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2,591 results for “remotization”
Self-reported data for Sustainable Development from people living in rural and remote areas
<p>Anonymous self-reported data from people living in rural/remote areas as part of a research project on Sustainable Development.</p> <p>The data collection has been approved by the University of Technology Sydney (UTS HREC REF NO. ETH24-9191).</p> <p>The version 1.0 of the dataset includes 212 valid answers to 40 core questions (+ additional info) collected in 2024 in Saudi Arabia.</p>
Installing a EC Sensor on a remote location.
<p><strong>1.Introduction</strong></p> <p>The objective of this document is to provide a description of the dataset entitled “Installing an EC Sensor on a remote location”.</p> <p>The guidelines on how to use the files are included in this document.</p> <p>The dataset is part of the deliverables D9.4 (First data management plan) and D9.5 (Final data management plan).</p> <p><strong>2.Description of the data</strong></p> <p><strong>2.1.Origin</strong></p> <p>This dataset includes data collected from the experiments related to the task “Installing a EC Sensor on a remote location” as part of the WP8 “validation in the industrial scenario”.</p> <p><strong>2.2.Type</strong></p> <p>The data consists of Eddy Current (EC) measurements.</p> <p><strong>2.3.Formats</strong></p> <p>The acquired data are available in several formats.</p> <p>2.3.1.*.sidata files</p> <p>These files are proprietary format that can be opened with the software “UPecView” supplied by Sensima Inspection (http://www.sensimainsp.com).<br> This software provides an interface familiar to what expected by eddy-current inspectors.</p> <p>Each file includes all the relevant information that may be used for analysis: the measurements and the instrument configuration (ex. Excitation frequency of the probe) is contained in this file.</p> <p>2.3.2.*.csv files</p> <p>The csv files contain an export of the measurements only (without instrument settings); a comma separator is used. Each row is composed of the following variables: Time (s), Signal (in-phase), Signal (out-of-phase), Channel/state, Extra signal (ADC), Encoder coordinate 1 (x), Encoder coordinate 2 (y), Encoder coordinate 3 (z), Encoder error status.</p> <p> </p> <p> </p> <p><strong>3.Measurements indexing</strong></p> <p>Folder</p> <p>Filename</p> <p>Creation date</p> <p>Description</p> <p>Target</p> <p>Location</p> <p> </p> <p>EXP001</p> <p>0001A</p> <p>12.03.2019</p> <p>Calibration block scan</p> <p>Calibration block</p> <p>Seville, Spain</p> <p> </p> <p>EXP001</p> <p>0001B</p> <p>12.03.2019</p> <p>Manual reference scan on weld pipe</p> <p>Calibration block</p> <p>Seville, Spain</p> <p> </p> <p>EXP001</p> <p>0002A</p> <p>12.03.2019</p> <p>Drone overall scan inspection and sensor deployment</p> <p>Cement kiln</p> <p>Seville, Spain</p> <p> </p> <p>EXP001</p> <p>0002B</p> <p>12.03.2019</p> <p>Deployed sensor</p> <p>Cement kiln</p> <p>Seville, Spain</p> <p> </p> <p>EXP001</p> <p>0002C</p> <p>12.03.2019</p> <p>Deployed sensor</p> <p>Cement kiln</p> <p>Seville, Spain</p> <p> </p> <p>EXP001</p> <p>0002D</p> <p>12.03.2019</p> <p>Permanent sensor removal</p> <p>Cement kiln</p> <p>Seville, Spain</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Rethinking the fundamental unit of ecological remote sensing: Estimating individual level plant traits at scale
<p>derived data of leaf and plant structural traits for two National Ecological Observatory Network (NEON) Airborne Observatory Platform (AOP) sites. Dataset contains spatial explicit information for 4.5 million trees, and include: Nitrogen (%mass), Phosphorus (%mass), Leaf mass per area (g m<sup>-2</sup>), diameter at breast height (cm), crown area (m2), tree height (m) and other physical topographic variables (Albedo, Elevation, Slope, Aspect). data are associated to the </p>
Data and code from: Evaluating spatially explicit density estimates of unmarked wildlife detected by remote cameras.
<p>Detection data from American black bears and code used in "Evaluating spatially explicit density estimates of unmarked wildlife detected by remote cameras" published in the Journal of Applied Ecology (Evans & Rittenhouse 2018). Unmarked detection data was collected using remote cameras in northwest Connecticut in 2014, and individual detection data was determined from unique genotypes obtained from non-invasive hair snares constructed at camera sampling locations.</p> <p>EN14.rds contains detection data as an R list:</p> <p>$y (num): J (sites) x K (occasions) matrix containing detection counts</p> <p>$X (int): 2 x J matrix of site coordinates</p> <p>$xlims (num): bounding x-coordinates</p> <p>$ylims (num): bounding y-coordinates</p> <p>$M (int): upper bound for super population of individuals</p> <p>$nTraps (int): number of sampling sites (J)</p> <p>$nReps (int): number of MCMC interations</p> <p>$forest (num): vector of site-specific covariates</p> <p>$mark (int): K x I matrix storing site numbers at which individual (i) was detected on occasion k</p> <p>FullModel.R provides functions used to fit constant density models to unmarked detections incorporating covariates of detection probability.</p> <p>partialID.R provides functions and code used to estimate density from mixtures of marked and unmarked detection data</p> <p>VariableDensity.R provides functions and code used to fit variable density models to unmarked detection data incorporating spatial covariates of density.</p> <p> </p>
Data set: Industrial IoT-driven remote path planning
<p>This compressed file contains data from three different experiments during the IIoT-REPLAN experimentation phase (Industrial IoT-drive remote path planning). IIoT-REPLAN was funded by an open call from the H2020 Fed4FIRE+ project.</p> <p>Contents:</p> <p>A. astar.csv<br> This file contains the timestamp of each movement of the Robot and the uncertainty (d) at each specific time that Switch 1 was checked. The first two columns refer to seconds while the third one is a scalar value. The total duration of the experiment is 62.33 sec and the setup of this experiment is the real time application of the Astar Algorithm with the localization being based only on the sensor of the Robot</p> <p><br> B. Dijkstra.csv <br> In this experiment the full functionality of the switching system proposed in this work is highlighted. </p> <p>C. Cloud.csv<br> In this experiment the localization algorithm and the path planning algorithm are always executed on the cloud.</p> <p>In both B,C experiments the values of each column are explained inside the Dijkstra.csv </p> <p>Also, two pictures of singlie vision-based self localization are included. </p> <p>A more detailed exposition on all of the above can be found at <br> github link : https://github.com/maravger/alphabot-ppl</p>
OpenMapCD: A Multimodal Benchmark Dataset for Change Detection Between Optical Remote Sensing and Map Data
<p><strong>Overview: </strong></p> <ol> <li>OpenMapCD, the <strong>first large-scale multimodal dataset</strong> for change detection on optical remote sensing imagery and map (OpenStreetMap) data, <strong>supporing basic binary change detection and further semantic change detection</strong></li> <li>OpenMapCD is highly geographically diverse, with <strong>1288</strong> benchmark samples with 1024x1024 pixels from <strong>40 </strong>regions across six continents and out-of-distribution data in two areas in Japan</li> <li>Advancing land-cover mapping, binary change detection and semantic change detection tasks, and GIS system updating<br><br></li> </ol> <p><strong>Research Paper: <br></strong></p> <ul> <li>Arxiv paper: <a href="https://arxiv.org/abs/2310.02674v3">https://arxiv.org/html/2310.02674v3</a></li> <li>TGRS paper: <a href="https://ieeexplore.ieee.org/document/10551264">https://ieeexplore.ieee.org/document/10551264</a></li> </ul> <p><strong><br>Project Page:</strong><br>The benchmark code is available at: <a href="https://github.com/ChenHongruixuan/ObjFormer">https://github.com/ChenHongruixuan/ObjFormer</a><br><br><strong>Reference:</strong></p> <pre><code>@ARTICLE{Chen2024ObjFormer, author={Chen, Hongruixuan and Lan, Cuiling and Song, Jian and Broni-Bediako, Clifford and Xia, Junshi and Yokoya, Naoto}, journal={IEEE Transactions on Geoscience and Remote Sensing}, title={ObjFormer: Learning Land-Cover Changes From Paired OSM Data and Optical High-Resolution Imagery via Object-Guided Transformer}, year={2024}, volume={62}, number={}, pages={1-22}, doi={10.1109/TGRS.2024.3410389} }</code></pre>
Data from: Three decades of pastoralist settlement dynamics in the Ethiopian Omo Delta based on remote sensing data
<p>Data from the paper:</p> <p><em>Amos, S., Mengistu, S., Kleinschroth, F. (2021): Three decades of pastoralist settlement dynamics in the Ethiopian Omo Delta based on remote sensing.</em></p> <p>Based on Landsat 5, 7, 8, RapidEye Ortho, and Sentinel-2 satellite imagery, we manually mapped the settlements of the Dasanech people in the most populated parts of the Omo River Delta in Ethiopia from 1992 to 2019 using QGIS. We used the data to answer the following questions: (1) How have pastoralist settlements in the delta changed in extent and persistence over the past three decades? And (2) how have the settlements changed structurally during the construction, filling, and operation of Gibe III Dam?</p> <p>We conducted two independent remote sensing analyses. Firstly, we used Landsat data from 1992 to 2019 to track land that is inhabited by pastoralists people within the evergreen part of the Delta. Secondly, the higher spatial resolution of the RapidEye Ortho (5m) and Sentinel-2 (10m) images allowed the detailed identification of settlements as well as infrastructure (tin-roof houses and road) in the Delta during a shorter period from 2009 to 2019. <strong>For more information on the data, please refer to the README.txt or the paper.</strong></p>
Data related to: Hippocampal ripples and their coordinated dialogue with the default mode network during recent and remote recollection, Norman et al. (2021)
<p>This dataset contains intra-cranial EEG recordings and analysis code related to the paper: "Hippocampal ripples and their coordinated dialogue with the default mode network during recent and remote recollection" by Norman et al. (https://doi.org/10.1016/j.neuron.2021.06.020)<br> The study investigates the role of hippocampal ripples in the human brain during retrieval of recent and remote autobiographical memories and semantic facts. The intracranial recordings underwent standard preprocessing as described in the paper and were stored in EEGLAB datasets. The analysis code that accompanies the dataset implements the main analyses described in the paper.</p> <p>The dataset includes the following zip files:</p> <ul> <li>iEEG data and main analysis code: <ul> <li>“Norman_et_al_2021_iEEG_data_and_code_1.zip" </li> <li>“Norman_et_al_2021_iEEG_data_and_code_2.zip" </li> </ul> </li> <li>Patients' anatomical data: <ul> <li>“Norman_et_al_2021_Freesurfer.zip”</li> </ul> </li> <li>Additional toolboxes (developed by others): <ul> <li>“MATLAB_toolboxes.zip”</li> </ul> </li> </ul> <p>The code is written primarily in Matlab (version R2018b) and runs on a desktop computer with a 3.4Ghz Intel Core i7-6700 CPU with 64GB RAM. Matlab's Signal Processing Toolbox is required, as well as EEGLAB, Unfold toolbox, and several other open-source toolboxes.</p>
Remote and Local Processes Controlling Decadal Sea Ice Variability in the Weddell Sea
<p>These datasets are based on the 270-yr simulation results of CTR and SAOWED experiments, which include annual average of atmospheric and ocean variables used to make figures in a paper by Morioka and Behera (2021).</p>
Remote-sensing measurements and model simulations of peroxyacetyl nitrate (PAN)
<p>Ground-based FTIR and IASI-A and -B measurements of PAN, supplemented with GEOS-Chem simulations.</p> <p>End users of these data sets are invited to contact the authors to make sure they are using the data properly and check about the possible availability of more recent products.</p>
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>
Brilliantia kiribatiensis, a new genus and species of Cladophorales (Chlorophyta) from the remote coral reefs of the Southern Line Islands, Pacific Ocean
<p>Data associated with the study "<em>Brilliantia kiribatiensis</em>, a new genus and species of Cladophorales (Chlorophyta) from the remote coral reefs of the Southern Line Islands, Pacific Ocean".</p> <p><strong>ITS.fasta, ITS_bmge.fasta, LSU.fasta, LSU_bmge.fasta, SSU.fasta, SSU_bmge.fasta: </strong>SSU rDNA, LSU rDNA and rDNA ITS sequences used in phylogenetic analyses. Sequences of Brilliantia kiribatiensis were added to updated phylogenetic datasets used previously (Leliaert et al. 2007a, Leliaert et al. 2009b), aligned in MAFFT v7.215 (Katoh and Standley 2013), and stripped of hypervariable sites in BMGE v1.1 (Criscuolo and Gribaldo 2010) by using the -h 0.4 -g 0.35 parameters. Alignments were visually checked and concatenated in Seaview v4.4.2.</p> <p><strong>concatenated_SSU_ITS_LSU.fasta</strong>: concatenated alignment with following partitions: SSU: 1-1797, ITS1+5.8S+ITS2: 1798-3335, LSU: 3336-3926.</p> <p><strong>Table_S1_sequence_sources.xls: </strong>GenBank accessions, sample isolate codes and sites of collection for sequences included in the concatenated phylogenetic data set.</p> <p><strong>Table S2. </strong>Percent cover of different algal groups in 1 m2 photoquadrats. Algal groups are identified to genus level for fleshy macroalgae or functional group for turf algae, branched red algae, crustose coralline algae, and cyanobacteria.</p> <p><strong>Table SX.</strong> Morphological measurements of <em>Brilliantia kiribatiensis</em>.</p>
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>
Sampling metadata for the publication: "Deep-sea sponge derived environmental DNA analysis reveals demersal fish biodiversity of a remote Arctic ecosystem "
<p>Meta data of sampling location, time and depth of eDNA samples used in the study: "Deep-sea sponge derived environmental DNA analysis reveals demersal fish biodiversity of a remote Arctic ecosystem". As well as taxonomic identification of sponges, their microbial abundance and growth form.</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>
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>
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.