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Sila National Park - 3D Point cloud data
<p>This dataset contains 3 types of data.</p> <ul> <li>GPS data (the ones starting with <em>"GPS"</em>) of sampling plot centers collected with a Trimble GPS and post processed to ensure positioning errors lower than 2 meters.</li> <li>TLS data, (the ones starting with <em>"ID_"</em>): such data were collected in the end of August 2019 with a mobile terrestrial laser scanner (mobile ZEB TLS) in a squared area of approximatively 30x30m. Data have been normalized using TreeLS package in R.</li> <li>ALS data collected in the end of July 2019. For the entire study area, we upload 2 different ALS data: "<em>merged.las</em>" is the original point cloud; "<em>myLas_norm_lt22.las</em>" is the normalised point cloud, cut at 22 meters from the ground in order to perform specific analysis (i.e. paper under submission).</li> </ul> <p>Data collection was founded by the <em>AGRIDIGIT Selvicoltura</em> project.</p>
Detailed point cloud data on stem size and shape of Scots pine trees
<p>This data set is comprised of three packed zip files and they include text files of 3D information from terrestrial laser scanning (TLS) and aerial imagery from unmanned aerial vehicle (UAV) from individual Scots pine trees within 27 sample plots from three test sites located in southern Finland.</p> <p>TLS data acquisition was carried out with Trimble TX5 3D laser scanner (Trible Navigation Limited, USA) for all three study sites between September and October 2018. Eight scans were placed to each sample plot and scan resolution of point distance approximately 6.3 mm at 10-m distance was used. Artificial constant sized spheres (i.e. diameter of 198 mm) were placed around sample plots and used as reference objects for registering the eight scans onto a single, aligned coordinate system. The registration was carried out with FARO Scene software (version 2018). Aerial images were obtained by using an UAV with Gryphon Dynamics quadcopter frame. Two Sony A7R II digital cameras were mounted on the UAV in +15° and -15° angles. Images were acquired in every two seconds and image locations were recorded for each image. The flights were carried out on October 2, 2018. For each study site, eight ground control points (GCPs) were placed and measured. Flying height of 140 m and a flying speed of 5 m/s was selected for all the flights, resulting in 1.6 cm ground sampling distance. Total of 639, 614 and 663 images were captured for study site 1, 2, and 3, respectively, resulting in 93% and 75% forward and side overlaps, respectively. Photogrammetric processing of aerial images was carried out following the workflow as presented in Viljanen et al. (2018). The processing produced photogrammetric point clouds for each study site with point density of 804 points/m<sup>2</sup>, 976 points/m<sup>2</sup>, and 1030 points/m<sup>2</sup> for study site 1, 2, and 3, respectively.</p> <p>The sample plots within the three test sites have been managed with different thinning treatments in either 2005 or 2006. The experimental design of the sample plots includes two levels of thinning intensity and three thinning types resulting in six different thinning treatments, namely i) moderate thinning from below, ii) moderate thinning from above, iii) moderate systematic thinning, iv) intensive thinning from below, v) intensive thinning from above, and vi) intensive systematic thinning, as well as a control plot where no thinning has been carried out since the establishment. More information about the study sites and samples plots as well as the thinning treatments can be found in Saarinen et al. (2020a).</p> <p>The data set includes stem points of individual Scot pine trees extracted from the point clouds. More about the method of extraction can be found in Saarinen et al. (2020a, 2020b) and Yrttimaa et al. (2020). The title of the zip file refers to the study sites 1, 2, and 3. The title of the text files includes the information on the test site, the plot within the test site, and the tree within the plot. The text files contain stem points extracted from the TLS point clouds. The columns “x” and “y” contain x- and y-coordinates in a local coordinate system (in meters), in column “h” is the height of each point in meters above ground, and treeID is the tree identification number. The columns are separated by space.</p> <p>Based on the study site and plot number, files from different thinning treatments can be identified by using the information in Table 1 in Saarinen et al. (2020b).</p> <p> </p> <p><strong>References</strong></p> <p>Saarinen, N., Kankare, V., Yrttimaa, T., Viljanen, N., Honkavaara, E., Holopainen, M., Hyyppä, J., Huuskonen, S., Hynynen, J., Vastaranta, M. 2020a. Assessing the effects of stand dynamics on stem growth allocation of individual Scots pines. bioRxiv 2020.03.02.972521. <a href="https://doi.org/10.1101/2020.03.02.972521">https://doi.org/10.1101/2020.03.02.972521</a></p> <p>Saarinen, N., Kankare, V., Yrttimaa, T., Viljanen, N., Honkavaara, E., Holopainen, M., Hyyppä, J., Huuskonen, S., Hynynen, J., Vastaranta, M. 2020b. Detailed point cloud data on stem size and shape of Scots pine trees. bioRxiv 2020.03.09.983973. <a href="https://doi.org/10.1101/2020.03.09.983973">https://doi.org/10.1101/2020.03.09.983973</a></p> <p>Viljanen, N., Honkavaara, E., Näsi, R., Hakala, T., Niemeläinen, O., Kaivosoja, J. 2018. A Novel Machine Learning Method for Estimating Biomass of Grass Swards Using a Photogrammetric Canopy Height Model, Images and Vegetation Indices Captured by a Drone. Agriculture 8: 70. <a href="https://doi.org/10.3390/agriculture8050070">https://doi.org/10.3390/agriculture8050070</a></p> <p>Yrttimaa, T., Saarinen, N., Kankare, V., Hynynen, J., Huuskonen, S., Holopainen, M., Hyyppä, J., Vastaranta, M. 2020. Performance of terrestrial laser scanning to characterize managed Scots pine (<em>Pinus sylvestris</em> L.) stands is dependent on forest structural variation. EarthArXiv. March 5. <a href="https://doi.org/10.31223/osf.io/ybs7c">https://doi.org/10.31223/osf.io/ybs7c</a></p>
Chameleon bare metal cloud traces (2020-09-03)
<p>This cloud trace contains instance events and machine events for the bare-metal clouds on the Chameleon testbed since it was built in 2015. Several text fields have been anonymized to hide user information.</p> <ul> <li>This trace contains data from 2015-06-17T17:54:29.000Z until 2020-09-03T01:51:34.000Z. </li> <li>The epoch time for timestamps is 2015-06-17T00:00:00.</li> <li>It follows <strong>version 0.3</strong> of the <a href="http://press3.mcs.anl.gov/scienceclouds/cloud-traces/cloud-trace-format/">cloud trace format</a>.</li> </ul> <p>You can find more information and trace datasets at <a href="https://www.scienceclouds.org/cloud-traces">Science Clouds</a>.</p>
Chameleon KVM cloud traces (2020-09-04)
<p>This cloud trace contains Nova compute events for the OpenStack KVM cloud on the Chameleon testbed since it was built in 2015. Several text fields have been anonymized to hide user information.</p> <ul> <li>This is the last set of traces for the KVM cloud built in 2015 in a file "chameleon_legacy_kvm_tacc_2020_02_03.zip". Chameleon deprecated the legacy KVM site in February 2020 and future versions of this deposition will not include this historical trace data.</li> <li>This trace contains data from 2015-09-17T15:37:05.000Z until 2020-02-03T06:23:40.000Z.</li> <li>The epoch time for timestamps is 2015-09-06T00:00:00.</li> <li>It follows version 0.3 of the <a href="http://press3.mcs.anl.gov/scienceclouds/cloud-traces/cloud-trace-format/">cloud trace format</a>.</li> </ul> <p>You can find more information and trace datasets at <a href="https://www.scienceclouds.org/cloud-traces">Science Clouds</a>.</p>
Sentinel-2 Cloud Mask Catalogue
<p><strong>Overview</strong></p> <p>This dataset comprises cloud masks for 513 1022-by-1022 pixel subscenes, at 20m resolution, sampled random from the 2018 Level-1C Sentinel-2 archive. The design of this dataset follows from some observations about cloud masking: (i) performance over an entire product is highly correlated, thus subscenes provide more value per-pixel than full scenes, (ii) current cloud masking datasets often focus on specific regions, or hand-select the products used, which introduces a bias into the dataset that is not representative of the real-world data, (iii) cloud mask performance appears to be highly correlated to surface type and cloud structure, so testing should include analysis of failure modes in relation to these variables.</p> <p>The data was annotated semi-automatically, using the <a href="https://github.com/ESA-PhiLab/iris">IRIS toolkit</a>, which allows users to dynamically train a Random Forest (implemented using <a href="https://github.com/microsoft/LightGBM">LightGBM</a>), speeding up annotations by iteratively improving it's predictions, but preserving the annotator's ability to make final manual changes when needed. This hybrid approach allowed us to process many more masks than would have been possible manually, which we felt was vital in creating a large enough dataset to approximate the statistics of the whole Sentinel-2 archive.</p> <p>In addition to the pixel-wise, 3 class (CLEAR, CLOUD, CLOUD_SHADOW) segmentation masks, we also provide users with binary<br> classification "tags" for each subscene that can be used in testing to determine performance in specific circumstances. These include:</p> <ul> <li><strong>SURFACE TYPE</strong>: <em>11 categories</em></li> <li><strong>CLOUD TYPE</strong>: <em>7 categories</em></li> <li><strong>CLOUD HEIGHT</strong>: <em>low, high</em></li> <li><strong>CLOUD THICKNESS</strong>: <em>thin, thick</em></li> <li><strong>CLOUD EXTENT</strong>: <em>isolated, extended</em></li> </ul> <p> </p> <p>Wherever practical, cloud shadows were also annotated, however this was sometimes not possible due to high-relief terrain, or large ambiguities. In total, 424 were marked with shadows (if present), and 89 have shadows that were not annotatable due to very ambiguous shadow boundaries, or terrain that cast significant shadows. If users wish to train an algorithm specifically for cloud shadow masks, we advise them to remove those 89 images for which shadow was not possible, however, bear in mind that this will systematically reduce the difficulty of the shadow class compared to real-world use, as these contain the most difficult shadow examples.</p> <p>In addition to the 20m sampled subscenes and masks, we also provide users with shapefiles that define the boundary of the mask on the original Sentinel-2 scene. If users wish to retrieve the L1C bands at their original resolutions, they can use these to do so.</p> <p>Please see the README for further details on the dataset structure and more.</p> <p> </p> <p><strong>Contributions & Acknowledgements</strong></p> <p>The data were collected, annotated, checked, formatted and published by Alistair Francis and John Mrziglod.</p> <p>Support and advice was provided by Prof. Jan-Peter Muller and Dr. Panagiotis Sidiropoulos, for which we are grateful.</p> <p>We would like to extend our thanks to Dr. Pierre-Philippe Mathieu and the rest of the team at <em>ESA PhiLab</em>, who provided the environment in which this project was conceived, and continued to give technical support throughout.</p> <p>Finally, we thank the <em>ESA Network of Resources</em> for sponsoring this project by providing ICT resources.</p> <p> </p> <p> </p>
Example Cloud-Resolving Model Output (Dec 2013 run)
<p>This dataset contains a selection of hourly, domain-mean quantities from the large-ensemble of realistic cloud-resolving model experiments described here: https://acp.copernicus.org/articles/20/6291/2020/</p>
Wind field and cloud layer data for Stuttgart, Germany
<p>This dataset contains computed wind field, cloud base and mixing layer height time series for locations in the City Centre of Stuttgart, Germany. The observations were made using a network of Doppler lidar and ceilometer instrumentation. The measurements were conducted within the framework of the Urban Climate under Change [UC]2 program. </p> <p>Up-to-date information about the program can be found at: <a href="http://uc2-program.org">http://uc2-program.org</a></p> <p>Versions:</p> <ul> <li>1.x.y: Data are in compliance with the [UC]2 Data Standard (<a href="http://uc2-program.org/uc2_data_standard.pdf">http://uc2-program.org/uc2_data_standard.pdf</a>). Included are computed variables for days during the two Intensive Observation Periods (IOPs) in winter and summer of 2017.</li> <li>2.x.y: Data include computed variables for most of the available observations during 2017. The data stores conform to an approach developed for ScaleX.</li> </ul>
GUV total ozone column and effective cloud transmittance from three Norwegian sites 1995-2019
<p>Total ozone column (TOC) and effective cloud transmittance (eCLT) from GUV-511 in Oslo (Norway), GUV-541 from Andøya/Tromsø (Norway), and GUV-541 from Ny-Ålesund (Svalbard, Norway).</p> <p>Responsible institute: NILU - Norwegian Institute for Air Research<br> Collaborative institute: Norwegian Radiation and Nuclear Safety Authority, DSA</p> <p>Method described in: Dahlback, A. (1996), Measurements of biologically effective UV doses, total ozone abundances, and cloud effects with multichannel, moderate bandwidth filter instruments, Appl. Opt. 35, 6514–6521</p> <p>1h average noon-time values, based on data with 1-minute time resolution.</p> <p>TOC retrievals from 305/320 nm channel ratio<br> eCLT retrievals from 340 nm channel </p> <p>Calibrations based on the FARIN2005-campaign and annual site visits with a travelling reference GUV instrument from DSA (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2007JD009731)</p> <p>Funding: NILU - Norwegian Institute for Air Research, Norwegian Environment Agency, Norwegian Ministry of health and Care Services</p>
Terrestrial laser scanning - RIEGL VZ-1000, individual tree point clouds and cylinder models, Belgian hedgerows and tree rows
<p>Terrestrial laser scans were acquired for 69 trees (<em>Quercus robur</em>: 39 trees; <em>Alnus glutinosa</em>: 19 trees; <em>Betula pendula: </em>11 trees) in hedgerows and tree rows in agricultural lands in Flanders, Belgium. We used a RIEGL VZ-1000 terrestrial laser scanner (RIEGL Laser Measurement Systems GmbH, Austria) with a beam divergence of 0.35 mrad operating in the infrared (wavelength 1550 nm) with a range up to 1000 m. We scanned leaf-off and all recorded variables are valid for overbark measurements. Individual trees were manually extracted from the co-registered point cloud in RiSCAN PRO software (provided by RIEGL). To the extracted trees, quantitative structure models (QSM) were fitted. We used the QSMs to derive branch length (m), total wood volume (m³) and merchantable wood volume (m³, using only cylinders with diameter > 7 cm). From the point clouds, we extracted the tree structural features such as crown projection (m²), maximum crown diameter (m) and tree height (m). Biomass expansion factors (BEF) were calculated by dividing total tree volume to merchantable tree volume. We expressed the age dependency of the BEF values via non-linear regression models. See Van Den Berge et al. (2021) for further information (DOI: 10.1007/s12155-021-10250-y).</p>
OptiSpot: Minimizing Application Deployment Cost using Spot Cloud Resources
<p>1. Attached files: </p> <p>This archive contains 1800 MATLAB files, each one containing the results of a single experiment.<br /> The name of each file follows the following format:</p> <p> A_B_C_D_E_F_G.mat</p> <p>Where the fields A, B, C, D, E, F, and G are described as follows.</p> <p>A: number of users.<br /> Considered values are: 1000, 2000, 5000, 10000.</p> <p>B: maximum response time in milliseconds.<br /> Considered values are: 60, 80, 100, 200.</p> <p>C: overbid time cap in hours.<br /> Considered values are: 5, 20, 80, 0 (note: 0 is a code used to express infinite hours).</p> <p>D: Amazon region.<br /> Considered values are: us-east, eu-west.</p> <p>E: Operating system.<br /> Considerede values are: Windows, Linux.</p> <p>F: Optimization algorithm.<br /> Considered values are: heuristic (which is OptiSpot), fmincon.</p> <p>G: Experiment seed.<br /> Considered values are from 1 to 30</p> <p>2. Data format:</p> <p>MATLAB data format, can be loaded from MATLAB using the following command:</p> <p>results = load(filename);</p> <p>results is defined as a structure with the following fields:</p> <p>results.cost<br /> Type: scalar, positive real number.<br /> Desc: hourly cost in US dollars.</p> <p> <br /> results.time<br /> Type: scalar, positive real number.<br /> Desc: total time (in seconds) needed by the algorithm to compute the solution.</p> <p>results.evaluations<br /> Type: scalar, positive integer number.<br /> Desc: number of constraints evaluations needed by the algorithm to compute the <br /> solution.</p> <p><br /> results.d<br /> Type: matrix, non negative positive real number. <br /> Desc: association matrix between rented resources (columns) and application <br /> components (rows). The sum of all the elements of this matrix is equal to<br /> the ECUs used by the application.</p>
Dataset: Six years of surface remote sensing of stratiform warm clouds in marine and continental air over Mace Head, Ireland
<p>A total of 118 stratiform water clouds observed by ground-based remote sensing instruments at the Mace Head Atmospheric Research Station at the West coast of Ireland from 2009 to 2015 were analyzed in terms of microphysical and optical characteristics as well as the impact of aerosols on these properties. The microphysical and optical cloud properties in the files were obtained using the algorithm SYRSOC (SYnergistic Remote Sensing Of Clouds).</p>
Data from Portable Ice Nucleation Experiment during the Pallas Cloud Experiment 2022
<div> </div> <div> </div> <div>The data set contains data from the Portable Ice Nucleation Experiment during the Pallas Cloud Experiment 2022. The Level 1 data is given in its raw temporal resolution and the data is flagged. Invalid data should be removed before analysis.</div>
ForensicVM Windows 11 Virtualization Dataset: Cloud and Local Application Data Retrieval with Virtualization and Password Bypass Challenge
<p>A Windows 11 Pro dataset designed in VirtualBox, complete with local and cloud apps, is to be virtually analyzed for crucial evidence. Bypassing the straightforward 'Bart' password is essential for access, yet original passwords should remain unchanged for others to attempt the same challenge. The task involves determining the password's nature, Bart's motives, identifying involved cloud applications, and extracting data both offline and online. The viability of dead box forensics for complete data retrieval is questioned, alongside what additional information network access could unveil. The challenge includes identifying two financial applications, extracting their data, and gathering cryptocurrency-related information, presented as an engaging forensicVM showcase by Nuno Mourinho, Mario Candeias, and Rogerio Bravo (Escola Superior de Tecnologia e Gestão de Beja, Instituto Politécnico de Beja).</p>
Data from the Untethered Balloon Launch of a Holographic Imager Into Cloud Near Adelaide, South Australia in August 2020
<p>This dataset was obtained from an untethered balloon launch into cloud near Adelaide, South Australia in August 2020. The balloon payload train consisted of a holographic imager, polarsonde, webcam, and meteorological sensors.</p> <p>Data is organised as follows:</p> <ol> <li>A netcdf file consisting of; <ul> <li>Raw RS41 radiosonde and data logger temperature and relative humidity profiles</li> <li>Mean number density (30-second averaged) from the holographic imager </li> <li>Mean equivalent diameter (30-second averaged) from the holographic imager </li> <li>Raw polarsonde co and cross polarisation signals</li> </ul> </li> <li>A selection of focussed particle images </li> <li>A selection of webcam images</li> <li>A figure summarising the results of HYSPLIT back trajectory modelling for the launch (FigS1)</li> <li>A figure showing the HIMAWARI Cloud Type classification for the region surrounding the launch site (FigS2)</li> </ol> <p>This dataset can be used to reproduce the key figures of the following paper submitted to Atmospheric Measurement Techniques (under review 2023): </p> <p>'A Light-Weight Holographic Imager for Cloud Microphysical Studies from an Untethered Balloon'.</p> <p>Additional details about the instruments and measurements can be found in that publication.</p>
Precision viticulture dataset for detailed vineyard mapping composed of geotagged smartphone ground images, phytosanitary status, UAV orthomosaics, 3D point clouds, and RTK GNSS data - Northern Spain, July 2022
<p>This dataset offers a rich multimodal collection of data from vineyards, designed to enhance agricultural research with a focus on vineyard management and disease monitoring. It includes geotagged smartphone ground images in ".7z" format for detailed plant-level analysis, a ".csv" file detailing plants' phytosanitary status for health assessment, UAV-derived 3D Point Clouds and orthomosaics in ".las" and ".tiff" formats for aerial landscape views, and RTK GNSS data in ".shp" format for precise plant geolocations.</p> <p>This dataset can be combined with other datasets to enable a comprehensive view of the vineyards and improve its value:</p> <div> <ul> <li>Ariza-Sentís, Mar, Sergio Vélez, and João Valente. ‘Dataset on UAV RGB Videos Acquired over a Vineyard Including Bunch Labels for Object Detection and Tracking’. <em>Data in Brief</em> 46 (February 2023): 108848. <a href="https://doi.org/10.1016/j.dib.2022.108848">https://doi.org/10.1016/j.dib.2022.108848</a>.</li> <li>Vélez, Sergio, Mar Ariza-Sentís, and João Valente. ‘VineLiDAR: High-Resolution UAV-LiDAR Vineyard Dataset Acquired over Two Years in Northern Spain.’ <em>Data in Brief</em>, October 2023, 109686. <a href="https://doi.org/10.1016/j.dib.2023.109686">https://doi.org/10.1016/j.dib.2023.109686</a>.</li> <li> <div> <div>Vélez, Sergio, Mar Ariza-Sentís, and João Valente. ‘Dataset on Unmanned Aerial Vehicle Multispectral Images Acquired over a Vineyard Affected by Botrytis Cinerea in Northern Spain’. <em>Data in Brief</em> 46 (February 2023): 108876. <a href="https://doi.org/10.1016/j.dib.2022.108876">https://doi.org/10.1016/j.dib.2022.108876</a>.</div> <div> </div> </div> </li> </ul> </div>
Data Tables for Enrichment by Extragalactic First Stars in the Large Magellanic Cloud
<p>Machine-readable data tables for the article, "Enrichment by Extragalactic First Stars in the Large Magellanic Cloud"-- DOI: 10.1038/s41550-024-02223-w. Full descriptions of the tables are in the article, but we briefly summarize here-</p> <p>Table 1 (Table1.csv): This table provides general information on the stars for which we obtained long-exposure Magellan/MIKE data to derive their detailed elemental abundances. Columns include names, coordinates, SkyMapper g magnitudes from the Gaia XP spectra, and stellar parameters and metallicities with their respective random uncertainties.</p> <p>Table 2 (Table2.csv): This table provides names, radial velocities, metallicities, and selected elemental abundances with random uncertainties for stars listed in Table 1. [C/Fe]_c indicates carbon abundances that are corrected for the evolutionary state of the star following Placco et al. (2014). Abundances that are upper limits are flagged by the ul_[X/Fe] columns and have "nan" values for the uncertainty.</p> <p>Extended Data Table 1 (Extended_Data_Table1.csv): This table summarizes all of our observations, by providing names, coordinates, SkyMapper g magnitudes from the Gaia XP spectra, exposure times, dates of observation, and the instrument for these observations. This table includes stars observed with MagE, those observed with short-exposures with MIKE for just metallicities and carbon abundances, and those flagged as more metal-rich upon initial exposure and hence, not further observed. </p> <p>Extended Data Table 2 (Extended_Data_Table2.csv): This table provides names, followed by stellar parameters, metallicities, and carbon abundances, along with their respective uncertainties, for stars observed with MagE or MIKE for short exposures to just obtain a metallicity and carbon abundance. As in Table 2, abundances that are upper limits are flagged by the ul_[C/Fe] column and "nan" entries for the uncertainty.</p> <p>Supplementary Data 1 (Summary_Data_1.csv or Summary_Data_1.ascii): This table provides the suite of detailed element abundances and uncertainties from the long-exposure MIKE spectra of the stars in Table 1. Columns include the name, atomic number and ionization state of the element (element), the number of features used to estimate the elemental abundance (N), the solar abundance of that element (Solar), the absolute abundance (logeps), the chemical abundance scaled by the solar abundance relative to hydrogen ([X/H]), the ratio with respect to the iron abundance ([X/Fe]), the random uncertainty ([X/H]_err) and an upper limit flag (ul), and errors from propagating the uncertainties in the individual stellar parameters and the overall systematic and total uncertainty ([X/H]_errteff, [X/H]_errlogg, [X/H]_errvt, [X/H]_errsys, [X/H]_errtot). These are followed by the same columns, but with respect to iron (e.g., [X/Fe]_errteff, [X/Fe]_errlogg). Abundances of the CH molecule are indicated by 106.0 in the "element" column. This table is provided as a machine readable csv file and as an ascii file, the latter for easier visual readability.</p> <p>Supplementary Data 2 (Summary_Data_2.csv or Summary_Data_2.ascii): This table summarizes the chemical abundances from individual absorption features for the LMC stars with long-exposure MIKE spectra. The columns include the star name, the atomic number and ionization state of the element (species), the solar abundance of that element (Solar), followed by the wavelength, excitation potential, and loggf of the line (wavelength, expot, loggf), and then the measured equivalent width (EW), absolute abundance (logeps), and a flag indicating whether the abundance is an upper limit (ul). Abundances derived via spectral synthesis have "nan" entries for expot, loggf, and EW, and abundances of the CH molecular band are indicated by 106.0. This table is provided as a machine readable csv file and as an ascii file, the latter for easier visual readability. </p>
ACCESS-AM2 Southern Ocean cloud and radiation data and code for SHAP analysis
<p>The ACCESS-AM2 (Australian Community Climate and Earth-System Simulator - Atmospheric Model Version 2) and SHAP analysis code and data used for the study described in Fiddes et al. (2024) '<em>A machine learning approach for evaluating Southern Ocean cloud-radiative biases over the Southern Ocean in a global atmosphere model</em>' accepted in Geoscientific Model Development</p> <p>Included files: </p> <p>- code.zip, inc: </p> <p> - pre-process_modis.ipynb: process the modis data, described in Fiddes et al. 2022 (https://doi.org/10.5194/acp-22-14603-2022)<br> - pre-process.ipynb: organises model and modis data for analysis. Produces the files: COSP_vars_MODIS_2015-2019.nc, COSP_vars_cg207_2015-2019.nc and COSP_vars_bx400_2015-2019.nc<br> - run_XGBoost+SHAP_control.ipynb: run the XGBoost model and SHAP analysis for the control run (bx400). Produces the files: SHAP_values_SWCRE_2015-2019_bx4002.nc, XGBoost_predicted_SWCRE_2015-2019_bx4002.nc, SHAP_interactions_bx400.nc<br> - run_XGBoost+SHAP_ice.ipynb: run the XGBoost model and SHAP analysis for the ice experiment run (cg207). Produces the files: SHAP_values_SWCRE_2015-2019_cg2072.nc, XGBoost_predicted_SWCRE_2015-2019_cg2072.nc<br> - analysis+plots_ML.ipynb: plots and stats presented in paper </p> <p>- COSP_vars_MODIS_2015-2019.nc</p> <p>- COSP_vars_cg207_2015-2019.nc</p> <p>- COSP_vars_bx400_2015-2019.nc</p> <p>- SHAP_values_SWCRE_2015-2019_bx4002.nc</p> <p>- SHAP_values_SWCRE_2015-2019_cg2072.nc</p> <p>- XGBoost_predicted_SWCRE_2015-2019_cg2072.nc</p> <p>- XGBoost_predicted_SWCRE_2015-2019_bx4002.nc</p> <p>- SHAP_interaction_bx400.nc</p> <p>The cloud types used in this work can be found at https://doi.org/10.5281/zenodo.6004061 </p>
Mangrove terrestrial laser scanning (TLS) point clouds and quantitative structural models (QSMs)
<p>Datasets for a publication entitled, "Terrestrial laser scanning for the estimation of above ground biomass of mangrove roots by modelling them as inverted trees."</p> <p>See the file "Data dictionary for Mangrove terrestrial laser scanning.pdf" for a description of the datasets included in the zipped folder. </p>
Large-scale 3D building and tree datasets constructed from airborne LiDAR point clouds in Glasgow, UK
<p>This is the updated version of building 3D model data. The revision includes appending attributes to the lod1 and lod2 shapefile and creating cityjson file for each 3D building model. All 3D building models are available in mesh (.obj), multipath shapefile, and cityjson (.json) now.</p> <p><strong>IMPORTANT NOTE: We suggest using the building footprint, lod1, and lod2 data of this version (Version v4).</strong></p> <p>Urban Big Data Centre of the University of Glasgow generates 3D city models via the airborne LiDAR point clouds acquired between 2020-2021 on behalf of Glasgow City Council. It is a large-scale 3D city model containing 3D information on terrain, trees, and buildings in Glasgow City. This dataset comprises terrain, tree canopy, and building products derived from high-density airborne LiDAR point clouds. </p> <p>The terrain products include Digital Terrain Model (DTM), Digital Surface Model (DSM), and normalized Digital Surface Model (nDSM) in 0.5 m spatial resolution. The DTM and DSM rasters were provided by the vendor and nDSM rasters were obtained by subtracting DTM from DSM. Terrain products are provided in 5 km by 5 km GeoTIF format raster.</p> <p>The tree canopy products are composed of canopy height models (CHM) and tree top locations. Classified tree point clouds were applied with pit-free algorithm to generate CHM in 0.5 m grid raster in GeoTIF format [1]-[2]. Treetop locations were identified by using Local Maximum Filter based on CHM and are recorded as points in Shapefile format. The tree canopy products are provided in 5 km by 5 km tiles.</p> <p>Building 3D model products include footprint polygons with building height attributes and 3D mesh of building models in LoD1 and LoD2 levels. A series of processes such as converting building point clouds to building height models (BHM), converting BHM to polygons, and polygon regularization were conducted to obtain the building footprint polygons. Building height attributes were calculated from BHM for each footprint. The building footprint data are provided in Shapefile format. LoD1 models were generated based on the footprint and average height of the building. LoD2 models were constructed based on footprint and building point cloud with City3D tool[3]. LoD1 and LoD2 models are provided in OBJ and shapefile format. Building 3D model products are provided in 5 km by 5 km tiles. The RMSE of Euclidean distances between each point in the point cloud to the reconstructed model was calculated to evaluate the LoD2 model construction. A table of RMSE and a note for a few problematic models are provided.</p>
Polar mesospheric clouds from the Balloon Lidar Experiment (BOLIDE) during the PMC Turbo balloon mission
<p>This dataset contains BOLIDE lidar data obtained during the PMC Turbo balloon mission that was launched on 7 July 2018 from Esrange, Sweden and landed in Nunavut, Canada on 14 July 2018. The mission was designed to study small-scale atmospheric dynamics induced by breaking atmospheric gravity waves within the polar mesospheric cloud layer at ~82 km altitude. PMC Turbo floated at around 40 km altitude and carried seven digital cameras to image the polar mesospheric cloud layer and the first Rayleigh lidar to successfully operate from a balloon.</p> <p>The lidar data consists of volume backscatter coefficients of polar mesospheric clouds, available at 20 m vertical and 10 s temporal resolution, contained in a compressed netcdf file. The magnitude of volume backscatter coefficients scales with<br> the brightness of clouds imaged by the PMC Turbo cameras. The netcdf file further includes floating altitude, rotator angle (azimuth) as well as latitude and longitude of the lidar beam at 82 km altitude.</p> <p>Users are encouraged to contact us for discussion when using BOLIDE data.</p> <p>Data contact: natalie.kaifler@dlr.de</p> <p>References:</p> <p>PMC Turbo camera videos: https://svs.gsfc.nasa.gov/13073</p> <p>NASA Space Physics Data Facility: https://cdaweb.gsfc.nasa.gov/index.html/, select PMC Turbo</p> <p>DLR Institute mission database: https://halo-db.pa.op.dlr.de/mission/112</p> <p>Kaifler, N., Kaifler, B., Rapp, M., Fritts, D.C. The polar mesospheric cloud dataset of the Balloon Lidar Experiment BOLIDE. Earth System Science Data. In preparation.</p> <p>Kaifler, B., Rempel, D., Roßi, P., Büdenbender, C., Kaifler, N., and Baturkin, V.: A technical<br> description of the Balloon Lidar Experiment (BOLIDE), Atmos. Meas. Tech., 13, 5681–5695,<br> https://doi.org/10.5194/amt-13-5681-2020, 2020.</p> <p>Fritts, D. C., Miller, A. D., Kjellstrand, C. B., Geach, C., Williams, B. P., Kaifler, B.,<br> et al. (2019). PMC Turbo: Studying gravity wave and instability dynamics in the summer mesosphere<br> using polar mesospheric cloud imaging and profiling from a stratospheric balloon. Journal of<br> Geophysical Research: Atmospheres, 124, 6423– 6443. https://doi.org/10.1029/2019JD030298</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.