https://data.opendatascience.eu/geonetwork/srv/api/records
type: PagedCollection
type: PartialCollectionView
last page: https://data.opendatascience.eu/geonetwork/srv/api/records
total items: 147
next page: https://data.opendatascience.eu/geonetwork/srv/api/records
items per page: 10

Open Data Science Europe Metadata Catalog
type: Catalog
label: Open Data Science Europe Metadata Catalog (OpenGeoHub, CTU Prague, mundialis GmbH & Co KG, TerraSigna, MultiOne)
open search description: eng/portal.opensearch
uri lookup endpoint: eng/rdf.search?any=
publisher: OpenGeoHub, CTU Prague, mundialis GmbH & Co KG, TerraSigna, MultiOne
themes: INSPIRE media-types register
themes: GEMET - INSPIRE themes, version 1.0
language: eng
dataset: Short-term forecasting model ICON-D2
record: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
dataset: Regional model ICON-D2
dataset: Ensemble forecasting system ICON-D2 EPS
dataset: gh:veg_salix.caprea_pnv.eml_p_30m_0..0cm_2018..2020_eumap_epsg3035_v0.2
dataset: zenodo.6147830
dataset: https://www.ufz.de/arcgis/services/extern/land_system_archetypes/MapServer/WMSServer
dataset: https://zenodo.org/record/7427010
dataset: https://geoserver.deap.terrasigna.io/geoserver/opendatascience/ows
dataset: My service

OpenGeoHub, CTU Prague, mundialis GmbH & Co KG, TerraSigna, MultiOne
type: Organization

INSPIRE media-types register
type: ConceptScheme
uri: https://data.opendatascience.eu:/geonetwork/srv/eng/thesaurus.download

GEMET - INSPIRE themes, version 1.0
type: ConceptScheme
uri: https://data.opendatascience.eu:/geonetwork/srv/eng/thesaurus.download

Continents, countries, sea regions of the world.
type: ConceptScheme
uri: https://data.opendatascience.eu:/geonetwork/srv/eng/thesaurus.download

https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
type: CatalogRecord
primary topic: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
issued: 2021-10-25
modified: 2021-10-25
references: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
format: text/html
  type: IMT
  label: HTML

https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
format: application/xml
  type: IMT
  label: XML

Short-term forecasting model ICON-D2
type: Dataset
abstract: Regional model ICON-D2 The DWD's ICON-D2 model is a forecast model which is operated for the very-short range up to +27 hours (+45 hours for the 03 UTC run). Due to its fine mesh size, the ICON-D2 especially provides for improved forecasts of hazardous weather conditions, e.g. weather situations with high-level moisture convection (super and multi-cell thunderstorms, squall lines, mesoscale convective complexes) and weather events that are influenced by fine-scale topographic effects (ground fog, Föhn winds, intense downslope winds, flash floods). The model area of ICON-D2 covers the whole German territory, Benelux, Switzerland, Austria and parts of the other neighbouring countries at a horizontal resolution of 2.2 km. In the vertical, the model defines 65 atmosphere levels. The fairly short forecast periods make perfect sense because of the purpose of ICON-D2 (and its small model area). Based on model runs at 00, 06, 09, 12, 15, 18 and 21 UTC, ICON-D2 provides new 27-hour forecasts every 3 hours. The model run at 03 UTC even covers a forecast period of 45 hours. The ICON-D2 forecast data for each weather element are made available in standard packages at our free DWD Open Data Server, both on a rotated grid and on a regular grid. Regional ensemble forecast model ICON-D2 EPS The ensemble forecasting system ICON-D2 EPS is based on the DWD's numerical weather forecast model ICON-D2 and currently includes 20 ensemble members. All ensemble members are calculated at the same horizontal grid spacing as the operational configuration of ICON-D2 (2.2 km). Like ICON-D2, the ICON-D2 EPS ensemble system provides forecasts up to +27 hours for the same model area (up to +45 hours based on the 03 UTC run). For generating the ensemble members, some of the features of the forecasting system are changed. The method currently used to generate the ensemble members involves varying the - lateral boundary conditions - initial state - soil moisture - and model physics. For varying the lat…
keyword: Weather
keyword: Forecast
keyword: Germany
theme: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
spatial: Polygon
  as WKT: <http://www.opengis.net/def/crs/OGC/1.3/CRS84> Polygon((-2.00 43.00, -2.00 57.00, 19.00 57.00, 19.00 43.00, -2.00 43.00))
temporal: 2021-02-10 /
publisher: DWD
accrual periodicity: asNeeded
language: eng
license: otherRestrictions
license: license
license: The freely accessible data may be re-used without any restrictions provided that the source reference is indicated, as laid down in the GeoNutzV ordinance ("Verordnung zur Festlegung der Nutzungsbestimmungen für die Bereitstellung von Geodaten des Bundes = Ordinance to Determine the Conditions for Use for the Provision of Spatial Data of the Federation https://gdz.bkg.bund.de/). (see https://opendata.dwd.de/climate_environment/CDC/Terms_of_use.txt)
license: {"id":"geoNutz/20130319","name":"Nutzungsbestimmungen für die Bereitstellung von Geodaten des Bundes","url":"https://sg.geodatenzentrum.de/web_public/gdz/lizenz/geonutzv.pdf","quelle":"Source: DWD"}
distribution: Regional model ICON-D2
distribution: Ensemble forecasting system ICON-D2 EPS
data quality: Modelled forecast
title: Decadal time series of spatially enhanced relative humidity for Europe at 30 arc seconds resolution (2000 - 2021) derived from ERA5-Land data
abstract: Overview: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Processing steps: The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (https://chelsa-climate.org/). Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 07/2021. Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997): maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta)) actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td)) relative humidity = actual water pressure / maximum water pressure The resulting relative humidity has been aggregated to decadal averages. Each month is divided into three decades: the first decade of a month covers days 1-10, the second decade covers days 11-20, and the third decade covers days 21-last day of the month. Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000]. File naming scheme (YYYY = year; MM = month; dD = number of decade): ERA5_land_rh2m_avg_decadal_YYYY_MM_dD.tif Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) Spatial extent: north: 82:00:30N south: 18N west: 32:00:30W …
keyword: relative humidity
keyword: geospatial analysis
keyword: environment
keyword: meteorology
keyword: ERA5-Land
keyword: CHELSA
keyword: MOOD-H2020
keyword: Europe
spatial: Polygon
  as WKT: <http://www.opengis.net/def/crs/OGC/1.3/CRS84> Polygon((-36.00 18.00, -36.00 82.00, 70.00 82.00, 70.00 18.00, -36.00 18.00))
temporal: 2000-01-01 / 2021-07-31
publisher: mundialis GmbH & Co. KG
license: Creative Commons Namensnennung – 4.0 International (CC BY 4.0)
license: { "id": "cc-by/4.0", "name": "Creative Commons Namensnennung – 4.0 Internationa", "url": "http://creativecommons.org/licenses/by/4.0/", "quelle": "Source: mundialis GmbH & Co. KG" }
distribution: zenodo.6147830
relation: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
data quality: derived from ERA5-Land reanalysis data, spatially enhanced by image fusion with CHELSA data
title: Decadal time series of spatially enhanced relative humidity for Europe at 1000 m resolution (2000 - 2021) derived from ERA5-Land data
abstract: Overview: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Processing steps: The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: 1. spatially aggregate CHELSA to the resolution of ERA5-Land 2. calculate difference of ERA5-Land - aggregated CHELSA 3. interpolate differences with a Gaussian filter to 30 arc seconds. 4. add the interpolated differences to CHELSA Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 07/2021. Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997): maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta)) actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td)) relative humidity = actual water pressure / maximum water pressure The resulting relative humidity has been aggregated to decadal averages. …
keyword: Tularaemia
keyword: Lyme
keyword: TBE
keyword: WNV
keyword: USUTU
keyword: Mosquito borne Flaviviruses
spatial: Polygon
  as WKT: <http://www.opengis.net/def/crs/OGC/1.3/CRS84> Polygon((-36.00 18.00, -36.00 82.00, 70.00 82.00, 70.00 18.00, -36.00 18.00))
distribution: https://zenodo.org/record/7427010

https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
type: CatalogRecord
primary topic: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
issued: 2021-01-14
modified: 2021-01-14
references: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
format: text/html
  type: IMT
  label: HTML

https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
format: application/xml
  type: IMT
  label: XML

PNV - Probability distribution for Salix caprea
type: Dataset
abstract: Overview: Potential Natural Vegetation (PNV): potential probability of occurrence for the Goat willow from 2018 to 2020 Traceability (lineage): This is an original dataset produced with a machine learning framework which used a combination of point datasets and raster datasets as inputs. Point dataset is a harmonized collection of tree occurrence data, comprising observations from National Forest Inventories (EU-Forest), GBIF and LUCAS. The complete dataset is available on Zenodo. Raster datasets used as input are: monthly time series air and surface temperature and precipitation from a reprocessed version of the Copernicus ERA5 dataset; long term averages of bioclimatic variables from CHELSA; elevation, slope and other elevation-derived metrics and long term monthly averages snow probability. For a more comprehensive list refer to Bonannella et al. (2022) (in review, preprint available at: https://doi.org/10.21203/rs.3.rs-1252972/v1). Scientific methodology: Probability and uncertainty maps were the output of a spatiotemporal ensemble machine learning framework based on stacked regularization. Three base models (random forest, gradient boosted trees and generalized linear models) were first trained on the input dataset and their predictions were used to train an additional model (logistic regression) which provided the final predictions. More details on the whole workflow are available in the listed publication. Usability: Probability maps are particularly useful when compared with existing products of potential distribution of species or when combined with maps of realized distribution: gaps in potential and realized distribution can be identified and used as information for future programs of tree planting or forest restoration. Uncertainty quantification: Uncertainty is quantified by taking the standard deviation of the probabilities predicted by the three components of the spatiotemporal ensemble model. Data validation approaches: Distribution maps were validat…
keyword: Vegetation
keyword: Geoharmonizer
keyword: geoharvester
keyword: geodata
keyword: Europe
theme: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
thumbnail: https://geoserver.opendatascience.eu/geoserver/gh/wms
spatial: Polygon
  as WKT: <http://www.opengis.net/def/crs/OGC/1.3/CRS84> Polygon((-11.50 35.30, -11.50 81.40, 43.20 81.40, 43.20 35.30, -11.50 35.30))
temporal: - / -
accrual periodicity: asNeeded
license: copyright
license: otherRestictions
distribution: gh:veg_salix.caprea_pnv.eml_p_30m_0..0cm_2018..2020_eumap_epsg3035_v0.2
data quality: derived from XY
title: RGB Landsat (yearly)
abstract: rgb: Landsat RGB time-series, derived by the median pixel values obtained between June 25 and September 12 on a specific year.
keyword: Land cover, land use and administrative data
temporal: 2000-01-01 / 2019-12-31
title: ANV - Probability distribution for Quercus cerris
abstract: Overview: Actual Natural Vegetation (ANV): probability of occurrence for the Turkey oak in its realized environment for the period 2000 - 2031 Traceability (lineage): This is an original dataset produced with a machine learning framework which used a combination of point datasets and raster datasets as inputs. Point dataset is a harmonized collection of tree occurrence data, comprising observations from National Forest Inventories (EU-Forest), GBIF and LUCAS. The complete dataset is available on Zenodo. Raster datasets used as input are: harmonized and gapfilled time series of seasonal aggregates of the Landsat GLAD ARD dataset (bands and spectral indices); monthly time series air and surface temperature and precipitation from a reprocessed version of the Copernicus ERA5 dataset; long term averages of bioclimatic variables from CHELSA, tree species distribution maps from the European Atlas of Forest Tree Species; elevation, slope and other elevation-derived metrics; long term monthly averages snow probability and long term monthly averages of cloud fraction from MODIS. For a more comprehensive list refer to Bonannella et al. (2022) (in review, preprint available at: https://doi.org/10.21203/rs.3.rs-1252972/v1). Scientific methodology: Probability and uncertainty maps were the output of a spatiotemporal ensemble machine learning framework based on stacked regularization. Three base models (random forest, gradient boosted trees and generalized linear models) were first trained on the input dataset and their predictions were used to train an additional model (logistic regression) which provided the final predictions. More details on the whole workflow are available in the listed publication. Usability: Probability maps can be used to detect potential forest degradation and compositional change across the time period analyzed. Some possible applications for these topics are explained in the listed publication. Uncertainty quantification: Uncertainty is quantified by tak…
temporal: 2000-2002-01-01 / 2018-2020-12-31
title: Sea and ocean
abstract: 523: Zone seaward of the lowest tide limit.
title: Salines
abstract: Overview: 422: Sections of salt marsh exploited for the production of salt by evaporation, active or in processof abandonment, distinguishable from marsh by parcellation or embankment systems. Traceability (lineage): This dataset was produced with a machine learning framework with several input datasets, specified in detail in Witjes et al., 2022 (in review, preprint available at https://doi.org/10.21203/rs.3.rs-561383/v3 ) Scientific methodology: The single-class probability layers were generated with a spatiotemporal ensemble machine learning framework detailed in Witjes et al., 2022 (in review, preprint available at https://doi.org/10.21203/rs.3.rs-561383/v3 ). The single-class uncertainty layers were calculated by taking the standard deviation of the three single-class probabilities predicted by the three components of the ensemble. The HCL (hard class) layers represents the class with the highest probability as predicted by the ensemble. Usability: The HCL layers have a decreasing average accuracy (weighted F1-score) at each subsequent level in the CLC hierarchy. These metrics are 0.83 at level 1 (5 classes):, 0.63 at level 2 (14 classes), and 0.49 at level 3 (43 classes). This means that the hard-class maps are more reliable when aggregating classes to a higher level in the hierarchy (e.g. 'Discontinuous Urban Fabric' and 'Continuous Urban Fabric' to 'Urban Fabric'). Some single-class probabilities may more closely represent actual patterns for some classes that were overshadowed by unequal sample point distributions. Users are encouraged to set their own thresholds when postprocessing these datasets to optimize the accuracy for their specific use case. Uncertainty quantification: Uncertainty is quantified by taking the standard deviation of the probabilities predicted by the three components of the spatiotemporal ensemble model. Data validation approaches: The LULC classification was validated through spatial 5-fold cross-validation as detailed in the accompa…

https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
type: CatalogRecord
primary topic: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
issued: 2022-03-15
modified: 2022-03-15
references: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
format: text/html
  type: IMT
  label: HTML

https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
format: application/xml
  type: IMT
  label: XML

https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
type: CatalogRecord
primary topic: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
issued: 2021-01-14
modified: 2021-01-14
references: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
format: text/html
  type: IMT
  label: HTML

https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
format: application/xml
  type: IMT
  label: XML

https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
type: CatalogRecord
primary topic: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
issued: 2021-10-12
modified: 2021-10-12
references: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
format: text/html
  type: IMT
  label: HTML

https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
format: application/xml
  type: IMT
  label: XML

Land system archetypes (WMS)
abstract: This service visualizes land system archetypes data. Land use is a key driver of global environmental change. Unless major shifts in consumptive behaviours occur, land-based production will have to increase drastically to meet future demands for food and other commodities. To better understand the drivers and impacts of agricultural intensification, identifying global, archetypical patterns of land systems is needed. However, current approaches focus on broad-scale representations of dominant land cover with limited consideration of land-use intensity. In this study, we derived a new global representation of land systems based on more than 30 high-resolution datasets on land-use intensity, environmental conditions and socioeconomic indicators. Using a self-organizing map algorithm, we identified and mapped twelve archetypes of land systems for the year 2005. Our analysis reveals unexpected similarities in land systems across the globe but the diverse pattern at sub-national scales implies that there are no one-size-fits-all solutions to sustainable land management. Our results help to identify generic patterns of land pressures and environmental threats and provide means to target regionalized strategies to cope with the challenges of global change. Mapping global archetypes of land systems represents a first step towards better understanding the driving forces and environmental and social outcomes of land system dynamics.
keyword: inspireidentifiziert
keyword: land system archetypes
keyword: BESTMAP-H2020
keyword: WMS
keyword: infoMapAccessService
theme: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
issued: 2013-02-07
publisher: Helmholtz Centre for Environmental Research - UFZ
license: otherRestrictions
license: unknown
distribution: https://www.ufz.de/arcgis/services/extern/land_system_archetypes/MapServer/WMSServer

https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
type: CatalogRecord
primary topic: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
issued: 2023-12-15
modified: 2023-12-15
references: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
format: text/html
  type: IMT
  label: HTML

https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
format: application/xml
  type: IMT
  label: XML

https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
type: CatalogRecord
primary topic: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
issued: 2021-01-14
modified: 2021-01-14
references: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
format: text/html
  type: IMT
  label: HTML

https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
format: application/xml
  type: IMT
  label: XML

https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
type: CatalogRecord
primary topic: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
issued: 2021-01-14
modified: 2021-01-14
references: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
format: text/html
  type: IMT
  label: HTML

https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
format: application/xml
  type: IMT
  label: XML

https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
type: CatalogRecord
primary topic: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
issued: 2021-10-01
modified: 2021-10-01
references: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
format: text/html
  type: IMT
  label: HTML

https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
format: application/xml
  type: IMT
  label: XML

Geo-harmonizer DEM WMS
abstract: Geo-harmonizer DEM WMS for serving 3D data for https://maps.opendatascience.eu 3D clients.
publisher: Terrasigna
license: otherRestrictions
license: No constrains
distribution: https://geoserver.deap.terrasigna.io/geoserver/opendatascience/ows
distribution: My service

https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
type: CatalogRecord
primary topic: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
issued: 2021-01-14
modified: 2021-01-14
references: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
format: text/html
  type: IMT
  label: HTML

https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
format: application/xml
  type: IMT
  label: XML

Atmospheric conditions
type: Concept
in scheme: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search

Human health and safety
type: Concept
in scheme: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search

Meteorological geographical features
type: Concept
in scheme: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search

Geographical grid systems
type: Concept
in scheme: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search

Land use
type: Concept
in scheme: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search

Regional model ICON-D2
type: Distribution
format: WWW:LINK-1.0-http--download

Ensemble forecasting system ICON-D2 EPS
type: Distribution
format: WWW:LINK-1.0-http--download

gh:veg_salix.caprea_pnv.eml_p_30m_0..0cm_2018..2020_eumap_epsg3035_v0.2
type: Distribution
format: OGC:WMS

veg salix.caprea pnv.eml p 30m 0..0cm 2018..2020 eumap epsg3035 v0.2.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

zenodo.6147830
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv rgb landsat.glad.ard p50 30m 0..0cm 2019 eumap epsg3035 v1.0.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv rgb landsat.glad.ard p50 30m 0..0cm 2018 eumap epsg3035 v1.0.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv rgb landsat.glad.ard p50 30m 0..0cm 2017 eumap epsg3035 v1.0.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv rgb landsat.glad.ard p50 30m 0..0cm 2016 eumap epsg3035 v1.0.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv rgb landsat.glad.ard p50 30m 0..0cm 2015 eumap epsg3035 v1.0.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv rgb landsat.glad.ard p50 30m 0..0cm 2014 eumap epsg3035 v1.0.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv rgb landsat.glad.ard p50 30m 0..0cm 2013 eumap epsg3035 v1.0.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv rgb landsat.glad.ard p50 30m 0..0cm 2012 eumap epsg3035 v1.0.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv rgb landsat.glad.ard p50 30m 0..0cm 2011 eumap epsg3035 v1.0.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv rgb landsat.glad.ard p50 30m 0..0cm 2010 eumap epsg3035 v1.0.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv rgb landsat.glad.ard p50 30m 0..0cm 2009 eumap epsg3035 v1.0.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv rgb landsat.glad.ard p50 30m 0..0cm 2008 eumap epsg3035 v1.0.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv rgb landsat.glad.ard p50 30m 0..0cm 2007 eumap epsg3035 v1.0.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv rgb landsat.glad.ard p50 30m 0..0cm 2006 eumap epsg3035 v1.0.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv rgb landsat.glad.ard p50 30m 0..0cm 2005 eumap epsg3035 v1.0.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv rgb landsat.glad.ard p50 30m 0..0cm 2004 eumap epsg3035 v1.0.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv rgb landsat.glad.ard p50 30m 0..0cm 2003 eumap epsg3035 v1.0.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv rgb landsat.glad.ard p50 30m 0..0cm 2002 eumap epsg3035 v1.0.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv rgb landsat.glad.ard p50 30m 0..0cm 2001 eumap epsg3035 v1.0.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv rgb landsat.glad.ard p50 30m 0..0cm 2000 eumap epsg3035 v1.0.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

https://www.ufz.de/arcgis/services/extern/land_system_archetypes/MapServer/WMSServer
type: Distribution
format: OGC:WMS-http-get-capabilities

https://www.ufz.de/arcgis/services/extern/land_system_archetypes/MapServer/WMSServer
type: Distribution
access URL: https://www.ufz.de/arcgis/services/extern/land_system_archetypes/MapServer/WMSServer?version=1.3.0&service=WMS&request=GetMap&layers=1&crs=epsg:4326&bbox=-90,-180,90,180&width=1140&height=1620&format=image/png&styles=default
format: OGC:WMS-http-get-map

https://zenodo.org/record/7427010
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

veg quercus.cerris anv.eml p 30m 0..0cm 2018..2020 eumap epsg3035 v0.2.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

veg quercus.cerris anv.eml p 30m 0..0cm 2014..2018 eumap epsg3035 v0.2.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

veg quercus.cerris anv.eml p 30m 0..0cm 2010..2014 eumap epsg3035 v0.2.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

veg quercus.cerris anv.eml p 30m 0..0cm 2006..2010 eumap epsg3035 v0.2.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

veg quercus.cerris anv.eml p 30m 0..0cm 2002..2006 eumap epsg3035 v0.2.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

veg quercus.cerris anv.eml p 30m 0..0cm 2000..2002 eumap epsg3035 v0.2.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.523 lucas.corine.rf p 30m 0..0cm 2019 eumap epsg3035 v0.1.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.523 lucas.corine.rf p 30m 0..0cm 2018 eumap epsg3035 v0.1.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.523 lucas.corine.rf p 30m 0..0cm 2017 eumap epsg3035 v0.1.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.523 lucas.corine.rf p 30m 0..0cm 2016 eumap epsg3035 v0.1.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.523 lucas.corine.rf p 30m 0..0cm 2015 eumap epsg3035 v0.1.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.523 lucas.corine.rf p 30m 0..0cm 2014 eumap epsg3035 v0.1.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.523 lucas.corine.rf p 30m 0..0cm 2013 eumap epsg3035 v0.1.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.523 lucas.corine.rf p 30m 0..0cm 2012 eumap epsg3035 v0.1.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.523 lucas.corine.rf p 30m 0..0cm 2011 eumap epsg3035 v0.1.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.523 lucas.corine.rf p 30m 0..0cm 2010 eumap epsg3035 v0.1.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.523 lucas.corine.rf p 30m 0..0cm 2009 eumap epsg3035 v0.1.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.523 lucas.corine.rf p 30m 0..0cm 2008 eumap epsg3035 v0.1.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.523 lucas.corine.rf p 30m 0..0cm 2007 eumap epsg3035 v0.1.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.523 lucas.corine.rf p 30m 0..0cm 2006 eumap epsg3035 v0.1.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.523 lucas.corine.rf p 30m 0..0cm 2005 eumap epsg3035 v0.1.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.523 lucas.corine.rf p 30m 0..0cm 2004 eumap epsg3035 v0.1.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.523 lucas.corine.rf p 30m 0..0cm 2003 eumap epsg3035 v0.1.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.523 lucas.corine.rf p 30m 0..0cm 2002 eumap epsg3035 v0.1.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.523 lucas.corine.rf p 30m 0..0cm 2001 eumap epsg3035 v0.1.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.523 lucas.corine.rf p 30m 0..0cm 2000 eumap epsg3035 v0.1.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

https://geoserver.deap.terrasigna.io/geoserver/opendatascience/ows
type: Distribution
format: OGC:WMS

My service
type: Distribution

lcv landcover.422 lucas.corine.eml p 30m 0..0cm 2020 eumap epsg3035 v0.2.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.422 lucas.corine.eml p 30m 0..0cm 2019 eumap epsg3035 v0.2.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.422 lucas.corine.eml p 30m 0..0cm 2018 eumap epsg3035 v0.2.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.422 lucas.corine.eml p 30m 0..0cm 2017 eumap epsg3035 v0.2.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.422 lucas.corine.eml p 30m 0..0cm 2016 eumap epsg3035 v0.2.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.422 lucas.corine.eml p 30m 0..0cm 2015 eumap epsg3035 v0.2.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.422 lucas.corine.eml p 30m 0..0cm 2014 eumap epsg3035 v0.2.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.422 lucas.corine.eml p 30m 0..0cm 2013 eumap epsg3035 v0.2.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.422 lucas.corine.eml p 30m 0..0cm 2012 eumap epsg3035 v0.2.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.422 lucas.corine.eml p 30m 0..0cm 2011 eumap epsg3035 v0.2.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.422 lucas.corine.eml p 30m 0..0cm 2010 eumap epsg3035 v0.2.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.422 lucas.corine.eml p 30m 0..0cm 2009 eumap epsg3035 v0.2.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.422 lucas.corine.eml p 30m 0..0cm 2008 eumap epsg3035 v0.2.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.422 lucas.corine.eml p 30m 0..0cm 2007 eumap epsg3035 v0.2.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.422 lucas.corine.eml p 30m 0..0cm 2006 eumap epsg3035 v0.2.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.422 lucas.corine.eml p 30m 0..0cm 2005 eumap epsg3035 v0.2.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.422 lucas.corine.eml p 30m 0..0cm 2004 eumap epsg3035 v0.2.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.422 lucas.corine.eml p 30m 0..0cm 2003 eumap epsg3035 v0.2.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.422 lucas.corine.eml p 30m 0..0cm 2002 eumap epsg3035 v0.2.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.422 lucas.corine.eml p 30m 0..0cm 2001 eumap epsg3035 v0.2.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv landcover.422 lucas.corine.eml p 30m 0..0cm 2000 eumap epsg3035 v0.2.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

DWD
type: Organization
member: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search

mundialis GmbH & Co. KG
type: Organization
member: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search

Helmholtz Centre for Environmental Research - UFZ
type: Organization
member: Tomas Vaclavik

Terrasigna
type: Organization
member: Codrina Ilie

Carmelo Bonannella
type: Agent

Tomislav Hengl
type: Agent

Tomas Vaclavik
type: Agent

Martijn Witjes
type: Agent

Martijn Witjes
type: Agent

Leandro Parente
type: Agent

Codrina Ilie
type: Agent

Polygon
as WKT: <http://www.opengis.net/def/crs/OGC/1.3/CRS84> Polygon((-11.50 35.30, -11.50 81.40, 43.20 81.40, 43.20 35.30, -11.50 35.30))

Polygon
as WKT: <http://www.opengis.net/def/crs/OGC/1.3/CRS84> Polygon((-11.50 35.30, -11.50 81.40, 43.20 81.40, 43.20 35.30, -11.50 35.30))

Polygon
as WKT: <http://www.opengis.net/def/crs/OGC/1.3/CRS84> Polygon((-11.50 35.30, -11.50 81.40, 43.20 81.40, 43.20 35.30, -11.50 35.30))

Polygon
as WKT: <http://www.opengis.net/def/crs/OGC/1.3/CRS84> Polygon((-11.50 35.30, -11.50 81.40, 43.20 81.40, 43.20 35.30, -11.50 35.30))