qualitat-der-metadaten-opendata-swiss

title_for_slug: qualitat-der-metadaten-opendata-swiss

de: Qualität der Metadaten opendata.swiss

de: Dieses von der Geschäftsstelle OGD entwickelte Dashboard hilft Datenabietenden, die Qualität der auf opendata.swiss veröffentlichten Metadaten zu verbessern.

fr: Qualité des métadonnées opendata.swiss

fr: Élaboré par le Secrétariat OGD, ce tableau de bord aide les fournisseurs de données à améliorer la qualité des métadonnées publiées sur opendata.swiss.

gove

de: Regierung und öffentlicher Sektor


name: Ufficio di statistica del Canton Ticino (Ustat)

state: active

type: dataset

media_type: application/zip

it: Questo archivio zip contiene un file csv con i risultati in forma lunga (con una sola colonna numerica) e un file txt con la descrizione del contenuto (metadati)

format: ZIP

it: Questo archivio zip contiene un cubo di dati in formato PC Axis px.

it: Questo archivio zip contiene un file xlsx con tre fogli: uno con i risultati statistici (Tabella), uno con l'elenco e la descrizione delle variabili (Definizioni) e uno con il titolo, la fonte e la data dell'ultima modifica (Informazioni).

it:
Popolazione residente permanente di 15 e più anni, secondo lo stato di attività, il sesso, la nazionalità, la classe d'età e il grado di formazione, per grande regione, in Svizzera, dal 2010

[Fonte: Rilevazione sulle forze di lavoro in Svizzera (RIFOS), Ufficio federale di statistica, Neuchâtel](https://www3.ti.ch/DFE/DR/USTAT/index.php?fuseaction=definizioni.fonti&tema=35&id=51&proID=155)
[Elaborazione: Ufficio di statistica (Ustat), Giubiasco](https://www.ti.ch/ustat)
Versione dei dati: 20.02.2025

Ultima modifica: 10.03.2025

Variabili presenti nel cubo di dati:
anno: l'anno dell'inchiesta
grande_regione: la grande regione svizzera
Stato_att: lo stato di attività della persona (vedi il Glossario)
sesso: il sesso della persona
nazionalità: la nazionalità della persona (svizzeri / stranieri)
classe_eta: la classe d'età della persona
grado_formazione: il grado di formazione della persona

La colonna "info" riporta una delle informazioni seguenti:
ok: esistono delle stime con le caratteristiche descritte in quella riga
...: dato non disponibile
X: dato non pubblicato per motivi legati alla protezione dei dati
( ): stima basata su meno di 90 osservazioni. I risultati sono da interpretare con molta precauzione.

[Segni, simboli, abbreviazioni, sigle e concetti statistici usati nei prodotti dell'Ustat](https://www4.ti.ch/fileadmin/DFE/DR-USTAT/Prodotti/Definizioni/segni_sigle_simboli.pdf)

Glossario:
[Occupati (RIFOS)](https://www3.ti.ch/DFE/DR/USTAT/index.php?fuseaction=definizioni.glossario&id=532&tema=)
[Disoccupati ai sensi dell'ILO](https://www3.ti.ch/DFE/DR/USTAT/index.php?fuseaction=definizioni.glossario&id=343&tema=)
[Persone non attive (RIFOS)](https://www3.ti.ch/DFE/DR/USTAT/index.php?fuseaction=definizioni.glossario&id=496&tema=)

enquete-suisse-sur-la-population-active

display_name: enquete-suisse-sur-la-population-active

econ

fr: Économie et finances

de: Wirtschaft und Finanzen

en: Economy and finance

it: Economia e finanze

ustat

it:
L'Ufficio di statistica del Cantone Ticino (Ustat) è il servizio centrale della statistica cantonale e il suo coordinatore ai sensi della [Legge sulla statistica cantonale](https://www4.ti.ch/dfe/dr/ustat/chi-siamo/basi-legali/leggi-cantonali) del 22 settembre 2009.

Nella prima veste l'Ustat produce e diffonde per la collettività e per lo Stato: dati, elaborazioni, contributi analitici di vario genere e livello di approfondimento, consulenze statistiche e tematiche, su praticamente tutti i settori della statistica pubblica.
Alla base di questi risultati vi è un'attività che copre tutte le fasi del processo statistico: dalla definizione del fabbisogno alla rilevazione, trattamento e validazione dei dati, alla loro archiviazione e documentazione, all'elaborazione, analisi ed interpretazione e alla diffusione dei risultati. Progressivamente, le attività di analisi e di ricerca tematica e la diffusione stanno progressivamente assumendo maggior peso.

Nella veste di coordinatore della statistica cantonale l'Ustat opera in prima misura attraverso la preparazione, l'aggiornamento annuale e la rendicontazione del [Programma pluriennale](https://www4.ti.ch/dfe/dr/ustat/chi-siamo/programma-dattivita/programma-pluriennale), in collaborazione con la [Commissione scientifica](https://www4.ti.ch/dfe/dr/ustat/chi-siamo/la-commissione-scientifica) e gli altri attori della [statistica cantonale](https://www4.ti.ch/dfe/dr/ustat/chi-siamo/statistica-cantonale). L'Ustat assicura pure il coordinamento con l'esterno, in primis con il programma e l'attività della [statistica nazionale](https://www4.ti.ch/dfe/dr/ustat/chi-siamo/il-sistema-statistico), ma pure con gli altri servizi statistici e di ricerca regionali, nazionali e transfrontalieri.

political_level: canton

ti

capacity: public

type: organization

approval_status: approved

key: uri

value: DCAT Harvester Kt. TI


wasser-leitungen-offentlich

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

ener

fr: Énergie

de: Energie

en: Energy

it: Energia

regi

fr: Régions et villes

de: Regionen und Städte

en: Regions and cities

it: Regioni e città

envi

fr: Environnement

de: Umwelt

en: Environment

it: Ambiente

title_for_slug: wasser-leitungen-offentlich

label: Web GIS Client des Kanton Uri. Die Daten können direkt via dem Portal in den unterschiedlichsten Datenformaten heruntergeladen werden.

label: geocat.ch permalink

fr: WMS-Dienst des Kanton Uri

de: WMS-Dienst des Kanton Uri

en: WMS-Dienst des Kanton Uri

it: WMS-Dienst des Kanton Uri

format: WMS

fr: WFS-Dienst des Kanton Uri

de: WFS-Dienst des Kanton Uri

en: WFS-Dienst des Kanton Uri

it: WFS-Dienst des Kanton Uri

format: WFS

fr: In diesem Datensatz werden Leitungen für die Wasserversorgung verwaltet. Im Kanton Uri sind nicht alle Wasserversorgungen digital erfasst. Die Gemeindeverwaltungen oder Wasserversorgungen bestimmen zur Zeit die Verfügbarkeit des öffentlichen oder geschützten Zugangs.

spatial: Canton of Uri (UR)

geoinformation-kanton-uri

de: Lisag AG, Geoinformation Kanton Uri

de: Die Lisag AG bewirtschaftet das geografische Informationssystem des Kantons Uri (GIS Uri), ist die verantwortliche Stelle für den ÖREB-Kataster Uri und zudem zuständig für die Durchführung der amtlichen Vermessung auf Urner Kantonsgebiet.

value: Uri-Kt - Geocat Harvester

fr: Wasser Leitungen öffentlich

de: Wasser Leitungen öffentlich

en: Wasser Leitungen öffentlich

it: Wasser Leitungen öffentlich


fernwarme-leitungen

title_for_slug: fernwarme-leitungen

fr: Fernwärme Trasse und Leitungspunkt.

de: Fernwärme Trasse und Leitungspunkt.

en: Fernwärme Trasse und Leitungspunkt.

it: Fernwärme Trasse und Leitungspunkt.

fr: Fernwärme Leitungen

de: Fernwärme Leitungen

en: Fernwärme Leitungen

it: Fernwärme Leitungen


swisscom-leitungen

title_for_slug: swisscom-leitungen

fr: Swisscom Leitungen, Bauwerke und Schächte

fr: Swisscom Leitungen

de: Swisscom Leitungen

en: Swisscom Leitungen

it: Swisscom Leitungen


gemeindestrassenplan-genehmigt

tran

fr: Transports

de: Verkehr

en: Transport

it: Trasporti

title_for_slug: gemeindestrassenplan-genehmigt

fr: Gemeindestrassenplan gemäss dem Strassengesetz (StrG) Artikel 17. Beinhaltet die Nationalstrassen, Kantonstrassen, Gemeindestrassen, Korporationsstrassen, Privatstrassen. Noch nicht genehmigte Gemeinden sind farblich markiert und werden nach der Genehmigung freigeschalten.

fr: Gemeindestrassenplan (genehmigt)

de: Gemeindestrassenplan (genehmigt)

en: Gemeindestrassenplan (genehmigt)

it: Gemeindestrassenplan (genehmigt)


abwasseranlagen

title_for_slug: abwasseranlagen

de: leitungskataster geobasisdaten

en: pipeline-cadastre official-geodata

it: catasto-delle-condotte geodati-di-base

fr: In diesem Datensatz werden alle Leitungen für verschmutztes und nicht verschmutztes Abwasser erfasst (Art. 26. Kantonales Umweltschutzgesetz, KUG). Weiter enthält dieser Datensatz Abwasserreinigungsanlagen, Versickerungsanlagen und Sonderbauwerke.

fr: Abwasseranlagen

de: Abwasseranlagen

en: Abwasseranlagen

it: Abwasseranlagen


abwasseranlagen-darstellung-nutzungsart

educ

fr: Éducation, culture et sport

title_for_slug: abwasseranlagen-darstellung-nutzungsart

fr: Dieser Datensatz hat den gleichen Inhalt wie der Datensatz "34-UR_Abwasseranlagen". Die Abwasserleitungen sind gemäss dem Attribut "Nutzungsart" verschiedenfarbig dargestellt.

fr: Abwasseranlagen Darstellung Nutzungsart

de: Abwasseranlagen Darstellung Nutzungsart

en: Abwasseranlagen Darstellung Nutzungsart

it: Abwasseranlagen Darstellung Nutzungsart


elektro-leitungen

educ

de: Bildung, Kultur und Sport

ener

fr: Énergie

regi

fr: Régions et villes

envi

fr: Environnement

title_for_slug: elektro-leitungen

fr: Elektro Trasse, Bauwerke und Trassepunkte.

fr: Elektro Leitungen

de: Elektro Leitungen

en: Elektro Leitungen

it: Elektro Leitungen


ener

de: Energie

regi

de: Regionen und Städte

de: gebaude-und-anlagen strahlung telekommunikation ver--und-entsorgung

protocol: https

de: Datenshop

de: Produktansicht Geodatenshop

format: SERVICE

de: Zeigt Antennenstandorte mit Angaben zu Betreiber und Mobilfunkstandard. Angezeigt werden Basisstationen für 2G, 3G, 4G und 5G - Mobilkommunikation.

spatial: Kanton Luzern

fr: Géoinformation du canton de Lucerne

de: Geoinformation Kanton Luzern

en: Geoinformation Canton of Lucerne

it: Geoinformazione del Cantone di Lucerna

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

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

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

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

value: LU-Kt - Geocat Harvester

de: Antennenstandorte Mobilfunk


envi

de: Umwelt

de: abfall altlasten betriebe boden eigentumsbeschrankungoreb risiken umweltbeobachtung umweltschutz wirtschaftliche-aktivitaten

de: Altlasten sind belastete Standorte, die wegen der Auswirkungen auf die Umwelt saniert werden müssen.

de: Kataster der belasteten Standorte (KbS)


soci

fr: Population et société

de: Bevölkerung und Gesellschaft

en: Population and society

it: Popolazione e società

educ

en: Education, culture and sport

it: Istruzione, cultura e sport

de: eigentumsbeschrankungoreb gebaude-und-anlagen kulturdenkmal kulturguterschutz nutzungsplanung ortsbildschutz zonenplan

de: Im Bauinventar werden schützenswerte und erhaltenswerte Baudenkmäler des Kantons erfasst. Besonders schutzwürdige Kulturobjekte werden ins Kantonale Denkmalverzeichnis eingetragen und unter Schutz gestellt.

de: Kantonales Denkmalverzeichnis und Bauinventar


wasserrechte

regi

en: Regions and cities

it: Regioni e città

envi

en: Environment

it: Ambiente

title_for_slug: wasserrechte

de: konzession wasserrecht entnahme ruckgabe kanal wasserrechtskanal stauwehr stauwehrreglement wasserrechtskonzession eheliche-konzession wasserrechtsbezirk wasserrechtsnummer

fr: WFS für Open-Government-Data des Kantons Zürich (OGD ZH WFS)

fr: Öffentliche Oberflächengewässer, Gewässerraum und Wasserrechte

fr: Die konzessionierten Wasserrechte wurden ab 1997 mit den öffentlichen Oberflächengewässern im GIS erfasst und werden seit 1998 als eigener Datensatz in Abstimmung zu den öffentlichen Oberflächengewässern vom AWEL, Abt. Wasserbau nachgeführt. Seit Januar 2022 werden aktive und gelöschte Wasserrechte in getrennten Datensätzen zu Verfügung gestellt. Die Daten sind auf dem SDE Server vom GIS-ZH abgelegt unter: GISZHPUB.WASSERRECHTE_F für die Wasserrechtsweiher (Flächendaten) GISZHPUB.WASSERRECHTE_GELOESCHT_F für die gelöschten Wasserrechtsweiher (Flächendaten) GISZHPUB.WASSERRECHTE_L für die Wasserrechtskanäle und Leitungen (Liniendaten) GISZHPUB.WASSERRECHTE_GELOESCHT_L für die gelöschten Wasserrechtskanäle und Leitungen (Liniendaten) GISZHPUB.WASSERRECHTE_P für die Entnahme- und Rückgabeorte (Punktdaten) GISZHPUB.WASSERRECHTE_GELOESCHT_P für die gelöschten Entnahme- und Rückgabeorte (Punktdaten)

spatial: Kanton Zürich

geoinformation-kanton-zuerich

fr: Département de Géoinformation du Canton de Zurich

de: Geoinformation Kanton Zürich

en: Department of Geoinformation of the Canton of Zurich

it: Dipartimento della geoinformazione del Cantone di Zurigo

fr:
Le département de géomatique est le centre de compétence du system d'information géographique du canton de Zurich.
Nous coordinons l'aquisition des géodonnées d'intérêt commun sur le territoire cantonal et les mettons à disposition des
secteurs public et privé et des particuliers.

en:
The department of geoinformation is the geospatial competency center of the Canton of Zurich.
We coordinate the acquisition of geodata within the cantonal territory and provide public, private, and government sectors with data of outstanding quality.

de: Die Abteilung Geoinformation ist das Kompetenzzentrum für räumliche Informationen im Kanton Zürich. Wir versorgen Gesellschaft, Wirtschaft und öffentliche Verwaltung mit qualitativ hochstehenden Geodaten und Informationen über den Kanton Zürich.

it: La divisione della geoinformazione è il centro d'eccellenza del Cantone di Zurigo per dati georeferenziati. Mettiamo a disposizione dati geografici e informazioni di alta qualità per l'economia, la società e la pubblica amministrazione su tutto il cantone.

value: ZH - Geocat Harvester

fr: Wasserrechte

de: Wasserrechte

en: Wasserrechte

it: Wasserrechte


de: Richtplan

de: Der Richtplan ist behördenverbindlich. Er legt fest, wie sich der Kanton und die Gemeinden langfristig räumlich entwickeln sollen. Dabei geht es um die Zukunft des Natur-, Landwirtschafts-, Siedlungs-, Wirtschafts- und Erholungsraums.

raumplanung

display_name: raumplanung

name: Amt für Raum und Verkehr des Kantons Zug

kanton-zug

fr: Canton de Zoug

de: Kanton Zug

en: Canton of Zug

it: Cantone di Zugo

de:
Hier veröffentlicht das Team von Open Data Zug von der kantonalen Fachstelle für Daten und Statistik schrittweise offene Behördendaten der kantonalen Verwaltung.

Weitere Informationen unter https://zg.ch/opendata

key: guid

value: ZG-Kt - Geocat Harvester


okoinfomobil

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

ener

de: Energie

en: Energy

it: Energia

regi

de: Regionen und Städte

title_for_slug: okoinfomobil

de: oekomobil okomobil

fr: Map (Preview)

de: Map (Preview) Stadtplan

en: Map (Preview)

it: Map (Preview)

de: Link zum Thema im Internet-Stadtplan der Stadt Bern

de: WMS OekoInfoMobil

de: Web Map Services WMS

de: WFS OekoInfoMobil

de: Web Feature Services WFS

de: Haltestellen, an welchen das ÖkoInfoMobil einmal die Woche für 20 Minuten hält und kleine Abfallmengen entgegennimmt. Der Fahrplan ist fix und die Haltezeiten für die jeweiligen Standorte sind angegeben. Die Mitarbeitenden im Mobil beraten kompetent zu Entsorgungsfragen.

spatial: Stadt Bern

geoinformation-der-stadt-bern

fr: L’Office du cadastre de la ville de Berne

de: Geoinformation Stadt Bern

en: The Land Surveying Office of the City of Bern

it: L'ufficio del catasto nella Città di Berna

fr: L’Office du cadastre (Geoinformation Stadt Bern) est l’organisme compétent pour la collecte, l’accès et la diffusion d’informations géographiques dans la ville de Berne.

de: Geoinformation Stadt Bern (ehemals Vermessungsamt) ist die Kompetenzstelle für die Beschaffung, den Zugang und die Verbreitung von Geoinformationen in der Stadt Bern.

en: The Surveying Office (Geoinformation Stadt Bern) is the centre of competence in the City of Bern for the procurement of, access to and dissemination of geographic information.

it: L'ufficio del catasto (Geoinformation Stadt Bern) è il centro di competenza per il reperimento, l'accesso e la diffusione di geoinformazioni nella Città di Berna.

political_level: commune

value: Geoinformation Stadt Bern - Geocat Harvester

de: ÖkoInfoMobil


abfallanlagen-und-sammelstellen

ener

fr: Énergie

regi

fr: Régions et villes

envi

fr: Environnement

de: Umwelt

title_for_slug: abfallanlagen-und-sammelstellen

de: abfall-sammelstelle abfallanlage

de: WMS Abfallsammelstellen

de: WFS Abfallsammelstellen

de: Standorte der Entsorgungshöfe und öffentlichen Sammelstellen in der Stadt Bern. Informationen, welche Abfallarten abgegeben werden können. Die Standorte von privaten Unterflursammelstellen für Kehricht und Papier/Karton werden nicht abgebildet.

de: Abfallanlagen und Sammelstellen


ladebedarfsszenarien

educ

de: Bildung, Kultur und Sport

ener

de: Energie

regi

de: Regionen und Städte

title_for_slug: ladebedarfsszenarien

protocol: WWW:DOWNLOAD:OGC Geopackage (GPKG)

de: Steckerfahrzeuge Geopackage

format: GPKG

de: Strombedarf Geopackage

de: Ladepunkte-Geplant Geopackage

de: Ladepunkte-Bequem Geopackage

de: Ladepunkte-Flexibel Geopackage

de: Heimladeverfügbarkeit-Bequem Geopackage

protocol: WWW:DOWNLOAD:TEXT, comma separated (CSV)

de: Weiterführende Informationen: Fahrzeuge

format: CSV

de: Weiterführende Informationen: Energie

de: Weiterführende Informationen: Weitere

fr: De quelle infrastructure de recharge aurons-nous besoin demain pour les véhicules électriques ? Et où exactement ? Pour répondre à ces questions, il faut connaître les besoins de recharge futurs. Les « scénarios de besoins de recharge » livrent cette information pour chaque commune suisse et simplifient ainsi la planification. Les « scénarios de besoins de recharge » répartissent la future demande de recharge de la Suisse entre les différentes communes. L’outil va ainsi un pas plus loin que l’étude « Conception Infrastructure de recharge 2050 ».Les communes, exploitants de réseaux de recharge et exploitants de réseaux de distribution ainsi que l’économie immobilière se voient ainsi proposer de nouvelles possibilités de planification, d’investissement et de décision et ce, sur la base de données. Il est donc possible d’avoir des indications sur les endroits où les bornes de recharge seront demandées à l'avenir, quelle doit en être la puissance et quelle quantité d’énergie elles nécessiteront. Les paramètres principaux ont été traités sous forme de cartes : ces cartes montrent notamment le nombre de véhicules électriques attendus dans une commune déterminée et le nombre de bornes de recharge publiques qui seront nécessaires. L’année clé est 2035 car d’ici là, les véhicules électriques représenteront plus de la moitié de tous les véhicules immatriculés. Les données peuvent être téléchargées sous forme de géopackages (.gpkg-file). De plus, toutes les données brutes de l’étude « Conception Infrastructure de recharge 2050 » sont disponibles, comme la puissance et la densité du réseau de recharge, la puissance installée cumulée des infrastructures de recharge ou les nouvelles immatriculations de voitures. Les données vont jusqu’en 2050, par paliers de cinq années, et permettent de réaliser de propres calculs. Les hypothèses pour les différents univers de recharge sont basées sur un processus de parties prenantes reposant sur l’outil « Conception Infrastructure de recharge 2050 ». Ce processus représente le consensus sectoriel concernant les différents groupes d’utilisateurs et est sans cesse actualisé. Les données simulées sont le résultat d’algorithmes et du modèle d’EBP. Les données brutes ne sont pas seulement disponibles pour les communes, mais aussi pour quelque 8000 zones de circulation et pour les cantons. De plus, de nombreux autres attributs sont disponibles, comme l’énergie chargée par besoin de recharge, la disponibilité d’une infrastructure de recharge à domicile ou sur le lieu de travail, le nombre de bornes de recharge par option de recharge et la puissance de recharge installée dans les bâtiments et aux bornes de recharge publiques, ainsi que la puissance du réseau de recharge public.

de: Welche Ladeinfrastruktur für Steckerfahrzeuge brauchen wir morgen? Und wo genau? Dies kann nur beantworten, wer den künftigen Ladebedarf kennt. Die «Ladebedarfsszenarien» liefern diese Information für jede Schweizer Gemeinde und erleichtern damit die Planung. Die «Ladebedarfsszenarien» brechen den künftigen Ladebedarf der Schweiz auf einzelne Gemeinden herunter. Das Werkzeug geht damit einen Schritt weiter als die Studie «Verständnis Ladeinfrastruktur 2050», auf der es basiert. Damit eröffnen sich Gemeinden, Ladenetzbetreibern, Verteilnetzbetreibern und der Immobilienwirtschaft neue Möglichkeiten zum Planen, Investieren und Entscheiden – und zwar auf einer datengestützten Grundlage. Sie gibt Hinweise darauf, wo künftig Ladepunkte gefragt sein werden, wie leistungsfähig sie sein müssen und wie viel Energie sie benötigen. Die wichtigsten Parameter sind in Karten aufbereitet: Sie zeigen unter anderem, wie viele Steckerfahrzeuge in einer bestimmten Gemeinde zu erwarten sind und wie viele allgemein zugängliche Ladepunkte nötig sein werden. Schlüsseljahr ist 2035, denn bis dahin werden Steckerfahrzeuge mehr als die Hälfte aller zugelassenen Fahrzeuge ausmachen. Die Daten können als Geopackages (.gpkg-File) heruntergeladen werden. Zudem sind sämtliche Rohdaten (.csv-File) aus der Studie «Verständnis Ladeinfrastruktur 2050» verfügbar, beispielsweise die Leistungsfähigkeit und Dichte des Ladenetzes, die kumulierte installierte Leistung von Ladeinfrastrukturen oder die Neuzulassungen von Personenwagen. Die Daten reichen in Fünfjahresschritten bis ins Jahr 2050 und bieten sich für eigene Berechnungen an. Die Annahmen für die verschiedenen Ladewelten basieren auf einem Stakeholder-Prozess, der dem Werkzeug «Verständnis Ladeinfrastruktur 2050» zugrunde liegt. Es stellt den aktuellen Branchenkonsens verschiedenster Nutzergruppen dar und wird regelmässig aktualisiert. Die simulierten Daten sind das Ergebnis von Algorithmen und der Modelllandschaft von EBP. Die Rohdaten liegen nicht nur für Gemeinden vor, sondern auch für rund 8000 Verkehrszonen und Kantone. Ausserdem sind viele weitere Attribute verfügbar, etwa die geladene Energie pro Ladebedürfnis, die Verfügbarkeit der Ladeinfrastruktur zu Hause oder am Arbeitsplatz, die Anzahl Ladepunkte je Ladeoption sowie die installierte Ladeleistung in Gebäuden und an allgemein zugänglichen Ladepunkten sowie die Leistungsfähigkeit des allgemein zugänglichen Ladenetzes.

en: What charging infrastructure for plug-in electric vehicles will we need tomorrow? And where exactly? This can only be answered by knowing the future charging requirements. The “charging demand scenarios” provide this information for each Swiss municipality and thus facilitate planning. The “charging demand scenarios” break down Switzerland's future charging demand into each individual municipality. The tool thus goes one step further than the “Understanding charging infrastructure 2050” study on which it is based. This opens up new opportunities for municipalities, charging network operators, distribution network operators and the real estate industry to plan, invest and make decisions on a data-driven basis. It provides information on where charging points will be in demand in the future, how powerful they need to be and how much energy they require. The most important parameters are presented in maps: Among other things, they show how many plug-in electric vehicles can be expected in a specific municipality and how many generally accessible charging points will be needed. The key year is 2035, because by then plug-in electric vehicles will account for more than half of all registered vehicles. The data can be downloaded as geopackage files (.gpkg). All raw data (.csv file) from the “Understanding charging infrastructure 2050” study is also available, such as the performance and density of the charging network, the cumulative installed capacity of charging infrastructure and new car registrations. The data extends in five-year increments up to the year 2050 and is suitable for our own calculations. The assumptions for the various charging worlds are based on a stakeholder process underlying the “Understanding charging infrastructure 2050” tool. It represents the current industry consensus of various user groups and is updated regularly. The simulated data is the result of algorithms and the EBP model landscape. The raw data is not only available for municipalities, but also for around 8000 traffic zones and cantons. Many other attributes are also available, such as the energy charged per charging requirement, the availability of charging infrastructure at home or at work, the number of charging points per charging option and the installed charging capacity in buildings and at generally accessible charging points, as well as the performance of the generally accessible charging network.

it: Di quale infrastruttura di ricarica per veicoli plug-in avremo bisogno in futuro? E dove esattamente? A queste domande può rispondere solo chi conosce il futuro fabbisogno di ricarica. Gli «scenari del fabbisogno di ricarica» forniscono questa informazione per ogni Comune in Svizzera, facilitando così la pianificazione. Gli «scenari del fabbisogno di ricarica» mostrano il futuro fabbisogno di ricarica della Svizzera per ogni singolo Comune. Lo strumento compie quindi un passo avanti rispetto allo studio «Verständnis Ladeinfrastruktur 2050» su cui è basato. Per i Comuni, i gestori delle reti di ricarica, i gestori delle reti di distribuzione ed il settore immobiliare si aprono così nuove opportunità per pianificare, investire e prendere decisioni facendo affidamento su uno strumento supportato da dati in grado di fornire indicazioni su dove in futuro saranno necessari punti di ricarica, quale deve essere la loro potenza e quanta energia richiedono. I parametri più importanti sono disponibili sotto forma di mappe: mostrano, tra le altre cose, quanti veicoli plug-in sono previsti in un determinato Comune e quanti punti di ricarica a libero accesso saranno necessari. L’anno chiave è il 2035: entro quella data, infatti, i veicoli plug-in rappresenteranno oltre la metà di tutti i veicoli immatricolati. I dati possono essere scaricati come geopacchetti (file .gpkg). Inoltre sono disponibili tutti i dati grezzi dello studio «Verständnis Ladeinfrastruktur 2050», come la potenza e densità della rete di ricarica, la potenza cumulata installata delle infrastrutture di ricarica o le nuove vetture immatricolate. I dati, raggruppati in periodi di cinque anni, arrivano fino al 2050 e possono essere utilizzati per i propri calcoli. Le ipotesi per i vari scenari di ricarica si basano su un processo con coinvolgimento di stakeholder che è alla base dello strumento «Verständnis Ladeinfrastruktur 2050». Esso rappresenta l’attuale consenso settoriale di vari gruppi di utenti e viene aggiornato regolarmente. I dati simulati sono il risultato di algoritmi e del modello composito di EBP. I dati grezzi non sono disponibili solo per i Comuni, ma anche per circa 8000 zone di traffico e per i Cantoni. Inoltre sono disponibili molti altri parametri, come ad esempio l’energia ricaricata per ogni esigenza di ricarica, la disponibilità dell’infrastruttura di ricarica a domicilio o sul posto di lavoro, il numero di punti di ricarica per opzione di ricarica e la potenza di ricarica installata negli edifici e nei punti di ricarica a libero accesso, ma anche la potenza della rete di ricarica a libero accesso.

spatial: Schweiz

bundesamt-fur-energie-bfe

fr: Office fédéral de l'énergie OFEN

de: Bundesamt für Energie BFE

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).


envi

de: Umwelt

de: AGIS Direktdownload

de: Höhenlinien aus LiDAR DTM 2014 (Äquidistanz 5m)

de: Höhenlinien berechnet aus dem LiDAR-DTM 20140405, Äquidistanz 5m. Die Generalisierung ist für den Darstellungsmassstab 1:5'000 optimiert.

de: AGIS Service Center Kanton Aargau

de: Das AGIS Service Center unterstützt als kantonale Koordinationsstelle die Bereitstellung von Geoinformationen und Metadaten der kantonalen Verwaltung. Die zuständigen kantonalen Fachstellen stellen die öffentlichen Geodaten des Kantons Aargau mittels der Infrastruktur des AGIS Service Center in der Informatik Aargau als Webservices und zum Download bereit.

value: Aargau-Kt - Geocat Harvester


sew-atb-abwasser-schacht-ausser-betrieb

regi

de: Regionen und Städte

title_for_slug: sew-atb-abwasser-schacht-ausser-betrieb

de: Der Datensatz beinhaltet die ausser Betrieb stehenden Normaschächte (inkl. Strassenabläufe) der Kantonsstrassenentwässerung sowie der Transportleitungen bis zur ARA oder in den Vorfluter. Der Umfang beschränkt sich auf die Elemente im Eigentum des Kantons Aargau.

de: SEW: ATB Schacht ausser Betrieb


sew-atb-abwasser-pendenzen

title_for_slug: sew-atb-abwasser-pendenzen

de: SEW: ATB Pendenzen

de: Der Datensatz beinhaltet die räumliche Abgrenzung von Pendenzen im Strassenentwässerungskataster der Kantonsstrassen.


kantonale-denkmalschutzobjekte

soci

fr: Population et société

de: Bevölkerung und Gesellschaft

educ

de: Bildung, Kultur und Sport

title_for_slug: kantonale-denkmalschutzobjekte

de: Kantonale Denkmalschutzobjekte

de: Der vorliegende Datensatz enthält alle Objekte (Gebäude, Archäologische Fundstellen, Einzelobjekte, Sammlungen u.ä. des Kantons Aargau, die vom Regierungsrat gestützt auf das Kulturgesetz unter kantonalen Schutz gestellt werden.


fr: eigentumsbeschrankungoreb zonenplan bauzone grundplan nutzungsplan flachennutzungsplan

fr: WFS-Dienst des Kantons Schwyz

de: WFS-Dienst des Kantons Schwyz

en: WFS-Dienst des Kantons Schwyz

it: WFS-Dienst des Kantons Schwyz

fr: ÖREB: Nutzungsplanung kommunal, Ingenbohl, überlagernde Festlegung

fr: ÖREB: Nutzungsplanung kommunal, Ingenbohl, objektbezogene Festlegungen

fr: Nutzungsplanung kommunal Gemeinde Ingenbohl

fr: ÖREB: Nutzungsplanung kommunal, Ingenbohl, Grundnutzung

fr: ÖREB: Nutzungsplanung kommunal, Ingenbohl, linienbezogene Festlegung

fr: Nutzungsplanung kommunal. Geobasisdatensätze Nr. 73D - 73F gemäss KGeoiV Kanton Schwyz (SRSZ 214.111). Dieser Datensatz ist ÖREB-relevant. D.h. diejenigen Objekte, die die Verbindlichkeit "Nutzungsplanfestlegung" aufweisen, sind auch im ÖREB-Kataster ausgewiesen. Unter den Fachdaten sind alle Objekte aufgeführt, also auch Objekte mit den Verbindlichkeiten "orientierend", "hinweisend" und "wegleitend".

spatial: Ingenbohl

amt-geoinformation-sz

de: Amt für Geoinformation Kanton Schwyz

de: Das Amt für Geoinformation verwaltet und veröffentlicht die Geodaten des Kantons Schwyz als OGC-Geodienste und als Datei-Download. Alle aktuell verfügbaren Geodaten, welche dem OGD unterliegen, sind hier aufgeführt.

value: SZ-Kt Geoinformation - Geocat Harvester


fr: larmempfindlichkeitsstufe eigentumsbeschrankungoreb nutzungsplan larmbelastung

label: WebGIS Kanton Schwyz, Thema einblenden

fr: Lärmempfindlichkeitsstufen

de: Lärmempfindlichkeitsstufen

en: Lärmempfindlichkeitsstufen

it: Lärmempfindlichkeitsstufen

fr: Lärmempfindlichkeitsstufen (in Nutzungszonen). Geobasisdatensatz Nr. 145 gemäss KGeoiV Kanton Schwyz (SRSZ 214.111). Der Datensatz ist auch Bestandteil des Katasters der öffentlich-rechtlichen Eigentumsbeschränkungen (ÖREB)

spatial: Kanton Schwyz (SZ)


de: eigentumsbeschrankungoreb zonenplan bauzone grundplan nutzungsplan flachennutzungsplan

fr: ÖREB: Nutzungsplanung kommunal, Wollerau, überlagernde Festlegung

fr: ÖREB: Nutzungsplanung kommunal, Wollerau, objektbezogene Festlegungen

fr: Nutzungsplanung kommunal Gemeinde Wollerau

fr: ÖREB: Nutzungsplanung kommunal, Wollerau, Grundnutzung

fr: ÖREB: Nutzungsplanung kommunal, Wollerau, linienbezogene Festlegung

de: Nutzungsplanung kommunal. Geobasisdatensätze Nr. 73D - 73F gemäss KGeoiV Kanton Schwyz (SRSZ 214.111). Dieser Datensatz ist ÖREB-relevant. D.h. diejenigen Objekte, die die Verbindlichkeit "Nutzungsplanfestlegung" aufweisen, sind auch im ÖREB-Kataster ausgewiesen. Unter den Fachdaten sind alle Objekte aufgeführt, also auch Objekte mit den Verbindlichkeiten "orientierend", "hinweisend" und "wegleitend".

spatial: Wollerau


maintainer: Amelie Fees

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

en: Glide distances for opening glide-cracks

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

en: SNOWPACK simulations

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

en: 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: README

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

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

educ

fr: Éducation, culture et sport

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

value: Envidat Harvester

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


high-resolution-climate-data-for-europe

maintainer: EnviDat Support

title_for_slug: high-resolution-climate-data-for-europe

en: air-temperature daily-climate eur11 maximum-temperature cordex precipitation europe minimum-temperature

en: CHELSA EUR11 Technical Documentation

media_type: application/pdf

en: As the CHELSA project is continuously expanded, certain new parameters, time scales etc. are not included anymore in the original publication (Karger et al. 2017). We are therefore provide this document as a guideline of the current climatic parameters available and will expand it as new parameters or time periods will become available. Please refer to the version history for changes made in the document.

format: PDF

en: Data Access

en:
High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present downscaled climate data for the CORDEX EUR11 domain at a high resolution of 30 arc sec. The temperature algorithm is based on statistical downscaling of atmospheric temperature lapse rates. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height. The resulting data consist of a daily temperature and precipitation timeseries.

The data is distributed under a: Creative Commons: Attribution 4.0 International (CC BY 4.0) license.

air-temperature

display_name: air-temperature

cordex

display_name: cordex

daily-climate

display_name: daily-climate

europe

display_name: europe

maximum-temperature

display_name: maximum-temperature

minimum-temperature

display_name: minimum-temperature

precipitation

display_name: precipitation

educ

en: Education, culture and sport

it: Istruzione, cultura e sport

en: High resolution climate data for Europe


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

en: Site photo of the forest station (NEB)

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

mimetype: image/jpeg

en: Field station data (NEF)

media_type: text/csv

en: Data file.

mimetype: text/csv

en: Data plot of the forest station (NEB)

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

mimetype: application/pdf

en: Site photo of the field station (NEF)

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

en: Site aerial photo (Neunkirch)

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

en: Forest station data (NEB)

en: Data plot of the field station (NEF)

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Neunkirch in Switzerland where one station is located within a natural deciduous forest stand (NEB) with European beech (_Fagus sylvatica_; 160 yrs), sycamore maple (_Acer pseudoplatanus_; 160 yrs) and lime trees (_Tilia sp._; 110 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, NEF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Neunkirch is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

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


greenland-shrubs-and-microclimate

maintainer: Nathalie Chardon

title_for_slug: greenland-shrubs-and-microclimate

en: validation accessible-shrub-forest field-data arctic sdm climate

en: Nuup Kangerlua

en:
 Collected July-August 2011-2013 (PI: Jacob Nabe-Nielsen)

These data collected July-August 2011-2013 (PI: Jacob Nabe-Nielsen). Please contact J. Nabe-Nielsen (jnn_at_ecos.au.dk) to request access to data on all surveyed shrub species. Available here are only data used in Chardon et al. 2022.

 COLUMN ABBREVIATIONS

 Metadata for 90 m spatial grid

year: sampling year

alt: average altitude of grid as recorded during field surveys [m]

long: WGS84 longitude

lat: WGS84 latitude

x_stere: longitude in Polar Stereographic (EPSG 4326)

y_stere: latitude in Polar Stereographic (EPSG 4326)

 Species presence [1] or absence [0] at grid level

pa_bena: _Betula nana_

pa_sagl: _Salix glauca_

 Shrub species cover (calculated from pin hits in 0.7 x 0.7 m frame, based on total of 25 hits) [%]

cov_bena: _Betula nana_ cover

cov_sagl: _Salix glauca_ cover

 Downscaled climate (time series 1984 – 2013)

tempjja: average maximum June-August temperature [ºC]

tempmax: yearly maximum temperature [ºC]

tempmin: yearly minimum temperature [ºC]

tempcont: yearly maximum – minimum temperature [ºC]

precipmam: cumulative March-May precipitation [mm]

precipjja: cumulative June-August precipitation [mm]

insoljja: average June-August insolation [MJ cm-2 yr-1]

 Topography and terrain wetness (calculated from)

twi: SAGA wetness index (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015)

slope: slope angle (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015) [º]

asp: aspect (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015) [º]

tcws: Tasseled Cap Wetness component (Sentinal images)

 Community characteristics

rich: shrub species richness averaged across pin-point frames per grid

shan: Shannon diversity (‘vegan’ package; Oksanen et al. 2019)

comp_bena: shrub competition on Betula nana (i.e. summed cover of non-_B. nana_ species) [% cover]

comp_sagl: shrub competition on Salix glauca (i.e. summed cover of non-_S. glauca_ species) [% cover]

en: GitLab Repository

en: Zip drive of GitLab repository contents (https://gitlab.com/nathaliechardon/gl_microclim).

en: Kangerluarsunnguaq

en:
 Collected August 2020 (PI: Nathalie Isabelle Chardon)

These data collected August 2020 (PI: Nathalie Isabelle Chardon). Please contact N. I. Chardon (nathalie.chardon_at_gmail.com) for individual quadrat (i.e. 1 m x 1 m) data, which are averaged at the 90 m spatial grid here.

 COLUMN ABBREVIATIONS

 Metadata for 90 m spatial grid

id: sampling zone and unique 90 m grid identifier

long: WGS84 longitude

lat: WGS84 latitude

x_stere: longitude in Polar Stereographic (EPSG 4326)

y_stere: latitude in Polar Stereographic (EPSG 4326)

date: sampling day.month.year

 Species presence [1] or absence [0] at grid level

alcr: _Alnus crispa_

bena: _Betula nana_

dila: _Diapensia lapponica_

emni: _Empetrum nigrum_

hahy: _Harimanella hypoides_

lopr: _Loiseleuria procumbens_

oxpa: _Oxycoccus palustre_

phca: _Phyllodoce caerulea_

rhod: _Rhododendron_ sp.

saar: _Salix arctica_

sagl: _Salix glauca_

sahe: _Salix herbacea_

vaul: _Vaccinium uliginosum_

drin: _Dryas integrifolia_

thpr: _Thymus praecox_

 Habitat characteristics [% cover]

shrub: all shrub species

gram: graminoids

forb: forbs

bryo: bryophytes

lich: lichens

litt: litter

bare: bare ground

stone: stones (> 2 cm)

org: organic crust

 Shrub species height (maximum vegetative height) [mm] and cover (estimated in 1x1 m quadrat) [%]

alcr_c: _Alnus crispa_ cover

alcr_h: _Alnus crispa_ height

bena_c: _Betula nana_ cover

bena_h: _Betula nana_ height

dila_c: _Diapensia lapponica_ cover

dila_h: _Diapensia lapponica_ height

emni_c: _Empetrum nigrum_ cover

emni_h: _Empetrum nigrum_ height

hahy_c: _Harimanella hypoides_ cover

hahy_h: _Harimanella hypoides_ height

lopr_c: _Loiseleuria procumbens_ cover

lopr_h: _Loiseleuria procumbens_ height

oxpa_c: _Oxycoccus palustre_ cover

oxpa_h: _Oxycoccus palustre_ height

phca_c: _Phyllodoce caerulea_ cover

phca_height: _Phyllodoce caerulea_ height

rhod_c: _Rhododendron_ sp. cover

rhod_h: _Rhododendron_ sp. height

saar_c: _Salix arctica_ cover

saar_h: _Salix arctica_ height

sagl_c: _Salix glauca_ cover

sagl_h: _Salix glauca_ height

sahe_c: _Salix herbacea_ cover

sahe_h: _Salix herbacea_ height

vaul_c: _Vaccinium uliginosum_ cover

vaul_h: _Vaccinium uliginosum_ height

drin_c: _Dryas integrifolia_ cover

drin_h: _Dryas integrifolia_ height

 Community characteristics

rich_pa: shrub species richness at grid level

rich: shrub species richness averaged across 3 1x1 m sampling quadrats per grid

shan: Shannon diversity (‘vegan’ package; Oksanen et al. 2019)

comp_bena: shrub competition on _Betula nana_ (i.e. summed cover of non-_B. nana_ species) [% cover]

comp_sagl: shrub competition on _Salix glauca_ (i.e. summed cover of non-_S. glauca_ species) [% cover]

ht_cwm: maximum shrub canopy height (i.e. community weighted mean by abundance of maximum shrub height) [mm]

 Topography and terrain wetness (calculated from)

tcws: Tasseled Cap Wetness component (Sentinal images)

slope: slope angle (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015) [º]

twi: SAGA wetness index (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015)

asp: aspect (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015) [º]

 Downscaled climate (time series 1984 – 2013)

insoljja: average June-August insolation [MJ cm-2 yr-1]

precipmam: cumulative March-May precipitation [mm]

tempcont: yearly maximum – minimum temperature [ºC]

tempmin: yearly minimum temperature [ºC]

precipjja: cumulative June-August precipitation [mm]

tempjja: average maximum June-August temperature [ºC]

tempmax: yearly maximum temperature [ºC]

en:
 Study Aim

We collected these data to alternatively train and validate high resolution (~ 90 m) Species Distribution Models (SDMs) and Species Abundance Models (SAMs) for _Betula nana_ L. (dwarf birch, Betulaceae) and _Salix glauca_ L. (grey willow, Salicaceae) in Southwest Greenland to assess how well such models can predict local-scale patterns.

 Data Description

Individual (presence-absence, abundance, maximum vegetative height) and community (species composition, maximum canopy height) shrub data for two fjords near Nuuk, Southwest Greenland. Also provided are corresponding downscaled climate data as well as calculated topographic and terrain wetness indicator variables.

 Nuup Kangerlua (Godthåbsfjord)

_Betula nana_ and _Salix glauca_ presence-absence, abundance, community species richness

 Kangerluarsunnguaq (Kobbefjord)

Shrub presence-absence, abundance, maximum vegetative height, community composition, maximum shrub canopy height

 Methods

 Field survey in Nuup Kangerlua

We conducted a stratified systematic plant survey along the length of Nuup Kangerlua (NK) fjord in Soutwesth Greenland (Fig. 1 in Chardon et al. 2022; following Nabe-Nielsen et al., 2017). At five distinct sites, we sampled along elevational gradients to collect data on presences, absences, abundance, and species composition of all woody species using a 0.7 x 0.7 m pin-point frame (Fig. 1e in Chardon et al. 2022). For model training, we converted these pin-point data to percent cover estimates based on the number of pins dropped (n = 25 per plot) and averaged them across the 119 spatio-climatic grids (see next section) corresponding to the plot locations (for details see Appendix S2 in Chardon et al. 2022).

 Field survey in Kangerluarsunnguaq

We conducted a random stratified plant survey in Kangerluarsunnguaq (K) fjord in Southwest Greenland. We used a preliminary Species Abundance Model trained with summed pin counts of _Betula nana_ in NK fjord (see Fig. S1.3 in Chardon et al. 2022) to stratify the ~ 27 x 17 km fjord landscape into low, medium, and high abundances classes. We randomly selected 90 x 90 m spatio-climatic grids to survey in each class for a total of 200 grids, ensuring that they were accessible by foot or boat (for details see Appendix S2 in Chardon et al. 2022). Within each grid, we sampled within three 1 m2 quadrats arranged in a randomly rotated equilateral triangle centered on the mid-point of the cell. We used a gridded sampling quadrat with 1% delineations (Fig. 1h in Chardon et al. 2022) to record woody species presences, absences, and composition, estimated percent cover, and measured maximum shrub species vegetatitve height. At every plot, we also visually scanned the area in a 20 m radius from the plot and recorded the presence of any additional shrub species to estimate grid-level species richness. As in NK fjord, we averaged these data at the grid level (for details see Appendix S2 in Chardon et al. 2022).

 Biotic variables

We calculated biotic microscale variables from the plant survey data collected in NK and K fjords. We calculated shrub species richness, diversity, and competition (i.e. sum of non-B. nana or non-S. glauca pin hits or percent cover). In K fjord, we also calculated canopy height as the community weighted mean (by abundance) of maximum vegetative shrub height.

 Climate variables

We computed high resolution temperature, precipitation, and insolation for local scale data for the study area by statistically downscaling climate time series (1982 - 2013) from the monthly CHELSA data (Karger et al. 2017). We downscaled these data from 30 arc sec (~ 400 m at the latitude of our study) to our target grid size of ~ 90 m with geographic weighted regression and using the MEaSUREs Greenland Ice Mapping Project (GIMP) Digital Elevation Model (DEM) v. 1 (Howat et al., 2014, 2015). We then calculated 30-year averages of the climate parameters: average summer (June – August) maximum temperature, yearly maximum temperature, yearly minimum temperature, temperature continentality (yearly max. - min. temperatures), cumulative Spring (March – May) precipitation, cumulative summer precipitation, and average summer incident solar radiation (henceforth, insolation) (for calculation details see Appendices S2, S3 in Chardon et al. 2022 and Appendix S2 in von Oppen et al. 2021).

 Topography and terrain wetness indicator variables

We calculated several topographic and terrain wetness indices at a local scale. We derived slope, aspect, and the SAGA wetness index (hereafter TWI; Boehner et al., 2002; Boehner and Selige, 2006) from the GIMP DEM. TWI is a measure of how ‘wet’ an area is, based on water drainage from the surrounding landscape. We also calculated the tasseled cap wetness component (hereafter TCW, Crist and Cicone 1984) from satellite images (for details see Appendices S2, S3 in Chardon et al. 2022) as an alternative measure of wetness.

 Computer code

Attached as zip file and available on GitLab (https://gitlab.com/nathaliechardon/gl_microclim)

 Third-party data

Data used to calculate climate, topography, and terrain wetness indicator variables are publicly available (see Appendix S2 in Chardon et al. 2022 for all data references).

accessible-shrub-forest

display_name: accessible-shrub-forest

arctic

display_name: arctic

climate

display_name: climate

field-data

display_name: field-data

sdm

display_name: sdm

validation

display_name: validation

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Greenland shrubs and microclimate


en: snow-stratigraphy weissfluhjoch water-front snowmicropen ice-layers snow-temperature

en: WFJ_ICE_LAYERS dataset

mimetype: application/zip

en: README

media_type: text/plain

format: TXT

mimetype: text/plain

en: The WFJ_ICE_LAYERS dataset contains multi-instrument snowpack measurements at high temporal resolution, which enable to monitor the formation of deep ice layers due to preferential water flow, at the Weissfluhjoch research site, Davos, Switzerland. It covers the winter 2016/2017, with a focus on the early melting season. This dataset includes traditional snowpack profiles (weekly resolution, 15/11/2016-29/05/2017), SnowMicroPen penetration resistance profiles (daily resolution, 01/02/2017-19/04/2017), snow temperatures measured at different heights in the snowpack (half-hourly resolution, 01/03/2017-15/04/2017) and the water front height derived from an upward-looking ground penetrating radar (3-hour resolution, 04/03/2017-08/04/2017). The measurements are complemented by initialization files for SNOWPACK model simulations with the ice reservoir parameterization at Weissfluhjoch for the winter 2016/2017.

ice-layers

display_name: ice-layers

snow-stratigraphy

display_name: snow-stratigraphy

snow-temperature

display_name: snow-temperature

snowmicropen

display_name: snowmicropen

water-front

display_name: water-front

weissfluhjoch

display_name: weissfluhjoch

en: WFJ_ICE_LAYERS: Multi-instrument data for monitoring deep ice layer formation in an alpine snowpack


maintainer: Matthias Haeni

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

en: Site photo of the forest station (VSB)

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

en: Site aerial photo (Visp)

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

en: Data plot of the forest station (VSB)

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

en: Forest station data (VSB)

en: Data file.

en: Data plot of the field station (VSF)

en: Field station data (VSF)

en: Site photo of the field station (VSF)

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

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Visp in Switzerland where one station is located within a natural mixed forest stand (VSB) with Scots pine (_Pinus sylvestris_; 40-80 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, VSF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Visp is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

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


maintainer: Franziska Gerber

en: cryowrf reproducibility blowing-snow surface-mass-balance antarctica

en: Static input data from the WRF simulations (used in the python scripts in Scripts_Reproducibility).

en: Python scripts to reproduces figures in Gerber et al., 2022, submitted.

en: Namelist and input files and information to run the WRF and CRYOWRF simulations used in the paper.

en: Processed WRF output (run with Noah-MP land surface model) used for the analysis and the figures in the paper.

en: Python scripts to reproduces figures in Gerber et al., 2022, revised

mimetype: application/x-zip-compressed

en: Processed CRYOWRF output used for the analysis and the figures in the paper.

en:
This dataset contains data and scripts for "CRYOWRF - a validation and the effect of blowing snow on the Antarctic SMB" (Gerber et al., submitted).

* Simulation_setup: Namelists and input information to run the simulation. Some input files need to be downloaded from Sharma et a., 2021.
* Static_input: Static topography input file of WRF (geo_em.d01).
* WRF_27km_NoahMP: Preprocessed WRF output of the simulation run with the WRF using the surface parameterization Noah-MP to reproduce the figures and results in the paper.
* WRF_27km_CRYOWRF: Preprocessed WRF output of the simulation run with CRYOWRF to reproduce the figures and results in the paper.
* Scripts_Reproducibility: Python scripts to reproduce the figures and results in the paper.

Note:

* To run some of the scripts Atmospheric Weather station data needs to be prepared using Gerber and Lehning, 2022.
* AWS data is not provided and needs to be downloaded from the corresponding databases. Please make sure to comply with the respective terms and conditions.

antarctica

display_name: antarctica

blowing-snow

display_name: blowing-snow

cryowrf

display_name: cryowrf

reproducibility

display_name: reproducibility

surface-mass-balance

display_name: surface-mass-balance

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Reproducibility dataset for CRYOWRF validation


maintainer: Martin Schneebeli

en: new-snow 3d-print 3d-model snow-cast snow depth-hoar rounded-snow weak-layer

en: Snow models in STL-format

en: The zipped file contains all data described in the file 3d_snow_models_metadata. The stl-files should be ready for additive manufacturing.

en: Description of snowtypes, filenames and other metadata

en: This file contains information about the stl-files of the different snow models. Snowtype (Schneeart), International Snow Classification, preview picture, notes about the origin of the snow, filename of the stl-data, and physical size of the model are given.

format: XLS

en: The dataset contains several snow models in the Standard Tesselated Geometry File Format (stl) for 3D visualization, printing and additive manufacturing. Different snow types are available (new snow, rounded snow, depth hoar, buried surface hoar, graupel).

depth-hoar

display_name: depth-hoar

new-snow

display_name: new-snow

rounded-snow

display_name: rounded-snow

snow

display_name: snow

snow-cast

display_name: snow-cast

weak-layer

display_name: weak-layer


maintainer: Tobias Wechsler

en: gis hydropower decision-making ecosystem-protection integral-water-management

format: SHP

en: Indicators

en: The indicators capture supporting ecosystem functions.

en:
 A spatial dataset and tool to simultaneously assess hydropower potential and ecological potential of the Swiss river network (Version 2016)

 Introduction
The steadily growing demand for energy and the simultaneous pursuit of decarbonisation are increasing interest in the expansion of renewable energies worldwide. In Switzerland, various funding projects have been launched to promote technologies in the field of renewable energies and their application as quickly as possible. With the introduction of a funding instrument in 2009, the number of projects submitted to produce renewable energies increased rapidly. The applications for small hydropower plants (≤ 10 MW) were correspondingly numerous. However, the assessment of the environmental impact and its comparison with hydropower importance is still not standardized.
To provide a basis for decision-making, a methodology was developed to determine the overall hydropower potential of a region. A detailed assessment of each river reach, and the systematic and holistic assessment of small hydropower projects at a regional scale are combined here. The assessment of a river reach is conducted at the river space (i.e., the river and adjacent areas) and at the surrounding landscape level. The HYDROpot_integral methodology was developed as part of Carol Hemund's dissertation (2012) at the University of Bern. It allows the evaluation of river reaches holistically, regarding ecological, social, economic and cultural criteria. As a second part of the overall project, the theoretical hydropower (or hydraulic) potential was calculated for the entire river network, which complemnets the spatial assessment. In particular, it is possible to classify river reaches into those that are more suitable for hydropower production (=”use”) and those that are more suitable for protection.

 Material and method
The HYDROpot_Integral method was developed and tested on the basis of cantonal and national data (Hirschi et al. 2013). The method relies on 73 geodata sets. This holistic assessment is the key element of the entire assessment procedure. Its aim is to quantify the importance of the ecosystem functions in terms of services. The river network (GWN07) is divided into reaches of about 450m and for each reach two study units are defined. The river space (RS) records the ecosystem functions of the water body and the nearby riparian area. The length of the RS is 315 m on average in Switzerland and a maximum of 450 m, whereas the width is based on the FOEN definition (BWG 2001: 18f) and varies between 7-107 m. The surrounding landscape (SLS) is the second survey unit that records the ecosystem functions of the surrounding area over a range of 21 m to 321 m. The SLS is calculated over three times the RS width. The length of the SLS is identical to the length of the RS. The ecosystem functions are divided into three types: regulating (service A), cultural (service B) and provisioning (service C) functions. Accordingly, the assessment of the functions is divided into three parts and three values are assigned to each river reach. The more functions there are and the greater their performance, the higher these values are and the more important the corresponding functions are. Hence, these values quantify the importance of the ecosystem functions and the ecological, cultural and economic ecosystem services of each river reach. The concatenation of ecosystem services results in a value (ABC) that can occur in 125 different versions due to the chosen five-level value scale; i.e. each digit of the three-digit number sequence can be assigned a value between 1 and 5. Each of the 125 combinations, and thus each river reach, has its own characteristics determined by the assessments of the three function types. To record the suitability, the combinations are ranked according to their ecological, cultural and economic ecosystem services. These rules mean that the combination that is most suitable for hydropower production at minimum cost in terms of ecological and cultural ecosystem services and has a high economic potential is ranked first; rank 125 indicates the highest ecological and cultural ecosystem services and the lowest economic services and is therefore most suitable for protection. A river reach that is excluded from hydropower use due to legislation, a so-called priority reach, is given rank 126 from the outset and specially marked. A more detailed description of the methods can be found in Hirschi et al. 2013 [Link]. The dataset presented here presents the latest state of the HYDROpot_integral methodology applied at the national level. Only national data that is easily accessible was used in the preparation of the dataset. The cantonal data, such as renaturation and revitalization, would have to be requested by each canton individually and was excluded here. The nationwide value synthesis was made with R.
A list of data sources can be found here [link to text file]
A list of all parameters can be downloaded here [link to PDF and text files]

 Dataset description
Data is presented as a single shapefile. It contains the river network and all assessment results obtained with HYDROpot_Integral.

 Changes in the methodology compared to the original method (Hirschi et. al 2013)

 * RS_A11 Ecomorphology: recorded for the whole of Switzerland and zero values equated with NA;
individual cantons such as Zug and St. Gallen have no mapped values according to the modular
concept of the federal government, Valais and Graubünden only the main valleys, Ticino and Fribourg not
completely (BAFU 2009).
 * RS_A14 Renaturation and revitalization data: not centrally available at the time of data collection.
centrally available, therefore values in GR were equated with NA.
 * RS_A15 Dilution ratio at wastewater treatment plants (WWTPs) for discharges: Zero values
equal to NA.
 * RS_A20 Water flow: use WASTA (2013) with hydroelectric power plants (> 300 kW) under
Federal control and dams serving hydroelectricity (Dam, as of 2013).
 * RS_C05 Synoptic hazard maps: are cantonally managed at the time of data collection,
Values in GR are marked with a 5 so that the systematics in the decision tree is not affected.
is affected.
 * Water quality (RS_A15, RS_A16, RS_A17, RS_A18, RS_A19): for the evaluation of the function type.
A Nature, it is important whether the median of the five values is less than or equal to 3 in total. This
evaluation is based on the decision tree for evaluating GR (Hirschi et al. 2013:22).
Therefore, an evaluation of the station data is made where critical and possible river segments
with poor quality (median less than 3) exist. Only two longer and one short
sections in Switzerland receive a lower median than 3 for water quality.
 * SLS_B06 Visibility: For 99 percent of the river segments (30,733 of 31,062) in the canton of
Bern (2015 reduced version), the landscape area is considered to be visible. Due to this high number
of sections, a large number of viewpoints in the layer of Swisstopo and
the computation time and computability in ArcGIS, the landscape area is classified as generally viewable
(equal to 1).
16 Method
Additional indicators were added (see Appendix B.2):
 * SLS_A21 Dissection
 * SLS_A22 Forest areas
 * SLS_B03 Hiking trails
 * SLS_B10 Residential and vacation homes
 * SLS_B11 Tourist infrastructure
 * SLS_C01 Landfill
 * SLS_C03 Infrastructure
 * SLS_C05 Industry
 * SLS_C06 Agricultural land
Not to be added, although present to some extent:
 * SLS_B06 Cultural assets of national importance: here, too, the calculability of the visibility analysis is
for the whole of Switzerland is limited
 * SLS_A15 Legally binding protection and land use planning: the individual river sections are not
clearly designated, i.e. no geodata exist
The following data are also not supplemented, as they are cantonal data:
 * SLS_A10 Cantonal nature reserves
 * SLS_A16 Forest reserves
 * SLS_A17 Cantonal inventories and contractually protected areas

decision-making

display_name: decision-making

ecosystem-protection

display_name: ecosystem-protection

gis

display_name: gis

hydropower

display_name: hydropower

integral-water-management

display_name: integral-water-management


en: hygroscopicity cloud-droplets aerosol ccn hac2

en: This repository contains the datasets that were used for used for the paper:: Drivers of Droplet Formation in East Mediterranean Orographic Clouds, EGUsphere, https://doi.org/10.5194/egusphere-2024-490, 2024.

aerosol

display_name: aerosol

ccn

display_name: ccn

cloud-droplets

display_name: cloud-droplets

hygroscopicity

display_name: hygroscopicity

en: Dataset for the publication Drivers of Droplet Formation in East Mediterranean Orographic Clouds


maintainer: Henning Löwe

en: discrete-element-modelling grain-shape fast-sintering angle-of-repose granular-snow

en: Angle of repose and 3D CT images

en:
5 Snow types were examined and glass beads as a cohesion-less reference, named as follows:

RG: rounded grains (sieved with 0.7 - 1.4 mm)

FC: faceted grains (1.4 - 2 mm)

FCS: small faceted grains (0.7 - 1.4 mm)

IB: ice beads (4 mm)

IBS: small ice beads (2 - 2.2 mm)

GB: glass beads(2 mm)

en:
Angle of repose experiments were performed with different snow types at temperatures between -2 and -40°C. They were used to examine granular snow dynamics on the grain-scale with focus on the role of grain shape and cohesion. The angle of repose was observed by sieving snow onto a round, freestanding base until a stationary heap was formed.

This dataset consists of 1) the images of the experimental heaps that were taken to determine the angle of repose, 2) one binary 3D micro computed tomography image of each snow type. The CT images were taken with the SLF micro-CT40 to characterize snow properties and grain shape.

The experiments with natural snow types (rounded and faceted grains) and spherical model snow allowed for an examination of the differences in granular properties between natural grain shapes and spherical particles in view of Discrete Element Modelling. With the chosen temperatures, the effect of sintering could be observed that increases the angle of repose with increasing temperature.

angle-of-repose

display_name: angle-of-repose

discrete-element-modelling

display_name: discrete-element-modelling

fast-sintering

display_name: fast-sintering

grain-shape

display_name: grain-shape

granular-snow

display_name: granular-snow

educ

en: Education, culture and sport

en: Angle of repose experiments with natural and spherical snow


intratrait

maintainer: Christian Rixen

title_for_slug: intratrait

en: plant-traits neighbour-interactions elevation-range climate-change cold-adapted-plants elevation-gradient

en: Plant trait data along elevation gradients on mountains

en:
This data set was used to test whether species specialized to high elevations or with narrow elevational ranges show more conservative (i.e. less variable) trait responses across their elevational distribution, or in response to neighbours, than species from lower elevations or with wider elevational ranges. We did so by studying intraspecific trait variation of 66 species along 40 elevational gradients in four countries (Switzerland, Australia, New Zealand, China) in both hemispheres. As an indication of potential neighbour interactions that could drive trait variation, we also analysed plant species’ height ratio, its height relative to its nearest neighbour.
The following traits and parameters were measured and are available in this data set:
As an indication of plant stature, we measured vegetative and generative height, where vegetative height was distance from soil to highest vegetative leaf and generative height was distance to the highest point on the reproductive shoot. As a measure of reproductive investment, we noted the presence of flowers on the randomly chosen individuals (see below). As a measure of individual and genet basal area, we measured individual plant and patch diameters, in two dimensions (along the largest diameter and perpendicular to it). In clonal plant species, plant diameter was equivalent to an individual rosette, whereas patch diameter referred to the whole genet and could represent the size of a tuft, tussock or cushion. For genera with more singular growth forms (e.g., some Gentiana species) plant and patch diameter were the same. The two diameter measurements were made at right angles, allowing estimates of patch and plant areas to be calculated as an ellipse (i.e., area = 0.5 a 0.5 b Π). All traits were measured on ten randomly selected individuals per site. Flower count data was considered in a binary fashion on a per individual basis (because for some species individuals only produce one flower when flowering) so that the presence or absence of flower(s) was a nominal value between 0 and 10 for each species at each site. We then collected at least three leaves (up to 30 for small and light leaves) from each of the first three individuals selected from each species for determination of leaf dry matter content (LDMC) and specific leaf area (SLA). For calculations of LDMC and SLA, fresh leaves were scanned on a flatbed scanner to determine leaf area. Leaves were then weighed on a balance to a precision of +/- 0.001g, prior to being air dried and reweighed with a balance to a precision of +/- 0.0001g. LDMC was calculated by dividing dry leaf mass by fresh leaf mass. SLA was calculated by dividing leaf area by dry leaf mass. Additionally, within an area of 10 cm diameter around the target individual, we determined the tallest neighbouring species and measured its vegetative and generative height, and estimated the percent cover of the target species, other vegetation, rock, and bare soil.
For more details see Rixen et al. 2022, Journal of Ecology.

climate-change

display_name: climate-change

cold-adapted-plants

display_name: cold-adapted-plants

elevation-gradient

display_name: elevation-gradient

elevation-range

display_name: elevation-range

neighbour-interactions

display_name: neighbour-interactions

plant-traits

display_name: plant-traits

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: intratrait


soil-property-maps-for-the-swiss-forest

title_for_slug: soil-property-maps-for-the-swiss-forest

en: soil-depth soc soil forest dsm switzerland soil-properties soil-texture soil-ph

en:
We used 2071 forest soil profiles to model a wide range of soil properties for the forested area of Switzerland. The spatial prediction is based on the principle of «digital soil mapping». This involves linking soil profiles with soil forming factors using statistical or machine learning methods.

A quantile regression forest (QRF) approach was applied to predict the following soil properties at six depth ranges: clay, gravel, sand, fine earth density, SOC. The depth ranges correspond to the standard depths of the [GlobalSoilMap.Net](https://www.isric.org/) specification: 0–5, 5–15, 15–30, 30–60, 60–100 and 100–200 cm. In addition, the total soil depth down to a non-root-permeable layer or solid rock soil thick was predicted. To quantify the uncertainty for each predicted pixel, the upper and lower limit of the 90% prediction interval derived from QRF was calculated. More details on the methods and results are described in Baltensweiler et al. 2021 and Baltensweiler et al 2022.

The soil property maps, and the uncertainty maps are provided as a GeoTIFF files at 25 m resolution. The excel file (xlsx) provides a short description of the raster layers.

**The soil and the uncertainty maps can be viewed in a simple web-GIS application available at:**
[www.wsl.ch/soilmaps](https://www.wsl.ch/soilmaps).

dsm

display_name: dsm

forest

display_name: forest

soc

display_name: soc

soil

display_name: soil

soil-depth

display_name: soil-depth

soil-ph

display_name: soil-ph

soil-properties

display_name: soil-properties

soil-texture

display_name: soil-texture

switzerland

display_name: switzerland

en: Soil property maps for the Swiss forest


en: lidar radiation-transfer solar-energy sky-view-fraction light-availability transmissivity solar-irradiance shortwave-radiation terrain forests

en: DataLink

en:
The SwissRad10 dataset contains light availability maps at 10 m and hourly resolution across an entire annual cycle with wall-to-wall coverage across the whole of Switzerland.
Areas below and within 25 m of forests include light availability calculated for both leaf-on and leaf-off canopy structure, and terrain only data is available across the full domain.
The variables sky-view fraction and direct-beam transmissivity are both included, allowing estimation of both direct and diffuse light at the ground surface.
The dataset was calculated using the detailed synthetic hemispheric image-based ray-tracing Canopy Radiation Model (CanRad) with the latest airborne lidar and terrain surface data from the swissSURFACE3D dataset.

forests

display_name: forests

lidar

display_name: lidar

light-availability

display_name: light-availability

radiation-transfer

display_name: radiation-transfer

shortwave-radiation

display_name: shortwave-radiation

sky-view-fraction

display_name: sky-view-fraction

solar-energy

display_name: solar-energy

solar-irradiance

display_name: solar-irradiance

terrain

display_name: terrain

transmissivity

display_name: transmissivity

en: SwissRad10 Hourly light availability maps at 10 m resolution over Switzerland


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

en: metadata and description of dataset

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

afforestation

display_name: afforestation

genetic-structure

display_name: genetic-structure

molecular-markers

display_name: molecular-markers

pinus-cembra

display_name: pinus-cembra

reproductive-matieral

display_name: reproductive-matieral

en: Nuclear microsatellite genotypes of Pinus cembra


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

en: Data access

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

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

caribbean-region

display_name: caribbean-region

coral-reefs

display_name: coral-reefs

curacao

display_name: curacao

edna

display_name: edna

environmental-dna

display_name: environmental-dna

fish

display_name: fish

fish-composition

display_name: fish-composition

marine

display_name: marine

teleo

display_name: teleo

educ

en: Education, culture and sport

en: Environmental DNA Marine Curacao 2020


en: agro-food-trade national-dishes food-preferences food-taste-similarity latent-semantic-analysis

en: information on ingredients of national dishes

en: shows information of ingredients of all national dishes of 171 countries and their names

en: dyadic food similarity measures, pairwise similarity and dissimilarity (chord diagram)

en:
The figures show the distribution of two dyadic food tastes similarity measures on the base of
the national dishes: (i) the Manhattan distance and the (ii) latent semantic analysis. The chord diagram
shows the results for country pairs with most and less similarity on basis of the LSA-approach.

en: National dishes, migration data and data for bilateral food trade flows

en:
The data include information on national dishes, migration data and data for bilateral food
trade flows for 5 years as well as importer and exporter GDP, population, distance between
most populated cities, bilateral information on Preferential Trade Agreements, common
official primary langauge, common colonizer post-1945, colonial relationship for overall
171 countries. The final data set contains 145,350 observations (29,070 country pairs for
5 years)

en: The data described in this article were collected daily over the period 4 June 2018 to 23 August 2018 and contains information of several data sources. The database includes information on national recipes and their ingredients for 171 countries, measures for food taste similarities between all 171 countries as well as bilateral migration and agro-food trade data for 5 years. The database can be used for analyzing e.g., the relation between food preferences and international trade or food preferences and health outcomes (e.g., obesity) across countries.

agro-food-trade

display_name: agro-food-trade

food-preferences

display_name: food-preferences

food-taste-similarity

display_name: food-taste-similarity

latent-semantic-analysis

display_name: latent-semantic-analysis

national-dishes

display_name: national-dishes

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: Data of national dishes, their similarity and trade flows


maintainer: James W. Kirchner

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

en: Benchmark demonstration scripts and results

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

en: Impulse Response Function script

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

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

arma-noise

display_name: arma-noise

de-mixing

display_name: de-mixing

deconvolution

display_name: deconvolution

greens-function

display_name: greens-function

impulse-response-function

display_name: impulse-response-function

irf

display_name: irf

irfnnhsr

display_name: irfnnhsr

nonlinear-deconvolution

display_name: nonlinear-deconvolution

system-identification

display_name: system-identification

time-series-analysis

display_name: time-series-analysis

transfer-function

display_name: transfer-function

en: Impulse response functions for nonlinear nonstationary and heterogeneous systems


backward-trajectories

maintainer: Michael Sprenger

title_for_slug: backward-trajectories

en: lagrangian-analysis atmospheric-dynamics raclets complex-topopgraphy backward-trajectories alps cosmo

en: Backward Trajectories

en: The trajectories are started from surface up to 5000 m altitude, with an equidistant vertical spacing of 200 m, and calculated 120 h backward in time. As long as the trajectories stay in the COSMO1 domain (essentially the Alpine region), as run operationally at MeteoSwiss, the COSMO analysis wind fields are used. When the trajectories leave this domain, they are extended further backward in time based on ECMWF analysis data. The following fields are traced along the trajectories: longitude, latitude, geopotential height, pressure, specific humidity, temperature and surface topography. Furthermore, it is indicated for each trajectory time instance on which model data, COSMO or ECMWF, the trajectory time is based. Finally, the trajectories are started every hour in the specified time period, and positions and fields are available every 10 min. Each day is saved as a separate file. The last 8 digits of the file names indicate the starting date of the trajectories.

en: Backward trajectories were calculated from two positions: Davos Wolfgang (LON: 9.85361, LAT: 46.83551) and Weissfluhjoch (LON: 9.80646 LAT: 46.83304) for the time period February 2 until March 27 2019 using COSMO or ECMWF, respectively.

alps

display_name: alps

atmospheric-dynamics

display_name: atmospheric-dynamics

backward-trajectories

display_name: backward-trajectories

complex-topopgraphy

display_name: complex-topopgraphy

cosmo

display_name: cosmo

lagrangian-analysis

display_name: lagrangian-analysis

raclets

display_name: raclets

educ

en: Education, culture and sport

en: Backward  Trajectories


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

en: Site photo of the field station (LAF)

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

en: Data plot of the forest station (LAB)

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

en: Data plot of the field station (LAF)

en: Site photo of the forest station (LAB)

en: Site photo of the fforest station from the side.

en: Field station data (LAF)

en: Data file.

en: Site aerial photo (Lausanne)

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

en: Forest station data (LAB)

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Lausanne in Switzerland where one station is located within a natural mixed forest stand (LAB) with European beech (_Fagus sylvatica_; 160-170 yrs), European silver fir (_Abies alba_; 160-170 yrs) and Norway spruce (_Picea abies_; 160-170 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, LAF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Lausanne is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

en: Lausanne, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1996 onwards


maintainer: Vanessa Burg

en: review switzerland manure biogas bioenergy

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

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

bioenergy

display_name: bioenergy

biogas

display_name: biogas

manure

display_name: manure

review

display_name: review

switzerland

display_name: switzerland

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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


en: water-level catchment-data catchment dynamics drainage drainage-network channel precipitation

en: The dataset consists of time series of measured surface and groundwater levels, calculated flowing drainage network lengths, and nearby precipitation data for two sub-catchments of the Haute-Mentue catchment, Switzerland (Summer-Autumn 2022). The details of the dataset are described in the provided documentation (short-term_drainage_density_dynamics_dataset_documentation_for_the_haute-mentue_catchment.pdf)

en: Documentation for the Short-term Drainage Density Dynamics Dataset for the Haute-Mentue Catchment (dataset_of_flowing_drainage_network_lengths_water_levels_and_precipitation_for_the_haute-mentue_catchment.csv). This document provides a detailed description of the collected data, the locations where it was recorded, the data format, and the curation process applied.

en: The dataset contains time series of water levels, precipitation measured in the two sub-catchments of the Haute-Mentue catchment and its vicinity during summer and autumn 2022, as well as flowing drainage network lengths calculated for these areas using the CEASE method developed by the authors. Detailed description of the dataset is provided in the documentation.

catchment

display_name: catchment

catchment-data

display_name: catchment-data

channel

display_name: channel

drainage

display_name: drainage

drainage-network

display_name: drainage-network

dynamics

display_name: dynamics

water-level

display_name: water-level


maintainer: Irmi Seidl

en: energy-cooperatives renewable-energy switzerland cooperatives community-energy

en: Raw data (.csv)

en: Raw data in csv. format. For comprehension of the data and variable structure, please consult the codebook and the modified questionnaire with variable names.

en: Questionnaire with variable names

en: Modified questionnaire with variable names for each item. Should be used together with the data files (.csv or .sav) to reconstruct the data and variable structures.

en: Codebook

en: Description of variable attributes and valid n

en: Raw date (SPSS)

en: Raw data in sav. format (SPSS). For comprehension of the data and variable structure, please consult the modified questionnaire with variable names.

en:
 Topic of Survey
The data at hand on energy cooperatives in Switzerland were collected in 2016 as part of the project "Collective financing of renewable energy projects in Switzerland and Germany" of the National Research Programme 71 "Managing Energy Consumption". The cooperatives were surveyed on their organizational structure, their activities in electricity and heat generation, their finances, the political context and their assessments of the future.

 Survey Method
The survey was targeted at all energy cooperatives in Switzerland (this is the basic population). The Swiss Commercial Register was searched for cooperatives and specific keywords in order to determine this basic population and collect addresses. This search in May 2016 resulted in a total of 304 energy cooperatives, to which a questionnaire was sent in July 2016. A pre-test with 8 persons had been carried out before the questionnaire was sent out. The questionnaire was provided in German and French. It was sent by mail and an attached letter referred to a link for the digital version if preferred. The online version was designed with the software "Sawtooth". After three weeks, a first, and after six weeks a second reminder letter was sent to those cooperatives that had not yet completed the questionnaire. The returned hardcopy questionnaires were manually entered into the database and then combined with the electronic data from the online survey. In the course of the survey, the total population was reduced from 304 to 289: in 4 cases the survey was not deliverable, 4 cooperatives had dissolved, 6 were not actually energy cooperatives, 1 case had recently changed its legal form. With a response rate of 47%, the final data set comprises 136 responses (from 77 digital and 59 hardcopy questionnaires). However, not all 136 of the returned questionnaires were filled out completely. We checked for answers that seemed contradictory or incomprehensible. If an error could be clearly identified and the correct answer derived, the answer was adjusted, otherwise the answer was replaced by "missing data".

 Anonymization
Participating cooperatives have been assured that their information will be kept confidential and will only be made public anonymously. For this reason, the data have been anonymized in in order to prevent any identification of individual cooperatives.

 How to Use the Data
* The data are available in CSV and SPSS (sav.) format.
* A codebook and a modified version of the used questionnaire are provided to illustrate the data and variable structure. In the questionnaire, the variable names are assigned to the corresponding questions. In the codebook, further information on these variables (valid n, answer categories) can be found. This information (of the codebook) is already integrated in the SPSS file.

 Current Embargo on Data
These data are currently under embargo and will only be released when the project is completed (not before 2020).

#Additional Information
* The used questionnaire is provided in German and French.
* Descriptive results of the survey were published in a WSL report: https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:18943

community-energy

display_name: community-energy

cooperatives

display_name: cooperatives

energy-cooperatives

display_name: energy-cooperatives

renewable-energy

display_name: renewable-energy

en: Energy Cooperatives in Switzerland: Survey Results // Energiegenossenschaften in der Schweiz: Befragungsergebnisse


maintainer: EnviDat

en: research-data-management meteorological-data-format csv metadata-convention nead

en: NEAD on GitHub

media_type: text/html

format: HTML

en: NEAD syntax v0.1 (EBNF)

en: PyNEAD (future reference implementation)

en: MeteoIO (future reference implementation)

mimetype: text/html

en:
Acknowledgement: The NEAD format includes NetCDF metadata and is proudly inspired by both SMET and NetCDF formats. NEAD is designed as a long-term data preservation and exchange format.

The NEAD specifications were presented at the "WMO Data Conference 2020 - Earth System Data Exchange in the 21st Century" (Virtual Conference).

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

Summary:
The Non-Binary Environmental Data Archive (NEAD) format is being developed as a generic and intuitive format that combines the self-documenting features of NetCDF with human readable and writeable features of CSV. It is designed for exchange and preservation of time series data in environmental data repositories.

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

csv

display_name: csv

metadata-convention

display_name: metadata-convention

meteorological-data-format

display_name: meteorological-data-format

nead

display_name: nead

research-data-management

display_name: research-data-management

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Non-Binary Environmental Archive Data (NEAD) format