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12,346 results for “derivatives”
The Adult Adansonia digitata L. (baobab tree) distribution map derived from very high resolution satelite imagery for 2010s across the Sahel at 1km resolution
<p>The baobab tree (<em>Adansonia digitata</em> <em>L.</em>) is an integral part of rural livelihoods throughout the African continent. However, the combined effects of climate change and increasing global demand for baobab products are currently exerting pressure on the sustainable utilization of these resources. Here we employ sub-meter resolution satellite imagery to identify nearly 3 million baobab trees in the Sahel, a dryland region of 1.5 million km<sup>2</sup>. This achievement is considered an essential step towards improving valuable woody species' management and monitoring system. The map's overall underestimate bias is 0.27. To prevent mismanagement of this specific tree species, we aggregated every single adult baobab tree map to 1 × 1 km grids. We also classified the baobab trees using the tree crown diameters( small: 3-9m; medium 9m-13m; large: >13m). The baobab tree count map is also available for these three different size classes. </p>
Phenoxytacrine derivatives: Low-toxicity neuroprotectants exerting affinity to ifenprodil-binding site and cholinesterase inhibition
<p><a title="Learn more about Tacrine from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/chemistry/tacrine">Tacrine</a> (THA), a long withdrawn drug, is still a popular scaffold used in medicinal <a title="Learn more about chemistry from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/chemistry/chemistry">chemistry</a>, mainly for its good reactivity and multi-targeted effect. However, THA-associated hepatotoxicity is still an issue and must be considered in <a title="Learn more about drug discovery from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/pharmacology-toxicology-and-pharmaceutical-science/drug-discovery">drug discovery</a> based on the THA scaffold. Following our previously identified hit compound 7-phenoxytacrine (7-PhO-THA), we systematically explored the chemical space with 30 novel derivatives, with a focus on low hepatotoxicity, <a title="Learn more about anticholinesterase from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/pharmacology-toxicology-and-pharmaceutical-science/cholinesterase-inhibitor">anticholinesterase</a> action, and antagonism at the GluN1/GluN2B subtype of the <a title="Learn more about NMDA receptor from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/pharmacology-toxicology-and-pharmaceutical-science/n-methyl-dextro-aspartic-acid-receptor">NMDA receptor</a>. Applying the down-selection process based on <em>in vitro</em> and <em>in vivo</em> <a title="Learn more about pharmacokinetic from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/pharmacology-toxicology-and-pharmaceutical-science/pharmacokinetics">pharmacokinetic</a> data, two candidates, <strong>I-52</strong> and <strong>II-52,</strong> selective GluN1/GluN2B inhibitors thanks to the interaction with the ifenprodil-binding site, have entered <em>in vivo</em> <a title="Learn more about pharmacodynamic from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/pharmacodynamics">pharmacodynamic</a> studies. Finally, compound <strong>I-52,</strong> showing only minor affinity to <a title="Learn more about AChE from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/pharmacology-toxicology-and-pharmaceutical-science/acetylcholinesterase">AChE</a>, was identified as a lead candidate with favorable behavioral and <a title="Learn more about neuroprotective from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/pharmacology-toxicology-and-pharmaceutical-science/neuroprotective-agent">neuroprotective</a> effects using open-field and <a title="Learn more about prepulse inhibition from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/prepulse-inhibition">prepulse inhibition</a> tests, along with scopolamine-based behavioral and NMDA-induced hippocampal lesion models. Our data show that compound <strong>I-52</strong> exhibits low toxicity often associated with <a title="Learn more about NMDA receptor from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/nmda-receptor">NMDA receptor</a> ligands, and low hepatotoxicity, often related to THA-based compounds.</p>
Dataset for publication "Chemical-Dealloying-Derived PtPdPb-Based Multimetallic Nanoparticles: Dimethyl Ether Electrocatalysis and Fuel Cell Application" in ACS Applied Materials & Interfaces 2023, 15, 49, 56930–56944
<p>Dataset for publication "Chemical-Dealloying-Derived PtPdPb-Based Multimetallic Nanoparticles: Dimethyl Ether Electrocatalysis and Fuel Cell Application" in ACS Applied Materials & Interfaces 2023, 15, 49, 56930–56944, https://doi.org/10.1021/acsami.3c11003</p> <p>Dataset contains XRD, SAXS, Particle size distribution, HRTEM, Electrochemical characterisation, Fuel cell current-voltage curve, DFT calculations, Elemental maps and electrode stability measurements made on pristine and chemically dealloyed carbon supported Pt2PdPb2 nanoparticles.</p>
Ellipsoidal Harmonic Forward Model derived from Earth2014 topographies up to d/o 7200: EHFM_Earth_7200
<p>The model named EHFM_Earth_7200 was derived by layer-based forward modeling technique in ellipsoidal harmonics, the maximum degree of this model reaches 7200. The relief information was provided by Earth2014 relief model. EHFM_Earth_7200 provides very detailed (~3 km) information for the Earth’s short-scale gravity field, and it is expected to be able to augment or refine existing global gravity models. To meet the existing standard, here we provide spherical harmonic coefficients, which are transformed from original ellipsoidal harmonic coefficients.</p>
Global Pasture Watch - Grassland sampling design derived by Feature Space Coverage Sampling (FSCS) at 1-km spatial resolution
<p>Sampling design used in the production of the <strong>global maps of grassland dynamics 2000–2022 at 30 m spatial resolution</strong> in the scope of the Global Pasture Wath initiative. The sampling desing was based in Feature Space Coverage Sampling and resulted in 10,000 sample tiles (1x1 km) distributed across the World, which were visual interpreted in Very-High Resolution imagery thorugh the QGIS plugin <a href="https://plugins.qgis.org/plugins/qgis-fgi-plugin/">QGIS Fast Grid Inspection</a>.</p> <p>FSCS steps include:</p> <ul> <li>Short vegetation mask that includes all pixels mapped as mosaic, shrubland, grassland, and sparse vegetation in at least one year from 1993 to 2021 according to <a href="https://www.esa-landcover-cci.org/">ESA/CCI global land cover</a> (<code>gpw_short.veg.mask_esacci.lc_p_1km_s_19920101_20201231_go_epsg.3857_v1.tif</code>),</li> <li>87 input raster layers (including vegetation indices, terrain, land temperature, climate and water variable),</li> <li>Principal Components Analysis (PCA) using all input layers,</li> <li>Selection of the 10 first components (explaining 75% of variance),</li> <li> K-Means with 10,000 clusters (targeted number of samples - <br><code>gpw_grassland_fscs.kmeans.cluster_c_1km_20000101_20221231_go_epsg.3857_v1.tif</code>)</li> <li>Calculation of euclidean distance (in the principal component space) of all 1-km pixels to the centre of each cluster,</li> <li>Selection of the pixel with the shortest distance for each cluster,</li> <li>Conversion of the selected pixels into sample tiles ()</li> </ul> <p>The file <code>gpw_grassland_fscs_tile.samples_1km_20000101_20221231_go_epsg.3857_v1.gpkg</code> provides the sample tiles and include the follow collumns:</p> <ul> <li><strong>X</strong>: Latitude in Web Mercator projection (EPSG:3857),</li> <li><strong>Y</strong>: Longitude in Web Mercator projection (EPSG:3857),</li> <li><strong>cluster_id</strong>: K-Means output ranging from 0—9999,</li> <li><strong>cluster_distance</strong>: Distance from the selected sample to the centre of the cluster,</li> <li><strong>cluster_size</strong>: Number o 1-km pixels inside the K-Means cluster, estimated using Web Mercator projection (<a href="https://epsg.io/3857">EPSG:3857</a>)</li> <li><strong>cluster_size_equal_area</strong>: Number o 1-km pixels inside the K-Means cluster, estimated using Goode Homolosine Land projection (<a href="https://epsg.io/54052">ESRI:54052</a>)</li> <li><strong>cluster_size_corr</strong>: Correction factor to adjust the area distortion due to Web Mercator projection, estimated by the difference in normalized propotional values of cluster_size and cluster_size_equal_area.</li> <li><strong>rf_n_pred</strong>: Number of pixels predicted by a RF model trained to estimate probability to select the pixel closer to the centre of the KMeans cluster. The RF models were trained individually per each cluster using the 10 first components derived by PCA (<code>gpw_comps_fscs.pca_m_1km_20000101_20221231_go_epsg.3857_v1.tar.gz</code>).</li> <li><strong>rf_samp_prob</strong>: Sampling probability based on RF model (<em>rf_n_pred / cluster_size</em>)</li> <li><strong>rf_samp_wei</strong>: Sampling weight estimated in Web Mercator projection.</li> <li><strong>rf_samp_wei_coor</strong>: Corrected sampling weight estimated in Goode Homolosine Land projection.</li> </ul> <h3>Related resources</h3> <ul> <li><strong>Maps of dominant grassland:</strong><br><a href="https://zenodo.org/records/13890400">2000-2002</a> <a href="https://zenodo.org/records/13890402">2003-2005</a> <a href="https://zenodo.org/records/13890404">2006-2008</a> <a href="https://zenodo.org/records/13890408">2009-2011</a> <a href="https://zenodo.org/records/13890410">2012-2014</a> <a href="https://zenodo.org/records/13890412">2015-2017</a> <a href="https://zenodo.org/records/13890414">2018-2020</a> <a href="https://zenodo.org/records/13890416">2021-2022</a></li> <li><strong>Probability maps of cultivated grassland:</strong><br><a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2022 (All URLs)</a></li> <li><strong>Probability maps of natural/semi-natural grassland:</strong><br><a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2022 (All URLs)</a></li> <li><strong>Grassland reference samples based on VHR imagery (2000–2022):</strong><br><a href="https://doi.org/10.5281/zenodo.11281157">GeoPackage files</a></li> <li><strong>Global machine learning models (Random Forest):</strong><br><a href="https://doi.org/10.5281/zenodo.13952806">Parquet and joblib python files</a></li> <li><strong>Reference sampling design derived by FSCV:</strong><br><a href="https://doi.org/10.5281/zenodo.11391517">GeoPackage and raster files</a></li> <li><strong>Harmonized reference samples based on existing LULC dataset:</strong><br><a href="https://doi.org/10.5281/zenodo.13951976">GeoPackage and raster files</a></li> <li><strong>Source code for reproducibility:<br></strong><a href="https://doi.org/10.5281/zenodo.13952867">GitHub release</a><strong><br></strong></li> <li><strong>Mapping feedback tool:</strong><br><a href="https://geo-wiki.org">GeoWiki</a></li> <li><strong>Data catalogues:</strong><br><a href="https://stac.openlandmap.org/gpw_ggc-30m/collection.json?.language=en">OpenLandMap STAC</a> <a href="https://global-pasture-watch.projects.earthengine.app/view/ggc-30m">Google Earth Engine</a></li> </ul> <h3>Support</h3> <p>For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in <a href="https://github.com/wri/global-pasture-watch">https://github.com/wri/global-pasture-watch</a></p>
Dataset for "Exploring the Potential of Various Cyclodextrin-based Derivatives in Enzyme Supramolecular Engineering" research article
<p>The dataset for the paper titled "Exploring the Potential of Various Cyclodextrin-based Derivatives in Enzyme Supramolecular Engineering".<br>The dataset includes the following items:<br><br>1. "alpha-CD-TES_Characterization" xls file (1 file) including 5 datasheets;<br>These data sheets provide raw characterization data regarding the synthesis of the alpha-CD-TES molecule including 1H NMR, 13C NMR, FTIR, ESI-MS, and MALDI.</p> <p>2. "beta-CD-TES_Characterization" xls file (1 file) including 5 datasheets;<br>These data sheets provide raw characterization data regarding the synthesis of the beta-CD-TES molecule including 1H NMR, 13C NMR, FTIR, ESI-MS, and MALDI.</p> <p>3. "gamma-CD-TES_Characterization" xls file (1 file) including 5 datasheets;<br>These data sheets provide raw characterization data regarding the synthesis of the gamma-CD-TES molecule including 1H NMR, 13C NMR, FTIR, ESI-MS, and MALDI.<br><br>4. "Activities" xls file (1 file) including 6 datasheets;<br>These datasheets contain the raw data of the enzymatic activities measured for both LipMRD9 and A50 enzymes for each set of stability experiments reported in the manuscript or the supporting information documents.</p> <p>5. "Layer growth - A50" zip file including 5 files:<br>The unprocessed SEM micrographs of A50 enzyme shielding with alpha/beta/gamma-CD-TES building blocks (after 75 min reaction) and the corresponding size measurements in an xls file.</p> <p>6. "Layer growth - LipMRD9" zip file including 15 files:<br>The unprocessed SEM micrographs of the shielded LipMRD9 enzyme with α-, β-, and γ-CD-TES building blocks (after 30, 60, 90, and 120 min reaction) and the corresponding size measurements (using ImageJ software) saved in separate xls files.</p>
Land surface temperature (heatmaps) derived from earth observation data to assess thermal behaviour of 3 European cities: Milano, Logroño and Athens.
<p>Next tables present the detail description of the datasets developed in REACHOUT to characterize heat phenomena at city level by providing an assessment of the <strong>land surface temperature (heatmaps)</strong> of three European cities: Milan, Logroño and Athens. TECNALIA is the responsible partner for these datasets.</p> <p>There is a wide range of methods that can be used to characterise the thermal behaviour of a city, each of them with its advantages and disadvantages. One of these methods uses the land surface temperature that is obtained from remote sensing observations. Although thermal indices are considered more suitable when characterising thermal comfort, still the LST can provide a useful information about the behaviour of a citiy’s surfaces and materials. This has implications for several applications such as urban energy efficiency or urban environmental health. </p> <p>The input data used by the current version of the dataset came from Landsat 8. All the images acquired since 2013 by this satellite for Milan, Logroño and Athens were downloaded and processed to characterise not only the current (2019-2023) thermal behaviour of the city, but also its evolution considering the last seven 5-year windows.</p> <p>- 2013-2017<br>- 2014-2018<br>- 2015-2019<br>- 2016-2020<br>- 2017-2021<br>- 2018-2022<br>- 2019-2023</p> <p>The input data used in this dataset come from Landsat 8 downloaded from <a href="https://earthexplorer.usgs.gov/">Earth Explorer (usgs.gov)</a>.</p> <p>The format of this dataset is organized in two ZIP format files:</p> <p>- LANDSAT_8_L2SP_000000-milan_LST_peak.zip</p> <p>- LANDSAT_8_L2SP_000000-logrono_LST_peak.zip</p> <p>- LANDSAT_8_L2SP_000000-athens_LST_peak.zip</p> <p>Each of these zip files contain seven TIF images that represent the peak LST map according to the images of the above mentioned seven periods. The peak LST is obtained after getting the Annual Cycle Parameters of each of the periods and selecting a 30-day window centred on the day that the city reaches the maximum LST.</p> <p>The values of the images are in degree Celsius and nodata value is -9999.</p> <p> </p>
Effectively controlling for sample relatedness in large-scale GWAS: application to 79 EHR-derived longitudinal traits
<p><span>Sample relatedness is a major confounder in genome-wide association studies (GWAS), potentially leading to inflated type I error rates if not appropriately controlled. A common strategy is to incorporate a random effect related to genetic relatedness matrix (GRM) into regression models. However, this approach is challenging for large-scale GWAS of complex traits, such as longitudinal traits. Here we propose a scalable and accurate analysis framework, SPA<sub>GRM</sub>, which controls for sample relatedness via a precise approximation of the joint distribution of genotypes. SPA<sub>GRM</sub> can utilize GRM-free models and thus is applicable to various trait types and statistical methods, including linear mixed models and generalized estimation equations for longitudinal traits. A hybrid strategy incorporating saddlepoint approximation greatly increases the accuracy to analyze low-frequency and rare genetic variants, especially in unbalanced phenotypic distributions. We also introduce SPA<sub>GRM(CCT)</sub> to aggregate the results following different models via Cauchy combination test. Extensive simulations and real data analyses demonstrated that SPA<sub>GRM</sub> maintains well-controlled type I error rates and SPA<sub>GRM(CCT)</sub> can serve as a broadly effective method. Applying SPA<sub>GRM</sub> to 79 longitudinal traits extracted from</span><span> </span><span>UK Biobank primary care data, we identified 7,463 genetic loci, making a pioneering attempt to conduct GWAS for these traits as longitudinal traits.</span></p>
Global hydrology and water quality data from 1980-2019, derived from the dynamical surface water quality model (DynQual) at 5 arcmin spatial resolution
<p>Global ~10km (5 arcmin) output data from the dynamical surface water quality model (DynQual) from 1980-2019, with annual and monthly temporal resolution. Simulations are made following the ISIMIP3a protocol (https://protocol.isimip.org/#/ISIMIP3a).</p> <p>Output data includes:</p> <ul> <li>Discharge (m3 s-1)</li> <li>Channel storage (m3) </li> <li>Water temperature (K)</li> <li>Total dissolved solids (TDS) load (g s-1)</li> <li>Biological oxygen demand (BOD) load (g s-1)</li> <li>Fecal coliform (FC) load (million cfu s-1)</li> <li>Salinity; as indicated by TDS concentrations (mg l-1)</li> <li>Organic pollution; as indicated by BOD concentrations (mg l-1)</li> <li>Pathogen/bacterial pollution; as indicated by FC concentrations (cfu 100ml-1)</li> </ul> <p>Note. a minimum discharge threshold of 0.1 m3 s-1 was used when computing salinity (TDS), organic (BOD) and pathogen (FC) concentrations, as uncertainties in absolute values of water availabilities have large impacts on resulting in-stream concentrations. Thus, if the the average discharge for the month was below 0.1 m3 s-1, concentrations are not calculated (assigned as NA).</p> <p>In-stream water quality aggregated to 0.5 degree (i.e. 30 arcmin) spatial resolution (daily, monthly and annual) can be found at: <a href="https://zenodo.org/records/14675270">https://zenodo.org/records/14675270</a>. </p>
Global surface water quality data from 1980 - 2019, derived from the dynamical surface water quality model (DynQual) at 30 arcmin spatial resolution
<p>Global ~50km (30 arcmin) surface water quality data from the dynamical surface water quality model (DynQual) from 1980-2019, with annual, monthly and daily temporal resolution. Simulations are made following the ISIMIP3a protocol (https://protocol.isimip.org/#/ISIMIP3a).</p> <p>Output data includes:</p> <ul> <li>Salinity; as indicated by TDS concentrations (mg l-1)</li> <li>Organic pollution; as indicated by BOD concentrations (mg l-1)</li> <li>Pathogen/bacterial pollution; as indicated by FC concentrations (cfu 100ml-1)</li> </ul> <p>Simulations were originally made at 5-arcmin resolution and aggregated to 30 arcmin 0.5 degree by summing the in-stream (routed) loadings and channel storage over the aggregated area (at daily, monthly and annual timesteps), and subsequently calculating in-stream concentrations. Please note the aggregation technique is provisional and thus the data is subject to change.</p> <p>Note. A minimum discharge threshold of 0.1 m3 s-1 was used when computing TDS, BOD and FC concentrations, as uncertainties in absolute values of water availabilities have large impacts on resulting in-stream concentrations. Concentrations in these gridcells are assigned as NA.</p> <p>Hydrology and water quality simulations made at DynQuals native spatial resolution (5 arcmin) can be found at: <a href="https://zenodo.org/records/14673871">https://zenodo.org/records/14673871</a>.</p>
Global surface water quality datasets under uncertain climate and socio-economic change, derived from the dynamical surface water quality model (DynQual) at 5 arcmin spatial resolution
<pre>Global ~10km (5 arcmin) surface water quality data from the dynamical surface water quality model (DynQual) from 2005-2100, with annual and monthly temporal resolution. Simulations are made under three combined climate and socio-economic scenarios (SSP1-RCP2.6; SSP3-RCP7.0 and SSP5-RCP8.5) and using five general circulation model (GFDL-ESM4; UKESM1-0-LL; MPI-ESM1-2-hr; IPSL-CM6A-LR and MRI-ESM2-0), following the ISIMIP3b protocol (<a href="https://protocol.isimip.org/#/ISIMIP3b">https://protocol.isimip.org/#/ISIMIP3b</a>). Output data are provided at annual and monthly temporal resolution over WorldClim time periods (2005-2020; 2021-2040; 2041-2060; 2061-2080; 2081-2100). Output data includes: - Discharge (m<sup>3</sup> s<sup>-1</sup>) - Water temperature (K)<br>- Total dissolved solids (TDS) load (g s<sup>-1</sup>)<br>- Biological oxygen demand (BOD) load (g s<sup>-1</sup>)<br>- Fecal coliform (FC) load (million cfu s<sup>-1</sup>) - Salinity; as indicated by TDS concentrations (mg l<sup>-1</sup>) - Organic pollution; as indicated by BOD concentrations (mg l<sup>-1</sup>) - Pathogen/bacterial pollution; as indicated by FC concentrations (cfu 100ml<sup>-1</sup>)<br><br>Note. A minimum discharge threshold of 0.1 m<sup>3</sup> s<sup>-1</sup> was used when computing TDS, BOD and FC concentrations, as uncertainties in absolute values of water availabilities have large impacts on resulting in-stream concentrations. Concentrations in these gridcells are assigned as NA.<br><br>Full time series of these variables at 30 arcmin (0.5 degree) can be found at: <a href="https://zenodo.org/records/14677534">https://zenodo.org/records/14677534</a>.</pre>
Derived daily timeseries of weather, soil moisture and temperature, flow and nitrogen species (nitrate and nitrite, ammonium) concentrations data for the North Wyke Farm Platform National Biosciences Research Infrastructure, England
<p>For a selection of catchments from the North Wyke Farm Platform in southwest England, where land use conversions have been introduced, daily time series data covering weather conditions (minimum temperature, maximum temperature, total rainfall, wind speed and solar radiation), near-surface soil status (moisture content and temperature), flow and concentrations of key nitrogen species (nitrate and nitrite, ammonium) have been filtered based on attached data quality tags . The datasets run between 2013 and March 2024. For the main climate variables, data gaps were infilled with preceding- and following-on daily data, observations from a nearby weather station or existing national datasets to generate a continuous data series for modelling. For the other data series, annual and seasonal summary statistics on data coverage are provided. Information on significant field events, such as ploughing, drilling and harvest, fertiliser applications and manure spreading were also tabulated.</p>
CLDF dataset derived from Grollemund et al.'s "Bantu expansion shows habitat alters the route and pace of human dispersals" from 2015
<p>Cite the source of the dataset as:</p> <blockquote> <p>Grollemund, Rebecca, Branford, Simon, Bostoen, Koen, Meade, Andrew, Venditti, Chris, & Pagel, Mark (2015) Bantu expansion shows habitat alters the route and pace of human dispersals. Proc Natl Acad Sci USA. doi:10.1073/pnas.1503793112.</p> </blockquote>
CLDF dataset derived from the ECAI digitization of Wurm and Hattori's "Language Atlas of the Pacific Area" from 1981 and 1983
<p>Cite the source of the dataset as:</p> <blockquote> <p>Wurm, S. & Hattori, S. Language Atlas of the Pacific Area: New Guinea area, Oceania, Australia, vol. 66 of Pacific Linguistics: Series C (Canberra: Research School of Pacific and Asian Studies, Australian National University, Canberra, 1981). Wurm, S. & Hattori, S. Language Atlas of the Pacific Area: Japan area, Taiwan-Formosa, Philippines, Mainland and insular South-East Asia, vol. 67 of Pacific Linguistics: Series C (Canberra: Research School of Pacific and Asian Studies, Australian National University, Canberra, 1983). Registered scans and GIS dataset released by the Electronic Cultural Atlas Initiative (ECAI).</p> </blockquote>
Multidecadal satellite-derived Portuguese Burn Severity Atlas (1984 -2022)
<p>The <strong>Portuguese burn severity atlas (1984 -2022)</strong> provides satellite-derived burn severity estimates of each historical fire with its start and end dates recorded and equal to or larger than<strong>100 ha</strong> for fires from 1984 to 2022 in Portugal. The fire perimeters are provided by the Instituto da Conservação da Natureza e das Florestas (ICNF) (https://sig.icnf.pt/portal/home/item.html?id=983c4e6c4d5b4666b258a3ad5f3ea5af). <strong>Landsat </strong>imagery is applied for the creation of this atlas. The burn severity indices applied within this atlas are differenced Normalized Burn Ratio (<strong>dNBR</strong>), Relative differenced Normalized Burn Ratio (<strong>RdNBR</strong>), Relative Burn Ratio (<strong>RBR</strong>), and index combining dNBR with enhanced vegetation index (<strong>dNBR-EVI</strong>).</p> <p>Since 1984 to 2022, the total burned area recorded in Portugal is 4.85 million ha. Only <strong>valid fires</strong>, which are fires equal to or larger than 100 ha with known dates, were considered for the burn severity estimates. The total area of valid fires is 3.29 million ha. The <strong>Portuguese Burn Severity Atlas</strong> provides estimates for 3.17 million ha, accounting for 65% of all fires and <strong>96% of valid fires</strong>.</p> <p>The <strong>Portuguese burn severity atlas </strong>is organized in subfolders, each entitled as the corresponding year and containing shapefile, maps and a table with details on pairs of images used for burn severity estimates. Within each subfolder, the following data are stored:</p> <ul> <li> the annual fires’ perimeters shapefile (.dbf, .prj, .shp, .shx)</li> <li>dNBR map (.tiff)</li> <li>RdNBR map (.tiff)</li> <li>RBR map (.tiff)</li> <li>dNBR-EVI map (.tiff)</li> <li>confidence map: average “SUITABILITY in each pixel within the area of the fire (.tiff)</li> <li>comma separated value (.csv) file containing details on the pair of images used for burn severity estimates (year, ID, iteration number, pre-fire time lag(day), pre-fire cloud%, pre-fire suitability (%), post-fire time lag (day), post-fire cloud%, post-fire suitability(%), confidence in the iteration(%), area with dNBR estimation(ha), area of fire (ha), fire i number (to refer in GEE code), dNBR offset value, RdNBR offset value, RBR offset value, and dNBR-EVI offset value).</li> </ul> <p>To the best of our knowledge, <strong>Portuguese Burn Severity Atlas </strong>is the first open access atlas providing burn severity estimates of historical fires for an entire European country, in this case, <strong>Portugal</strong>, with 38 years of coverage. Target audience who can benefit from this atlas can be policymakers at national levels, field managers, project managers, and agency and academic researchers.</p> <p><strong>***</strong> The second version of this atlas provides updated fire data and burn severity estimates of fires from 1984 to 2000 with corrected dates area equal or larger than 100 ha. Moreover, for 2012, aside from burn severity estimates provided via imagery from Terra abroad Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat7-derived maps are included. </p>
EcoDes-DK15: High-resolution ecological descriptors of vegetation and terrain derived from Denmark's national airborne laser scanning data set
<p><strong>Eighteen high-resolution ecological descriptors of vegetation and terrain for Denmark "EcoDes-DK15"</strong></p> <p>The data are derived from the nationwide airborne laser scanning / LiDAR campaign of Denmark from 2014-2015 provided by the Danish Agency for Data Supply and Efficiency.</p> <p><strong>Update: EcoDes-DK15 v1.1.0 (4 Dec. 2021)</strong></p> <p>Following the recommendations and feedback during the first round of peer-review, we updated the EcoDes-DK processing pipeline and EcoDes-DK15 data set. The key changes are:</p> <ul> <li>New version of the source data optimised to contain only point data collected before the end of 2015. The source data for EcoDes-DK15 v1.0.0 unintentionally contained data from 2018. The new source data is documented <a href="https://github.com/jakobjassmann/ecodes-dk-lidar/blob/master/documentation/source_data/readme.md">here</a>.</li> <li>New "date_stamp_*" auxiliary variables that illustrate the survey dates for the vegetation points in each cell. See updated descriptor documentation <a href="https://github.com/jakobjassmann/ecodes-dk-lidar/blob/master/documentation/descriptors.md">here</a>.</li> <li>Re-scaling of "solar_radiation" variable to MJ per 100 m<sup>2</sup> per year.</li> </ul> <p><strong>Detailed documentation for the data set can be found in the accompanying manuscript and GitHub repository:</strong></p> <p>Assmann, J. J., Moeslund, J. E., Treier, U. A., and Normand, S.: EcoDes-DK15: High-resolution ecological descriptors of vegetation and terrain derived from Denmark's national airborne laser scanning data set, Earth Syst. Sci. Data Discuss. [preprint], <a href="https://doi.org/10.5194/essd-2021-222">https://doi.org/10.5194/essd-2021-222</a>, in review, 2021<strong><em>.</em></strong></p> <p><a href="https://github.com/jakobjassmann/ecodes-dk-lidar">https://github.com/jakobjassmann/ecodes-dk-lidar</a></p> <p>Files are compressed using bzip2 and tar archiving. The compressed archives can be extracted using commonly available archiving tools (for example <a href="https://www.7-zip.org/">7z </a>on Windows, the archiving tool on macOS and bz2 on Linux). </p> <p>A small example "teaser" subset (5 MB) of the data set, covering the Husby Klit area from Figure 7 in the manuscript, can be found <a href="https://github.com/jakobjassmann/ecodes-dk-lidar/blob/master/manuscript/figure_7/EcoDes-DK15_teaser.zip">here</a>.</p> <p><strong>Abstract (from manuscript)</strong></p> <p>Biodiversity studies could strongly benefit from three-dimensional data on ecosystem structure derived from contemporary remote sensing technologies, such as Light Detection and Ranging (LiDAR). Despite the increasing availability of such data at regional and national scales, the average ecologist has been limited in accessing them due to high requirements on computing power and remote-sensing knowledge. We processed Denmark’s publicly available national Airborne Laser Scanning (ALS) data set acquired in 2014/15 together with the accompanying elevation model to compute 70 rasterized descriptors of interest for ecological studies. With a grain size of 10 m, these data products provide a snapshot of high-resolution measures including vegetation height, structure and density, as well as topographic descriptors including elevation, aspect, slope and wetness across more than forty thousand square kilometres covering almost all of Denmark’s terrestrial surface. The resulting data set is comparatively small (~94 GB, compressed 16.8 GB) and the raster data can be readily integrated into analytical workflows in software familiar to many ecologists (GIS software, R, Python). Source code and documentation for the processing workflow are openly available via a code repository, allowing for transfer to other ALS data sets, as well as modification or re-calculation of future instances of Denmark’s national ALS data set. We hope that our high-resolution ecological vegetation and terrain descriptors (EcoDes-DK15) will serve as an inspiration for the publication of further such data sets covering other countries and regions and that our rasterized data set will provide a baseline of the ecosystem structure for current and future studies of biodiversity, within Denmark and beyond.</p> <p><strong>Acknowledgements (from manuscript)</strong></p> <p>We would like to thank Andràs Zlinszky for his contributions to earlier versions of the data set, Charles Davison for feedback regarding data use and handling, as well as Matthew Barbee and Zsófia Koma for sharing their insights on the source data merger and Zsófia’s script to generate summary statistics for the different versions of the DHM point clouds. Funding for this work was provided by the Carlsberg Foundation (Distinguished Associate Professor Fellowships) and Aarhus University Research Foundation (AUFF-E-2015-FLS-8-73) to Signe Normand (SN). This work is a contribution to SustainScapes – Center for Sustainable Landscapes under Global Change (grant NNF20OC0059595 to SN).</p>
DATASET: Evaluation of sorbents for high temperature removal of tars, hydrogen sulphide, hydrogen chloride and ammonia from biomass-derived syngas by using Aspen Plus
<p>Effect of S/B ratio on syngas; Effect of temperature on syngas; sensitivity analysis of H2S concentration respect to steam content in syngas; sensitivity analysis of H2S concentration respect to temperature.</p>
Text-mining-derived cancer associations - PMED
<p>JSON file containing gene::cancer, variant::cancer and gene/variant::cancer:drug-response associations derived from an in-house text-mining pipeline utilizing full-text articles from PMC.</p>
TROPOMI-derived ground level NO2 concentrations (2019 & 2020 Monthly Means)
<p>Monthly mean ground level NO2 concentrations derived from TROPOMI satellite NO<sub>2</sub> observations for January-June 2019 and 2020. Ground level concentrations are derived from observed column densities using the GEOS-Chem chemical transport model constrained with ground monitor observations following the method outlined in Cooper et al 2021 (DOI: 10.1038/s41586-021-04229-0)</p> <p><br> Annual mean data are provided at ~1x1 km<sup>2</sup> resolution at satellite overpass time (~1:30 PM local). Datasets are in netcdf (.nc) format.<br> </p>
Satellite-derived ground level NO2 concentrations, 2005-2019
<p>Ground level NO2 concentrations derived from OMI and TROPOMI satellite NO2 observations, as presented in Cooper et al 2021 (DOI: 10.1038/s41586-021-04229-0). NO2 column densities for the given year are determined using OMI observations, and downscaled to finer resolution using TROPOMI observations. Ground level concentrations are derived from downscaled column densities using the GEOS-Chem chemical transport model constrained with ground monitor observations following the method outlined in Cooper et al 2020 (https://doi.org/10.1088/1748-9326/aba3a5) and Cooper et al 2021.</p> <p> </p> <p>Annual mean data are provided at ~1x1 km2 resolution at satellite overpass time (~1:30 PM local). Datasets are in netcdf (.nc) format.</p> <p> </p>
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