fr: Population dite légale par groupe d'âge et par commune, dès 2010 (csv)

de: Zivilrechtliche Bevölkerung nach Altersgruppe und Gemeinde ab 2010 (csv)

fr: Population dite légale par groupe d'âge et par commune, dès 2010 (n3)

de: Zivilrechtliche Bevölkerung nach Altersgruppe und Gemeinde ab 2010 (n3)

de: xlsx

fr: Population dite légale par groupe d'âge et par commune, dès 2010 (xlsx)

de: Zivilrechtliche Bevölkerung nach Altersgruppe und Gemeinde ab 2010 (xlsx)

de: parquet

fr: Population dite légale par groupe d'âge et par commune, dès 2010 (parquet)

de: Zivilrechtliche Bevölkerung nach Altersgruppe und Gemeinde ab 2010 (parquet)

fr: Population dite légale par groupe d'âge et par commune, dès 2010 (json)

de: Zivilrechtliche Bevölkerung nach Altersgruppe und Gemeinde ab 2010 (json)

fr: Population dite légale par groupe d'âge et par commune, dès 2010 (jsonl)

de: Zivilrechtliche Bevölkerung nach Altersgruppe und Gemeinde ab 2010 (jsonl)

de: rdfxml

fr: Population dite légale par groupe d'âge et par commune, dès 2010 (rdfxml)

de: Zivilrechtliche Bevölkerung nach Altersgruppe und Gemeinde ab 2010 (rdfxml)

fr: Population dite légale par groupe d'âge et par commune, dès 2010 (turtle)

de: Zivilrechtliche Bevölkerung nach Altersgruppe und Gemeinde ab 2010 (turtle)

fr: Population dite légale par groupe d'âge et par commune, dès 2010 (jsonld)

de: Zivilrechtliche Bevölkerung nach Altersgruppe und Gemeinde ab 2010 (jsonld)

fr: Ce jeu de données présente la population dite légale par groupe d'âge et par commune depuis 2010.

altersgruppen

display_name: altersgruppen

commune

display_name: commune

gemeinde

display_name: gemeinde

groupe-dage

display_name: groupe-dage

population-dite-legale

display_name: population-dite-legale

zivilrechtliche-bevolkerung

display_name: zivilrechtliche-bevolkerung

soci

fr: Population et société

de: Bevölkerung und Gesellschaft

ssd

fr: Service de la statistique et de la donnée Fribourg

de: Amt für Statistik und Daten Freiburg

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.


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

fr: emplois entreprises commune

de: unternehmen beschaftigte gemeinde

state: active

type: dataset

de: json

media_type: application/json

fr: Fermetures d'entreprises et emplois perdus, dès 2013 (json)

de: Unternehmensschliessungen und verlorene Arbeitsstellen nach Gemeinde ab 2013 (json)

format: JSON

de: jsonl

fr: Fermetures d'entreprises et emplois perdus, dès 2013 (jsonl)

de: Unternehmensschliessungen und verlorene Arbeitsstellen nach Gemeinde ab 2013 (jsonl)

media_type: application/rdf+xml

fr: Fermetures d'entreprises et emplois perdus, dès 2013 (rdfxml)

de: Unternehmensschliessungen und verlorene Arbeitsstellen nach Gemeinde ab 2013 (rdfxml)

format: RDF XML

fr: Fermetures d'entreprises et emplois perdus, dès 2013 (n3)

de: Unternehmensschliessungen und verlorene Arbeitsstellen nach Gemeinde ab 2013 (n3)

de: turtle

media_type: text/turtle

fr: Fermetures d'entreprises et emplois perdus, dès 2013 (turtle)

de: Unternehmensschliessungen und verlorene Arbeitsstellen nach Gemeinde ab 2013 (turtle)

format: RDF Turtle

de: jsonld

fr: Fermetures d'entreprises et emplois perdus, dès 2013 (jsonld)

de: Unternehmensschliessungen und verlorene Arbeitsstellen nach Gemeinde ab 2013 (jsonld)

format: JSON-LD

de: csv

media_type: text/csv

fr: Fermetures d'entreprises et emplois perdus, dès 2013 (csv)

de: Unternehmensschliessungen und verlorene Arbeitsstellen nach Gemeinde ab 2013 (csv)

format: CSV

fr: Fermetures d'entreprises et emplois perdus, dès 2013 (xlsx)

de: Unternehmensschliessungen und verlorene Arbeitsstellen nach Gemeinde ab 2013 (xlsx)

format: XLS

fr: Fermetures d'entreprises et emplois perdus, dès 2013 (parquet)

de: Unternehmensschliessungen und verlorene Arbeitsstellen nach Gemeinde ab 2013 (parquet)

format: PARQUET

fr: Ce jeu de données présente les chiffres annuels des fermetures d'entreprises et des emplois perdus du canton de Fribourg selon la commune, depuis 2013. Ne font partie du relevé que les entreprises ayant définitivement cessé une activité économique marchande durant l'année de référence. Les entreprises disparues à la suite d'une fusion, d'une reprise, d'une scission ou d'une restructuration ne sont pas considérées. Sont par contre incluses les entreprises «dormantes» si elles ne sont pas réactivées dans un délai de deux ans. Le secteur primaire est exclu des données.Les valeurs "GDPr" dans le jeu de données sont incluses pour être conforme à la nouvelle loi sur la protection des données (nLPD, effective depuis le 1er septembre 2023).Les données de Clavaleyres, commune anciennement bernoise fusionnée avec Morat au 1er janvier 2022, sont incluses à celles du canton de Fribourg et de la commune de Morat rétroactivement pour toute la période d'observation.

de: Dieser Datensatz enthält die jährlichen Zahlen der Unternehmensschliessungen und der verlorenen Arbeitsplätze im Kanton Freiburg nach Gemeinde, seit 2013. In der Erhebung sind nur Unternehmen enthalten, die im Berichtsjahr eine marktwirtschaftliche Tätigkeit endgültig eingestellt haben. Unternehmen, die ihre Tätigkeit infolge Fusionen, Übernahmen, Spaltungen oder Restrukturierungen eingestellt haben, werden nicht berücksichtigt. Erfasst werden hingegen ruhende Einheiten, die ihre Tätigkeit innerhalb von zwei Jahren nicht wieder aufnehmen. Der primäre Sektor ist aus den Daten ausgeschlossen. Die "GDPr"-Werte im Datensatz sind enthalten, um dem neuen Datenschutzgesetz (nLPD, wirksam seit dem 1. September 2023) zu entsprechen.Die Daten von Clavaleyres, einer ehemals bernischen Gemeinde, die am 1. Januar 2022 mit Murten fusionierte, sind rückwirkend für den gesamten Beobachtungszeitraum in die Daten des Kantons Freiburg bzw. der Gemeinde Murten integriert.

beschaftigte

display_name: beschaftigte

emplois

display_name: emplois

entreprises

display_name: entreprises

unternehmen

display_name: unternehmen

econ

fr: Économie et finances

de: Wirtschaft und Finanzen

en: Economy and finance

it: Economia e finanze

de: ssd

en: Statistics and Data Service Fribourg

it: Servizio Statistiche e Dati Friburgo

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


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

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

de: inverkehrsetzungen konjunkturindikatoren freiburger-konjunkturspiegel neue-personenwagen

author: ssd

de: xlsx

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

de: Inverkehrsetzungen neuer Personenwagen ab 2006 (xlsx)

de: parquet

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

de: Inverkehrsetzungen neuer Personenwagen ab 2006 (parquet)

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

de: Inverkehrsetzungen neuer Personenwagen ab 2006 (jsonld)

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

de: Inverkehrsetzungen neuer Personenwagen ab 2006 (n3)

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

de: Inverkehrsetzungen neuer Personenwagen ab 2006 (turtle)

de: rdfxml

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

de: Inverkehrsetzungen neuer Personenwagen ab 2006 (rdfxml)

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

de: Inverkehrsetzungen neuer Personenwagen ab 2006 (jsonl)

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

de: Inverkehrsetzungen neuer Personenwagen ab 2006 (csv)

de: json

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

de: Inverkehrsetzungen neuer Personenwagen ab 2006 (json)

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

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: deces genre causes

de: geschlecht ursachen todesfalle

fr: Causes principales de décès, dès 1995 (n3)

de: Häufigste Todesursachen, ab 1995 (n3)

fr: Causes principales de décès, dès 1995 (parquet)

de: Häufigste Todesursachen, ab 1995 (parquet)

de: jsonl

fr: Causes principales de décès, dès 1995 (jsonl)

de: Häufigste Todesursachen, ab 1995 (jsonl)

de: csv

fr: Causes principales de décès, dès 1995 (csv)

de: Häufigste Todesursachen, ab 1995 (csv)

de: jsonld

fr: Causes principales de décès, dès 1995 (jsonld)

de: Häufigste Todesursachen, ab 1995 (jsonld)

fr: Causes principales de décès, dès 1995 (rdfxml)

de: Häufigste Todesursachen, ab 1995 (rdfxml)

de: turtle

fr: Causes principales de décès, dès 1995 (turtle)

de: Häufigste Todesursachen, ab 1995 (turtle)

de: xlsx

fr: Causes principales de décès, dès 1995 (xlsx)

de: Häufigste Todesursachen, ab 1995 (xlsx)

fr: Causes principales de décès, dès 1995 (json)

de: Häufigste Todesursachen, ab 1995 (json)

fr: Le jeu de données fournit des informations sur les principales causes de décès enregistrées dans le canton de Fribourg, ventilées par année et par genre. Les données proviennent de l'Office fédéral de la statistique (OFS).Les valeurs manquantes sont représentées par un tiret ("-").

de: Der Datensatz liefert Informationen zu den wichtigsten im Kanton Freiburg registrierten Todesursachen, aufgeschlüsselt nach Jahr und Geschlecht. Die Daten stammen vom Bundesamt für Statistik (BFS).Fehlende Werte werden durch einen Bindestrich ("-") dargestellt.

causes

display_name: causes

deces

display_name: deces

genre

display_name: genre

geschlecht

display_name: geschlecht

todesfalle

display_name: todesfalle

ursachen

display_name: ursachen

soci

fr: Population et société

de: Bevölkerung und Gesellschaft

en: Population and society

it: Popolazione e società

heal

fr: Santé

de: Gesundheit

en: Health

it: Salute

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.

fr: Causes principales de décès, dès 1995

de: Häufigste Todesursachen, ab 1995


fr: indicateurs-conjoncturels conjoncture-fribourgeoise faillites registre-du-commerce

de: konjunkturindikatoren konkurse freiburger-konjunkturspiegel handelsregister

de: json

fr: Ouvertures de faillites inscrites au Registre du commerce, par mois, dès 2019 (json)

de: Im Handelsregister eingetragene Konkurseröffnungen, pro Monat ab 2019 (json)

fr: Ouvertures de faillites inscrites au Registre du commerce, par mois, dès 2019 (csv)

de: Im Handelsregister eingetragene Konkurseröffnungen, pro Monat ab 2019 (csv)

de: parquet

fr: Ouvertures de faillites inscrites au Registre du commerce, par mois, dès 2019 (parquet)

de: Im Handelsregister eingetragene Konkurseröffnungen, pro Monat ab 2019 (parquet)

fr: Ouvertures de faillites inscrites au Registre du commerce, par mois, dès 2019 (xlsx)

de: Im Handelsregister eingetragene Konkurseröffnungen, pro Monat ab 2019 (xlsx)

de: jsonl

fr: Ouvertures de faillites inscrites au Registre du commerce, par mois, dès 2019 (jsonl)

de: Im Handelsregister eingetragene Konkurseröffnungen, pro Monat ab 2019 (jsonl)

de: rdfxml

fr: Ouvertures de faillites inscrites au Registre du commerce, par mois, dès 2019 (rdfxml)

de: Im Handelsregister eingetragene Konkurseröffnungen, pro Monat ab 2019 (rdfxml)

fr: Ouvertures de faillites inscrites au Registre du commerce, par mois, dès 2019 (n3)

de: Im Handelsregister eingetragene Konkurseröffnungen, pro Monat ab 2019 (n3)

fr: Ouvertures de faillites inscrites au Registre du commerce, par mois, dès 2019 (turtle)

de: Im Handelsregister eingetragene Konkurseröffnungen, pro Monat ab 2019 (turtle)

fr: Ouvertures de faillites inscrites au Registre du commerce, par mois, dès 2019 (jsonld)

de: Im Handelsregister eingetragene Konkurseröffnungen, pro Monat ab 2019 (jsonld)

fr: Ce jeu de données présente les chiffres mensuels du nombre d'inscriptions des ouvertures de faillites d'entités juridiques du canton de Fribourg, depuis 2019. Ces chiffres ne comprennent pas les dissolutions et successions répudiées.

de: Dieser Datensatz enthält die monatlichen Zahlen der Eintragungen der Konkurseröffnungen von juristischen Personen im Kanton Freiburg ab 2019. Diese Zahlen beinhalten keine Auflösungen und ausgeschlagene Erbschaften.

faillites

display_name: faillites

handelsregister

display_name: handelsregister

konkurse

display_name: konkurse

registre-du-commerce

display_name: registre-du-commerce

econ

fr: Économie et finances

de: Wirtschaft und Finanzen

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: sante subsides assurance-maladie primes

de: gesundheit pramien pramienverbilligung krankenversicherung

de: turtle

fr: Réduction des primes dans l'assurance obligatoire des soins (AOS), dès 1996 (turtle)

de: Prämienverbilligung in der obligatorischen Krankenpflegeversicherung (OKP), seit 1996 (turtle)

fr: Réduction des primes dans l'assurance obligatoire des soins (AOS), dès 1996 (n3)

de: Prämienverbilligung in der obligatorischen Krankenpflegeversicherung (OKP), seit 1996 (n3)

de: csv

fr: Réduction des primes dans l'assurance obligatoire des soins (AOS), dès 1996 (csv)

de: Prämienverbilligung in der obligatorischen Krankenpflegeversicherung (OKP), seit 1996 (csv)

de: jsonld

fr: Réduction des primes dans l'assurance obligatoire des soins (AOS), dès 1996 (jsonld)

de: Prämienverbilligung in der obligatorischen Krankenpflegeversicherung (OKP), seit 1996 (jsonld)

fr: Réduction des primes dans l'assurance obligatoire des soins (AOS), dès 1996 (parquet)

de: Prämienverbilligung in der obligatorischen Krankenpflegeversicherung (OKP), seit 1996 (parquet)

de: json

fr: Réduction des primes dans l'assurance obligatoire des soins (AOS), dès 1996 (json)

de: Prämienverbilligung in der obligatorischen Krankenpflegeversicherung (OKP), seit 1996 (json)

fr: Réduction des primes dans l'assurance obligatoire des soins (AOS), dès 1996 (rdfxml)

de: Prämienverbilligung in der obligatorischen Krankenpflegeversicherung (OKP), seit 1996 (rdfxml)

de: xlsx

fr: Réduction des primes dans l'assurance obligatoire des soins (AOS), dès 1996 (xlsx)

de: Prämienverbilligung in der obligatorischen Krankenpflegeversicherung (OKP), seit 1996 (xlsx)

fr: Réduction des primes dans l'assurance obligatoire des soins (AOS), dès 1996 (jsonl)

de: Prämienverbilligung in der obligatorischen Krankenpflegeversicherung (OKP), seit 1996 (jsonl)

fr: Ce jeu de données présente les chiffres relatifs aux subsides pour la réduction des primes dans l'assurance obligatoire des soins (AOS), pour le canton de Fribourg et l'ensemble de la Suisse, depuis 1996. Il contient des informations sur le nombre de bénéficiaires de subsides et le taux de bénéficiaires en % de l'effectif moyen des assurés, ainsi que sur le montant total alloué aux subsides en millions de francs avec les parts supportées respectivement par le(s) canton(s) et par la Confédération (en %) et la contribution moyenne par bénéficiaire.

de: Dieser Datensatz enthält Zahlen zu den Prämienverbilligungen in der obligatorischen Krankenpflegeversicherung (OKP) für den Kanton Freiburg und die gesamte Schweiz seit 1996. Er enthält Informationen über die Anzahl der Bezüger von Prämienverbilligungen und den Anteil der Bezüger in % des durchschnittlichen Versichertenbestands sowie über den Gesamtbetrag der Prämienverbilligungen in Millionen Franken mit den Anteilen (in %), die jeweils vom Kanton/von den Kantonen und vom Bund getragen werden, und den durchschnittlichen Beitrag pro Bezüger.

assurance-maladie

display_name: assurance-maladie

gesundheit

display_name: gesundheit

krankenversicherung

display_name: krankenversicherung

pramien

display_name: pramien

pramienverbilligung

display_name: pramienverbilligung

primes

display_name: primes

sante

display_name: sante

subsides

display_name: subsides

heal

de: Gesundheit

de: ssd


fr: votations canton

de: kanton abstimmungen

de: parquet

fr: Votations cantonales fribourgeoises, dès 2015 (parquet)

de: Kantonale Abstimmungen in Freiburg, ab 2015 (parquet)

fr: Votations cantonales fribourgeoises, dès 2015 (n3)

de: Kantonale Abstimmungen in Freiburg, ab 2015 (n3)

fr: Votations cantonales fribourgeoises, dès 2015 (xlsx)

de: Kantonale Abstimmungen in Freiburg, ab 2015 (xlsx)

de: turtle

fr: Votations cantonales fribourgeoises, dès 2015 (turtle)

de: Kantonale Abstimmungen in Freiburg, ab 2015 (turtle)

fr: Votations cantonales fribourgeoises, dès 2015 (jsonld)

de: Kantonale Abstimmungen in Freiburg, ab 2015 (jsonld)

de: jsonl

fr: Votations cantonales fribourgeoises, dès 2015 (jsonl)

de: Kantonale Abstimmungen in Freiburg, ab 2015 (jsonl)

fr: Votations cantonales fribourgeoises, dès 2015 (json)

de: Kantonale Abstimmungen in Freiburg, ab 2015 (json)

de: rdfxml

fr: Votations cantonales fribourgeoises, dès 2015 (rdfxml)

de: Kantonale Abstimmungen in Freiburg, ab 2015 (rdfxml)

fr: Votations cantonales fribourgeoises, dès 2015 (csv)

de: Kantonale Abstimmungen in Freiburg, ab 2015 (csv)

fr: Ce jeu de données présente les résultats des votations cantonales au niveau du canton dès 2015.

de: Dieser Datensatz enthält die Ergebnisse der kantonalen Volksabstimmungen auf Kantonsebene ab 2015.

abstimmungen

display_name: abstimmungen

canton

display_name: canton

kanton

display_name: kanton

votations

display_name: votations

gove

fr: Gouvernement et secteur public

de: Regierung und öffentlicher Sektor

en: Government and public sector

it: Governo e settore pubblico

ssd

fr: Service de la statistique et de la donnée Fribourg

de: Amt für Statistik und Daten Freiburg

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: constructions autorisations

de: bau bewilligung

de: json

fr: Constructions autorisées (nombre, coût), par mois, dès 1993 (json)

de: Bewilligte Bauten (Anzahl, Kosten) pro Monat ab 1993 (json)

de: xlsx

fr: Constructions autorisées (nombre, coût), par mois, dès 1993 (xlsx)

de: Bewilligte Bauten (Anzahl, Kosten) pro Monat ab 1993 (xlsx)

fr: Constructions autorisées (nombre, coût), par mois, dès 1993 (jsonl)

de: Bewilligte Bauten (Anzahl, Kosten) pro Monat ab 1993 (jsonl)

de: jsonld

fr: Constructions autorisées (nombre, coût), par mois, dès 1993 (jsonld)

de: Bewilligte Bauten (Anzahl, Kosten) pro Monat ab 1993 (jsonld)

de: csv

fr: Constructions autorisées (nombre, coût), par mois, dès 1993 (csv)

de: Bewilligte Bauten (Anzahl, Kosten) pro Monat ab 1993 (csv)

fr: Constructions autorisées (nombre, coût), par mois, dès 1993 (turtle)

de: Bewilligte Bauten (Anzahl, Kosten) pro Monat ab 1993 (turtle)

fr: Constructions autorisées (nombre, coût), par mois, dès 1993 (n3)

de: Bewilligte Bauten (Anzahl, Kosten) pro Monat ab 1993 (n3)

fr: Constructions autorisées (nombre, coût), par mois, dès 1993 (rdfxml)

de: Bewilligte Bauten (Anzahl, Kosten) pro Monat ab 1993 (rdfxml)

de: parquet

fr: Constructions autorisées (nombre, coût), par mois, dès 1993 (parquet)

de: Bewilligte Bauten (Anzahl, Kosten) pro Monat ab 1993 (parquet)

fr:
Ce jeu de données présente les constructions autorisées
(nombre, coût), par mois, dès 1993.Donnée estimée :
Les données estimées proviennent directement de FRIAC, [l’application de gestion des demandes de permis de construire du Canton de Fribourg](https://www.fr.ch/territoire-amenagement-et-constructions/permis-de-construire-et-autorisations/friac).
Elles reflètent les informations telles que saisies par les requérants dans
l’application.
Elles ne font l’objet d’aucun contrôle ou plausibilisation.Donnée provisoire :
Les données provisoires sont issues des projets de construction saisis dans le
registre des bâtiments et logements ([RegBL](https://www.housing-stat.ch/fr/accueil.html)) par les communes.
Ces données sont validées et plausibilisées trimestriellement. Afin de tenir
compte des éventuelles corrections et annonces tardives de projets, ces données
sont considérées comme provisoires durant 12 mois.
Donnée définitive :
Au-delà des 12 mois de présence dans la partie projets de construction du
RegBL, les données provisoires deviennent des données définitives.
Elles servent à établir la statistique annuelle de la construction (BAU)
publiée par l’OFS.

de: Dieses Datenset zeigt die bewilligten Bauvorhaben (Anzahl, Kosten) pro Monat ab 1993.Geschätzte Daten:Die geschätzten Daten stammen direkt aus FRIAC, der[ Anwendung zur Verwaltung von Baugesuchsanträgen des Kantons Freiburg](https://www.fr.ch/de/raum-planung-und-bau/baubewilligung-und-bewilligungen/friac). Sie spiegeln die Informationen wider, wie sie von den Antragstellern in der Anwendung erfasst wurden.Diese Daten unterliegen keiner Kontrolle oder Plausibilitätsprüfung.Provisorische Daten:Die provisorischen Daten stammen aus den Bauprojekten, die von den Gemeinden im Gebäude- und Wohnungsregister ([GWR](https://www.housing-stat.ch/de/accueil.html)) erfasst wurden.Diese Daten werden vierteljährlich validiert und plausibilisiert. Um allfällige Korrekturen und verspätete Meldungen von Projekten zu berücksichtigen, gelten diese Daten während 12 Monaten als provisorisch.Definitive Daten:Nach 12 Monaten in der Bauprojektsektion des GWR werden die provisorischen Daten zu definitiven Daten.Sie dienen der Erstellung der jährlichen Baustatistik (BAU), die vom BFS veröffentlicht wird.

autorisations

display_name: autorisations

bau

display_name: bau

bewilligung

display_name: bewilligung

constructions

display_name: constructions

econ

fr: Économie et finances

de: Wirtschaft und Finanzen

de: ssd


data-analysis-toolkits

maintainer: EnviDat Support

title_for_slug: data-analysis-toolkits

en: statistics data-analysis bayesian-inference error-propagation hypothesis-testing uncertainty

en: Toolkit 11: serial correlation

media_type: application/pdf

en: Tools for detecting and correcting for serial correlation in analyses of time series

format: PDF

mimetype: application/pdf

en: toolkit 6: averaging in nonlinear functions

en: The average of a nonlinear function will not equal the function evaluated at its average inputs (the mean of the function will differ from from the function of the mean). This toolkit shows how to estimate that averaging bias.

en: Toolkit 4: confidence intervals

en: Toolkit 7: hypothesis testing, significance, and power

en: An overview of basic concepts -- and common pitfalls -- in statistical hypothesis testing, statistical significance, and power analysis

en: Toolkit 8: Hodges-Lehmann estimators

en: A quick introduction to Hodges-Lehmann estimators (nonparametric measures of effect size)

en: Toolkit 12: weighted averages and their uncertainties

en: There are two different ways to calculate the uncertainty of a weighted average. Software packages often don't tell you which one they are reporting, and the resulting errors can be huge.

en: Toolkit 1: graphically displaying data distributions

en: Toolkit 9: Experimentwise error rates and statistical fishing expeditions

en: Statistical fishing expeditions, and how to avoid being misled by them

format: SERVICE

en: Toolkit 5: uncertainty analysis and error propagation

en: Gaussian error propagation, monte carlo methods, and first-order, second-moment error propagation

en: Toolkit 10: linear regression

en: An overview of the math, logic, and pitfalls of ordinary least-squares linear regression

en: Toolkit 3: tools for transforming data

en: The ladder of powers and a few other cool transformations

en: Toolkit 2: quantifying data distributions using descriptive statistics

en:
These are condensed notes covering selected key points in data analysis and statistics. They were developed by James Kirchner for the course "Analysis of Environmental Data" at Berkeley in the 1990's and 2000's. They are not intended to be comprehensive, and thus are not a substitute for a good textbook or a good education!

License: These notes are released by James Kirchner under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license.

bayesian-inference

display_name: bayesian-inference

data-analysis

display_name: data-analysis

error-propagation

display_name: error-propagation

hypothesis-testing

display_name: hypothesis-testing

statistics

display_name: statistics

uncertainty

display_name: uncertainty

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.

political_level: other

value: Envidat Harvester

en: Data analysis toolkits


maintainer: Felix Gugerli

en: monitoring conservation-genetics genetic-structure genetic-diversity melanargia-galathea

en:
Individual genotypes assessed at six nuclear microsatellite loci (two alleles per locus) for individuals of the butterfly Melanargia galathea (Marbled white) that were collected throughout Switzerland, along the regular grid of the Biodiversity Monitoring (BDM) of Switzerland. For each individual, the sampling site (number) and the genotype are given. Note that due to privacy restrictions, the original geographic coordinates remain disclosed. For original coordinates, data holders should be contacted (Swiss Federal Office for the Environment; Biodiversity Monitoring Switzerland).
The data refer to the following publication: Terzer et al., Distinct spatial patterns of genetic structure and diversity in the butterfly Marbled White (Melanargia galathea) inhabiting fragmented grasslands. Conservation Genetics, https://doi.org/0.1007/s10592-023-01593-4.

conservation-genetics

display_name: conservation-genetics

genetic-diversity

display_name: genetic-diversity

genetic-structure

display_name: genetic-structure

melanargia-galathea

display_name: melanargia-galathea

monitoring

display_name: monitoring

en: Nuclear microsatellite genotypes of the butterfly Melanargia galathea


maintainer: Michael Lehning

en: Coordinates of Grid Points

en: Data Source

en: Hourly Gridded Wind Power Time Series

en: Description

en: Full Data Description

en:
This dataset consist of simulated hourly power production from an Enercon E82 Turbine at 100 m hub-height. It describes the hourly power output a 1MW turbine would produce in each 0.01° grid cell for the years 2016 and 2017.

100 m wind speed data was taken from the COSMO-1 model (Consortium for Small-scale Modeling 2017), which has a 0.01° horizontal resolution. The domain covered is the whole of Switzerland, with the exclusion of lakes. As such, the number of 0.01◦ pixels within Switzerland amounts to 48657.
Conversion to power output was done based on the power curve of the Enercon E82 Turbine. As power output is lower at altitude due to lower air density, we corrected for this effect as described in (Kruyt et al. 2017).

Please cite the following paper in connection with the dataset:

Paper Citation:
 > _Bert Kruyt, Jérôme Dujardin, and Michael Lehning: Improvement of wind power assessment in complex terrain: The case of COSMO-1 in the Swiss Alps, Front. Energy Res., [doi:10.3389/fenrg.2018.00102] (https://doi.org/10.3389/fenrg.2018.00102)_

mountain-wind-potential

display_name: mountain-wind-potential

topography

display_name: topography

wind-energy

display_name: wind-energy

en: Dataset on Cosmo-1 based Energy Potential in Swiss Alps


en: treeline long-term-experiment mountain-forest stillberg afforestation

en: Site photo

en: This site photo is outdated and has been replaced with an updated version (see description).

en: Dataset V1

en: This dataset is outdated and has been replaced with an updated version (see description).

format: GDB

en: Documentation

en: This data documentation is outdated and has been replaced with an updated version (see description).

mimetype: application/msword

en:
 Important
This EnviDat entry is outdated. The most recent, usable version of the data can be found under the new EnviDat entry "Long-term afforestation experiment at the Alpine treeline site Stillberg, Switzerland." The entry can be found under this link <https://www.envidat.ch/dataset/afforestation-stillberg> and with this DOI 10.16904/envidat.397.

afforestation

display_name: afforestation

long-term-experiment

display_name: long-term-experiment

mountain-forest

display_name: mountain-forest

stillberg

display_name: stillberg

treeline

display_name: treeline

en: Long-term treeline research dataset at Stillberg, Davos


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

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 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: 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 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)

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

raclets

display_name: raclets

wind

display_name: wind

wind-direction

display_name: wind-direction

wind-speed

display_name: wind-speed

wind-velocity

display_name: wind-velocity

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: Snow Drift Station - 3D Ultrasonic


causal-effect-of-mfoz

title_for_slug: causal-effect-of-mfoz

en: causality the-major-function-oriented-zone built-up-land-change fujian-province propensity-score-matching selection-bias

en: Data and Stata code for repeating the results

media_type: application/zip

en:
Data.xlsx is the variables of 952 townships
Table 1-2 NN(1-1)_noreplace_caliper(0.01).do is STATA code to produce the results in Table 1 and 2
Figure 5 Distribution of the propensity scores.R is R code rto produce Figure 5
Figure 5 Standard mean difference.R is R code rto produce Figure 5

format: ZIP

en:
Title: Closer to causality: How effective is spatial planning in governing built-up land expansion in Fujian Province, China?

Research objective: The Major Function Oriented Zone (MFOZ), the first strategic spatial plan in China, is developed to achieve a coordinated regional development, through spatial regulation and zoning of development. The MFOZ he MFOZ divided land into four major function-oriented zones: The development-optimized zone, the development-prioritised zone, the development-restricted zone, and the development-prohibited zone. We used propensity score marching to evaluate the effect of the MFOZ on built-up land expansion in Fujian Province over three time intervals (2013–2015, 2013–2018 and 2013–2020).

Data: Data.xlsx contains the variables of 954 towns in Fujian Province.
Town_ID is the town unique ID;
County_ID is the county unique ID;
City_ID is the city unique ID;
MFOZ is the the development-prioritised zone and the development-restricted zone (The development-optimized zone and the development-prohibited zone are excluded);
Builtup_13_15 is the built-up land expansion from 2013 to 2015;
Builtup_13_18 is the built-up land expansion from 2013 to 2018;
Builtup_13_20 is the built-up land expansion from 2013 to 2020;
Dis2water is the Euclidean distance from the town to the nearest waterbody;
Slope is the the average slope within the town;
GDP is the average GDP in 2010 within the town;
Pop is the average population in 2010 within the town;
Road is the average population in 2010 within the town;
Dis2city is the Euclidean distance from the town to the nearest prefectural city centre;
Nei_Arable, Nei_Forest, and Nei_Built.up are the area of arable land, forest land, and built-up land neighbouring town i in 2010.

Method: we used the propensity score matching to compare the changes in the amount of built-up land in the towns of the development-prioritised zone with the matched towns of the development-restricted zone. Additionally, we used three evaluation intervals (2013–2015, 2013–2018 and 2013–2020) to evaluate temporal variation in the causal effect of the MFOZ on built-up land expansion.

built-up-land-change

display_name: built-up-land-change

causality

display_name: causality

fujian-province

display_name: fujian-province

propensity-score-matching

display_name: propensity-score-matching

selection-bias

display_name: selection-bias

the-major-function-oriented-zone

display_name: the-major-function-oriented-zone

en: Causal effect of MFOZ


en: stream-water catchment-data groundwater oxygen-isotopes catchment solutes hydrogen-isotopes erlenbach precipitation

en: This dataset includes mean concentrations of solutes (Calcium, Iron, Magnesium, Sodium, Potassium, Silicon, Nitrate, Sulfate, and Chloride) and stable isotope values (hydrogen and oxygen) in water molecules. These were measured in precipitation, groundwater, soil water, and surface water samples collected from the Erlenbach catchment and its close vicinity in Switzerland during Spring and Fall 2021. Detailed information is available in the accompanying documentation (documentation_for_the_hydrochemical_data_collected_during_spring_and_fall_2021_in_the_erlenbach_catchment.pdf).

en: This document provides a detailed description of the hydrochemical dataset, including, among other things, an overview of the dataset content, an explanation of the column headers, a description of the locations where samples were collected, and an explanation of the abbreviations used in the dataset.

en: The dataset contains hydrochemical data collected from spring to fall 2021 in the Erlenbach catchment, Switzerland. This hydrochemical data includes solute concentrations and stable isotopes of oxygen and hydrogen in water molecules, measured in streamwater, soil water, groundwater, and rainfall samples. Samples were collected during seven rainfall events with higher temporal resolution (hourly) as well as at lower resolution between events. A detailed description of the dataset is provided in the documentation.

catchment

display_name: catchment

catchment-data

display_name: catchment-data

erlenbach

display_name: erlenbach

groundwater

display_name: groundwater

hydrogen-isotopes

display_name: hydrogen-isotopes

oxygen-isotopes

display_name: oxygen-isotopes

precipitation

display_name: precipitation

solutes

display_name: solutes

stream-water

display_name: stream-water

en: Hydrochemical Data Collected during Spring-Fall 2021 in the Erlenbach Catchment


maintainer: Yael Frischholz

en: snow-transport preferential-deposition alpine photovoltaic renewable-energy

en: Browse simulation files

en:
This file contains the snowBedFoam simulations carried out to produce the results presented in: _article not published yet_.
 Content
 1. /simulations
Groups all simulations folders organised by parameter (azimuth, groupsize, height, interspace) and by type of experiment (single_ref, group_ref).
 2. /simulations/[parameter]/
For each parameter multiple simulations were carried out. Each simulation folder follows the same naming systematic:
`helioplant_n#_h#_ARL#_BL#_az#_windDir#`
- n: number of Helioplant units
- h: height-above-surface
- sp: regular distance between the units
- ARL: additional refinement level
- BL: boundary layer first cell height
- windDir: wind direction
- stag: staggered arangement (suffix)
Here is an example of a folder content for a specific parameter
(simulations/azimuth/):
- `helioplant_n9_h07_sp10_ARL45_BL0015_az30_windDir5`: following the naming systematic this folder corresponds to a simulation with 9 aligned Helioplant units with 0.7m of height-above-surface, an inter-unit distance of 10m an azimuth of 30° and a wind direction of 5°.
 3. /simulations/[parameter]/[helioplant_n#_h#_ARL#_BL#_az#_windDir#]
Each simulation folder contains multiple sub-folders (0/, 100/, 300/, constant/ etc.) and files that define the experiment. For each simulation, the files used in the processing [scripts] are the following:
- 0/Cx: contains the x coordinates of sampling grid cells and of the Helioplants structures.
- 0/Cy: contains the y coordinates of sampling grid cells and of the Helioplants structures.
- 300/kinematicCloud-massCheckPatterns: contains the snow mass distribution values [kg/m2] for each grid cell.

[scripts]: https://github.com/frischwood/snowbed-helio.git

en:
This dataset groups numerical simulation outputs and validation measurement data produced in the context of the following [publication]: _not published yet_
The snow transport model [snowBedFoam] was used to analyse snow deposition around a specific type of Alpine PV structures named HELIOPLANT®.
The results of a sensitivity analysis of multiple key parameters that govern the spatial organisation of these structures are provided here. Measurements of snow distribution taken from the test-site of the [Gondosolar] project, are provided too.

To reproduce the results of the aforementioned publication, follow the instructions on this [repository].
[publication]: https://
[snowBedFoam]: https://www.doi.org/10.16904/envidat.223
[Gondosolar]: https://www.gondosolar.ch
[repository]: https://github.com/frischwood/snowbed-helio.git

alpine

display_name: alpine

photovoltaic

display_name: photovoltaic

preferential-deposition

display_name: preferential-deposition

renewable-energy

display_name: renewable-energy

snow-transport

display_name: snow-transport

en: Drifting and blowing snow distribution around structures for Alpine PV applications


climarctic-seasons

maintainer: Aline Frossard

title_for_slug: climarctic-seasons

en: arctic microbial-biodiversity soil greenhouse-gases microbial-functions

en: Climarctic seasons dataset Envidat.xlsx

en:
Samples description
•	sample number: sample list number (S1 to S126)
•	season: season during which the samples were collected
•	location: one of the two location where the samples were collected in the vicinity of Ny Alesund on Svalbard
•	slope site: sites where the samples were collected along the toposequence (upslope = top of the slope, midslope = middle of the slope, downslope = bottom of the slope)
•	soil layer: depth layer where the sample were collected (biocrust = 0-2 cm and mineral soil = 5-10 cm)
•	replicate: replicated sampled (3 replicates per site)
Environmental variables
•	pH = pH value of the soil
•	sand = proportion of sand of the soil [%]
•	silt = proportion of silt of the soil [%]
•	clay = proportion of clay of the soil [%]
•	GWC = Gravimetric Water content in the soil [%]
•	OM = Organic Matter content in the soil [g]
•	N = Total Nitrogen content in soil [mg/g]
•	C = Total Carbon content in soil [mg/g]
•	C/N = ration of C content over N content [ratio]
•	NO3 = Nitrate content in soil [mg/kg]
•	NO2 = Nitrite content in soil [mg/kg]
•	NH4 = Ammonium content in soil [mg/kg]
•	Ptot = Total Phosphorus content in soil [mg/kg]
•	Pavail = Available Phosphorus content in soil [mg/kg]
•	PO4 = Phsphate content in soil [mg/kg]
•	CO2 = CO2 fluxes from soil [mmol CO2 m-2 d-1]
•	CH4 = CH4 fluxes from soil [umol CH4 m-2 d-1]
•	N2O = N2O fluxes from soil [umol CH4 m-2 d-1]
•	GPP = Gross Primary Production [mmol CO2 m-2 d-1]
•	ER = Ecosystem respiration [mmol CO2 m-2 d-1]
•	NEE = Net Ecosystem Exchange [mmol CO2 m-2 d-1]
•	glucoOM = glucosidase enzyme activity [mmol h-1 gOM-1]
•	23 xyloOM = xylosidase enzyme activity [mmol h-1 gOM-1]
•	chitOM = N-acetyl-glucosaminidase (chitinase) enzyme activity [mmol h-1 gOM-1]
•	phosOM = phosphatase enzyme activity [mmol h-1 gOM-1]
•	leuOM = leucine-aminopeptidase enzyme activity [mmol h-1 gOM-1]
•	PPOM = Polyphenol Peroxidase enzyme activity [mmol h-1 gOM-1]
•	POOM = Polyphenol Oxidase enzyme activity [mmol h-1 gOM-1]
•	30. Bac16SOM = Bacterial 16S gene abundance [copies gOM-1]
•	31. BacamoAOM = Bacterial amoA gene abundance [copies gOM-1]
•	32. ArchamoAOM = Archaeal 16S gene abundance [copies gOM-1]
•	33. nifHOM = nifH gene abundance [copies gOM-1]
•	34. nirSOM = nirS gene abundance [copies gOM-1]
•	35. nosZOM = nosZ gene abundance [copies gOM-1]
•	36. nxrBOM = nxrB gene abundance [copies gOM-1]
•	37. mcrAOM = mcrA gene abundance [copies gOM-1]
•	38. pmoAOM = pmoA gene abundance [copies gOM-1]

en: This dataset includes measurements of potential microbial functions (potential extracellular enzyme activities and microbial functional genes abundance) and soil physico-chemical properties (pH, nutrients, texture, etc) in biocrusts and underlying mineral soils in High-Arctic tundra near Ny-Ålesund (Svalbard) during each season along a toposequence.

arctic

display_name: arctic

greenhouse-gases

display_name: greenhouse-gases

microbial-biodiversity

display_name: microbial-biodiversity

microbial-functions

display_name: microbial-functions

soil

display_name: soil

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: Climarctic seasons


maintainer: Alexis Shakas

en: ert drone-data pinus-sylvestris irrigation 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: forest-ecosystems soil-solution xylem-water water-budget water-isotopes

media_type: image/png

en: Overview of most xylem water isotopic signatures.

format: PNG

en: Column descriptions for soil solution, deposition, xylem and bulk soil samples.

en: Water isotope signatures of soil solution samples.

en: Overview of most deposition water isotopic signatures.

en: Water isotope signatures and gravimetric water content of xylem samples.

en: Overview of most soil solution water isotopic signatures.

en: Water isotopic signature of deposition samples.

en: Overview of most bulk soil water isotopic signatures.

en: Water isotope signatures and gravimetric water content of bulk soil samples.

en:
The isotope composition of different forest water pools was analyzed at 10 sites of the Long-term Forest Ecosystem Research programme in 2021 and 2022. The sampling was done during the vegetation season and included fortnightly xylem water samples taken by stem increment cores and each year 3 bulk soil water sampling campaigns (in April, June, September of 2021 and 2022). Both sample types were cryogenically extracted at WSL and analyzed on a Picarro 2140 with Micro-Combustion-Module. Gravimetric water content in both sample types was computed based on pre-and post-extraction weights. In addition to xylem and bulk soil waters, we analyzed the isotope composition of soil solution from a suction cup lysimeters in three soil depths 0, 15, 50, 80 cm and precipitation from samplers below the canopy and outside of the forest, all collected fortnightly from collection periods of varying length (longest from 2017-2022).
Precipitation samples are further described [here](https://www.doi.org/10.16904/envidat.lwf.15). Lysimeter samples are described further [here](https://www.doi.org/10.16904/envidat.lwf.28).

forest-ecosystems

display_name: forest-ecosystems

soil-solution

display_name: soil-solution

water-budget

display_name: water-budget

water-isotopes

display_name: water-isotopes

xylem-water

display_name: xylem-water

en: Water isotope signatures in Swiss forest ecosystems in 2021 and 2022


en: wind radiation atmospheric-conditions temperature-profiles precipitation

en: DATA ACCESS

en: This data access is outdated and has been replaced with an updated version (see description).

en: README

media_type: text/plain

en: This description is outdated and has been replaced with an updated version (see description).

format: TXT

en: Site photo 2

en: This site photo is outdated and has been replaced with an updated version (see description).

en: Site photo 1

en: STB2 CSV

en: This dataset is outdated and has been replaced with an updated version (see description).

mimetype: text/csv

en: Overview plot

en: This overview plot is outdated and has been replaced with an updated version (see description).

en: Latest data from Stillberg climate station

en: Site photo 3

en:
 Important
This EnviDat entry is outdated. The most recent, usable version of the data can be found under the new EnviDat entry "Long-term meteorological station Stillberg, Davos, Switzerland at 2090 m a.s.l.." The entry can be found under this link <https://www.envidat.ch/#/metadata/meteo-stillberg> and with this DOI <https://doi.org/10.16904/envidat.396>.

atmospheric-conditions

display_name: atmospheric-conditions

precipitation

display_name: precipitation

radiation

display_name: radiation

temperature-profiles

display_name: temperature-profiles

wind

display_name: wind

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: Long-term meteorological and snow station at 2090 m a.s.l., Stillberg, Davos, Switzerland (1975 - present)


present-weather-sensor-klosters

maintainer: Maxime Hervo

title_for_slug: present-weather-sensor-klosters

en: precipitation raclets weather sensor visibility

en: Present Weather Sensor Klosters

en: Present Weather sensor measuring visibility and weather type installed at Klosters.

en: A present weather sensor (Vaisala PWD22) was deployed in Klosters (LON: 9.880413, LAT: 46.869019) for weather observation, combining the functions of a forwardscatter visibility meter and a present weather sensor. Besides measuring ambient light, it detects the intensity as well as the amount of both liquid and solid precipitation. More information can be found in the [User's Manual](ftp://ftp.cmdl.noaa.gov/aerosol/doc/manuals/PWD22_Manual.pdf).

raclets

display_name: raclets

sensor

display_name: sensor

visibility

display_name: visibility

weather

display_name: weather


en: research-data-management geodata time-series core sentinel-2 raster video-encoding gridded-data

en: Disclaimer: provided for the Geomatics peer review in 2021 and left as additional information for the dataset. Software dependencies and libraries, as well as cloud storage paths, may change over time (after 2021).

en: CORE Dataset (80 GB)

en: View publication in the Digital Object Repository at WSL (DORA)

media_type: text/html

format: HTML

en:
Acknowledgements: The CORE format was proudly inspired by the Cloud Optimized GeoTIFF ([COG](https://www.cogeo.org/)) format, by considering how to leverage the ability of clients issuing HTTP GET range requests for a time-series of remote sensing and aerial imagery (instead of just one image).

License: The Cloud Optimized Raster Encoding (CORE) specifications are released to the public domain under a Creative Commons 1.0 CC0 "No Rights Reserved" international license. You can reuse the information contained herein in any way you want, for any purposes and without restrictions.

-----------------------

Summary:
The Cloud Optimized Raster Encoding (CORE) format is being developed for the efficient storage and management of gridded data by applying video encoding algorithms. It is mainly designed for the exchange and preservation of large time series data in environmental data repositories, while in the same time enabling more efficient workflows on the cloud. It can be applied to any large number of similar (in pixel size and image dimensions) raster data layers. CORE is not designed to replace COG but to work together with COG for a collection of many layers (e.g. by offering a fast preview of layers when switching between layers of a time series).

WARNING: Currently only applicable to RGB/Byte imagery. The final CORE specifications may probably be very different from what is written herein or CORE may not ever become productive due to a myriad of reasons (see also 'Major issues to be solved').

With this early public sharing of the format we explicitly support the Open Science agenda, which implies "shifting from the standard practices of publishing research results in scientific publications towards sharing and using all available knowledge at an earlier stage in the research process" (quote from: European Commission, Directorate General for Research and Innovation, 2016. Open innovation, open science, open to the world).

CORE Specifications:

1) a MP4 or WebM video digital multimedia container format (or any future video container playable as HTML video in major browsers)

2) a free to use or open video compression codec such as H.264, VP9, or AV1 (or any future video codec that is open sourced or free to use for end users)

Note: H.264 is currently recommended because of the wide usage with support in all major browsers, fast encoding due to acceleration in hardware (which is currently not the case for AV1 or VP9) and the fact that MPEG LA has allowed the free use for streaming video that is free to the end users. However, please note that H.264 is restricted by patents and its use in proprietary or commercial software requires the payment of royalties to [MPEG LA](https://www.mpegla.com/programs/avc-h-264/). However, when AV1 matures and accelerated hardware encoding becomes available, AV1 is expected to offer 30% to 50% smaller file size in comparison with H.264, while retaining the [same quality](https://trac.ffmpeg.org/wiki/Encode/AV1).

3) the encoding frame rate should be of one frame per second (fps) with each layer segmented in internal tiles, similar to COG, ordered by the main use case when accessing the data: either layer contiguous or tile contiguous;

Note: The internal tile arrangement should support an easy navigation inside the CORE video format, depending on the use case.

4) a CORE file is optimised for streaming with the moov atom at the beginning of the file (e.g. with -movflags faststart) and optional additional optimisations depending on the codec used (e.g. -tune fastdecode -tune zerolatency for H.264)

5) metadata tags inside the moov atom for describing and using geographic image data (that are preferably compatible with the [OGC GeoTIFF standard](https://www.ogc.org/standards/geotiff) or any future standard accepted by the geospatial community) as well as list of original file names corresponding to each CORE layer

6) it needs to encode similar source rasters (such as time series of rasters with the same extent and resolution, or different tiles of the same product; each input raster should be having the same image and pixel size)

7) it provides a mechanism for addressing and requesting overviews (lower resolution data) for a fast display in web browser depending on the map scale (currently external overviews)

Major issues to be solved:

- Internal overviews (similar to COG), by chaining lower resolution videos in the same MP4 container for fast access to overviews first); Currently, overviews are kept as separate files, as external overviews.

- Metadata encoding (how to best encode spatial extent, layer names, and so on, for each of the layer inside the series, which may have a different geographical extent, etc...; Known issues: adding too many tags with FFmpeg which are not part of the standard MP4 moov atom; metadata tags have a limited string length.

- Applicability beyond RGB/Byte datasets; defining a standard way of converting cell values from Int16/UInt16/UInt32/Int32/Float32/Float64/ data types into multi-band Byte values (and reconstructing them back to the original data type within acceptable thresholds)

Example

Notice: The provided CORE (.mp4) examples contain modified Copernicus Sentinel data [2018-2021].

For generating the CORE examples provided, 50 original Sentinel 2 (S-2) TCI data images from an area located inside Switzerland were downloaded from www.copernicus.eu, and then transformed into CORE format using ffmpeg with H.264 encoding using the [x264 library](https://www.videolan.org/developers/x264.html).

DISCLAIMER: Basic scripts are provided for the Geomatics peer review (in 2021) and kept as additional information for the dataset. Nevertheless, please note that software dependencies and libraries, as well as cloud storage paths, may quickly become deprecated over time (after 2021). For compatibility, stable dependencies and libraries released around 2020 should be used.

core

display_name: core

geodata

display_name: geodata

gridded-data

display_name: gridded-data

raster

display_name: raster

research-data-management

display_name: research-data-management

time-series

display_name: time-series

video-encoding

display_name: video-encoding


en: long-term wind-speed precipitation par meteorology temperature relative-humidity climate-change swiss-forest-lab meteo

en: Site photo of the forest station (LEB)

en: Site photo of the forest station from below (viewing upwards the slope)

mimetype: image/jpeg

en: Forest station data (LEB)

en: Data file.

en: Data plot of the forest station (LEB)

en: Data overview for air temperature, relative humidity, precipitation, PAR, wind speed.

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for one meteorological station in Lens in Switzerland which is located within a natural coniferous forest with Scots pine (_Pinus sylvestris_; 150-170 yrs)) as dominant tree species. The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Lens is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

en: Lens, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards


en: avalanche-activity avalanche snow-profile soil-moisture glide-snow-avalanche

en: This file contains all soil measruements (liquid water content and matric potential) recorded at the reference location.

en: This file contains all sensor recordings in the Seewer Berg slope (time interval: 15 minutes).

en: This file provides an overview of the dataset, the detailed measurement setup, and points out limitations.

en: This file contains the aggregated data from manually recorded snow profiles in the reference location, 100m northwest of the Seewer Berg slope and in release areas of glide-snow avalanches in the Seewer Berg slope.

en: This file contains the coordinates of the reference location and the sensors in the Seewer Berg slope as a shapefile.

en: This file contains SNOWPACK simulations (snow height, air temperature, rain) at a virtual station in the reference location. The virtual station was interpolated between the Weissfluhjoch (WFJ2) and Klosters Madrisa (KLO2) IMIS Stations.

en: This file contains the daily avalanche activity on Dorfberg as extracted from time-lapse photographs.

en: This file contains the release areas (polygon) of glide-snow avalanches as a shapefile.

en: This file explains all provided datasets, variables and their units.

en:
This dataset consists of spatio-temporal soil measurements and local snow measurements which were recorded to investigate glide-snow avalanche release in the Seewer Berg slope (Davos, Switzerland) from Nov 2021 to June 2024. Investigations based on this dataset were published in the research article:
Fees A., Lombardo M., van Herwijnen A., Lehmann P., Schweizer J. (2025). The source, quantity, and spatial distribution of interfacial water during glide-snow avalanche release: experimental evidence from field monitoring. The Cryosphere
The dataset consists of:
- Soil measurements (temperature and liquid water content) at soil depths of -5cm, -10cm, and -20cm
- Matric potential at depths of -5cm, -10cm, and -20cm
- Manual snow profiles (Snow height, temperature, liquid water content, bulk density)
- Metadata and manual snow profiles on glide-snow avalanches that released in the Seewer Berg slope
- Snow height, air temperature and rain simulated with SNOWPACK.

avalanche

display_name: avalanche

avalanche-activity

display_name: avalanche-activity

glide-snow-avalanche

display_name: glide-snow-avalanche

snow-profile

display_name: snow-profile

soil-moisture

display_name: soil-moisture

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: Spatio-temporal soil and local snow monitoring in a glide-snow avalanche prone slope above Davos Switzerland


en: hydrologic-and-hydrodynamic-modelling climate-change switzerland perialpine-lakes lake-level-regulation

en: Lake characteristics

en: Lake characteristics: stage-area relation for Lake Walen, Lake Zurich, Lake Brienz and Lake Thun, stage-discharge relations for the unregulated Lake Walen and semi-regulated Lake Brienz and, lake level management rules for the two regulated lakes Zurich and Thun.

en: Lake outflow scenarios CH2018

en: Lake outflow scenarios for Lake Walen, Lake Zurich, Lake Brienz and Lake Thun.

en: Lake water level scenarios CH2018

en: Lake water level scenarios for Lake Walen, Lake Zurich, Lake Brienz and Lake Thun.

en:
The dataset "Lake_climate_change_scenarios_CH2018" provides simulation-based climate change impact scenarios for perialpine lakes in Switzerland. These transient future scenarios were produced by combining the hydrologic model PREVAH with the hydrodynamic model MIKE11 to simulate daily lake water level (Lake_water_level_scenarios_CH2018.xls) and outflow scenarios (Lake_outflow_scenarios_CH2018.xls) from 1981 to 2099, using the Swiss Climate Change Scenarios CH2018. The future scenarios contain a total of 39 model members for three Representative Concentration Pathways, RCP2.6 (concerted mitigation efforts), RCP4.5 (limited climate mitigation) and RCP8.5 (no climate mitigation measures). These scenarios result from the study titled "Lower summer lake levels in regulated perialpine lakes, caused by climate change," authored by Wechsler et al. in 2023.

The dataset emphasizes four specific Swiss lakes, each subject to different degrees of lake level management: an unregulated lake (Lake Walen), a semi-regulated lake (Lake Brienz), and two regulated lakes (Lake Zurich and Lake Thun).

In addition, the file (Lake_characteristics.xlsx) includes data used in the modeling process, encompassing the stage-area relation for the four lakes, stage-discharge relations for the unregulated and semi-regulated lakes, and lake level management rules for the two regulated lakes.

hydrologic-and-hydrodynamic-modelling

display_name: hydrologic-and-hydrodynamic-modelling

lake-level-regulation

display_name: lake-level-regulation

perialpine-lakes

display_name: perialpine-lakes

switzerland

display_name: switzerland

en: Lake climate change scenarios CH2018


en: discharge hydrology climate-change-impact water-temperature model

en: Data access

en:
This work presents the first extensive study of climate change impacts on rivers temperature in Switzerland. Results show that even for low emissions scenarios, water temperature increase will lead to adverse effect for both ecosystems and socioeconomic sectors (such as nuclear plant cooling) throughout the 21st century. For high emissions scenarios, the effect will be worsen. This study also shows that water warming in summer will be more important in Alpine regions than in lowlands.
This material is distributed under CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/legalcode).

climate-change-impact

display_name: climate-change-impact

discharge

display_name: discharge

hydrology

display_name: hydrology

model

display_name: model

water-temperature

display_name: water-temperature

en: Dataset for: Future water temperature of rivers in Switzerland under climate change investigated with physics-based models


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

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

educ

en: Education, culture and sport

en: Nuclear microsatellite genotypes of Pinus cembra


en: water-supply reservoirs water-scarcity water-demand climate-change

en: Water demand version 2 (corrected version)

mimetype: application/zip

en: Water supply

en: Simulated discharge series for a reference period 1981-2010 and till the end of the century. Data are provided for 39 climate model chains and a control run based on the observed meteorology. Data are provided at daily resolution (mm/d).

en: Reservoir volumes

en: Reservoir volume for the 307 medium-sized catchments for different storage categories: natural lakes, artificial reservoirs, reservoirs for artificial snow production, drinking water reservoirs, and usable volumes of natural lakes. Unit: Mio m³. One column per catchment (see shapefile).

en: Water demand (deprecated)

en:
Warning: please use the corrected version 2, since this file contains errors for certain categories such as Livestock, Industry or Houshold.

Water demand estimates for the 307 medium-sized catchments for different categories for current and future conditions. One text file is provided per demand category. One column corresponds to one catchment (IDs compare to shapefile), The data are provided at monthly resolution in mm/d and can be converted to volumes by using the catchment area. E.g.
Q[mm/d] * catchment area [m²] /1000 = Q[m³/d]
or to obtain discharge in m³/s
Q[mm/d] * catchment area [m²]/1000/24/3600 = Q [m³/s].
Future irrigation and hydropower water demand are provided per climate model chain.

en: Shapefile of 307 catchments in Switzerland

en: Shapefile of 307 catchments for which estimates of water supply, demand, and reservoir volumes are provided. The OBJECTID entry corresponds to the catchment ID which is used as a reference in the other datasets provided.

en:
The dataset Hydro-CH2018 reservoirs provides estimates of current and future water supply, water demand, and storage volumes for 307 medium-sized catchments in Switzerland.
Water supply for current (1981-2010) and future (2070-2099) climate conditions was simulated using the hydrological model PREVAH. For modeling current water supply, observed meteorological time series were used as input, while simulated meteorological time series derived from 39 model chains of the CH2018 initiative were used as an input for simulating future climate conditions.

Water demand was estimated for six categories:

- 1) Drinking water (households and tourism),
- 2) industry (second and third sector),
- 3) artificial snow production,
- 4) agriculture (irrigation and livestock feeding),
- 5) ecology (residual flows), and
- 6) hydropower.

Future estimates consider changes in demand related to population growth and changes in the hydrological conditions.
Storage volumes are provided for natural lakes (storage capacities and usable volumes), artificial reservoirs, reservoirs for artificial snow production, and drinking reservoirs.

A detailed description of the simulation and estimation procedures can be found in

*  Brunner, M.I., Björnsen Gurung, A., Zappa, M., Zekollari, H., Farinotti, D., Stähli, M., 2019. Present and future water scarcity in Switzerland: Potential for alleviation through reservoirs and lakes. Sci. Total Environ. 666, 1033–1047. https://doi.org/10.1016/j.scitotenv.2019.02.169.

This dataset contains the following information:

1.	Shapefile with the 307 medium-sized Swiss catchments.
2.   Textfiles with the water supply simulations for the control run and the 39 climate model chains (one file per chain) at daily resolution for the 307 catchments.
3.	Textfiles with the current and future demand estimates per category at monthly resolution for the 307 catchments.
4.	Textfiles with the storage volumes per category and catchment.

climate-change

display_name: climate-change

reservoirs

display_name: reservoirs

water-demand

display_name: water-demand

water-scarcity

display_name: water-scarcity

water-supply

display_name: water-supply

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: Hydro-CH2018 reservoirs


en: weisfluhjoch snow-density snow-water-equivalent snow-stratigraphy snow-ice-temperature

en: README

en: README file giving use metadata.

en: WFJ Snow Profiles 1999-2014

en:
Biweekly manual snow profiles from Weissfluhjoch, Davos, Switzerland.
Version: 2015-09-14T16:25

en: Dataset of manual bi-weekly snow profiles from Weissfluhjoch, Davos, Switzerland. Typical snow profile measurements and observations are included (temperature, density, grain size, grain type, hardness, wetness), following the guidelines of the The International Classification for Seasonal Snow on the Ground (ICSSG) [Fierz, C., Armstrong, R.L., Durand, Y., Etchevers, P., Greene, E., McClung, D.M., Nishimura, K., Satyawali, P.K. and Sokratov, S.A. 2009. The International Classification for Seasonal Snow on the Ground. IHP-VII Technical Documents in Hydrology N°83, IACS Contribution N°1, UNESCO-IHP, Paris].

snow-density

display_name: snow-density

snow-ice-temperature

display_name: snow-ice-temperature

snow-stratigraphy

display_name: snow-stratigraphy

snow-water-equivalent

display_name: snow-water-equivalent

weisfluhjoch

display_name: weisfluhjoch

en: Manual bi-weekly snow profiles from Weissfluhjoch, Davos, Switzerland


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

en: Elk and bison carcasses in Yellowstone, USA