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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: germany 2016 2019 MAJA cd total v02.sld
dataset: germany 2016 2019 MAJA cd total v02.qml
dataset: LICENSE.html
dataset: gh:lcv_farmyard_osm_p_30m_0..0cm_2021_eumap_epsg3035_v0.1
dataset: germany 2019 2020 MAJA cd total v02.sld
dataset: germany 2019 2020 MAJA cd total v02.qml

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: Change Detection map of Germany 2016-2019 based on Sentinel-2 data
abstract: This change map was produced as an intermediate result in the course of the project incora (Inwertsetzung von Copernicus-Daten für die Raumbeobachtung, mFUND Förderkennzeichen: 19F2079C) in cooperation with ILS (Institut für Landes- und Stadtentwicklungsforschung gGmbH) and BBSR (Bundesinstitut für Bau-, Stadt- und Raumforschung) funded by BMVI (Federal Ministry of Transport and Digital Infrastructure). The goal of incora is an analysis of settlement and infrastructure dynamics in Germany based on Copernicus Sentinel data. The map indicates land cover changes between the years 2016 and 2019. It is a difference map from two classifications based on Sentinel-2 MAJA data (MAJA L3A-WASP: https://geoservice.dlr.de/web/maps/sentinel2:l3a:wasp; DLR (2019): Sentinel-2 MSI - Level 2A (MAJA-Tiles)- Germany). More information on the two basis classifications can be found here: https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/db130a09-fc2e-421d-95e2-1575e7c4b45c https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/36512b46-f3aa-4aa4-8281-7584ec46c813 To keep only significant changes in the change detection map, the following postprocessing steps are applied to the initial difference raster: - Modefilter (3x3) to eliminate isolated pixels and edge effects - Information gain in a 4x4 window compares class distribution within the window from the two timesteps. High values indicate that the class distribution in the window has changed, and thus a change is likely. Gain ranges from 0 to 1, all changes < 0.5 are omitted. - Change areas < 1ha are removed The resulting map has the following nomenclature: 0: No Change 1: Change from low vegetation to forest 2: Change from water to forest 3: Change from built-up to forest 4: Change from bare soil to forest 5: Change from agriculture to forest 6: Change from forest to low vegetation 7: Change from water to low vegetation 8: Change from built-up to low vegetation 9: Change from bare soil to low vegetat…
keyword: Sentinel-2
keyword: Classification
keyword: Land Cover
keyword: mFUND
keyword: MAJA
keyword: Infrastuktur
keyword: Umwelt
keyword: Regionen und Städte
keyword: mfund-projekt:incora
keyword: mfund-fkz:19F2079C
keyword: Change Detection
keyword: Settlement Dynamics
keyword: incora
spatial: Polygon
  as WKT: <http://www.opengis.net/def/crs/OGC/1.3/CRS84> Polygon((5.8525 47.271, 5.8525 55.065, 15.022 55.065, 15.022 47.271, 5.8525 47.271))
temporal: 2016-01-01 / 2019-12-31
publisher: mundialis GmbH & Co. KG
accrual periodicity: irregular
license: Data licence Germany - attribution - version 2.0 or later (DL-DE->BY-2.0) | Datenlizenz Deutschland - Namensnennung - Version 2.0 oder neuer
license: { "id": "dl-by-de/2.0", "name": "Datenlizenz Deutschland Namensnennung 2.0", "url": "https://www.govdata.de/dl-de/by-2-0", "quelle": "Source: mundialis GmbH & Co. KG" }
distribution: germany 2016 2019 MAJA cd total v02.sld
distribution: germany 2016 2019 MAJA cd total v02.qml
distribution: LICENSE.html
relation: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
data quality: derived from Sentinel-2 MSI - Level 3A-WASP
title: Change Detection map of Germany 2019-2020 based on Sentinel-2 data
abstract: This change map was produced on the basis of a classification method developed in the project incora (Inwertsetzung von Copernicus-Daten für die Raumbeobachtung, mFUND Förderkennzeichen: 19F2079C) in cooperation with ILS (Institut für Landes- und Stadtentwicklungsforschung gGmbH) and BBSR (Bundesinstitut für Bau-, Stadt- und Raumforschung) funded by BMVI (Federal Ministry of Transport and Digital Infrastructure). The goal of incora is an analysis of settlement and infrastructure dynamics in Germany based on Copernicus Sentinel data. The map indicates land cover changes between the years 2019 and 2020. It is a difference map from two classifications based on Sentinel-2 MAJA data (MAJA L3A-WASP: https://geoservice.dlr.de/web/maps/sentinel2:l3a:wasp; DLR (2019): Sentinel-2 MSI - Level 2A (MAJA-Tiles)- Germany). More information on the two basis classifications can be found here: https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/36512b46-f3aa-4aa4-8281-7584ec46c813 https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/9246503f-6adf-460b-a31e-73a649182d07 To keep only significant changes in the change detection map, the following postprocessing steps are applied to the initial difference raster: - Modefilter (3x3) to eliminate isolated pixels and edge effects - Information gain in a 4x4 window compares class distribution within the window from the two timesteps. High values indicate that the class distribution in the window has changed, and thus a change is likely. Gain ranges from 0 to 1, all changes < 0.5 are omitted. - Change areas < 1ha are removed The resulting map has the following nomenclature: 0: No Change 1: Change from low vegetation to forest 2: Change from water to forest 3: Change from built-up to forest 4: Change from bare soil to forest 5: Change from agriculture to forest 6: Change from forest to low vegetation 7: Change from water to low vegetation 8: Change from built-up to low vegetation 9: Change from bare soil to l…
spatial: Polygon
  as WKT: <http://www.opengis.net/def/crs/OGC/1.3/CRS84> Polygon((5.8525 47.271, 5.8525 55.065, 15.022 55.065, 15.022 47.271, 5.8525 47.271))
temporal: 2019-01-01 / 2020-12-31
distribution: germany 2019 2020 MAJA cd total v02.sld
distribution: germany 2019 2020 MAJA cd total v02.qml

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
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  label: HTML

https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
format: application/xml
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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

OSM farmyard
type: Dataset
abstract: osm: Farmyard rasterized from OSM landuse polygons, first to 10m spatial resolution and after downsampled to 30m by spatial average.
keyword: Land cover, land use and administrative data
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:lcv_farmyard_osm_p_30m_0..0cm_2021_eumap_epsg3035_v0.1
data quality: derived from XY
title: Non-irrigated arable land prob. slope
abstract: Overview: 211: Slope of non-irrigated arable land derived by OLS regression over the probabilities values (2000—2019). The std. error of the model was considered as uncertainty. 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 accompanying publicati…
title: AQ PM2.5
abstract: pm2.5: Monthly PM2.5 maps aggregated from the daily PM2.5 predictions.
keyword: Environmental quality maps
temporal: 201801-01-01 / 201812-12-31
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.
temporal: 2000-01-01 / 2019-12-31
title: Complex cultivation patterns
abstract: Overview: 242: Mosaic of small cultivated land parcels with different cultivation types(annual and permanent crops, as well as pastures), potentially with scattered houses or gardens. 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 accompanying pub…

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
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https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
format: application/xml
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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
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https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
format: application/xml
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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

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format: application/xml
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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

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

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format: application/xml
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Polygon
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type: CatalogRecord
primary topic: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
issued: 2021-01-14
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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

Land cover
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

Geographical grid systems
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

germany 2016 2019 MAJA cd total v02.tif
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germany 2016 2019 MAJA cd water v02.tif
type: Distribution
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germany 2016 2019 MAJA cd lowvegetation v02.tif
type: Distribution
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germany 2016 2019 MAJA cd forest v02.tif
type: Distribution
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germany 2016 2019 MAJA cd builtup v02.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

germany 2016 2019 MAJA cd baresoil v02.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

germany 2016 2019 MAJA cd agriculture v02.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

LICENSE.html
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

gh:lcv_farmyard_osm_p_30m_0..0cm_2021_eumap_epsg3035_v0.1
type: Distribution
format: OGC:WMS

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type: Distribution
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lcv landcover.211 lucas.corine.slope p 30m 0..0cm 2000 eumap epsg3035 v0.1.tif
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germany 2019 2020 MAJA cd total v02.tif
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germany 2019 2020 MAJA cd water v02.tif
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germany 2019 2020 MAJA cd lowvegetation v02.tif
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germany 2019 2020 MAJA cd forest v02.tif
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germany 2019 2020 MAJA cd builtup v02.tif
type: Distribution
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germany 2019 2020 MAJA cd baresoil v02.tif
type: Distribution
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germany 2019 2020 MAJA cd agriculture v02.tif
type: Distribution
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LICENSE.html
type: Distribution
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aq pm25 et.eml m 1km na 201812 eumap epsg3035 v0.1prebeta.tif
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aq pm25 et.eml m 1km na 201810 eumap epsg3035 v0.1prebeta.tif
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aq pm25 et.eml m 1km na 201809 eumap epsg3035 v0.1prebeta.tif
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aq pm25 et.eml m 1km na 201808 eumap epsg3035 v0.1prebeta.tif
type: Distribution
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aq pm25 et.eml m 1km na 201807 eumap epsg3035 v0.1prebeta.tif
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aq pm25 et.eml m 1km na 201806 eumap epsg3035 v0.1prebeta.tif
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aq pm25 et.eml m 1km na 201805 eumap epsg3035 v0.1prebeta.tif
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aq pm25 et.eml m 1km na 201804 eumap epsg3035 v0.1prebeta.tif
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aq pm25 et.eml m 1km na 201803 eumap epsg3035 v0.1prebeta.tif
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aq pm25 et.eml m 1km na 201802 eumap epsg3035 v0.1prebeta.tif
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