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  • Rural Wards of Dodoma Urban District shapefile was extracted from the 2012 Wards shapefile obtained from the National Bureau of Statistics (NBS) of Tanzania downloaded at https://www.nbs.go.tz/index.php/en/census-surveys/gis/386-2012-phc-shapefiles-level-three. The shapefile of the randomly sampled nine Rural Wards studied in this study was extracted from the Rural Wards of Dodoma Urban District shapefile. The household size of each of the nine Rural Wards was extracted from the 2012 Population and Housing Census report of the NBS [24]. Waterpoint data were derived from the Directorate of Rural Water Supply (DRWS) of Tanzania through the Government Basic Statistics Portal. The catchment areas were generated in this study using water point data as input and buffer tool in QGIS 3.10.5 software. Households shapefile was generated by extracting households within catchment areas from Google Earth through digitization.
    Data Types:
    • Software/Code
    • Geospatial Data
    • Tabular Data
    • Dataset
  • The data contains a shapefile with IDs in "Regionalization shapefile.zip" and a respective KML layer (for Google Earth) in "regionalization_layer_v1.kmz". It is meant to be used to aggregate, store and share regionalized results from LCA and LCIA, as the layer covers the main features for impact assessment and policy level. It contains 50’626 units (48’612 terrestrial and 2'014 marine regions). The ID description and correspondance to the original layer are provided in "Description shapefile layer.xlsx", incl. some additional information from the original layers. The information related to countries is in yellow, ecoregions in green, urban in grey and watersheds in light blue. Coastal marine ecoregions are in marked dark blue and fisheries purple. Through the information in this layer, existing regionalized LCIA results such as recommended methods for land and water use can be directly linked. However, they can also be linked just based on ecoregions, watersheds, countries or urban areas using the original shapefiles used to compile this dataset through respective IDs. The following 6 layers have been used: - Urban areas: Natural Earth, Urban Areas, version 4.0.0, 11877 Urban areas Schneider, A., M. A. Friedl and D. Potere (2009) A new map of global urban extent from MODIS data. Environmental Research Letters, volume 4, article 044003. - Country boundaries (subunits) Natural Earth, Admin 0 – Details, version 4.1.0, 197 countries https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details/ - Ecoregions Terrestrial Ecoregions of the World, WWF (2012), 867 terrestrial ecoregions Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., D'Amico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., Kassem, K. R. 2001. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51(11):933-938. - Watersheds Aware method: Input data (WaterGAP), 11049 watersheds; http://www.wulca-waterlca.org/aware.html Müller Schmied, H., Eisner, S., Franz, D., Wattenbach, M., Portmann, F. T., Flörke, M., and Döll, P.: Sensitivity of simulated global-scale freshwater fluxes and storages to input data, hydrological model structure, human water use and calibration, Hydrol. Earth Syst. Sci., 18, 3511-3538, doi:10.5194/hess-18-3511-2014, 2014 - Marine ecosystems: Marine Ecoregions of the World, WWF (2007) Marine Ecoregions of the World: A Bioregionalization of Coastal and Shelf Areas (2007) Spalding M Fox H Allen G Davidson N Ferdaña Z et. al. BioScience 2007 vol: 57 (7) pp: 573-583 - Fishing areas: FAO Statistical Areas for Fishery Purposes - FAO Statistical areas (Marine ) - No coastline (for use with custom coastline resolutions) - GIS data (WFS - SHP). FAO (2019). FAO Statistical Areas for Fishery Purposes. In: FAO Fisheries and Aquaculture Department [online]. Rome.
    Data Types:
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  • It is a data for paper named "A progressive and combined building simplification approach with local structure classification and backtracking strategy". Three datasets are provided. One is the original data, which is collected from an open data product named OS Open Map –Local and provided by Ordnance Survey. Another two datasets are buildings which are simplified based on the original dataset into scales of 1: 25, 000 and 1: 50, 000. The simplification approach are proposed in the paper "A progressive and combined building simplification approach with local structure classification and backtracking strategy" .
    Data Types:
    • Software/Code
    • Geospatial Data
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    • Document
    • File Set
  • The current study aims to investigate the relationships between personal relative deprivation and employees’ attitudes towards job and organization and the underlying psychological mechanisms of the associations. Drawing on the self-determination theory, we propose the personal relative deprivation leads to the three work-related basic needs unsatisfied, which in turn lower the job satisfaction and affective commitment. We collected data from 390 participants recruited from a professional research participation system. The results indicated that higher level of personal relative deprivation significantly reduce individual’s job satisfaction and affective commitment. The indirect effects of personal relative deprivation on the job satisfaction or affective commitment through the satisfaction of the autonomy needs or the satisfaction of the relatedness needs are significant, while the mediating effects of the satisfaction of competence needs are nonsignificant.
    Data Types:
    • Software/Code
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    • Document
  • Code and data files associated with Current Biology publication. Please contact lead author if you are interested in using data for any reason.
    Data Types:
    • Software/Code
    • Geospatial Data
    • Tabular Data
    • Dataset
  • Climate change has a significant impact on seasonal snow cover. However, obtaining robust data on snow cover remains a challenge. There is a significant lack of ground-based data for verification of remote and model data. Observation network in Siberia is quite rare, and the location of the snow stations does not always represent the characteristics of the territory. We aimed to extend the observation coverage of climate stations and to assess variability in different ecosystems. We focused on the representation of different ecosystem types in the southern West Siberian Plain and Altai low mountain area. We carried out our research in two catchments - Kasmala and Maima, located in the forest-steppe and lowland areas. The observations were conducted during the peak snow accumulation (late February - early March). In the Kasmala catchment, the observations were conducted in 2011-2014 and 2017-2019, in the Mayma catchment from 2015 to 2019. These works were funded by state projects of the Institute for Water and Environmental Problems SB RAS. In 2019, a joint 3S (South Siberian Snowpack) project funded by RFBR (N 19-35-60006, 2019-2022) was launched at Lomonosov Moscow State University. As part of this project, we expanded the observation network and conducted observations during the whole winter season 2019-2020 in three catchments: Kuchuk (steppe), Kasmala (forest-steppe), and Mayma (low mountains). Also, the 3S project merged existing data into a single dataset on snow properties (depth, density, SWE). Observations till 2019 were carried out on snow courses and small snow sites. Courses were 500 m to 2 km long. Depth measurements were made every 20 m, density measurements every 100/200 m. The snow sites were two perpendicular transects of 50 or 20 meters long, including 20 depth and 5 density measurements. In the 3S project, we changed the observation scheme (data 2019-2020). All observations were made at the snow sites, which included 61 depth and 13 density measurements. The sampling scheme was proposed by Jost et al., 2007. In total, in the Kasmala catchment, we carried out about 600 depth/70 density measurements, in the Mayma catchment about 800 depth/200 density measurements. Within the 3S project, we carried out 8781 depth and 1873 density measurements during the winter season. We highly recommend aggregating the data by courses, sites or catchments (do not use individual values).
    Data Types:
    • Software/Code
    • Geospatial Data
    • Tabular Data
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  • As spatial analysis can contribute to the understanding of COVID-19 epidemic, we compiled and georeference data for Mexico. Data were compiled from the National Population Council (CONAPO), Google, the National Institute of Statistics and Geography (INEGI), and the Secretary of Health. The data describe the cases of COVID and characteristics of the population, such as distribution, mobility, and prevalence of chronic diseases such as diabetes, hypertension, and obesity. These data were processed to be compatible and georeferenced to a common geographic framework to facilitate spatial analysis in a geographic information system (GIS). The dataset comprises GIS layers (shapefiles), tables (CSV formatted), and R scripts. A complete description will be submitted to the journal Data in Brief (https://www.journals.elsevier.com/data-in-brief/)
    Data Types:
    • Software/Code
    • Geospatial Data
    • Tabular Data
    • Dataset
  • assessment and prediction of short term and long term coastline change
    Data Types:
    • Software/Code
    • Geospatial Data
    • Tabular Data
    • Dataset
    • Document
    • File Set
  • 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. Unedited versions of all photographs used in the figures are also included.
    Data Types:
    • Geospatial Data
    • Image
    • Dataset
    • Document
  • RAMAS files from our model for covid in brazil
    Data Types:
    • Geospatial Data
    • Dataset