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We simulated the surface evolution for a pre-Nectarian surface unit and found that relative to their size, large complex craters are less destructive to the surrounding terrain than small simple craters. The data is structured as follows: 01_CTEM_Outputs - .dat files in which the craters from the simulations are stored - Python script to convert the .dat files to shapefiles 02_Shapefiles_From_CTEM_Outputs - Shapefiles in which the craters from the simulations are stored - Shapefile of the surrounding area (created manually) 03_CSFD_Measurements - A modified version of CSFD Tools to conduct Cartesian crater size-frequency distribution measurements - .scc files which contain the results from the Traditional Crater Counting and Non-sparseness Correction techniques (for further analysis in Craterstats) 04_Crater_Statistics - .stat files which contain crater size-frequency distribution statistics (obtained from the Craterstats software) 05_Crater_Equilibrium - simplified .stat files which contain cumulative number density information - Python script to fit a power law function to the simplified .stat files 06_Geometric_Saturation_Levels - .stat files which contain crater size-frequency distribution statistics (obtained from the Craterstats software) - Python script to calculate geometric saturation levels from .stat files - .txt files containing geometric satration levels Data by Orgel et al. (2018) are availiable here: http://www.planet.geo.fu-berlin.de/Orgel_etal_2017_Lunar_basins.zip
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The database, version 26 (first version was available in 2002), contains now 13239 site forms, (most of them with their geographical coordinates), comprising 16695 radiometric data: Conv. 14C and AMS 14C (12922 items), TL (10143 items), OSL (6510 items), ESR, Th/U and AAR (2093 items) from the European (Russian Siberia included) Lower, Middle and Upper Palaeolithic. All 14C dates are conventional dates BP. This improved version 26 replaces the older version 25. 170 new sites are incorporated and 267 sites have a corrected or an updated content.
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We examined the role of employee justice perceptions in explaining the distinct effects of two forms of pay transparency– process versus outcome pay transparency– on counterproductive workplace behavior (CWB). Study 1, a field study of 321 employees, revealed that process pay transparency is inversely related to CWB-O, with this effect mediated by greater procedural justice perceptions. In contrast, among employees perceiving their pay position as being lower than that of referent others, outcome pay transparency is positively associated with both CWB-O and CWB-I, with this effect mediated by reduced distributive justice perceptions. Study 2, using an online simulation-based experiment conducted on 394 employees and assessing actual deception behaviors, replicated and extended these findings. Specifically, when pay allocations were transparent (vs. secretive) and participant's pay was manipulated to be lower than that of teammates, participants reported lower distributive justice perceptions leading to heightened deception behaviors, with this effect mediated by a more negative emotional state. Analyses were done using MPlus 8.4. Files (.dat , and .inp , files are attached) for both CFA (Study 1) and path analyses (Studies 1 and 2). *-alt* files were used for testing alternative models reported in the papaer. R file and .csv file (Study 1) were used to compute alpha and omega values for ordinal indicators. Finally, *-omega* files (Study 2) were used to compute omegas in Study 2.
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The dataset contains the Shu-Talas Transboundary Basin shapefiles. The shapefiles are produced using the data from HydroSHEDS project that provides watershed delineations at a global scale. Shu-Talas Basin has two major rivers, Shu and Talas. Their boundary shapefiles are included separately. Very small sub-basins within the Shu-Talas Basin are merged and dissolved.
Data Types:
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In support of the manuscript by Bagley et al. (2020; see below) on quaking aspen phylogeography and ecological niche modeling (ENM), this accession provides 1) the in-house laboratory protocol used to extract DNA from aspen leaf tissues (modified from Strauss Lab); 2) the Supporting Information files for the corresponding manuscript (Bagley et al. 2020); 3) code used to conduct independent runs of the TASSEL-GBSv2 SNP discovery pipeline (Glaubitz et al. 2014) on our final (combined) genotyping-by-sequencing (GBS) dataset; 4) resulting SNP variant files from TASSEL-GBSv2 and final filtered variant call format (VCF) and genotype data files used during our genomic analyses; and 5) unfiltered vs filtered species occurrence data files and computer code used during our ENM analyses of our focal taxon, Populus tremuloides. REFERENCES Bagley, J. C., Heming, N. M., Gutiérrez, E. E., Devisetty, U. K., Mock, K. E., Eckert, A. J., & Strauss, S. H. (2020). Genotyping-by-sequencing and ecological niche modeling illuminate phylogeography, admixture, and Pleistocene range dynamics in quaking aspen (Populus tremuloides). Ecology and Evolution. Glaubitz, J. C., Casstevens, T. M., Lu, F., Harriman, J., Elshire, R. J., Sun, Q., & Buckler, E. S. (2014). TASSEL-GBS: a high capacity genotyping by sequencing analysis pipeline. PLoS One, 9(2): e90346
Data Types:
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Files and tables in support of the manuscript “Mineral precipitation as a mechanism of fault core growth” submitted to the Journal of Structural Geology. Table S1 contains structural measurements from Dixie Comstock, Nevada, USA. Map S1 is a .kmz file that can be downloaded and opened with Google Earth that includes a geologic map of the Dixie Comstock area, approximate locations for several other figures from the submitted text, sample locations, and scanline locations presented in Table S2.
Data Types:
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The dataset contain ground rainfall data, radar rainfall grid data, and polarimetric data. These data are taken as an example for analyses of quantitative precipitation estimates by X-band polarimetric weather radar in Yogyakrta Region.
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In this folder are stored the data used in the Data in Brief article "Hydro-stratigraphic datasets for the reconstruction of a large scale 3D FEM numerical model in the Milan metropolitan area (northern Italy)" (Previati A., et al., 2020). 1. "SHP" folder contains the side boundaries of the model (Model_Domain.shp), the top and bottom boundaries (Ground_Surface_Elevation.shp, Model_Bottom.shp), the limit surface (Phreatic_Aquifer_Bottom.shp) between the shallow phreatic and the lower semi-confined aquifers and the hydraulic head isolines as on 2016 (Hydraulic_head_Isolines.shp). 2. "3DFEM_Mesh_Parameters.txt" contains the location (X, Y, Z Coordinate system EPSG:3003) of the mesh nodes and the associated parameters (hydraulic conductivity and porosity) as well as the grain size class, the aquifer system and the depositional system (see the associated article for a detailed description).
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Data Set S1. Satellite data as digitized KMZ file of the ~6,000 landslides shown in Figure 9, indicating the manually digitized axial trends using the Google Earth© satellite image of the Hokkaido landslide region. Data Set S2. Aeromagnetic data digitized as a Google Earth KMZ file (raw data for Figure 11d)
Data Types:
  • Geospatial Data
  • Dataset
The objective of this dataset was to present the forage biomass production over time in different pasture management systems. We selected two farms located in the Western region of São Paulo State, Brazil. Pasture field data collection was carried out in two farms during three dates (June and November 2018 and March 2019) over two seasons (wet and dry). Samples were regularly taken through time to monitor forage biomass. These fields represent a wide variety of pasture management, as follow: Farm 1 (Santa Clara): i) traditional, low forage productivity, cattle rotation; ii) traditional, intermediate forage productivity, fertilized, cattle rotation; iii) intensified pasture, high forage productivity, reformed, cattle rotation. Farm 2 (Poderosa): i) traditional degraded*, recently reformed with millet + grass, cattle rotation; ii) traditional, low forage productivity, signs of degradation, fertilized, cattle rotation. *degraded was based on visual analysis of pasture area with sparse grass and exposed soil in some areas. With the support of NDVI images from the MODIS sensor, sample pixels were used to allocate the sample points. The areas of these pixels were divided into nine sampling points and in each of these points, the forage biomass was collected. Soil analyses were also carried out in two seasons (June 2018 and March 2019). The data files were organized in three folders. Each folder represents one field campaign. These folders have a shapefile of all the fields, the same file in kml extension (to open on Google Earth) and a zip file with photography of each field during the field campaign. The attribute table of the shapefile has a description of the fields and biomass. Excel files show the same information of the attribute table and a description of the items. A figure with the template of the biomass collection scheme is also available. Soil analyses are in the folders 'June 2018' and 'March 2019'. A more detailed description and discussion about these data and their association with soil chemical analysis were described in a scientific report (available by request). The biomass collection allowed the analysis of the forage production and better diagnoses about resource utilization strategies over the different pasture systems. This work was funded by the São Paulo Research Foundation (process numbers 2018/10770-1, 2017/06037-4, 2016/08741-8, 2017/08970-0, 2018/11052-5 and 2014/26767-9) as part of the Global Sustainable Bioenergy Initiative.
Data Types:
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  • File Set