Contributors: Abdikaiym Zhiyenbek
... This dataset contains Aral Sea Basin boundary shapefiles and its sub-basins. The shapefiles are produced using data from HydroSHEDS project that provides watershed delineations at a global scale. Aral Sea basin has two major rivers, Syr-Darya and Amu-Darya, and their boundary shapefiles are included separately. Small sub-basins between these two major rivers were joined and merged to produce the Aral Sea Basin boundary.
Contributors: Andrea Vigliotti
... This is a static collection of the scripts needed to reproduce the examples of the paper: Vigliotti A., Auricchio F., "Automatic differentiation for solid mechanics", Archives of Computational Methods in Engineering, 2020, In the press DOI 10.1007/s11831-019-09396-y The same data are also availble from the following github repository: https://github.com/avigliotti/AD4SM.jl the above repository includes the AD4SM.jl package files and will be updated with new versions, new examples, bug corrections, etc. The scripts included in this data set are written in the Julia programming language and will need a working installation in order to run properly. Julia is an open-source, high-level, high-performance, dynamic programming language. Refer to the Julia language website for more information and downloads at https://julialang.org/ Following the content of the individual files: - adiff.jl : main module implementing the dual number algebra needed for the forward differentiation - materials.jl : module implementing the strain energy density functions for the different material models - elements.jl : module implementing the element integration rules, the functions for evaluating the deformation energy of the entire model, together with the Lagrange multipliers, and the solvers - example_01_non_linear_truss.jl : julia file for the first example 1 of the paper, this file produce as output the openscad model of the deformed truss for producing preety images - example_01_non_linear_truss.ipynb : jupyther notebook file for example 1 - example_02_Euler_beams.ipynb : julia file for the first example 2 of the paper - example_02_Euler_beams.jl : jupyther notebook file for example 2 - example_03_plane_stress.ipynb : jupyther notebook file for the first example 3 of the paper - example_03_plane_stress.jl : julia file for example 3 - example_04_AxSymDomain.ipynb : notebook file for example 4 - example_04_AxSymDomain.jl : julia file for example 4 - example_05_3DSpring.jl : julia file for example 5, this files produces output files readable with paraview - Pattern2D03FinerMesh02j.inp : input file for example 3 - AxSymDomainj.inp : input file for example 4 - 3DSpringHexaj.inp : input file for example 5 - polyhedron_hedges.scad : helper file to produce the openscad files for the deformed lattices of example 1 - description.txt : this file - step_to_reproduce.txt : the file with the steps to reproduce te ecamples
Contributors: Hichem Omrani, Bilel Omrani, Benoit Parmentier, Marco Helbich
... Monitoring of air pollution is an important task in public health. Availability of data is often hindered by the paucity of the ground monitoring station network. We present here a new spatio-temporal dataset collected and processed from the Sentinel-5P remote sensing platform aiming at the monitoring of air pollution for public institutions. As an example application, we applied the full workflow to process measurements of Nitrogen dioxide (NO2) collected over the territory of mainland France from May 2018 to June 2019. The data stack generated is daily measurements at a 4×7km spatial resolution. The supplementary code package used to collect and process the data is made publicly available to ease the access and processing for any location and product. The dataset provided in this article is of value for policy-makers and health assessment. Please find the full dataset in a Dropbox shared repository using this link: https://drive.google.com/drive/folders/1t5vbQq1g0LtJa37Sc6NYoq45bkLP2EWp?usp=sharing The raw data file is zipped to save disk space. The original raw data have a size of 60 Gigabyte
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Contributors: Gaetan Montero, Cécile Tannier, Isabelle Thomas
... Contributors: Gaëtan Montero, Cécile Tannier, Isabelle Thomas Date:2019-16-10 Description: This data set can be used to reproduce the analyses made by the authors in their paper “Morphological delineation of cities based on scaling properties of urban patterns: a comparison of three methods”. It contains 12 shapefiles that represent theoretical urban patterns and 4 shapefiles that can be used to delineate the morphological agglomeration of Brussels (Belgium). It also contains a R script to calculate the carrying capacity of a logistic percolation function. Description of each file 2_Figure_1: theoretical street network for testing the Natural Cities method 3_Figure_2: theoretical street network for the comparison of two variants of the Natural Cities method 4_Figure_3: theoretical street network to evaluate the effects of the spatial extent of the study area on the delineation of Natural Cities 5_Figure_5a: theoretical pattern for testing MorphoLim (building footprints) – dense urban core 6_Figure_5b: theoretical pattern for testing MorphoLim (building footprints) – less dense urban core 7_Figure_6: theoretical pattern (building footprints) to evaluate the effects of the geographic extent of the study area on the delineation with MorphoLim 8_Percolation_C_Calculation: R code to calculate the carrying capacity of a logistic function (Hierarchical Percolation) 9_Figure_7: theoretical street network for testing Hierarchical Percolation 10_Figure_8: theoretical polycentric street network for testing Hierarchical Percolation 11_Figure_9ac: theoretical urban pattern crossed by a large non built area (road intersections) 12_Figure_9b: theoretical urban pattern crossed by a large non built area (building footprints) 13_Figure_10ac: theoretical pattern where a built ribbon links two urban centres (roads intersections ) 14_Figure_10b: theoretical pattern where a built ribbon links two urban centres (building footprints) 15_Belgium_buildings: cadastral data of buildings (2D) for Belgium (© 2009 Administration Générale de la Documentation Patrimoniale) 16_Brabant_buildings: cadastral data of buildings (2D) for the province of Brabant (© 2009 Administration Générale de la Documentation Patrimoniale) 17_Belgium_roads: road network data come from the platform Geofabrik of OpenStreetMap (http://download.geofabrik.de, accessed 08/21/2018) for Belgium 18_Brabant_roads: Road network data come from the platform Geofabrik of OpenStreetMap (http://download.geofabrik.de, accessed 08/21/2018) for the province of Brabant.
Utilizing Indicator Kriging to Identify Suitable Zones for Manual Drilling in Weathered Crystalline Basement Aquifers
Contributors: Philip Deal
... Manual drilling offers a practical and affordable method of increasing access to groundwater supply in regions struggling with economic water scarcity. However, manual techniques are limited to specific hydrogeological contexts and must be sited appropriately. Indicator kriging is proposed as an interpolation method that builds upon previous efforts to identify suitable zones for manual drilling, particularly in weathered crystalline basement aquifers. This approach allows for heterogeneity within weathering profiles and provides probability mapping of success for regional planning. Modeling was conducted in the Upper East Region of Ghana using available borehole-log data, including: transmissivity, static water depth, laterite thickness, depth to hard rock, water quality parameters, and the degree of weathering. Indicator kriging interpolations predicted binary variables with over 90% accuracy. The model predicts that drilling into highly weathered layers will be common, and percussion techniques will be necessary to reach sufficient depths. The results suggest that suitable zones occur near Bolgatanga, Bawku, and Zebila, which coincide with historical drilling efforts in the central and eastern portions of the region. The original dataset was derived from the Hydrogeological Assessment of the Northern Regions of Ghana Project (HAP) implemented by SNC-Lavalin, Institut national de Recherche Scientifique (INRS) and the Water Resources Comission (WRC) of Ghana, and was supported by the Canadian International Development Agency. Hydrogeological data was collected and aggregated for the Voltaian Sedimentary Basin and Precambrian Basement complexes in Ghana from numerous sources. The data was compiled into a GIS databased for further study and analysis of the groundwater resources in Ghana. For this study, the dataset was obtained from the University of Ghana upon request with a focus on manual drilling feasibility. Borehole records were manipulated with various interpolation methods within the Upper East Region in ArcGIS, as described within the journal article.
Contributors: Pranav Pandya, Kartikey Hadiya, Arnava Ghatak
... Resource Mapping data was collected from field survey and all points such as markets, atms, schools were located and appropriate tags were given. Data was uploaded on Google sheets and addons of Fusion Mas and point map were installed and addons were run to form virtual maps in their own particular webpages. Source link of those webpages are determined and were added in a iframe in src link. In web html design a table was made and all three iframe are added in table. The final html was added as html element in sites.google.com to create a custom website. The website link: www.sites.google.com/site/pranavrsmap Webpage and Sheets are the most important data here. Other data are optional and are uploaded for your Geospatial Location research
Contributors: Rick J. Hogeboom, Davey de Bruin, Joep Schyns, Maarten Krol, Arjen Hoekstra
... Excelfile containing several water footprint caps, runoff and environmental flow statistics for 11,000+ river basins worldwide. Shapefile containing geometries for these basins.
Contributors: Tetsuji Okada
... DSA files of human (N to Z, by gene name) : UniProt ID is used for a protein to which no gene name is assigned.