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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: Fruit trees and berry plantations
record: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
dataset: gh:lcv-landcoverX222-lucasXcorineXrf-p-30m
dataset: Regional model ICON-D2
dataset: Ensemble forecasting system ICON-D2 EPS
dataset: Daily gap-filled MODIS LST data (2003-2019)
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: 2021-01-14
modified: 2021-01-14
references: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
format: text/html
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https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
format: application/xml
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  label: XML

Fruit trees and berry plantations
type: Dataset
abstract: 222: Cultivated parcels planted with fruit trees and shrubs, intended for fruit production, including nuts. The planting pattern can be by single or mixed fruit species, both in association with permanently grassy surfaces.
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
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temporal: 2000-01-01 / 2019-12-31
accrual periodicity: asNeeded
license: copyright
license: otherRestictions
distribution: gh:lcv-landcoverX222-lucasXcorineXrf-p-30m
data quality: derived from XY
title: PNV - Probability distribution for Picea abies
abstract: Overview: Potential Natural Vegetation (PNV): potential probability of occurrence for the Norway spruce 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…
keyword: Vegetation
temporal: - / -
title: Coniferous forest
abstract: Overview: 312: Vegetation formation composed principally of trees, including shrub and bush understorey, where coniferous species predominate. The predominant classifying parameter for this class is a crown cover density of > 30 % or a minimum 500 subjects/ha density, with coniferous trees representing > 75 % of the formation. The minimum tree height is 5 m (with the exception of Christmas tree plantations). 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…
title: Broad-leaved forest prob. slope
abstract: 311: Slope of broad-leaved forest derived by OLS regression over the probabilities values (2000—2019). The std. error of the model was considered as uncertainty.
title: Sclerophyllous vegetation
abstract: 323: Bushy sclerophyllous vegetation in a climax stage of development, including maquis, matorral and garrigue.

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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  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: 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
keyword: MOOD-H2020
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)
relation: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
data quality: Dataset has been gap-filled from the original MODIS LST products MOD11A1/MYD11A1 at a spatial resolution of 1km

https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
type: CatalogRecord
primary topic: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
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type: CatalogRecord
primary topic: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
issued: 2021-10-25
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type: CatalogRecord
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https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
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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
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format: application/xml
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type: CatalogRecord
primary topic: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
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Polygon
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type: CatalogRecord
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issued: 2021-01-14
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Geographical grid systems
type: Concept
in scheme: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search

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

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

gh:lcv-landcoverX222-lucasXcorineXrf-p-30m
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