{"type": "FeatureCollection", "features": [{"id": "10.7910/DVN/MM1QQZ", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:24:32Z", "type": "Dataset", "created": "2020-07-15", "title": "Replication Data and statistical analyses for: Implications of the existence of different sexual forms on the interaction with arbuscular mycorrhizal fungi in a dioecious population of Opuntia robusta Wendl. 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It contains the following shapefiles: PO6_BAU_NoZoning_50.shp PO6_BAU_NoZoning_100.shp PO6_BAU_Zoning_50.shp PO6_Sprawl_Zoning_50.shp PO6_BAU_NoZoning_50.shp PO6_Compact_Zoning_50.shp PO6_Compact_NoZoning_50.shp The metronamica Model was applied on six scenarios with combinations of business as usual, suburban sprawl or compact city development which build on the socio-economic projections and density assumptions of the ESPON-ET2050 project, and use the land use allocation parameters from the RECARE and SoilCare Integrated Assessment Models. Spatial development (zoning) was for some scenarios restricted in high productive fields. The model results give probabilities (0 \u2013 1) of urban development within the 1 km\u00b2 cells. Based on these probability percentages the different soil functions are reduced (100% of the probability and 50% of the probability) compared to the current soil functioning and, for the 50% scenarios, partly replaced by low productive grasslands as gardens and other public greenery. Z-scores are calculated from the spatial SF maps for each of the environmental zones. These environmental zones are derived from the Metzger et al. (2013). The z-scores give the signed fractional number of standard deviations by which SF means for an environmental zone are above or below the mean value and allow us indicate which areas have a higher or lower soil function performance compared to the mean value. Z-scores from the current SF maps and scenario maps were then compared to each other to calculate the change in z-scores. This change in z-scores is given in the shapefiles and describes the relative change in soil function performance. Positive values indicate an improvement in soil functioning compared to the current situation, negative values a decrease. More information regarding calculation and interpretation of both this dataset and the soil function maps used to calculate the z-scores can be found in: Vrebos D., F. Bampa, R. Creamer, A. Jones, E. Lugato, L. O\u2019Sullivan, P. Meire, R.P.O. Schulte, J. Schr\u00f6der and J. Staes (2018). Scenarios maps: visualizing optimized scenarios where supply of soil functions matches demands. LANDMARK Report 4.3. and Jones A. et al. (2019). An options document to propose future policy tools for functional soil management. LANDMARK 5.3. All available from www.landmark2020.eu.", "keywords": ["Water resources", "Food Safety", "Food Safety and Toxicology", "Nutritional Sciences", "Social Sciences", "7. Clean energy", "Pathology and Forensic Medicine", "Health and Life Sciences", "Farming Systems and Practices", "11. Sustainability", "13. 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It contains the following shapefiles: PO6_BAU_NoZoning_50.shp PO6_BAU_NoZoning_100.shp PO6_BAU_Zoning_50.shp PO6_Sprawl_Zoning_50.shp PO6_BAU_NoZoning_50.shp PO6_Compact_Zoning_50.shp PO6_Compact_NoZoning_50.shp The metronamica Model was applied on six scenarios with combinations of business as usual, suburban sprawl or compact city development which build on the socio-economic projections and density assumptions of the ESPON-ET2050 project, and use the land use allocation parameters from the RECARE and SoilCare Integrated Assessment Models. Spatial development (zoning) was for some scenarios restricted in high productive fields. The model results give probabilities (0 \u2013 1) of urban development within the 1 km\u00b2 cells. 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For thin soils, we generally sampled the uppermost horizon (<25cm). Soil and plant tissue analysis was carried out at the KALRO laboratories.   During field seasons in 2013 and 2014 a total of 163 soil samples and 160 plant tissue samples in the Kenya Rift from Lake Magadi in the south to Lake Baringo in the north. All samples were tested for concentration of the following trace elements and nutrients: calcium (Ca), copper (Cu), iron (Fe), manganese (Mn), magnesium (Mg), nitrogen (N), potassium (K), sodium (Na), phosphorus (P), and zinc (Zn). Further, soil samples were tested for pH-value, electrical conductivity, and total organic carbon (Table S1). Water samples from springs and boreholes around Lake Elmenteita to test for fluoride are also shown. The location of sample sites are tabulated (Table S2). 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PM2.5 is a critical air pollutant with potential health and environmental impacts. This data highlights the individual components that contribute to PM2.5 levels, offering valuable information for air quality research and policymaking.  This dataset is a processed and aggregated from Randall Martin's PM2.5 componets data (https://sites.wustl.edu/acag/datasets/surface-pm2-5/). The processing involves applying a downscaling rasterization strategy using TIGER/Line Shapefiles.  The dataset covers various PM2.5 components, including but not limited to:  BC (Black Carbon): A fine particulate matter produced by incomplete combustion of carbon-based fuels. NH4 (Ammonium): A compound formed from ammonia gas, commonly found in airborne particles. NIT (Nitrate): Nitric acid and nitrate salts, which are major constituents of PM2.5 particles. OM (Organic Matter): Carbon-containing compounds from organic sources, contributing to particle mass. SO4 (Sulfate): Sulfuric acid and sulfate salts, originating from industrial and natural sources. SOIL (Soil Dust): Particles from soil erosion and mineral dust suspended in the air. SS (Sea Salt): Particles generated from ocean spray, containing various minerals. The data is organized by year and ZCTA, providing annual averages for each PM2.5 component. This dataset aids in understanding the composition and variations of PM2.5 across different geographical areas. It plays a crucial role in studying pollution sources, assessing health risks, and formulating air quality regulations.  This dataset represents an aggregation of Randall Martin's ground-level fine particulate matter (PM2.5) data at the ZCTA level. The processing of this dataset involves the application of a downscaling rasterization strategy using TIGER/Line Shapefiles. 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