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4,276 results for “Estimation”
Point-Quarter Distance and Dimension Measurements to Calculate Shrub Density and Estimate Shrub ANPP in a Chihuahuan Desert Creosote Shrubland at the Sevilleta National Wildlife Refuge, New Mexico
In an effort to better quantify NPP of Creosotebush in the Five-Points region, it was decided to test the Point-Quarter method against the standard 1-m2 quadrat method that has been in use since 1998. Transects were laid out across the 5 mammal trapping webs as well as across burned and unburned plots of the Mixed Shrub site (MS). Repeated measures of the same bushes are performed seasonally. Whole shrubs of various size classes are collected, sorted, and weighed to develop regressions for biomass. Purpose: Data was collected initially to determine density and dimensions of creosote bush in the Five points area on core rodent webs and on burned and unburned plots following the 2003 prescribed burn. It was decided to expand the project by continuing measurements through time to quantify the change in shrub size and with simultaneous harvest of shrubs to measure NPP.
ReFAB 10,000 Year Statistical Estimate of Aboveground Woody Biomass, Midwest US, Level 2
How terrestrial biomass changed before the advent of industrial society is a major gap in our understanding of the Earth's carbon cycle. Here, we archive data used to reconstruct 10,000 years of aboveground woody biomass across the US Upper Midwest using statistical models based on historical forest surveys and fossil pollen assemblages. From our analyses we document a 5,000 year long carbon sink into vegetation, primarily caused by the range expansion of two late-successional species into the region during the late Holocene. The importance of such large slow-growing tree species in storing carbon during the pre-industrial past argues for protecting similar species in wild forests today. This material is based upon work supported by the National Science Foundation under grants #DEB-1241874, 1241868, 1241870, 1241851, 1241891, 1241846, 1241856, 1241930.
Large-Scale Dataset for Radio Frequency based Device-Free Crowd Estimation
<p>This dataset serves to estimate the status, in particular the size, of a crowd given the impact on radio frequency communication links within a wireless sensor network. To quantify this relation, signal strengths across sub-GHz communication links are collected at the premises of the Tomorrowland music festival. The communication links are formed between the network nodes of wireless sensor networks deployed in three of the festival's stage environments. </p> <p>The table below lists the eighteen dataset files. They are collected at the music festival's 2017 and 2018 editions. There are three environments, labeled: ‘Freedom Stage 2017’, ‘Freedom Stage 2018’, and ‘Main Comfort 2018’. Each environment has both 433 MHz and 868 MHz data. The measurements at each environment were collected over a period of three festival days. The dataset files are formatted as Comma-Separated Values (CSV).</p> <pre><code class="language-markdown">| Dataset file | Reference file | Number of messages | |-------------------- |------------------------- |-------------------- | | free17_433_fri.csv | None | 393 852 | | free17_868_fri.csv | None | 472 202 | | free17_433_sat.csv | free17_transactions.csv | 996 033 | | free17_868_sat.csv | free17_transactions.csv | 1 023 059 | | free17_433_sun.csv | free17_transactions.csv | 1 007 066 | | free17_868_sun.csv | free17_transactions.csv | 1 036 456 | | free18_433_fri.csv | None | 765 024 | | free18_868_fri.csv | None | 757 657 | | free18_433_sat.csv | free18_transactions.csv | 711 438 | | free18_868_sat.csv | free18_transactions.csv | 714 390 | | free18_433_sun.csv | free18_transactions.csv | 648 329 | | free18_868_sun.csv | free18_transactions.csv | 656 290 | | main18_433_fri.csv | None | 791 462 | | main18_868_fri.csv | None | 908 407 | | main18_433_sat.csv | main18_counts.csv | 863 666 | | main18_868_sat.csv | main18_counts.csv | 884 682 | | main18_433_sun.csv | main18_counts.csv | 903 862 | | main18_868_sun.csv | main18_counts.csv | 894 496 |</code></pre> <p>In addition to the datasets and reference files, a software example is provided to illustrate the data use and visualise the initial findings and relation between crowd size and network signal strength impact.</p> <p>In order to use the software, please retain the following file structure: </p> <pre><code class="language-markdown">. ├── data ├── data_reference ├── graphs └── software</code></pre> <p>The peer-reviewed data descriptor for this dataset has now been published in MDPI Data - an open access journal aiming at enhancing data transparency and reusability, and can be accessed here: <a href="https://doi.org/10.3390/data5020052">https://doi.org/10.3390/data5020052</a>.<br> Please cite this when using the dataset.</p>
Data from paper: "Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates"
<p>Data from the paper:</p> <p>Dalagnol, R. <em>et al.</em> Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates. <em>Sci Rep</em> <strong>11, </strong>1388 (2021). https://doi.org/10.1038/s41598-020-80809-w</p> <p>Link: https://www.nature.com/articles/s41598-020-80809-w</p> <p> </p> <p>This repository contains:</p> <p>1) Data frame with data from static and dynamic gaps used in Figure 2 (Dalagnol_2020_Data_Multitemporal_gaps.csv). Each row is the aggregated measurement at 5-km resolution. The site component referes to the five site studied with multitemporal data. Site order from 1 to 5 is DUC, TAP, FN1, BON and TAL.</p> <p>2) Data frame with data from static gaps and environmental factors used in Table 1, Figure 3, 4, 5 (Dalagnol_2020_Data_Singledate_gaps_Modeling.csv). Each row is the aggregated measurement of one site observed by airborne lidar data.</p> <p>3) Raster file at 5-km resolution with dynamic gap fraction estimates presented in Figure 5 (dynamic_gap_fraction_amazon.tif).</p> <p> </p> <p>If you need anything else, please contact the corresponding author: Ricardo Dalagnol (ricds@hotmail.com).</p>
Two-minute averaged net community production in seawater estimated along the Antarctic Circumnavigation Expedition during the austral summer of 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>During the Antarctic Circumnavigation Expedition (ACE) in the austral summer of 2016/2017, seawater O2/Ar ratios were measured underway from the ship's flow-through seawater system. High-resolution net community production (NCP) in units of mmol O2 m-2 day-1 was derived from delta O2/Ar and National Centers for Environmental Prediction (NCEP) reanalysis winds followed by a linear correction for sea-ice.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_ncp_linear_correction.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> <li>change_log.txt, metadata, text</li> </ul> <p><strong>Change log</strong></p> <p>v1.1 - data reprocessed using different version of sea ice.</p> <ul> <li>data file reprocessed</li> <li>README updated to reflect change in processing</li> <li>change log added to dataset</li> </ul> <p>v1.0 - initial release of dataset</p>
Dataset of "Advanced machine learning techniques for State-of-Health estimation in lithium-ion batteries: A comparative study"
This research focuses on State-of-Health (SOH) estimation of lithium-ion (Li-ion) batteries to enhance lifespan and reliability. Using Samsung INR18650-35E cells, 600 cycles were analyzed with machine learning (ML) techniques, including Gaussian Process Regression (GPR), Support Vector Regression (SVR), Feed-Forward Neural Network (FFNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). Input features from charging and discharging cycles were selected with Pearson Correlation Analysis (PCA) and Exhaustive Search (ES) to optimize inputs for each ML method. Models were tested on datasets of varying sizes to evaluate performance and overfitting, including an experiment where SOH estimation of one battery was performed using training data from another. The findings highlight each model's strengths and limitations, guiding their application in battery health prediction.
Figure data and code used in Inconsistent definitions of GDP: Implications for estimates of decoupling
<p>Figure code in R and underlying data to reproduce all figures in the article "Inconsistent definitions of GDP: Implications for estimates of decoupling".</p>
Land Subsidence in Iran Estimated from a Nationwide InSAR Analysis of Sentinel-1 Observations 2014-2020
<p><strong>Overview</strong></p> <p>This dataset is a supplementary material to the paper "Haghighi and Motagh, 2024. Uncovering the Impacts of Depleting Aquifers: A Remote Sensing Analysis of Land Subsidence in Iran, Science Advances". It provides detailed insights into land subsidence across Iran, derived from Sentinel-1 InSAR observations. This dataset is intended for use by researchers, policymakers, and practitioners interested in land subsidence, groundwater depletion, and related fields.</p> <p><strong>Dataset Contents</strong></p> <ol> <li><em>Iran_subsidence_rate_2014-2020_Sentinel-1_InSAR_desc_v1.0.0.tif</em><br>Annual rate of land subsidence in Iran over the six-year period, projected from satellite Line of Sight to vertical.</li> <li><em>Iran_subsidence_rate_2014-2020_Sentinel-1_InSAR_desc_v1.0.0.jpg</em><br>Subsidence map of Iran visualized as jpg</li> <li><em>Iran_subsidence_seasonal_amplitude_2014-2020_Sentinel-1_InSAR_desc_v1.0.0.tif</em><br>Amplitude of seasonal ground deformation, projected from satellite Line of Sight to vertical.</li> <li><em>Iran_subsidence_mask_2014-2020_Sentinel-1_InSAR_desc_v1.0.0.tif</em><br>Land subsidence mask, based on the annual rate of land subsidence.</li> </ol> <p><strong>Methodology</strong></p> <p>The data were derived using Interferometric Synthetic Aperture Radar (InSAR) analysis of Sentinel-1 satellite imagery. The original SAR data includes more than 6000 scenes of Sentinel-1 images collected across 10 descending tracks between 2014 and 2020. The details can be found in the original paper.</p> <p><strong>Acknowledgements</strong></p> <p>We acknowledge the European Space Agency (ESA) for providing the Sentinel-1 satellite data used in this analysis.</p> <p><strong>License</strong></p> <p>This dataset is shared under CC BY 4.0 license, which allows for reuse and distribution, provided that the original authors and source are credited.</p> <p><strong>Citation</strong></p> <p>Please cite the following if you use this dataset:</p> <ol> <li>Haghighi and Motagh, 2024. Uncovering the Impacts of Depleting Aquifers: A Remote Sensing Analysis of Land Subsidence in Iran, Science Advances.</li> <li>Haghighi and Motagh, 2024. Land Subsidence in Iran Estimated from a Nationwide InSAR Analysis of Sentinel-1 Observations 2014-2020. Zenodo. doi:10.5281/zenodo.10815578</li> <li>The dataset contains modified Copernicus Sentinel data 2014-2020, processed by ESA.</li> </ol> <p><strong>Contact</strong></p> <p>Please contact Mahmud Haghighi for inquiries related to this dataset.<br>https://www.ipi.uni-hannover.de/en/haghighi</p>
Modelled gridded population estimates for the Kasaï-Oriental Province in the Democratic Republic of Congo (2024) version 4.2
<h2><strong>Content</strong></h2> <p>This repository contains the input data and scripts used to create the modeled gridded population estimates for Kasaï-Oriental Province in the Democratic Republic of Congo. It also includes the grid-cell posterior distributions and scripts to aggregate them within user-defined geographic boundaries.</p> <p> In particular, this repository contains two compressed files (.zip):</p> <p><strong>1. <code>population_estimates.zip</code></strong></p> <ul> <li>Includes raster files (<code>.tif</code>) with summaries of population count posterior predictions at the grid-cell level, specifically the mean, median, lower credible interval, and upper credible interval.</li> <li>Includes spatial files (<code>.gpkg</code>) with summaries of population count posterior predictions at the health-area and health-zone levels, specifically the mean, median, lower credible interval, and upper credible interval.</li> </ul> <p><strong>2. <code>population_model.zip</code></strong></p> <p>This directory comprises five subdirectories with scripts, input data, and output data necessary to replicate the population model:</p> <ul> <li><code><strong>01_model_stan</strong></code>: Contains the Stan model, input data, and an R script (<code>01_model_stan.R</code>) with a function to run the model.</li> <li><code><strong>02_model_run</strong></code>: Includes an R script (<code>02_model_run.R</code>) for running the model, along with output data.</li> <li><code><strong>03_model_evaluate</strong></code>: Features a Quarto report template (<code>03_model_evaluate.qmd</code>) and model evaluation summary files(.pdf).</li> <li><code><strong>04_predict_posterior</strong></code>: Provides R scripts (<code>04_predict_posterior.R</code> and <code>04_predict_run.R</code>) for generating predictions, along with input and output data, namely the posterior predictions files (.rds).</li> <li><code><strong>05_aggregate_posterior</strong></code>: Contains R scripts (<code>05_aggregate_posterior.R</code> and <code>05_aggregate_run.R</code>) and associated input and output data, namely the population count posterior summaries as presented in the file <code>population_estimates.zip</code> .</li> </ul> <p>The work was carried out in <code>R</code> (version 4.4.0), with the packages <code>tidyverse</code> (version 2.0.0), <code>terra</code> (version 1.7-78), <code>sf</code> (version 1.0-16), <code>furrr</code> (version 0.3.1), <code>doParallel</code> (version 1.0.17), <code>foreach</code> (version 1.5.2), <code>rstudioapi</code> (version 0.16.0), and <code>rstan</code> (version 2.32.6), on macOS Sequoia (version 15.1.1). While the scripts are designed to be portable, minor adjustments may be required for compatibility with other operating systems.</p> <h2><strong>Important</strong></h2> <p>This version includes changes in the STAN model <code>10h_survey_survey_covariate_building_random_effect_hierarchy_building_covariate_density_fixed_effect_hierarchy_density.stan</code>. Consequentely, all the files generated in the previous versions are now changed.</p> <p> </p> <p>For inquiries regarding the model and the data, please contact Gianluca Boo at gianluca.boo@soton.ac.uk.</p>
Datasets for "A unified framework to estimate the origins of atmospheric moisture and heat using Lagrangian models"
<p>This repository contains the post-processed model outputs from HAMSTER v1.2.0 as used in the following paper: </p> <p>Keune, J., Schumacher, D. L., and Miralles, D. G.: A unified framework to estimate the origins of atmospheric moisture and heat using Lagrangian models, Geosci. Model Dev., 15, 1875–1898, https://doi.org/10.5194/gmd-15-1875-2022, 2022.<br> <br> The data set contains (1) global validation statistics for the three fluxes (evaporation, precipitation, sensible heat), and (2) the climatological source regions of precipitation and heat for Denver, Beijing and Windhoek. The former are found in the directory 'validation/global', and the latter are found in the directories '1001' (Denver), '3001' (Beijing) and '5002' (Windhoek). Multiple experiments were performed to assess the uncertainty of the source regions. Thus, multiple files exist, that show the same variables but for multiple experiments (indicated by the names "ALLPBL", "RH-10-20", "SOD08-SCH19", "SCH20", "FAS19" in the file name). For the moisture source regions, the uncertainty of the attribution methodology was assessed; these are indicated by the different folders, i.e. 'linear_upscaled' and 'random2_upscaled'. For each city and each experiment, the climatologically averaged source regions ('_mean.nc') and the climatologically averaged individual backward day contributions ('_bwmean.nc') are provided. Data sets are in the netCDF format and contain metadata following the CF convention.</p>
Downscaled 8km March Snow Water Equivalent Estimates for the Western US, 1901-2010
<p>Downscaled estimates of March mean snow water equivalent at approximately 8km resolution across the western United States for the years 1901-2010. Data downscaled from the CERA-20c reanalysis using UA-SWE daily observations. Downscaled data using both the CERA-20c ensemble mean as well as each individual ensemble member as predictors are included. Units are in millimeters of snow water equivalent.</p>
RiceFloodIT: Water Management in the Italian Rice Paddies Estimated from MODIS data
<p>This repository includes two datasets used in Ranghetti et al. (2018) and Ranghetti & Boschetti (2022) to analyse the magnitude of a decreasing trend in the extent of submerged rice paddies during the rice-sowing period in the Italian rice district: methods used to generate these data from MODIS remote sensing imagery are described in these papers.</p> <ul> <li><strong>ffavg_2021.csv</strong>: this dataset includes values of yearly FF<sub>avg</sub> (averaged Flooding Fraction) at pixel level. Each record represent the FF<sub>avg</sub> value of a specific pixel in a specific year. <ul> <li><strong>x</strong> and <strong>y</strong> identifies the latitude and longitude of each record (in UTM32 coordinates);</li> <li><strong>subdistrict</strong> represent the sub-district ID of each pixel ("A" to "G");</li> <li><strong>year</strong> is the year whose each record refers to;</li> <li><strong>ff</strong> is the FFavg value (range 0-1);</li> <li><strong>count</strong> is the number of MODIS images used to generate each FF<sub>avg</sub> aggregated value.</li> </ul> </li> <li><strong>ws_2021.csv</strong>: this dataset includes values of WS (proportion of Water-Seeded rice surface) at sub-district and district levels. <ul> <li><strong>subdistrict</strong> represent the sub-district ID of each record ("A" to "G", plus "all" which identifies values aggregated at district level);</li> <li><strong>year</strong> is the year whose each record refers to;</li> <li><strong>ws</strong> is the WS value (range 0-1);</li> <li><strong>count</strong> is the number of pixels used to generate each WS aggregated record.</li> </ul> </li> </ul> <p>Current data version (2021.01) includes estimated values in the period 2000-2021.</p> <p>References:</p> <p>Ranghetti, Luigi, Elisa Cardarelli, Mirco Boschetti, Lorenzo Busetto and Mauro Fasola. 2018. “Assessment of Water Management Changes in the Italian Rice Paddies from 2000 to 2016 Using Satellite Data: A Contribution to Agro-Ecological Studies.” <em>Remote Sensing</em> 10 (3). doi:<a href="https://doi.org/10.3390/rs10030416">10.3390/rs10030416</a>.</p> <p>Ranghetti, Luigi and Mirco Boschetti. 2022. “Updated trends of water management practice in the Italian rice paddies from remotely sensed imagery.” <em>European Journal of Remote Sensing</em> 55 (1), pp. 1-9. doi:<a href="https://doi.org/10.1080/22797254.2021.2002726">10.1080/22797254.2021.2002726</a>.</p>
Annotations to direct and indirect image rotation estimation methods of orthopedic X-ray images
<p>The annotation file contains labels for AP wrist images of the MURA dataset on the center line of the radius bone. The annotations are stored in json format. For each annotated image file of the MURA dataset an entry is provided with the coordinates of the start and end point of the radius' center line.</p>
shallowgroundwater: estimated zones with shallow groundwater in the Flemish region of Belgium
<p><strong>General</strong></p> <p>The data source <code>shallowgroundwater</code> is a geospatial dataset of multipolygons that represent the estimated areas, in the Flemish region of Belgium, where the mean lowest groundwater level (MLW; Knotters & Van Walsum, 1997; Van Heesen, 1970) is less than approximately 2 m below soil surface (hence, “shallow” groundwater). We expect groundwater dependent species and communities to be present within these areas. Outside these areas we assume they are groundwater independent. We combined several data sources in order to estimate these areas.</p> <p><strong>Compilation of the data source</strong></p> <p>We compiled the dataset through an iterative process of adding specific data sources (referred in the description of attributes below), followed by validation steps based on both the actual presence of groundwater dependent habitat types or regionally important biotopes (Natura 2000 habitat map) and in situ measurements of groundwater levels (<a href="http://data.inbo.be/watina/Pages/Common/Default.aspx">Watina+</a> database). The coverage of these validation data by <code>shallowgroundwater</code> was 96.9% and 98.6% respectively.</p> <p>Most steps to compile the data source were done manually using QGIS. Final steps were done in R; see R-code in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/1b004e1/src/update_shallowgroundwater">'n2khab-preprocessing' at commit 1b004e1</a>.</p> <p><strong>Detailed properties</strong></p> <p>The data source is a GeoPackage with a single spatial multipolygon layer <code>shallowgroundwater</code> in the ‘Belge 1972 / Belgian Lambert 72’ coordinate reference system (EPSG-code <a href="https://epsg.io/31370">31370</a>).</p> <p>All attributes are boolean (true/false), each indicating if a polygon was selected from the corresponding data source by applying a set of criteria. Multiple attributes can be true for a given polygon. In order to reduce file size, polygons were dissolved by each unique combination of the values of all attributes. Hence the dataset consists of multipolygons (also multipart polygons) rather than single (part) polygons. The different attributes of this dataset reveal for each polygon (1) the data source(s) we relied on and (2) the selection criteria we applied to judge if the mean lowest groundwater level is less than approximately 2 m below soil surface. As far as possible, we reference each used data source in the description of attributes below. If one is interested in the original polygons of each datasource, selections can be made by consulting the referenced data sources and applying the specified criteria.</p> <p>These are the attributes:</p> <ul> <li> <p><code>geomorph_wcoast</code>:</p> <ul> <li> <p>source: Cosyns et al. (2019)</p> </li> <li> <p>description: polygon belonging to geomorphological entities that are expected to harbour groundwater dependent types, and thus to exhibit shallow groundwater levels. It mainly concerns dune slacks, mud flats and salt marshes.</p> </li> <li> <p>selection: <code>"Code" IN ('ms', 'msl', 'sm', 'ss', 'ys', 'ysl', 'yfs') OR ("Code" = 't' and "Subtype" like '%vlakte%')</code> with ms = medium old dune slack, msl = leveled medium old dune slack, sm = mud flat, ss = salt marsh, ys = young dune slack, ysl = leveled young dune slack, yfs = young frontal dune (intruded), (<code>"Code" = 't' AND "Subtype" like '%vlakte%'</code>) = fossil beach</p> </li> </ul> </li> <li> <p><code>anthrop_gwdep:</code></p> <ul> <li> <p>source: <code>soilmap_simple</code> (<a href="https://doi.org/10.5281/zenodo.3732903">10.5281/zenodo.3732903</a>) as derived from the <a href="https://www.dov.vlaanderen.be/geonetwork/srv/dut/catalog.search#/metadata/5c129f2d-4498-4bc3-8860-01cb2d513f8f">digital soil map of the Flemish Region</a> (version <code>soilmap_2017-06-20</code>; <a href="https://doi.org/10.5281/zenodo.3387008">10.5281/zenodo.3387008</a>); <code>habitatmap_terr</code> (<a href="https://doi.org/10.5281/zenodo.3468948">10.5281/zenodo.3468948</a>) as derived from the Natura 2000 habitat map of Flanders (<a href="https://doi.org/10.5281/zenodo.3354381">10.5281/zenodo.3354381</a>)</p> </li> <li> <p>description: zones located within a 100 m buffer around (almost) everywhere groundwater dependent habitat types (or regionally important biotopes) ánd situated within zones classified as “anthropogenic” areas within the soil map. Within the zones of the soil map that are designated as “anthropogenic”, we lack information on soil characteristics. However, (almost) everywhere groundwater dependent types are present in these zones according to the Natura 2000 habitat map of Flanders, implying shallow groundwater levels. By including 100 m buffer zones around these types, restricted to the anthropogenic zones of the soil map, we consider the combined areas to have (potentially) shallow groundwater levels. So in practice, we first select the habitatmap polygons with (almost) everywhere groundwater dependent types that intersect the anthropogenic soil polygons, we buffer them, and then clip the result by the anthropogenic soil polygons.</p> </li> <li> <p>selection: see <a href="https://github.com/inbo/n2khab-preprocessing/pull/61">https://github.com/inbo/n2khab-preprocessing/pull/61</a> for the adopted workflow</p> <ul> <li> <p>from <code>soilmap_simple</code>: <code>bsm_mo_soilunitype starts with 'O'</code></p> </li> <li> <p>from <code>habitatmap_terr</code>: (almost) everywhere groundwater dependent types only; list of this category of types is available through <a href="https://inbo.github.io/n2khab">n2khab</a> R-package.</p> </li> </ul> </li> </ul> </li> <li> <p><code>narrowanthrop_gwdep</code>:</p> <ul> <li> <p>source: <code>soilmap_simple</code> (<a href="https://doi.org/10.5281/zenodo.3732903">10.5281/zenodo.3732903</a>) as derived from the <a href="https://www.dov.vlaanderen.be/geonetwork/srv/dut/catalog.search#/metadata/5c129f2d-4498-4bc3-8860-01cb2d513f8f">digital soil map of the Flemish Region</a> (version <code>soilmap_2017-06-20</code>; <a href="https://doi.org/10.5281/zenodo.3387008">10.5281/zenodo.3387008</a>); <code>habitatmap_terr</code> (<a href="https://doi.org/10.5281/zenodo.3468948">10.5281/zenodo.3468948</a>) as derived from the Natura 2000 habitat map of Flanders (<a href="https://doi.org/10.5281/zenodo.3354381">10.5281/zenodo.3354381</a>)</p> </li> <li> <p>description: narrow zones classified as “anthropogenic” areas within the soil map that include (almost) everywhere groundwater dependent habitat types (or regionally important biotopes). Regarding the anthropogenic soil type polygons, it appears that the narrow ones containing (almost) everywhere groundwater dependent types are interesting to include as a whole as zones with supposed shallow groundwater levels. Hence we select them as a whole instead of selecting buffers around the polygons with (almost) everywhere groundwater dependent types (cfr. <code>anthrop_gwdep</code> and <code>dunes_gwdep</code>). An appropriate algorithm selects meaningful polygons, based on a “thinness” criterion and the fraction of (almost) everywhere groundwater dependent types that are present within the polygons.</p> </li> <li> <p>selection: see https://github.com/inbo/n2khab-preprocessing/pull/61</p> </li> </ul> </li> <li> <p><code>drainage</code>:</p> <ul> <li> <p>source: <code>soilmap_simple</code> (<a href="https://doi.org/10.5281/zenodo.3732903">10.5281/zenodo.3732903</a>) as derived from the <a href="https://www.dov.vlaanderen.be/geonetwork/srv/dut/catalog.search#/metadata/5c129f2d-4498-4bc3-8860-01cb2d513f8f">digital soil map of the Flemish Region</a> (version <code>soilmap_2017-06-20</code>; <a href="https://doi.org/10.5281/zenodo.3387008">10.5281/zenodo.3387008</a>)</p> </li> <li> <p>description: Drainage classification is based on a combination of groundwater depth, soil permeability, presence of impermeable layers, soil depth and topography (see Van Ranst & Sys, 2000). </p> </li> <li> <p>selection: <code>bsm_mo_drain in ('c-d', 'd', 'e', 'f', 'g', 'h', 'i', 'e-f', 'e-i', 'h-i')</code>; these are soils that are at least moderately gleyic or wet. </p> </li> </ul> </li> <li> <p><code>dunes_gwdep</code>:</p> <ul> <li> <p>source: <code>soilmap_simple</code> (<a href="https://doi.org/10.5281/zenodo.3732903">10.5281/zenodo.3732903</a>) as derived from the <a href="https://www.dov.vlaanderen.be/geonetwork/srv/dut/catalog.search#/metadata/5c129f2d-4498-4bc3-8860-01cb2d513f8f">digital soil map of the Flemish Region</a> (version <code>soilmap_2017-06-20</code>; <a href="https://doi.org/10.5281/zenodo.3387008">10.5281/zenodo.3387008</a>); <code>habitatmap_terr</code> (<a href="https://doi.org/10.5281/zenodo.3468948">10.5281/zenodo.3468948</a>) as derived from the Natura 2000 habitat map of Flanders (<a href="https://doi.org/10.5281/zenodo.3354381">10.5281/zenodo.3354381</a>)</p> </li> <li> <p>description: zones located within a 100 m buffer around (almost) everywhere groundwater dependent habitat types (or regionally important biotopes) ánd situated within zones classified as “dunes” areas within the soil map. Within the zones of the Belgian soil map that are designated as “dunes”, we lack information on soil characteristics. However, (almost) everywhere groundwater dependent types are present in these zones according to the Natura 2000 habitat map of Flanders, implying shallow groundwater levels. By including 100 m buffer zones around these types, restricted to the “dunes” of the soil map, we consider the combined areas to have shallow groundwater levels. So in practice, we first select the habitatmap polygons with (almost) everywhere groundwater dependent types that intersect the “dunes” polygons, we buffer them, and then clip the result by the “dunes” polygons.</p> </li> <li> <p>selection: see https://github.com/inbo/n2khab-preprocessing/pull/61</p> <ul> <li> <p>from <code>soilmap_simple</code>: <code>bsm_mo_soilunitype = 'X'</code></p> </li> <li> <p>from <code>habitatmap_terr</code>: (almost) everywhere groundwater dependent types only; list of this category of types available through <a href="https://inbo.github.io/n2khab">n2khab</a> R-package.</p> </li> </ul> </li> </ul> </li> <li> <p><code>peat_profile</code>:</p> <ul> <li> <p>source: <code>soilmap_simple</code> (<a href="https://doi.org/10.5281/zenodo.3732903">10.5281/zenodo.3732903</a>) as derived from the <a href="https://www.dov.vlaanderen.be/geonetwork/srv/dut/catalog.search#/metadata/5c129f2d-4498-4bc3-8860-01cb2d513f8f">digital soil map of the Flemish Region</a> (version <code>soilmap_2017-06-20</code>; <a href="https://doi.org/10.5281/zenodo.3387008">10.5281/zenodo.3387008</a>)</p> </li> <li> <p>description: variant of the soil profile indicates a superficial peaty cover, mostly on gleyic or permanently water saturated soil with or without profile development (‘(v)’), eventually combined with strong anthropogenic influence (‘(o)’)</p> </li> <li> <p>selection: <code>bsm_mo_profvar in ('(o)(v)', '(v)')</code></p> </li> </ul> </li> <li> <p><code>peat_substr</code>:</p> <ul> <li> <p>source: <code>soilmap_simple</code> (<a href="https://doi.org/10.5281/zenodo.3732903">10.5281/zenodo.3732903</a>) as derived from the <a href="https://www.dov.vlaanderen.be/geonetwork/srv/dut/catalog.search#/metadata/5c129f2d-4498-4bc3-8860-01cb2d513f8f">digital soil map of the Flemish Region</a> (version <code>soilmap_2017-06-20</code>; <a href="https://doi.org/10.5281/zenodo.3387008">10.5281/zenodo.3387008</a>)</p> </li> <li> <p>description: soil substrate (layer underlying superficial layer, and lithologically diverging from it) consists of peat material starting at small (less than 75 cm; ‘v’) or moderate depths (75-125 cm; ‘(v)’), or a combination of the previous (‘v-’)</p> </li> <li> <p>selection: <code>bsm_mo_substr in ('(v)', 'v', 'v-')</code></p> </li> </ul> </li> <li> <p><code>peat_parentmat</code>:</p> <ul> <li> <p>source: <code>soilmap_simple</code> (<a href="https://doi.org/10.5281/zenodo.3732903">10.5281/zenodo.3732903</a>) as derived from the <a href="https://www.dov.vlaanderen.be/geonetwork/srv/dut/catalog.search#/metadata/5c129f2d-4498-4bc3-8860-01cb2d513f8f">digital soil map of the Flemish Region</a> (version <code>soilmap_2017-06-20</code>; <a href="https://doi.org/10.5281/zenodo.3387008">10.5281/zenodo.3387008</a>)</p> </li> <li> <p>description: parent material contains a mixture of at least 30% of peaty material</p> </li> <li> <p>selection: <code>bsm_mo_parentmat = 'v'</code></p> </li> </ul> </li> <li> <p><code>peat_texture</code>:</p> <ul> <li> <p>source: <code>soilmap_simple</code> (<a href="https://doi.org/10.5281/zenodo.3732903">10.5281/zenodo.3732903</a>) as derived from the <a href="https://www.dov.vlaanderen.be/geonetwork/srv/dut/catalog.search#/metadata/5c129f2d-4498-4bc3-8860-01cb2d513f8f">digital soil map of the Flemish Region</a> (version <code>soilmap_2017-06-20</code>; <a href="https://doi.org/10.5281/zenodo.3387008">10.5281/zenodo.3387008</a>)</p> </li> <li> <p>description: soil consists of plain peat material </p> </li> <li> <p>selection: <code>bsm_mo_tex in ('V-E', 'V')</code></p> </li> </ul> </li> <li> <p><code>phys_system</code>:</p> <ul> <li> <p>source: Fysische systeemkaart - Gegeneraliseerde bodemkaart voor Vlaanderen (<a href="https://www.geopunt.be/catalogus/datasetfolder/80a496cc-aa90-4ff9-8eb8-c89418f2407d">here</a> available at geopunt.be). Lhermitte & Honnay (1994).</p> </li> <li> <p>description: polygons designated as seepage areas where groundwater is supposed to gather after having infiltrated elsewhere (infiltration areas) and being transported through the landscape (passage areas). The classification of infiltration/passage/seepage areas is entirely based on the soil map polygons of Flanders (<a href="https://doi.org/10.5281/zenodo.3387008">10.5281/zenodo.3387008</a>). Each polygon is attributed to one of these classes by considering soil, topographical and hydrological characteristics. In the seepage areas, we consider the groundwater to be shallow below topography.</p> </li> <li> <p>selection: <code>INKWEL = 3</code></p> </li> </ul> </li> <li> <p><code>zwin</code>:</p> <ul> <li> <p>source: Open Street Map, consulted 2021-10-06 by QGIS plugin QuickOSM</p> </li> <li> <p>description: the contour of the Zwin Nature Reserve in the most eastern part of the Flemish coastal area, as mapped in the Open Streep Map on 2021-10-06</p> </li> <li> <p>selection: <code>leisure = nature_reserve</code></p> </li> </ul> </li> <li> <p><code>habitat_1130</code>:</p> <ul> <li> <p>source: Natura 2000 habitat map of Flanders (<a href="https://doi.org/10.5281/zenodo.3354381">10.5281/zenodo.3354381</a>)</p> </li> <li> <p>description: polygons located within estuaries (habitat type <code>1130</code>); these areas are located outside dike infrastructure and harbour several types that rely on tidal dynamics or brackish water, and at the same time are considered (almost) everywhere groundwater dependent, a.o. brackish pioneer habitats, salt marshes and meadows (habitat types <code>1310</code>, <code>1320</code>, <code>1330</code> resp.), reed beds (<code>6430_mr</code>, <code>rbbmr</code>) and riverine Salix forests and shrubs (<code>91E0_sf</code>, <code>rbbsf</code>).</p> </li> <li> <p>selection: <code>Hab1 = '1130' or Hab2 = '1130' or Hab3 = '1130' or Hab4 = '1130' or Hab5 = '1130'</code></p> </li> </ul> </li> <li> <p><code>gwdepth_coast</code>:</p> <ul> <li> <p>source: interpolated groundwater level of point measurements along the Flemish coast, taken from the <a href="http://data.inbo.be/watina/Pages/Common/Default.aspx">WATINA database</a> of the Research Institute for Nature and Forest (<a href="https://www.vlaanderen.be/inbo/home/">INBO</a>)</p> </li> <li> <p>description: locations with estimated average lowest groundwater table less than 2.5 m below soil surface (shallow groundwater), based on interpolation of measured groundwater levels in areas along the Flemish coast with sufficient gauge densities; in these coarse sandy areas, groundwater levels are supposed to behave independently of topography, allowing simple interpolation among point measurements</p> </li> <li> <p>selection:</p> <ul> <li> <p>calculation of mean yearly spring groundwater level at each gauge (GVG; i.e. average of three groundwater head measurements in meter Ostend height, <a href="https://epsg.io/5710">EPSG:5710</a>, closest to 1st April, but with a minimum time lapse of 14 days, averaged over years)</p> </li> <li> <p>calculation of mean yearly amplitude of groundwater level at each gauge (i.e. difference between min and max water level per hydrological year, averaged over years)</p> </li> <li> <p>inverse distance weighted interpolation of both these datasets (IDW; factor 3, resolution 5 m) for areas with sufficient gauge densities</p> </li> <li> <p>calculation of relative GVG (depth below soil surface) by subtracting soil surface level (<a href="https://www.geopunt.be/catalogus/datasetfolder/f52b1a13-86bc-4b64-8256-88cc0d1a8735">Flemish DTM</a>) from GVG, both in meter Ostend height (EPSG:5710)</p> </li> <li> <p>subtract average amplitude from relative GVG as an estimate of lowest groundwater level (GLG)</p> </li> <li> <p>select locations with estimated <code>GLG >= -2.5</code> m located within the coastal dunes ecodistrict (see <a href="https://www.geopunt.be/catalogus/datasetfolder/58279e22-b9c0-4bcc-a774-95eb76208e7e">Flemish ecoregions and ecodistricts</a>)</p> </li> </ul> </li> </ul> </li> <li> <p><code>gwdepth_local</code>:</p> <ul> <li> <p>source: Batelaan et al. (2012)</p> </li> <li> <p>description: mean lowest groundwater level less than 2 m below soil surface (MLW) for the present day situation. The modelled area is a large military training site for which no information is available in the soil map of Flanders.</p> </li> <li> <p>selection: <code>GLG <= 2</code> m</p> </li> </ul> </li> <li> <p><code>seepage</code>:</p> <ul> <li> <p>source: Batelaan & De Smedt (1994)</p> </li> <li> <p>description: area with modelled seepage fluxes of at least 0.8 mm/day. In these areas we inherently consider the groundwater level to be shallow. The extent of the regional groundwater model encompasses the valleys of the rivers Demer, Dijle en Nete in the central part of eastern Flanders</p> </li> <li> <p>selection: <code>gridcode >= 3</code></p> </li> </ul> </li> <li> <p><code>peat_survey</code>:</p> <ul> <li> <p>source: peat pricks executed or compiled by the Research Institute for Nature and Forest (INBO)</p> </li> <li> <p>layer with interpreted presence of peaty zones evaluated by simple measurements of the depth of plain and superficial peaty soil layers at regular intervals (about 20 m), sampled by manually stabbing the peaty layers with a simple stick until the underlying mineral layer is reached (i.e. resistance is felt). These measurements originate from localised inventories and are thus sporadically available only.</p> </li> </ul> </li> <li> <p><code>duneslack</code>:</p> <ul> <li> <p>source: Provoost et al. (2020)</p> </li> <li> <p>description: polygons with dune slack vegetations along the Flemish coast that typically imply shallow groundwater levels. </p> </li> <li> <p>selection: see source</p> </li> </ul> </li> </ul> <p><br> A reading function to return <code>shallowgroundwater</code> in a standardized way into the R environment is provided by the R-package <a href="https://inbo.github.io/n2khab/">n2khab</a>.</p>
Distancia-Covid Individual Contact Estimates for Spain
<p>Individual estimates of age-specific contact patterns in Spain during the Covid-19 pandemic. This data was generated from the CSIC Distancia-Covid survey (https://distancia-covid.csic.es/). It includes estimated numbers of coresidents and non-coresident contacts for each individual represented in the Spanish Labor Force Survey during 2020 and 2021. These estimates do not relate to any identifiable person; rather they provide information about the overall distribution of contacts across the population. These individual estimates have been used to calculate the mean age-specific contacts provided in <a href="https://doi.org/10.5281/zenodo.5983902">https://doi.org/10.5281/zenodo.5983902</a>. </p> <p>This dataset contains the following files:</p> <ul> <li>distancia_covid_individual_contact_estimates_metadata_dictionary.csv: Variable definitions</li> <li>distancia_covid_individual_contact_estimates_spain_cores_wave1.csv.gz: Estimates of coresidents during wave 1 of the Distancia-Covid survey (14 May 2020 through 10 June 2020)</li> <li>distancia_covid_individual_contact_estimates_spain_cores_wave2.csv.gz: Estimates of coresidents during wave 2 of the Distancia-Covid survey (24 July 2020 through 31 August 2020)</li> <li>distancia_covid_individual_contact_estimates_spain_cores_wave3.csv.gz: Estimates of coresidents during wave 3 of the Distancia-Covid survey (14 December 2020 through 10 January 2021)</li> <li>distancia_covid_individual_contact_estimates_spain_noncores_wave1.csv.gz: Estimates of non-coresident contacts during wave 1 of the Distancia-Covid survey (14 May 2020 through 10 June 2020)</li> <li>distancia_covid_individual_contact_estimates_spain_noncores_wave2.csv.gz: Estimates of non-coresident contacts during wave 2 of the Distancia-Covid survey (24 July 2020 through 31 August 2020)</li> <li>distancia_covid_individual_contact_estimates_spain_noncores_wave3.csv.gz: Estimates of non-coresident contacts during wave 3 of the Distancia-Covid survey (14 December 2020 through 10 January 2021)</li> <li>CITATION.cff: Citation file.</li> </ul> <p> </p>
Distancia-Covid Contact Estimates for Spain
<p>Estimates of age-specific contact patterns in Spain during the Covid-19 pandemic. This data was generated from the CSIC Distancia-Covid survey (https://distancia-covid.csic.es/). It includes estimated mean numbers of coresidents and non-coresident contacts by age group during 2020 and 2021, for all of Spain and disaggregated by autonomous community. (See `data/distancia_covid_contact_estimates_spain_metadata_dictionary.csv` for variable descriptions.) This repository also includes the survey instrument used in each wave.</p> <p><em>File Descriptions</em></p> <p>- distancia_covid_contact_estimates_spain_metadata_dictionary: Data dictionary<br> - distancia_covid_contact_estimates_spain.csv: Contact estimates<br> - distancia_covid_instrument_wave_1.xlsx: Survey instrument used in Wave 1<br> - distancia_covid_instrument_wave_2.xlsx: Survey instrument used in Wave 2<br> - distancia_covid_instrument_wave_3_4.xlsx: Survey instrument used in Waves 3 and 4<br> - CITATION.cff: Citation information </p> <p>This data is also hosted in the <a href="https://github.com/Distancia-COVID/Distancia-Covid-Open-Data">Distancia-Covid-Open-Data</a> GitHub repository. This version corresponds with:</p> <p><a href="https://github.com/Distancia-COVID/Distancia-Covid-Open-Data/releases/tag/v1.2.0">https://github.com/Distancia-COVID/Distancia-Covid-Open-Data/releases/tag/v1.2.0</a></p>
Finite amplitude sound propagation effects in volume backscattering measurements for fish abundance estimation
<p>The upload contains measurement and simulation data for finite-amplitude sound propagation effects in volume backscattering measurements. The experimental data are from a trawl survey conducted in the North Sea with R/V "G. O. Sars", 6-7 November 2004, passing several times over a group of Atlantic mackerel schools. The measurements are of the relative area backscattering coefficient, relative to 38 kHz, 2000 W power setting, at</p> <p>(1) 120 kHz with 250 W transmit power setting, 200 kHz with 120 W transmit power setting<br> (2) 120 kHz with 1000 W power setting, 200 kHz with 1000 W power setting.</p> <p>A Simrad EK60 echosounder system was used, alternating between the low (1) and high (2) power settings through the measurement series.</p> <p>The corresponding simulation data are calculated using the Bergen Code numerical solver of the KZK Equation. The medium parameters input to the simulations are based on CTD data from the field survey . The transducer and amplitude data were found by laboratory measurements on echo sounders of the same type as used in the survey.</p> <p>.m files are included for both .mat data files, with details on how to read the data.</p> <p>An article describing the data has been submitted by the authors to Acta Acustica, 2022.</p>
Data for "Modelling soil carbon stocks following reduced tillage intensity: a framework to estimate decomposition rate constant modifiers for RothC-26.3, demonstrated in north-west Europe"
<p>Dataset of paired observations of conventional tillage (CT) with no tillage (NT) and reduced tillage (RT) from studies in temperate oceanic regions of Western Europe, extracted from a recent systematic review (Jordon et al. preprint, see DOI below).</p> <p>R code of modelling framework to estimate tillage rate modifiers (TRM) for simulating adoption of RT and NT using RothC-26.3, and meta-estimates of TRM across studies.</p>
Regional Estimates of Chemical Composition of Fine Particulate Matter Using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors: V4.NA.02.MAPLE
<p>We estimate ground-level fine particulate matter (PM<sub>2.5</sub>) total and compositional mass concentrations over North America by combining Aerosol Optical Depth (AOD) retrievals from the NASA MODIS, MISR, and SeaWIFS instruments with the GEOS-Chem chemical transport model, and subsequently calibrated to regional ground-based observations of both total and compositional mass using Geographically Weighted Regression (GWR) as detailed in the provided reference for V4.NA.02. V4.NA.02.MAPLE further modified the V4.NA.02 GWR method with additional developments as part of the MAPLE (Mortality–Air Pollution Associations in Low-Exposure Environments) project. This adjustment was of particular value over low concentrations. The GWR method of individual components remains unchanged from V4.NA.02, but are provided are percentages to ensure mass closure and recommended to be applied to the V4.NA.02.MAPLE total PM<sub>2.5</sub>.</p> <p>Annual datasets are provided in NetCDF [.nc]. Gridded files use the WGS84 projection. Compositional estimates are provided for sulfate (SO4), nitrate (NO3), ammonium (NH4), organic matter (OM), black carbon (BC), mineral dust (DUST), and sea-salt (SS). Percentages are denoted with a ‘p’ after component identifiers within filenames. A slight change in file name has been included for 2017, corresponding to minor internal changes compared to earlier years. Overall, however, the dataset is consistent throughout its entire time period and can be appropriately used for trend analysis.</p> <p><strong>Reference:</strong><br> van Donkelaar, A., R. V. Martin, et al. (2019). <strong>Regional Estimates of Chemical Composition of Fine Particulate Matter using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors.</strong> Environmental Science & Technology, 2019, doi:10.1021/acs.est.8b06392.</p>
Training data for bathymetry estimation via EO satellite - Hel Peninsula
<p>This dataset contains satellite image from Sentiel-2A (bands B2, B3, B4, B8) and reference sonar based bathymetry measurements.</p> <p>The reference data was acquired from Polish Maritime Administration (htttp://www.um.gdy.pl) and is publicly available. File reference_data_34.csv contains in-situ measrements aquired at northen shore of Hel Peninsula. Points coordinates are expressed in UTM34N coordinate system.</p> <p>The satellite and the reference datasets were preprocessed by the authors for adjust them for Machine Learning algorithms used in the research.</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.