The LUCAS LUC future dataset consists of annual land use and land cover maps from 2016 to 2100 for Europe. It is based on land cover data from the LANDMATE PFT dataset for the year 2015. The LANDMATE PFT consists of 16 plant functional types and non-vegetated classes that were converted from the ESA-CCI LC land cover data according to the method of Reinhart et al. (2022). For version 1.1 of the LUCAS LUC dataset, the improved LANDMATE PFT map version 1.1 was employed. The land use change information from the Land-Use Harmonization Data Set version 2 (LUH2 v2.1f, Hurtt et al. 2020) was imposed using the land use translator developed by Hoffmann et al. (2021). The projected land use change information was derived for different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) combinations used in the framework of the 6th phase of Coupled Model Intercomparison Project (CMIP6). For each year, a map is provided that contains 16 fields. Each field holds the fraction of the respective plant functional types and non-vegetated classes in the total grid cell (0-1). The LUCAS LUC dataset was constructed within the HICSS project LANDMATE and the WCRP flagship pilot study LUCAS to meet the requirements of downscaling experiments within EURO-CORDEX and other CORDEX regions. Plant functional types and non-vegetated classes: 1 - Tropical broadleaf evergreen trees 2 - Tropical deciduous trees 3 - Temperate broadleaf evergreen trees 4 - Temperate deciduous trees 5 - Evergreen coniferous trees 6 - Deciduous coniferous trees 7 - Coniferous shrubs 8 - Deciduous shrubs 9 - C3 grass 10 - C4 grass 11 - Tundra 12 - Swamp 13 - Non-irrigated crops 14 - Irrigated crops 15 - Urban 16 - Bare

The Land Use/Cover Area frame Survey (LUCAS) in the European Union (EU) was set up to provide statistical information. It represents a triennial in-situ landcover and land-use data-collection exercise that extends over the whole of the EU's territory. LUCAS collects information on land cover and land use, agro-environmental variables, soil, and grassland. The surveys also provide spatial information to analyse the mutual influences between agriculture, environment, and countryside, such as irrigation and land management. The dataset presented here is the harmonized version of all yearly LUCAS surveys with a total of 106 attributes. Each point's location is using the fields 'th_lat' and 'th_lon', that is, the LUCAS theoretical location (THLOC), as prescribed by the LUCAS grid. For more information please see Citations. Note that not every field is present for every year - see the "Years" section in property descriptions. The text "C1 (Instructions)" in the table schema descriptions refers to this document. See also the 2018 LUCAS polygons dataset.

The LUCAS LUC historical dataset consists of annual land use and land cover maps from 1950 to 2015 for Europe. It is based on land cover data from the LANDMATE PFT dataset that was generated from ESA-CCI LC data. The ESA-CCI LC land cover classes are converted into 16 plant functional types and non-vegetative classes employing the method of Reinhart et al. (2022). For version 1.1 of the LUCAS LUC dataset, the improved LANDMATE PFT map version 1.1 was employed. The land use change information from the Land-Use Harmonization Data Set version 2 (LUH2 v2h, Hurtt et al. 2020) was imposed using the land use translator developed by Hoffmann et al. (2021). For each year, a map is provided that contains 16 fields. Each field holds the fraction of the respective plant functional types and non-vegetative classes in the total grid cell (0-1). The LUCAS LUC dataset was constructed within the HICSS project LANDMATE and the WCRP flagship pilot study LUCAS to meet the requirements of downscaling experiments within EURO-CORDEX and other CORDEX regions. Plant functional types and non-vegetated classes: 1 - Tropical broadleaf evergreen trees 2 - Tropical deciduous trees 3 - Temperate broadleaf evergreen trees 4 - Temperate deciduous trees 5 - Evergreen coniferous trees 6 - Deciduous coniferous trees 7 - Coniferous shrubs 8 - Deciduous shrubs 9 - C3 grass 10 - C4 grass 11 - Tundra 12 - Swamp 13 - Non-irrigated crops 14 - Irrigated crops 15 - Urban 16 - Bare

The LUCAS LUC historical dataset consists of annual land use and land cover maps from 1950 to 2015 for Europe. It is based on land cover data from the LANDMATE PFT dataset that was generated from ESA-CCI LC data. The ESA-CCI LC land cover classes are converted into 16 plant functional types and non-vegetative classes employing the method of Reinhart et al. (2022). For version 1.1 of the LUCAS LUC dataset, the improved LANDMATE PFT map version 1.1 was employed. The land use change information from the Land-Use Harmonization Data Set version 2 (LUH2 v2h, Hurtt et al. 2020) was imposed using the land use translator developed by Hoffmann et al. (2021). For each year, a map is provided that contains 16 fields. Each field holds the fraction of the respective plant functional types and non-vegetative classes in the total grid cell (0-1). The LUCAS LUC dataset was constructed within the HICSS project LANDMATE and the WCRP flagship pilot study LUCAS to meet the requirements of downscaling experiments within EURO-CORDEX and other CORDEX regions. Plant functional types and non-vegetated classes: 1 - Tropical broadleaf evergreen trees 2 - Tropical deciduous trees 3 - Temperate broadleaf evergreen trees 4 - Temperate deciduous trees 5 - Evergreen coniferous trees 6 - Deciduous coniferous trees 7 - Coniferous shrubs 8 - Deciduous shrubs 9 - C3 grass 10 - C4 grass 11 - Tundra 12 - Swamp 13 - Non-irrigated crops 14 - Irrigated crops 15 - Urban 16 - Bare

The LUCAS LUC future dataset consists of annual land use and land cover maps from 2016 to 2100 for North America. It is based on land cover data from the LANDMATE PFT dataset for the year 2015. The LANDMATE PFT consists of 16 plant functional types and non-vegetated classes that were converted from the ESA-CCI LC land cover data according to the method of Reinhart et al. (2022). For version 1.1 of the LUCAS LUC dataset, the improved LANDMATE PFT map version 1.1 was employed. The land use change information from the Land-Use Harmonization Data Set version 2 (LUH2 v2.1f, Hurtt et al. 2020) were imposed using the land use translator developed by Hoffmann et al. (2023). The projected land use change information was derived for different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) combinations used in the framework of the 6th phase of Coupled Modelling Intercomparison Project (CMIP6). For each year, a map is provided that contains 16 fields. Each field holds the fraction the respective plant functional types and non-vegetated classes in the total grid cell (0-1). The LUCAS LUC dataset was constructed within the HICSS project LANDMATE and the WCRP flagship pilot study LUCAS to meet the requirements of downscaling experiments within CORDEX. Plant functional types and non-vegetative classes: 1 - Tropical broadleaf evergreen trees 2 - Tropical deciduous trees 3 - Temperate broadleaf evergreen trees 4 - Temperate deciduous trees 5 - Evergreen coniferous trees 6 - Deciduous coniferous trees 7 - Coniferous shrubs 8 - Deciduous shrubs 9 - C3 grass 10 - C4 grass 11 - Tundra 12 - Swamp 13 - Non-irrigated crops 14 - Irrigated crops 15 - Urban 16 - Bare

The LUCAS LUC historical dataset consists of annual land use and land cover maps from 1950 to 2015 for North America. It is based on land cover data from the LANDMATE PFT dataset that was generated from ESA-CCI LC data. The ESA-CCI LC land cover classes are converted into 16 plant functional types and non-vegetative classes employing the method of Reinhart et al. (2022). For version 1.1 of the LUCAS LUC dataset, the improved LANDMATE PFT map version 1.1 was employed. The land use change information from the Land-Use Harmonization Data Set version 2 (LUH2 v2h, Hurtt et al. 2020) were imposed using the land use translator developed by Hoffmann et al. (2023). For each year, a map is provided that contains 16 fields. Each field holds the fraction the respective plant functional types and non-vegetative classes in the total grid cell (0-1). The LUCAS LUC dataset was constructed within the HICSS project LANDMATE and the WCRP flagship pilot study LUCAS to meet the requirements of downscaling experiments within CORDEX. Plant functional types and non-vegetative classes: 1 - Tropical broadleaf evergreen trees 2 - Tropical deciduous trees 3 - Temperate broadleaf evergreen trees 4 - Temperate deciduous trees 5 - Evergreen coniferous trees 6 - Deciduous coniferous trees 7 - Coniferous shrubs 8 - Deciduous shrubs 9 - C3 grass 10 - C4 grass 11 - Tundra 12 - Swamp 13 - Non-irrigated crops 14 - Irrigated crops 15 - Urban 16 - Bare

The dataset contains gridded daily soil moisture simulations for Baden-Württemberg. The simulations were caried out with the hydroloigcal model TRAIN. The TRAIN model was set up for a 1 km resolution grid over the study region, which encompasses a variety of different soil, land use and climate characteristics. Next to the raw data, the dataset contains two animations that display the development and persistence of soil moisture drought during two of the more prominent drought years (2003 and 2018).

pclucas14/gistify-grid-search-prompts dataset hosted on Hugging Face and contributed by the HF Datasets community

A number of data layers are provided that accompany the publication "Assessment of soil organic carbon stocks under future climate and land cover changes in Europe" by Yusuf Yigini and Panos Panagos in "Science of The Total Environment, Volumes 557–558, 1 July 2016, Pages 838–850" (http://dx.doi.org/10.1016/j.scitotenv.2016.03.085) Soil organic carbon plays an important role in the carbon cycling of terrestrial ecosystems, variations in soil organic carbon stocks are very important for the ecosystem. Soil is the largest organic carbon pool of the terrestrial ecosystems on earth which interacts strongly with climate, and land cover change. In this study, a geo-statistical model is used to estimate the current and the future soil organic carbon (SOC) stocks in Europe. A geo-statistical approach is proposed to achieve spatiotemporal prediction of soil organic carbon stocks in Europe. The model consists of two sub-models (Figure 1). The base model predicts current soil organic carbon stocks at European scale using regression-kriging, and future model uses the regression coefficients and projects the estimation to the near future (2050). Figure 1. Prediction and Projection Workflow Model and Model Outputs It was hypothesized that soil organic carbon is driven largely by climate, land use and inherent soil properties. Moreover, it is anticipated that the complex relationship between soil organic carbon and its drivers is time independent and will remain in the future. From this point of view, the covariates which have been used to predict current soil organic carbon stocks in Europe can also help to predict future conditions by transferring the knowledge from today to the future. The first phase of the study predicts current soil organic carbon content (Figure 2) by using stepwise multiple linear regression and ordinary kriging and the second phase of the study projects the soil organic carbon to the near future (2050) by using a set of environmental predictors (Table 1). An approach is demonstrated to predict present and future soil organic carbon stocks by using climate, land cover, terrain and soil data and their projections. The covariates were selected for their role in the carbon cycle and their availability for the future model. The regression-kriging as a base model is predicting current SOC stocks in Europe by using a set of covariates and dense SOC measurements coming from LUCAS Soil Database. The base model delivers coefficients for each of the covariates to the future model. The overall model produced soil organic carbon maps which reflect the present and the future predictions (2050) based on climate and land cover projections. The data of the present climate conditions (long-term average (1950 - 2000)) and the future projections for 2050 were obtained from WorldClim data portal. The future climate projections are the recent climate projections mentioned in the Fifth Assessment IPCC report. These projections were extracted from the global climate models (GCMs) for four representative concentration pathways (RCPs). The results suggest an overall increase in SOC stocks by 2050 in Europe (EU26) under all climate and land cover scenarios, but the extent of the increase varies between the climate model and emissions scenarios. Figure 2. Soil organic carbon prediction map which represents the present conditions simulated by the base model (background map: ESRI, USGS, NOAA). Table 1. Present and Projected Soil organic Carbon Stocks (Pg) for EU26. (Cyprus and Croatia were excluded due to data unavailability) Available Data: Soil Organic Carbon Stocks (Current), tonnes.ha-1 Soil Organic Carbon Stocks (2050) by Climate Scenarios (CCSM4, HadGEM2-AO , IPSL-CM5A-LR MRI-CGCM3) and Representative Concentration Pathways (RCPs). (EU26), tonnes.ha-1 Metadata for "Soil Organic Carbon Stocks (Current)" Spatial Coverage: European Union, 26 Member States (no data for Cyprus and Croatia) Resolution: 1000m Format: Raster (GRID) Projection: ETRS89 Lambert Azimuthal Equal Area Input data: Climate Data (Current) from WorldClim Data Portal: Bio-climatic parameters, Annual Precipitation, Grid Size: 1000m Land Cover 2010, European Commission, Joint Research Centre, Sustainability Assessment Unit: Pan-European Land Use Modelling Platform (LUMP), Grid Size: 1000m Soil Data, Joint Research Centre European Soil Database, Ballabio et al. 2016: Clay, Silt, Sand, Soil Structure, Available Water Capacity, Grid Size: 1000m Output Data Layers: SOC_Stocks_EU26 : Current Predication of European Soil Organic Carbon Stocks (tonnes.ha-1) Metadata for "Soil Organic Carbon Stocks (2050) by Climate Scenarios (CCSM4, HadGEM2-AO , IPSL-CM5A-LR MRI-CGCM3) and Representative Concentration Pathways (RCPs). (EU26), tonnes.ha-1" Spatial Coverage: European Union, 26 Member States (no data for Cyprus and Croatia) Resolution: 1000m Format: Raster (GRID) Projection: ETRS89 Lambert Azimuthal Equal Area Input data: Climate Data (2050), WorldClim Data Portal: Bio-climatic parameters, Annual Precipitation, Grid Size: 1000m Land Cover 2050, European Commission, Joint Research Centre, Sustainability Assessment Unit: Pan-European Land Use Modelling Platform (LUMP), Grid Size: 1000m Soil Data, Joint Research Centre European Soil Database, Ballabio et al. 2016: Clay, Silt, Sand, Soil Structure, Available Water Capacity, Grid Size: 1000m Output Data Layers: Projected Soil Organic Carbon Stocks (2050) by Climate Scenarios (CCSM4, HadGEM2-AO , IPSL-CM5A-LR MRI-CGCM3), (tonnes.ha-1) - the names are self-explanatory after reading the paper (http://dx.doi.org/10.1016/j.scitotenv.2016.03.085) cc426: CCSM4, RCP 2.6 cc445: CCSM4, RCP 4.5 cc460: CCSM4, RCP 6 cc485: CCSM4, RCP 8.5 hd26: HadGEM2-AO, RCP 2.6 hd45: HadGEM2-AO, RCP 4.5 hd60: HadGEM2-AO, RCP 6 hd85: HadGEM2-AO, RCP 8.5 ip26: IPSL-CM5A-LR, RCP 2.6 ip45: IPSL-CM5A-LR, RCP 4.5 ip60: IPSL-CM5A-LR, RCP 6 ip85: IPSL-CM5A-LR, RCP 8.5 mg26: MRI-CGCM3, RCP 2.6 mg45: MRI-CGCM3, RCP 4.5 mg60: MRI-CGCM3, RCP 6 mg85: MRI-CGCM3, RCP 8.5

This dataset provides tabular data output from a series of modeling simulations for forest ecosystems of the U.S. state of California under different initial conditions scenarios. We used the LUCAS state and transition simulation model with carbon stocks and fluxes based on the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) to simulate changes in forest ecosystem carbon balance resulting from historical land use and land cover change, annual climate variability, and disturbance from wildfire and drought-induced forest die-off. The model was run at a 1-km spatial resolution on an annual timestep for the years 1985 to 2020. We simulated 36 initial conditions scenarios based on unique combinations of spatial datasets used to define forest extent, forest composition and forest age at the beginning of the simulation period. Results presented here have been aggregated from the individual grid cell level and summarized for the entire state of California.

Total magnetic intensity (TMI) data measures variations in the intensity of the Earth's magnetic field caused by the contrasting content of rock-forming minerals in the Earth crust. Magnetic …Show full descriptionTotal magnetic intensity (TMI) data measures variations in the intensity of the Earth's magnetic field caused by the contrasting content of rock-forming minerals in the Earth crust. Magnetic anomalies can be either positive (field stronger than normal) or negative (field weaker) depending on the susceptibility of the rock. The data are processed via standard methods to ensure the response recorded is that due only to the rocks in the ground. The results produce datasets that can be interpreted to reveal the geological structure of the sub-surface. The processed data is checked for quality by GA geophysicists to ensure that the final data released by GA are fit-for-purpose. This magnetic grid has a cell size of 0.004 degrees (approximately 430m). The data used to produce this grid was acquired in 1980 by the WA Government, and consisted of 57126 line-kilometres of data at 1500m line spacing and 150m terrain clearance.

Project CROSSBOW is an EU funded project with grant agreement Nº 773430 in the topic LCE-04-2017.

This dataset corresponds to the regional grid model used as input for the tests done for RES-CC products during the preliminary demostration phase.

This grid model is in xiidm format (POWSYBL grid format) and the data is anonymised due to security issues. The grid model is generated from regional Common Grid Model (CGM), generated in turn by SCC (Balkan region regional security center) by compiling and merging the countries Individual Grid Models (IGM).

for more info: http://crossbowproject.eu/

This data and code archive provides the raw data and code used to create the figures for the paper "Transmission Impossible? Prospects for Decarbonizing the US Grid" under preparation for the Journal of Economic Perspectives.

This dataset presents the mean topsoil (0-15 cm) organic carbon concentration (g kg-1) and measures of its variability at 1 km resolution across Great Britain. The mean and variability metrics were calculated from an ensemble of eight previously published digital soil maps applied to all 1 km grid cells across GB where data were available from all eight maps. Four of the maps were generated from the 2007 UKCEH Countryside Survey topsoil data, two of which are available online to use. Two maps are from the International Soil Reference and Information Centre, ISRIC: SoilGrids250m v.1 (2017) and v.2 (2020) which are free to download. Two maps are from the EU’s Joint Research Centre (JRC): the OCTOP map of 2004 and LUCAS map of 2014 which are both free to download. The dataset comes in the form of a seven-band raster tiff file, with each band representing the following: 1. Mean predicted soil organic carbon concentration (g kg-1) of all eight maps at each 1 km grid cell 2. Standard deviation (g kg-1) of all eight maps at each 1 km grid cell 3. Coefficient of variation (unitless; the standard deviation divided by the mean) at each 1 km grid cell 4. Signal to noise ratio (unitless; the mean divided by the standard deviation) at each 1 km grid cell 5. Name of the map that deviates the most from the ensemble mean at each 1 km grid cell 6. Relative size (%) of the largest difference from the ensemble mean at each 1 km grid cell 7. Relative size of the largest difference from the ensemble mean expressed as the number of standard deviations exceeded at each 1 km grid cell This work was supported by the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability. Full details about this dataset can be found at https://doi.org/10.5285/c3b400ea-f603-4bc2-9225-5f1abfa9fe65

AUSGeoid98 data files contain a 2 minute grid of AUSGeoid98 data covering the Australian region, which you can use to interpolate geoid-ellipsoid separations for the positions required.You can use your own interpolation software, or you can use Geoscience Australia's Windows Interpolation software (Winter). The data files are text files in a standard format that cover the same area as standard topographic map areas. Files covering both 1:250,000 (approximately 100 x 150 km) and 1:1,000,000 (approximately 400 x 600 km) map areas are available. There is a 4 minute overlap on all sides of each area. Data format: AUSGeoid98 data files have a header record at the start of each file, to distinguish them from the superseded AUSGeoid93 data files. AUSGeoid98 data files show the geoid-ellipsoid separation to 3 decimal places, while the superseded AUSGeoid93 data files showed only 2 decimal places. AUSGeoid98 deflections of the vertical were computed from the geoid-ellipsoid separation surface, while the AUSGeoid93 deflections of the vertical were computed from OSU91A.

The LUCAS LUC future dataset consists of annual land use and land cover maps from 2016 to 2100 for North America. It is based on land cover data from the LANDMATE PFT dataset for the year 2015. The LANDMATE PFT consists of 16 plant functional types and non-vegetated classes that were converted from the ESA-CCI LC land cover data according to the method of Reinhart et al. (2022). For version 1.1 of the LUCAS LUC dataset, the improved LANDMATE PFT map version 1.1 was employed. The land use change information from the Land-Use Harmonization Data Set version 2 (LUH2 v2.1f, Hurtt et al. 2020) were imposed using the land use translator developed by Hoffmann et al. (2023). The projected land use change information was derived for different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) combinations used in the framework of the 6th phase of Coupled Modelling Intercomparison Project (CMIP6). For each year, a map is provided that contains 16 fields. Each field holds the fraction the respective plant functional types and non-vegetated classes in the total grid cell (0-1). The LUCAS LUC dataset was constructed within the HICSS project LANDMATE and the WCRP flagship pilot study LUCAS to meet the requirements of downscaling experiments within CORDEX. Plant functional types and non-vegetative classes: 1 - Tropical broadleaf evergreen trees 2 - Tropical deciduous trees 3 - Temperate broadleaf evergreen trees 4 - Temperate deciduous trees 5 - Evergreen coniferous trees 6 - Deciduous coniferous trees 7 - Coniferous shrubs 8 - Deciduous shrubs 9 - C3 grass 10 - C4 grass 11 - Tundra 12 - Swamp 13 - Non-irrigated crops 14 - Irrigated crops 15 - Urban 16 - Bare

The Norwegian Institute for Nature Research (www.nina.no) has conducted area representative surveys of butterflies and bumblebees since 2009, on behalf of the Norwegian Environment Agency (https://www.miljodirektoratet.no/). The monitoring project is designed to provide indicators of bumblebees and butterflies to the Nature Index of Norway (https://naturindeks.no/), which measures the condition of the biodiversity in Norway. The monitoring project is supervised by the Norwegian Institute for Nature Research, but the field inventories are done by citizen scientists administered by Sabima (https://www.sabima.no/). More information (in Norwegian) can be found at the project web-page, which also has a rudimentary data display of the projects results (https://www.nina.no/V%C3%A5re-fagomr%C3%A5der/Milj%C3%B8overv%C3%A5king-p%C3%A5-land/Humler-og-dagsommerfugler ). The project started in 2009 in the former counties Østfold (now part of county Viken) and Vestfold (now part of county Vestfold and Telemark). Citizen scientists joined the project in 2010, and the project was extended geographically the following year to include also the county Trøndelag. Since 2013, the project also includes the former county of Vest-Agder (now part of county Agder), and county Rogaland. The surveys are currently performed at a total of 52 sites from the Lucas-grid (country covering grid network with 18 km distance between grids) in the lower parts of the regions (i.e. excluding alpine areas). The grid network is made up of square polygons, placed 18 x 18 km apart, where every square is 1.5 * 1.5 km. In each square, a total of 1 km transects (20 transects á 50 m) are placed in suitable environments (approximately evenly distributed between the habitat types open forest- and grassland), where inventories of butterflies and bumblebees are perfomed three times each summer following a standardized protocol. This includes visual identification and sweep netting along the fixed transects. Earlier versions of this dataset contained specific absences of all unobserved taxa within the scope of the study. These are now removed, but can be inferred by the user by adding zeroes for all butterfly species (Papilionoidea) and bumblebee species (Bombus). A recipe for how to download and arrange the data to a more traditional format can be found here: https://github.com/jenast/NBBM_data_export/blob/master/NBBM_GBIF_to_BMS_export.md

The dataset is a collection of presence and absence points for forest tree species for Europe. Each unique combination of longitude, latitude and year was considered as an independent sample. Presence data was obtained from the harmonized tree species occurrence dataset by Heising and Hengl (2020) and absence data from the LUCAS (in-situ source) dataset.

A set of 50 different forest tree species was selected from the harmonized tree species dataset and data lacking a temporal observation was overlaid with yearly forest masks derived from land cover maps produced by Parente et al. (2021). We overlaid the points with the probability maps for the classes:

• 311: Broad-leaved forest,
• 312: Coniferous forest,
• 313: Mixed forest,
• 323: Sclerophyllous forest,
• 324: Transitional woodland-shrub,
• 333: Sparsely vegetated area.

Points were included in the dataset only if the probability value extracted for at least one of the above classes was ≥ 50% for all the years considered. An additional quality flag was added to distinguish points coming from this operation and the points with original year of observation coming from source datasets.

The final dataset contains 4,359,999 observations for and a total of 630 columns.

The first 8 columns of the dataset contain metadata information used to uniquely identify the points:

• id: unique point identifier,
• year: year of observation,
• postprocess: quality flag to identify if the temporal reference of an observation comes from the original dataset or is the result of spatiotemporal overlay with forest masks,
• Tile_ID: contains the tile id from a 30 km grid,
• easting: longitude coordinates in Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035),
• northing: latitude coordinates in Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035),
• Atlas_class: name of the tree species according to the European Atlas of Forest Tree Species or NULL in case of absence point,
• lc1: contains original LUCAS land cover class or NULL if it's a presence point.

The remaining columns contain the extracted values of a series of predictor variables (temperature, precipitation, elevation, topographical information, spectral reflectance) useful for species distribution modeling applications. These points were used to model the potential and realized distribution of a series of 16 target species for the period 2000 - 2020. The approach involved training three ML models to predict probability of presence (i.e. Random Forest, XGBoost, GLM), which served as input to train a linear meta-model (i.e. Logistic regression classifier), responsible for predicting the final probability of presence for each species.

The 10 most important variables used by each of the three base models are available in the "variable importance" plots for both potential and realized distribution in a PDF format.

The RDS file is created from a data.table object and suitable for fast reading in the R-programming environment. The CSV.GZ file contains records as a table with easting and northing in Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035) and can be fed in a GIS after being unzipped.

To access the predictions of the meta-model (probabilities and uncertainties) produced for these species access:

• Open Data Science Europe viewer: https://maps.opendatascience.eu

If you would like to know more about the creation of this dataset and the modeling, watch the talk at Open Data Science Workshop 2021 (TIB AV-PORTAL)

A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is under preparation. To suggest any improvement/fix use https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues.

Geologic, sediment texture, and physiographic zone maps characterize the sea floor of Vineyard and western Nantucket Sounds, Massachusetts. These maps were derived from interpretations of seismic-reflection profiles, high-resolution bathymetry, acoustic-backscatter intensity, bottom photographs, and surficial sediment samples. The interpretation of the seismic stratigraphy and mapping of glacial and Holocene marine units provided a foundation on which the surficial maps were created. This mapping is a result of a collaborative effort between the U.S. Geological Survey and the Massachusetts Office of Coastal Zone Management to characterize the surface and subsurface geologic framework offshore of Massachusetts.

The data comprises the measurements read at the Konjsko substation before, during and after a curtailment in Pometeno Brno plant.

The measurements gathered from the SCADA are:

• P, Q and V of the 110, 220 and 400 Kv buses at the substation

P, Q and V of the line connecting the substation with the plant