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Dataset for the manuscript "Are remote sensing evapotranspiration models reliable 2 across South American ecoregions?" published in WRR
<p><strong>Metadata of ‘<em>Are remote sensing evapotranspiration models reliable across South American ecoregions?</em>’ </strong></p> <p>This document describes the file formatting and data used to run and evaluate the evapotranspiration models in this study. Because forcing data varies among models, each input file contains a different set of meteorological data placed within a folder named after the corresponding model.</p> <p> </p> <p><strong>File format and time stamps</strong></p> <p>Data files are CSV formatted with timestamps in the first column of the file. The following timestamps are used:</p> <ul> <li>GLEAM: Year (YYYY); Day of Year (DDD)</li> <li>PT-JPL: Year (YYYY); Month (MM); Day (DD)</li> <li>PM-MOD: Year (YYYY); Month (MM); Day (DD)</li> <li>PM-VI: Date (MM/DD/YYYY)</li> </ul> <p> </p> <p><strong>Missing data</strong></p> <p>Missing data are reported using ‘NaN’ as a replacement flag. Data for all days in a leap year are reported. </p> <p> </p> <p><strong>Data format</strong></p> <p>The column headers Name, Description and Units are adopted used in the data files to describe the following variables::</p> <ul> <li>ETo, Penman-Monteith FAO-56 reference evapotranspiration (mm day<sup>-1</sup>);</li> <li>ETobs, Observed evapotranspiration (mm day<sup>-1</sup>);</li> <li>Rn, Surface Net Radiation (w m<sup>-2</sup>);</li> <li>Rg, Daylight shortwave Incoming Radiation (w m<sup>-2</sup>);</li> <li>Rgs_out, Shortwave Radiation - outgoing (w m<sup>-2</sup>);</li> <li>G, Soil heat flux (w m<sup>-2</sup>);</li> <li>P, Rainfall (mm day<sup>-1</sup>);</li> <li>T, Surface Air Temperature (ºC);</li> <li>Tmax, Maximum Temperature (ºC);</li> <li>Tmin, Minimum Temperature (ºC);</li> <li>Tday, Daytime Temperature (ºC);</li> <li>TminDay, Daytime Minimum Temperature (ºC);</li> <li>TminNight, Nighttime Minimum Temperature (ºC);</li> <li>Patm, Atmospheric Air Pressure (Pa);</li> <li>ea, Actual Vapor Pressure (kPa);</li> <li>es, Saturation Vapor Pressure (kPa);</li> <li>VPD, Vapor Pressure Deficit (kPa);</li> <li>eaDay, Daytime Actual Vapor Pressure (kPa);</li> <li>eaNight, Nighttime Actual Vapor Pressure (kPa);</li> <li>RH, Air Relative Humidity;</li> <li>RHDayTime, Daytime Air Relative Humidity;</li> <li>RHNightTime, Nighttime Air Relative Humidity;</li> <li>LAI, Leaf Area Index (m² m<sup>-</sup>²);</li> <li>SWC, Soil Water Content (mm m<sup>-1</sup>).</li> </ul> <p> </p> <p><strong>Forcing data per model</strong></p> <p>Each model requires a different set of forcing data, as follows:</p> <ul> <li>GLEAM: Rn, P, T, Rgs_out;</li> <li>PT-JPL: Tmax, Rn, RH (or e<sub>a</sub>);</li> <li>PM-MOD: Rg, Tday, TminDay, TminNight, RHDayTime, RHNighttime, eaDay, eaNight;</li> <li>PM-VI: ETo.</li> </ul> <p> </p> <p><strong>Tower sites (IDs) and co-authors/PIs:</strong></p> <ul> <li>SDF: J. P. Quezada and M. Galleguillos;</li> <li>TF1 and TF2: L. Kutzbach and D. Holl;</li> <li>GRO and SLU: G. Posse;</li> <li>BAL and MCC: M. Gassman and C. Perez;</li> <li>PDG, EUC and USR: O. Cabral;</li> <li>FM and SIN: J.S. Nogueira and T. Range;</li> <li>CAA: M. Moura;</li> <li>CST: A. C. D. Antonino;</li> <li>SJO: E. S. Souza and J. R. S. Lima;</li> <li>ESEC: B. Bezerra.</li> </ul>
Pre-Publication Dataset: Is Remote Sensing a Better Measure of Internet Censorship than Expert Analysis? Analyzing Tradeoffs for International Donors and Advocacy Organizations
<p>These are the underlying data and do file to support the analysis in the forthcoming paper "Is Remote Sensing a Better Measure of Internet Censorship than Expert Analysis? Analyzing Tradeoffs for International Donors and Advocacy Organizations" that has been submitted to the <em>Data & Policy </em>Journal. This is an expanded and updated version of the Data For Policy conference paper "Comparing Measures of Internet Censorship: Analyzing the Tradeoffs between Expert Analysis and Remote Measurement" (10.5281/zenodo.3967398).</p>
Detecting coarse beach sediment using remotely sensed imagery at the FRF, Duck, NC, USA: Labeled images, deep learning model, testing data, and predictions.
<p>This data record contains 5 zip files all used to build and use a semantic segmentation model to operate on beach imagery taken at the Field Research Facility (FRF) in Duck, North Carolina, USA. All data is from 2015-2021</p> <p>The `training_data.zip` contains all data used to train the ML model. All images come from the north facing (c1) camera. This zip file includes: a list of classes used to label the imagery, and folders of 107 images, 107 sparse annotations (doodles), 107 labels, and 107 overlays. All labeling was done with the open-source labeling tool ‘Doodler (Buscombe et al., 2021).</p> <p>The `model.zip` file contains the ML model, and associated metadata. This includes: a JSON model configuration file, a figure showing model training statistics, an `.npz` file of model training output, a list of training and validation files, the model as an h5 file and in the Tensorflow ‘saved model’ format. All modeling was done with Segmentation Gym (Buscombe & Goldstein 2022).</p> <p>The `test_data_c6.zip` file contains all data from the south facing (c6) camera to test the ML model. This includes: a list of classes used to label the imagery, and folders of 10 images, 10 sparse annotations (doodles), 10 labels, and 10 overlays. All labeling was done with the open-source labeling tool ‘Doodler (Buscombe et al., 2021). Testing the model with this data was done with codes in: https://github.com/ebgoldstein/FRF_GrainSize</p> <p>The `test_data_c1.zip` file contains all data from the north facing (c1) camera to test the ML model. This includes: a list of classes used to label the imagery, and folders of 10 images, 10 sparse annotations (doodles), 10 labels, and 10 overlays. All labeling was done with an open-source labeling tool ‘Doodler (Buscombe et al., 2021). Testing the model with this data was done with codes in: https://github.com/ebgoldstein/FRF_GrainSize</p> <p>The `predictions.zip` file contains 4418 images from the north facing (c1) camera that were run through the trained segmentation model as well as the resulting output (presented as side-by-side image and overlays). These images were created using codes in Segmentation Gym (Buscombe & Goldstein 2022).</p>
Decreasing trends of ammonia emissions over Europe seen from remote sensing and inverse modelling
<p>The set consists of 5 files that constitute the main calculations of ammonia emissions over Europe for the years 2013-2020. <br> The detailed description of variables follows:</p> <p>1) PriorEmission.nc<br> - Pall: tensor of the size 240 x 200 x 12 x 8 (lat x lon x months x years) with ammonia prior emissions used in the study [ng/m2/s]</p> <p>2) PosteriorEmission.nc<br> - Xall: tensor of the size 240 x 200 x 12 x 8 (lat x lon x months x years) with ammonia posterior emissions [ng/m2/s]</p> <p>3) UncertaintyEmission.nc<br> - Uall: tensor of the size 240 x 200 x 12 x 8 (lat x lon x months x years) with uncertainty of posterior emissions [ng/m2/s]</p> <p>4) stations_vmodVSobs.mat <br> - st_list: list of stations identifiers, 53 stations in total<br> - st_coord: stations coordinates [lot,lat]<br> - st_OBSdays: matrix of the size 53 x (366*8) with observations in daily resolution [ug/m3]<br> - st_ind_obs: logical matrix of the size 53 x (366*8) with indicators when each station provides observation (1) and when not (0)<br> - st_prior_vmod_days: matrix of the size 53 x (366*8) with calculated concentrations using model with prior emission [ug/m3]- st_post_vmod_days: matrix of the size 53 x (366*8) with calculated concentrations using model with posterior emission [ug/m3]<br> - st_prior1_vmod_days: same as st_prior_vmod_days for EC6G4 prior<br> - st_prior2_vmod_days: same as st_prior_vmod_days for EGG prior<br> - st_prior3_vmod_days: same as st_prior_vmod_days for NE prior<br> - st_prior4_vmod_days: same as st_prior_vmod_days for VD prior</p>
Remote Sensing based Sea Surface partial pressure of CO2 (pCO2) and air-sea CO2 flux (FCO2) in the East China Sea (2003-2019)
<p>Based on <em>in situ</em> seawater <em>p</em>CO<sub>2</sub> data collected on 51 cruises/legs over the past two decades, a satellite retrieval algorithm for seawater <em>p</em>CO<sub>2</sub> was developed by combining the semi-mechanistic algorithm and machine learning method (MeSAA-ML). MeSAA-ML introduces semi-analytical parameters, including the temperature-dependent seawater <em>p</em>CO<sub>2</sub> (<em>p</em>CO<sub>2,therm</sub> ) and upwelling index (<em>UI<sub>SST</sub></em>), to characterise the combined effect of atmospheric CO<sub>2</sub> forcing, thermodynamic effects, and multiple mixing processes on seawater <em>p</em>CO<sub>2</sub>. Additionally, considering the biological effects and various sub-regional features, multiple ocean colour parameters were also used as inputs in XGBoost, the best-selected machine learning algorithm. Independent cruise-based data were used to validate the satellite-derived <em>p</em>CO<sub>2</sub>, which achieved excellent performance in this complicated marginal sea, with low root mean square error (RMSE=19.6 μatm) and mean absolute percentage deviation (APD=4.12%). Air-sea CO2 fluxes are calculated based on retrieved seawater <em>p</em>CO<sub>2</sub>. </p>
Dataset provided for : Sensing Ultrasound Localization Microscopy reveals glomeruli in rats and humans
<p><strong>Abstract :</strong> Estimation of glomerular function is a key element in the diagnosis of kidney disease. However, the study of glomeruli in the clinic remains indirect through urine and blood tests. Recent imaging technique called Ultrasound Localization Microscopy (ULM) originated from the ability to record continuous movements of individual microbubbles in the bloodstream. Although it improved the resolution of vascular imaging up to tenfold, the imaging of the smallest vessels had yet to be reported.</p> <p>We acquired ultrasound sequences from living humans and rats and then applied filtering dividing the data set into slow-moving and fast-moving microbubbles. We performed a double tracking to highlight and characterize this new population of microbubbles with singular behaviors: we called this technique “sensing ULM” (sULM). We used post-mortem micro-CT for side-by-side confirmation in rats.</p> <p>In this study, we report the observation of microbubbles flowing in capillaries bundles, i.e. the glomeruli, in the kidney in living humans and rats. We introduce a set of analysis tools dedicated to extracting quantitative information from individual microbubbles, like the remanence time or the normalized distance.</p> <p>As glomeruli play a key role in kidney function, their observation could yield a deeper understanding of kidney diseases and provide a diagnostic tool for patients. More generally, it will bring imaging capabilities closer to the functional units of organs, which is one of the keys to understanding most diseases, like cancers, diabetes, or kidney failures. </p> <p><strong>Academic reference to be cited : </strong>Denis, Bodard, Hingot, Chavignon, Battaglia, Renault, Lager, Aissani, Hélénon, Correas, and Couture. <em>Sensing Ultrasound Localization Microscopy reveals glomeruli in rats and humans,</em> eBioMedicine, 2023.</p> <p><strong>Related scripts and software application</strong> : <a href="https://github.com/EngineerJB/akebia">https://github.com/EngineerJB/akebia</a></p> <p><strong>(New ! published in June 2024) Raw data :</strong> <a href="../records/11395562">https://zenodo.org/records/11395562</a></p> <p><strong>Corresponding authors : </strong></p> <ul> <li>Article : Louise Denis, <a href="mailto:louise.denis@sorbonne-universite.fr">louise.denis@sorbonne-universite.fr</a>, Sylvain Bodard, <a href="mailto:sylvain.bodard@aphp.fr">sylvain.bodard@aphp.fr</a></li> <li>Scripts, and codes : Louise Denis, <a href="mailto:louise.denis@sorbonne-universite.fr">louise.denis@sorbonne-universite.fr</a>, Jacques Battaglia, <a href="mailto:jacques.battaglia@sorbonne-universite.fr">jacques.battaglia@sorbonne-universite.fr</a></li> <li>Materials, collaborations, rights and others: Olivier Couture, <a href="mailto:olivier.couture@sorbonne-universite.fr">olivier.couture@sorbonne-universite.fr</a></li> </ul>
Remote Sensing based Sea Surface partial pressure of CO2 (pCO2) and air-sea CO2 flux (FCO2) in the South China Sea (2003-2019)
<p>The South China Sea (SCS) is one of the largest marginal seas worldwide. It includes a river-dominated, highly productive marginal sea on the north shelf and a wide, oligotrophic ocean-dominated basin with various dynamic sub-regions. Based on an <em>in situ</em> seawater partial pressure of CO<sub>2</sub> (<em>p</em>CO<sub>2</sub>) datasets of 44 cruises/legs collected for the last two decades in the SCS, we proposed a seawater <em>p</em>CO<sub>2</sub> retrieval algorithm by combining the semi-mechanistic and machine learning (ML) methods (MeSAA-ML). The parameter selection strategy was based on the mechanistic analysis of <em>p</em>CO<sub>2</sub> variation, separating impacts of thermodynamics, biological activities, water mixing, and the atmospheric CO<sub>2</sub> forcing. We set a few semi-analytical parameters: <em>p</em>CO<sub>2</sub><sub>_<em>therm</em></sub>, which was a proxy for the combined effect of thermodynamics and the atmospheric CO<sub>2</sub> forcing on seawater <em>p</em>CO<sub>2</sub>; an upwelling index (UI<em><sub>SST</sub></em>) and mixing layer depth (MLD) to characterize the multiple mixing processes; chlorophyll-a concentration (Chl-a) with remote sensing reflectance at 443 and 555 nm (Rrs(443) and Rrs(555)), which were the inputs to proxy the biological effect and other characteristics for distinguishing shelf, basin, and sub-regions. As the seawater <em>p</em>CO<sub>2 </sub>and atmospheric <em>p</em>CO<sub>2</sub> ( <em>p</em>CO<sub>2</sub><sup>air</sup>) have similar data values and characteristics in the vast SCS oligotrophic basin, it will cause instability of the model if one is input and the other is output; thus the difference between them (<em>Δp</em>CO<sub>2</sub><sup>sea-air</sup>) was set as the output, and the seawater <em>p</em>CO<sub>2</sub> was obtained finally by summing <em>p</em>CO<sub>2</sub><sup>air </sup>and <em>Δp</em>CO<sub>2</sub><sup>sea-air</sup>. We compared several ML models, and the XGBoost model was confirmed as the best model. Completely independent cruise-based and observed datasets from Southeastern Asia Time-series Study (SEATS) were used to validate the satellite products, with low root mean square error (RMSE = 11.69 μatm) and mean absolute percentage deviation (APD = 1.59%). The increasing trend of satellite-derived <em>p</em>CO<sub>2</sub> (2.44 ± 0.24 μatm/yr) at the location of SEATS was found to be consistent with observed data. We presented that the SCS as a whole is a source of atmospheric CO<sub>2</sub>, releasing an average of 11.00 ± 2.45 Tg C/yr from a total area of 3.32 × 10<sup>6</sup> km<sup>2,</sup> and the northern shelf is a sink (1.69 ± 0.53 Tg C/yr). The area-integrated CO<sub>2</sub> efflux over the entire SCS may decrease with a rate of 0.34 Tg C/yr during 2003–2019. This high-accuracy dataset with 1 km resolution provides a refined understanding of the air-sea CO<sub>2</sub> exchange dynamics in the SCS during 2003–2019.</p>
Low-level mixed-phase clouds at the high Arctic site of Ny-Ålesund: A comprehensive long-term dataset of remote sensing observations
<p>This dataset contains a comprehensive set of quality-controlled remote sensing observations of low-level mixed-phase clouds collected at the high Arctic site of Ny-Ålesund, between 10 October 2021 and 31 December 2022. Cornerstones of the dataset are observations from a 35-GHz polarimetric scanning Doppler cloud radar and a 94-GHz zenith-pointing Doppler cloud radar. Radar data are complemented with thermodynamic retrievals from a microwave radiometer, liquid base height from a ceilometer and wind fields from large-eddy simulations. All data have undergone extensive quality control, especially the cloud radar data, which are accurately calibrated, matched, and corrected for gas and liquid-hydrometeor attenuation, ground clutter and range folding. This dataset is especially suited for cloud microphysical studies, and the high number of events included allows for the compiling of robust statistics. The dataset is accompanied by a data descriptor article, which is available at <a href="https://doi.org/10.5194/essd-15-5427-2023" target="_blank" rel="noopener">doi.org/10.5194/essd-15-5427-2023</a>.</p> <p> </p> <p><strong>Dataset overview</strong><br>The files include only low-level mixed-phase cloud (LLMPC) events, as well as the 2 hours preceding and following events. Each file contains an individual event, unless multiple events are less than 4 hours apart, in which case they are combined into the same file. LLMPC events are detected by requiring that ice and liquid phase coexist in a cloud layer with top below 2500 m for at least one hour. All radar variables observed in zenith (Doppler moments at 35 and 94 GHz, linear depolarization ratio (LDR) at 35 GHz), as well as microwave radiometer retrievals (temperature (T), liquid water path (LWP), integrated water vapor (IWV)), liquid base height from the ceilometer, and model data (horizontal wind speed and direction) are brought to the same time and range grids (respectively named ‘time_zen’ and ‘range_zen’ in the files). Off-zenith radar variables (reflectivity, differential reflectivity (ZDR), maximum spectral ZDR (sZDRmax), correlation coefficient (RhoHV), differential phase shift (PhiDP), and specific differential phase (KDP)) are stored on separate coordinates (named ‘time_slant’ and ‘range_slant’). All derived corrections are already applied to the data, and stored in the files, in case the user is interested in reconstructing the original data. A number of flags have been included in the files: in particular ‘MPC_detected’ indicates whether a LLMPC event was detected, and ‘liquid_attenuation_correction_flag_zen’ and ‘liquid_attenuation_correction_flag_slant’ indicate whether radar reflectivities were corrected for attenuation due to liquid hydrometeors. Liquid attenuation corrections should be especially taken into account when computing the dual-wavelength ratio (i.e., the difference between reflectivity at 35 GHz and at 94 GHz, both expressed in dBZ), and performing quantitative analyses of reflectivity fields.</p>
Datasets of Radwin 2023 Great Salt Lake Remote Sensing Historical Assessment
<p>Included are the culminated datasets for an article in review to be published with the Utah Geological Association. This data helps an investigator reproduce or utilize the data. There are datasets for both Landsat and Sentinel, for both the North and South arms of the Great Salt Lake. Additionally, there are datasets documenting the NDWI threshold used for each Landsat image, the outlier images not used in analyses, NDWI error assessment, and calculation of stats/facts.</p> <p>Paper abstract:</p> <p>The Great Salt Lake has been rapidly shrinking since the highstand of the mid-1980s, creating cause for concern in recent decades as the lake has reached historic lows. Many investigators have assessed the evolution of lake elevation, geochemistry, anthropogenic impacts, and links to climate and atmospheric processes; however, the use of remote sensing to study the evolution of the lake has been significantly limited. Harnessing recent advancements in cloud-processing, specifically Google Earth Engine cloud computing, this study utilizes over 600 Landsat TM/OLI and Sentinel MSI satellite images from 1984-2023 to present time-series analyses of remotely sensed Great Salt Lake water area, exposed lakebed area, surface cover types, and chlorophyll-a analyses paired with modelled estimates for water and exposed lakebed area. Results show that since the highstand of 1986-1987, the water area has declined by 45% (~3,000 km<sup>2</sup>) and the exposed lakebed area has increased to ~3,500 km<sup>2</sup> from ~500 km<sup>2</sup>. The area of unconsolidated sediments not protected by vegetation or halite crusts has risen to ~2,400 km<sup>2</sup>. Significant halite crusts are observed in the North Arm, having a max extent of ~150 km<sup>2</sup> between 2002 and 2003, while only small extents of halite crusts are observed for the South Arm. Vegetation is more prevalent in the Bear River Bay and South Arm, with surface area increases over 400% since 1990. Gypsum is widely observed independent of halite crusts. The results highlight multiple instances of land-use/water-management that led to observable changes in water/exposed lakebed area and halite crust extent. This study demonstrates the important benefits of maintaining a lake elevation above ~4,194 ft to maximize lake and halite crust area, which would help mitigate possible dust events and maintain broad lake extent.</p> <p> </p> <p>Files should be self-explanatory based on filename, where BRB means Bear River Bay. Note there are two video files animating the evolution of the North and South Arms of the Great Salt Lake using satellite imagery from 1984 to 2023. </p> <p> </p> <p>Visit https://github.com/radwinskis for details on code used for this study.</p> <p>Please contact me at markradwin@gmail.com with any questions.</p> <p> </p>
Remote Sensing based Sea Surface partial pressure of CO2 (pCO2) and air-sea CO2 flux (FCO2) in the East China Sea (2003-2019)
<p>Based on <em>in situ</em> seawater <em>p</em>CO<sub>2</sub> data collected on 51 cruises/legs over the past two decades, a satellite retrieval algorithm for seawater <em>p</em>CO<sub>2</sub> was developed by combining the semi-mechanistic algorithm and machine learning method (MeSAA-ML). MeSAA-ML introduces semi-analytical parameters, including the temperature-dependent seawater <em>p</em>CO<sub>2</sub> (<em>p</em>CO<sub>2,therm</sub> ) and upwelling index (<em>UI<sub>SST</sub></em>), to characterise the combined effect of atmospheric CO<sub>2</sub> forcing, thermodynamic effects, and multiple mixing processes on seawater <em>p</em>CO<sub>2</sub>. Additionally, considering the biological effects and various sub-regional features, multiple ocean colour parameters were also used as inputs in XGBoost, the best-selected machine learning algorithm. Independent cruise-based data were used to validate the satellite-derived <em>p</em>CO<sub>2</sub>, which achieved excellent performance in this complicated marginal sea, with low root mean square error (RMSE=19.6 μatm) and mean absolute percentage deviation (APD=4.12%). Air-sea CO2 fluxes are calculated based on retrieved seawater <em>p</em>CO<sub>2</sub>. </p>
Quantum-enhanced sensing on optical transitions via finite-range interactions
<p>This upload contains the data set for the manuscript titled "Quantum-enhanced sensing on optical transitions via finite-range interactions". Notes describing the added data set are uploaded alongside. Please see the Arxiv version https://arxiv.org/abs/2303.10688 for plots.</p>
Remotely sensed geoarchaeological marks in the hinterland of Ravenna
<p>GeoPackage database with 994 fluvial and anthropogenic traces mapped in the hinterland of Ravenna through historical imagery, satellite images and drone photos, together with related metadata.</p> <p> </p> <p>Sources of aerial and satellite images analysed include:</p> <ul> <li>Istituto Geografico Militare Italiano (IGMI), Volo GAI (Gruppo Aeronautico Italiano) aerial photos are available at https://servizimoka.regione.emilia-romagna.it/mokaApp/apps/VIGMIGAI1954_H5/index.html (last accessed on 31 January 2025).</li> <li>Ministero dell'Ambiente e della Tutela del Territorio e del Mare (MATTM) aerial photos are available at http://www.pcn.minambiente.it/viewer/ (last accessed on 31 January 2025).</li> <li>Agenzia per le Erogazioni in Agricoltura (AGEA) aerial photos are available at https://servizimoka.regione.emilia-romagna.it/mokaApp/apps/CORERH5/index.html (last accessed on 31 January 2025).</li> <li>Google Earth satellite images, accessed through Google Earth Pro for desktop, which can be downloaded from https://www.google.it/earth/versions/ (last accessed on 31 January 2025).</li> <li>Microsoft Bing satellite images are available at https://www.bing.com/maps/aerial (last accessed on 31 January 2025). Historical imagery has been accessed through a third-party website freely available at https://zoom.earth/, but this service has been discontinued (last accessed on 31 January 2025).</li> <li>Esri World Imagery satellite images are available at https://livingatlas.arcgis.com/wayback/ (last accessed on 31 January 2025).</li> <li>Consorzio Telerilevamento Agricoltura (TeA) are available at https://servizimoka.regione.emilia-romagna.it/mokaApp/apps/CORERH5/index.html (last accessed on 31 January 2025).</li> <li>Compagnia Generale Riprese Aeree (CGR) are available at https://servizimoka.regione.emilia-romagna.it/mokaApp/apps/CORERH5/index.html (last accessed on 31 January 2025).</li> <li>PlanetScope satellite images were made available through Planet Education and Research Program: more information is available at https://www.planet.com/industries/education-and-research/ (last accessed on 31 January 2025).</li> </ul>
Supplement to "Low-level mixed-phase clouds at the high Arctic site of Ny-Ålesund: A comprehensive long-term dataset of remote sensing observations"
<p>This dataset is a supplement to "Low-level mixed-phase clouds at the high Arctic site of Ny-Ålesund: A comprehensive long-term dataset of remote sensing observations", available at <a href="http://doi.org/10.5281/zenodo.7803064">doi.org/10.5281/zenodo.7803064</a>. The additional variables here included are: slow edge velocity, fast edge velocity, and eddy dissipation rate (EDR). All variables are stored on the same time and range grids adopted for the main dataset. Similarly, the event selection and file structure are identical to those of the main dataset.<br><br>Slow and fast edge velocities are derived from Doppler spectra recorded by the zenith-pointing 94-GHz cloud radar. The slow (fast) edge velocity is calculated as the velocity associated with the slowest (fastest) Doppler bin above the peak noise level, belonging to a spectral cluster whose width is at least 5 Doppler bins.<br><br>The EDR is retrieved following the approach by Borque et al. (2016; <a href="http://doi.org/10.1002/2015JD024543">doi.org/10.1002/2015JD024543</a>), using as input the slow edge velocity, and model horizontal wind speed from the main dataset. EDR is retrieved in 5 minute intervals, up to a maximum range of 3 km.<br><br>The detailed documentation of the variables here included can be found in the Supporting Information to the following publication: <a href="https://doi.org/10.1029/2023GL106599" target="_blank" rel="noopener">doi.org/10.1029/2023GL106599</a>.</p>
GMSK Spectrum Sensing for Machine Learning
<p>This file contains measurement results achievied in the following scenario:<br>Single PC with GNU Radio and connected USRP transmits GMSK signal at center frequncy 2100 MHz with different amplifier gain.<br>Single PC with GNU Radio and connected USRP receives signal with central frequency 2100 MHz and bandwidth 40 MHz (treated like 40 x 1 MHz channels). It uses local osciallator offset equal 10 MHz and due to non-linear characteristics of amplifiers and filters in USRP the extreme 12 (on both sides) are removed. However to create (this) dataset only one channel where signal was transmitted were included.<br><br>Data that can be found in the files is stored in CSV format to be easly analyzed in ML models. </p><p>Data collected at during this experiment contains:<br>the first column of average received power in the analyzed channel (in dBm)<br>the second column of autocorrelation function skewness in the analyzed channel (in linear scale)<br>the third column of autocorrelation function kurtosis in the analyzed channel (in linear scale)<br>the fourth column with information about signal transmission (label; 0 - noise, 1 - signal transmitted)</p>
Pond Area Estimates: Nine Study Regions in Alaska for 3 time periods (1950s, 1978-1982, 1999-2001) using remotely sensed images
The data are ArcGIS shapefiles by USGS quadrangle within 9 study regions: Arctic Coastal Plain, Stevens Village area, Yukon Flats, Minto Flats, Denali Flats, Talkeetna, Innoko Flats, Tetlin Flats, and Copper River Basin. Each shapefile polygon represents the shoreline of a pond as visually interpreted from each georectified remotely sensed image. All images were rectified based on at least 25 control points from 1:63 360 USGS digital raster graphics topographic maps using a second-order polynomial with a RMS error of less than one satellite image pixel (30 meters). All closed-basin ponds greater than 0.2 hectares were visually delineated and manually traced as polygons using ArcGIS. Each pond polygon has an ID and Hectares field representing the pond ID and area in hectares for the time period of the remotely sensed image.
Tree ring, leaf mining, climate, and remote sensing data from aspen leaf miner survey sites: III - Climate, leaf mining, and NDVI data
This dataset contiains annual site-level measurements from 2004 - 2015 of growing season climate moisture index ( GS CMI; summed CMI from May - September), average site level leaf mining, and mean July - August normalized difference vegetation index (NDVI) derived from Landsat, GIMMS3g, MODIS Aqua, and MODIS Terra
MCR LTER: Reference: Satellite Remote Sensing: Landsat ETM from 2001 to 2010
This LTER Remote Sensing spatial raster dataset consists of Landsat Enhanced Thematic Mapper image data collected between 2001 and 2010 over the island of Moorea and Tahiti. These are reference data, from the USGS EROS archive, not data generated by MCR LTER. This product was created by the U.S. Geological Survey (USGS) and contains Landsat data files in Geographic Tagged Image-File Format (GeoTIFF). NASA Landsat Program, 2009, Landsat ETM+ scene 20010429-LE70540722001119EDC00, LPGS_11.2.1, USGS, Sioux Falls, 04/29/2001.
MCR LTER: Reference: Satellite Remote Sensing: Landsat 7 in 1999 and 2000
This LTER Remote Sensing spatial raster dataset consists of Landsat 7 Thematic Mapper image data collected in 1999 and 2000 over the island of Moorea and Tahiti. These are reference data, from the USGS EROS archive, not data generated by MCR LTER. This product was created by the U.S. Geological Survey (USGS) and contains Landsat data files in Geographic Tagged Image-File Format (GeoTIFF). NASA Landsat Program, 2001, Landsat ETM+ scene RNL71054072_07220000426, Unspecified processing, USGS, Sioux Falls, 04/26/2000. NASA Landsat Program, 2003, Landsat ETM+ scene L7054072_19990830, Ortho product, USGS, Sioux Falls, 08/30/1999.
MCR LTER: Reference: Satellite Remote Sensing: Landsat MSS in 1979
This LTER Remote Sensing spatial raster dataset consists of Landsat Multi-Spectral Scanner (MSS) image data collected in 1979 over the island of Moorea and Tahiti. These are reference data, from the USGS EROS archive, not data generated by MCR LTER. This product was created by the U.S. Geological Survey (USGS) and contains Landsat data files in Geographic Tagged Image-File Format (GeoTIFF). NASA Landsat Program, 2010, Landsat MSS scene L3057072_07219790206_MTL, LPGS_11.2.1, USGS, Sioux Falls, 1979-02-06.
Output data for manuscript "Tidal analysis of GNSS reflectometry applied for coastal sea level sensing in Antarctica and Greenland"
<p>We retrieve sea levels in polar regions via GNSS reflectometry (GNSS-R), using signal-to-noise ratio (SNR) observations from eight POLENET GNSS stations. Although geodetic-quality antennas are designed to boost the direct reception from GNSS satellites and to suppress indirect reflections from natural surfaces, the latter can still be used to estimate the sea level in a stable terrestrial reference frame. Here, typical GNSS-R retrieval methodology is improved in two ways, 1) constraining phase-shifts to yield more precise reflector heights and 2) employing an extended dynamic filter to account for the second-order height rate of change (vertical acceleration). We validate retrievals over a 4-year period at Palmer Station (Antarctica), where there is a co-located tide gauge (TG). Because ice contaminates the long-period tidal constituents, we focus on the main tidal species (daily and subdaily), by employing a deseasonalization filter. The difference between sub-hourly GNSS-R retrievals of the ocean surface and TG records has a root-mean-square error (RMSE) of 15.4 cm and a correlation of 0.903, while the tidal prediction has a RMSE of 1.9 cm and a correlation of 0.998. There is excellent millimetric agreement between the two sensors for most eight major tidal constituents, with the exception of luni-solar diurnal (<em>K<sub>1</sub></em>), principal solar (<em>S<sub>2</sub></em>), and luni-solar semidiurnal (<em>K</em><sub>2</sub>) components, which are biased in GNSS-R due to the leakage of the GPS orbital period. We also compare the GNSS-R tidal constituents from seven additional POLENET sites, without co-located TG, to global and local ocean tide models. We find that the root-sum-square-error (RSSE) of eight major constituents varies between 26.0 cm and 56.9 cm for different models. Given that the agreement in tidal constituents between the TG and GNSS-R was better at Palmer Station, we conclude that assimilating the GNSS-R retrievals into tidal models would improve their accuracy in Antarctica and Greenland, provided that care is exercised to avoid the orbital period overtones and also sea ice.</p>
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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)
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DANDI Archive for NWB datasets
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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.