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424 results for “Compilers”
A compilation of environmental geographic rasters for SDM covering France
<p>This dataset is a compilation of geographic rasters from multiple environmental data sources. It aims at making the life of SDM users easier. All rasters cover the metropolitan French territory, but have varying resolutions and projections. Each directory inside the main directory "<strong>0_mydata</strong>" contain a single environmental raster. Punctual extraction of raster values can be easily done for large sets of WGS84-(longitude,latitude) points coordinates and for multiple rasters at the same time through the R function <strong>get_variables</strong> of script <a href="https://github.com/ChrisBotella/SamplingEffort/blob/master/_functions.R">_functions.R</a> from Github repository: <a href="https://github.com/ChrisBotella/SamplingEffort">https://github.com/ChrisBotella/SamplingEffort</a>. All data sources are accessible on the web and free of use, at least for scientific purpose. They have various conditions of citations. Anyone diffusing a work using the present data must reference along with the present DOI, the original source data employed. Those source data are described in the paragraphs below. We provide the articles to cite, when required, and webpages for access.</p> <p><strong>Pedologic Descriptors of the ESDB v2: 1 km × 1 km Raster Library :</strong> The library contains multiple soil pedology (physico-chemical properties of the soil) descriptors raster layers covering Eurasia at a resolution of 1 km. We selected 11 descriptors from the library. They come from the PTRDB. The PTRDB variables have been directly derived from the initial soil classification of the Soil Geographical Data Base of Europe (SGDBE) using expert rules. For more details, see [1, 2] and [3]. The data is maintained and distributed freely for scientific use by the European Soil Data Centre (ESDAC) at <a href="http://eusoils.jrc.ec.europa.eu/content/european-soil-databasev2-raster">http://eusoils.jrc.ec.europa.eu/content/european-soil-databasev2-raster</a>. The 11 rasters are in the directories <strong>"awc_top", "bs_top", "cec_top", "dimp", "crusting", "erodi", "dgh", "text", "vs", "oc_top", "pd_top"</strong>.</p> <p><strong>Corine Land Cover 2012, Version 18.5.1, 12/2016 :</strong> It is a raster layer describing soil occupation with 48 categories across Europe (25 countries) at a resolution of 100 m. This data base of the European Union is freely accessible online for all use at <a href="http://land.copernicus.eu/pan-european/corine-land-cover/clc-2012">http://land.copernicus.eu/pan-european/corine-land-cover/clc-2012</a>. The raster of this variable is in the directory "<strong>clc</strong>".</p> <p><strong>Hydrographic Descriptor of BD Carthage v3: </strong>BD Carthage is a spatial relational database holding many informations on the structure and nature of the french metropolitan hydrological network. For the purpose of plants ecological niche, we focus on the geometric segments representing watercourses, and polygons representing hydrographic fresh surfaces. The data has been produced by the Institut National de l’information Géographique et forestière (IGN) from an interpretation of the BD Ortho IGN. It is maintained by the SANDRE under free license for non-profit use and downloadable at:<br> <a href="http://services.sandre.eaufrance.fr/telechargement/geo/ETH/BDCarthage/FX">http://services.sandre.eaufrance.fr/telechargement/geo/ETH/BDCarthage/FX</a><br> From this shapefile, we derived a raster containing the binary value raster proxi_eau_fast, i.e. proximity to fresh water, all over France.We used qgis to rasterize to a 12.5m resolution, with a buffer of 50m, the shapefile COURS_D_EAU.shp on<br> one hand, and the polygons of SURFACES_HYDROGRAPHIQUES.shp with attribute NATURE=“Eau douce<br> permanente” on the other hand.We then created the maximum raster of the previous ones (So the value of 1 correspond to an approximate distance of less than 50m to a watercourse or hydrographic surface of fresh water). The raster is in the directory named "<strong>proxi_eau_fast</strong>".</p> <p><strong>USGS Digital Elevation Data :</strong> The Shuttle Radar Topography Mission achieved in 2010 by Endeavour shuttle measured elevation at three arc second resolution over most of the earth surface. Raw measures have been post-processed by NASA and NGA in order to correct detection anomalies. The data is available from the U.S. Geological Survey, and downloadable on the Earthexplorer (<a href="https://earthexplorer.usgs.gov/">https://earthexplorer.usgs.gov/</a>). One may refer to <a href="https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-shuttle-radar-topography-mission-srtm-void?qt-science_center_objects=0#qt-science_center_objects">https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-shuttle-radar-topography-mission-srtm-void?qt-science_center_objects=0#qt-science_center_objects</a> for more informations. the elevation raster is in the directory named "<strong>alti</strong>".</p> <p><strong>Potential Evapotranspiration of CGIAR-CSI ETP : </strong>The CGIAR-CSI distributes this worldwide monthly potential-evapotranspiration raster data. It is pulled from a model developed by Antonio Trabucco [4, 5]. Those are estimated by the Hargreaves formula, using mean monthly surface temperatures and standard deviation from WorldClim 1:4 (<a href="http://www.cgiar-csi.org/data/global-aridity-and-pet-database#description">http://www.worldclim. org/</a>), and radiation on top of atmosphere. The raster is at a 1km resolution, and is<br> freely downloadable for a nonprofit use at: <a href="http://www.cgiar-csi.org/data/global-aridity-and-pet-database#description">http://www.cgiar-csi.org/data/global-aridity-and-pet-database#description</a>. This raster is in the directory "<strong>etp</strong>".</p> <p><strong>Bioclimatic Descriptors of Chelsea Climate Data 1.1:</strong> Those are raster data with worldwide coverage and 1 km resolution. A mechanistical climatic model is used to make spatial predictions of monthly mean-max-min temperatures, mean precipitations and 19 bioclimatic variables, which are downscaled with statistical models integrating historical measures of meteorologic stations from 1979 to today. The exact method is explained in the reference papers [6] and [7]. The data is under Creative Commons Attribution 4.0 International License and downloadable at (<a href="http://chelsa-climate.org/downloads/">http://chelsa-climate.org/downloads/</a>). The 19 bioclimatic rasters are located in the directories named "<strong>chbio_X</strong>".</p> <p><strong>ROUTE500 1.1:</strong> This database register classified road linkages between cities (highways, national roads, and departmental roads) in France in shapefile format, representing approxi-mately 500,000 km of roads. It is produced under free license (all uses) by the IGN. Data are available online at <a href="http://osm13.openstreetmap.fr/~cquest/route500/">http://osm13.openstreetmap.fr/~cquest/route500/</a>. For deriving the variable “<strong>droute_fast</strong>”, the distance to the main roads networks, we computed with qGis the distance raster to the union of all elements of the shapefile ROUTES.shp (segments).</p> <p><strong>References : </strong></p> <p>[1] Panagos, P. (2006). The European soil database. GEO: connexion, 5(7), 32–33.</p> <p>[2] Panagos, P., Van Liedekerke, M., Jones, A., Montanarella, L. (2012). European Soil Data<br> Centre: Response to European policy support and public data requirements. Land Use Policy,<br> 29(2),329–338.</p> <p>[3] Van Liedekerke, M. Jones, A. & Panagos, P. (2006). ESDBv2 Raster Library-a set of rasters<br> derived from the European Soil Database distribution v2. 0. European Commission and the<br> European Soil Bureau Network, CDROM, EUR, 19945.</p> <p>[4] Zomer, R., Bossio, D., Trabucco, A., Yuanjie, L., Gupta, D. & Singh, V. (2007). Trees and<br> water: smallholder agroforestry on irrigated lands in Northern India.</p> <p>[5] Zomer, R., Trabucco, A., Bossio, D. & Verchot, L. (2008). Climate change mitigation: A<br> spatial analysis of global land suitability for clean development mechanism afforestation and<br> reforestation. Agriculture, ecosystems & environment, 126(1), 67–80.</p> <p>[6] Karger, D. N., Conrad, O., Bohner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W. & Kessler,<br> M. (2016). Climatologies at high resolution for the earth’s land surface areas. arXiv preprint<br> arXiv:1607.00217.</p> <p>[7] Karger, D. N., Conrad, O., Bohner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W. & Kessler, M.<br> (2016). CHELSEA climatologies at high resolution for the earth’s land surface areas (Version<br> 1.1).</p>
Compilation of data used to implement floodplain DEM algorithm in the Logone Floodplain
<p>This document describes the dataset that was used in the article submitted to Geophysical Research Letters by Shastry and Durand entitled "Water Surface Elevation Constraints in a Data Assimilation Scheme to Infer Floodplain Topography: A Case Study in the Logone Floodplain". The dataset is distributed as a NetCDF file containing the Digital Elevation Models at different stages of the algorithm described in the article, and a set of excel files describing the location coordinates of flood boundaries used in the study.</p> <p><strong>Description of Files</strong></p> <p><strong>Excel files</strong><br> Each excel file provides the location coordinates of flood boundaries on a particular day; the date is mentioned in the file name as YYYYMMDD. Each Sheet is each excel file corresponds to a unique flooded region on the particular day. The first column corresponds to Northing and the second Easting. The coordinates are in the WGS 1984 UTM Zone 33 N coordinate system. </p> <p><strong>NetCDF file</strong><br> This file contains Digital Elevation Models (DEMs) at various stages of the algorithm. The various stages are described below.<br> 1. The Prior DEM: Multi-Error-Removed Improved-Terrain (MERIT) DEM (Yamazaki et al., 2017; http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/) of the Logone Floodplain in Cameroon upscaled to 500 m.<br> 2. Ensemble of particle DEMs: Spatially correlated errors added to the prior DEM to produce an ensemble of 50 particles.<br> 3. Channel smoothed ensemble of particles: The elevations of the river network smoothed in the ensemble of particles in 2.<br> 4. Flood boundary smoothed ensemble of particles: The elevations along flood boundaries provided in the excel files smoothed in the ensemble mentioned in 3.<br> 5. Water surface constrained ensemble: A water surface constraint is applied to the ensemble in 4 to produce the ensemble that goes into the particle batch smoother described in the article.</p> <p><strong>Reference:</strong></p> <p>Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O’Loughlin, F., Neal, J. C., Bates, P. D. (2017). A high-accuracy map of global terrain elevations. <em>Geophysical Research Letters</em>, 44 (11), 5844–5853. doi: 10.1002/2017GL072874</p>
Datasets, figures and simulation scripts for "Quantum circuit compilation with quantum computers"
<p>The files contain the datasets and figures with the results of the manuscript "<a title="Quantum circuit compilation with quantum computers" href="https://doi.org/10.48550/arXiv.2408.00077" target="_blank" rel="noopener">Quantum circuit compilation with quantum computers</a>".</p> <p>The repository URL links to the repository with the simulation scripts used to produce the datasets and figures.</p>
Compilation of δO2/N2 records, and accumulation rate and temperature reconstructions from various polar ice cores
<p>Compilation of δO2/N2 records, and accumulation rate and temperature reconstructions from various polar ice cores. The first file 'Compilation_Acc_T_2024.xlsx' contains published temperature and accumulation rate reconstructions which were used to make Figure 3 in the associated paper. The datasets are included as published (i.e., no interpolation onto age or depth scales). Depth/age ranges considered in study are indicated in Table 3 in associated paper. The second file 'Compilation_O2N2_2024.xlsx' contains published and unpublished δO2/N2 data. </p>
Fig. 30 in Compilation of personal tributes to William Roy Branch (1946-2018): a loving husband and father, a good friend, and a mentor
Fig. 30. DNA sampling of Spek's Hinged Tortoise, Kinixys spekii (Photo: Amber Jackson).
Fig. 28 in Compilation of personal tributes to William Roy Branch (1946-2018): a loving husband and father, a good friend, and a mentor
Fig. 28. Bill Branch and others at Bagamoyo, Tanzania, in 2014.
Fig. 24 in Compilation of personal tributes to William Roy Branch (1946-2018): a loving husband and father, a good friend, and a mentor
Fig. 24. Bill Branch in Angola with a dead on the road Vine Snake (Photo: Alex Paullin).
Fig. 22 in Compilation of personal tributes to William Roy Branch (1946-2018): a loving husband and father, a good friend, and a mentor
Fig. 22. Bill Branch in Angola looking for frogs (Photo: Alex Paullin).
Fig. 15 in Compilation of personal tributes to William Roy Branch (1946-2018): a loving husband and father, a good friend, and a mentor
Fig. 15. Bill Branch and Krystal Tolley in south-western Angola in 2009 (Photo: Krystal Tolley).
Fig. 26 in Compilation of personal tributes to William Roy Branch (1946-2018): a loving husband and father, a good friend, and a mentor
Fig. 26. Bill Branch photographing some lilies at Lake Langanao, Ethiopia (Photo: Steven Spawls).
Fig. 32. Bill with a in Compilation of personal tributes to William Roy Branch (1946-2018): a loving husband and father, a good friend, and a mentor
Fig. 32. Bill with a shoftshell terrapin he caught (Photo: Craig Weatherby).
Fig. 31 in Compilation of personal tributes to William Roy Branch (1946-2018): a loving husband and father, a good friend, and a mentor
Fig. 31. Bill reading science in 45 °C heat (Photo: Amber Jackson).
Compilation of mean monthly water table depth data (2015-2023) and linkages to further published sources of water table data, from European peatlands
<p>This dataset (WH_D1_4_meanmonthly.csv) contains mean monthly water table depth data for 211 point locations, for which the data were originally captured at a higher temporal resolution and were additionally clipped to the temporal window (2015 onwards) of the available Earth Observations in the Sentinel-1 and Sentinel-2 archive. Links to higher resolution/longer time series of these source data, where these are already in the public domain, have been identified in the data submission in case future data users require more detailed water table datasets.Information on site co-ordinates, data period, condition class, and other details, are provided in the associated metadata file (WH_D1_4_metadata.csv). Further links to 165 additional water table dynamics data have been provided for future users, but were not summarised as monthly means in this data submission in case the source data are updated in future. Please refer to the README file for methodological details and important disclaimers.</p>
Dataset S1 - Noelaerhabdaceae organic carbon isotope culture data compilation
<p class="BodyA">The carbon isotope fractionation in algal organic matter (E<sub>p</sub>), including the long-chain alkenones produced by the coccolithophorid family Noelaerhabdaceae, is used to reconstruct past atmospheric CO<sub>2</sub> levels. The conventional proxy linearly relates E<sub>p</sub> to changes in cellular carbon demand relative to diffusive CO<sub>2</sub> supply, with larger E<sub>p</sub> values occurring at lower carbon demand relative to supply (<i>i</i>.<i>e</i>. abundant CO<sub>2</sub>). However, the response of <i>Gephyrocapsa oceanica</i>, one of the dominant alkenone producers of the last few million years, has not been studied closely. Here we subject <i>G. oceanica</i> to various CO<sub>2</sub> levels by increasing pCO<sub>2</sub> in the culture headspace, as opposed to increasing dissolved inorganic carbon (DIC) and alkalinity concentrations at constant pH. We note no substantial change in physiology, but observe an increase in E<sub>p</sub> as carbon demand relative to supply decreases, consistent with DIC manipulations. We compile existing Noelaerhabdaceae E<sub>p</sub> data and show that the diffusive model poorly describes the data. A meta-analysis of individual treatments (unique combinations of lab, strain, and light conditions) shows that the slope of the E<sub>p</sub> response depends on the light conditions and range of carbon demand relative to CO<sub>2</sub> supply in the treatment, which is incompatible with the diffusive model. We model E<sub>p</sub> as a multilinear function of key physiological and environmental variables and find that both photoperiod duration and light intensity are critical parameters, in addition to CO<sub>2</sub> and cell size. While alkenone carbon isotope ratios indeed record CO<sub>2</sub> information, irradiance and other factors are also necessary to properly describe alkenone E<sub>p</sub>.</p>
Compilation of concavity index calculations
<p>This dataset contains the compilation of the reference concavity analysis calculated for the manuscript "Impact of changing concavity indices on channel steepness and divide migration metrics" - JGR:Earth Surface</p> <p>Boris Gailleton - boris.gailleton@gfz-potsdam.de<br> Simon M. Mudd<br> Fiona J. Clubb<br> Stuart W.D. Grieve<br> and Martin D. Hurst</p> <p><br> The files are organised by folders, each representing one field site. They contain a csv file with the different information used for table 1 in the main manuscript as well as few useful figures. The summary CSVs have the following collumns:</p> <p>raster_name: a unique ID<br> best_fit: the best fit concavity index<br> err_neg: the lower bound<br> err_pos: the higher bound<br> best_fit_norm_by_range: the best fit concavity index (calculated with the range method)<br> err_neg_norm_by_range: the lower bound (calculated with the range method)<br> err_pos_norm_by_range: the higher bound (calculated with the range method)<br> D*_XXX: disorder for each concavity index tested<br> D*_r_XXX: ranged disorder for each concavity index tested<br> X_median: the median X coordinate of the basin in local WGS84 - UTM coordinates<br> X_firstQ: the median X coordinate of the basin in local WGS84 - UTM coordinates<br> X_thirdtQ: the median X coordinate of the basin in local WGS84 - UTM coordinates<br> Y_median: the median X coordinate of the basin in local WGS84 - UTM coordinates<br> Y_firstQ: the median X coordinate of the basin in local WGS84 - UTM coordinates<br> Y_thirdtQ: the median X coordinate of the basin in local WGS84 - UTM coordinates</p> <p>The local UTM zones are the following (N: North, S: South):</p> <p>Andes_Chile: 19S<br> Arkansas: 15N<br> Bureinsky_range_russia: 52N<br> Carpathians: 35N<br> Caucasus: 38N<br> Central_sierra_madre: 13N<br> Corsica: 31N<br> Ethiopia: 37N<br> Lesotho: 35S<br> Luzon_Phillippines: 51S<br> North_of_Beijing: 50N<br> Nujang: 46N<br> Oregon_Coast_Ranges: 10N<br> San_Gabriel_Mts: 11N<br> Southern_Altai: 47N<br> Southern_Brazil: 23S<br> West_Zoid_Afrika: 33S<br> Wisconsin: 15N<br> Yemen: 38N<br> atlas: 29N<br> dolomites: 33N<br> hida: 54N<br> himalayas: 45N<br> kentucky_and_west_virginia: 17N<br> northern_appalachians: 17N<br> olympic: 10N<br> pyrenees: 31N<br> southern_appalachians: 10N<br> taiwan: 51N<br> tien_shan: 44N<br> zagros: 38N</p> <p><br> There is also a summary csv file compiling all the information in the root folder.</p> <p>Most of the field sites also have a number of figures:</p> <p>_CDF_IQR: Cumulative distributed function of the inter-quartile range of concavity indices' uncertainties for all the basins in the area<br> _histogram_all_fits: Histogram of all the best fits<br> _MAP_best_fits: Map of the best fits<br> _D_star_range_theta_X: Map of D_star_r for the median best fit of all the basins (i.e. how good the median best fit is for each basins)<br> _min_Dstar_for_each_basins: Map of minimum D_star for each basin, representing the quality of the best fit for each basins</p> <p><br> Note that few field sites only have the csv file, as they are themselves compilation of multiple analysis.</p> <p>All the calculations have been done usign lsdtopytools (10.5281/zenodo.4774992)</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Fig. 124. Chrysichthys auratus, 242.0 in The non-native freshwater fishes of Singapore: an annotated compilation
Fig. 124. Chrysichthys auratus, 242.0 mm SL, Bishan (Lim RHB).
Fig. 123 in The non-native freshwater fishes of Singapore: an annotated compilation
Fig. 123. Horabagrus brachysoma, length not recorded, 700 g, Kranji Reservoir (Looh CW).
Fig. 125. Leporinus fasciatus, 67.0 in The non-native freshwater fishes of Singapore: an annotated compilation
Fig. 125. Leporinus fasciatus, 67.0 mm SL, trade material.
Fig. 117. Trichopodus pectoralis, 96.6 in The non-native freshwater fishes of Singapore: an annotated compilation
Fig. 117. Trichopodus pectoralis, 96.6 mm SL, Sungei Buloh.
Fig. 120 in The non-native freshwater fishes of Singapore: an annotated compilation
Fig. 120. Number of first records of non-native fish species from 1849 to 2016.
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