Contributors:Raúl Roberto poppiel, Lacerda Marilusa Pinto Coelho, Safanelli José Lucas, Rizzo Rodnei, Manuel Pereira de Oliveira junior, Novais Jean Jesus, Jose Alexandre Dematte
Maps of clay, silt and sand contents (g kg-1) were predicted at 0-20 cm, 20-60 cm and 60-100 cm depths intervals by random forest regression in Google Earth Engine. Gridded soil information covers a part of the Midwest Brazil, from 12° S to 20° S and from 45° W to 54° W, and is available with 250m resolution. The maps were cross-validated and had Coefficient of Determination ranging from 0.64 to 0.85 at all depth intervals.
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.
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
The same data are also availble from the following github repository:
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
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:Omrani Hichem, Omrani Bilel, Parmentier Benoit , Helbich Marco
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:
The raw data file is zipped to save disk space. The original raw data have a size of 60 Gigabyte
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:Yapiyev Vadim, Skrzypek Grzegorz, Sagintayev Zhanay, Macdonald David, Verhoef Anne
The dataset (n=54) represents the results of analysis for hydrogen and oxygen isotope composition of water with calculated deuterium excess (d-excess: δ^2 H=8δ^18 O+10). The samples were collected during one hydrologic year from November 2015 to November 2016 at Burabay National Nature Park (BNNP), Kazakhstan. Lake water samples (n=30) were collected approximately each month during ice-free period, groundwater (n=13) and streamwater (n=6) samples quarterly. The lake water samples were collected by grab sampling at the shoreline usually at fixed locations (if the location for a given sample for a lake was different from the fixed point it was indicated in comment column and coordinates are provided). The lake water was sampled at approximately monthly intervals, from the start of the open water season (end of April) to the first days of November 2016 (about one week before permanent ice-cover; ice-on). Snow samples (n=2) collected near Lake Shortandy were melted in a sealed container at room temperature. Rainfall samples (n=3) were collected at Kazakh State Hydrometeorological Agency (Kazhydromet) weather station near Ulken Shabakty Lake. The rainfall samples were collected during abundant precipitation events using a large plastic container and immediately transferred into the vials and sealed. Groundwater samples were collected from boreholes (GrdW1-4) using a bailer. Groundwater was sampled during the open water season at approximately three-month intervals (end of April, mid-July, and end of October 2016). Stream samples were collected at approximately the same times as groundwater samples following the same sampling procedure used for lake water samples. The samples were analyzed at the Global Institute for Water Security, McDonnell Watershed Hydrology Laboratory (Saskatchewan, Canada) on a Liquid Water Isotope Analyzer (Los Gatos Research). The analyzer uses liquid water Off-Axis Integrated-Cavity Output Spectroscopy (Off-Axis ICOS) and has an uncertainty of ≤ ±1.0 for δ2H and ±0.2 for δ18O. The following reference materials were used to normalize obtained values to VSMOW international scale: ‘Saskatoon Snow Melt Water’ (SSMW): δ2H= -200.4 ‰, δ18O = -26.1 ‰; and ‘Enriched’: δ2H=3.2 ‰, δ18O=-0.3 ‰). All values are reported as parts per thousand (‰) according to the Vienna Standard Mean Ocean Water - Standard Light Antarctic Precipitation (VSMOW-SLAP) scales. See also the Materials and Methods sections (3.2 and 3.3) of the article. The sampling points and weather station location can also be found in Google Earth geospatial data format file (Burabay isotopes paper.kmz).
These data are a matrix of diploid genotypes of 14 microsatellite loci for 18 cattle breeds of European and Asian origin. For each locus and each individual, the genotype has 6-digit code (3 digits per allele). The first sheet includes the microsatellite data in the genind. Missing data indicated by 000000.