de: shp

fr: Projets de construction Immobilier (shp)

en: Real Estate construction projects (shp)

de: Bauprojekte Immobilien (shp)

it: Progetti di costruzione Immobili (shp)

fr: Projets de construction Immobilier (n3)

de: Bauprojekte Immobilien (n3)

en: Real Estate construction projects (n3)

it: Progetti di costruzione Immobili (n3)

de: gpx

fr: Projets de construction Immobilier (gpx)

de: Bauprojekte Immobilien (gpx)

en: Real Estate construction projects (gpx)

it: Progetti di costruzione Immobili (gpx)

de: turtle

fr: Projets de construction Immobilier (turtle)

de: Bauprojekte Immobilien (turtle)

en: Real Estate construction projects (turtle)

it: Progetti di costruzione Immobili (turtle)

de: rdfxml

fr: Projets de construction Immobilier (rdfxml)

de: Bauprojekte Immobilien (rdfxml)

en: Real Estate construction projects (rdfxml)

it: Progetti di costruzione Immobili (rdfxml)

de: geojson

fr: Projets de construction Immobilier (geojson)

de: Bauprojekte Immobilien (geojson)

en: Real Estate construction projects (geojson)

it: Progetti di costruzione Immobili (geojson)

de: jsonl

fr: Projets de construction Immobilier (jsonl)

de: Bauprojekte Immobilien (jsonl)

en: Real Estate construction projects (jsonl)

it: Progetti di costruzione Immobili (jsonl)

de: json

fr: Projets de construction Immobilier (json)

en: Real Estate construction projects (json)

de: Bauprojekte Immobilien (json)

it: Progetti di costruzione Immobili (json)

de: kml

fr: Projets de construction Immobilier (kml)

de: Bauprojekte Immobilien (kml)

en: Real Estate construction projects (kml)

it: Progetti di costruzione Immobili (kml)

fr: Projets de construction Immobilier (ov2)

en: Real Estate construction projects (ov2)

de: Bauprojekte Immobilien (ov2)

it: Progetti di costruzione Immobili (ov2)

de: jsonld

fr: Projets de construction Immobilier (jsonld)

en: Real Estate construction projects (jsonld)

de: Bauprojekte Immobilien (jsonld)

it: Progetti di costruzione Immobili (jsonld)

de: fgb

fr: Projets de construction Immobilier (fgb)

de: Bauprojekte Immobilien (fgb)

en: Real Estate construction projects (fgb)

it: Progetti di costruzione Immobili (fgb)

fr: Projets de construction Immobilier (xls)

fr: Projets de construction Immobilier (csv)

fr: Information sur les projets de construction en cours de CFF Immobilier.La mise à jour est quotidienne.

de: Informationen zu laufenden Bauprojekte der SBB Immobilien.Die Aktualisierung erfolgt täglich.Information on current construction projects of SBB Immobilien.Updating is done daily.

en: Information on ongoing SBB Real Estate construction projects.Updated daily.

it: Informazioni sui progetti di costruzione in corso presso FFS Immobili.L’aggiornamento è giornaliero.

schweizerische-bundesbahnen-sbb

fr:
Les CFF sont l’un des piliers d’une mobilité durable en Suisse. Chaque jour, les CFF acheminent 1,18 million de voyageurs et 210 000 tonnes de marchandises, de manière ponctuelle et respectueuse de l’environnement.

Le chemin de fer crée des liens à travers toute la Suisse, par-delà les frontières linguistiques, culturelles et politiques. Du point de vue économique et social, il contribue ainsi à la qualité de vie et à la compétitivité de notre pays. Mais les CFF ne sont pas qu’une entreprise de chemin de fer moderne et performante. En leur qualité d’employeur, de commanditaire, de propriétaire et de gérant d’immeubles, ils sont responsables de la Suisse, de ses régions et de l’environnement.

de:
Die SBB ist das Rückgrat einer nachhaltigen Mobilität in der Schweiz. Täglich sind 1,18 Millionen Reisende und 210'000 Tonnen Güter mit der SBB sicher, pünktlich und umweltfreundlich unterwegs.

Die Eisenbahn verbindet die Schweiz über alle sprachlichen, kulturellen und politischen Grenzen hinweg und leistet in wirtschaftlicher und gesellschaftlicher Hinsicht einen überaus wertvollen Beitrag an die Lebensqualität und Wettbewerbsfähigkeit unseres Landes. Die SBB ist aber weit mehr als ein modernes und leistungsfähiges Bahnunternehmen. Auch als Arbeits- und Auftraggeberin sowie als Immobilienbesitzerin und ‑bewirtschafterin übernimmt sie Verantwortung für die Schweiz, ihre Regionen und die Umwelt.

en:
SBB is the backbone of sustainable mobility in Switzerland. Every day, it safely transports 1.18 million passengers and 210,000 tonnes of freight on time and in an environmentally friendly manner.

The railway transcends all linguistic, cultural and political boundaries to bring Switzerland together, and in economic and social terms, it makes a vital contribution to the quality of life and competitiveness of our country. However, SBB is much more than just a modern, efficient railway company. As an employer and contractor, a property owner and a real estate manager, SBB takes responsibility for Switzerland, its regions and the environment.

it:
Le FFS assumono la loro responsabilità.

Consigliare ad altri

Condividere

Le FFS sono la colonna portante della mobilità sostenibile in Svizzera. Ogni giorno 1,18 milioni di viaggiatori e quasi 210 000 tonnellate di merci viaggiano con le FFS in modo sicuro, puntuale e rispettoso dell'ambiente.

La ferrovia unisce la Svizzera oltre i confini linguistici, culturali e politici e dal punto di vista economico e sociale assicura un contributo oltremodo prezioso alla qualità di vita e alla competitività del nostro Paese. Le FFS tuttavia sono ben più di un'impresa ferroviaria moderna e altamente performante. Esse assumono la loro responsalibità per la Svizzera, le sue regioni e l'ambiente anche come datore di lavoro, di committenti, così come di proprietarie e amministratrici di fondi e di immobili.


infrastrukturbetreiberinnen

title_for_slug: infrastrukturbetreiberinnen

state: active

type: dataset

de: gpx

media_type: application/gpx+xml

de: Infrastrukturbetreiberinnen (gpx)

format: gpx+xml

mimetype: application/gpx+xml

media_type: text/plain

de: Infrastrukturbetreiberinnen (ov2)

format: TXT

mimetype: text/plain

media_type: application/json

de: Infrastrukturbetreiberinnen (jsonl)

format: JSON

mimetype: application/json

de: xls

media_type: application/xls

de: Infrastrukturbetreiberinnen (xls)

format: XLS

mimetype: application/xls

de: Infrastrukturbetreiberinnen (geojson)

de: rdfxml

media_type: application/rdf+xml

de: Infrastrukturbetreiberinnen (rdfxml)

format: RDF XML

mimetype: application/rdf+xml

de: csv

media_type: text/csv

de: Infrastrukturbetreiberinnen (csv)

format: CSV

mimetype: text/csv

de: shp

media_type: application/zip

de: Infrastrukturbetreiberinnen (shp)

format: ZIP

mimetype: application/zip

de: Infrastrukturbetreiberinnen (kml)

format: KML

de: Infrastrukturbetreiberinnen (json)

media_type: application/octet-stream

de: Infrastrukturbetreiberinnen (fgb)

format: octet-stream

mimetype: application/octet-stream

de: Infrastrukturbetreiberinnen (n3)

de: turtle

media_type: text/turtle

de: Infrastrukturbetreiberinnen (turtle)

format: RDF Turtle

mimetype: text/turtle

media_type: application/ld+json

de: Welche Strecke wird von welcher Infrastrukturbetreiberin verantwortet


radiosondes

maintainer: Jörg Wieder

title_for_slug: radiosondes

en: radiosonde raclets wind relative-humidity temperature wind-direction holoballoon pressure

en: Radiosond Template Files

en:
The "template" files contain standard parameters, i.e. ambient pressure (mbar and Pa), temperature (°C), relative humidity (%), wind speed (m s‾ ¹) and wind direction (true deg), as well as additional information, e.g. time (UTC and local time), dew point (°C), mixing ration (g/kg), density (kg m³), altitude (m MSL), height above ground (ft and m AGL), and geographic coordinates (longitude, latitude). If the wind speed was zero, no wind direction is given.
The zip files contain ascents on the following days:
February 10 (morning and afternoon),
February 11 (morning),
February 16 (afternoon),
February 22 (morning),
March 1 (morning),
March 5 (morning),
March 11 (morning),
March 12 (morning),
March 13 (morning),
March 14 (morning),
March 17 (afternoon), and
March 18.
The data was managed with Windsond Receiver Application Demo Version 2.92, (C) Sparv Embedded AB 2016.

en: Radiosond Sounding Files

en:
The "soundings" radiosond data contains information on ambient pressure (mbar), temperature (C), relative humidity (%), wind speed (m s‾ ¹) and wind direction (true deg), and are ordered with respect to the height above ground (m AGL) with a step size of 20 m. The first line of each file idicates the geographic location and the start time (in UTC) of the ascent. If the wind speed was zero, no wind direction is given.
The zip files contain ascents on the following days:
February 10 (morning and afternoon),
February 11 (morning),
February 16 (afternoon),
February 22 (morning),
March 1 (morning),
March 5 (morning),
March 11 (morning),
March 12 (morning),
March 13 (morning),
March 14 (morning),
March 17 (afternoon), and
March 18.
The data was managed with Windsond Receiver Application Demo Version 2.92, (C) Sparv Embedded AB 2016.

en: Radiosondes (Windsond, Sparv Embedded AB) were started in Davos Wolfgang (LON: 9.853594, LAT: 46.835577) to report height profiles of pressure, relative humidity and temperature at specific days. In addition to regular launches of radiosondes, sondes were attached to [HoloBalloon](https://www.envidat.ch/group/clouds-in-situ-raclets) to report the ambient conditions of the in-situ measurements. Further profiles of meteorological measures were recorded at [HoloGondel](https://www.envidat.ch/group/clouds-in-situ-raclets) which was installed at the gondola moving between Gotschnaboden and Gotschnagrat at 2285 m a.s.l.

holoballoon

display_name: holoballoon

pressure

display_name: pressure

raclets

display_name: raclets

radiosonde

display_name: radiosonde

relative-humidity

display_name: relative-humidity

temperature

display_name: temperature

wind

display_name: wind

wind-direction

display_name: wind-direction

education

fr: Education, science

de: Bildung, Wissenschaft

en: Education and science

it: Formazione e scienza

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: Radiosondes


maintainer: Ansprechsstelle für Daten

de: Ein- und Aussteigende an Bahnhöfen (rdfxml)

de: geojson

de: Ein- und Aussteigende an Bahnhöfen (geojson)

de: Ein- und Aussteigende an Bahnhöfen (csv)

de: json

de: Ein- und Aussteigende an Bahnhöfen (json)

de: fgb

de: Ein- und Aussteigende an Bahnhöfen (fgb)

de: Ein- und Aussteigende an Bahnhöfen (turtle)

de: xls

de: Ein- und Aussteigende an Bahnhöfen (xls)

de: Ein- und Aussteigende an Bahnhöfen (shp)

de: jsonld

de: Ein- und Aussteigende an Bahnhöfen (jsonld)

format: JSON-LD

mimetype: application/ld+json

de: gpx

de: Ein- und Aussteigende an Bahnhöfen (gpx)

de: jsonl

de: Ein- und Aussteigende an Bahnhöfen (jsonl)

de: kml

de: Ein- und Aussteigende an Bahnhöfen (kml)

de: Ein- und Aussteigende an Bahnhöfen (n3)

de: Ein- und Aussteigende an Bahnhöfen (ov2)

de:
In den Zahlen sind die Ein- und Aussteigenden der in «EVU_EF_ITF» angegebenen Eisenbahnverkehrsunternehmen enthalten. Für die grösseren Bahnhöfe ist in den Bemerkungen angegeben, welche Eisenbahntransportunternehmen am jeweiligen Bahnhof nicht in den Zahlen enthalten sind. Ein- und Aussteigende anderer öffentlicher Verkehrsmittel sowie Passanten werden nicht berücksichtigt. Umsteigende zählen sowohl als Aus- wie als Einsteigende, also zweifach.
Letzter Update Juni.  2023
Detaillierte Informationen zu den Bahnhöfen und Haltestellen, beispielsweise deren Koordinaten, finden sich in der Liste der Stations- und Haltestellennamen, publiziert im Auftrag des BAV: https://opentransportdata.swiss/de/dataset/bav_liste

verkehr

display_name: verkehr

mobility

fr: Mobilité et transports

de: Mobilität und Verkehr

en: Mobility and Transport

it: Mobilità e trasporti

schweizerische-bundesbahnen-sbb

de:
Die SBB ist das Rückgrat einer nachhaltigen Mobilität in der Schweiz. Täglich sind 1,18 Millionen Reisende und 210'000 Tonnen Güter mit der SBB sicher, pünktlich und umweltfreundlich unterwegs.

Die Eisenbahn verbindet die Schweiz über alle sprachlichen, kulturellen und politischen Grenzen hinweg und leistet in wirtschaftlicher und gesellschaftlicher Hinsicht einen überaus wertvollen Beitrag an die Lebensqualität und Wettbewerbsfähigkeit unseres Landes. Die SBB ist aber weit mehr als ein modernes und leistungsfähiges Bahnunternehmen. Auch als Arbeits- und Auftraggeberin sowie als Immobilienbesitzerin und ‑bewirtschafterin übernimmt sie Verantwortung für die Schweiz, ihre Regionen und die Umwelt.


maintainer: Jana von Freyberg

en: alptal stable-water-isotope precipitation hydrology stream-water catchment

media_type: PDF

format: PDF

media_type: .txt

en:
Version 2 of the dataset contains additional information on:
- line-conditioned (lc) excess
- precipitation sample volumes
- isotope data quality

format: SERVICE

media_type: TXT

en: Single comma-separated txt-file with daily data of stable water isotopes measured in streamwater and precipitation in the Swiss Alptal catchment and two of its tributaries (Erlenbach and Vogelbach), as well as daily data of key hydrometeorological variables from the Alptal valley

en: Data Set Documentation: alptal_Isotopes_daily_2015-2019_v2

media_type: .pdf

en: Documentations contains meta data of the data columns and information about the study catchments.

en: This dataset contains four years of daily measurements of the natural isotopic composition (2H, 18O) of precipitation and stream water at the Alp catchment (area 47 km2) in Central Switzerland and two of its tributaries (0.73 km2 and 1.55 km2). In addition, the dataset contains daily measurements of key hydrometeorological variables.

alptal

display_name: alptal

catchment

display_name: catchment

hydrology

display_name: hydrology

precipitation

display_name: precipitation

stable-water-isotope

display_name: stable-water-isotope

stream-water

display_name: stream-water

education

de: Bildung, Wissenschaft

envidat

de: EnviDat: das Umwelt-Datenportal

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.

en: Four years of daily stable water isotope data in stream water and precipitation from three Swiss catchments


maintainer: Sektion energieeffizienter Verkehr

title_for_slug: Kennzahlen alternativ angetriebene LNF

de: ogd93-bfe elektromobilitat leichte-nutzfahrzeuge alternative-antriebe lieferwagen elektrische-fahrzeuge hybridfahrzeuge co2

fr: Chiffres clés des véhicules utilitaires légers neufs à propulsion alternative (VUL)

de: Kennzahlen alternativ angetriebene LNF

en: Key figures for alternatively powered LCVs

it: Indicatori VCL a propulsione alternativa

label: Storymap

fr: Modèles de VUL entièrement électriques (modèles BEV) les plus prisés

de: Beliebteste vollelektrische LNF-Modelle (BEV)

en: Most popular all-electric LCV models (BEV)

it: VCL completamente elettrici più diffusi (modelli BEV)

fr:
L’Office fédéral de l’énergie évalue depuis 2020 les modèles de VUL entièrement électriques (modèles BEV) les plus prisés. Pour l’année 2020, des chiffres trimestriels sont disponibles; à partir de 2021, les valeurs sont disponibles à un rythme mensuel.

La numérotation des cantons est celle de l’OFS.

en:
The Swiss Federal Office of Energy has since 2020 ranked the most popular all-electric LCV models (BEV). Quarterly figures are provided for 2020; from 2021 on, the values are available on a monthly basis.

Numbering of cantons according to FSO

de:
Das Bundesamt für Energie wertet seit 2020 bei den LNF die beliebtesten, vollelektrischen Modellen (BEV) aus. Für das Jahr 2020 sind Quartalszahlen bereitgestellt, ab 2021 sind die Werte in monatlicher Auflösung verfügbar.

Nummerierung der Kantone gemäss BFS

it:
Dal 2020 l'Ufficio federale dell'energia analizza i modelli di VCL completamente elettrici più diffusi. Per il 2020 vengono forniti dati trimestrali, mentre dal 2021 in poi i valori sono disponibili con risoluzione mensile.

Numerazione dei Cantoni secondo UST.

fr: Chiffres clés des VUL à propulsion alternative

fr:
Sur la base des données anonymisées des nouvelles immatriculations publiées par l’OFROU, l’Office fédéral de l’énergie évalue depuis 2020 des chiffres clés importants concernant les voitures de livraison et tracteurs à sellette légers à propulsion alternative (véhicules utilitaires légers, VUL). Sont réputés véhicules à propulsion alternative les véhicules ne disposant pas exclusivement d’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.

Légende: t1 = émissions moyennes de CO2, t2 = part BEV, t3 = part PHEV essence, t4 = part PHEV diesel, t5 = part hybride essence, t6 = part hybride diesel, t7 = part gaz naturel, t8 = part nouvelles immatriculations, t10 = part hydrogène

La numérotation des cantons est celle de l’OFS.

en:
On the basis of the anonymised data for new registrations published by FEDRO, the Swiss Federal Office of Energy has evaluated since 2020 the key figures for alternatively powered vans and light articulated vehicles (collectively: light commercial vehicles, LCVs). Vehicles are considered to be alternatively powered if they do not have an exclusively petrol or diesel-driven 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.

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

Numbering of cantons according to FSO

de:
Auf Basis der vom ASTRA publizierten, anonymisierten Neuzulassungsdaten wertet das Bundesamt für Energie ab 2020 wichtige Kennzahlen zu den alternativ angetriebenen Lieferwagen und leichten Sattelschlepper (Sammelbegriff: leichte Nutzfahrzeuge, LNF) aus. Als alternativ angetrieben gelten Fahrzeuge, bei denen nicht ausschliesslich ein Benzin- oder Diesel-Verbrennungsmotor eingebaut ist. Dieser Datensatz enthält neben aggregierten jährlichen Kennzahlen auch Quartals- und Monatszahlen der Marktanteile sowie Angaben zu den einzelnen Kantonen.

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

Nummerierung der Kantone gemäss BFS

it:
Sulla base dei dati anonimizzati sulle nuove immatricolazioni pubblicati dall'USTRA, dal 2020 l'Ufficio federale dell'energia analizza importanti indicatori relativi agli autofurgoni e ai trattori a sella leggeri (iperonimo: veicoli commerciali leggeri VCL) a propulsione alternativa. Per veicoli a propulsione alternativa si intendono veicoli sui quali non è installato esclusivamente 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.

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

Numerazione dei Cantoni secondo UST.

fr: Modèles de VUL hybrides rechargeables (modèles PHEV) les plus prisés

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

en: Most popular plug-in models (LCV)

it: VCL plug-in più diffusi (modelli PHEV)

fr:
L’Office fédéral de l’énergie évalue depuis 2020 les modèles de VUL hybrides rechargeables (modèles PHEV) les plus prisés. Pour l’année 2020, des chiffres trimestriels sont disponibles; à partir de 2021, les valeurs sont disponibles à un rythme mensuel.

La numérotation des cantons est celle de l’OFS.

en:
The Swiss Federal Office of Energy has since 2020 ranked the most popular plug-in LCV models (PHEV). Quarterly figures are provided for 2020; from 2021 on, the values are available on a monthly basis.

Numbering of cantons according to FSO

de:
Das Bundesamt für Energie wertet seit 2020 bei den LNF die beliebtesten Plug-In LNF-Modelle (PHEV) aus. Für das Jahr 2020 sind Quartalszahlen bereitgestellt, ab 2021 sind die Werte in monatlicher Auflösung verfügbar.

Nummerierung der Kantone gemäss BFS

it:
Dal 2020 l'Ufficio federale dell'energia analizza i modelli di VCL plug-in più diffusi (modelli PHEV). Per il 2020 vengono forniti dati trimestrali, mentre dal 2021 in poi i valori sono disponibili con risoluzione mensile.

Numerazione dei Cantoni secondo UST.

fr:
Sur la base des données anonymisées des nouvelles immatriculations publiées par l’OFROU, l’Office fédéral de l’énergie évalue depuis 2020 des chiffres clés importants portant sur les voitures de livraison et tracteurs à sellette légers à propulsion alternative (véhicules utilitaires légers, VUL). 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’OFEN évalue en outre les modèles de VUL entièrement électriques 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 since 2020 evaluated key figures for alternatively powered vans and light articulated vehicles (collectively: light commercial vehicles, LCVs). 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 LCV models.

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

de:
Auf Basis der vom ASTRA publizierten, anonymisierten Neuzulassungsdaten wertet das Bundesamt für Energie seit 2020 die wichtigsten Kennzahlen zu den alternativ angetriebenen Lieferwagen und leichten Sattelschlepper (Sammelbegriff: leichte Nutzfahrzeuge, LNF) aus. Als alternativ angetrieben gelten Fahrzeuge, die nicht ausschliesslich über einen Benzin- oder Diesel-Verbrennungsmotor angetrieben werden. Dieser Datensatz enthält neben aggregierten jährlichen Kennzahlen auch Quartal- und Monatszahlen der Marktanteile sowie Angaben zu den einzelnen Kantonen. Zudem wertet das Bundesamt für Energie bei den LNF die beliebtesten, vollelektrischen Modellen aus.

Rechtsgrundlage: Energieeffizienzverordnung (EnEV) Art. 11

it:
Sulla base dei dati anonimizzati sulle nuove immatricolazioni pubblicati dall'USTRA, dal 2020 l'Ufficio federale dell'energia analizza importanti indicatori relativi agli autofurgoni e ai trattori a sella leggeri (iperonimo: veicoli commerciali leggeri VCL) 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 VCL completamente elettrici 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

leichte-nutzfahrzeuge

display_name: leichte-nutzfahrzeuge

lieferwagen

display_name: lieferwagen

energy

fr: Energie

de: Energie

en: Energy

it: Energia

territory

fr: Territoire et environnement

de: Raum und Umwelt

en: Territory and environment

it: Territorio e ambiente

bundesamt-fur-energie-bfe

fr: Office fédéral de l'énergie OFEN

de: Bundesamt für Energie BFE

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).

political_level: confederation


modeling-snow-failure-with-dem

title_for_slug: modeling-snow-failure-with-dem

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

en: Dataset

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

en: Python file

media_type: text/x-python

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

mimetype: text/x-python

en: Read-me

media_type: application/pdf

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

education

en: Education and science

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: Modeling snow failure with DEM


fr: immobilien gare

de: bahnhof immobilien

en: station immobilien

it: stazione immobilien

de: xls

fr: Images historiques de gares ferroviaires (xls)

de: Historische Bahnhofbilder / historical Pictures of trainstations (xls)

en: Historic pictures of railway stations (xls)

it: Immagini storiche di stazioni (xls)

de: turtle

fr: Images historiques de gares ferroviaires (turtle)

de: Historische Bahnhofbilder / historical Pictures of trainstations (turtle)

en: Historic pictures of railway stations (turtle)

it: Immagini storiche di stazioni (turtle)

de: shp

fr: Images historiques de gares ferroviaires (shp)

de: Historische Bahnhofbilder / historical Pictures of trainstations (shp)

en: Historic pictures of railway stations (shp)

it: Immagini storiche di stazioni (shp)

de: jsonl

fr: Images historiques de gares ferroviaires (jsonl)

en: Historic pictures of railway stations (jsonl)

de: Historische Bahnhofbilder / historical Pictures of trainstations (jsonl)

it: Immagini storiche di stazioni (jsonl)

de: csv

fr: Images historiques de gares ferroviaires (csv)

en: Historic pictures of railway stations (csv)

de: Historische Bahnhofbilder / historical Pictures of trainstations (csv)

it: Immagini storiche di stazioni (csv)

de: geojson

fr: Images historiques de gares ferroviaires (geojson)

de: Historische Bahnhofbilder / historical Pictures of trainstations (geojson)

en: Historic pictures of railway stations (geojson)

it: Immagini storiche di stazioni (geojson)

fr: Images historiques de gares ferroviaires (n3)

de: Historische Bahnhofbilder / historical Pictures of trainstations (n3)

en: Historic pictures of railway stations (n3)

it: Immagini storiche di stazioni (n3)

de: gpx

fr: Images historiques de gares ferroviaires (gpx)

de: Historische Bahnhofbilder / historical Pictures of trainstations (gpx)

en: Historic pictures of railway stations (gpx)

it: Immagini storiche di stazioni (gpx)

de: json

fr: Images historiques de gares ferroviaires (json)

de: Historische Bahnhofbilder / historical Pictures of trainstations (json)

en: Historic pictures of railway stations (json)

it: Immagini storiche di stazioni (json)

de: rdfxml

fr: Images historiques de gares ferroviaires (rdfxml)

en: Historic pictures of railway stations (rdfxml)

de: Historische Bahnhofbilder / historical Pictures of trainstations (rdfxml)

it: Immagini storiche di stazioni (rdfxml)

de: kml

fr: Images historiques de gares ferroviaires (kml)

de: Historische Bahnhofbilder / historical Pictures of trainstations (kml)

en: Historic pictures of railway stations (kml)

it: Immagini storiche di stazioni (kml)

de: fgb

fr: Images historiques de gares ferroviaires (fgb)

de: Historische Bahnhofbilder / historical Pictures of trainstations (fgb)

en: Historic pictures of railway stations (fgb)

it: Immagini storiche di stazioni (fgb)

de: jsonld

fr: Images historiques de gares ferroviaires (jsonld)

en: Historic pictures of railway stations (jsonld)

de: Historische Bahnhofbilder / historical Pictures of trainstations (jsonld)

it: Immagini storiche di stazioni (jsonld)

fr: Images historiques de gares ferroviaires (ov2)

de: Historische Bahnhofbilder / historical Pictures of trainstations (ov2)

en: Historic pictures of railway stations (ov2)

fr: Photos historiques des gares de Suisse. Données fournies par CFF Historic

en: Historic pictures of railway stations in Switzerland. The data is provided by SBB Historic

de: historische Bilder von Bahnhöfen der Schweiz. Die Daten werden von SBB Historic zur Verfügung gestellt

it: Immagini storiche delle stazioni in Svizzera. Fonte dei dati: FFS Historic

schweizerische-bundesbahnen-sbb

fr:
Les CFF sont l’un des piliers d’une mobilité durable en Suisse. Chaque jour, les CFF acheminent 1,18 million de voyageurs et 210 000 tonnes de marchandises, de manière ponctuelle et respectueuse de l’environnement.

Le chemin de fer crée des liens à travers toute la Suisse, par-delà les frontières linguistiques, culturelles et politiques. Du point de vue économique et social, il contribue ainsi à la qualité de vie et à la compétitivité de notre pays. Mais les CFF ne sont pas qu’une entreprise de chemin de fer moderne et performante. En leur qualité d’employeur, de commanditaire, de propriétaire et de gérant d’immeubles, ils sont responsables de la Suisse, de ses régions et de l’environnement.

en:
SBB is the backbone of sustainable mobility in Switzerland. Every day, it safely transports 1.18 million passengers and 210,000 tonnes of freight on time and in an environmentally friendly manner.

The railway transcends all linguistic, cultural and political boundaries to bring Switzerland together, and in economic and social terms, it makes a vital contribution to the quality of life and competitiveness of our country. However, SBB is much more than just a modern, efficient railway company. As an employer and contractor, a property owner and a real estate manager, SBB takes responsibility for Switzerland, its regions and the environment.


maintainer: EnviDat Support

en: tls preferential-deposition alpine3d lidar lee-side-flow-field

en: Dataset

media_type: ZIP

en:
The data presented here corresponds to the publication "A Close-Ridge Small-Scale Atmospheric Flow Field and its Influence on Snow Accumulation" (Gerber et al., 2017), which investigates an eddy-like structure in the vicinity of the Sattelhorn in the Dischma valley (Davos Switzerland) and its influence on snow accumulation. The dataset contains:

* Alpine3D: Alpine3D snow depth grids (25 m resolution) for two simulations with and without snow redistribution.
* ARPS: 10 ARPS simulations (25 m horizontal resolution) with different model setups (wind direction, wind speed, stability).
* LiDAR: Processed LiDAR PPI (D2_PPI_1h) and RHI (D2_cross_1h) across the valley with a hourly resolution for the period 27 October 2015 01:00 - 29 October 2015c 21:00 (spatial resolution: 25 m).
* meteostations-dischma: Meteorological station data of two meteorological stations in the Dischma valley with 10 minute resolution for the period 28 October 2015 - 30 October 2015.
* TLS: Snow depth change data (m) between 28 October 2015 and 30 October 2015 based on terrestrial laser scans.

For more details about the simulation and observation data, see Gerber et al., 2017.

Publication: Gerber et al., 2017: A Close-Ridge Small-Scale Atmospheric Flow Field and its Influence on Snow Accumulation, submitted to JGR - Atmospheres.

lee-side-flow-field

display_name: lee-side-flow-field

lidar

display_name: lidar

preferential-deposition

display_name: preferential-deposition

tls

display_name: tls

education

fr: Education, science

de: Bildung, Wissenschaft

en: Education and science

it: Formazione e scienza

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.

en: DISCHMEX - Observations and simulations of the close-ridge small-scale atmospheric flow field and snow accumulation at Sattelhorn, Dischma valley, Davos, Switzerland.


maintainer: Bundesamt für Energie BFE

title_for_slug: Gebäudeprogramm: Jahresbericht und Jahresstatistik

fr: ogd18-bfe batiments renovation energie-renouvelable chauffage isolation proprietaires subventions

de: ogd18-bfe gebaude sanierung erneuerbare-energie heizung dammung hauseigentumer fordermittel

fr: Programme Bâtiments: rapports annuels et statistique annuelles

it: Programma Edifici: rapporti e statistiche annuali

author: Bundesamt für Energie BFE

fr: Versements des subventions selon domaine de mesures par canton et année de référence

fr: Versements des subventions selon domaine de mesures (isolation thermique, installations techniques, rénovation du système, nouvelle construction, approvisionnement en chaleur centralisé, mesures indirectes) par canton et année de référence

de: Auszahlungen nach Massnahmenbereich (Haustechnik, Wärmedämmung, Systemsanierung, Neubau, Zentrale Wärmeversorgung, Indirekte Massnahmen) pro Kanton und Berichtsjahr

de: Vordefinierte SPARQL Query auf LINDAS für Auszahlungen nach Massnahmenbereich pro Kanton und Berichtsjahr

format: SPARQL

fr: Nombre de demandes soutenues (toutes les mesures directes)

de: Anzahl unterstützter Gesuche (alle direkten Massnahmen)

fr: Aperçu du nombre de demandes et bâtiments pour les années de référence (2017 à 2022) (projets agrégés par année de versement)

de: Übersicht zur Anzahl Gesuche und Gebäude für Berichtsjahre (2017 bis 2022) (Projekte zusammengefasst nach Auszahlungsjahr)

de: Vordefinierte SPARQL Query auf LINDAS für Anzahl unterstützter Gesuche

de: Energiewirkungen je Massnahmenbereich

fr: Effets énergétiques induits par domaine de mesures (isolation thermique, installations techniques, rénovation du système, nouvelle construction, approvisionnement en chaleur centralisé, mesures indirectes), depuis le début du Programme Bâtiments (calculés sur la base du ModEnHa 2015)

de: Anhaltende Energiewirkungen je Massnahmenbereich (Haustechnik, Wärmedämmung, Systemsanierung, Neubau, Zentrale Wärmeversorgung, Indirekte Massnahmen), seit Start des Gebäudeprogramms (berechnet auf Basis des HFM 2015)

de: Vordefinierte SPARQL Query auf LINDAS für Energiewirkungen je Massnahmenbereich

fr: Effets sur les émissions de CO2 induits

de: CO2-Wirkungen je Massnahmenbereich

fr: Effets sur les émissions de CO2 induits par domaine de mesures (isolation thermique, installations techniques, rénovation du système, nouvelle construction, approvisionnement en chaleur centralisé, mesures indirectes), depuis le début du Programme Bâtiments (calculés sur la base du ModEnHa 2015)

de: Anhaltende CO2-Wirkungen je Massnahmenbereich (Haustechnik, Wärmedämmung, Systemsanierung, Neubau, Zentrale Wärmeversorgung, Indirekte Massnahmen), seit Start des Gebäudeprogramms (berechnet auf Basis des HFM 2015)

de: Vordefinierte SPARQL Query auf LINDAS für CO2-Wirkungen je Massnahmenbereich

fr:
Le Programme Bâtiments a été lancé en 2010 pour inciter les propriétaires à assainir leurs biens sur le plan énergétique et à apporter par là une contribution concrète à l’amélioration de l’efficacité énergétique et à la protection du climat. Jusqu’à fin 2016, le Programme Bâtiments comprenait un volet A commun à tout le pays pour l’assainissement de l’enveloppe du bâtiment et un volet B variable selon les cantons pour les énergies renouvelables, la récupération de chaleur et l’amélioration des installations techniques.
En six années d’exploitation, le programme a versé près de 1,184 milliard de francs de subventions. Sur toute leur durée de vie, les mesures ainsi soutenues permettront d’économiser environ 18,6 millions de tonnes de CO2 et 93 724 gigawattheures d’énergie.
Informations supplémentaires sur les résultats et les effets du Programme Bâtiments dans les rapports annuels et les statistiques annuelles.

de:
Seit 2010 motiviert Das Gebäudeprogramm Eigentümer/-innen, mit der energetischen Sanierung ihrer Liegenschaft einen konkreten Beitrag zur Energieeffizienz und zum Klimaschutz zu leisten. Bis Ende 2016 bestand das Programm aus einem schweizweit einheitlichen Teil A (energetische Sanierung der Gebäudehülle) und einem kantonal unterschiedlichen Teil B (Einsatz erneuerbarer Energien, Abwärmenutzung und Optimierung der Gebäudetechnik).
Nach sechs Betriebsjahren hat Das Gebäudeprogramm insgesamt rund 1,184 Milliarden Franken an Fördermitteln ausbezahlt. Damit können über die Lebensdauer der geförderten Massnahmen insgesamt rund 18,6 Millionen Tonnen CO2 vermieden bzw. rund 93‘724 Gigawattstunden Energie eingespart werden.
Mehr Informationen zu den Resultaten und Wirkungen des Gebäudeprogramms können Sie den Jahresberichten und -statistiken entnehmen.

statistical-basis

fr: Bases statistiques

de: Statistische Grundlagen

en: Statistical basis

it: Basi statistiche

construction

fr: Construction, logement

de: Bau- und Wohnungswesen

en: Construction and housing

it: Costruzione e abitazione

national-economy

fr: Economie nationale

de: Volkswirtschaft

en: National economy

it: Economia

energy

fr: Energie

de: Energie

en: Energy

it: Energia

bundesamt-fur-energie-bfe

fr: Office fédéral de l'énergie OFEN

de: Bundesamt für Energie BFE

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).


aufzugsstammdaten

title_for_slug: aufzugsstammdaten

de: barrierefrei infrastruktur immobilien

de: xls

fr: Données de base sur les ascenseurs (xls)

de: Aufzugsstammdaten (xls)

en: Elevator master data (xls)

it: Dati anagrafici ascensori (xls)

de: rdfxml

fr: Données de base sur les ascenseurs (rdfxml)

en: Elevator master data (rdfxml)

de: Aufzugsstammdaten (rdfxml)

it: Dati anagrafici ascensori (rdfxml)

fr: Données de base sur les ascenseurs (n3)

en: Elevator master data (n3)

de: Aufzugsstammdaten (n3)

it: Dati anagrafici ascensori (n3)

de: shp

fr: Données de base sur les ascenseurs (shp)

en: Elevator master data (shp)

de: Aufzugsstammdaten (shp)

it: Dati anagrafici ascensori (shp)

de: jsonl

fr: Données de base sur les ascenseurs (jsonl)

en: Elevator master data (jsonl)

de: Aufzugsstammdaten (jsonl)

it: Dati anagrafici ascensori (jsonl)

de: json

fr: Données de base sur les ascenseurs (json)

de: Aufzugsstammdaten (json)

en: Elevator master data (json)

it: Dati anagrafici ascensori (json)

de: fgb

fr: Données de base sur les ascenseurs (fgb)

de: Aufzugsstammdaten (fgb)

en: Elevator master data (fgb)

it: Dati anagrafici ascensori (fgb)

de: jsonld

fr: Données de base sur les ascenseurs (jsonld)

de: Aufzugsstammdaten (jsonld)

en: Elevator master data (jsonld)

it: Dati anagrafici ascensori (jsonld)

de: gpx

media_type: application/gpx+xml

fr: Données de base sur les ascenseurs (gpx)

de: Aufzugsstammdaten (gpx)

en: Elevator master data (gpx)

it: Dati anagrafici ascensori (gpx)

de: csv

fr: Données de base sur les ascenseurs (csv)

en: Elevator master data (csv)

de: Aufzugsstammdaten (csv)

it: Dati anagrafici ascensori (csv)

fr: Données de base sur les ascenseurs (turtle)

fr: Données de base sur les ascenseurs (ov2)

fr: Données de base sur les ascenseurs (kml)

fr: Données de base sur les ascenseurs (geojson)

fr: Données de base sur les ascenseurs et escaliers mécaniques des CFF

de: Stammdaten zu den Liften und Rolltreppen der SBBMaster data on SBB lifts and escalators

it: Dati anagrafici su ascensori e scale mobili delle FFS

schweizerische-bundesbahnen-sbb

fr:
Les CFF sont l’un des piliers d’une mobilité durable en Suisse. Chaque jour, les CFF acheminent 1,18 million de voyageurs et 210 000 tonnes de marchandises, de manière ponctuelle et respectueuse de l’environnement.

Le chemin de fer crée des liens à travers toute la Suisse, par-delà les frontières linguistiques, culturelles et politiques. Du point de vue économique et social, il contribue ainsi à la qualité de vie et à la compétitivité de notre pays. Mais les CFF ne sont pas qu’une entreprise de chemin de fer moderne et performante. En leur qualité d’employeur, de commanditaire, de propriétaire et de gérant d’immeubles, ils sont responsables de la Suisse, de ses régions et de l’environnement.

de:
Die SBB ist das Rückgrat einer nachhaltigen Mobilität in der Schweiz. Täglich sind 1,18 Millionen Reisende und 210'000 Tonnen Güter mit der SBB sicher, pünktlich und umweltfreundlich unterwegs.

Die Eisenbahn verbindet die Schweiz über alle sprachlichen, kulturellen und politischen Grenzen hinweg und leistet in wirtschaftlicher und gesellschaftlicher Hinsicht einen überaus wertvollen Beitrag an die Lebensqualität und Wettbewerbsfähigkeit unseres Landes. Die SBB ist aber weit mehr als ein modernes und leistungsfähiges Bahnunternehmen. Auch als Arbeits- und Auftraggeberin sowie als Immobilienbesitzerin und ‑bewirtschafterin übernimmt sie Verantwortung für die Schweiz, ihre Regionen und die Umwelt.

it:
Le FFS assumono la loro responsabilità.

Consigliare ad altri

Condividere

Le FFS sono la colonna portante della mobilità sostenibile in Svizzera. Ogni giorno 1,18 milioni di viaggiatori e quasi 210 000 tonnellate di merci viaggiano con le FFS in modo sicuro, puntuale e rispettoso dell'ambiente.

La ferrovia unisce la Svizzera oltre i confini linguistici, culturali e politici e dal punto di vista economico e sociale assicura un contributo oltremodo prezioso alla qualità di vita e alla competitività del nostro Paese. Le FFS tuttavia sono ben più di un'impresa ferroviaria moderna e altamente performante. Esse assumono la loro responsalibità per la Svizzera, le sue regioni e l'ambiente anche come datore di lavoro, di committenti, così come di proprietarie e amministratrici di fondi e di immobili.


de: turtle

en: Special Train - Polyhack (turtle)

de: xls

en: Special Train - Polyhack (xls)

en: Special Train - Polyhack (n3)

media_type: text/plain

en: Special Train - Polyhack (ov2)

en: Special Train - Polyhack (fgb)

en: Special Train - Polyhack (shp)

en: Special Train - Polyhack (jsonld)

de: geojson

en: Special Train - Polyhack (geojson)

en: Special Train - Polyhack (csv)

en: Special Train - Polyhack (jsonl)

de: rdfxml

en: Special Train - Polyhack (rdfxml)

de: kml

en: Special Train - Polyhack (kml)

en: Special Train - Polyhack (json)

en: This Dataset includes all Special Trains from 1.1.2018 to the 10.10.2020. It is dedicated to the Polyhack. The Source of the information is https://data.sbb.ch/explore/dataset/ist-daten-sbb/

schweizerische-bundesbahnen-sbb

en:
SBB is the backbone of sustainable mobility in Switzerland. Every day, it safely transports 1.18 million passengers and 210,000 tonnes of freight on time and in an environmentally friendly manner.

The railway transcends all linguistic, cultural and political boundaries to bring Switzerland together, and in economic and social terms, it makes a vital contribution to the quality of life and competitiveness of our country. However, SBB is much more than just a modern, efficient railway company. As an employer and contractor, a property owner and a real estate manager, SBB takes responsibility for Switzerland, its regions and the environment.


maintainer: Leo Gallus Bont

en: forest geoprocessing qgis accessible-forest seilaplan openstreetmap

en: Railways vector data (close to the sample forest range)

en: Power lines vector data (close to the area of the sample forest range)

en: Preview

media_type: image/png

format: PNG

mimetype: image/png

en: Juypter Notebook for Data Preparation

media_type: ipynb

en: DEM (for the area of the sample forest range)

media_type: image/tiff

format: TIFF

mimetype: image/tiff

en: Juypter Notebook for Data Preparation (online)

en: Forest vector data (for sample forest range)

en: Digitised sample perimeter

en:
This dataset contains open vector data for railways, forests and power lines, as well an open digital elevation model (DEM) for a small area around a sample forest range in Europe (Germany, Upper Bavaria, Kochel Forest Range, some 70 km south of München, at the edge of Bavarian Alps).
The purpose of this dataset is to provide a documented sample dataset in order to demonstrate geospatial preprocessing at FOSS4G2019 based on open data and software. This sample has been produced based on several existing open data sources (detailed below), therefore documenting the sources for obtaining some data needed for computations related to forest accessibility and wood harvesting. For example, they can be used with the open methodology and QGIS plugin [Seilaplan]( https://doi.org/10.16904/envidat.software.1) for optimising the geometric layout cable roads or with additional open software for computing the forest accessibility for wood harvesting.
The vector data (railways, forests and power lines) was extracted from OpenStreetMap (data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org). The railways and forests were downloaded and extracted on 18.05.2019 using the open sources QGIS (https://www.qgis.org) with the QuickOSM plugin, while the power lines were downloaded a couple of days later on 23.05.2019.

Additional notes for vector data:
Please note that OpenStreeMap data extracts such as forests, roads and railways (except power lines) can also be downloaded in a GIS friendly format (Shapefile) from http://download.geofabrik.de/ or using the QGIS built-in download function for OpenStreetMap data. The most efficient way to retrieve specific OSM tags (such as power=line) is to use the QuickOSM plugin for QGIS (using the Overpass API - https://wiki.openstreetmap.org/wiki/Overpass_API) or directly using overpass turbo (https://overpass-turbo.eu/). Finally, the digitised perimeter of the sample forest range is also made available for reproducibility purposes, although any perimeter or area can be digitised freely using the QGIS editing toolbar.

The DEM was originally adapted and modified also with QGIS (https://www.qgis.org) based on the elevation data available from two different sources, by reprojecting and downsampling datasets to 25m then selecting, for each individual raster cell, the elevation value that was closer to the average. These two different elevation sources are:

- Copernicus Land Monitoring Service - EU-DEM v.1.1 (TILE ID E40N20, downloaded from https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1; this original DEM was produced by the Copernicus Land Monitoring Service “with funding by the European Union” based on SRTM and ASTER GDEM)
- Digitales Geländemodell 50 m Gitterweite (https://opendata.bayern.de/detailansicht/datensatz/digitales-gelaendemodell-50-m-gitterweite/), produced by the Bayerische Vermessungsverwaltung – www.geodaten.bayern.de –and downloaded from http://www.geodaten.bayern.de/opendata/DGM50/dgm50_epsg4258.tif

This methodology was chosen as a way of performing a basic quality check, by comparing the EU-DEM v.1.1 derived from globally available DEM data (such as SRTM) with more authoritative data for the randomly selected region, since using authoritative data is preferred (if open and available). For other sample regions, where authoritative open data is not available, such comparisons cannot longer be performed.

Additional notes DEM: a very good DEM open data source for Germany is the open data set collected and resampled by Sonny (sonnyy7@gmail.com) and made available on the Austrian Open Data Portal http://data.opendataportal.at/dataset/dtm-germany. In order to simplify end-to-end reproducibility of the paper planned for FOSS4G2019, we use and distribute an adapted (reprojected and resampled to 25 meters) sample of the above mentioned dataset for the selected forest range.

This sample dataset is accompanied by software in Python, as a Jupiter Notebook that generates harmonized output rasters with the same extent from the input data. The extent is given by the polygon vector dataset (Perimeter). These output rasters, such as obstacles, aspect, slope, forest cover, can serve as input data for later computations related to forest accessibility and wood harvesting questions. The obstacles output is obtained by transforming line vector datasets (railway lines, high voltage power lines) to raster. Aspect and slope are both derived from the sample digital elevation model.

accessible-forest

display_name: accessible-forest

forest

display_name: forest

geoprocessing

display_name: geoprocessing

openstreetmap

display_name: openstreetmap

qgis

display_name: qgis

seilaplan

display_name: seilaplan

education

fr: Education, science

de: Bildung, Wissenschaft

en: Education and science

it: Formazione e scienza

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.

en: Sample Geodata and Software for Demonstrating Geospatial Preprocessing for Forest Accessibility and Wood Harvesting at FOSS4G2019


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

en: Sonic temperature

media_type: npy

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

en: Sonic wind component v

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

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

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

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

en: Sonic wind component u

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

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

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

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

en: Sonic wind component w

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

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

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

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

en: Sonic time

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

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

anemometer

display_name: anemometer

raclets

display_name: raclets

wind

display_name: wind

wind-direction

display_name: wind-direction

wind-speed

display_name: wind-speed

wind-velocity

display_name: wind-velocity

en: Snow Drift Station - 3D Ultrasonic


causal-effect-of-lup

title_for_slug: causal-effect-of-lup

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

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

education

en: Education and science

en: Causal effect of LUP


en: lidar mobile-laser-scanning terrestrial-photogrammetry terrestrial-laser-scanning unmanned-aerial-vehicle-laser-scanning forest-inventory structure-from-motion 3d-point-cloud

en:
Close Range Remote Sensing Benchmark for different LiDAR and photogrammetric Sensors in a mixed temperate forest.
Benchmarks are needed to evaluate the performance of different close-range remote sensing devices and approaches, both in terms of efficiency as well as accuracy. In this study we evaluate the performance of two terrestrial (TLS), one handheld mobile (PLS) and two drone based (UAVLS) laser scanning systems to detect trees and extract the diameter at breast height (DBH) in three plots with a steep gradient in tree and understorey vegetation density. As a novelty, we also tested the acquisition of 3D point-clouds using a low-cost action camera (GoPro) in conjunction with the Structure from Motion (SfM) technique and compared its performance with those of the more costly LiDAR devices.

forest-inventory

display_name: forest-inventory

lidar

display_name: lidar

mobile-laser-scanning

display_name: mobile-laser-scanning

structure-from-motion

display_name: structure-from-motion

terrestrial-laser-scanning

display_name: terrestrial-laser-scanning

terrestrial-photogrammetry

display_name: terrestrial-photogrammetry

unmanned-aerial-vehicle-laser-scanning

display_name: unmanned-aerial-vehicle-laser-scanning

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: Ramerenwald Close Range Remote Sensing Benchmark


en: mountain albedo energy irradiance snow photovoltaic reflection

en: PV Data

media_type: application/zip

en: Windows users use the 7-zip app to extract the zip-file.

en:
 Overview
The SUNWELL Modelling Environment is a combination of data and code that models electricity production from satellite-derived irradiance data and other spatial data sets for all of Switzerland. This ensemble accompanies the publication "The bright side of PV production in snow-covered mountains", published in the Proceedings of the National Academy of Science and reproduces all results and figures of. Code and resources are in their original form (with documentation). A new version with a more generalized application to PV modelling and with more flexibility in terms of input and output formats will be released in the coming months.

 Format
All code is written and has to be executed in Matlab. The input and output data sets are also in the Matlab-specific .mat format. Whenever publicly available, the original data is provided as geotif, .xlsx or other common format.
This is the case for:

- Digital Elevation Model (InputsFromMatlab/MSG/OriginalData/ASTERDEM),
- Landsurface cover type (InputsFromMatlab/MSG/OriginalData/CORINE),
- Population Density (InputsFromMatlab/MSG/OriginalData/popdensRaster,
- Electricity production from three of our validation sites (/Validation/WSL),
- Measured irradiance for two validation sites (/Validation/ASRB)
The ‘Metadata’ documents in the respective folders provide further information about the data sources and processing. Figures are produced either in .pdf or .png format.

 Structure
The central level of the SUNWELL environment holds the 5 Mains, which run the different modelling aspects of the paper; each code is documented separately. Additional code is located in the ‘DataProcessing’ and the ‘functions’ folder. Functions are called in the different Mains.

‘InputsFromMatlab’ contains the radiation and albedo input data sets in separate subfolders (SIS/SISDIR/ALB). The original data is not publicly available, but can be requested for research purposes free of charge. We provide a processed subset of the data set that was used to run the SUNWELL simulations. The MSG subfolder contains additional spatial input data sets.

‘Outputs’ contains the output files from the different mains (matching names, Main_CHallpixels.m  Prod_CHallpixels)

‘Publication_figures’ contains all individual figures from the PNAS publication, as well as the generating code (/code_plot) and the power point figures (/ppts) that provide the combined final figures.

‘Validation’ contains the data sets used in the model validation:

- Electricity production from three of our validation sites (/WSL),
- Measured irradiance for two validation sites (/ASRB)

Electricity production from a validation site at Lac des Toules in Wallis (/LDT), this data set was provided under an NDA and cannot be made publicly available.

Paper Citation:
 > _Annelen Kahl; Jérôme Dujardin; Michael Lehning (2018). Dataset on PV Production in Snow Covered Mountains. PNAS - Proceedings of the National Academy of Sciences. (in press)_

albedo

display_name: albedo

energy

display_name: energy

irradiance

display_name: irradiance

mountain

display_name: mountain

photovoltaic

display_name: photovoltaic

reflection

display_name: reflection

snow

display_name: snow


en: low-flow extreme-values reservoir-inflow return-periods alpine-precipitation

en: Daily Luzzone Reservoir Inflow 01-01-2000 to 31-12-2019

media_type: .csv

en: Daily total water inflow to Luzzone reservoir. Natural inflow (LUZ_inflow_raw) is back-calculated based on daily water level in reservoir (removing hydropower inputs and spills). Linear interpolation between daily values is used to bring all inflow values into positive space (LUZ_inflow_intrp).

en: R codes for Extreme Values Analysis

media_type: zip-compresses folder of R scripts

en: Zip file containing R codes for running extreme value analyses for high precipitation and inflow events as well as code for determining return levels of low inflow and low precipitation events

en:
 Summary

* Dataset of daily inflow to Luzzone reservoir in Ticino, Switzerland

* R scripts used to generate return levels for low reservoir inflow, low precipitation, high inflow, and extreme high precipitation based on various methods from extreme value analysis

 Data
The dataset included here is the "natural" reservoir inflow for the Luzzone reservoir.
Additional analyses were conducted on daily total precipitation of 6 meteorological stations (abbreviations: TIOLI, TIOLV, COM, VRN, VLS, ZEV). These precipitation data are freely available for teaching and research from the MeteoSwiss IDAweb portal (https://www.meteoswiss.admin.ch/services-and-publications/service/weather-and-climate-products/data-portal-for-teaching-and-research.html).

 Codes
R scripts used to determine return levels of the data set are included for both extreme high events and low events. The scripts include the following methods for calculating return levels:

* GEV (Generalized Extreme Value)

* GPD and GPDd (Generalized Pareto Distribution including declustered version)

* eGPD (extended Generalized Pareto Distribution)

* MEV (Metastatistical Extreme Value)

alpine-precipitation

display_name: alpine-precipitation

extreme-values

display_name: extreme-values

low-flow

display_name: low-flow

reservoir-inflow

display_name: reservoir-inflow

return-periods

display_name: return-periods

en: Data and Code on Extreme Inflow and Lowflow Analysis for Alpine Reservoirs


sensitivity-of-modeled-snow-instability

maintainer: Bettina Richter

title_for_slug: sensitivity-of-modeled-snow-instability

en: global-sensitivity-analysis snow-cover-modeling snowpack snow-instability numerical-avalanche-forecasting

en: Reference run

en: Reference run for the sensitivity analysis at Weissfluhjoch field site for winter season 2016-2017. Meteorological data from the automatic weather stations are used. No biases were introduced to the input data.

en: Case SL

en: Extracted output data for case SL. Here biases were introduced to meteorological input data during the period of slab formation. Data include quasi random biases, which were introduced, extracted output of simulated snow profiles and sobol indices for each output variable.

en: Case WL

en: Extracted output data for case ALL. Here biases were introduced to meteorological input data during the period of weak layer formation. Data include quasi random biases, which were introduced, extracted output of simulated snow profiles and sobol indices for each output variable.

en: Case ALL

en: Extracted output data for case ALL. Here biases were introduced to meteorological input data during the whole simulation period. Data include quasi random biases, which were introduced, extracted output of simulated snow profiles and sobol indices for each output variable.

en: Manually observed snow profiles

en: Manually observed snow profiles at Weissfluhjoch field site for winter season 2016-2017, including layer depth, hand hardness, grain size and grain shape for each layer.

en: We investigated the sensitivity of modeled snow instability to meteorological input data using SNOWPACK. We therefore used input data from the automatic weather station at the Weissfluhjoch field site for the year 2016-2017. We investigated three scenarios and performed 14'000 simulations for each scenario. The dataset contains extracted output data from modeled SNOWPACK simulations, including setup files to reproduce the simulations. For further information read the README file.

global-sensitivity-analysis

display_name: global-sensitivity-analysis

numerical-avalanche-forecasting

display_name: numerical-avalanche-forecasting

snow-cover-modeling

display_name: snow-cover-modeling

snow-instability

display_name: snow-instability

snowpack

display_name: snowpack

education

en: Education and science

en: Sensitivity of modeled snow instability


en: avalanche-forecast verification avalanche-forecasting avalanche-bulletin avalanche-danger-level

en: D_QC for forecasting seasons 2001/2002 until 2019/2020.

en: D_QC for forecasting seasons 2020/2021 - 2022/2023.

en:
The data set contains the re-analyzed (or quality-checked) regional avalanche danger levels (D_QC) for Switzerland.

D_QC relates to dry-snow avalanche conditions only. Measuring the avalanche danger level D is not possible; forecast, nowcast, and hindcast assessments of D are judgments by humans interpreting data. However, combining several pieces of information indicating the same D, it can be expected that it is more likely that D_QC represents the avalanche conditions well.

For the **forecasting seasons 2001/2002 until 2019/2020**, the approach to obtain D_QC is described in detail in Appendix A of [Pérez-Guillén et. al. (2022)](https://nhess.copernicus.org/articles/22/2031/2022/nhess-22-2031-2022.html).

For the **forecasting seasons 2020/2021 and later**, D_QC is derived using the following approach:
1. *Combination of forecast (D_forecast) and nowcast (D_nowcast)*: If there was only one assessment available by an observer after a day in the field for a region, and if D_forecast = D_nowcast --> D_QC = D_forecast.
2. *Combination of several nowcast assessments (D_nowcast)*: If two (or more) observers agreed (or majority opinion) in their (independent) assessments of D_nowcast after a day in the field in the same warning region. --> D_QC = D_nowcast.
3. *Hindcast analysis (D_hindcast)*: In Switzerland, avalanche forecasters re-evaluate all situations when D = 4 (high) or D = 5 (very high) were either forecast, should have been forecast, or when forecasters discussed given one of these two levels but had not given them. Generally, two forecasters assess each situation. In these cases, D_QC = D_hindcast. The hindcast analysis, only available since the forecasting season 2020/2021, replaces what was step (2) in Appendix A of [Pérez-Guillén et. al. (2022)](https://nhess.copernicus.org/articles/22/2031/2022/nhess-22-2031-2022.html).

All other cases - ties in case of (1) or (2), no new information from the warning region in question, or if no D_hindcast was available - are not considered quality-checked, and are, thus, not contained in the data set.

In addition to D_QC, the file contains information on the elevation and aspect, where D_QC likely prevails.
- The indicated elevation is the mean of the respective elevations in (1), (2), or (3). At danger level 1 (low), when no elevation is indicated in the Swiss forecast, a value of 1500 m is set.
- For the four cardinal aspects N, E, S, and W, a value of 1 means that there was agreement that D was reached in this aspect and a value of 0 means that there was agreement that D was not reached in this aspect. Intermediate values correspondingly mark disagreements in the assessments.

avalanche-bulletin

display_name: avalanche-bulletin

avalanche-danger-level

display_name: avalanche-danger-level

avalanche-forecast

display_name: avalanche-forecast

avalanche-forecasting

display_name: avalanche-forecasting

verification

display_name: verification

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: Re-analyzed regional avalanche danger levels in Switzerland


en: biodiversity climate-change-impact lichen lichen-growth-form photobiont liverwort endemic-species climate-change moss bryophyte lichen-growthform functional-taxonomic-groups endemic madeira-island elevational-gradient bryophyte-life-strategy functional-taxonomic-group disturbance

en: MadCrypto – Bryophyte and macrolichen diversity in laurel forests of Madeira

en:
This dataset includes species lists of bryophytes and macrolichens (presence/absence) sampled on the forest floor and on trees in disturbed and undisturbed plots along elevation gradients in the laurel forests of Madeira island. It also contains species specific information (bryophytes: red list status, endemic status, taxonomic group, life strategy; macrolichens: photobiont type, growth form) as well as plot information (Plot_ID, sampling date, coordinates, elevation a.s.l. (m), disturbance type, sampled host tree species).

The dataset was used for the paper
Boch S, Martins A, Ruas S, Fontinha S, Carvalho P, Reis F, Bergamini A, Sim-Sim M (2019) Bryophyte and macrolichen diversity show contrasting elevation relationships and are negatively affected by disturbances in laurel forests of Madeira island. Journal of Vegetation Science.

en:
This dataset includes species lists of bryophytes and macrolichens (presence/absence) sampled on the forest floor and on trees in disturbed and undisturbed plots along elevation gradients in the laurel forests of Madeira island. It also contains species specific information (bryophytes: red list status, endemic status, taxonomic group, life strategy; macrolichens: photobiont type, growth form) as well as plot information (Plot_ID, sampling date, coordinates, elevation a.s.l. (m), disturbance type, sampled host tree species).

The dataset was used for the paper
Boch S, Martins A, Ruas S, Fontinha S, Carvalho P, Reis F, Bergamini A, Sim-Sim M (2019) Bryophyte and macrolichen diversity show contrasting elevation relationships and are negatively affected by disturbances in laurel forests of Madeira island. Journal of Vegetation Science 30: 1122–1133.

The excel file contains 5 sheets:
1) Plot information
2) Bryophyte data with species specific information, separated per substrate
3) Macrolichen data with species specific information, separated per substrate
4) Bryophyte data with species specific information (plot level data). Species lists of the two substrates were merged; if one substrate in a plot hosted a species identified only to genus level (genus spec.) but the second investigated substrate hosted an identified species of the same genus, we removed the genus spec. entry in the particular plot. The species list of sheet 2 and 4 might therefore differ slightly.
5) Macrolichen data with species specific information (plot level data). Species lists of the two substrates were merged; if one substrate in a plot hosted a species identified only to genus level (genus spec.) but the second investigated substrate hosted an identified species of the same genus, we removed the genus spec. entry in the particular plot. The species list of sheet 3 and 5 might therefore differ slightly.

biodiversity

display_name: biodiversity

bryophyte

display_name: bryophyte

bryophyte-life-strategy

display_name: bryophyte-life-strategy

climate-change

display_name: climate-change

climate-change-impact

display_name: climate-change-impact

disturbance

display_name: disturbance

elevational-gradient

display_name: elevational-gradient

endemic

display_name: endemic

endemic-species

display_name: endemic-species

functional-taxonomic-group

display_name: functional-taxonomic-group

functional-taxonomic-groups

display_name: functional-taxonomic-groups

lichen

display_name: lichen

lichen-growth-form

display_name: lichen-growth-form

lichen-growthform

display_name: lichen-growthform

liverwort

display_name: liverwort

madeira-island

display_name: madeira-island

moss

display_name: moss

photobiont

display_name: photobiont


en: tree-rings stillberg treeline fertilization nitrogen-isotope dwarf-shrubs

en: Project Description Nutrient Addition Stillberg

media_type: HTML

en: Link to Nutrient Addition project description on the WSL website (in German).

format: HTML

en: Metadata Nutrient Addition Stillberg Experimental Design

media_type: PNG

en: Nutrient addition map of the study area with the investigated plots. Each circle (study block) contains one control plot and two plots that received NPK fertilizer corresponding to 15 (NPK15) or 30 (NPK30) kg N ha-1 a-1. Contour lines specify the elevation (m a.s.l.) in steps of 10 m.

en: Data Nutrient Addition Stillberg

media_type: https

en: This link leads to the figshare repository, which contains all nutrient addition data.

en:
 Background information

The availability of nitrogen (N) and phosphorus (P) is considered to be a major factor limiting growth and productivity in terrestrial ecosystems globally. This project aimed to determine whether the growth stimulation documented in previous short‐term fertilisation trials persisted in a longer‐term study (12 years) in the treeline ecotone, and whether possible negative effects of nutrient addition offset the benefits of any growth stimulation.
Over the course of the 12 study years, NPK fertiliser corresponding to 15 or 30 kg N ha−1 a−1 was added annually to plots containing 30‐year‐old *Larix decidua* or 32‐year-old *Pinus uncinata* individuals with an understorey of mainly ericaceous dwarf shrubs. To quantify growth, annual shoot increments of trees and dwarf shrubs as well as radial growth increments of trees were measured. Nutrient concentrations in the soil were also measured and the foliar nutritional status of trees and dwarf shrubs was assessed.

 Experimental design

Over an elevation gradient of 140 m across the treeline afforestation site Stillberg, 22 locations were chosen that covered the whole range of microenvironmental conditions (*see* Nutrient addition experimental design.png). Half of the blocks included European larch (*L. decidua*) and the other half included mountain pine (*P. uncinata*). Within each block, three plantation quadrats were randomly selected as experimental plots and each plot was assigned to a control (no fertilisation) or to one of two fertiliser dose treatments (15 kg and 30 kg N ha−1 a−1). Treatments were assigned randomly but confined so that the location of fertilised plots within a block was not directly above control plots to avoid nutrient input from drainage. For details about the experiment, *see* Möhl et al (2019).

 Data description

The available datasets contain climate variables (2004-2016), nutrient isotope measurements (2010 & 2016), shrub growth measurements (2004-2016), soil parameter measurements and annual ring and shoot measurements (2004-2016). All data can be found here:
<https://figshare.com/articles/dataset/Twelve_years_of_low_nutrient_input_stimulates_growth_of_trees_and_dwarf_shrubs_in_the_treeline_ecotone/7025858>

dwarf-shrubs

display_name: dwarf-shrubs

fertilization

display_name: fertilization

nitrogen-isotope

display_name: nitrogen-isotope

stillberg

display_name: stillberg

tree-rings

display_name: tree-rings

treeline

display_name: treeline

en: Nutrient addition experiment at the Alpine treeline site Stillberg, Switzerland


dischmex-meteorological-measurements

maintainer: Rebecca Mott

title_for_slug: dischmex-meteorological-measurements

en: meteorological-data meteo-station dischmex meteorological-measurements dischma

en: Meteorological measurements 2016

en: Location of the meteorological stations

en: data

en: Meteorological measurement 2015

en: Overview sensors

en: Meteorological measurement 2014

en: Meteorological measurements 2014

en:
Meteorological measurements recorded in the Dischma valley from 2014-2016. In 2014 and 2015 we used 11 mobile weather stations from sensorscope to record meteorological parameter in the upper Dischma valley in the closer surroundings of the Gletschboden area. The meteorological stations are eqiupped with at least air temperature/humidity, wind velocity and wind direction sensors. Some stations are additionally equipped with precipitation, shortwave radiation and snow surface temperature sensors.
Three transects were installed at different aspects and were equipped with air temperature/humidity and wind sensors. Transect 1 (stations 2-4) provides meteorological Information on an east-north-east facing slope at elevations ranging between 2100 m and 2500 m. Transect 2 (stations 5-7) provides meteorological Information on a south-west slope and transect 3 (stations 8-10) on a north-west slope. Station 1 is fully equipped with meteorological sensors (temperature/humidity, wind, IR, up and downwand shortwave radiation and precipitation).
In 2016, mobile stations from sensorscope were replaced with six permanent meteorological stations. Meteorological stations 1-3 are equipped with an air temperature/humidity sensor, two wind speed sensors, a wind direction sensor and an incoming and outgoing shortwave radiation sensor. Stations 4 and 6 are equipped with an air temperature/humidity sensor and a wind speed/direction sensor. Station 5 is a equipped with an air temperature/humidity sensor, a wind speed/direction sensor, a snow surface temperature sensor, an incoming and outgoing shortwave radiation sensor and an incoming longwave radiation sensor.

dischma

display_name: dischma

dischmex

display_name: dischmex

meteo-station

display_name: meteo-station

meteorological-data

display_name: meteorological-data

meteorological-measurements

display_name: meteorological-measurements

education

en: Education and science

en: DISCHMEX - Meteorological measurements


en: davos weisfluhjoch radiation shortwave-radiation longwave-radiation

en: Processing doc

en: Description of processing code that was ported to R by P. Frei (2016).

en: DATA ACCESS

media_type: SMET

en: ASRB radiation station at the Weissfluhjoch versuchsfeld.

en: Parameter file

en: Description of ASRB parameters.

en: Metadata

en:
Incoming and outgoing shortwave and longwave 2 min radiation measurements at the Weissfluhjoch research site, Davos, CH. The experimental site at the Weissfluhjoch (WFJ, 46.83 N, 9.81 E) is located at an altitude of 2540 m in the Swiss Alps near Davos. During the winter months, almost all precipitation falls as snow at this altitude. As a consequence, a continuous seasonal snow cover builds up every winter, with a maximum snow height ranging from 153–366 cm over the period 1934–2012. The measurement site is located in an almost flat part of a southeast oriented slope.

 References
1. Marty, C., Philipona, R., Frohlich, C., Ohmura, A.. Altitude dependence of surface radiation fluxes and cloud forcing in the alps: results from the alpine surface radiation budget network. 2002. Theoretical and Applied Climatology. Volume 72. Issue 3-4. 137-155. http://dx.doi.org/10.1007/s007040200019. 10.1007/s007040200019.
2. Christoph Marty. Surface Radiation, Cloud Forcing and Greenhouse Effect in the Alps. 2000. Institute fuer Klimaforschung ETH. Zuercher Klima-Schriften. Volume 79. http://e-collection.library.ethz.ch/eserv/eth:23491/eth-23491-01.pdf.

davos

display_name: davos

longwave-radiation

display_name: longwave-radiation

radiation

display_name: radiation

shortwave-radiation

display_name: shortwave-radiation

weisfluhjoch

display_name: weisfluhjoch

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: ASRB_WFJVF: Shortwave and longwave radiation measurements (2 min) at the Weissfluhjoch research site, Davos


lwf-tea-bag-sites

maintainer: Marcus Schaub

title_for_slug: lwf-tea-bag-sites

en: roiboos forest decomposition verification modeling carbon green-tea tea-bag litter

en: Site Information

media_type: text/csv

en: Descriptive information on sites

en:
Decomposition of plant litter is a key process for the transfer of carbon and nutrients in ecosystems. Carbon contained in the decaying biomass is released to the atmosphere as respired CO2, and may contribute to global warming. Litterbag studies have been used to improve our knowledge of the drivers of litter decomposition, but they lack comparability because litter quality is plant species-specific. The use of commercial tea bags as a standard substrate was suggested in order to harmonize studies, where green tea and rooibos represent more labile and more recalcitrant C compounds as surrogates of local litter.
The tea bag approach was implemented on eight sites of the Swiss long-term Forest Ecosystem Research (LWF) network (https://www.wsl.ch/LWF). This allowed us to take advantage from the existing infrastructure and data from a previous litterbag study with local litter. In Beatenberg and Schaenis, additional elevation transects were established (1200-1800 m and 540-1150 m, respectively) to examine particularly the effect of temperature on decomposition. In Pfynwald (https://www.wsl.ch/de/ueber-die-wsl/versuchsanlagen-und-labors/flaechen-im-wald/pfynwald.html) and Salgesch, infrastructure of running projects was used to examine the effect of drought and understory removal, respectively. In Novaggio, tea bags were incubated in summer and winter to study the effect of seasonality particularly precipitation. Tea bags are collected after 3, 12, 24, and 36 months; for the two time-shifted experiments additionally after 6 and 9 months.
The study has two primary objectives. Firstly, it contributes to TeaComposition initiative (http://teacomposition.org/) which aims at investigating long-term litter decomposition and its key drivers at present as well as under different future climate scenarios using a common protocol and standard litter (tea) across nine terrestrial biomes. Secondly, the data are used to further develop decomposition models such as Yasso (http://en.ilmatieteenlaitos.fi/yasso) which is used by several countries, including Switzerland to estimate the annual carbon fluxes in dead wood, litter, and soil for reporting in National Greenhouse Gas Inventories under the United Nations Framework Convention on Climate Change and the Kyoto Protocol.

carbon

display_name: carbon

decomposition

display_name: decomposition

forest

display_name: forest

green-tea

display_name: green-tea

litter

display_name: litter

modeling

display_name: modeling

roiboos

display_name: roiboos

tea-bag

display_name: tea-bag

verification

display_name: verification

en: LWF-Tea bag sites


maintainer: Elisabeth Hafner

en: hazard-mapping satellite avalanche snow natural-hazard

en: SPOT6 Avalanche Outlines 16 January 2019

media_type: SHP

en: Shapefile of the manually mapped avalanche outlines. Please only use this data together with the example key document provided with this dataset and within the limitations of the dataset described in the publications.

format: SHP

en:
Outlines of 6'041 avalanches mapped from SPOT6 satellite data over the Swiss Alps on 16 January 2019. The dataset was acquired following a period with very high avalanche danger. The outlines have different attributes described in the data example key (ExampleKey_AvalMapping16012019.pdf)

The generation of the data is described in:

Bühler, Y., E. D. Hafner, B. Zweifel, M. Zesiger, and H. Heisig (2019), Where are the avalanches? Rapid SPOT6 satellite data acquisition to map an extreme avalanche period over the Swiss Alps, The Cryosphere, 13(12), 3225-3238, doi:10.5194/tc-13-3225-2019.

The data was comprehensivly validated in a subset area in

Hafner, E.D.; Techel, F.; Leinss, S.; Bühler, Y., 2021: Mapping avalanches with satellites - evaluation of performance and completeness. Cryosphere, 15, 2: 983-1004. doi: 10.5194/tc-15-983-2021

avalanche

display_name: avalanche

hazard-mapping

display_name: hazard-mapping

natural-hazard

display_name: natural-hazard

satellite

display_name: satellite

snow

display_name: snow

education

en: Education and science

en: SPOT6 Avalanche outlines 16 January 2019


en: angiospermae antwerp poznan zurich floral-resources tartu floral-inventories plants urban paris

en: Trait values for the sampled plants

en: Floral units, mean number of flowers per floral unit and floral abundance of the different plants counted in quadrats in the study sites in Zurich during the four sampling periods

en: List of plant species sampled in the five cities (Antwerp, Paris, Poznan, Tartu and Zurich) during the different sampling perionds (four for Zurich, three for the other cities) with their frequency (oc1-oc16) within a 100 m radius

en: Data of a survey of flowering plants in 80 sites in five European cities and urban agglomerations (Antwerp, Belgium; greater Paris, France; Poznan, Poland; Tartu, Estonia; and Zurich, Switzerland) sampled between April and July 2018.

angiospermae

display_name: angiospermae

antwerp

display_name: antwerp

floral-inventories

display_name: floral-inventories

floral-resources

display_name: floral-resources

paris

display_name: paris

plants

display_name: plants

poznan

display_name: poznan

tartu

display_name: tartu

urban

display_name: urban

zurich

display_name: zurich

en: Flowering Plants (Angiospermae) in Urban Green Areas in five European Cities


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

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

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: Seilaplan Tutorial: DTM download from swisstopo website


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

en: SNOWPACK simulations

media_type: .pro

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

media_type: .txt

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

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: Glide distances for opening glide-cracks

media_type: zip-compressed folder of csv

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:
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