fr: force-des-partis politique suffrage

de: parteistarke stimmen politik

fr: Elections au Conseil national (suffrages des partis et force des partis depuis 1971: districts et communes) (csv)

de: Nationalratswahlen (Parteistimmen und Parteistärke seit 1971: Bezirke und Gemeinden) (csv)

fr: Elections au Conseil national (suffrages des partis et force des partis depuis 1971: districts et communes) (rdfxml)

de: Nationalratswahlen (Parteistimmen und Parteistärke seit 1971: Bezirke und Gemeinden) (rdfxml)

de: xlsx

fr: Elections au Conseil national (suffrages des partis et force des partis depuis 1971: districts et communes) (xlsx)

de: Nationalratswahlen (Parteistimmen und Parteistärke seit 1971: Bezirke und Gemeinden) (xlsx)

fr: Elections au Conseil national (suffrages des partis et force des partis depuis 1971: districts et communes) (jsonl)

de: Nationalratswahlen (Parteistimmen und Parteistärke seit 1971: Bezirke und Gemeinden) (jsonl)

fr: Elections au Conseil national (suffrages des partis et force des partis depuis 1971: districts et communes) (json)

de: Nationalratswahlen (Parteistimmen und Parteistärke seit 1971: Bezirke und Gemeinden) (json)

de: geojson

fr: Elections au Conseil national (suffrages des partis et force des partis depuis 1971: districts et communes) (geojson)

de: Nationalratswahlen (Parteistimmen und Parteistärke seit 1971: Bezirke und Gemeinden) (geojson)

fr: Elections au Conseil national (suffrages des partis et force des partis depuis 1971: districts et communes) (jsonld)

de: Nationalratswahlen (Parteistimmen und Parteistärke seit 1971: Bezirke und Gemeinden) (jsonld)

fr: Elections au Conseil national (suffrages des partis et force des partis depuis 1971: districts et communes) (n3)

de: Nationalratswahlen (Parteistimmen und Parteistärke seit 1971: Bezirke und Gemeinden) (n3)

de: fgb

fr: Elections au Conseil national (suffrages des partis et force des partis depuis 1971: districts et communes) (fgb)

de: Nationalratswahlen (Parteistimmen und Parteistärke seit 1971: Bezirke und Gemeinden) (fgb)

format: OCTET

de: shp

media_type: application/zip

fr: Elections au Conseil national (suffrages des partis et force des partis depuis 1971: districts et communes) (shp)

de: Nationalratswahlen (Parteistimmen und Parteistärke seit 1971: Bezirke und Gemeinden) (shp)

format: ZIP

de: parquet

fr: Elections au Conseil national (suffrages des partis et force des partis depuis 1971: districts et communes) (parquet)

de: Nationalratswahlen (Parteistimmen und Parteistärke seit 1971: Bezirke und Gemeinden) (parquet)

de: kml

fr: Elections au Conseil national (suffrages des partis et force des partis depuis 1971: districts et communes) (kml)

de: Nationalratswahlen (Parteistimmen und Parteistärke seit 1971: Bezirke und Gemeinden) (kml)

format: KML

media_type: text/plain

fr: Elections au Conseil national (suffrages des partis et force des partis depuis 1971: districts et communes) (ov2)

de: Nationalratswahlen (Parteistimmen und Parteistärke seit 1971: Bezirke und Gemeinden) (ov2)

format: TXT

de: turtle

fr: Elections au Conseil national (suffrages des partis et force des partis depuis 1971: districts et communes) (turtle)

de: Nationalratswahlen (Parteistimmen und Parteistärke seit 1971: Bezirke und Gemeinden) (turtle)

de: gpx

fr: Elections au Conseil national (suffrages des partis et force des partis depuis 1971: districts et communes) (gpx)

de: Nationalratswahlen (Parteistimmen und Parteistärke seit 1971: Bezirke und Gemeinden) (gpx)

format: GPX

fr: Ce jeu de données présente le suffrage des partis et la force des partis par commune (à l'état du 31.12.2023), depuis 1971. Aucune donnée n'est disponible pour les fribourgeois de l'étranger entre 1971 et 2011.

de: Dieser Datensatz zeigt die Parteistimmen und die Parteistärke nach Gemeinde (Stand 31.12.2023), seit 1971. Für die Freiburger im Ausland sind zwischen 1971 und 2011 keine Daten verfügbar.

force-des-partis

display_name: force-des-partis

parteistarke

display_name: parteistarke

politik

display_name: politik

politique

display_name: politique

stimmen

display_name: stimmen

suffrage

display_name: suffrage

gove

fr: Gouvernement et secteur public

de: Regierung und öffentlicher Sektor

en: Government and public sector

it: Governo e settore pubblico

ssd

de: Das Amt für Statistik und Daten ist das kantonale Kompetenzzentrum für die öffentliche Statistik. Es erhebt, analysiert und verbreitet statistische Daten über die Lage und die Entwicklung des Kantons, unterstützt die Dienststellen des Staats bei der Verwaltung ihrer Daten und koordiniert die Veröffentlichung der offenen Verwaltungsdaten.


maintainer: Centre de compétences et de coordination OGD / Kompetenz- und Koordinationszentrum für OGD

fr: conjoncture-fribourgeoise voitures-de-tourisme-neuves immatriculations indicateurs-conjoncturels

de: neue-personenwagen konjunkturindikatoren freiburger-konjunkturspiegel inverkehrsetzungen

author: ssd

state: active

type: dataset

de: xlsx

fr: Mises en circulation de voitures de tourisme neuves, dès 2006 (xlsx)

de: Inverkehrsetzungen neuer Personenwagen ab 2006 (xlsx)

format: XLS

de: csv

media_type: text/csv

fr: Mises en circulation de voitures de tourisme neuves, dès 2006 (csv)

de: Inverkehrsetzungen neuer Personenwagen ab 2006 (csv)

format: CSV

de: jsonld

fr: Mises en circulation de voitures de tourisme neuves, dès 2006 (jsonld)

de: Inverkehrsetzungen neuer Personenwagen ab 2006 (jsonld)

format: JSON-LD

fr: Mises en circulation de voitures de tourisme neuves, dès 2006 (parquet)

de: Inverkehrsetzungen neuer Personenwagen ab 2006 (parquet)

format: PARQUET

de: jsonl

media_type: application/json

fr: Mises en circulation de voitures de tourisme neuves, dès 2006 (jsonl)

de: Inverkehrsetzungen neuer Personenwagen ab 2006 (jsonl)

format: JSON

fr: Mises en circulation de voitures de tourisme neuves, dès 2006 (n3)

de: Inverkehrsetzungen neuer Personenwagen ab 2006 (n3)

de: rdfxml

media_type: application/rdf+xml

fr: Mises en circulation de voitures de tourisme neuves, dès 2006 (rdfxml)

de: Inverkehrsetzungen neuer Personenwagen ab 2006 (rdfxml)

format: RDF XML

de: json

fr: Mises en circulation de voitures de tourisme neuves, dès 2006 (json)

de: Inverkehrsetzungen neuer Personenwagen ab 2006 (json)

media_type: text/turtle

fr: Mises en circulation de voitures de tourisme neuves, dès 2006 (turtle)

de: Inverkehrsetzungen neuer Personenwagen ab 2006 (turtle)

format: RDF Turtle

fr: Ce jeu de données présente l'aperçu mensuel des nouvelles immatriculations de voitures de tourisme neuves dans le canton de Fribourg et dans l'ensemble de la Suisse, depuis 2006.Les résultats de l'année en cours sont toujours provisoires. Des annonces complémentaires relatives aux mois précédents sont probables.

de: Dieser Datensatz enthält eine monatliche Übersicht über die Neuzulassungen von neuen Personenwagen im Kanton Freiburg und in der gesamten Schweiz seit 2006.Die Resultate des laufenden Jahres sind immer provisorisch. Nachmeldungen für vergangene Monate sind wahrscheinlich.

conjoncture-fribourgeoise

display_name: conjoncture-fribourgeoise

freiburger-konjunkturspiegel

display_name: freiburger-konjunkturspiegel

immatriculations

display_name: immatriculations

indicateurs-conjoncturels

display_name: indicateurs-conjoncturels

inverkehrsetzungen

display_name: inverkehrsetzungen

konjunkturindikatoren

display_name: konjunkturindikatoren

neue-personenwagen

display_name: neue-personenwagen

voitures-de-tourisme-neuves

display_name: voitures-de-tourisme-neuves

tran

fr: Transports

de: Verkehr

en: Transport

it: Trasporti

de: ssd

fr: Service de la statistique et de la donnée Fribourg

de: Amt für Statistik und Daten Freiburg

en: Statistics and Data Service Fribourg

it: Servizio Statistiche e Dati Friburgo

fr: Le Service de la statistique et de la donnée est le centre de compétence cantonal pour la statistique publique. Il collecte, analyse et diffuse des données statistiques sur la situation et l’évolution du canton, soutient les services de l’État dans la gestion des données et coordonne la diffusion des données administratives en libre accès.

en: The Statistics and Data Service is the cantonal competence center for public statistics. It collects, analyzes, and disseminates statistical data on the situation and development of the canton, supports state services in data management, and coordinates the dissemination of administrative data in open access.

it: Il Servizio Statistiche e Dati è il centro di competenza cantonale per la statistica pubblica. Raccoglie, analizza e diffonde dati statistici sulla situazione e l'evoluzione del cantone, supporta i servizi statali nella gestione dei dati e coordina la diffusione dei dati amministrativi ad accesso aperto.

political_level: canton

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: DCAT Harvester Kt. FR


name: Centre de compétences et de coordination OGD / Kompetenz- und Koordinationszentrum für OGD

fr: commune population-residante

de: gemeinde wohnbevolkerung

de: xlsx

fr: Population résidante par commune, de 1900 à 2000 (xlsx)

de: Wohnbevölkerung nach Gemeinde von 1900 bis 2000 (xlsx)

fr: Population résidante par commune, de 1900 à 2000 (jsonld)

de: Wohnbevölkerung nach Gemeinde von 1900 bis 2000 (jsonld)

de: csv

fr: Population résidante par commune, de 1900 à 2000 (csv)

de: Wohnbevölkerung nach Gemeinde von 1900 bis 2000 (csv)

de: turtle

fr: Population résidante par commune, de 1900 à 2000 (turtle)

de: Wohnbevölkerung nach Gemeinde von 1900 bis 2000 (turtle)

fr: Population résidante par commune, de 1900 à 2000 (json)

de: Wohnbevölkerung nach Gemeinde von 1900 bis 2000 (json)

de: parquet

fr: Population résidante par commune, de 1900 à 2000 (parquet)

de: Wohnbevölkerung nach Gemeinde von 1900 bis 2000 (parquet)

fr: Population résidante par commune, de 1900 à 2000 (jsonl)

de: Wohnbevölkerung nach Gemeinde von 1900 bis 2000 (jsonl)

fr: Population résidante par commune, de 1900 à 2000 (rdfxml)

de: Wohnbevölkerung nach Gemeinde von 1900 bis 2000 (rdfxml)

fr: Population résidante par commune, de 1900 à 2000 (n3)

de: Wohnbevölkerung nach Gemeinde von 1900 bis 2000 (n3)

fr: Ce jeu de données présente la population résidante par commune de 1900 à 2000.

de: Dieser Datensatz zeigt die Wohnbevölkerung nach Gemeinde von 1900 bis 2000.

commune

display_name: commune

gemeinde

display_name: gemeinde

population-residante

display_name: population-residante

wohnbevolkerung

display_name: wohnbevolkerung

soci

fr: Population et société

de: Bevölkerung und Gesellschaft

en: Population and society

it: Popolazione e società

ssd

de: Das Amt für Statistik und Daten ist das kantonale Kompetenzzentrum für die öffentliche Statistik. Es erhebt, analysiert und verbreitet statistische Daten über die Lage und die Entwicklung des Kantons, unterstützt die Dienststellen des Staats bei der Verwaltung ihrer Daten und koordiniert die Veröffentlichung der offenen Verwaltungsdaten.


fr: carburant voitures energie

de: treibstoff personenwagen energie

fr: Voitures de tourisme, par type de carburant/énergie, dès 2017 (turtle)

de: Personenwagen nach Treibstoffart/Energie ab 2017 (turtle)

de: jsonl

fr: Voitures de tourisme, par type de carburant/énergie, dès 2017 (jsonl)

de: Personenwagen nach Treibstoffart/Energie ab 2017 (jsonl)

fr: Voitures de tourisme, par type de carburant/énergie, dès 2017 (n3)

de: Personenwagen nach Treibstoffart/Energie ab 2017 (n3)

fr: Voitures de tourisme, par type de carburant/énergie, dès 2017 (csv)

de: Personenwagen nach Treibstoffart/Energie ab 2017 (csv)

fr: Voitures de tourisme, par type de carburant/énergie, dès 2017 (xlsx)

de: Personenwagen nach Treibstoffart/Energie ab 2017 (xlsx)

de: json

fr: Voitures de tourisme, par type de carburant/énergie, dès 2017 (json)

de: Personenwagen nach Treibstoffart/Energie ab 2017 (json)

fr: Voitures de tourisme, par type de carburant/énergie, dès 2017 (parquet)

de: Personenwagen nach Treibstoffart/Energie ab 2017 (parquet)

de: jsonld

fr: Voitures de tourisme, par type de carburant/énergie, dès 2017 (jsonld)

de: Personenwagen nach Treibstoffart/Energie ab 2017 (jsonld)

de: rdfxml

fr: Voitures de tourisme, par type de carburant/énergie, dès 2017 (rdfxml)

de: Personenwagen nach Treibstoffart/Energie ab 2017 (rdfxml)

fr: Ce jeu de données présente les voitures de tourisme, par type de carburant/énergie, dès 2017.

de: Dieser Datensatz enthält die Personenwagen nach Treibstoffart/Energie ab 2017.

carburant

display_name: carburant

energie

display_name: energie

personenwagen

display_name: personenwagen

treibstoff

display_name: treibstoff

voitures

display_name: voitures

tran

de: Verkehr

de: ssd

de: Amt für Statistik und Daten Freiburg


fr: population-dite-legale commune

de: gemeinde zivilrechtliche-bevolkerung

de: parquet

fr: Population dite légale par commune, dès 1980 (parquet)

de: Zivilrechtliche Bevölkerung nach Gemeinde ab 1980 (parquet)

de: csv

fr: Population dite légale par commune, dès 1980 (csv)

de: Zivilrechtliche Bevölkerung nach Gemeinde ab 1980 (csv)

de: turtle

fr: Population dite légale par commune, dès 1980 (turtle)

de: Zivilrechtliche Bevölkerung nach Gemeinde ab 1980 (turtle)

fr: Population dite légale par commune, dès 1980 (json)

de: Zivilrechtliche Bevölkerung nach Gemeinde ab 1980 (json)

fr: Population dite légale par commune, dès 1980 (jsonl)

de: Zivilrechtliche Bevölkerung nach Gemeinde ab 1980 (jsonl)

fr: Population dite légale par commune, dès 1980 (n3)

de: Zivilrechtliche Bevölkerung nach Gemeinde ab 1980 (n3)

de: xlsx

fr: Population dite légale par commune, dès 1980 (xlsx)

de: Zivilrechtliche Bevölkerung nach Gemeinde ab 1980 (xlsx)

fr: Population dite légale par commune, dès 1980 (rdfxml)

de: Zivilrechtliche Bevölkerung nach Gemeinde ab 1980 (rdfxml)

fr: Population dite légale par commune, dès 1980 (jsonld)

de: Zivilrechtliche Bevölkerung nach Gemeinde ab 1980 (jsonld)

fr: Ce jeu de données présente la population dite légale par commune depuis 1980.

population-dite-legale

display_name: population-dite-legale

zivilrechtliche-bevolkerung

display_name: zivilrechtliche-bevolkerung

soci

de: Bevölkerung und Gesellschaft

ssd

fr: Service de la statistique et de la donnée Fribourg

fr: Le Service de la statistique et de la donnée est le centre de compétence cantonal pour la statistique publique. Il collecte, analyse et diffuse des données statistiques sur la situation et l’évolution du canton, soutient les services de l’État dans la gestion des données et coordonne la diffusion des données administratives en libre accès.

de: Das Amt für Statistik und Daten ist das kantonale Kompetenzzentrum für die öffentliche Statistik. Es erhebt, analysiert und verbreitet statistische Daten über die Lage und die Entwicklung des Kantons, unterstützt die Dienststellen des Staats bei der Verwaltung ihrer Daten und koordiniert die Veröffentlichung der offenen Verwaltungsdaten.


fr: prix-de-vente frequentation-de-la-clientele stocks chiffre-daffaires volume-des-ventes conjoncture-fribourgeoise personnes-occupees indicateurs-conjoncturels situation-des-affaires

de: beschaftigte konjunkturindikatoren freiburger-konjunkturspiegel geschaftslage kundenfrequenz mengenmassiger-absatz umsatzentwicklung verkaufspreise lager

fr: Indicateurs mensuels pour le commerce de détail, dès 2010 (xlsx)

de: Monatliche Indikatoren für den Detailhandel ab 2010 (xlsx)

de: rdfxml

fr: Indicateurs mensuels pour le commerce de détail, dès 2010 (rdfxml)

de: Monatliche Indikatoren für den Detailhandel ab 2010 (rdfxml)

de: jsonl

fr: Indicateurs mensuels pour le commerce de détail, dès 2010 (jsonl)

de: Monatliche Indikatoren für den Detailhandel ab 2010 (jsonl)

fr: Indicateurs mensuels pour le commerce de détail, dès 2010 (n3)

de: Monatliche Indikatoren für den Detailhandel ab 2010 (n3)

fr: Indicateurs mensuels pour le commerce de détail, dès 2010 (turtle)

de: Monatliche Indikatoren für den Detailhandel ab 2010 (turtle)

de: jsonld

fr: Indicateurs mensuels pour le commerce de détail, dès 2010 (jsonld)

de: Monatliche Indikatoren für den Detailhandel ab 2010 (jsonld)

fr: Indicateurs mensuels pour le commerce de détail, dès 2010 (parquet)

de: Monatliche Indikatoren für den Detailhandel ab 2010 (parquet)

fr: Indicateurs mensuels pour le commerce de détail, dès 2010 (csv)

de: Monatliche Indikatoren für den Detailhandel ab 2010 (csv)

de: json

fr: Indicateurs mensuels pour le commerce de détail, dès 2010 (json)

de: Monatliche Indikatoren für den Detailhandel ab 2010 (json)

fr: Ce jeu de données présente les réponses au questionnaire mensuel de l'enquête conjoncturelle du KOF dans le commerce de détail, du canton de Fribourg et de la Suisse, depuis 2010.

de: Dieser Datensatz enthält die Antworten auf den monatlichen Fragebogen der KOF-Konjunkturumfrage im Einzelhandel, im Kanton Freiburg und in der Schweiz, seit 2010.

beschaftigte

display_name: beschaftigte

chiffre-daffaires

display_name: chiffre-daffaires

conjoncture-fribourgeoise

display_name: conjoncture-fribourgeoise

freiburger-konjunkturspiegel

display_name: freiburger-konjunkturspiegel

frequentation-de-la-clientele

display_name: frequentation-de-la-clientele

geschaftslage

display_name: geschaftslage

kundenfrequenz

display_name: kundenfrequenz

lager

display_name: lager

mengenmassiger-absatz

display_name: mengenmassiger-absatz

personnes-occupees

display_name: personnes-occupees

prix-de-vente

display_name: prix-de-vente

situation-des-affaires

display_name: situation-des-affaires

stocks

display_name: stocks

umsatzentwicklung

display_name: umsatzentwicklung

verkaufspreise

display_name: verkaufspreise

volume-des-ventes

display_name: volume-des-ventes

econ

fr: Économie et finances

de: Wirtschaft und Finanzen

en: Economy and finance

it: Economia e finanze

de: ssd

de: Amt für Statistik und Daten Freiburg


de: csv

fr: Consommation de gaz, en milliers de kWh, par semestre, dès 2021 (csv)

de: Gasverbrauch, in Tausend kWh, pro Halbjahr ab 2021 (csv)

fr: Consommation de gaz, en milliers de kWh, par semestre, dès 2021 (json)

de: Gasverbrauch, in Tausend kWh, pro Halbjahr ab 2021 (json)

de: turtle

fr: Consommation de gaz, en milliers de kWh, par semestre, dès 2021 (turtle)

de: Gasverbrauch, in Tausend kWh, pro Halbjahr ab 2021 (turtle)

fr: Consommation de gaz, en milliers de kWh, par semestre, dès 2021 (jsonld)

de: Gasverbrauch, in Tausend kWh, pro Halbjahr ab 2021 (jsonld)

de: xlsx

fr: Consommation de gaz, en milliers de kWh, par semestre, dès 2021 (xlsx)

de: Gasverbrauch, in Tausend kWh, pro Halbjahr ab 2021 (xlsx)

fr: Consommation de gaz, en milliers de kWh, par semestre, dès 2021 (rdfxml)

de: Gasverbrauch, in Tausend kWh, pro Halbjahr ab 2021 (rdfxml)

de: parquet

fr: Consommation de gaz, en milliers de kWh, par semestre, dès 2021 (parquet)

de: Gasverbrauch, in Tausend kWh, pro Halbjahr ab 2021 (parquet)

fr: Consommation de gaz, en milliers de kWh, par semestre, dès 2021 (n3)

de: Gasverbrauch, in Tausend kWh, pro Halbjahr ab 2021 (n3)

fr: Consommation de gaz, en milliers de kWh, par semestre, dès 2021 (jsonl)

de: Gasverbrauch, in Tausend kWh, pro Halbjahr ab 2021 (jsonl)

fr: Ce jeu de données présente l'aperçu par semestre de la consommation de gaz naturel du canton de Fribourg, dès 2021.

consommation-gaz-naturel

display_name: consommation-gaz-naturel

gasverbrauch

display_name: gasverbrauch

ener

fr: Énergie

de: Energie

en: Energy

it: Energia

ssd

de: Das Amt für Statistik und Daten ist das kantonale Kompetenzzentrum für die öffentliche Statistik. Es erhebt, analysiert und verbreitet statistische Daten über die Lage und die Entwicklung des Kantons, unterstützt die Dienststellen des Staats bei der Verwaltung ihrer Daten und koordiniert die Veröffentlichung der offenen Verwaltungsdaten.


fr: genre nationalite population-residante commune

de: nationalitat wohnbevolkerung gemeinde geschlecht

fr: Bilan démographique de la population résidante permanente, par commune, sexe et catégorie de nationalité, dès 1981 (csv)

de: Bevölkerungsbilanz der ständigen Wohnbevölkerung nach Gemeinde, Geschlecht und Staatsangehörigkeitskategorie ab 1981 (csv)

fr: Bilan démographique de la population résidante permanente, par commune, sexe et catégorie de nationalité, dès 1981 (n3)

de: Bevölkerungsbilanz der ständigen Wohnbevölkerung nach Gemeinde, Geschlecht und Staatsangehörigkeitskategorie ab 1981 (n3)

de: json

fr: Bilan démographique de la population résidante permanente, par commune, sexe et catégorie de nationalité, dès 1981 (json)

de: Bevölkerungsbilanz der ständigen Wohnbevölkerung nach Gemeinde, Geschlecht und Staatsangehörigkeitskategorie ab 1981 (json)

de: rdfxml

fr: Bilan démographique de la population résidante permanente, par commune, sexe et catégorie de nationalité, dès 1981 (rdfxml)

de: Bevölkerungsbilanz der ständigen Wohnbevölkerung nach Gemeinde, Geschlecht und Staatsangehörigkeitskategorie ab 1981 (rdfxml)

fr: Bilan démographique de la population résidante permanente, par commune, sexe et catégorie de nationalité, dès 1981 (turtle)

de: Bevölkerungsbilanz der ständigen Wohnbevölkerung nach Gemeinde, Geschlecht und Staatsangehörigkeitskategorie ab 1981 (turtle)

fr: Bilan démographique de la population résidante permanente, par commune, sexe et catégorie de nationalité, dès 1981 (parquet)

de: Bevölkerungsbilanz der ständigen Wohnbevölkerung nach Gemeinde, Geschlecht und Staatsangehörigkeitskategorie ab 1981 (parquet)

de: jsonl

fr: Bilan démographique de la population résidante permanente, par commune, sexe et catégorie de nationalité, dès 1981 (jsonl)

de: Bevölkerungsbilanz der ständigen Wohnbevölkerung nach Gemeinde, Geschlecht und Staatsangehörigkeitskategorie ab 1981 (jsonl)

fr: Bilan démographique de la population résidante permanente, par commune, sexe et catégorie de nationalité, dès 1981 (xlsx)

de: Bevölkerungsbilanz der ständigen Wohnbevölkerung nach Gemeinde, Geschlecht und Staatsangehörigkeitskategorie ab 1981 (xlsx)

de: jsonld

fr: Bilan démographique de la population résidante permanente, par commune, sexe et catégorie de nationalité, dès 1981 (jsonld)

de: Bevölkerungsbilanz der ständigen Wohnbevölkerung nach Gemeinde, Geschlecht und Staatsangehörigkeitskategorie ab 1981 (jsonld)

fr: Ce jeu de données présente le Bilan démographique de la population résidante permanente, par commune, sexe et catégorie de nationalité, dès 1981

de: Dieser Datensatz zeigt die demografische Bilanz der ständigen Wohnbevölkerung nach Gemeinde, Geschlecht und Staatsangehörigkeitskategorie ab 1981.

commune

display_name: commune

gemeinde

display_name: gemeinde

genre

display_name: genre

geschlecht

display_name: geschlecht

nationalitat

display_name: nationalitat

nationalite

display_name: nationalite

population-residante

display_name: population-residante

wohnbevolkerung

display_name: wohnbevolkerung

soci

fr: Population et société

de: Bevölkerung und Gesellschaft

de: ssd

fr: Service de la statistique et de la donnée Fribourg

fr: Le Service de la statistique et de la donnée est le centre de compétence cantonal pour la statistique publique. Il collecte, analyse et diffuse des données statistiques sur la situation et l’évolution du canton, soutient les services de l’État dans la gestion des données et coordonne la diffusion des données administratives en libre accès.


windmessungen-cote-du-glaney

title_for_slug: windmessungen-cote-du-glaney

fr: vent eolienne mesures energie

de: messungen wind windenergie energie

de: turtle

fr: Mesures de vents - Côte du Glâney (turtle)

de: Windmessungen - Côte du Glâney (turtle)

de: parquet

fr: Mesures de vents - Côte du Glâney (parquet)

de: Windmessungen - Côte du Glâney (parquet)

fr: Mesures de vents - Côte du Glâney (json)

de: Windmessungen - Côte du Glâney (json)

fr: Mesures de vents - Côte du Glâney (rdfxml)

de: Windmessungen - Côte du Glâney (rdfxml)

de: xlsx

fr: Mesures de vents - Côte du Glâney (xlsx)

de: Windmessungen - Côte du Glâney (xlsx)

fr: Mesures de vents - Côte du Glâney (jsonl)

de: Windmessungen - Côte du Glâney (jsonl)

de: csv

fr: Mesures de vents - Côte du Glâney (csv)

de: Windmessungen - Côte du Glâney (csv)

fr: Mesures de vents - Côte du Glâney (jsonld)

de: Windmessungen - Côte du Glâney (jsonld)

fr: Mesures de vents - Côte du Glâney (n3)

de: Windmessungen - Côte du Glâney (n3)

fr:
Ce jeu de données fournit des mesures de vent enregistrées toutes les 10 minutes pour le périmètre « Côte du Glâney », situé dans le canton de Fribourg. Collectées dans le cadre du mandat du Grand Conseil pour la planification éolienne cantonale, ces données comprennent la vitesse et la direction du vent, la température, l'humidité relative à différentes hauteurs, ainsi que le potentiel de production éolienne (productible éolien). Ces informations sont essentielles pour la planification énergétique et sont mises à disposition en libre accès sur la plateforme Open Government Data du canton de Fribourg dans un souci de transparence et d'accès à l'information pour tous. Le périmètre « Côte du Glâney » se situe dans le district de la Glâne à environ 800 mètres d’altitude, à l’ouest et sud-ouest de la commune de Romont.L'heure
UTC (temps universel coordonné) sert de référence mondiale et ne change pas
avec les saisons. Pour obtenir l'heure locale, ajoutez 1 heure en hiver (UTC+1)
et 2 heures en été (UTC+2).

de: Dieser Datensatz enthält Windmessungen, die alle 10 Minuten für das Gebiet « Côte du Glâney » im Kanton Freiburg aufgezeichnet werden. Diese Daten wurden im Rahmen des Mandats des Grossen Rates für die kantonale Windenergieplanung erhoben und umfassen Windgeschwindigkeit und -richtung, Temperatur, relative Luftfeuchtigkeit in verschiedenen Höhen sowie das Potenzial für die Windenergieerzeugung (Windenergieertrag). Diese Informationen sind für die Energieplanung von entscheidender Bedeutung und werden im Sinne der Transparenz und des Zugangs zu Informationen für alle auf der Open Government Data Plattform des Kantons Freiburg frei zugänglich gemacht. Das Gebiet «Côte du Glâney» liegt im Bezirk Glâne, auf rund 800 Metern Höhe, westlich und südwestlich der Gemeinde Romont.Die UTC-Zeit (koordinierte Weltzeit) dient als weltweite Referenz und ändert sich nicht mit den Jahreszeiten. Um die Ortszeit zu erhalten, addieren Sie im Winter 1 Stunde (UTC+1) und im Sommer 2 Stunden (UTC+2).

energie

display_name: energie

eolienne

display_name: eolienne

messungen

display_name: messungen

mesures

display_name: mesures

vent

display_name: vent

wind

display_name: wind

windenergie

display_name: windenergie

ener

de: Energie

ssd

de: Amt für Statistik und Daten Freiburg

de: Das Amt für Statistik und Daten ist das kantonale Kompetenzzentrum für die öffentliche Statistik. Es erhebt, analysiert und verbreitet statistische Daten über die Lage und die Entwicklung des Kantons, unterstützt die Dienststellen des Staats bei der Verwaltung ihrer Daten und koordiniert die Veröffentlichung der offenen Verwaltungsdaten.

fr: Mesures de vents - Côte du Glâney

de: Windmessungen - Côte du Glâney


regi

fr: Régions et villes

de: Regionen und Städte

en: Regions and cities

it: Regioni e città

envi

fr: Environnement

de: Umwelt

en: Environment

it: Ambiente

fr: carte-historique carte-nationale opendata evolution-historique directive-inspire occupation-des-terres nationales topographie cartogramme

de: historische-karte landeskarte opendata historische-entwicklung inspire bodenbedeckung national topografie karte

it: carta-storica carta-nazionale opendata evoluzione-storica inspire copertura-del-suolo nazionali topografia mappa

fr: geocat.ch permalien

de: geocat.ch Permalink

en: geocat.ch permalink

it: geocat.ch link permanente

de: Farbige Landeskarte Liechtensteins im Massstab 1:25'000. Das historische Kartenwerk ist eine Zusamensetzung au den Landeskarten 1:25'000 der Schweiz (Blätter 1115, 1116, 1135, 1136, 1155, 1156) mit dem Nachführungsstand 1952.

en: Colored national map of Liechtenstein at a scale of 1:25,000. This historical map series is a compilation of the Swiss national maps 1:25,000 (sheets 1115, 1116, 1135, 1136, 1155, 1156) with the update status of 1952.

spatial: Liechtenstein

fr: Amt für Tiefbau und Geoinformation

de: Amt für Tiefbau und Geoinformation

it: Amt für Tiefbau und Geoinformation

amt-fuer-tiefbau-und-geoinformation-fl

en: The Civil Engineering and Geoinformation Office is responsible for the construction,  operation and maintenance of main roads, the coordination of geodata infrastructure, official surveying and the catastre of Public Law Restrictions on Landownership (PLR-cadastre).

de: Das Amt für Tiefbau und Geoinformation ist zuständig für den Bau,  Betrieb und Unterhalt der Landstrassen, die Koordination der Geodateninfrastrukur, die Amtliche Vermessung und den ÖREB-Kataster.

political_level: other

value: Liechtenstein - Geocat Harvester

de: Pixelkarte Liechtenstein 1:25'000 (1952)

en: Pixel Map Liechtenstein 1:25,000 (1952)


fr: carte-historique opendata carte-nationale evolution-historique directive-inspire occupation-des-terres nationales topographie cartogramme

de: historische-karte opendata landeskarte historische-entwicklung inspire bodenbedeckung national topografie karte

it: carta-storica opendata carta-nazionale evoluzione-storica inspire copertura-del-suolo nazionali topografia mappa

de: Die Topographische Karte der Schweiz 1:100 000 (Dufourkarte) ist das erste amtliche Kartenwerk, das die Schweiz landesweit abdeckt. Der Karteninhalt im Hoheitsgebiet von Liechtenstein ist geringer. Es fehlen unter anderem Flurnamen und Geländeschraffuren.

en: The Topographic Map of Switzerland 1:100,000 (Dufour Map) is the first official map series to cover Switzerland nationwide. The map content within the territory of Liechtenstein is more limited. Among other things, field names and terrain hachures are missing.

en: Civil Engineering and Geoinformation Office


de: Datenstruktur auf Englisch

en: Data structure in English

de: Datenwürfel auf Englisch

en: Data cube in English

media_type: text/html

format: HTML

de: Datenstruktur auf Deutsch

en: Data structure in German

de: Datenwürfel auf Deutsch

en: Data cube in German

de: Personentransportfahrzeugbestand bei Stichtag, Karosserie, Marke, Anzahl Sitzplätze und Altersklasse (Jahre)

en: Licensed passenger vehicles by Reference date, Type of body, Make, Number of seats and Age category (years)

fahrzeugbestand

display_name: fahrzeugbestand

licensed-vehicles

display_name: licensed-vehicles

tran

fr: Transports

de: Verkehr

amt-fuer-statistik-fl

de: Amt für Statistik

en: Office of Statistics

en: The Office of Statistics is Liechtenstein's statistical information center. It provides the national and municipal authorities as well as the public with relevant, reliable and coherent statistical information on society, economy and environment in Liechtenstein.

de: Das Amt für Statistik ist das statistische Informationszentrum Liechtensteins. Es liefert den Landes- und Gemeindebehörden sowie der Öffentlichkeit relevante, zuverlässige und kohärente statistische Informationen zu Gesellschaft, Wirtschaft und Umwelt in Liechtenstein.

value: DCAT Harvester Amt für Statistik FL

de: Personentransportfahrzeugbestand nach Karosserie, Marke, Anzahl Sitzplätze und Altersklasse seit 2015

en: Licensed passenger vehicles by type of body, make, number of seats and age category since 2015


de: Wegpendelnde bei Jahr, Voll-/ Teilzeit, Wirtschaftszweig, Altersklasse und Geschlecht

en: Outward commuters by Year, Part-/ full-time, Economic activity, Age group and Sex

erwerbstatigkeit

display_name: erwerbstatigkeit

population-in-gainful-employment

display_name: population-in-gainful-employment

econ

fr: Économie et finances

de: Wirtschaft und Finanzen

en: Economy and finance

de: Wegpendelnde nach Voll-/ Teilzeit, Wirtschaftszweig, Altersklasse und Geschlecht seit 2008

en: Outward commuters by part-/ full-time, economic activity, age group and sex since 2008


de: Erstzugelassene Motorräder bei Jahr, Fahrzeugart und Treibstoffart

en: Motorcycles registered for the first time by Year, Type of vehicle and Type of fuel

fahrzeug-erstzulassungen

display_name: fahrzeug-erstzulassungen

vehicle-registration

display_name: vehicle-registration

tran

en: Transport

de: Erstzugelassene Motorräder nach Fahrzeugart und Treibstoffart

en: Motorcycles registered for the first time by type of vehicle and type of fuel


de: Bauprojekte bei Bewilligungsjahr, Kategorie der Arbeiten, Art der Verfahren und Gemeinde

en: Construction projects by Year of approval, Category of work, Type of procedures and Municipality

bautatigkeit

display_name: bautatigkeit

construction-activity

display_name: construction-activity

de: Bauprojekte nach Kategorie der Arbeiten, Art der Verfahren und Gemeinde seit 2010

en: Construction projects by category of work, type of procedures and municipality since 2010


de: Erstzugelassene Personenwagen bei Jahr, Marke, Hubraumklasse und Leistungsklasse

en: Passenger cars registered for the first time by Year, Make, Engine capacity class and Engine power class

de: Erstzugelassene Personenwagen nach Marke, Hubraumklasse und Leistungsklasse

en: Passenger cars registered for the first time by make, engine capacity class and engine power class


de: Datenwürfel auf Englisch

en: Data cube in English

de: Datenstruktur auf Englisch

en: Data structure in English

de: Ständige Bevölkerung nach Stichtag, Stellung im Haushalt, Lebensalter, Geschlecht und Gemeinde

en: Permanent resident population by Reference date, Status in household, Age, Sex and Municipality

bevolkerungsstruktur

display_name: bevolkerungsstruktur

population-structure

display_name: population-structure

soci

fr: Population et société

de: Bevölkerung und Gesellschaft

en: Population and society

de: Ständige Bevölkerung nach Stellung im Haushalt, Lebensalter, Geschlecht und Gemeinde

en: Permanent resident population by status in household, age, sex and municipality


maintainer: Amt für Statistik

de: Datenwürfel auf Deutsch

en: Data cube in German

de: Datenstruktur auf Deutsch

en: Data structure in German

de: Monatlicher Bruttolohn nach Jahr, Vollzeit/Teilzeit, Unternehmensgrösse, Wirtschaftssektor, Geschlecht und Kennzahl

en: Gross monthly wage by Year, Full-time/Part-time, Enterprise size, Economic sector, Sex and Key figure

lohne

display_name: lohne

wages

display_name: wages

amt-fuer-statistik-fl

de: Amt für Statistik

en: Office of Statistics

en: The Office of Statistics is Liechtenstein's statistical information center. It provides the national and municipal authorities as well as the public with relevant, reliable and coherent statistical information on society, economy and environment in Liechtenstein.

de: Monatlicher Bruttolohn nach Voll-/Teilzeit, Unternehmensgrösse, Wirtschaftssektor und Geschlecht

en: Gross monthly wage by full-/part-time, enterprise size, economic sector and sex


de: Erleichterte Einbürgerung infolge längerfristigem Wohnsitz bei Jahr, Geschlecht und Altersklasse

en: Facilitated naturalisation due to long-term residence by Year, Sex and Age group

einburgerungen

display_name: einburgerungen

naturalisations

display_name: naturalisations

intr

fr: Questions internationales

de: Internationale Themen

en: International issues

it: Tematiche internazionali

de: Das Amt für Statistik ist das statistische Informationszentrum Liechtensteins. Es liefert den Landes- und Gemeindebehörden sowie der Öffentlichkeit relevante, zuverlässige und kohärente statistische Informationen zu Gesellschaft, Wirtschaft und Umwelt in Liechtenstein.

de: Erleichterte Einbürgerung infolge längerfristigem Wohnsitz nach Geschlecht und Altersklassen seit 2000

en: Facilitated naturalisation due to long-term residence by sex and age group since 2000


de: 463.004 Versicherungsleistungen bei Jahr, Versicherungszweig und Kostenart

accident-insurers

display_name: accident-insurers

unfallversicherer

display_name: unfallversicherer

heal

fr: Santé

de: Gesundheit

en: Health

it: Salute

econ

de: Wirtschaft und Finanzen

en: Economy and finance

de: 463.004 Versicherungsleistungen nach Versicherungszweig und Kostenart seit 2005


de: Datenwürfel auf Englisch

de: Datenstruktur auf Englisch

de: Arealstatistik bei Jahr und Bodenbedeckung

en: Land use statistics by Year and Land cover

raum-flachennutzung

display_name: raum-flachennutzung

territory-land-use

display_name: territory-land-use

envi

de: Umwelt

en: Environment

de: Arealstatistik nach Bodenbedeckung seit 1984

en: Land use statistics by land cover since 1984


en: Data cube in English

en: Data structure in English

de: Datenstruktur auf Deutsch

de: Datenwürfel auf Deutsch

de: Demografische Indikatoren nach Szenario, Kennzahl und Jahr

en: Demographic indicators by Scenario, Indicator and Year

bevolkerungsszenarien

display_name: bevolkerungsszenarien

population-scenarios

display_name: population-scenarios

soci

de: Bevölkerung und Gesellschaft

de: Demografische Indikatoren nach Szenario und Kennzahl von 2023 bis 2060

en: Demographic indicators by Scenario and Indicator from 2023 to 2060


en: Data cube in German

en: Data structure in German

de: Gebäude bei Jahr, Bauperiode, Energieträger der Heizung und Gemeinde

en: Buildings by Year, Period of construction, Energy source of heating and Municipality

buildings-dwellings

display_name: buildings-dwellings

gebaude-wohnungen

display_name: gebaude-wohnungen

en: Population and society

amt-fuer-statistik-fl

de: Amt für Statistik

de: Gebäude nach Bauperiode, Energieträger der Heizung und Gemeinde seit 2010

en: Buildings by period of construction, energy source of heating and municipality since 2010


de: Verpackungsabfälle bei Jahr und Materialkategorie

en: Packaging waste by Year and Material category

abfall

display_name: abfall

waste

display_name: waste

en: Office of Statistics

en: The Office of Statistics is Liechtenstein's statistical information center. It provides the national and municipal authorities as well as the public with relevant, reliable and coherent statistical information on society, economy and environment in Liechtenstein.

de: Das Amt für Statistik ist das statistische Informationszentrum Liechtensteins. Es liefert den Landes- und Gemeindebehörden sowie der Öffentlichkeit relevante, zuverlässige und kohärente statistische Informationen zu Gesellschaft, Wirtschaft und Umwelt in Liechtenstein.

de: Verpackungsabfälle nach Materialkategorie seit 2006

en: Packaging waste by material category since 2006


weather-station-davos-wolfgang

maintainer: Jörg Wieder

title_for_slug: weather-station-davos-wolfgang

en: weather raclets temperature relative-humidity pressure

en: Weather Station Davos Wolfgang

en: The data file contains date and time (YYYY-MM-DD hh:mm:ss) in UTC, temperature (°C), relative humidity (%), pressure (hPa) at 10 m above ground and above sea level. The data was averaged over 10 minutes.

mimetype: text/csv

en: The dataset contains weather parameters measured at Davos Wolfgang (LON: 9.853594, LAT: 46.835577).

pressure

display_name: pressure

raclets

display_name: raclets

relative-humidity

display_name: relative-humidity

temperature

display_name: temperature

weather

display_name: weather

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

fr: EnviDat est le portail de données du WSL; il offre un accès uniformisé et réglementé aux données de surveillance et de recherche en matière d’environnement. Ce portail est capable d’héberger et de publier des ensembles de données précieuses et uniques, qui sont partagées aux quatre coins de la planète. Ce faisant, EnviDat favorise la recherche et contribue à l’avancement des sciences environnementales.

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.

de: EnviDat ist das Datenportal der WSL für einen einheitlichen und verwalteten Zugang zu Umweltdaten aus Monitoring- und Forschungsprojekten. Auf dem Portal können Datensätze gehostet und veröffentlicht werden. Durch die weltweite Bereitstellung einzigartiger und wertvoller Datensätze leistet EnviDat einen Beitrag zur Forschung im Bereich der Umweltwissenschaften.

it: EnviDat è il portale dati del WSL che fornisce un accesso unificato e gestito ai dati di monitoraggio ambientale e di ricerca. Il portale ha la capacità di ospitare e pubblicare serie di dati preziosi e unici che vengono condivisi in tutto il mondo. Così facendo, EnviDat promuove la ricerca e contribuisce al progresso della scienza ambientale.

value: Envidat Harvester


maintainer: EnviDat Support

en: wind-direction anemometer wind wind-speed raclets wind-velocity

en: Sonic wind component u

en:
The data contains values for u direction of the wind in a sampling frequency of 20 Hz.
Erroneour spikes were filterd in two steps.

1. Define moving window uₓ = u(i-3:i+3)
2. If u(i)-mean(uₓ) > 2*std(uₓ):u(i) = NaN

Frozen detectors resulted in continuous zero values (not filtered).
The wind speed and direction were calculated as follows:

wind speed = sqrt(u² + v²)
wind direction = 180° + (180° / PI)* arctan2 (-u, -v)

format: SERVICE

en: Sonic wind component v

en:
The data contains values for v direction of the wind in a sampling frequency of 20 Hz.
Erroneour spikes were filterd in two steps.

1. Define moving window uₓ = u(i-3:i+3)
2. If u(i)-mean(uₓ) > 2*std(uₓ):u(i) = NaN

Frozen detectors resulted in continuous zero values (not filtered).
The wind speed and direction were calculated as follows:

wind speed = sqrt(u² + v²)
wind direction = 180° + (180° / PI)* arctan2 (-u, -v)

en: Sonic temperature

en: The data set contains values of temperature (°C) measured with the anemometer at a sampling frequency of 20 Hz.

en: Sonic time

en: The data set contains the time in YYYY-mm-dd HH:MM:SS.sss for the anemometer data. The time is given in UTC+1.

en: Sonic wind component w

en:
The data contains values for w direction of the wind in a sampling frequency of 20 Hz.
Erroneour spikes were filterd in two steps.

1. Define moving window uₓ = u(i-3:i+3)
2. If u(i)-mean(uₓ) > 2*std(uₓ):u(i) = NaN

Frozen detectors resulted in continuous zero values (not filtered).
The wind speed and direction were calculated as follows:

wind speed = sqrt(u² + v²)
wind direction = 180° + (180° / PI)* arctan2 (-u, -v)

en: A Young 81000 sonic anemomenter was deployed at Gotschnagrat (LON: 46.859 LAT: 9.849) to record three components of the wind velocity (u, v, w in [m s‾ ¹]) and air temperature (Ts in [°C]). The anemomenter was mounted in direction North at a height of 1.5 m above snow surface at the beginning. The time within each data set is given in UTC+1. Instrument specifications can be found [here](http://www.youngusa.com/Manuals/81000-90(I).pdf) .

anemometer

display_name: anemometer

wind

display_name: wind

wind-direction

display_name: wind-direction

wind-speed

display_name: wind-speed

wind-velocity

display_name: wind-velocity

en: Snow Drift Station - 3D Ultrasonic


en: antarctica landuse topography wrf cryowrf

en: Documentation

media_type: application/pdf

en: Documentation about how the provided dataset was produced including details to reproduce the dataset.

format: PDF

mimetype: application/pdf

en: WPSinput

en: Input files to run the Weather Research and Forecasting model (WRF) preprocessing system WPS with REMA topography and AntarcticaLC2000 landuse data (see Documentation for details).

mimetype: application/zip

en: Scripts

en: Scripts to run the preprocessing to produce WRF binary tiles from REMA and AntarcticaLC2000 for topography and landuse (see Documentation for procedure).

en:
Reference Elevation Model of Antarctica (REMA) topography and AntarcticaLC2000 landuse data
are now available as static data input for the Weather Research and Forecasting model (WRF). Topography
and landuse are made available at a spatial resolution of 1 km. This documentation describes the methods
applied to convert REMA and AntarcticaLC2000 to WRF readable format and shows how this improves
the representation of the Antarctic topography and landuse categories over coastal Antarctic regions.

antarctica

display_name: antarctica

cryowrf

display_name: cryowrf

landuse

display_name: landuse

topography

display_name: topography

wrf

display_name: wrf

en: REMA topography and AntarcticaLC2000 for WRF


maintainer: Michael Eisenring

en: herbivory phytochemistry climate-change drought-legacy fagus-sylvatica

en:
What are the research data files about:
Raw data on various beech (Fagus sylvatica) leaf traits. Beech leaf chemistry (primary and specialized metabolites), leaf damage measurements from various herbivore feeding guilds and crown health assessment

Which methods were used:
Field surveys on trees that showed either prematured defoliation due to drought-stress or showed not pre-mature defoliation. .

When and where was the data collected/analyzed:
Field data was collected in 2019, 2020
Data was analyzed in 2021-2023

climate-change

display_name: climate-change

drought-legacy

display_name: drought-legacy

fagus-sylvatica

display_name: fagus-sylvatica

herbivory

display_name: herbivory

phytochemistry

display_name: phytochemistry

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.

en: Legacy effects of premature defoliation in response to an extreme drought event modulate phytochemical profiles with subtle consequences for leaf herbivory in European beech


en: curacao marine fish caribbean-region coral-reefs edna teleo environmental-dna fish-composition

en: Data access

en:
Fish taxonomic, functional and phylogenetic composition variations recovered from eDNA metabarcoding along the coast of Curacao

In February 2020, we collected a total of 20 water samples, from 10 stations, with two filtration replicates per station, in the two investigated coastal areas. Each station consisted of a transect of 2 km at an overall constant distance from the coast. We recorded the GPS coordinates at the start and end of the transect, which we used to map the transect positions. We conducted eDNA sampling by using a filtration device composed of an Athena® peristaltic pump (Proactive Environmental Products LLC, Bradenton, Florida, USA; nominal flow of 1.0 L/min), a VigiDNA® 0.20 μM cross-flow filtration capsule (SPYGEN, le Bourget du Lac, France) and disposable sterile tubing for each filtration capsule. We performed two filtration replicates in parallel on each side of a boat, at each station, for 30 min corresponding to a volume of ~30 L of water filtered by each capsule. At the end of each filtration, the water inside the capsules was emptied, and we filled the capsules with 80 ml of CL1 Conservation buffer (SPYGEN, le Bourget du Lac, France) and stored at room temperature. We followed a strict contamination control protocol in both field and laboratory stages (Valentini et al., 2016). Each water sample processing included the use of disposable gloves and single-use filtration equipment to avoid any risk of contamination. Libraries were prepared with ligation using the MetaFast protocol (Fasteris).

caribbean-region

display_name: caribbean-region

coral-reefs

display_name: coral-reefs

curacao

display_name: curacao

edna

display_name: edna

environmental-dna

display_name: environmental-dna

fish

display_name: fish

fish-composition

display_name: fish-composition

marine

display_name: marine

teleo

display_name: teleo

en: Environmental DNA Marine Curacao 2020


en: senegal teleo metabarcoding fish edna

en: Please use cyberduck to download for the moment

en:
Monitoring fish diversity through eDNA metabarcoding along the Casamance river

In 2018, water samples were collected in 13 locations along the Casamance in Senegal. Samples were taken either from a boat or by filtering from the side of the river. At each station, two filtration replicates were performed using a peristaltic pump to conduct environmental DNA (eDNA) sampling. Each filtration targeted a maximum duration of 30 min, during which approximately 30 liters of water were filtered through each filtration capsule. After filtration, the water inside the capsules was removed, and the capsules were filled with 50 ml of conservation buffer for preservation at room temperature. We followed strict contamination control protocols throughout both the fieldwork and laboratory processes, adhering to the guidelines of Valentini et al. (2016). To prevent contamination, each sample was processed using disposable gloves and single-use filtration equipment. We use primers for eukaryote and fish. The MiSeq Reagent Kit v3 (2x75 bp) (Illumina, San Diego, CA, USA) was used for paired-end sequencing at a theoretical sequencing depth of 200,000 reads per sample.

Data content:
	* rawdata/: contains the raw reads for each individual sample. One archive contains the paired-end reads specified by the _R1 or _R2 suffix as well as individually tagged PCR replicates (if available) together with an archive containing all extraction and PCR blank samples of the library. Reads have been demultiplexed using cutadapt but not trimmed, individual demultiplexing tags and primers remain present in the sequences.
	* taxadata/: contains the table with all detected taxonomy for each sample after bioinformatic processing (see Polanco et al. 2020 for details; https://doi.org/10.1002/edn3.140) and associated field metadata.
	* metadata/: contains two metadata files, one related to the data collected in the field for each filter, and the second related to the sequencing process in the lab (including the tag sequence, library name, and marker information for each sample)

metabarcoding

display_name: metabarcoding

senegal

display_name: senegal

en: Environmental DNA Freshwater Senegal Casamance 2018


en: meteoswiss complex-terrain numerical-simulation cosmo-wrf high-resolution

en: MeteorologicalData

en: Scripts to process meteorological data (see Section 2 in COSMOWRFdocumentation.pdf).

en: WPSSetup

en: Scripts that need to be changed to compile WPS (see COSMOWRFdocumentation.pdf, Section 4)

en: StaticData

en:
Scripts to process geographical/static data (see Section 3 in COSMOWRFdocumentation.pdf).
- ascii_to_bin_landuse.f90
- ascii_to_bin_soiltype.f90
- ascii_to_bin_topo_wgs84_ws2sf1.f90
- see_slopes.ncl
- soiltype_writing.py
- toposmooth_wgs84_cosmobound.ncl
- write_geogrid.o

en: WPSrun

en: Scripts to run the WRF pre-processing (WPS) for COSMO-WRF simulation (see Section 5 in COSMOWRFdocumentation.pdf)

en: WRFrun

en: Scripts to run COSMO-WRF simulation (see Section 6 in COSMOWRFdocumentation.pdf)

en: Postprocessing

en: Postprocessing scripts to map WRF output on swiss coordinates (see Section 7 in COSMOWRFdocumentation.pdf)

en: COSMO-WRF documentation v1.0

en:
Gerber, Franziska; Sharma, Varun (2018). Running COSMO-WRF on very-high resolution over complex terrain. EnviDat. doi:10.16904/envidat.35

Documentation about the procedure to run COSMO-WRF

en: This is a technical documentation of the procedure to run the Weather Research and Forecasting (WRF) model over complex alpine terrain using Consortium for Small-Scale Modeling (COSMO) reanalysis by the Federal Office of Meteorology and Climatology (MeteoSwiss) as initial and boundary conditions (COMSO-WRF). The setup is adapted for very high resolution simulations based on COSMO-2 (2.2 km resolution) reanalysis. This document gives an overview over steps to setup COSMO-WRF and adaptations needed to run COSMO-WRF. Additionally, the calculation of precipitation rate at a horizontal plane and remapping COSMO-WRF output on Swiss coordinates are documented.

complex-terrain

display_name: complex-terrain

cosmo-wrf

display_name: cosmo-wrf

high-resolution

display_name: high-resolution

meteoswiss

display_name: meteoswiss

numerical-simulation

display_name: numerical-simulation


maintainer: Andrin Caviezel

en: stonenode rockfall-experiments sensor-stream natural-hazards deposition-points rockfall-runout dem rockfall stonenodedata induced-rockfall

en: Induced Rockfall Data, Tschamut, GR

en:
 Induced Rockfall Experimental Data, Tschamut, GR, Switzerland

The archive contains site specific geographical data as well as the deposition points of manually induced rockfall by releasing differently shaped boulders with 30–80 kg of mass. Additionally availabe are the StoneNode v1.0 sensor streams for the equipped rocks.

The _OverviewFigures_ file serves as quick representation of the included data. It also contains the overview table for the rock masses in order to set the dimensions accordingly in RAMMS::ROCKFALL.

The archive contains the following folders:
<\ul>
* DEM: ArcGIS compatible DEM Tschamut_DEM_2m (grid resolution of 2 m) as well as the .asc grid used in RAMMS::ROCKFALL simulations
* _DepositionPoints_: containing two folders _RF Tschamut Frozen_ and _RF Tschamut WET_ containing each a shapefile with the complete deposition data of each series as well as deposition shapfiles for each rock shape.
* _DigitizedRocks_: Digitized point clouds of the used rocks as used for RAMMS::ROCKFALL simulations (.pts files) (dimensions have to be checked).
* _Orthofoto_: Orthophoto of the experimental site obtained via UAV with 5 cm resolution.

en: StoneNode v1.0 Data for equipped experimental Runs.

en: Corresponding StoneNode v1.0 data streams belonging to the deposition points.

en:
 Dataset of an experimental campaign of induced rockfall in Tschamut, Grisons, Switzerland.

The data archive contains site specific geographical data such as DEM and orthophoto as well as the deposition points of manually induced rockfall by releasing differently shaped boulders with 30–80 kg of mass. Additionally available are all the StoneNode data streams for rocks equipped with a sensor. The data set consists of

* Deposition points from two series (wet (27/10/2016) and frozen (08/12/2016) ground)
* Digital Elevation Model (grid resolution 2 m) obtained via UAV
* Orthophoto (5 cm resolution) obtained via UAV
* Digitized rock point clouds (.pts input files for RAMMS::ROCKFALL)
* StoneNode v1.0 raw data stream for equipped rocks.

Further information is found in

* A. Caviezel et al., _Design and Evaluation of a Low-Power Sensor Device for Induced Rockfall Experiments_, IEEE Transactions on Instrumentation and Measurement, 2018, 67, 767-779, http://ieeexplore.ieee.org/document/8122020/
* P. Niklaus et al., _StoneNode: A low-power sensor device for induced rockfall experiments_, 2017 IEEE Sensors Applications Symposium (SAS), 2017, 1-6, http://ieeexplore.ieee.org/document/7894081/

dem

display_name: dem

deposition-points

display_name: deposition-points

induced-rockfall

display_name: induced-rockfall

natural-hazards

display_name: natural-hazards

rockfall

display_name: rockfall

rockfall-experiments

display_name: rockfall-experiments

rockfall-runout

display_name: rockfall-runout

sensor-stream

display_name: sensor-stream

stonenode

display_name: stonenode

stonenodedata

display_name: stonenodedata

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.

en: Induced Rockfall Dataset (Small Rock Experimental Campaign), Tschamut, Grisons, Switzerland


maintainer: Daniel Scherrer

en: climate-change forest-management forest ground-vegetation fagus-sylvatica air-pollution

en: Site Information

en: This table contains information about the 254 forest sites used in the publication of Scherrer et al. 2024.

en: Taxa Data

en: This table contains information about the individual taxa found in the historic and current vegetation relevés in Scherrer et al. 2024.

en: This dataset comprised 254 historical (1937 – 1948) and resurveyed (2019 – 2022) vegetation relevés of beech-dominated forests sites in the Swiss Jura mountains. The data contains species lists as well as site information.

air-pollution

display_name: air-pollution

climate-change

display_name: climate-change

fagus-sylvatica

display_name: fagus-sylvatica

forest

display_name: forest

forest-management

display_name: forest-management

ground-vegetation

display_name: ground-vegetation

en: Resurveyed vegetation releves of beech forests in the Swiss Jura Mountains


maintainer: Marco Moretti

en: remote-sensing honeybees urban-biodiversity pollination urbanization sustainability

en: Dataset 2

en: Dataset 2: Processed data for modelling balance urban greenspace

en: Dataset 3

en: Dataset on the the number of beehives and apiaries on the years 2012, 2013, 2014, 2015, 2016, 2017, 2018 in several Swiss cities.

en: Not used:

en: Not used for the analyses and not link to the paper.

en: Dataset 1

en: Dataset 1: Processed data for urban beekeeping 2012 & 2018

en: Data on: (1) (Dataset 1) spatial distribution of urban beekeeping (number of hives and number of beekeeping locations) in 14 Swiss cities (Geneva, Lausanne, Biel, Neuchatel, Basel, Zurich, Chur, Luzern, St. Gallen, Winterthur, Bern, Lugano, Bellinzona, Thun) for the period 2012-2018; (2) (Dataset 2) aggregated data to model the sustainability of urban beekeeping.

honeybees

display_name: honeybees

pollination

display_name: pollination

remote-sensing

display_name: remote-sensing

sustainability

display_name: sustainability

urban-biodiversity

display_name: urban-biodiversity

urbanization

display_name: urbanization

en: Challenging the sustainability of urban beekeeping: evidence from Swiss cities


en: yellowstone biogeochemical-cycling bacteria fungi detritus soil-biodiversity wolf decomposition carrion carcass

en:
Site	ID for different carcasses across the Yellowstone Landscape
Location	Sampling location, either under carcass or control plot
Species	Indicating whether carcass was from bison or elk
Gender	Gener of carcass; F = female, M = male, ? = unknown
Lat	Latitude
Long	Longitude
DSK	Days since kill; in days
Soil.N	Soil N concentration (%)
Soil.C	Soil C concentration (%)
Veg.C	Vegetation C concentration (%); dominant grasses sampled
Veg.N	Vegetation N concentration (%); dominant grasses sampled
Veg.CtoN	Vegetation C to N ratio; dominant grasses sampled
Soil.resp	Soil respiration (g m-2 h-1)
soil.temp	Soil temperature (°C)
Soil.moist	Soil moisture (volumentric; %)
CRR	Cotton rotting rate
bacteria.richness	Bacteria richness
bacteria.diversity	Bacteria diverstiy
fungi.richness	Fungi richness
fungi.diversity	Fungi diversity
bacteria.abundance	Bacteria abundance
fungi.abundance	Fungi abundance

en:
This dataset contains all data on which the following publication below is based.

Paper Citation:

Risch, A.C., Frossard, A., Schütz, M., Frey, B., Morris, A.W., Bump, J.K. (accepted) Effects of elk and bison carcasses on soil microbial communities and ecosystem functions in Yellowstone, USA. (accepted). Functional Ecology
doi: ...

Methods
Study area and study sites
This study was conducted in YNP’s Northern Range (NR), located in north-western Wyoming and south-western Montana, USA (~44.9163° N, 110.4169° W). The NR expands over ~1000 km2 and features long cold winters and short dry summers. Grasslands and shrublands dominate the NR that is the home of large migratory herds of bison (winter counts 2017: ~3919 individuals; Geremia, Wallen, & White, 2017) and elk (~5349 individuals) as well as their main predators, approximately five packs of wolves with a total of 33 individuals (Smith et al., 2017). As part of a long-term research program within YNP, wolf predation has been studied since their reintroduction in 1995.
For our study, we received ground-truthed coordinates of bison and elk carcasses from winter 2016/17 (November 2016 through April 2017) from the YNP Wolf Project. Between June 20 and July 1, 2017, we visited 24 carcasses in total. At five sites, we could not sample as the carcasses were no longer found. In total we located remains (hairmats, rumen content, bones, teeth) of 19 adult male and female carcasses (7 bison, 12 elk; Supplementary Table 1). Live body weights of adult bison and elk are approximately 730 kg (male bison), 450 kg (female bison), 330 kg (male elk), and 235 kg (female elk, Meagher, 1973; Quimby & Johnson, 1951).
The kills and subsequent consumption happened between 34 and 173 days prior to our sampling (hereafter “days since kill”, DSK), for which we accounted in our statistics. Note that wolves and other scavengers consumed the soft tissue of the carcasses quickly, hence, there is close to no soft tissue left for decomposition as compared to an intact body left on the soil surface. The 19 carcass sites covered the extent of YNP’s NR, with both bison and elk carcasses showing similar distributions; elevation ranged from 1703 to 2884 m a.s.l. (Supplementary Fig 1 & Supplementary Table 1). The carcasses were all located in grassland or sage-brush shrubland, with or without sparsely scattered trees, and both bison and elk carcasses showed the same distribution of DSK. At each study site, we selected a reference plot (hereafter “control”) that was of comparable size, slope aspect and vegetation to the carcass location (hereafter “carcass”). The control was at least 10 m away (Danell, Berteaux, & Brathen, 2002; Melis et al., 2007) from the carcass itself to ensure the absence of potential direct and indirect carcass effects (paired design; (Bump, Webster, et al., 2009; Bump, Peterson, et al., 2009).

Ecosystem functions and soil properties
We randomly collected 50 g of mineral soil from three locations on both control and carcass plots to a depth of 5 cm with sterile techniques and gently mixed the material to obtain a composite sample. Half the soil sample was immediately bagged in plastic bags (whirl packs), stored in a cooler with ice packs (~5 ºC), sieved (2-mm) and frozen within 4-6 hours of collection to assess soil microbial communities. For this purpose, we extracted total genomic DNA from 0.5 g soil using the PowerSoil DNA Isolation Kit (Qiagen, Hilden, Germany). DNA concentrations were measured using PicoGreen (Molecular Probes, Eugene, OR, USA). PCR amplifications of partial bacterial small-subunit ribosomal RNA genes (region V3–V4 of 16S rRNA) and fungal ribosomal internal transcribed spacers (region ITS2) were performed as described previously (Frey et al., 2016). Each sample consisting of 40 ng DNA was amplified in triplicate and pooled before purification with Agencourt AMPure XP beads (Beckman Colter, Berea, CA, USA) and quantified with the Qubit 2.0 fluorometric system (Life Technologies, Paisley, UK). Amplicons were sent to the Genome Quebec Innovation Center (Montreal, Canada) for barcoding using the Fluidigm Access Array technology and paired-end sequencing on the Illumina MiSeq v3 platform (Illumina Inc., San Diego, CA, USA).
Quality control of bacterial and fungal reads was performed using a customized pipeline (Supplementary Table 2; Frey et al., 2016). Paired-ends reads were matched with USEARCH (Edgar & Flyvbjerg, 2015), substitution errors were corrected using Bayeshammer (Nikolenko, Korobeynikov, & Alekseyev, 2013) and PCR primers were trimmed (allowing for 1 mismatch, read length >300 bp for 16S and >200 bp for ITS primers) using Cutadapt (M. Martin, 2011). Sequences were dereplicated and singleton reads removed prior to clustering into operational taxonomic units (OTUs) at 97% identity using USEARCH (Edgar, 2013). The remaining centroid sequences were tested for the presence of ribosomal signatures using Metaxa2 (Bengtsson-Palme et al., 2015) or ITSx (Bengtsson-Palme et al., 2013). Taxonomic assignments of the OTUs were obtained using Bayesian classifier (Wang, Garrity, Tiedje, & Cole, 2007) with a minimum bootstrap support of 60% implemented in mothur (Schloss et al., 2009) by querying the bacterial and fungal reads against the SILVA Release 128 (Quast et al., 2013) and UNITE 8.0 (Abarenkov et al., 2010) reference databases for 16S and ITS OTUs, respectively.
Abundances of the bacterial 16S rRNA gene and fungal ITS amplicon were determined by quantitative real-time PCR (qPCR) on an ABI7500 Fast Real-Time PCR system (Applied Biosystems, Foster City, CA, USA) as described previously (Frossard et al., 2018). The same primers (without barcodes) and cycling conditions as for the sequencing approach were used for the 16S and ITS qPCR. Three standard curves per target region were obtained using tenfold serial dilutions of plasmids generated from cloned targets (Frey, Niklaus, Kremer, Lüscher, & Zimmermann, 2011). Data were converted to represent mean copy number of targets per gram of soil (dry weight).
The other half of the soil sample was bagged in paper, dried to constant weight at 60°C, passed through a 2 mm sieve and analyzed for total C and N concentration with a CE Instruments NC 2100 soil analyzer (CE Elantech Inc., Lakewood NJ, USA). We also collected 20 mature and undamaged leaves of the dominant grass species growing on control and carcass sites, but taxa were not recorded. The plant material was dried at 60°C, finely ground till homogenized and also analyzed to obtain total C and N concentrations. Soil temperature (10 cm depth) was measured with a waterproof digital thermometer (Barnstead International, Dubuque IA, USA) at three locations each at the control and carcass site. Soil moisture (0 – 10 cm depth) was measured with time domain reflectometry (Field-Scout TDR-100; Spectrum Technologies, Plainfield IL, USA) at five randomly chosen points on control and carcass sites. We measured soil respiration at five randomly chosen points at both control and carcass sites with a PP-Systems SRC-1 soil respiration chamber (closed circuit) attached to a PP-Systems EGM-4 infrared gas analyzer (PP-Systems, Amesbury, MA, USA). For each measurement the soil chamber (15 cm high; 10 cm diameter) was tightly placed on the soil surface, after clipping plants to avoid measuring plant respiration or photosynthesis. Measurements were conducted over 120 s.
In addition, we assessed the decomposition rates of standardized OM using the cotton strip assay (Latter & Howson, 1977; Latter & Walton, 1988). Cotton cloth tensile strength loss (CTSL) is a measure of decomposition, and an index to express the combined effect of soil microclimatic, physical, chemical and biological properties on decomposition while accounting for OM quality (Latter & Walton, 1988; Risch, Jurgensen, & Frank, 2007; Withington & Sanford Jr., 2007). We placed five 20 cm wide x 13 cm long sheets of 100% unbleached cotton cloth (American Type SM 1/18’’, Warp: 34/1, Weft: 20/1, Weave plain, 29.5 picks/cm warp, 22 picks/cm weft, 237 g/m2; Daniel Jenny & Co., Switzerland;) at each carcass and control site vertically into the soil by making slits with a flat spade to a depth of 12 cm. We inserted each cloth with the spade, and then pushed the slit closed to assure tight contact with the soil. The cloths were retrieved after 18 to 27 days. After retrieval, the cloths were air-dried, remaining soil gently removed by hand, and 1.5 cm wide strips were cut at the 3.5-5.0 cm (top) and the 9-10.5 cm (bottom) soil depth. The strips were equilibrated at 50 % relative humidity and 20°C for 48 hours (climate chamber) prior to strength testing (Scanpro Awetron TH-1 tensile strength tester; AB Lorentzen and Wettre, Kista, Sweden). Cotton rotting rate (CRR) = (CTScontrol - CTSfinal/CTSfinal)1/3 * (365/t), where CTScontrol is the cotton tensile strength of a control cloth and CTSfinal the cotton tensile strength of the incubated sample, t is the incubation period in days. Control cloths were inserted into the ground and immediately retrieved to account for tensile strength loss associated with cloth insertion. We averaged the CRR of top and bottom strips for further analyses as no difference was found between the two. All sampling and cloth insertion took place between June 20 and July 1, 2017, cloths were retrieved between July 17 and 20, 2017. Soil respiration, average CRR, vegetation N concentration and vegetation C:N ratio are defined as ecosystem functions, soil C and N concentration, soil temperature and moisture as soil abiotic properties, and bacterial and fungal richness (number of taxa), diversity (Shannon) and abundance as soil biotic properties.

Statistical analyses
Univariate analyses for ecosystem functions, soil biotic and abiotic properties
We tested whether individual ecosystem functions, soil biotic and abiotic properties differed between carcass and control (“Location”), bison and elk (“Species”) and days since kill (“DSK”). For this purpose, we used linear mixed effect models (LMM, “nlme” package v 3.1 – 131.1 in R v 3.4.4; Pinheiro, Bates, DebRoy, & Sarkar, 2018; R Core Team, 2019) with Location, Species, Location x Species and DSK as fixed effects. Site was included as random effect to account for the paired design. We developed a separate model for all dependent variables. All but bacterial richness, fungal richness, fungal diversity and vegetation N concentration were natural-log transformed to meet model assumptions. For each LMM, we calculated contrasts to assess the specific comparisons we were interested in with the “lsmeans” package v 2.27-62 (Lenth & Love, 2018): 1) carcass vs control, 2) carcass bison vs control bison, and 3) carcass elk vs control elk. We also tested whether we had differences between bison and elk carcasses or the sites where bison and elk were killed and included contrasts 4) carcass bison vs carcass elk and 5) control bison vs control elk.
We calculated the log response ratio (LRR = ln[carcass/control]) to obtain carcass effects for all variables for both species separately. LRR < 0 indicates higher value at control compared to carcass, LRR > 0 indicates higher values at carcass compared control. We used LRRs for visualization and to assess spatial patterns in carcass effects across YNP. For this purpose we calculated the Moran’s I statistic for each ecosystem function, soil biotic and abiotic property based on a latitude-longitude matrix with the “moran.test” function in the “spdep” package version 1.1-3 (Bivand et al., 2019).
Multivariate analyses
Rare OTUs, defined as OTUs with a low abundance of reads, were retained in multivariate methods because they only marginally influence these analyses (Gobet, Quince, & Ramette, 2010). Bray–Curtis dissimilarity matrices were generated based on square-root-transformed matrices. We used Principal Coordinate Analyses (PCoA) to assess how soil bacterial and fungal communities differed between control and carcass of bison and elk (“vegan” package v 2.5-4, Oksanen et al., 2019). We then extracted PCoA axes scores 1 and 2 and used LMM (“nlme” package) with Location, Species, Location x Species and DSK as fixed effects. Site was, again, included as random effect. We again calculated the contrasts as described above using the “lsmeans” package. We also assessed how ecosystem functions, and soil abiotic and biotic properties were related to the soil bacteria and fungi community structure associated with bison and elk control and carcasses using the “envfit” function in the “vegan” package (Oksanen et al., 2019).
Indicator species analyses were performed using the multipatt function implemented in the “indicspecies” package version 1.7.6 with 100000 permutations (De Caceres & Jansen, 2016). This step allowed to identify OTUs that led to changes in multivariate patterns between control and carcass of both bison and elk separately (De Cáceres, Legendre, & Moretti, 2010). The multipatt function uses a point biserial correlation coefficient statistical test. Indicator OTUs were defined as bacterial and fungal OTUs with more than 50 sequences, i.e., removing rare taxa and taxa with low abundances containing little indicator information (Rime et al., 2015) and that were significantly correlated with Location (p < 0.05, correlation coefficient > 0.3). A heatmap of these OTUs were generated with the vegan and ggplot2 packages. The indicator analyses were performed in R version 3.3.3 (R Core Team, 2017).

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Quimby, D. C., & Johnson, D. E. (1951). Weights and measurements of Rocky Mountain elk. Journal of Wildlife Management, 15, 57–62.
R Core Team. (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Zurich, Switzerland.
R Core Team. (2019). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
Rime, T., Hartmann, M., Brunner, I., Widmer, F., Zeyer, J., & Frey, B. (2015). Vertical distribution of the soil microbiota along a successional gradient in a glacier forefield. Molecular Ecology, 24(5), 1091–1108. doi:10.1111/mec.13051
Risch, A. C., Jurgensen, M. F., & Frank, D. A. (2007). Effects of grazing and soil micro-climate on decomposition rates in a spatio-temporally heterogeneous grassland. Plant and Soil, 298(1–2), 191–201. doi:10.1007/s11104-007-9354-x
Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., … Weber, C. F. (2009). Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and Environmental Microbiology, 75(23), 7537–7541. doi:10.1128/AEM.01541-09
Smith, D., Stahler, D., Cassidy, K., Stahler, E., Metz, M., Cassidy, B., … Cato, E. (2018). Yellowstone National Park wolf project annual report 2017. Yellowstone National Park, Mammoth, WY, USA: National Park Service, Yellowstone Center of Resources.
Wang, Q., Garrity, G. M., Tiedje, J. M., & Cole, J. R. (2007). Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Applied and Environmental Microbiology, 73(16), 5261–5267. doi:10.1128/AEM.00062-07
Withington, C., & Sanford Jr., R. (2007). Decomposition rates of buried substances increase with altitude in a forest-alpine tundra ecotone. Soil Biology and Biochemistry, (39), 68–75.

Please cite this paper together with the citation for the datafile.

bacteria

display_name: bacteria

biogeochemical-cycling

display_name: biogeochemical-cycling

carcass

display_name: carcass

carrion

display_name: carrion

decomposition

display_name: decomposition

detritus

display_name: detritus

fungi

display_name: fungi

soil-biodiversity

display_name: soil-biodiversity

wolf

display_name: wolf

yellowstone

display_name: yellowstone

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.

en: Elk and bison carcasses in Yellowstone, USA


modeling-snow-failure-with-dem

title_for_slug: modeling-snow-failure-with-dem

en: snow-avalanche snow-failure snow snow-micro-mechanics discrete-element-modelling

en: Dataset

en: The zip file contains the data underlying the figures presented in Bobillier et al. (2019). The figures can be reproduced with the python file, which is also provided.

en: Python file

en: Python file to reproduce the figures in Bobillier et al. (2019)

mimetype: text/x-python

en: Read-me

en: The read-me file contains a description of all the data provided in the zip file.

en: This data set includes the modeling results described in the research article by Bobiller et al. (2020). All the figures in the article can be reproduced with the data provided.

discrete-element-modelling

display_name: discrete-element-modelling

snow

display_name: snow

snow-avalanche

display_name: snow-avalanche

snow-failure

display_name: snow-failure

snow-micro-mechanics

display_name: snow-micro-mechanics

en: Modeling snow failure with DEM


maintainer: Robert Pazúr

en: vegetation-indices cropland switzerland remote-sensing grassland habitat sentinel-2 agriculture machine-learning phenology

en: (current version) Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.7 (v1.5)

format: GeoTIFF

en: (current version) Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.6 (v1.5)

en: Assuming that sealed surfaces have little or no phenological response we calculated a mask of unsealed surfaces based on the 3 years (2017-2019) Sentinel-2 NDVI 95th percentile threshold value: 0.6.

en: (current version) Cropland, grassland, and shrubland map of Switzerland (v.1.5)

en: Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.6  (v1.0)

media_type: image/tiff

en:
!!! Please check out the newer version in the repository !!!
Mask of unsealed surfaces of Switzerland based on 3 years (2017-2019) Sentinel-2 NDVI 95th percentile threshold value: 0.6

format: TIFF

en: Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.7 (v1.0)

en: !!! Please check out the newer version in this repository !!! Assuming that sealed surfaces have little or no phenological response we calculated a mask of unsealed surfaces based on the 3 years (2017-2019) Sentinel-2 NDVI 95th percentile threshold value: 0.7.

en: Cropland, grassland, and shrubland map of Switzerland (v1.0)

en: !!! Please check out the newer version in this repository !!! (1) cropland; (2) grassland; (3) shrubland; other LC classes are masked out from the map

en: (current version) Cropland and grassland map of Switzerland (v.1.5)

en: GEE (Google Earth Engine) script (JavaScript) to derived the output map  (v1.5)

en: The code used in Google Earth Engine to derive the cropland, grassland and shrubland map of Switzerland. Compare to the previous version the newer version includes the shadow masking procedure which might be important in the mountainous areas.

en: GEE (Google Earth Engine) script (JavaScript) to derived the output map  (v1.0)

en: !!! Please check out the newer version in the repository !!! The code used in Google Earth Engine to derive the cropland, grassland and shrubland map of Switzerland

en: Cropland and grassland map of Switzerland  (v.1.0)

en: !!! Please check out the newer version in this repository !!! (1) cropland; (2) grassland; other LC classes are masked out from the map

en: We developed a map of cropland and grassland allocation for Switzerland based on several indices dominantly derived from Sentinel-2 satellite imagery captured over multiple growing seasons. The classification model was trained based on parcel-based data derived from landholder reporting. The mapping was conducted on Google Earth Engine platform using random forest classifier. Areas of high vegetation, shrubland, sealed surface and non-vegetated areas were masked out from the country-wide map. The resulting map has high accuracy in lowlands as well as mountainous areas.

agriculture

display_name: agriculture

cropland

display_name: cropland

grassland

display_name: grassland

habitat

display_name: habitat

machine-learning

display_name: machine-learning

phenology

display_name: phenology

remote-sensing

display_name: remote-sensing

switzerland

display_name: switzerland

vegetation-indices

display_name: vegetation-indices

en: Cropland and grassland map of Switzerland based on Sentinel-2 data


maintainer: Michael Lehning

en: sublimation stable-water-isotopes paleoclimate snow post-depositional-processes weissfluhjoch latent-heat natural-tracer

en: Details about the different folders

en: Stable water isotopes in vapor

en:
Stable water isotopes in vapor measurements. Processed (calibrated) and raw data for February - June 2017. Raw data for January and February 2020.
For the time in between July 2017 and April 2020, raw vapor isotopic measurements are available, but no calibrations were performed during that time. For data delivery, please contact one of the authors.
This folder also contains the post-processing scripts for stable water isotopes in vapor measurements.

en:
Details on the data collected during the different field campaigns and available in this dataset.
Information on the sampling procedure of the snow isotopes samples.

en: Weather

en: Meteorological data, snow height measurement, and SNOWPACK simulations covering the different field campaigns.

en: Stable water isotopes in snow

en: Stable water isotopes in snow measurements

en:
Notice: Changes to the dataset are still possible. Please do not use this dataset until the final publication with a DOI. Contact the authors if you have questions about this.

This dataset contains measurements of stable water isotopes in snow and vapor on the Weissfluhjoch from different field campaigns (Winter 2017 (Trachsel, 2019), January 2020, December 2020, and March 2021 (Sadowski et al., 2022).
Snow profiles and surface samples are available at different frequencies for each campaign. Please see "Data_description.pdf" for details.

Scripts and SNOWPACK simulations used in (Trachsel, 2019) and (Sadowski et al., 2022) are also provided.

latent-heat

display_name: latent-heat

natural-tracer

display_name: natural-tracer

paleoclimate

display_name: paleoclimate

post-depositional-processes

display_name: post-depositional-processes

stable-water-isotopes

display_name: stable-water-isotopes

sublimation

display_name: sublimation

weissfluhjoch

display_name: weissfluhjoch

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.

en: Stable Water Isotopes in snow and vapor on the Weissfluhjoch


en: dry-snow-avalanche climate-projections avalanche wet-snow-avalanche snow-instability snow snow-stratigraphy climate-change

en: SNOWPACK settings

en: contains .ini and .sno file indicating the settings used to drive SNOWPACK with the climate scenarios

en: Projections of avalanche activity

en: contains one file per IMIS station that indicates the daily probability for the occurrence of dry- or wet-snow avalanches in the vicinity of the respective station for each day during the time period 1.12.1981-31.05.2099 (months December to May) and all climate model chains and RCP scenarios.

en: Climate scenarios .smet

en: contains one .smet file per combination of station, climate model chain and RCP scenario. Each .smet file contains the downscaled meteorological data (time span 1.9.1981-31.8.2099) that was used to drive the SNOWPACK simulations.

en:
This data set includes 21st century projections of dry- and wet-snow avalanche activity for seven sites in the Swiss Alps as well as the downscaled climate scenarios and SNOWPACK configuration files used to produce these projections. The methods used to produce these data are described in the research article:
Mayer, S., Hendrick, M., Michel, A., Richter, B., Schweizer, J., Wernli, H., and van Herwijnen, A.: Changes in snow avalanche activity in response to climate warming in the Swiss Alps, EGUsphere, 2024, 1-32, https://doi.org/10.5194/egusphere-2024-1026, 2024.
We used downscaled climate projections to drive the snow cover model SNOWPACK and evaluated future dry- and wet-snow avalanche occurrences in the vicinity of seven automatic weather stations from the IMIS network using machine learning models trained with observed records of avalanche activity. Projections are available for three different emission scenarios (Representative Concentration Pathways, RCP2.6, RCP4.5 and RCP8.5) and eight climate model chains from the CH2018 ensemble. The output of the model chain is given by a daily probability for dry- or wet-snow avalanche occurrences. These probabilities can then be used to classify a day as a dry- and wet-snow avalanche day (AvD) or non-avalanche day.

avalanche

display_name: avalanche

climate-change

display_name: climate-change

climate-projections

display_name: climate-projections

dry-snow-avalanche

display_name: dry-snow-avalanche

snow

display_name: snow

snow-instability

display_name: snow-instability

snow-stratigraphy

display_name: snow-stratigraphy

wet-snow-avalanche

display_name: wet-snow-avalanche


en: snow-avalanche climate-change ramms climada avalanche risk

en: By using hazard scenarios based on CH2018 climate projections and the RAMMS avalanche model, we generated large-scale hazard indication maps for future avalanche hazards. By employing the open-source probabilistic risk assessment platform CLIMADA, along with building data and vulnerability functions, we estimated risks for the present time and two future time frames: mid-century (2060) and the end of the century (2085). An uncertainty and sensitivity analysis complemented the study to account for potential fluctuations in model assumptions. The data contains the hazard information and data used for the Climada risk analysis.

climada

display_name: climada

ramms

display_name: ramms

risk

display_name: risk

snow-avalanche

display_name: snow-avalanche

en: Assessing the impacts of climate change on snow avalanche-induced risk in alpine regions


maintainer: Alexis Shakas

en: ert irrigation drone-data pinus-sylvestris drought

en: This .csv file contains the soil water potential data for all treatments (irrigation, irrigation-stop and control) and for the whole year of 2022. The data is given as a mean value and with an upper and lower uncertainty bound, given from the manufacturer. The depth of the sensor is given in cm, and the measured values are in units of kPa.

en: This zip file contains all raw and processed data for the electrical resistivity and drone campaigns that took place in Pfynwald, 2022

en:
This collection of datasets consists of various measurements taken during the year 2022 in Pfynwald. It combines 2 electrical resistivity transects which were monitored in May and July, before and after the irrigation season. The transects transverse all treatments (irrigation, control, irrigation stop). Each transect was repeated twice during the day for a period of 3 days to a week.
In addition there are raw and post-processed drone images (and resulting PRI maps), which were used to compare the below ground responses (from resistivity) to the above ground (crown) stress.
Here, the raw data is stored and a link to a git project is provided where python code is stored to reproduce all the results the published manuscript: "Does optimality partitioning theory fail for belowground traits? Insights from geophysical imaging of a drought-release experiment in a dry Scots Pine forest", New Phytologist, Shakas et al., 2024. The in-depth explanations from each processing step are found in the code (git project).

drone-data

display_name: drone-data

drought

display_name: drought

ert

display_name: ert

irrigation

display_name: irrigation

pinus-sylvestris

display_name: pinus-sylvestris

en: Pfynwald Geoelectric Experiment 2022


en: snow-depth boundary-layer-winds snow-cover saltation vertical-wind-motion

en: Data Compilation

en: Compressed TXT files

en:
The data correspond to the experiments presented and discussed in a paper regarding the interaction between turbulent wind fluctuations and snow saltation mass-fluxes (Paterna, 2016). Each of the nine data files corresponds to a different experiment presented in the paper and conducted in the winter 2014/2015 in the WSL/SLF cold wind tunnel in Davos. For each file the five columns indicate the time from the beginning of the experiment, the streamwise (u’) and the vertical (w’) wind velocity fluctuations, the streamwise (qx) and the vertical (qz) snow mass-flux components. From these time-series the scales of the snow saltation and of the turbulent flow are obtained with respect to the eddy-cycles and snow saltation cycles. From spectral analysis of the time-series a decoupling of the snow saltation from the turbulence forcing reveals two regimes of interaction: a turbulence-dependent regime occurring with weak saltation, and a turbulence-independent regime with strong saltation. Further details can be found at the link below.

Publication http://onlinelibrary.wiley.com/doi/10.1002/2016GL068171/abstract

boundary-layer-winds

display_name: boundary-layer-winds

saltation

display_name: saltation

snow-cover

display_name: snow-cover

snow-depth

display_name: snow-depth

vertical-wind-motion

display_name: vertical-wind-motion

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.

en: Wind tunnel measurement data of drifting snow and turbulent wind fluctuations


en: reproductive-matieral molecular-markers pinus-cembra genetic-structure afforestation

en: metadata and description of dataset

en: The dataset contains individual genotypes at 11 nuclear microsatellite markers of samples of Swiss stone pine (*Pinus cembra*). Samples were collected in 12 natural and presumably planted stands in the canton of Fribourg (Switzerland). The dataset is complemented by individual genotypes (same markers) from samples of a subset of 40 populations across the European Alps (taken from Gugerli et al., Journal of Biogeography 2023; https://doi.org/10.1111/jbi.14586; dataset archived at Dryad, https://doi.org/10.5061/dryad.866t1g1v6).

afforestation

display_name: afforestation

genetic-structure

display_name: genetic-structure

molecular-markers

display_name: molecular-markers

pinus-cembra

display_name: pinus-cembra

reproductive-matieral

display_name: reproductive-matieral

en: Nuclear microsatellite genotypes of Pinus cembra


maintainer: Vanessa Burg

en: biogas bioenergy review switzerland manure

en: Biogas from animal manure in Switzerland: energy potential, technology development and resource mobilization

en: Aim of this white paper is to provide decision-makers, administrations and stakeholders with the most current research findings in order to promote the optimal use of bioenergy from manure in the Swiss energy transition. For this purpose, the results of the Swiss competence center for bioenergy research - SCCER BIOSWEET - are summarized and presented in a broader context. If nothing else is mentioned, the results refer to Switzerland and in case of the feedstock to the domestic biomass potentials.

bioenergy

display_name: bioenergy

biogas

display_name: biogas

manure

display_name: manure

review

display_name: review

switzerland

display_name: switzerland