maintainer: Sektion energieeffizienter Verkehr

title_for_slug: Kennzahlen alternativ angetriebene PW

de: alternative-antriebe elektromobilitat hybridfahrzeuge co2 ogd92-bfe personenwagen elektrische-fahrzeuge

fr: Chiffres clés des voitures de tourisme neuves à propulsion alternative

de: Kennzahlen alternativ angetriebene PW

en: Key figures for alternatively powered passenger cars

it: Indicatori relativi alle automobili a propulsione alternativa

de: Storymap

fr: Modèles de voitures de tourisme hybrides rechargeables les plus prisés

de: Beliebteste Plug-In PW-Modelle (PHEV)

en: Most popular plug-in models (cars)

it: Automobili plug-in più diffuse (modelli PHEV)

fr: Depuis 2015, l’Office fédéral de l’énergie recense chaque trimestre les modèles de voitures de tourisme hybrides rechargeables (PHEV) les plus prisés; depuis 2021, les données sont disponibles à un rythme mensuel. Tous les modèles hybrides rechargeables disponibles en Suisse sont pris en compte.

en: Since 2015, the Swiss Federal Office of Energy has reported the most popular plug-in models (PHEVs) among passenger cars on a quarterly basis; since 2021, the data has been available on a monthly basis. All plug-in models from Switzerland are counted together.

de: Ab 2015 weist das Bundesamt für Energie quartalsweise die beliebtesten Plug-In Modelle (PHEV) bei den Personenwagen aus, ab 2021 sind die Daten in monatlicher Auflösung verfügbar. Dabei werden alle Plug-In Modelle aus der Schweiz zusammengezählt.

it: Dal 2015 l'Ufficio federale dell'energia indica su base trimestrale i modelli più diffusi di automobili plug-in (PHEV); dal 2021 i dati sono disponibili con risoluzione mensile. Vengono conteggiati tutti i modelli plug-in presenti in Svizzera.

fr: Statistiques des motorisations alternatives des voitures de tourisme

fr:
Sur la base des données anonymisées des nouvelles immatriculations publiées par l’OFROU, l’Office fédéral de l’énergie évalue depuis 2015 les statistiques des motorisations alternatives des voitures de tourisme.
Sont réputés véhicules à propulsion alternative les véhicules dont la propulsion ne se fait pas exclusivement avec un moteur à combustion à essence ou à diesel.
Ce jeu de données montre le nombre de voitures de tourisme à motorisation alternative par type de motorisation et par canton. Les données sont présentées sous forme de totaux annuels (2015-actuel), trimestriels (2015-2020) ou mensuels (2021-actuel).
Légende: t1 = émissions moyennes de CO2, t2 = nombre de BEV, t3 = nombre de PHEV essence, t4 = nombre de PHEV diesel, t5 = nombre de hybride essence, t6 = nombre de hybride diesel, t7 = nombre de gaz naturel, t8 = nombre de nouvelles immatriculations, t10 = nombre de hydrogène

[La numérotation des cantons est celle de l’OFS](https://www.bfs.admin.ch/asset/fr/453856)

Trimestre 1-4 = Q1-Q4, Trimestre 5 = Total annuel
Mois 1-12 = Janvier-Février, Mois 13 = Total annuel

Le total annuel peut être un peu différent des totaux mensuels ou trimestriels cumulés en raison des ajustements rétroactifs dans la source de données utilisée.

en:
On the basis of the anonymised data for new registrations published by FEDRO, the Swiss Federal Office of Energy has evaluated key figures on alternatively powered passenger cars since 2015.
Alternatively powered vehicles are vehicles that are not solely powered by a petrol or diesel internal combustion engine.
This data set shows the number of alternatively powered passenger cars by type of drive per canton. The data is reported as annual totals (2015-current) as well as quarterly totals (2015-2020) and monthly totals (2021-current).

Key: t1 = Average CO2 emissions, t2 = Number of BEVs, t3 = Number of petrol PHEVs, t4 = Number of diesel PHEVs, t5 = Number of hybrid petrol, t6 = Number of hybrid diesel, t7 = Number of natural gas, t8 = Number of new registrations, t10 = Number of hydrogen

[Numbering of cantons according to FSO](https://www.bfs.admin.ch/bfs/en.assetdetail.453856.html)

Quarter 1-4 = Q1-Q4, Quarter 5 = Annual total
Month 1-12 = January-February, Month 13 = Annual total

The annual total may differ slightly from the cumulative monthly or quarterly totals due to retroactive adjustments in the data source used.

de:
Auf Basis der vom ASTRA publizierten, anonymisierten Neuzulassungsdaten wertet das Bundesamt für Energie seit 2015 wichtige Kennzahlen zu den alternativ angetriebenen Personenwagen aus. Als alternativ angetriebenen Fahrzeuge gelten Fahrzeuge, die nicht ausschliesslich über einen Benzin- oder Diesel-Verbrennungsmotor angetrieben werden.
Dieser Datensatz weist die Anzahl alternativ angetriebener Personenwagen nach Antriebsart pro Kanton aus. Die Daten werden als Jahressummen (2015-aktuell) sowie Quartalssummen (2015-2020) bzw. Monatssummen (2021-aktuell) ausgewiesen.

Legende: t1 = Durchschnittliche CO2-Emissionen, t2 = Anzahl BEV, t3 = Anzahl Benzin PHEV, t4 = Anzahl Diesel PHEV, t5 = Anzahl Hybrid Benzin, t6 = Anzahl  Hybrid Diesel, t7 = Anzahl Erdgas, t8 = Anzahl Neuzulassungen, t10 = Anzahl Wasserstoff

[Nummerierung der Kantone gemäss BFS](https://www.bfs.admin.ch/asset/de/453856)

Quartale 1-4 = Q1-Q4, Quartal 5 = Jahressumme
Monate 1-12 = Januar-Dezember, Monat 13 = Jahressumme

Die Jahressumme kann minimal von den kumulierten Monats- bzw. Quartalssummen abweichen. Dies ist auf laufende rückwirkende Anpassungen in der zugrundeliegenden Datenquelle zurückzuführen.

it:
Sulla base dei dati anonimizzati sulle nuove immatricolazioni pubblicati dall'USTRA, dal 2015 l'Ufficio federale dell'energia analizza importanti indicatori relativi alle automobili a propulsione alternativa.
Per veicoli a propulsione alternativa si intendono veicoli che non sono mossi esclusivamente da un motore a combustione a benzina o diesel.
Questo set di dati mostra il numero di auto turistiche con motore alternativo per tipo di motore e per cantone. I dati sono presentati come totali annuali (dal 2015 ad oggi), trimestrali (dal 2015 al 2020) o mensili (dal 2021 ad oggi).

Legenda: t1 = Emissioni medie di CO2, t2 = Numero di BEV, t3 = Numero di PHEV benzina, t4 = Numero di PHEV diesel, t5 = Numero di ibride benzina, t6 = Numero di ibride diesel, t7 = Numero di gas naturale, t8 = Numero nuove immatricolazioni, t10 = Numero di idrogeno

[Numerazione dei Cantoni secondo UST](https://www.bfs.admin.ch/bfs/it.assetdetail.453856.html)

Trimestre 1-4 = Q1-Q4, Trimestre 5 = Totale annuale
Mese 1-12 = Gennaio-Febbraio, Mese 13 = Totale annuale

Il totale annuale può essere leggermente diverso dai totali mensili o trimestrali cumulati a causa di rettifiche retroattive nella fonte dei dati usata.

fr: Modèles de voitures de tourisme entièrement électriques les plus prisés

de: Beliebteste vollelektrische PW-Modelle (BEV)

en: Most popular all-electric models (cars)

it: Modelli più diffusi di automobili completamente elettriche

fr: Depuis 2015, l’Office fédéral de l’énergie recense chaque trimestre les modèles de voitures de tourisme entièrement électriques (BEV) les plus prisés; depuis 2021, les données sont disponibles à un rythme mensuel. Tous les modèles entièrement électriques disponibles en Suisse sont pris en compte.

en: Since 2015, the Swiss Federal Office of Energy has reported the most popular all-electric models (BEVs) among passenger cars on a quarterly basis; since 2021, the data has been available on a monthly basis. All fully electric models from Switzerland are counted together.

de: Ab 2015 weist das Bundesamt für Energie quartalsweise die beliebtesten vollelektrische Modelle (BEV) bei den Personenwagen aus, ab 2021 sind die Daten in monatlicher Auflösung verfügbar. Dabei werden alle vollelektrischen Modelle aus der Schweiz zusammengezählt.

it: Dal 2015 l'Ufficio federale dell'energia indica su base trimestrale i modelli più diffusi di automobili completamente elettriche (BEV); dal 2021 i dati sono disponibili con risoluzione mensile. Vengono conteggiati tutti i modelli completamente elettrici presenti in Svizzera.

fr:
Sur la base des données anonymisées des nouvelles immatriculations publiées par l’OFROU, l’Office fédéral de l’énergie évalue depuis 2015 des chiffres clés importants concernant les voitures de tourisme à propulsion alternative. Sont réputés véhicules à propulsion alternative les véhicules dont la propulsion ne se fait pas exclusivement avec un moteur à combustion à essence ou à diesel. Ce jeu de données comprend, outre les statistiques annuelles agrégées, les chiffres trimestriels et mensuels des parts de marché ainsi que des indications sur les différents cantons. L’Office fédéral de l’énergie évalue par ailleurs les modèles entièrement électriques et hybrides rechargeables les plus prisés.

Base légale: art. 11 de l’ordonnance sur les exigences relatives à l’efficacité énergétique (OEEE)

en:
On the basis of the anonymised data for new registrations published by FEDRO, the Swiss Federal Office of Energy has evaluated key figures for alternatively powered passenger cars since 2015. Alternatively powered vehicles are vehicles that are not solely powered by a petrol or diesel internal combustion engine. In addition to aggregated annual key figures, this data set also contains quarterly and monthly market share figures as well as information regarding the individual cantons. Also, the Swiss Federal Office of Energy ranks the most popular all-electric and plug-in car models.

Legal basis: Energy Efficiency Ordinance (EnEO), Art. 11

de:
Auf Basis der vom ASTRA publizierten, anonymisierten Neuzulassungsdaten wertet das Bundesamt für Energie seit 2015 wichtige Kennzahlen zu den alternativ angetriebenen Personenwagen (PW) aus. Als alternativ angetriebenen Fahrzeuge gelten Fahrzeuge, die nicht ausschliesslich über einen Benzin- oder Diesel-Verbrennungsmotor angetrieben werden. Dieser Datensatz enthält neben aggregierten jährlichen Kennzahlen auch Quartals- und Monatszahlen der Marktanteile sowie Angaben zu den einzelnen Kantonen. Zudem wertet das Bundesamt für Energie bei den PW die beliebtesten vollelektrischen und Plug-In Modellen aus.

Rechtsgrundlage: Energieeffizienzverordnung (EnEV) Art. 11

it:
Sulla base dei dati anonimizzati sulle nuove immatricolazioni pubblicati dall'Ufficio federale delle strade (USTRA), dal 2015 l'Ufficio federale dell'energia analizza importanti indicatori relativi alle automobili a propulsione alternativa. Per veicoli a propulsione alternativa si intendono veicoli che non sono mossi esclusivamente da un motore a combustione a benzina o diesel. Oltre a indicatori annuali aggregati, questo set di dati contiene anche dati trimestrali e mensili relativi alle quote di mercato e indicazioni sui singoli Cantoni. Inoltre l'Ufficio federale dell'energia analizza i modelli di automobili completamente elettriche e plug-in più diffusi.

Base legale: ordinanza sull’efficienza energetica (OEEne), nello specifico l'articolo 11

alternative-antriebe

display_name: alternative-antriebe

elektrische-fahrzeuge

display_name: elektrische-fahrzeuge

elektromobilitat

display_name: elektromobilitat

hybridfahrzeuge

display_name: hybridfahrzeuge

personenwagen

display_name: personenwagen

ener

fr: Énergie

de: Energie

en: Energy

it: Energia

tran

fr: Transports

de: Verkehr

en: Transport

it: Trasporti

de: Bundesamt für Energie BFE

bundesamt-fur-energie-bfe

fr: Office fédéral de l'énergie OFEN

en: Swiss Federal Office of Energy SFOE

it: Ufficio federale dell'energia UFE

fr: L'Office fédéral de l'énergie (OFEN) est le centre de compétences du Département fédéral de l'environnement, des transports, de l'énergie et de la communication (DETEC) pour les questions liées à l'approvisionnement en énergie et à son utilisation.

de: Das Bundesamt für Energie (BFE) ist das Kompetenzzentrum für Fragen der Energieversorgung und der Energienutzung im Eidgenössischen Departement für Umwelt, Verkehr, Energie und Kommunikation (UVEK).

en: The Swiss Federal Office of Energy (SFOE) is the country's competence centre for issues relating to energy supply and energy use at the Federal Department of the Environment, Transport, Energy and Communications (DETEC).

it: L'Ufficio federale dell'energia (UFE) è il centro di competenza in materia di approvvigionamento e di impiego dell'energia in seno al Dipartimento federale dell'ambiente, dei trasporti, dell'energia e delle comunicazioni (DATEC).


de: regierungsratswahlen nationalratswahlen ersatzwahl vorname wahlresultate geschlecht standeratswahlen jahrgang wahlarchiv name landratswahlen wahlergebnisse

state: active

type: dataset

de: turtle

media_type: text/turtle

de: Eidgenössische und kantonale Wahlen (seit 2003): Kandidierendenresultate nach Gemeinde (turtle)

format: RDF Turtle

de: Eidgenössische und kantonale Wahlen (seit 2003): Kandidierendenresultate nach Gemeinde (n3)

de: json

media_type: application/json

de: Eidgenössische und kantonale Wahlen (seit 2003): Kandidierendenresultate nach Gemeinde (json)

format: JSON

de: jsonld

de: Eidgenössische und kantonale Wahlen (seit 2003): Kandidierendenresultate nach Gemeinde (jsonld)

format: JSON-LD

de: xls

de: Eidgenössische und kantonale Wahlen (seit 2003): Kandidierendenresultate nach Gemeinde (xls)

format: XLS

de: jsonl

de: Eidgenössische und kantonale Wahlen (seit 2003): Kandidierendenresultate nach Gemeinde (jsonl)

de: csv

media_type: text/csv

de: Eidgenössische und kantonale Wahlen (seit 2003): Kandidierendenresultate nach Gemeinde (csv)

format: CSV

de: rdfxml

media_type: application/rdf+xml

de: Eidgenössische und kantonale Wahlen (seit 2003): Kandidierendenresultate nach Gemeinde (rdfxml)

format: RDF XML

de: parquet

de: Eidgenössische und kantonale Wahlen (seit 2003): Kandidierendenresultate nach Gemeinde (parquet)

format: PARQUET

de: Kandidierendenresultate auf Ebene GemeindeZu weiteren Kandidierendenresultaten[Kandidierendenresultate auf Ebene Kanton (Nationalratswahlen) bzw. Wahlkreis (Landratswahlen)](https://data.bl.ch/explore/dataset/12770)[Herkunft der Kandidierendenstimmen auf Ebene Gemeinde](https://data.bl.ch/explore/dataset/12790)

ersatzwahl

display_name: ersatzwahl

geschlecht

display_name: geschlecht

jahrgang

display_name: jahrgang

landratswahlen

display_name: landratswahlen

name

display_name: name

nationalratswahlen

display_name: nationalratswahlen

regierungsratswahlen

display_name: regierungsratswahlen

standeratswahlen

display_name: standeratswahlen

vorname

display_name: vorname

wahlarchiv

display_name: wahlarchiv

wahlergebnisse

display_name: wahlergebnisse

wahlresultate

display_name: wahlresultate

gove

fr: Gouvernement et secteur public

de: Regierung und öffentlicher Sektor

en: Government and public sector

it: Governo e settore pubblico

de: kanton-basel-landschaft

kanton-basel-landschaft

fr: Canton de Bâle-Campagne

de: Kanton Basel-Landschaft

en: Canton of Basel-Country

it: Cantone di Basilea-Campagna

en:
The Canton of Basel-Country publishes open datasets of cantonal offices and agencies.
Further information at https://ogd.bl.ch/

de:
Der Kanton Basel-Landschaft veröffentlicht nach und nach Open Government Datensätze von kantonalen Ämtern und Dienststellen.
Weitere Informationen unter https://ogd.bl.ch/

political_level: canton

type: organization

approval_status: approved

key: guid

key: uri

value: BL - DCAT


de: wahlergebnisse landratswahlen wahlarchiv jahrgang wahlkreis standeratswahlen name geschlecht wahlresultate vorname ersatzwahl nationalratswahlen regierungsratswahlen

de: Eidgenössische und kantonale Wahlen (seit 2003): Kandidierendenresultate (jsonl)

de: json

de: Eidgenössische und kantonale Wahlen (seit 2003): Kandidierendenresultate (json)

de: Eidgenössische und kantonale Wahlen (seit 2003): Kandidierendenresultate (csv)

de: Eidgenössische und kantonale Wahlen (seit 2003): Kandidierendenresultate (parquet)

de: Eidgenössische und kantonale Wahlen (seit 2003): Kandidierendenresultate (xls)

de: jsonld

de: Eidgenössische und kantonale Wahlen (seit 2003): Kandidierendenresultate (jsonld)

de: turtle

de: Eidgenössische und kantonale Wahlen (seit 2003): Kandidierendenresultate (turtle)

de: Eidgenössische und kantonale Wahlen (seit 2003): Kandidierendenresultate (rdfxml)

de: Eidgenössische und kantonale Wahlen (seit 2003): Kandidierendenresultate (n3)

de: Kandidierendenresultate auf Ebene Kanton oder WahlkreisZu weiteren Kandidierendenresultaten[Kandidierendenresultate auf Ebene Gemeinde](https://data.bl.ch/explore/dataset/12780)[Herkunft der Kandidierendenstimmen auf Ebene Gemeinde](https://data.bl.ch/explore/dataset/12790)

wahlkreis

display_name: wahlkreis


name: Fachstelle Register - Kantonales Gebäude- und Wohnungsregister (kGWR)

de: forstwirtschaft industrie gesundheit privater-bau bildung registerdaten versorgung kommunikation wohnen kultur baubewilligung hochbau verkehr dienstleistungen entsorgung bauprojekt bauwerkstyp freizeit forschung gewerbe offentlicher-bau infrastruktur baugesuch landwirtschaft

de: rdfxml

de: Baubewilligungen nach Typ der Bauwerke und Quartal (seit 2005) (rdfxml)

de: csv

de: Baubewilligungen nach Typ der Bauwerke und Quartal (seit 2005) (csv)

de: Baubewilligungen nach Typ der Bauwerke und Quartal (seit 2005) (n3)

de: Baubewilligungen nach Typ der Bauwerke und Quartal (seit 2005) (turtle)

de: jsonl

de: Baubewilligungen nach Typ der Bauwerke und Quartal (seit 2005) (jsonl)

de: parquet

de: Baubewilligungen nach Typ der Bauwerke und Quartal (seit 2005) (parquet)

de: Baubewilligungen nach Typ der Bauwerke und Quartal (seit 2005) (jsonld)

de: xls

de: Baubewilligungen nach Typ der Bauwerke und Quartal (seit 2005) (xls)

de: Baubewilligungen nach Typ der Bauwerke und Quartal (seit 2005) (json)

de: Dieser Datensatz basiert auf dem kantonalen Gebäude- und Wohnungsregister (kGWR) und gibt den jeweiligen Informationsstand zum Zeitpunkt der Auswertung wieder. Es ist möglich, dass ein eingereichtes Bauprojekt nicht bewilligt oder ein bereits bewilligtes Bauprojekt anschliessend nicht realisiert wird.

baubewilligung

display_name: baubewilligung

baugesuch

display_name: baugesuch

bauprojekt

display_name: bauprojekt

bauwerkstyp

display_name: bauwerkstyp

bildung

display_name: bildung

dienstleistungen

display_name: dienstleistungen

entsorgung

display_name: entsorgung

forschung

display_name: forschung

forstwirtschaft

display_name: forstwirtschaft

freizeit

display_name: freizeit

gesundheit

display_name: gesundheit

gewerbe

display_name: gewerbe

hochbau

display_name: hochbau

industrie

display_name: industrie

infrastruktur

display_name: infrastruktur

kommunikation

display_name: kommunikation

kultur

display_name: kultur

landwirtschaft

display_name: landwirtschaft

offentlicher-bau

display_name: offentlicher-bau

privater-bau

display_name: privater-bau

registerdaten

display_name: registerdaten

verkehr

display_name: verkehr

versorgung

display_name: versorgung

wohnen

display_name: wohnen

soci

fr: Population et société

de: Bevölkerung und Gesellschaft

en: Population and society

it: Popolazione e società


maintainer: Basel-Landschaft

fr: election regional-district

de: wahl regional-district

it: elezione regionale-district

fr: Descripziun dal format

de: Descripziun dal format

en: Descripziun dal format

it: Descripziun dal format

fr: Description du format

de: Description du format

en: Description du format

it: Description du format

fr: Descrizione del formato

de: Descrizione del formato

en: Descrizione del formato

it: Descrizione del formato

fr: Formatbeschreibung

de: Formatbeschreibung

en: Formatbeschreibung

it: Formatbeschreibung

mimetype: application/json

identifier: json

identifier: parties-json

mimetype: text/csv

identifier: csv

identifier: parties-csv

rm: Resultats finals da l'elecziun regiunala "Landratswahlen 2019 Wahlkreis Oberwil", 31 da mars 2019, Kanton Basel-Landschaft, resultats da tut las candidatas e da tut ils candidats tenor vischnancas.

fr: Résultats finaux de l'élection régionale "Landratswahlen 2019 Wahlkreis Oberwil", 31 mars 2019, Kanton Basel-Landschaft, répartis par municipalités et par candidats.

de: Schlussresultate der regionalen Wahl "Landratswahlen 2019 Wahlkreis Oberwil", 31. März 2019, Kanton Basel-Landschaft, aufgeschlüsselt nach Kandidaten und Gemeinden.

it: Risultati finali della elezione regionale "Landratswahlen 2019 Wahlkreis Oberwil", 31 marzo 2019, Kanton Basel-Landschaft, suddivisi per municipalit e candidati.

election

display_name: election

elecziun

display_name: elecziun

elezione

display_name: elezione

plaun-regiunal-district

display_name: plaun-regiunal-district

regional-district

display_name: regional-district

regionale-district

display_name: regionale-district

wahl

display_name: wahl

gove

de: Regierung und öffentlicher Sektor

kanton-basel-landschaft

de: Kanton Basel-Landschaft

de:
Der Kanton Basel-Landschaft veröffentlicht nach und nach Open Government Datensätze von kantonalen Ämtern und Dienststellen.
Weitere Informationen unter https://ogd.bl.ch/

value: BL - Abstimmungen

rm: Landratswahlen 2019 Wahlkreis Oberwil

fr: Landratswahlen 2019 Wahlkreis Oberwil

de: Landratswahlen 2019 Wahlkreis Oberwil

it: Landratswahlen 2019 Wahlkreis Oberwil


bike-parking

maintainer: Open data platform mobility Switzerland

title_for_slug: bike-parking

fr: lmeta donnees bicyclette voyage-intermodal voyage-multimodal bicycliste proprietaire parking

de: parkplatz daten fahrradfahrer multi-modales-reisen inter-modales-reisen embag fahrrad proprietaer

en: data proprietary multi-modal-journey parking-space inter-modal-journey cyclist bicycle

it: dati bicicletta proprietario parcheggio multi-modal-transport ciclista inter-modal-transport lmeca

author: SBB via ROKAS

fr: Cookbook (Documentation)

de: Cookbook (Documentation)

en: Cookbook (Documentation)

it: Cookbook (Documentation)

fr: Le jeu de données contient environ 1200 parkings à vélos (possibilités de stationnement des vélos / places de stationnement). Les données ne concernent que les lieux disponibles pour les CFF.

de: Der Datensatz enthält rund 1200 Fahrrad-Parkings (Velo-Abstellmöglichkeiten / Abstellplätze). Die Daten beziehen sich nur auf die der SBB verfügbaren Orte.

en: The data set contains around 1200 bicycle parking spaces (bicycle parking facilities / parking spaces). The data refers only to the locations available to SBB.

it: Il set di dati contiene circa 1200 parcheggi per biciclette (strutture di parcheggio per biciclette / parcheggi). I dati si riferiscono solo alle sedi disponibili per le FFS.

bicicletta

display_name: bicicletta

bicycle

display_name: bicycle

bicyclette

display_name: bicyclette

bicycliste

display_name: bicycliste

ciclista

display_name: ciclista

cyclist

display_name: cyclist

data

display_name: data

daten

display_name: daten

dati

display_name: dati

donnees

display_name: donnees

embag

display_name: embag

fahrrad

display_name: fahrrad

fahrradfahrer

display_name: fahrradfahrer

inter-modal-journey

display_name: inter-modal-journey

inter-modal-transport

display_name: inter-modal-transport

inter-modales-reisen

display_name: inter-modales-reisen

lmeca

display_name: lmeca

lmeta

display_name: lmeta

multi-modal-journey

display_name: multi-modal-journey

multi-modal-transport

display_name: multi-modal-transport

multi-modales-reisen

display_name: multi-modales-reisen

parcheggio

display_name: parcheggio

parking

display_name: parking

parking-space

display_name: parking-space

parkplatz

display_name: parkplatz

proprietaer

display_name: proprietaer

proprietaire

display_name: proprietaire

proprietario

display_name: proprietario

proprietary

display_name: proprietary

voyage-intermodal

display_name: voyage-intermodal

voyage-multimodal

display_name: voyage-multimodal

spatial: Schweiz

tran

de: Verkehr

fr: Chemins de fer fédéraux suisses (CFF)

de: Schweizerische Bundesbahnen (SBB)

en: Swiss Federal Railways (SBB)

it: Ferrovie Federali Svizzere (FFS)

oevch

fr: Secrétariat des tâches du système d’information à la clientèle (CFF SA)

de: Geschäftsstelle Systemaufgaben Kundeninformation (SBB AG)

en: Business office of Customer Information System Tasks (SBB CFF FFS)

it: Servizio commerciale Attività di sistema Informazione alla clientela (FFS SA)

fr: Le Secrétariat des tâches du système d’information à la clientèle (SKI) publie pour le compte de l’OFT les données de l’ensemble des transports publics de Suisse.

en: Working on behalf of the Swiss Federal Office of Transport (FOT), the Customer Information System Tasks (SKI) business office publishes data on all public transport in Switzerland

de: Die Geschäftsstelle Systemaufgaben Kundeninformation (SKI) veröffentlicht im Auftrag des BAV die Daten des gesamten öffentlichen Verkehrs der Schweiz

it: Il servizio commerciale Attività di sistema Informazione alla clientela (SKI) pubblica, per conto dell’UFT, i dati di tutto il trasporto pubblico in Svizzera.

political_level: other

schweizerische-bundesbahnen-sbb

capacity: public

value: Opentransportdata - DCAT Harvester

fr: Parkings à bicyclette

de: Velo Abstellmöglichkeiten

en: Bike Parkings

it: Parcheggio per biciclette


maintainer: Fachstelle atlas

fr: en-route-sans-barrieres lhand proprietaire relation donnees sans-prestation-de-transport

de: ohne-befoerderungsleistung behig barrierefreies-reisen daten proprietaer verbindung

en: accessible-travel without-transport-service data proprietary relation prm

it: ldis collegamento dati in-viaggio-senza-barriere senza-servizio-di-transporto proprietario

author: atlas

fr: Cookbook

de: Cookbook

en: Cookbook

it: Cookbook

media_type: application/zip

format: ZIP

fr: Cet ensemble de données contient des informations sur l'accessibilité du trajet entre les points de référence et les différents objets (par ex. guichet, toilettes).

en: This data set contains information on the accessibility of the path between the reference points and the various objects (e.g. contact point, toilet).

de: Dieser Datensatz enthält Informationen zur Barrierefreiheit des Weges zwischen den Referenzpunkten und den verschiedenen Objekten (z.B. Schalter, Toilette).

it: Questo set di dati contiene informazioni sull'accessibilità del percorso tra i punti di riferimento e i vari oggetti (ad es. contatore, toilette).

accessible-travel

display_name: accessible-travel

barrierefreies-reisen

display_name: barrierefreies-reisen

behig

display_name: behig

collegamento

display_name: collegamento

data

display_name: data

daten

display_name: daten

dati

display_name: dati

donnees

display_name: donnees

en-route-sans-barrieres

display_name: en-route-sans-barrieres

in-viaggio-senza-barriere

display_name: in-viaggio-senza-barriere

ldis

display_name: ldis

lhand

display_name: lhand

ohne-befoerderungsleistung

display_name: ohne-befoerderungsleistung

prm

display_name: prm

relation

display_name: relation

sans-prestation-de-transport

display_name: sans-prestation-de-transport

senza-servizio-di-transporto

display_name: senza-servizio-di-transporto

verbindung

display_name: verbindung

without-transport-service

display_name: without-transport-service

tran

fr: Transports

en: Transport

it: Trasporti

fr: atlas (plateforme SKI Business)

de: atlas (SKI Business Plattform)

en: atlas (SKI Business Platform)

it: atlas (piattaforma commerciale SKI)

fr: Accessibilité - Relations (toutes les versions)

de: Barrierefreiheit - Verbindungen (alle Versionen)

en: Accessibility - Relations (all versions)

it: Accessibilità - Collegamenti (tutte le versioni)


bachapp-extras

maintainer: Fachstelle für OGD Basel-Stadt

title_for_slug: bachapp-extras

de: wc rhein

de: csv

de: BachApp: Extras (csv)

de: BachApp: Extras (n3)

de: jsonld

de: BachApp: Extras (jsonld)

mimetype: application/ld+json

de: rdfxml

de: BachApp: Extras (rdfxml)

mimetype: application/rdf+xml

de: json

de: BachApp: Extras (json)

de: parquet

de: BachApp: Extras (parquet)

de: turtle

de: BachApp: Extras (turtle)

mimetype: text/turtle

de: jsonl

de: BachApp: Extras (jsonl)

de: xls

de: BachApp: Extras (xls)

de: Dieser Datensatz enthält Informationen, welche noch in keinem anderen kantonalen System geführt werden, aber für die BachApp benötigt werden.

rhein

display_name: rhein

wc

display_name: wc

envi

fr: Environnement

de: Umwelt

en: Environment

it: Ambiente

de: Kantons- und Stadtentwicklung

kanton-basel-stadt

fr: Canton de Bâle-Ville

de: Kanton Basel-Stadt

en: Canton of Basel-City

it: Cantone di Basilea-Città

fr: Le canton de Bâle-Ville publie des ensembles de données ouvertes (Open Government Data) provenant des offices et services cantonaux. **Open Data Basel-Stadt** est responsable du soutien aux services administratifs qui publient des données.

en: The Canton of Basel-Stadt publishes open government datasets from cantonal offices and departments. **Open Data Basel-Stadt** is responsible for supporting the administrative units that publish data.

de: Der Kanton Basel-Stadt veröffentlicht Open Government-Datensätze von kantonalen Ämtern und Dienststellen. **Open Data Basel-Stadt** ist zuständig für die Unterstützung der datenpublizierenden Verwaltungsstellen.

it: Il Cantone di Basilea-Città pubblica set di dati aperti (Open Government Data) provenienti dagli uffici e dai servizi cantonali. **Open Data Basel-Stadt** è responsabile del supporto agli uffici amministrativi che pubblicano i dati.

value: BS Kanton Stadt - DCAT Harvester

de: BachApp: Extras


de: leine verbot leinenpflicht hund

author: Kantonales Veterinäramt

de: Hundesignalisation: Orte mit Leinenpflicht oder Hundeverbot (parquet)

de: Hundesignalisation: Orte mit Leinenpflicht oder Hundeverbot (xls)

de: gpx

de: Hundesignalisation: Orte mit Leinenpflicht oder Hundeverbot (gpx)

format: GPX

mimetype: application/gpx+xml

de: Hundesignalisation: Orte mit Leinenpflicht oder Hundeverbot (n3)

de: Hundesignalisation: Orte mit Leinenpflicht oder Hundeverbot (turtle)

de: kml

de: Hundesignalisation: Orte mit Leinenpflicht oder Hundeverbot (kml)

format: KML

de: rdfxml

de: Hundesignalisation: Orte mit Leinenpflicht oder Hundeverbot (rdfxml)

de: jsonld

de: Hundesignalisation: Orte mit Leinenpflicht oder Hundeverbot (jsonld)

de: csv

de: Hundesignalisation: Orte mit Leinenpflicht oder Hundeverbot (csv)

de: shp

de: Hundesignalisation: Orte mit Leinenpflicht oder Hundeverbot (shp)

mimetype: application/zip

de: Hundesignalisation: Orte mit Leinenpflicht oder Hundeverbot (json)

de: fgb

de: Hundesignalisation: Orte mit Leinenpflicht oder Hundeverbot (fgb)

format: OCTET

mimetype: application/octet-stream

de: geojson

de: Hundesignalisation: Orte mit Leinenpflicht oder Hundeverbot (geojson)

de: Hundesignalisation: Orte mit Leinenpflicht oder Hundeverbot (jsonl)

media_type: text/plain

de: Hundesignalisation: Orte mit Leinenpflicht oder Hundeverbot (ov2)

format: TXT

mimetype: text/plain

de: Der Plan zeigt Örtlichkeiten, an welchen eine Leinenpflicht oder ein Hundeverbot gilt.

hund

display_name: hund

leine

display_name: leine

leinenpflicht

display_name: leinenpflicht

verbot

display_name: verbot

gove

fr: Gouvernement et secteur public


de: konto finanzen bilanz investition gemeindefinanzen haushalt erfolgsrechnung gemeindehaushalt kontenrahmen gemeinde kennzahlen

de: Gemeinderechnungen 2020 (csv)

de: Gemeinderechnungen 2020 (rdfxml)

de: xls

de: Gemeinderechnungen 2020 (xls)

de: json

de: Gemeinderechnungen 2020 (json)

de: parquet

de: Gemeinderechnungen 2020 (parquet)

de: turtle

de: Gemeinderechnungen 2020 (turtle)

de: Gemeinderechnungen 2020 (jsonld)

de: Gemeinderechnungen 2020 (n3)

de: jsonl

de: Gemeinderechnungen 2020 (jsonl)

de: Der Datensatz enthält die Jahresberichte der politischen Gemeinden im Kanton Thurgau, bestehend aus den Kontensalden per 31.12. des entsprechenden Jahres für Erfolgsrechnung, Investitionsrechnung und Bilanz. Die Konten sind auf  4. Stelle verdichtet, die Funktionen auf 3. Stelle. Die Einteilung der Konten basiert auf dem Kontenplan, wie er in der Fachempfehlung 03 des Schweizerischen Rechnungslegungsgremiums für den öffentlichen Sektor (SRS-CSPCP) vorgesehen ist. Der Kontenplan umfasst eine vierstellige Struktur und wird regelmäßig (im Prinzip jährlich) aktualisiert. Die aktuellste Version kann hier eingesehen werden: [Harmonisierter Kontenplan | SRS-CSPCP](https://www.srs-cspcp.ch/de/harmonisierter-kontenplan-n17986).Datenquelle: Finanzverwaltung Kanton Thurgau

bilanz

display_name: bilanz

erfolgsrechnung

display_name: erfolgsrechnung

finanzen

display_name: finanzen

gemeinde

display_name: gemeinde

gemeindefinanzen

display_name: gemeindefinanzen

gemeindehaushalt

display_name: gemeindehaushalt

haushalt

display_name: haushalt

investition

display_name: investition

kennzahlen

display_name: kennzahlen

kontenrahmen

display_name: kontenrahmen

konto

display_name: konto

de: Regierung und öffentlicher Sektor

en: Government and public sector

it: Governo e settore pubblico

econ

fr: Économie et finances

de: Wirtschaft und Finanzen

en: Economy and finance

it: Economia e finanze

de: kanton-thurgau

kanton-thurgau

fr: Canton de Thurgovie

de: Kanton Thurgau

en: Canton of Thurgau

it: Cantone di Turgovia

de: Der Kanton Thurgau veröffentlicht hier schrittweise OGD-Datensätze der kantonalen Verwaltung.


nutzungsplanung-planungszonen

regi

fr: Régions et villes

de: Regionen und Städte

en: Regions and cities

it: Regioni e città

envi

de: Umwelt

title_for_slug: nutzungsplanung-planungszonen

de: bewilligungen eigentumsbeschrankungoreb nutzungsplanung raumplanung zonenplan

author: Kanton Luzern, Abteilung Geoinformation

fr: geocat.ch permalien

de: geocat.ch Permalink

en: geocat.ch permalink

it: geocat.ch link permanente

protocol: https

de: Datenshop

de: Produktansicht Geodatenshop

format: SERVICE

de: Mit Planungszonen wird sichergestellt, dass in den betroffenen Gebieten nur Vorhaben zulässig sind, die der geplanten künftigen Nutzung nicht widersprechen.

spatial: Kanton Luzern

de: Kanton Luzern, Abteilung Geoinformation

fr: Géoinformation du canton de Lucerne

de: Geoinformation Kanton Luzern

en: Geoinformation Canton of Lucerne

it: Geoinformazione del Cantone di Lucerna

fr: La géoinformation du canton de Lucerne est responsable de la collecte, du traitement et de la gestion des données à référence spatiale. Elle dirige, coordonne et surveille les travaux de la géoinformation et de la mensuration officielle.

en: Geoinformation of the Canton of Lucerne is responsible for the collection, processing and management of spatial data. It manages, coordinates and monitors the work of geoinformation and of the cadastral surveying.

de: Die Geoinformation des Kantons Luzern ist für die Erhebung, Verarbeitung und Verwaltung von raumbezogenen Daten zuständig. Sie leitet, koordiniert und überwacht die Arbeiten der Geoinformation und der amtlichen Vermessung.

it: La Geoinformation del Cantone di Lucerna è responsabile della raccolta, dell'elaborazione e della gestione dei dati territoriali. Gestisce, coordina e monitora il lavoro della geoinformazione e della misurazione ufficiale.

value: LU-Kt - Geocat Harvester

de: Nutzungsplanung: Planungszonen


name: Fachstelle Open Government Data des Kantons Zürich

de: bauvorhaben gerichte

de: Aktuelle Leitentscheide

media_type: application/xml

de: Alle in der jeweils jüngsten Ausgabe der Zeitschrift "Baurechtsentscheide Kanton Zürich" (BEZ) publizierten Leitentscheide des Baurekursgerichts des Kantons Zürich.

format: XML

de: Aktuelle Entscheide von öffentlichem Interesse

de: Alle in der jeweils jüngsten Ausgabe der Zeitschrift "Baurechtsentscheide Kanton Zürich" (BEZ) publizierten Entscheide des Baurekursgerichts des Kantons Zürich.

de: Entscheide des Baurekursgerichts des Kantons Zürich mit Entscheidnummer und Rechtsgebiet. Wichtige Hinweise: Die jeweiligen Urteilszusammenfassungen und Einleitungstexte werden durch die Kanzlei des Baurekursgerichts erstellt und sind nicht Bestandteil des Urteils. In den Suchergebnissen erscheinen auch Entscheide, welche aufgrund einer neuen Rechtspraxis oder von Gesetzesänderungen überholt und nicht mehr relevant sind.

bauvorhaben

display_name: bauvorhaben

gerichte

display_name: gerichte

soci

fr: Population et société

de: Bevölkerung und Gesellschaft

en: Population and society

it: Popolazione e società

fachstelle-ogd-kanton-zuerich

de: Fach- und Koordinationsstelle Open Government Data Kanton Zürich

de: Die Fach- und Koordinationsstelle Open Government Data (OGD) ist das kantonale Kompetenzzentrum für offene Behördendaten im Statistischen Amt. Wir unterstützen die Organisationseinheiten und Organe des Kantons bei der Bereitstellung ihrer nicht schützenswerten Daten als Open Data.

value: ZH-Kt Fachstelle OGD - DCAT Harvester


weather-station-davos-wolfgang

maintainer: Jörg Wieder

title_for_slug: weather-station-davos-wolfgang

en: relative-humidity raclets weather pressure temperature

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.

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


causal-effect-of-lup

maintainer: EnviDat Support

title_for_slug: causal-effect-of-lup

en: psm-did land-use-planning land-use-change annual-effect plan-evaluation

en: causal-effect-of-lup

en:
Data.dbf contains the varibles of 1662 villages in Zhangzhou Cities
PSM_DID.R can re-produce the results

en:
Title: Does zoning contain built-up land expansion? Causal evidence from Zhangzhou City, China.

Research objective: Built-up land zoning is an imporatant policy measure of land use planning (LUP) to contain built-up land expansion in China. We used a difference-indifference model with propensity score matching to estimate the average and annual effect of built-up land zoning on built-up land expansion in Zhangzhou City, China between 2010 and 2020.

Data: Data.dbf contains the varibles of 1662 villages in Zhangzhou Cities in 1995, 2000, 2005, 2010, 2013, 2015, 2018, and 2020.
XZQDM2 is villages' unique administrative ID;
Area is the land area of village i;
Dis2water is the Euclidean distance from village i to the nearest waterbody;
Dis2coastl is the Euclidean distance from village i to the nearest coastline;
Dis2city is the the Euclidean distance from village i to the city center;
Dis2county is the the Euclidean distance from village i to the nearest county center;
Elevation is the the average elevation within village i;
Dis2road is the the Euclidean distance from village i to the nearest road;
Nei_Built_ is the the area of built-up land (Nei Built.upit) in the neighboring villages of village i in year t;
Treated is a binary variable, Treated = 1 to the villages that were partially or entirely located inside the development-permitted zones, and Treated = 0 to the villages that were entirely located outside the development-permitted zones;
Intensity is the percentage of land that was assigned to the development-permitted zones in village i;
Year represent the year in 1995, 2000, 2005, 2010, 2013, 2015, 2018, and 2020;
BuLE is the dependent variable, representing built-up land expansion in village i in year t;
Town is town' unique administrative ID.

Method: First, we employed propensity score matching to overcome the selection bias and satisfy the parallel trend
assumption. Second, we built four Difference-in-Difference models to estimate the average and annual effect.

annual-effect

display_name: annual-effect

land-use-change

display_name: land-use-change

land-use-planning

display_name: land-use-planning

plan-evaluation

display_name: plan-evaluation

psm-did

display_name: psm-did

en: Causal effect of LUP


en: community-structure climate-change environmental-dna fish arctic

en: Data and code to reproduce the analysis of "Community structure and range shifts in Arctic marine fish under climate change"

en: Zip file with data and code

en:
Data and code for the paper published in Ecography: Community structure and range shifts in Arctic marine fish under climate change
Abstract:
Arctic marine ecosystems are rapidly transforming due to climate change. Warming temperatures and shrinking sea ice are enabling boreal fish to expand northward, possibly disturbing cold-adapted Arctic species assemblages. Species range shifts have been documented in the Bering and Barents Seas, raising concerns about ecosystem restructuring. Range shifts are especially difficult to detect in the Arctic due to sparse and inconsistent data. Here, we studied fish composition from eDNA water samples taken in East Greenland, Svalbard, the Barents Sea, and the Kara Sea during the TOPtoTOP and Arctic Century expeditions. We examined the environmental drivers of fish community structure using global dissimilarity models. We calculated the decadal rate of temperature change to identify the fastest-changing areas. We compared fish detections from eDNA with published historical records for the Kara Sea to assess possible range expansions. We found that temperature was the main factor influencing the taxa turnover of fish communities, with Gadidae and Liparis sp. driving the greatest compositional differences. Over the past 30 years, temperatures increased by 0.2 to 0.6°C per decade at our study sites, with the highest increases in western Svalbard and the lowest in the eastern Kara Sea. Despite the apparent dependence on temperature, we identified only one species detected outside its known latitudinal range, and five species in the Kara Sea with recent occurrences or representing an extended distribution. Our study suggests that temperature, the main driver of fish community assembly, is increasing rapidly in the Arctic, and a few species have likely already shifted recently, or at least their detections are new in some areas. While these detections cannot be definitively linked to range shifts, our results highlight the need to improve monitoring of high-latitude fish communities to detect and predict future ecosystem changes.
Article:
Marques, V., Fopp, F., Jaquier, M., Ellingsen, K. E., Yoccoz, N., Jucker, M., ...Pellissier, L. (2025). Community structure and range shifts in Arctic marine fish under climate change. Ecography, e8014. doi: 10.1002/ecog.08014
Data:
The resource contains a zip file with the entire project structure.
Data README:
 Intro
This repo presents data and code associated with the paper "Community structure and range shifts in Arctic marine fish under climate change" published in Ecography
 Usage
Launch the `main.R` script to reproduce the entire analysis.
It executes code blocks to create the data necessary for the analysis and then creates the figures.
💣 attention, total size is expected ~20 Go and the script will query the CMEMS database to fetch environmental data
 Tools
To make the scripts run, you need R, R packages (see session info below), python3, and the copernicusmarine python tool (https://pypi.org/project/copernicusmarine/).

 SessionInfo
```
sessionInfo()
R version 4.4.0 (2024-04-24)
Platform: aarch64-apple-darwin20
Running under: macOS 15.5
Matrix products: default
BLAS:  /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Europe/Zurich
tzcode source: internal
attached base packages:
[1] stats   graphics grDevices
[4] utils   datasets methods
[7] base
other attached packages:
 [1] lwgeom_0.2-14
 [2] metR_0.15.0
 [3] ggtext_0.1.2
 [4] scico_1.5.0
 [5] ggnewscale_0.5.0
 [6] colorspace_2.1-1
 [7] climetrics_1.0-15
 [8] rts_1.1-14
 [9] xts_0.14.0
[10] zoo_1.8-12
[11] ggpubr_0.6.0
[12] adespatial_0.3-23
[13] ggspatial_1.1.9
[14] colorplaner_0.1.4
[15] ape_5.8
[16] factoextra_1.0.7
[17] corrgram_1.14
[18] FactoMineR_2.11
[19] betapart_1.6
[20] gdm_1.5.0-9.1
[21] terra_1.7-78
[22] rnaturalearth_1.0.1
[23] ggrepel_0.9.5
[24] vegan_2.6-6.1
[25] lattice_0.22-6
[26] permute_0.9-7
[27] cowplot_1.1.3
[28] tidyterra_0.6.1
[29] sf_1.0-16
[30] egg_0.4.5
[31] gridExtra_2.3
[32] patchwork_1.2.0
[33] viridis_0.6.5
[34] viridisLite_0.4.2
[35] mgcv_1.9-1
[36] nlme_3.1-165
[37] MBA_0.1-0
[38] reshape2_1.4.4
[39] conflicted_1.2.0
[40] lubridate_1.9.3
[41] forcats_1.0.0
[42] stringr_1.5.1
[43] dplyr_1.1.4
[44] purrr_1.0.2
[45] readr_2.1.5
[46] tidyr_1.3.1
[47] tibble_3.2.1
[48] ggplot2_3.5.1
[49] tidyverse_2.0.0
[50] devtools_2.4.5
[51] usethis_2.2.3
loaded via a namespace (and not attached):
 [1] splines_4.4.0
 [2] later_1.3.2
 [3] bitops_1.0-7
 [4] minpack.lm_1.2-4
 [5] XML_3.99-0.16.1
 [6] lifecycle_1.0.4
 [7] rstatix_0.7.2
 [8] doParallel_1.0.17
 [9] MASS_7.3-60.2
 [10] flashClust_1.01-2
 [11] backports_1.5.0
 [12] magrittr_2.0.3
 [13] rmarkdown_2.27
 [14] yaml_2.3.8
 [15] remotes_2.5.0
 [16] httpuv_1.6.15
 [17] sp_2.1-4
 [18] sessioninfo_1.2.2
 [19] pkgbuild_1.4.4
 [20] mapproj_1.2.11
 [21] pbapply_1.7-2
 [22] DBI_1.2.3
 [23] RColorBrewer_1.1-3
 [24] ade4_1.7-22
 [25] maps_3.4.2
 [26] abind_1.4-5
 [27] pkgload_1.3.4
 [28] RCurl_1.98-1.14
 [29] itertools_0.1-3
 [30] yaImpute_1.0-34
 [31] rcdd_1.6
 [32] units_0.8-5
 [33] adegenet_2.1.10
 [34] codetools_0.2-20
 [35] xml2_1.3.6
 [36] adephylo_1.1-16
 [37] DT_0.33
 [38] tidyselect_1.2.1
 [39] RNeXML_2.4.11
 [40] raster_3.6-26
 [41] farver_2.1.2
 [42] jsonlite_1.8.9
 [43] e1071_1.7-14
 [44] phylobase_0.8.12
 [45] ellipsis_0.3.2
 [46] iterators_1.0.14
 [47] emmeans_1.10.4
 [48] systemfonts_1.1.0
 [49] foreach_1.5.2
 [50] progress_1.2.3
 [51] tools_4.4.0
 [52] ragg_1.3.2
 [53] snow_0.4-4
 [54] Rcpp_1.0.13
 [55] glue_1.8.0
 [56] xfun_0.44
 [57] withr_3.0.0
 [58] fastmap_1.2.0
 [59] boot_1.3-30
 [60] latticeExtra_0.6-30
 [61] fansi_1.0.6
 [62] spData_2.3.1
 [63] digest_0.6.35
 [64] timechange_0.3.0
 [65] R6_2.5.1
 [66] mime_0.12
 [67] estimability_1.5.1
 [68] wk_0.9.1
 [69] textshaping_0.4.0
 [70] jpeg_0.1-10
 [71] utf8_1.2.4
 [72] generics_0.1.3
 [73] data.table_1.15.4
 [74] class_7.3-22
 [75] prettyunits_1.2.0
 [76] httr_1.4.7
 [77] htmlwidgets_1.6.4
 [78] scatterplot3d_0.3-44
 [79] spdep_1.3-5
 [80] pkgconfig_2.0.3
 [81] gtable_0.3.5
 [82] picante_1.8.2
 [83] adegraphics_1.0-21
 [84] htmltools_0.5.8.1
 [85] carData_3.0-5
 [86] profvis_0.3.8
 [87] multcompView_0.1-10
 [88] scales_1.3.0
 [89] leaps_3.2
 [90] png_0.1-8
 [91] doSNOW_1.0.20
 [92] geometry_0.4.7
 [93] rnaturalearthhires_1.0.0.9000
 [94] knitr_1.47
 [95] rstudioapi_0.16.0
 [96] rncl_0.8.7
 [97] uuid_1.2-0
 [98] tzdb_0.4.0
 [99] checkmate_2.3.1
[100] coda_0.19-4.1
[101] magic_1.6-1
[102] proxy_0.4-27
[103] cachem_1.1.0
[104] KernSmooth_2.23-24
[105] parallel_4.4.0
[106] miniUI_0.1.1.1
[107] s2_1.1.6
[108] pillar_1.9.0
[109] grid_4.4.0
[110] vctrs_0.6.5
[111] urlchecker_1.0.1
[112] promises_1.3.0
[113] car_3.1-2
[114] xtable_1.8-4
[115] cluster_2.1.6
[116] evaluate_0.24.0
[117] mvtnorm_1.2-5
[118] cli_3.6.3
[119] compiler_4.4.0
[120] rlang_1.1.4
[121] crayon_1.5.2
[122] ggsignif_0.6.4
[123] labeling_0.4.3
[124] interp_1.1-6
[125] classInt_0.4-10
[126] plyr_1.8.9
[127] fs_1.6.4
[128] stringi_1.8.4
[129] deldir_2.0-4
[130] munsell_0.5.1
[131] Matrix_1.7-0
[132] hms_1.1.3
[133] seqinr_4.2-36
[134] shiny_1.8.1.1
[135] gridtext_0.1.5
[136] igraph_2.0.3
[137] broom_1.0.6
[138] memoise_2.0.1
[139] fastmatch_1.1-4
```
 Data content and reproducibility
Uncleaned raw table out of the bioinformatics pipeline can be found concatenated in `outputs/table_raw_before_cleaning.csv`, yet we caution readers to properly read the cleaning scripts should they wish to reproduce our analysis and this file is presently uncleaned.

arctic

display_name: arctic

climate-change

display_name: climate-change

community-structure

display_name: community-structure

environmental-dna

display_name: environmental-dna

fish

display_name: fish

en: Data and code for Community structure and range shifts in Arctic marine fish under climate change


alan-spongy-moth-laboratory-experiment

title_for_slug: alan-spongy-moth-laboratory-experiment

en: insect-development lab-experiment spongy-moth light-pollution alan

en: R code for the pupation analysis and producing the graphics.

en: Raw data modified and used for the Bodymass Analysis

en: R code for the Cox hazard analysis and producing the Kaplan Meier survival curves for both Mortality and Pupation.

en: R code for the bodymass analysis and producing the graphics.

en: Raw data used for the Pupation and Cox Hazard/Kaplan Meier Analysis.

en:
 Data and Code for ALAN Spongy Moth lab experiment
In this repository you will find all the raw datasets used for the data analysis performed in our paper about the impacts of artificial light at night (ALAN) on the development of Spongy Moth caterpillars. The files are attached as a csv file.
Also attached is all the R-code later used for the statistical analysis and for generating the graphics displayed in the paper. This files are attached as an R.file.

alan

display_name: alan

insect-development

display_name: insect-development

lab-experiment

display_name: lab-experiment

light-pollution

display_name: light-pollution

spongy-moth

display_name: spongy-moth

en: ALAN Spongy Moth Laboratory Experiment


maintainer: Daniel Scherrer

en: gui treemig forest-model forest-dynamics r-package spatial-modelling forest land-use bioclimatic-variables species-interaction species-migration

media_type: application/pdf

en: Tutorial for using the TreeMig framework; updated

format: PDF

en: TreeMig Simulation Environment and Input Data

en:
The zip file consists of a R-script to provide the framework, an example simulation environment and a set of input data.
- The R-script installAndRunTreeMigWrapperAndGUI.R installs the R-package, links to the input data and starts the graphical user interface.
- The gridded input datasets in TMInputData are specifically tailored to Switzerland and have the same coordinate reference system and a congruent 200m spatial resolution. They consist of climate, topographic, land use and soil data. For details see TMInputDataDetails.pdf.
- TMEnvironment is an example simulation environment, i.e. a set of hierarchically structured folders. For the first example simulation of the tutorial it contains the
  - the TreeMig executables for MacOS, Linux and Windows
  - simulation control files that define the setup and input files of the simulation (in folder C)
  - species parameters (in folder P)
  - bioclimatic data (in folder E)
  - folder R for results
  - folder Plots for plots of the simulation results.
  - This environment can be also used for the subsequent simulations of the tutorial.
- Some additional R-scripts demonstrate more complex applications described in the tutorial, e.g. coupling to another model.

en: TreeMig Tutorial

en: Description input data

en: Details where the input data come from and how they have been processed

en:
Starter kit for the TreeMig framework that allows for an easy application of the forest landscape model TreeMig for simulating forest dynamics in space under changing environmental and land use conditions.
The starter kit consists of a set of climate, land-use, topography and soil property input data for Switzerland, a script for installing the framework and running the GUI of the framework with the link to the input data, a tutorial covering a range of simulation examples for Switzerland and a transect through Europe, and an associated example simulation environment containing the model executables and other model inputs.

bioclimatic-variables

display_name: bioclimatic-variables

forest

display_name: forest

forest-dynamics

display_name: forest-dynamics

forest-model

display_name: forest-model

gui

display_name: gui

land-use

display_name: land-use

r-package

display_name: r-package

spatial-modelling

display_name: spatial-modelling

species-interaction

display_name: species-interaction

species-migration

display_name: species-migration

treemig

display_name: treemig

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: Starter Kit for the TreeMig framework v1 for easy simulation of spatio-temporal forest dynamics


modeling-snow-failure-with-dem

title_for_slug: modeling-snow-failure-with-dem

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

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.

mimetype: application/pdf

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


en: red-list range-size bryophytes endemic europe conservation-priority rarity

en: This dataset provides data on the occurrence of 672 European bryophyte species in European countries or territories. All species which are considered threatened or near threatened according to the European Red List (Hodgetts et al. 2019) are included (553 species). Further, all species evaluated in the European Red List assessed as LC, DD, NE and EX, which at the same time are either endemic, near endemic or globally rare, are included (119 species). Therefore, the list contains not only all European bryophyte species that are threatened or near threatened, but also all species endemic or near endemic to Europe as well as all globally rare species occurring in Europe. For each of the 553 threatened or near threatened species, we provide a conservation a priority score which was calculated based the Red List status and the proportion of the European population of the global population or the global rarity of the species. For details on the prioritisation approach see Bisang et al. (2025).

bryophytes

display_name: bryophytes

conservation-priority

display_name: conservation-priority

endemic

display_name: endemic

europe

display_name: europe

range-size

display_name: range-size

rarity

display_name: rarity

red-list

display_name: red-list

en: Priority ranking for the conservation of all European bryophyte species - dataset


en: colombia fish edna vert01 metabarcoding magdalena teleo

en: Data access

en: Please cyberduck to download for the moment

en:
Monitoring of vertebrates of the lower Magdalena river through eDNA metabarcoding

The Magdalena River basin harbors a large biodiversity of vertebrates, with numerous endemic species, many of which are threatened. Here, we used environmental DNA (eDNA) metabarcoding, with two primer sets targeting different regions of the mitochondrial DNA 12S ribosomal RNA gene, to detect vertebrate diversity in the Magdalena River. We detected a total of 159 vertebrate taxa, not only aquatic but also terrestrial, arboreal, and aerial. The diversity of these vertebrates increases in relation to the proximity to the river mouth with a change in the composition of the assemblage of aquatic vertebrates detected. We conclude that eDNA metabarcoding allows characterizing vertebrate assemblages in large rivers, assessing conservation status, and elucidating biodiversity patterns with minimal ecosystem disturbance. Samples were taken at the sides of the river or in the center using a boat. At each station, we performed two filtration replicates using a peristaltic pump to conduct environmental DNA (eDNA) sampling. Each filtration targeted a maximum duration of 1 hours, during which a maximum of 30 liters of water were filtered through each 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. 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)

colombia

display_name: colombia

edna

display_name: edna

fish

display_name: fish

magdalena

display_name: magdalena

metabarcoding

display_name: metabarcoding

teleo

display_name: teleo

en: Environmental DNA Freshwater Colombia Magdalena 2022


dataset-on-new-snow-water-equivalent

title_for_slug: dataset-on-new-snow-water-equivalent

en: snowfall new-snow precipitation data-assimilation snow-modelling

en: The .zip file contains all data as described in this repository and associated publication. The .zip file contains a readme file describing its content in detail.

en:
This dataset includes quality-controlled measurements of new snow depth (HN), new snow water equivalent (HNW), snow depth (HS), and snow water equivalent (SWE) from 41 stations located in Switzerland for the period from 2016-09-01 to 2022-08-31.
These data are the basis of the following publication: Magnusson J., Cluzet B., Quéno L., Mott R., Oberrauch M., Mazzotti G., Marty C., Jonas T., 2025, Evaluating methods to estimate the water equivalent of new snow from daily snow depth recordings, Cold Regions Science and Technology, https://doi.org/10.1016/j.coldregions.2025.104435.
Abstract
The water equivalent of new snow (HNW) plays a crucial role in various fields, including hydrological modeling, avalanche forecasting, and assessing snow loads on structures. However, in contrast to snow depth (HS), obtaining HNW measurements is challenging as well as time-consuming and is hence rarely measured. Therefore, we assess the reliability of two semi-empirical methods, HS2SWE and ΔSNOW, for estimating HNW. These methods are designed to simulate continuous water equivalent of the snowpack (SWE) from daily HS only, with changes in SWE yielding daily HNW estimates. We compare both parametric methods against HNW predictions from a physics-based snow model (FSM2oshd) that integrates daily HS recordings using data assimilation. Our findings reveal that all methods exhibit similar performance, with relative biases of less than ~3 % in replicating SWE observations commonly used for model evaluations. However, the ΔSNOW model tends to underestimate daily HNW by ~17 %, whereas HS2SWE and FSM2oshd combined with a particle filter data assimilation scheme provide nearly unbiased estimates, with relative biases below ~5 %. In contrast to the parsimonious parametric methods, we show that the physics-based approach can yield information about unobserved variables, such as total solid precipitation amounts, that may differ from HNW due to concurrent melt. Overall, our results underscore the potential of utilizing commonly available daily HS data in conjunction with appropriate modeling techniques to provide valuable insights into snow accumulation processes. Our study demonstrates that daily SWE observations or supplementary measurements like HNW are important for validating the day-to-day accuracy of simulations and should ideally already be incorporated during the calibration and development of models.
Acknowlegements
These data were recorded by SLF observers and staff members. Their contribution is gratefully acknowledged.

data-assimilation

display_name: data-assimilation

new-snow

display_name: new-snow

precipitation

display_name: precipitation

snow-modelling

display_name: snow-modelling

snowfall

display_name: snowfall

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: Dataset on new snow water equivalent


maintainer: Yves Bühler

en: snow-depth-mapping dsm photogrammetry wingtra orthophoto uav drone-data

en: Data Access

en:
- DSM (10 cm resolution)
- Ortho (3 cm resolution)
- HS (10 cm resolution)
- original RGB drone images
- Agisoft Metashape Processing Report

All products are also available as a cloud optimized geotiff.

en: To map and assess snow depth on different dates, 9 flights were conducted in the winter season of 2020/21 at the Latschüelfurgga in Davos. The data was captured with a Sony RX1R II mounted on a Wingtra drone and was processed with the Agisoft Metashape software. High-resolution DSMs, orthomosaics and snow height rasters, as well as the original RGB images from each flight are available.

drone-data

display_name: drone-data

dsm

display_name: dsm

orthophoto

display_name: orthophoto

photogrammetry

display_name: photogrammetry

snow-depth-mapping

display_name: snow-depth-mapping

uav

display_name: uav

wingtra

display_name: wingtra

en: Photogrammetric Drone Data Latschuelfurgga


maintainer: Amelie Fees

en: glide-snow-avalanche avalanche avalanche-activity snowpack-simulation time-lapse-photography

en: SNOWPACK simulations

en: SNOWPACK simulations (.pro and .smet) covering season 2008/09 to 2021/22 at 10 virtual locations on Dorfberg. The locations correspond to the most common glide-snow avalanche paths.

en: README

en: README.txt explaining the dataset and columns of tables in more detail.

en: Glide distances for opening glide-cracks

en: This folder contains all glide-cracks where glide-distances were extracted from time-lapse photographs. Glide-distances were calculated in x-direction (width) and y-direction (downslope, length) by georeferencing the images.

en: Glide-snow avalanche activity

en: Table that contains all glide-snow avalanche events that were detected in time-lapse photographs (season 2008/09 to 2021/22). The columns are explained in the README.txt

en:
This dataset includes the processed data of the glide-snow avalanche activity and dynamics on Dorfberg (Davos, Switzerland) covering seasons 2008/09 to 2021/22. This dataset was described in the research article:

Fees, A., van Herwijnen A., Altenbach, M., Lombardo, M., Schweizer, J.: Glide-snow avalanche characteristics at different time-scales extracted from time-lapse photography, Annals of Glaciology, 91

We extracted the dynamics of opening glide-cracks and the glide-snow avalanche activity from time-lapse photographs. Glide-snow avalanches were separated into surface and interface events using the liquid water content which was simulated with SNOWPACK at 10 virtual stations on Dorfberg.

avalanche

display_name: avalanche

avalanche-activity

display_name: avalanche-activity

glide-snow-avalanche

display_name: glide-snow-avalanche

snowpack-simulation

display_name: snowpack-simulation

time-lapse-photography

display_name: time-lapse-photography

en: Glide-snow avalanche activity on Dorfberg, Davos, Switzerland


photogrammetric-drone-data-schurlialp

title_for_slug: photogrammetric-drone-data-schurlialp

en: photogrammetry wingtra uav drone-data orthophoto dsm

en:
- 10 cm DSM
- 10 cm HS
- 3 cm orthophoto
- original RGB images
- Agisoft Metashape Processing Report

All of the products are also available as a cloud optimized geotiff.

en: The data was collected on 16.04.2021 and on 28.05.2021 with a Wingtra Gen II and a Sony RX1 II RGB sensor to obtain snow depth and distribution data. Following the data collection, the data was processed with Agisoft Metashape. A 10cm DSM, a 10cm snow depth raster, a 3mm orthophoto and the original drone images are available for download.

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: Photogrammetric Drone Data Schürlialp


en: acclimation invasive-species respiration photosynthesis leaf spac-model

en: Thermal tolerance of T. fortunei.zip

en:
This dataset originates from a two-year transplant experiment conducted across a temperature gradient in Europe (mean annual temperatures: 8.4–21.8 °C). It includes physiological measurements of one invasive palm species (Trachycarpus fortunei) and two co-occurring native species (Ilex aquifolium and Tilia cordata). The dataset captures plant acclimation responses to air temperature, focusing on photosynthetic and respiratory traits that influence carbon balance.
Variables that can be extracted:
- Optimal temperature of photosynthesis (Topt)
- Photosynthetic rate at optimal temperature (Aopt)
- Thermal breadth of photosynthesis (T80)
- Respiration rate at 25 °C (R25)
- Temperature sensitivity of respiration (Q10)
Raw data are reported for all species across multiple sites and time points, enabling assessment of their acclimation capacity to warming. The dataset supports comparisons of functional plasticity between an invasive species and native taxa, providing insights into climate responses and invasion ecology.

acclimation

display_name: acclimation

invasive-species

display_name: invasive-species

leaf

display_name: leaf

photosynthesis

display_name: photosynthesis

respiration

display_name: respiration

spac-model

display_name: spac-model

en: Thermal acclimation fails to confer a carbon budget advantage to invasive species over natives


ceilometer-klosters

title_for_slug: ceilometer-klosters

en: cloud-base-height klosters ceilometer raclets vertical-visibility

en: Ceilometer data Klosters

en: Ceilometer installed at Klosters measuring cloud base height, cloud cover and backscatter profile. The time resolution is 30 seconds, with time given in number of days from January 0, 0000, UTC. More information on the variables in the manufacturer manual.

en: Cloud base height (m) and vertical visibility (m) were measured with the VAISALA Ceilometer CL31 in Klosters (LON: 9.880413, LAT: 46.869019). The CL31 is an instrument with constant reliability for all weather conditions and simultaneous detection of three cloud layers in heights up to 7.6 km.

ceilometer

display_name: ceilometer

cloud-base-height

display_name: cloud-base-height

klosters

display_name: klosters

raclets

display_name: raclets

vertical-visibility

display_name: vertical-visibility

en: Ceilometer Klosters


maintainer: Christoph Marty

en: hydrology snowfall water-cycle snow climate-change-impacts

en: Synthesis report chapter

en: DORA Persistent URL

media_type: text/html

format: HTML

en:
This report was prepared as one of the synthesis report chapters of the Hydro-CH2018 project of the Federal Office for the Environment (FOEN). An important feature of snow cover is the fact that its volume and duration is subject to large year-to-year fluctuations. As frozen precipitation, snow cover is nothing other than a natural water reservoir that delays precipitation to runoff and is thus of outstanding importance for the seasonal water balance in Switzerland. Over a whole year, approximately 40% (22 km3) of the annual runoff currently comes from snow melting and only 1% from glacier melting. Typically, the snow cover in the Alpine region builds up over the autumn and winter months, reaches its maximum between February and May, depending on the altitude, and dominates the runoff processes during melting in the following spring and summer months.
Due to the great dependence on minus temperatures and precipitation, the snow cover reacts sensitively to temperatures above 0° Celsius and more or less precipitation. Due to climate change and the associated warming, the proportion of precipitation that falls as snow decreases measurably. In addition to this reduction in snowfall, the warmer temperatures also cause the snow cover to melt more quickly. The decline in snowfall has so far mainly affected lower altitudes, where winter temperatures often reach positive levels.
As climate change progresses, this trend is likely to continue and above all affect higher zones. Even at higher altitudes, the snow cover will then start later, melt away earlier and is increasingly no longer permanently present. This development will also have an effect on the water bodies. Today nival regimes, i.e. regimes shaped by snow, are shifting towards pluvial regimes, i.e. regimes dominated by rain. Overall, winter runoff increases, summer runoff decreases. By the end of the century, the proportion of runoff from snowmelt will decrease throughout Switzerland, albeit to a lesser extent than the proportion from glacier melt.

climate-change-impacts

display_name: climate-change-impacts

hydrology

display_name: hydrology

snow

display_name: snow

snowfall

display_name: snowfall

water-cycle

display_name: water-cycle

en: Snow and the water cycle in a changing climate


en: air-temperature wind-speed precipitation shortwave-radiation relative-humidity time-series speed2zero meteo-station climate-change

en: Data link

en:
This dataset provides 50 years of seasonal synthetic hourly time series for five meteorological variables: incoming shortwave radiation, total precipitation, relative humidity, 2-meter air temperature and wind speed.

The data are generated from observational records (1990–present) from nine MeteoSwiss stations (ABO, BAS, BER, LUG, PUY, SCU, SIO, STG, WFJ). We use a sampling approach that preserves both the temporal structure and inter-variable dependencies. Specifically, the time series are constructed by drawing 7-day blocks from historical observations, ensuring realistic short-term variability and maintaining the physical coherence between variables.

The synthetic sequences are constrained to follow daily temperature change trajectories corresponding to three future climate scenarios for the period 2040–2070: RCP2.6, RCP8.5 and RCP0: a counterfactual scenario with no long-term warming trend, but with temperature variability consistent with the RCP2.6 one.
The temperature constraints are derived from projections downscaled at the staiton level, available at: https://www.envidat.ch/#/metadata/climate-change-scenarios-at-hourly-resolution

air-temperature

display_name: air-temperature

climate-change

display_name: climate-change

meteo-station

display_name: meteo-station

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

shortwave-radiation

display_name: shortwave-radiation

time-series

display_name: time-series

wind-speed

display_name: wind-speed

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Meteorological Time Series Aligned with Projected Temperature Changes


maintainer: James W. Kirchner

en: de-mixing transfer-function irfnnhsr deconvolution arma-noise impulse-response-function time-series-analysis system-identification irf nonlinear-deconvolution greens-function

en: Benchmark demonstration scripts and results

en: README file for IRFnnhs.R and benchmark demonstration scripts

en: Impulse Response Function script

en:
The R script IRFnnhs.R, which efficiently estimates impulse response functions for environmental systems that are nonlinear, nonstationary, or heterogeneous, based on their input and output time series.

Scripts and results for a series of benchmark tests are also provided, to accompany Kirchner, J.W., Impulse response functions for heterogeneous, nonstationary, and nonlinear systems, estimated by deconvolution and demixing of noisy time series, _Sensors_, 22(9), 3291, https://doi.org/10.3390/s22093291, 2022.

arma-noise

display_name: arma-noise

de-mixing

display_name: de-mixing

deconvolution

display_name: deconvolution

greens-function

display_name: greens-function

impulse-response-function

display_name: impulse-response-function

irf

display_name: irf

irfnnhsr

display_name: irfnnhsr

nonlinear-deconvolution

display_name: nonlinear-deconvolution

system-identification

display_name: system-identification

time-series-analysis

display_name: time-series-analysis

transfer-function

display_name: transfer-function

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: Impulse response functions for nonlinear nonstationary and heterogeneous systems


maintainer: Matthias Haeni

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

en: Data plot of the forest station (CLB)

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

en: Data plot of the field station (CLF)

en: Data file.

en: Site aerial photo (Celerina)

en: Site aerial photo from both Celerina stations. Source: Google. 2016. Site aerial photo from both Celerina stations. Retrieved November 11, 2016, from https://www.google.ch/maps

mimetype: image/jpeg

en: Site photo of the forest station (CLB)

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

en: Site photo of the field station (CLF)

en: Site phote of the field station from the side.

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 two meteorological stations in Celerina in Switzerland where one station is located within a natural coniferous forest stand (CLB) with Swiss pine (_Pinus cembra_; 210-250 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, CLF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Celerina 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

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

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


bats-in-beaver-systems

title_for_slug: bats-in-beaver-systems

en: castor beaver nature-conservation deadwood ecosystem-engineer wetland habitat-heterogeneity chiroptera flying-arthropods

en:
site_info contains columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"location" (sampling location row within site),
"sample_nr" (sampling number within location)

en: README text file providing context for the data. Please do not hesitate to contact us if you have any questions

en: R script bats_pool_size to test the beaver pool size on bat richness, activity and feeding activity

en: R script bats_glmm to test the influence of beavers on bat richness, activity and feeding activity

en: sampling dates.xlsx

en:
For the time information about the research data, created (2022-09-23 to 2022-09-23) refers to the GIS data, collected (2022-06-13 to 2022-07-31) to the field sampling. Details can be found in the csv “sampling_dates”.

sampling_dates with columns
"site" (sampling site),
"bats_1" (first period of bat survey in June),
"bats_2" (second period of bat survey in June),
"bats_3" (first period of bat survey in July),
"bats_4" (second period of bat survey in July),
"flying_arthropods_1" (arthropod sampling in June),
"flying_arthropods_2" (arthropod sampling in July),
"deadwood survey" (date of deadwood survey),
"vegetation survey" (date of vegetation survey)

en: bats_raw_values_overview to produce raw value summaries and tables

en: R script bats_data_cleaning to clean the raw data

en:
Raw data data_bats with columns:
“Categories” (empty or "feeding", indicating that call sequence contains feeding buzz)
“Sequence” (sequence number)
“Timestamp” (recording time)
“Temperature” (temperature recorded by batlogger in degree celcius)
“Serial” (serial number of bat logger)
“Duration” (duration of call sequence in seconds)
“Call.Count” (call count number, how many calls could be detected)
“Verification.1” (bat sequence verification)
“Notes” (remarks for identification)
“treatment” (treatment ctrl = Control and Pool)
“slot” (slot number, 4 slots, two in June, two in July)
“Laufnummer” (unique identifier per sampling point)

en: R script bats_age to test the beaver pool age effect on bat richness, activity and feeding activity

en:
bats_biodiv.day with columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"community" (species group),
"date" (sampling date),
"month" (sampling month),
"feeding" (number of feeding buzzes summed per 5 minutes and identification level), "abundance" (sequences per 5 minutes and identification level),
"richness" (species richness),
"richness_feeding" (species richness of feeding bats)

en:
arthropods_flying the arthropod abundance with columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"community" (species group),
"month" (sampling month),
"abundance" (number of flying arthropods)

en: R script bats_SEM to test the influence of the beaver on bat richness, activity and feeding activity through feeding and habitat heterogeneity

en:
bat_guilds with columns
"SP" (identification level),
"EndGuilde" (information on feeding guild)

en:
bats.environment with columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"canopy" (modelled tree canopy cover, averaged across radius of 100 m, unitless),
"structure_ruggedness" (mean ruggedness of the total vertical structure > 3 m, averaged across radius of 100 m, unitless), "vegetation_ruggedness" (mean ruggedness of the canopy > 3 m, later called canopy_heterogenneity, averaged across radius of 100 m, unitless),
"pool_size" (maximum width of beaver pool in m),
"area_deadwood_monitoring" (total area of deadwood moonitoring in m3),
"age" (beaver presence in years)

en:
dead_wood for deadwood with columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"type" (deadwood type),
"height_length" (the height or lenghth of a piece of deadwood in m),
"DBH_diamlarge" (diameter at breast height or diameter of larger end if lying in m),
"diamsmall (diameter of smaller end if lying in m",
"volume" (calculated volume in m3)

en:
bats_biodiv.day.guilds with columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"community" (species group),
"date" (sampling date),
"month" (sampling month),
"guild" (which feeding guild information),
"feeding" (number of feeding buzzes summed per 5 minutes and identification level), "abundance" (sequences per 5 minutes and identification level),
"richness" (species richness),
"richness_feeding" (species richness of feeding bats)

en:
vegetation_characteristics data on vegetation_cover and cumulative_cover with columns "laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"Vegetation_cover" (estimated vegetation cover over 5x1 m plot in %),
"Cumulative cover" (summed up cover over tree, shrub and herb layer in %),
"Cumulative cover trees" (tree cover level, higher than 3 metres in %),

en: R script bats_data_exploration_Figure_nighly to explore the data and display nightly species richness, activity and feeding activity

en:
gewichtungsfaktor with columns
"Project" (sampling site),
"Treatment" (sampling location within site, with levels ctrl for Control and pool for Pool),
"date" (sampling date),
"gew" (weighting factor to standardize night length)

en:
This dataset contains processed bat acoustic data collected to evaluate the ecological impacts of beaver-engineered ponds on bat species richness, activity, and feeding activity. It includes nightly activity and feeding call counts identified to species or species group level, site and treatment information (beaver pond vs. control areas along the same stream), and associated environmental covariates such as deadwood volume, arthropod abundance, and vegetation structure.

Methods: Bats were recorded using passive acoustic detectors placed at paired beaver-influenced and control sites. The resulting data was processed in the software Batscope. Arthropod abundance was estimated using flight-interception traps. The amount of deadwood and vegetation cover was measured in the field. Canopy cover and canopy heterogeneity were modelled from GIS models.

Temporal and Spatial Coverage:
Data were collected from eight sites in Switzerland between June and July 2022, each site sampled for 16 nights.

beaver

display_name: beaver

castor

display_name: castor

chiroptera

display_name: chiroptera

deadwood

display_name: deadwood

ecosystem-engineer

display_name: ecosystem-engineer

flying-arthropods

display_name: flying-arthropods

habitat-heterogeneity

display_name: habitat-heterogeneity

nature-conservation

display_name: nature-conservation

wetland

display_name: wetland

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Bats in beaver systems


en: forest microclimate biodiversity climate-change statistical-interpolation radiation-transfer ecology

en: Predicted microclimate air temperatures at 100 cm above ground. 10 m spatial resolution raster files are provided for mean daily minimum (tmin), mean (tmean) and maximum (tmax) temperatures during each month from April (04) to October (10) over the period between 2012 to 2021.

en: Predicted microclimate topsoil temperatures at 5 cm below ground. 10 m spatial resolution raster files are provided for mean daily minimum (tmin), mean (tmean) and maximum (tmax) temperatures during each month from April (04) to October (10) over the period between 2012 to 2021.

en: Predicted microclimate air temperatures at 5 cm above ground. 10 m spatial resolution raster files are provided for mean daily minimum (tmin), mean (tmean) and maximum (tmax) temperatures during each month from April (04) to October (10) over the period between 2012 to 2021.

en: Climate data matching the scales at which organisms experience climatic conditions are often missing. Yet, such data on microclimatic conditions are required to better understand climate-change impacts on biodiversity and ecosystem functioning. Here we combine a national network of microclimate temperature measurements with a novel radiative transfer model to map monthly minimum, mean and maximum temperatures during the vegetation period at a 10 meter spatial resolution across Switzerland. The temperature measurements took place in 107 sampling plots distributed across different habitat types, with 62 plots in forests, 22 below trees outside forests, and 23 in open grasslands. In each plot we measured temperature in the topsoil (-5cm), as well as in the air at 5cm and 100cm height above ground. Spatial interpolation was achieved by using a hybrid approach based on linear mixed effects models with input from detailed radiation estimates that account for topographic and vegetation shading, as well as other predictor variables related to the macroclimate, topography and vegetation height. Our data reveals strong horizontal and vertical variability in microclimate temperature, particularly for maximum temperatures at 5 cm above the ground and within the topsoil. Compared to macroclimate conditions as measured by weather stations outside forests, diurnal air and topsoil temperature ranges inside forests were reduced by up to 3.0 and 7.8 °C, respectively, while below trees outside forests, e.g. in hedges and below solitary trees, this buffering effect was 1.8 and 7.2 °C. We also found that in open grasslands, maximum temperatures at 5 cm above ground are on average 3.4 °C warmer than that of macroclimate, suggesting that in such habitats heat exposure close to the ground is often underestimated when using macroclimatic data. After accounting for macroclimate effects, microclimate patterns were primarily driven by radiation, with particularly strong effects on maximum temperatures. Results from spatial block cross-validation revealed predictive accuracies as measured by RSME’s ranging from 1.18 to 3.43 °C, with minimum temperatures generally being predicted more accurately than maximum temperatures. The microclimate maps presented here enable a more biologically relevant perspective when analysing climate-species interactions, which is expected to lead to a better understanding of biotic and ecosystem responses to climate and land use change.

biodiversity

display_name: biodiversity

ecology

display_name: ecology

forest

display_name: forest

microclimate

display_name: microclimate

radiation-transfer

display_name: radiation-transfer

statistical-interpolation

display_name: statistical-interpolation

envidat

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: Monthly topsoil and near surface microclimate temperature data for Switzerland


maintainer: Franziska Gerber

en: wrf static-data topography cryowrf cosmo-wrf landuse

en: Static data for WRF - Landuse

en: Static data for WRF - Soiltype top

en: Documentation

en: Documentation of the dataset

en: Example namelist.wps to use with the 1s static data

en: Static data for WRF - Soiltype bottom

en: Static data for WRF - Topography

en:
Example GEOGRID.TBL to use with the 1s static data;
Note about naming:
* 121 -> 1-2-1 topography smoothing applied to dataset
* 1 -> one smoothing cycle applied to the data (needs to be changed depending on the chosen resolution to avoid too steep slope angles)

en: Static input data (topography, landuse and soiltype) for the WRF preprocessing system WPS is provided for Switzerland and its neighboring countries between 45-49 N and 4-12 E. The data is provided at a resolution of 1 s. Topography is based on the Aster dataset, while landuse is extracted from the Corine landuse dataset. Soil type is set to silty clay loam for the entire domain. This static input data is valid for WRF and CRYOWRF.

cosmo-wrf

display_name: cosmo-wrf

cryowrf

display_name: cryowrf

landuse

display_name: landuse

static-data

display_name: static-data

topography

display_name: topography

wrf

display_name: wrf

en: High resolution static data for WRF over Switzerland


en: climate-change precipitation snowmelt hydrology soil-moisture discharge

en: Shapefile of 307 catchments in Switzerland

en: Shapefile of the 307 medium-sized catchments in Switzerland. The "gridcode" provides the ID of the catchment used in the datafiles.

en: Daily simulated data for the future period

en: Daily simulated discharge, precipitation, snowmelt, and soil moisture for the period 1981-2100 in mm for the 307 catchments.

en: Daily simulated data for the reference period

en: Daily simulated discharge, precipitation, snowmelt, and soil moisture for the reference period 1981-2017 in mm.

en:
Daily discharge and the related hydro-meteorological variables precipitation, snowmelt, and soil moisture are provided for current (1981-2017) and for future climate conditions (1981-2100) for 307 medium-sized catchments in Switzerland. The catchments have a median catchment area of 117 km². The 307 catchments together form a set representative of the climatological conditions and runoff characteristics in Switzerland. The four variables were simulated at a daily resolution using the hydrological model PREVAH. PREVAH is a conceptual process-based model that was run in this study in its fully distributed version on a 500 m grid (Viviroli et al. 2009a). For the calibration, runoff time series from 140 mesoscale catchments covering the different runoff regimes were used. The model calibration was conducted over the period 1993-1997. Verification was performed on the period 1983-2005 using (i) volumetric deviation (Viviroli et al. 2007) and (ii) benchmark efficiency (Schäfli et al 2007) as objective functions. The calibration and validation procedures are described in detail in Köplin et al. (2010). The parameters for each model grid cell were derived by regionalizing the parameters obtained for the 140 catchments with a procedure based on ordinary kriging (Viviroli et al. 2009b, Köplin et al. 2010).
The calibrated and validated model was then driven with transient meteorological data (precipitation, temperature, radiation, and wind) representing both reference (1981-2017) and future climate conditions (2018-2099). The data were derived from the CH2018 climate scenarios (NCCS 2018) provided by the Swiss National Centre for Climate Services (NCCS). They were obtained from climate experiments produced with different climate modeling chains, consisting of a global and a regional circulation model each, within EUROCORDEX for three representative concentration pathways (RCP) emission scenarios. Downscaled output of ten climate model chains derived by quantile mapping were considered. The focus was on the chains of the EUR-11 domain with a horizontal resolution of 0.11 degrees (roughly 12.5 km). The climate model chains (GCM, RCM, RCP, and grid resolution) used are listed below:

- ICHEC-EC-EARTH	DMI-HIRHAM5	2.6	EUR-11
- ICHEC-EC-EARTH	DMI-HIRHAM5	4.5	EUR-11
- ICHEC-EC-EARTH	DMI-HIRHAM5	8.5	EUR-11
- ICHEC-EC-EARTH	SMHI-RCA4	2.6	EUR-11
- ICHEC-EC-EARTH	SMHI-RCA4	4.5	EUR-11
- ICHEC-EC-EARTH	SMHI-RCA4	8.5	EUR-11
- MOHC-HadGEM2-ES	SMHI-RCA4	4.5	EUR-11
- MOHC-HadGEM2-ES	SMHI-RCA4	8.5	EUR-11
- MPI-M-MPI-ESM-LR	SMHI-RCA4	4.5	EUR-11
- MPI-M-MPI-ESM-LR	SMHI-RCA4	8.5	EUR-11

*References*:

-	Köplin, N., D. Viviroli, B. Schädler, and R. Weingartner (2010), _How does climate change affect mesoscale catchments in Switzerland? - A framework for a comprehensive assessment_, Advances in Geosciences, 27, 111-119, doi:10.5194/adgeo-27-111-2010.
-	National Centre for Climate Services (2018), CH2018 - _Climate Scenarios for Switzerland_, Tech. rep., NCCS, Zurich.
-	Schäfli, B., and H. V. Gupta (2007), _Do Nash values have value?_, Hydrological Processes, 21, 2075-2080, doi:10.1002/hyp.6825.
-	Viviroli, D., J. Gurtz, and M. Zappa (2007), _The hydrological modelling system PREVAH. Part II - Physical model description_, Geographica Bernensia, 40, 1-89.
-	Viviroli, D., M. Zappa, J. Gurtz, and R. Weingartner (2009a), _An introduction to the hydrological modelling system PREVAH and its pre- and post-processing-tools_, Environmental Modelling & Software, 24, 1209-1222, doi:10.1016/j.envsoft.2009.04.001.
-	Viviroli, D., H. Mittelbach, J. Gurtz, and R. Weingartner (2009b), _Continuous simulation for flood estimation in ungauged mesoscale catchments of Switzerland-Part II: Parameter regionalisation and flood estimation results_, Journal of Hydrology, 377 (1), 208-225, doi:10.1016/j.jhydrol.2009.08.022.

climate-change

display_name: climate-change

discharge

display_name: discharge

hydrology

display_name: hydrology

precipitation

display_name: precipitation

snowmelt

display_name: snowmelt

soil-moisture

display_name: soil-moisture

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Simulated future discharge and climatological variables for medium-sized catchments in Switzerland


maintainer: Leo Gallus Bont

en: qgis-plugin cable-yarding swiss-geodata swisstopo open-source geoinformation-software seilaplan digital-planning digital-elevation-model

en: View video on youtube!

en: Herunterladen des DHM von swisstopo Website

en:
In order to use the QGIS plugin ‘Seilaplan’ for digital cable line planning, a digital terrain model (DTM) is required. As an alternative to using the ‘Swiss Geo Downloader’ plugin, the DTM can be obtained directly from Swisstopo. In this tutorial we explain step by step how to download the necessary DTM from the Swisstopo Website, and how to use it in QGIS for the digital planning of a cable line using the plugin ‘Seilaplan’. Please note that the tutorial language is German!

Link to the elevation model on the swisstopo website: https://www.swisstopo.admin.ch/de/geodata/height/alti3d.html#technische_details

Link to the rope map website: https://seilaplan.wsl.ch

********************

Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung ist ein digitales Höhenmodell (DHM) nötig. Als Alternative zum Swiss Geo Downloader erklären wir in diesem Tutorial Schritt für Schritt, wie man das nötige Höhenmodell von der Swisstopo Webseite herunterladen und in QGIS zur Seillinienplanung verwenden kann.

Link zum Höhenmodell auf der swisstopo Webseite: https://www.swisstopo.admin.ch/de/geodata/height/alti3d.html#technische_details

Link zur Seilaplan-Website: https://seilaplan.wsl.ch

cable-yarding

display_name: cable-yarding

digital-elevation-model

display_name: digital-elevation-model

digital-planning

display_name: digital-planning

geoinformation-software

display_name: geoinformation-software

open-source

display_name: open-source

qgis-plugin

display_name: qgis-plugin

seilaplan

display_name: seilaplan

swiss-geodata

display_name: swiss-geodata

swisstopo

display_name: swisstopo

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: Seilaplan Tutorial: DTM download from swisstopo website