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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: Monthly time series of spatially enhanced relative humidity for Europe at 1000 m resolution (2000 - 2022) derived from ERA5-Land data
record: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
dataset: https://zenodo.org/record/7427021
dataset: Regional model ICON-D2
dataset: Ensemble forecasting system ICON-D2 EPS
dataset: https://zenodo.org/records/14764686/files/MODIS_NDWI_4326_monthly_2019.zip
dataset: https://zenodo.org/records/14764686/files/MODIS_NDWI_4326_monthly_2020.zip
dataset: https://zenodo.org/records/14764686/files/MODIS_NDWI_4326_monthly_2021.zip
dataset: https://zenodo.org/records/14764686/files/MODIS_NDWI_4326_monthly_2022.zip
dataset: https://zenodo.org/records/14764686/files/MODIS_NDWI_4326_monthly_2023.zip
dataset: gh:lcv-landcoverX123-lucasXcorineXrf-p-30m

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: 2024-07-11
modified: 2024-07-11
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Monthly time series of spatially enhanced relative humidity for Europe at 1000 m resolution (2000 - 2022) derived from ERA5-Land data
type: Dataset
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 - 12/2023. 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 monthly averages. …
keyword: relative humidity
keyword: geospatial analysis
keyword: environment
keyword: meteorology
keyword: ERA5-Land
keyword: CHELSA
keyword: Europe
keyword: MOOD-H2020
keyword: Tularaemia
keyword: Lyme
keyword: TBE
keyword: WNV
keyword: USUTU
keyword: Mosquito borne Flaviviruses
theme: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
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temporal: 2000-01-01 / 2023-12-31
publisher: mundialis GmbH & Co. KG
accrual periodicity: asNeeded
language: eng
license: otherRestrictions
license: license
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: https://zenodo.org/record/7427021
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: Short-term forecasting model ICON-D2
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
spatial: Polygon
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temporal: 2021-02-10 /
publisher: DWD
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

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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: 2025-01-31
modified: 2025-01-31
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https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
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MODIS NDWI, monthly aggregated time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
type: Dataset
abstract: Normalized Difference Water Index (NDWI) from MODIS data for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023). Source data: - MODIS/Terra Surface Reflectance 8-Day L3 Global 500 m SIN Grid (MOD09A1 v061): https://lpdaac.usgs.gov/products/mod09a1v061/ The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra MOD09A1 Version 6.1 product provides an estimate of the surface spectral reflectance of Terra MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 meter (m) reflectance bands are two quality layers and four observation bands. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used. For the time period January 2019 - December 2023, the NDWI has been calculated from the Terra MOD09A1 Version 6.1 product. The layers B02 (near infrared) and B06 (shortwave infrared) of the original data have been processed. Bad quality pixels or pixels with snow/ice and/or cloud cover have been masked using the provided quality assurance (QA) layer. The time series has been gapfilled with a temporal and a spatial approach. Gaps in the time series were filled with a harmonic analysis of time series using six frequencies to also model relatively short-term changes in NDWI. Only missing values were replaced by modelled values. NDWI was calculated as the normalized difference of the bands B02 (near infrared) and B06 (shortwave infrared) with: NDWI = (B02 – B06) / (B02 + B06). This NDWI represents vegetation water content. The 8-day data are then aggregated to monthly temporal resolution using the average and reprojected to Latitude-Longitude/WGS84. File naming: ndwi_monthly_YYYY_MM_30arcsec.tif e.g.: ndwi_monthly_2023_12_30arcsec.tif The date within the filename are year an…
keyword: MOOD-H2020
keyword: Normalized Difference Water Index
keyword: NDWI
keyword: Rift Valley Fever
keyword: RVF
keyword: Mauritania
theme: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
spatial: Polygon
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temporal: 2019-01-01 / 2023-12-31
publisher: mundialis GmbH & Co. KG
accrual periodicity: asNeeded
language: eng
license: otherRestrictions
license: license
license: All data products distributed by NASA's Land Processes Distributed Active Archive Center (LP DAAC) are available at no charge.
license: All LP DAAC current data and products acquired through the LP DAAC have no restrictions on reuse, sale, or redistribution. The LP DAAC requests that any author using NASA data products in their work provide credit for the data, and any assistance provided by the LP DAAC, in the data section of the paper, the acknowledgement section, and/or as a reference. The recommended citation for each data product is available on its Digital Object Identifier (DOI) Landing page, which can be accessed through the Search Data Catalog interface. For full information on LP DAAC Data Citation and Policies, see: https://lpdaac.usgs.gov/data/data-citation-and-policies/
distribution: https://zenodo.org/records/14764686/files/MODIS_NDWI_4326_monthly_2019.zip
distribution: https://zenodo.org/records/14764686/files/MODIS_NDWI_4326_monthly_2020.zip
distribution: https://zenodo.org/records/14764686/files/MODIS_NDWI_4326_monthly_2021.zip
distribution: https://zenodo.org/records/14764686/files/MODIS_NDWI_4326_monthly_2022.zip
distribution: https://zenodo.org/records/14764686/files/MODIS_NDWI_4326_monthly_2023.zip
data quality: Derived from: MODIS/Terra Surface Reflectance 8-Day L3 Global 500 m SIN Grid (MOD09A1 v061): https://lpdaac.usgs.gov/products/mod09a1v061/ NDWI has been calculated from the Terra MOD09A1 Version 6.1 product. The layers B02 (near infrared) and B06 (shortwave infrared) of the original data have been processed. Bad quality pixels or pixels with snow/ice and/or cloud cover have been masked using the provided quality assurance (QA) layer. The time series has been gapfilled with a temporal and a spatial approach.

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Port areas
type: Dataset
abstract: 123: Infrastructure of port areas (land and water surface), including quays, dockyards and marinas.
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: 2000-01-01 / 2019-12-31
accrual periodicity: asNeeded
license: copyright
license: otherRestictions
distribution: gh:lcv-landcoverX123-lucasXcorineXrf-p-30m
data quality: derived from XY
title: PNV - Probability distribution for Quercus robur
abstract: Overview: Potential Natural Vegetation (PNV): potential probability of occurrence for the Pedunculate oak 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 val…
keyword: Vegetation
temporal: - / -
title: Peat bogs
abstract: Overview: 412: Wetlands with accumulation of considerable amount of decomposed moss (mostly Sphagnum)and vegetation matter. Both natural and exploited peat bogs 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 publication. Completeness:…
title: Mixed forest
abstract: Overview: 313: Vegetation formation composed principally of trees, including shrub and bush understory,where neither broad-leaved nor coniferous species predominate. 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 publication. Complete…

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Atmospheric conditions
type: Concept
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Meteorological geographical features
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Human health and safety
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Geographical grid systems
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Regional model ICON-D2
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Ensemble forecasting system ICON-D2 EPS
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