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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
themes: Continents, countries, sea regions of the world.
language: eng
dataset: Short-term forecasting model ICON-D2
record: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
dataset: ANV - Probability distribution for Pinus nigra
dataset: OpenGeoHub, CTU Prague, mundialis GmbH & Co KG, TerraSigna, MultiOne
dataset: INSPIRE media-types register
dataset: GEMET - INSPIRE themes, version 1.0
dataset: Continents, countries, sea regions of the world.
dataset: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
dataset: Atmospheric conditions
dataset: Human health and safety
dataset: Meteorological geographical features
dataset: Europe
dataset: Geographical grid systems
dataset: Regional model ICON-D2
dataset: Ensemble forecasting system ICON-D2 EPS
dataset: gh:veg-pinusXnigra-anvXeml-p-30m-0XX0cm--eumap-epsg3035-v0X2
dataset: zenodo.6147830
dataset: Daily gap-filled MODIS LST data (2003-2019)
dataset: DWD
dataset: mundialis GmbH & Co. KG
dataset: mundialis GmbH & Co KG
dataset: Carmelo Bonannella
dataset: Martijn Witjes
dataset: Leandro Parente
dataset: Tomislav Hengl

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
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https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
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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: Daily gap-filled MODIS LST data (2003-2019)
abstract: Temperature time series with high spatial and temporal resolutions are important for several applications. The new MODIS Land Surface Temperature (LST) collection 6 provides numerous improvements compared to collection 5. However, being remotely sensed data in the thermal range, LST shows gaps in cloud-covered areas. With a novel method [1] we fully reconstructed the daily global MODIS LST products MOD11A1/MYD11A1 (spatial resolution: 1 km). For this, we combined temporal and spatial interpolation, using emissivity and elevation as covariates for the spatial interpolation. Here we provide a time series of these reconstructed LST data aggregated as daily LST maps at overpass time (approx: 01:30 am, 10:30am, 1:30pm 10:30pm). [1] Metz M., Andreo V., Neteler M. (2017): A new fully gap-free time series of Land Surface Temperature from MODIS LST data. Remote Sensing, 9(12):1333. DOI: http://dx.doi.org/10.3390/rs9121333 The data are provided in GeoTIFF format. The Coordinate Reference System (CRS) is identical to the MOD11A1/MYD11A1 product (Sinusoidal) as provided by NASA. In WKT as reported by GDAL: PROJCRS["unnamed", BASEGEOGCRS["Unknown datum based upon the custom spheroid", DATUM["Not specified (based on custom spheroid)", ELLIPSOID["Custom spheroid",6371007.181,0, LENGTHUNIT["metre",1, ID["EPSG",9001]]]], PRIMEM["Greenwich",0, ANGLEUNIT["degree",0.0174532925199433, ID["EPSG",9122]]]], CONVERSION["unnamed", METHOD["Sinusoidal"], PARAMETER["Longitude of natural origin",0, ANGLEUNIT["degree",0.0174532925199433], ID["EPSG",8802]], PARAMETER["False easting",0, LENGTHUNIT["Meter",1], ID["EPSG",8806]], PARAMETER["False northing",0, LENGTHUNIT["Meter",1], ID["EPSG",8807]]], CS[Cartesian,2], AXIS["easting",east, ORDER[1], LENGTHUNIT["Meter",1]], AXIS["northing",north, ORDER[2], LENGTHUNIT["Meter",1]]] Acknowledgments: We are grateful to the NASA Land Processes Distributed Active Archive Center (LP DAAC) for making the MODIS LST data available. The dataset is based on MODIS Co…
keyword: land surface temperature
theme: Europe
spatial: Polygon
  as WKT: <http://www.opengis.net/def/crs/OGC/1.3/CRS84> Polygon((0.00 40.00, 0.00 60.00, 40.00 60.00, 40.00 40.00, 0.00 40.00))
temporal: 2003-01-01 / 2019-03-29
publisher: mundialis GmbH & Co KG
granularity: 3
license: Open Data Commons Open Database License v1.0 (ODbL)
license: { "id": "odbl", "name": "Open Data Commons Open Database License (ODbL)", "url": "http://www.opendefinition.org/licenses/odc-odbl", "quelle": "Source: mundialis GmbH & Co. KG" }
distribution: Daily gap-filled MODIS LST data (2003-2019)
data quality: Dataset has been gap-filled from the original MODIS LST products MOD11A1/MYD11A1 at a spatial resolution of 1km

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ANV - Probability distribution for Pinus nigra
type: Dataset
abstract: Overview: Actual Natural Vegetation (ANV): probability of occurrence for the Austrian pine in its realized environment for the period 2000 - 2027 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 …
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: 2000-2002-01-01 / 2018-2020-12-31
accrual periodicity: asNeeded
license: copyright
license: otherRestictions
distribution: gh:veg-pinusXnigra-anvXeml-p-30m-0XX0cm--eumap-epsg3035-v0X2
data quality: derived from XY
title: ANV - Probability distribution for Pinus halepensis
abstract: Overview: Actual Natural Vegetation (ANV): probability of occurrence for the Aleppo pine in its realized environment for the period 2000 - 2026 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 ta…
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))
title: Coniferous forest prob. slope
abstract: 312: Slope of coniferous forest derived by OLS regression over the probabilities values (2000—2019). The std. error of the model was considered as uncertainty.
keyword: Land cover, land use and administrative data
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: - / -
title: PNV - Probability distribution for Pinus nigra
abstract: Overview: Potential Natural Vegetation (PNV): potential probability of occurrence for the Austrian pine 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 valid…
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))
title: OSM commercial buildings
abstract: osm: Commercial building aggregated and rasterized from OSM polygons, first to 10m spatial resolution and after downsampled to 30m by spatial average.
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))
title: Terrain hillshade
abstract: dtm: Hillshade derived using combined compute_edges in gdaldem
keyword: Digital Terrain Model and parameters
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))
title: OSM industrial buildings
abstract: osm: Industrial building aggregated and rasterized from OSM polygons, first to 10m spatial resolution and after downsampled to 30m by spatial average.
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))

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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

Europe
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

gh:veg-pinusXnigra-anvXeml-p-30m-0XX0cm--eumap-epsg3035-v0X2
type: Distribution
format: OGC:WMS

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

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

veg pinus.nigra anv.eml p 30m 0..0cm 2010..2014 eumap epsg3035 v0.2.tif
type: Distribution
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veg pinus.nigra anv.eml p 30m 0..0cm 2006..2010 eumap epsg3035 v0.2.tif
type: Distribution
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veg pinus.nigra anv.eml p 30m 0..0cm 2002..2006 eumap epsg3035 v0.2.tif
type: Distribution
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veg pinus.nigra anv.eml p 30m 0..0cm 2000..2002 eumap epsg3035 v0.2.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

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

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

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

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

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

veg pinus.halepensis anv.eml p 30m 0..0cm 2000..2002 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

Daily gap-filled MODIS LST data (2003-2019)
type: Distribution
format: WWW:LINK-1.0-http--link

lcv landcover.312 lucas.corine.slope p 30m 0..0cm 2000 eumap epsg3035 v0.1.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

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

lcv building.commercial osm p 30m 0..0cm 2021 eumap epsg3035 v0.1.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

dtm hillshade.a315 gedi.eml mf 30m 0..0cm 2000..2018 eumap epsg3035 v0.3.tif
type: Distribution
format: WWW:DOWNLOAD-1.0-http--download

lcv building.industrial osm p 30m 0..0cm 2021 eumap epsg3035 v0.1.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

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

Carmelo Bonannella
type: Agent

Martijn Witjes
type: Agent

Martijn Witjes
type: Agent

Leandro Parente
type: Agent

Tomislav Hengl
type: Agent