; // uint float <em>PSD</em>(diameter=22, time=8741); :description = "Particle size distribution, 5 minutes interval, normalized by the observed volume."; :units = "m^-3 mm^-1"; :_ChunkSizes = 22U, 1U; // uint double <em>monthlySpectrum</em>(diameter=22, velocity=20); :description = "Matrix of particle counts in each of the 22 diameter sizes and 20 velocity ranges over the entire month."; :units = "counts"; double <em>monthlyPSD</em>(diameter=22); :description = "Particle size distribution for the whole month, normalized by the observed volume."; :units = "m^-3 mm^-1"; int <em>images</em>(x_image=12, y_image=12, n_image=20, time=8741); :description = "Images of samples of the detected precipitating particles. Images are 48x12 pixel maximum, for a max of 4 stacked 12x12 images. Most of the time less than 4 images are provided."; :units = "0-255 pixel values"; :_ChunkSizes = 12U, 12U, 20U, 1U; // uint int <em>image_count</em>(time=8741); :description = "How many images are registred by the instrument in the minute."; :units = "0-4 count"; :_ChunkSizes = 1024U; // uint int <em>precip_type</em>(n_image=20, time=8741); :description = "Precipitation type as classified by the instument based on shape, size, velocity and presence of water, according to the following table with 11 entries (0-10): 0-reserved value, 1-false positive, 2-rain or graupel, 3-drizzle, 4-drizzle with rain, 5-rain, 6-rain with snow, 7-snow, 8-ice prisms, 9-graupel, 10-hail."; :units = "0-10 code"; :_ChunkSizes = 20U, 1U; // uint int <em>particle_diam</em>(n_image=20, time=8741); :description = "Main diameter of the particles shown in the images."; :units = "mm"; :_ChunkSizes = 20U, 1U; // uint //<strong> global attributes</strong>: :<em>title </em>= "Thies Clima 3D Stereo disdrometer data, aggregated to 5min, monthly netCDF archive."; :<em>comment </em>= "Particle counts diveded in 22 size classes and 20 velocity classes. Note that this data has been processed regardless of precipitation type."; :<em>time_label </em>= "Jan 2023"; :<em>institution </em>= "CNR-ISAC, Rome (IT)"; :<em>contact_person </em>= "Luca Baldini, CNR-ISAC, Rome, l.baldini@isac.cnr.it"; :<em>source </em>= "TC 3DS disdrometer at MZS (Antarctica)"; :<em>location </em>= "Mario Zucchelli Station (74°42\'S, 164°07\'E, 15 m a.s.l.)"; :<em>author </em>= "Giacomo Roversi, Ca\' Foscari University, Venice (IT) and CNR-ISAC, Rome (IT), g.roversi@isac.cnr.it"; :<em>creation_date </em>= "23-Oct-2024 11:13:22 UTC"; :<em>coverage </em>= "Monthly coverage (Jan 2023): 100 %"; :<em>time_resolution </em>= "5 minutes"; :<em>history </em>= "Created from raw TC telegram TDD 163, aggregated to 5min temporal resolution with a sum of the 1-minute counts if least 3 out of 5 are not NaN."; </pre> <p> </p> <p> </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.