energie-reporter

maintainer: Christa Rohrbach

title_for_slug: Energie Reporter

fr: ReporterEnergie

de: Energie Reporter

en: Energy Reporter

it: Reporter Energetico

label: (DE) Energie Reporter

label: (FR) ReporterEnergie

label: (IT) Reporter Energetico

fr: Les données actuelles "energyreporter_latest.zip" contient l'état d'avancement actuel de toutes les communes, cantons et Suisse. Format: CSV (zippé).

en: The current data set "energyreporter_latest.zip" contains the current progress of all municipalities, cantons and Switzerland in CSV format (zipped).

de: Der aktuelle Datensatz "energyreporter_latest.zip" enthält den aktuellen Fortschritt aller Gemeinden, Kantone und der Schweiz im CSV-Format (gezippt).

it: Il set di dati attuali "energyreporter_latest.zip" contiene l'andamento attuale di tutti i comuni, i cantoni e la Svizzera. Formato: CSV (zippato).

format: ZIP

fr: Les données historisées "energyreporter_historized.zip" contient des instantanés mensuels depuis l'existence d'Energie Reporter (1. Mars 2021) avec l'évolution de toutes les communes et cantons ainsi que de la Suisse. Format: CSV (zippé).

en: The historized data set "energyreporter_historized.zip" contains monthly snapshots since the existence of Energie Reporter (1 March 2021) with the progress of all municipalities and cantons as well as Switzerland in CSV format (zipped).

de: Der historisierte Datensatz "energyreporter_historized.zip" enthält monatliche Zeitschnitte seit dem Bestehen von Energie Reporter (1. März 2021) mit dem Fortschritt aller Gemeinden und Kantone sowie der Schweiz im CSV-Format (gezippt).

it: Il set di dati storicizzati "energyreporter_historized.zip" contiene intervalli di tempo mensili a partire dalla data di pubblicazione del Reporter Energetico (1. Marzo 2021) con l'andamento di tutti i comuni e cantoni, nonché della Svizzera. Formato: CSV (zippato).

fr:
Découvrez où en est votre commune dans la transition énergétique grâce au [ReporterEnergie](https://www.reporterenergie.ch) et à ses chiffres-clés sur les voitures électriques, les chauffages renouvelables et l’énergie solaire.

Ces données sont publiées sous la licence Creative Commons [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/deed.fr). Ils peuvent être librement utilisés et transmis. En cas de publication, le nom "__Reporter Energie__" doit être indiqué comme source et "__geoimpact__" et "__SuisseEnergie__" comme contributeurs. Dans le cas d'un contenu en ligne, le lien vers [ReporterEnergie](https://www.reporterenergie.ch) doit également être indiqué.

Les données actuelles "__energyreporter_latest.zip__" contient l'état d'avancement actuel de toutes les communes, cantons et Suisse. Format: CSV (zippé).

Les données historisées "__energyreporter_historized.zip__" contient des instantanés mensuels depuis l'existence d'Energie Reporter (1. Mars 2021) avec l'évolution de toutes les communes et cantons ainsi que de la Suisse. Format: CSV (zippé).

en:
The [Energy Reporter](https://www.energiereporter.ch) is watching the energy future unfold in municipalities. How advanced is your municipality in terms of electric cars, renewable heating and solar power? Take a look at the data for your municipality.

This data is published under the Creative Commons licence [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/deed.en). It can be freely used and passed on. In the case of publication, the name "__Energy Reporter__" must be given as the source and "__geoimpact__" and "__EnergieSchweiz__" as contributors. For online content, the link to [Energy Reporter](https://www.energiereporter.ch) must also be provided.

The current data set "__energyreporter_latest.zip__" contains the current progress of all municipalities, cantons and Switzerland in CSV format (zipped).

The historized data set "__energyreporter_historized.zip__" contains monthly snapshots since the existence of Energie Reporter (1 March 2021) with the progress of all municipalities and cantons as well as Switzerland in CSV format (zipped).

de:
Der [Energie Reporter](https://www.energiereporter.ch) beobachtet die Entwicklung der Energiezukunft in den Gemeinden. Wo steht Ihre Gemeinde bei den Themen Elektroautos, erneuerbare Heizungen und Solarstrom? Schauen sie sich die Daten Ihrer Gemeinde an.

Diese Daten sind unter der Creative Commons Lizenz [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/deed.de) veröffentlicht. Sie können frei verwendet und weitergegeben werden. Bei einer Veröffentlichung müssen der Name "__Energie Reporter__" als Quelle sowie "__geoimpact__" und "__EnergieSchweiz__" als Mitwirkende angegeben werden. Bei Online-Inhalten ist zudem der Link zu [Energie Reporter](https://www.energiereporter.ch) anzugeben.

Der aktuelle Datensatz "__energyreporter_latest.zip__" enthält den aktuellen Fortschritt aller Gemeinden, Kantone und der Schweiz im CSV-Format (gezippt).

Der historisierte Datensatz "__energyreporter_historized.zip__" enthält monatliche Zeitschnitte seit dem Bestehen von Energie Reporter (1. März 2021) mit dem Fortschritt aller Gemeinden und Kantone sowie der Schweiz im CSV-Format (gezippt).

it:
Il [Reporter Energetico](https://www.reporterenergetico.ch) traccia l’evoluzione della transizione energetica nei Comuni. Dove si colloca il tuo Comune nell’ambito della mobilità elettrica, del riscaldamento da fonti rinnovabili e dell’energia solare? Verifica la stato del tuo Comune.

Questi dati sono pubblicati sotto la licenza Creative Commons [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/deed.it). Possono essere liberamente utilizzati e trasmessi. In caso di pubblicazione, deve essere indicato il nome "__Reporter Energetico__" come fonte e "__geoimpact__" e "__SvizzeraEnergia__" come contributori. Nel caso di contenuti online deve essere fornito anche il link al [Reporter Energetico](https://www.reporterenergetico.ch).

Il set di dati attuali "__energyreporter_latest.zip__" contiene l'andamento attuale di tutti i comuni, i cantoni e la Svizzera. Formato: CSV (zippato).

Il set di dati storicizzati "__energyreporter_historized.zip__" contiene intervalli di tempo mensili a partire dalla data di pubblicazione del Reporter Energetico (1. Marzo 2021) con l'andamento di tutti i comuni e cantoni, nonché della Svizzera. Formato: CSV (zippato).

energia

display_name: energia

energie

display_name: energie

energie-rinnovabili

display_name: energie-rinnovabili

energies-renouvelables

display_name: energies-renouvelables

energiewende

display_name: energiewende

energy

display_name: energy

energy-transition

display_name: energy-transition

erneuerbare-energien

display_name: erneuerbare-energien

renewable-energies

display_name: renewable-energies

tournant-energetique

display_name: tournant-energetique

transizione-energetica

display_name: transizione-energetica

energy

fr: Energie

de: Energie

en: Energy

it: Energia

geoimpact

fr: geoimpact AG

de: geoimpact AG

en: geoimpact AG

it: geoimpact AG

fr: Nous captons l'idée et l'intention d'un changement et relions les personnes qui veulent faire bouger les choses. Une équipe motivée s'est donné pour mission de développer un logiciel innovant pour la planification systématique des énergies renouvelables dans toute la Suisse. Pour que nous soyons tous - citoyens, planificateurs et maîtres d'ouvrage - à un clic du prochain pas vers l'avenir.

en: We capture the thought and intention of change and connect the people who want to make a difference. A motivated team has made it their mission to develop innovative software for the systematic planning of renewable energies throughout Switzerland. So that all of us - citizens, planners and builders - are just one click away from the next step into the future.

de: Wir fangen den Gedanken und die Absicht einer Veränderung ein und verbinden die Menschen, die etwas bewegen wollen. Ein motiviertes Team hat es sich zur Aufgabe gemacht, innovative Software zur schweizweiten systematischen Planung von erneuerbaren Energien zu entwickeln. Damit wir alle - Bürger, Planer und Bauherren - nur einen Klick vom nächsten Schritt in die Zukunft entfernt sind.

it: Catturiamo il pensiero e l'intenzione del cambiamento e colleghiamo le persone che vogliono fare la differenza. Un team motivato ha fatto la sua missione di sviluppare un software innovativo per la pianificazione sistematica delle energie rinnovabili in tutta la Svizzera. In modo che tutti noi - cittadini, pianificatori e sviluppatori - siamo solo un click di distanza dal prossimo passo verso il futuro.


geography

fr: Géographie

de: Geographie

en: Geography

it: Geografia

territory

fr: Territoire et environnement

de: Raum und Umwelt

en: Territory and environment

it: Territorio e ambiente

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

label: geocat.ch permalink

state: active

spatial: Kanton Luzern

type: dataset

protocol: https

de: Datenshop

de: Produktansicht Geodatenshop

format: SERVICE

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

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.

political_level: canton

capacity: public

type: organization

approval_status: approved

value: LU-Kt - Geocat Harvester

de: Kataster der belasteten Standorte (KbS)


gestaltungsplane-perimeter

title_for_slug: gestaltungsplane-perimeter

de: eigentumsbeschrankungoreb nutzungsplanung raumplanung sondernutzungsplanung

de: Perimeter der Gestaltungspläne

de: Gestaltungspläne: Perimeter


unuberbaute-bauzonen-in-kraft-genehmigt

title_for_slug: unuberbaute-bauzonen-in-kraft-genehmigt

de: gebaude-und-anlagen nutzungsplanung raumplanung wirtschaftliche-aktivitaten zonenplan

de: Überbauungsgrad der unüberbauten Bauzonen des genehmigten Zonenplans, laufend aktualisiert

de: Unüberbaute Bauzonen, in Kraft (genehmigt)


maintainer: Luca Hüsler

de: stromverbrauch elektrizitat

de: json

media_type: application/json

de: Endverbrauch von Elektrizität nach Gemeinde und Jahr (ab 1990) (json)

format: JSON

mimetype: application/json

license: CC BY 3.0 CH

de: csv

media_type: text/csv

de: Endverbrauch von Elektrizität nach Gemeinde und Jahr (ab 1990) (csv)

format: CSV

mimetype: text/csv

de: Energiestatistik

elektrizitat

display_name: elektrizitat

stromverbrauch

display_name: stromverbrauch

energy

de: Energie

kanton-basel-landschaft

fr: Canton de Bâle-Campagne

de: Kanton Basel-Landschaft

en: Canton of Basel-Country

it: Cantone di Basilea-Campagna

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

key: guid

key: uri

value: BL - DCAT


gewasserraum

geography

de: Geographie

territory

de: Raum und Umwelt

title_for_slug: gewasserraum

label: Geometadaten Kanton Zürich: GeoLion

spatial: Kanton Zürich

de: Download via GIS-Browser

de: Mit der Festlegung des Gewässerraums an den oberirdischen Gewässern soll der Raum gesichert werden, welcher nötig ist zur Gewährleistung der natürlichen Funktionen der Gewässer, des Hochwasserschutzes und der Gewässernutzung. Die Gewässerschutzverordnung des Bundes (GSchV) legt dazu konkrete Masse fest, wie gross der Gewässerraum mindestens sein muss (Art. 41a und 41b GSchV). Der Gewässerraum muss dort erhöht werden, wo dies zur Gewährleistung für den Hochwasserschutz, der Revitalisierung, sowie bei überwiegenden Interessen des Natur- und Landschaftsschutzes und der Gewässernutzung erforderlich ist. Im Gewässerraum dürfen nur standortgebundene, im öffentlichen Interesse liegende Bauten und Anlagen erstellt werden. In dicht überbauten Gebieten kann der Gewässerraum den baulichen Gegebenheiten angepasst werden, soweit der Schutz vor Hochwasser gewährleistet ist, bzw. kann die Behörde für zonenkonforme Bauten und Anlagen Ausnahmen bewilligen, sofern keine überwiegenden Interessen entgegenstehen. Rechtmässig erstellte und bestimmungsgemäss nutzbare Bauten und Anlagen im Gewässerraum sind in ihrem Bestand grundsätzlich geschützt. Der Gewässerraum darf nur extensiv gestaltet und bewirtschaftet werden (Art. 41 c GschV) Solange der Gewässerraum nicht festgelegt ist, regeln die Übergangsbestimmungen der GSchV direkt und grundeigentümerverbindlich die Bemessung der von Bauten und Anlagen freizuhaltenden Uferstreifen. Der Gewässerabstand von 5 Meter gemäss § 21 des kantonalen Wasserwirtschaftsgesetzes (WWG), behält bis zu einer allfälligen Anpassung des WWG auch mit der Festlegung des Gewässerraums weiterhin Gültigkeit. Nach der Ersterfassung werden die Daten in den ÖREB-Kataster überführt.

name:
Amt für Abfall, Wasser, Energie und Luft
             - Abteilung Wasserbau

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

de: Gewässerraum


agriculture

fr: Agriculture, sylviculture

de: Land- und Forstwirtschaft

en: Agriculture, forestry

it: Agricoltura, selvicoltura

de: fruchtfolgeflache fff boden bodenschutz

de: Die Karte gibt Auskunft über Lage, Umfang und Qualität der Fruchtfolgeflächen (FFF) im Kanton Zürich und konkretisiert die entsprechenden Festlegungen des kantonalen Richtplans (Stand 24. März 2014). Sie wurde gestützt auf die in den Jahren 2009 und 2010 durchgeführte Feldprüfung erarbeitet und dient als Grundlage für Massnahmen zum Erhalt des vom Bund vorgegebenen Mindestumfangs an FFF.

name:
Amt für Landschaft und Natur
             - Fachstelle Bodenschutz

de: Fruchtfolgeflächen


de: boden bodenkarte landwirtschaft landwirtschaftliche nutzung naturschutz umweltschutz gewasserschutz dunger dungen fruchtfolgeflachen fff fff-kompensation bodenschutz

de: Die Nutzungseignungskarte stellt für das Landwirtschaftsgebiet des Kantons Zürich die standortgerechte landwirtschaftliche Nutzungseignung in 10 Klassen dar, unter Angabe des limitierenden Standortfaktors. Methodische Grundlage ist ursprünglich der "Grundlagenbericht zur Bodenkartierung des Kantons Zürich" [FAL u. VD-ZH 1998] und heute die Kartieranleitung „Kartieren und Beurteilen von Landwirtschaftsböden“ [FAL 1997]. Beurteilungskriterien sind die Standorteigenschaften Boden, Relief und Klima. Die Nutzungseignungskarte soll dem landwirtschaftlichen Bewirtschafter und Berater als Hilfsmittel dienen, um für den entsprechenden Betrieb einen standortgerechten Pflanzenbau zu realisieren. Ferner bildet sie eine Grundlage für Orts- und Regionalplanung (z.B. Bauzonenerweiterungen) sowie für die Beurteilung von Planungs- und Bauvorhaben ausserhalb der Bauzone (Umweltverträglichkeitsprüfungen, Standortaufwertungen, Fruchtfolgeflächen-Kompensationen etc.). Die Karte ist ursprünglich ein Produkt der Bodenkartierung 1:5‘000 der Landwirtschaftsflächen im Kanton Zürich in den Jahren 1988-97. Seither finden auf einzelnen Standorten aus unterschiedlichen Gründen Neubeurteilungen statt, wobei sich die Methoden der Bodenansprache und -beurteilung weiterentwickelt haben (s. a. unter Datengrundlagen). Zu jeder Fläche wird deshalb neben den Angaben zur Nutzungseignung auch die Beurteilungsgrundlage ausgewiesen. Im Falle von weniger differenzierten Standortaufnahmen wird die Nutzungseignung u.U. als Bereich angegeben. Die Karte stellt den besten aktuell verfügbaren Informationsstand dar.

de: Landwirtschaftliche Nutzungseignung


dma-mediathek

maintainer: Digitale Medien der Armee DMA, Kundendienst Medienagentur

title_for_slug: dma-mediathek

fr: 360 groupes-thematiques ddps video photo mediatheque militaire

de: video themes vbs 360 mediathek militar foto

en: military video media-library theme-world ddps 360 photograph

it: foto mediateca 360 militare ambiti-tematici video ddps

fr: MNA - Médiathèque

en: AFDM - Media library

de: DMA - Mediathek

it: MDE - Mediateca

media_type: html

fr: Compétences en photos et en vidéos.

de: Kompetenz in Fotos und Videos.

en: Expertise for photographs and videos.

it: Competenza sotto forma di foto e video.

format: HTML

mimetype: html

fr: MNA - Ressources

de: DMA - Assets

en: AFDM - Assets

it: MDE - Asset

fr: Cette liste contient les ressources saisies par MNA.

de: Diese Liste enthält die von DMA erfassten Assets.

en: This list contains the assets of AFDM.

it: Questa lista contiene i asset dello MDE.

fr: MNA - Médiathèque historique

de: DMA - Historische Mediathek

en: AFDM - Historic Media Library

it: MDE - Mediateca storica

fr: Photos et films historiques.

en: Historical snapshots and video footage.

de: Historische Momentaufnahmen in Fotos und in Filmen.

it: Istantanee storiche in forma di foto e filmati.

fr: MNA - Métadonnées

de: DMA - Metadaten

en: AFDM - Metadata

it: MDE - Metadati

fr: Les métadonnées montrent les clients et ressources de la médiathèque.

de: Die Metadaten zeigen, welche Mandanten und Assets erfasst sind.

en: The metadata show the clients and assets of the media library.

it: I metadati mostrano i clienti e asset della mediateca.

fr: Sis à Berne, le MNA est le centre de compétences pour les produits et services dans les domaines de la formation et de la communication.

en: The AFDM, located in Bern, is the competence center for products and services in the fields of education and communication.

de: Das DMA mit Standort in Bern ist das Kompetenzzentrum für Produkte und Dienstleistungen in den Bereichen der Ausbildung und der Kommunikation.

it: Il MDE, con sede a Berna, è il centro di competenza per i prodotti e le prestazioni nei settori dell'istruzione e della comunicazione.

ambiti-tematici

display_name: ambiti-tematici

ddps

display_name: ddps

foto

display_name: foto

groupes-thematiques

display_name: groupes-thematiques

media-library

display_name: media-library

mediateca

display_name: mediateca

mediathek

display_name: mediathek

mediatheque

display_name: mediatheque

militaire

display_name: militaire

militar

display_name: militar

militare

display_name: militare

military

display_name: military

culture

fr: Culture, médias, société de l'information, sport

de: Kultur, Medien, Informationsgesellschaft, Sport

en: Culture, media, information society, sport

it: Cultura, media, società dell'informazione, sport

name: Digitale Medien der Armee (DMA), Departement VBS

fr: Sis à Berne, le Centre des médias numériques de l’armée MNA est le centre de compétences pour les produits et services dans les domaines de la formation et de la communication.

en: The Armed Forces Digital Media Centre AFDM, located in Bern, is the competence center for products and services in the fields of education and communication.

de: Das Zentrum digitale Medien der Armee DMA mit Standort in Bern ist das Kompetenzzentrum für Produkte und Dienstleistungen in den Bereichen der Ausbildung und der Kommunikation.

it: Il Centro dei media digitali dell’esercito MDE, con sede a Berna, è il centro di competenza per i prodotti e le prestazioni nei settori dell'istruzione e della comunicazione.


agriculture

de: Land- und Forstwirtschaft

geography

fr: Géographie

de: Geographie

en: Geography

it: Geografia

territory

fr: Territoire et environnement

de: Raum und Umwelt

en: Territory and environment

it: Territorio e ambiente

de: fruchtfolgeflachen fff kompensation boden bodenschutz

de: Download via GIS-Browser

de: Für verschiedene Fragestellungen im Zusammenhang mit Nutzung und Schutz des Bodens, z.B. bei geplanten Terrainveränderungen, ist es wichtig zu unterscheiden, ob es sich um einen natürlich gewachsenen oder einen durch menschliche Eingriffe wesentlich veränderten oder gar künstlich aufgebauten, sogenannt anthropogenen Boden handelt. Aus verschiedenen thematischen Datenebenen des GIS-ZH lassen sich Hinweise darauf gewinnen, wo mit anthropogen veränderten Böden zu rechnen ist. Für den vorliegenden Datensatz wurden diese Quellen ausgewertet und die entsprechenden Datenobjekte zusammengeführt. Je nach Datenquelle können die Hinweise mehr oder weniger spezifisch, präzise oder aktuell sein. Das Datenprodukt hat deshalb hauptsächlich orientierende Bedeutung und gibt keine Auskunft über die Bodenqualität. Ausprägung und genaue Lage von anthropogenen Bodenveränderungen müssen im Einzelfall durch Felduntersuchungen festgestellt werden.

geoinformation-kanton-zuerich

de: Geoinformation Kanton Zürich

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.

de: Hinweisflächen für anthropogene Böden


grueben

construction

fr: Construction, logement

de: Bau- und Wohnungswesen

en: Construction and housing

it: Costruzione e abitazione

title_for_slug: grueben

label: WebGIS Kanton Schwyz, Thema einblenden

spatial: Kanton Schwyz (SZ)

fr: Grueben

de: Grueben

en: Grueben

it: Grueben

format: WMS

format: WFS

fr: Der Kanton Schwyz ist zuständig für das Hauptwanderwegnetz und trägt dafür die Kosten für Planung, Bau, rechtliche Sicherung, Unterhalt und Markierung, soweit dafür nicht Dritte zuständig sind. Um die Attraktivität des Hauptwanderwegnetzes zu steigern, wurden an geeigneten Orten sogenannte Grueben (Schutzhütten / Grillstellen; alle in selber Bauform) erstellt. Es gibt über den ganzen Kanton verteilt weitere Schutzhütten (teils in ähnlichem Baustil), welche auf private Initiative oder von Gemeinden/Koporationen erstellt wurden und von diesen auch unterhalten werden. Diese Grueben sind in diesem Datensatz jedoch nicht enthalten.

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


maintainer: Bundesamt für Energie BFE

title_for_slug: Schweizerische Elektrizitätsstatistik: Schweizerische Elektrizitätsbilanz - Monatswerte

fr: ogd35-bfe electricite production importation exportation consommation

de: ogd35-bfe elektrizitat produktion import export verbrauch

en: ogd35-bfe electricity production import export

it: ogd35-bfe elettricita produzione importazione esportazione consumo

fr: Statistique suisse de l'électricité: bilan mensuel suisse de l'électricité

it: Statistica svizzera dell'elettricità: bilancio svizzero dell'elettricità - valori mensili

author: Bundesamt für Energie BFE

fr: Cette statistique recense sur une base mensuelle la production, l'importation, l'exportation et la consommation d'électricité en Suisse. Elle présente la production des centrales hydrauliques, des centrales nucléaires, des centrales thermiques classiques et renouvelables. Elle indique la consommation des installations de pompage d'accumulation et les pertes en ligne. Elle est établie dans le cadre de la Statistique de l'électricité, qui fait partie de la statistique publique de la Suisse (base légale: LSF).

de: Die Schweizerische Elektrizitätsbilanz - Monatswerte gibt Auskunft über Produktion, Import / Export und Verbrauch von Elektrizität in der Schweiz auf monatlicher Basis. Bei der Produktion werden Wasserkraftwerke, Kernkraftwerke, konventionell-thermische und erneuerbare Kraftwerke unterschieden. Der Verbrauch der Speicherpumpen und die Leitungsverluste werden ausgewiesen. Die monatliche Elektrizitätsbilanz wird im Rahmen der Elektrizitätsstatistik erstellt, welche Teil der öffentlichen Statistik der Schweiz ist (gesetzliche Grundlage: BStatG).

statistical-basis

fr: Bases statistiques

de: Statistische Grundlagen

en: Statistical basis

it: Basi statistiche

national-economy

fr: Economie nationale

de: Volkswirtschaft

en: National economy

it: Economia

energy

fr: Energie

de: Energie

en: Energy

it: Energia

bundesamt-fur-energie-bfe

fr: Office fédéral de l'énergie OFEN

de: Bundesamt für Energie BFE

en: Swiss Federal Office of Energy SFOE

it: Ufficio federale dell'energia UFE

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

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

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

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

political_level: confederation


fr: Contenu des bassins d'accumulation, dimanche 24:00 heures, bulletin hebdomadaire

it: Grado di riempimento dei bacini di accumulazione, domenica alle ore 24, rapporto settimanale livello di accumulazione

media_type: CSV

media_type: SERVICE

fr:
Ce relevé, élaboré chaque dimanche à 24h00, recense le contenu (en GWh et en %) des bassins d'accumulation suisses dans 4 régions différentes.
Le taux de remplissage des bassins d'accumulation est relevé dans le cadre de la Statistique de l'électricité, qui fait partie de la statistique publique de la Suisse (base légale: LSF).

de: Der Füllungsgrad der Speicherseen, Sonntag 24h, Wochenbericht Speicherinhalt gibt Auskunft über den Speicherinhalt der Schweizer Speicherseen für vier Regionen in GWh und als Füllgrad in % jeweils am Sonntag 24h auf wöchentlicher Basis. Der Füllungsgrad der Speicherseen wird im Rahmen der Elektrizitätsstatistik erhoben, welche Teil der öffentlichen Statistik der Schweiz ist (gesetzliche Grundlage: BStatG).

territory

de: Raum und Umwelt


maintainer: EnviDat Support

en: minimum-temperature free-climate-data climate-layers maximum-temperature timeseries precipitation century climate air-temperature

en: Data Access

media_type: geotiff

format: GeoTIFF

en: CHELSA 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

mimetype: application/pdf

en:
CHELSAcruts is a delta change monthly climate dataset for the years 1901-2016 for mean monthly maximum temperatures, mean monthly minimum temperatures, and monthly precipitation sum. Here we use the delta change method by B-spline interpolation of anomalies (deltas) of the CRU TS 4.01 dataset. Anomalies were interpolated between all CRU TS grid cells and are then added (for temperature variables) or multiplied (in case of precipitation) to high resolution climate data from CHELSA V1.2 (Karger et al. 2017, Scientific Data). This method has the assumption that climate only varies on the scale of the coarser (CRU TS) dataset, and the spatial pattern (from CHELSA) is consistent over time. This is certainly a rather crude assumption, and for time periods for which more accurate data is available CHELSAcruts should be avoided if possible (e.g. use CHELSA V1.2 for 1979-2015). Different to CHELSA V1.2, CHELSAcruts does not take changing wind patterns, or temperature lapse rates into account, but rather expects them to be constant over time, and similar to the long term averages.

CHELSAcruts is licensed under a Creative Commons Attribution 2.0 Generic (CC BY 2.0) license.

air-temperature

display_name: air-temperature

century

display_name: century

climate

display_name: climate

climate-layers

display_name: climate-layers

free-climate-data

display_name: free-climate-data

maximum-temperature

display_name: maximum-temperature

minimum-temperature

display_name: minimum-temperature

precipitation

display_name: precipitation

timeseries

display_name: timeseries

education

fr: Education, science

de: Bildung, Wissenschaft

en: Education and science

it: Formazione e scienza

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

value: Envidat Harvester

en: CHELSAcruts -  High resolution temperature and precipitation timeseries for the 20th century and beyond


en: urban-landscape landscape-planning concur urban-planning land-change

media_type: .pdf

en:
Book of abstracts from the virtual conference "From Plans to Land Change: Dynamics of Urban Regions"

Cities and urban regions are among the most dynamic land-use systems in the world, with dramatic consequences for the provision of ecosystem services and the livelihood of people. Planning is a multifaceted activity with extensive experience in the management of these urbanization processes. However, our understanding of planning’s contribution to shaping urban land use, form and structure is still incomplete, with serious consequences for the efficacy of urban planning and land change models.

This international conference aims to bring together the community of scholars working on planning evaluation and urban modelling. The participants are offered the opportunity to present their current research and to discuss how theoretical developments, data sources, comparative studies and modelling approaches might advance the field.

The conference was financially supported by the CONCUR project and sustained by Swiss Federal Research Institute WSL.

concur

display_name: concur

land-change

display_name: land-change

landscape-planning

display_name: landscape-planning

urban-landscape

display_name: urban-landscape

urban-planning

display_name: urban-planning

en: From Plans to Land Change: Dynamics of Urban Regions. Book of Abstracts


causal-effect-of-mfoz

title_for_slug: causal-effect-of-mfoz

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

en: Data and Stata code for repeating the results

media_type: ZIP

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

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

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

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

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

built-up-land-change

display_name: built-up-land-change

causality

display_name: causality

fujian-province

display_name: fujian-province

propensity-score-matching

display_name: propensity-score-matching

selection-bias

display_name: selection-bias

the-major-function-oriented-zone

display_name: the-major-function-oriented-zone

en: Causal effect of MFOZ


en: snow snow-water-equivalent wagital gcos meteodat-gmbh

media_type: TXT

format: TXT

en:
Total water reserves of the snow cover [mio m3] for Wägital catchment, Switzerland, for reference date April 1. Data is separated in 2 elevation zones 900m-1500m asl and 1500m-2300m asl. Time period 1943-2022, status 2022-04-20.

Funded currently or in the past by:

- Federal Office of Meteorology and Climatology MeteoSwiss in the context of GCOS Switzerland
- Meteodat GmbH
- Institute of Geography, University of Zurich
- WSL Institute for Snow and Avalanche Research SLF
- Institute of Geography, ETH Zurich (IAC ETH Zurich)
- AG Kraftwerk Wägital (AXPO and EWZ)

See also https://www.meteodat.ch/waegital.html

gcos

display_name: gcos

meteodat-gmbh

display_name: meteodat-gmbh

snow

display_name: snow

snow-water-equivalent

display_name: snow-water-equivalent

wagital

display_name: wagital

en: Snow water equivalent for reference date April 1 for Wägital catchment, starting 1943


maintainer: Yohann Chauvier

en: environmental-stratification covariate-correction cluster background-data target-group independent-dataset survey-effort random-stratified-sampling plant-species point-process-model

en: Non-copyright data, scripts, diversity outputs

en:
Aim: While species distribution models (SDMs) are standard tools to predict species distributions, they can suffer from observation and sampling biases, particularly presence-only SDMs that often rely on species observations from non-standardized sampling efforts. To address this issue, sampling background points with a target-group strategy is commonly used, although more robust strategies and refinements could be implemented. Here, we exploited a dataset of plant species from the European Alps to propose and demonstrate efficient ways to correct for observer and sampling bias in presence-only models.

Innovation: Recent methods correct for observer bias by using covariates related to accessibility in model calibrations (classic bias covariate correction, Classic-BCC). However, depending on how species are sampled, accessibility covariates may not sufficiently capture observer bias. Here, we introduced BCCs more directly related to sampling effort, as well as a novel corrective method based on stratified resampling of the observational dataset before model calibration (environmental bias correction, EBC). We compared, individually and jointly, the effect of EBC and different BCC strategies, when modelling the distributions of 1’900 plant species. We evaluated model performance with spatial block split-sampling and independent test data, and assessed the accuracy of plant diversity predictions across the European Alps.

Main conclusions: Implementing EBC with BCC showed best results for every evaluation method. Particularly, adding the observation density of a target group as bias covariate (Target-BCC) displayed most realistic modelled species distributions, with a clear positive correlation (r≃0.5) found between predicted and expert-based species richness. Although EBC must be carefully implemented in a species-specific manner, such limitations may be addressed via automated diagnostics included in a provided R function. Implementing EBC and bias covariate correction together may allow future studies to address efficiently observer bias in presence-only models, and overcome the standard need of an independent test dataset for model evaluation.

background-data

display_name: background-data

cluster

display_name: cluster

covariate-correction

display_name: covariate-correction

environmental-stratification

display_name: environmental-stratification

independent-dataset

display_name: independent-dataset

plant-species

display_name: plant-species

point-process-model

display_name: point-process-model

random-stratified-sampling

display_name: random-stratified-sampling

survey-effort

display_name: survey-effort

target-group

display_name: target-group

en: Novel methods to correct for observer and sampling bias in presence-only species distribution models


en: incoming outgoing longwave-radiation weissfluhjoch shortwave-radiation

en: Processing doc

en: Parameter file

media_type: text/plain

mimetype: text/plain

en: Metadata

en: DATA ACCESS

en:
Corrected incoming and outgoing shortwave and longwave 2 min radiation measurements at the Weissfluhjoch summit, Davos, CH.

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

incoming

display_name: incoming

longwave-radiation

display_name: longwave-radiation

outgoing

display_name: outgoing

shortwave-radiation

display_name: shortwave-radiation

weissfluhjoch

display_name: weissfluhjoch

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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

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


maintainer: Ionuț Iosifescu Enescu

en: map 0.2GB

media_type: image/tiff

format: TIFF

mimetype: image/tiff

en: huge map 100GB

en: map 0.5GB

en: Bulk Data Access

en: Link to the S3 bucket where all the data resources can be downloaded in bulk

en: map preview 0.5MB

media_type: image/jpeg

mimetype: image/jpeg

en: map 1GB

en: large map 10GB

en: map 2GB

en:
The attached data are some large GIS raster files (GeoTIFFs) made with Natural Earth data. Natural Earth is a free vector and raster map data @ naturalearthdata.com. The data used for creating these large files was the "Cross Blended Hypso with Shaded Relief and Water". Data was concatenated to achieve larger and larger files. Internal pyramids were created, in order that the files can be opened easily in a GIS software such as QGIS or by a (future) GIS data visualisation module integrated in EnviDat.

Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com

gis

display_name: gis

large-files

display_name: large-files

natural-earth

display_name: natural-earth

qgis

display_name: qgis

en: Large GIS raster data derived from Natural Earth Data (Cross Blended Hypso with Shaded Relief and Water)


en: landolt plants ecological-maps soil-ph ecological-indicator-values soil-mapping soil-moisture switzerland

en: Description

en: Description of the predictors provided in this dataset

format: XLS

en: Predictors

media_type: application/zip

en: Predictors generated and used in Descombes et al. (2020)

mimetype: application/zip

en:
Ecologically meaningful predictors are often neglected in plant distribution studies, resulting in incomplete niche quantification and low predictive power of species distribution models (SDMs). Because environmental data are rare and expensive to collect, and because their relationship with local climatic and topographic conditions are complex, mapping them over large geographic extents and at high spatial resolution remains a major challenge. Here, we derived environmental data layers by mapping ecological indicator values (EIVs) in space by using a large set of environmental predictors in Switzerland.

This dataset contains the predictors (raster layers) generated and used in the following publication (Descombes et al. 2020). Only predictors for which we have the rights to share them are provided. Other datasets and predictors can be accessed via the original data provider. Details on the predictors and sources are fully described in the publication. The predictors are provided as GeoTIFF files, at 93 m spatial resolution and Mercator projection ("+proj=merc +lon_0=0 +k=1 +x_0=0 +y_0=0 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs"). The excel file (xlsx) provides a short description of the raster layers.

Paper Citation:

Descombes, P. et al. (2020). Spatial modelling of ecological indicator values improves predictions of plant distributions in complex landscapes. Ecography. (accepted)

ecological-indicator-values

display_name: ecological-indicator-values

ecological-maps

display_name: ecological-maps

landolt

display_name: landolt

plants

display_name: plants

soil-mapping

display_name: soil-mapping

soil-moisture

display_name: soil-moisture

soil-ph

display_name: soil-ph

switzerland

display_name: switzerland


maintainer: Adam Winstral

en: jules-investigation-model snow-depth snow snow-simulation jim

en: DEM

en: 1-km resolution DEM used for downscaling gridded NWP data to stations and for distributing BDE evaluations at HS-stations to SWE-stations

en: File/data descriptor

en: Station forcing data

en: All forcing data need to run JIM at HS and SWE stations. These are data downscaled directly from COSMO NWP data (WY14, WY15) and further enhanced by additional station observations of snow depth (HS) and air temperature (Ta) in WY17. Further information can be found in README file. Note that in WY14 and WY15, shortwave radiation was not split into direct and diffuse (not necessary for JIM) and that the default method in JIM for splitting precipitation phase was used. In WY 17, direct and diffuse were combined as input to JIM and JIM was forced with pre-processed liquid and solid precipitation phases.

en: HS and SWE observations

en: Automated (HS) and manual (SWE) data used to evaluate model simulations

en: These data were used to drive and evaluate Jules Investigation Model (JIM) snow simulations. The data provided are the forcing data used for the "deterministic" runs as described in Winstral et al., 2019. The bias-detecting ensemble (Winstral et al., 2019) used observed snow depths (HS) to detect biases in these deterministic simulations related to precipitation and energy inputs to JIM. Simulations that included the BDE evaluations substantially improved JIM simulations.

jim

display_name: jim

jules-investigation-model

display_name: jules-investigation-model

snow

display_name: snow

snow-depth

display_name: snow-depth

snow-simulation

display_name: snow-simulation

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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

en: Meteorological data used to develop and validate the bias-detecting ensemble (BDE)


disdrometer-data-davos-wolfgang

title_for_slug: disdrometer-data-davos-wolfgang

en: disdrometer fall-velocity raclets precipitation davos

en: Disdrometer Data Davos Wolfgang

en:
The zip folder contains one netCDF file per day for Davos Wolfgang from February 8 to March 19 2019 with a temporal resolution of 30 seconds (time in UTC) and includes:

- characteristics of hydrometeors (e.g. diameter, fall velocity, amount per diameter class,...)
- precipitation rate
- radar reflectivity
- visibility range
- weather codes
- instrument performance

en: The dataset contains information on precipitation amount and type for Davos Wolfgang (LON: 9.853594, LAT: 46.835577) from February 8 to March 19 2019. It includes: characteristics of hydrometeors (e.g. diameter, fall velocity, amount per diameter class,...), precipitation rate, radar reflectivity, visibility range, weather codes and instrument performance.

davos

display_name: davos

disdrometer

display_name: disdrometer

fall-velocity

display_name: fall-velocity

precipitation

display_name: precipitation

raclets

display_name: raclets


maintainer: Frank Hagedorn

en: nitrogen zero-tension-lysimeters nutrient-fluxes fertilization dissolved-organic-phosphorus beech forest-topsoil soil-temperature phosphorus seasons

media_type: XLSX

en:
Data on dissolved organic and inorganic phosphorus and nitrogen concentrations in leachates and their corresponding
fluxes from the litter layer, the Oe/Oa horizon, and the A horizon of two German beech forest sites.
Leachate samples were taken in April 2018, July 2018, October 2018, Feb./Mar. 2019, and July 2019
with zero-tension lysimeters after artificial irrigation. Soil samples were taken in July 2019.
For more details please refer to the publication.

beech

display_name: beech

dissolved-organic-phosphorus

display_name: dissolved-organic-phosphorus

fertilization

display_name: fertilization

forest-topsoil

display_name: forest-topsoil

nitrogen

display_name: nitrogen

nutrient-fluxes

display_name: nutrient-fluxes

phosphorus

display_name: phosphorus

seasons

display_name: seasons

soil-temperature

display_name: soil-temperature

zero-tension-lysimeters

display_name: zero-tension-lysimeters

en: Phosphorus and nitrogen leaching from beech forest soils


en: longwave-radiation davos radiation shortwave-radiation weisfluhjoch

en: Parameter file

en: Description of ASRB parameters.

en: DATA ACCESS

media_type: SMET

en: ASRB radiation station at the Weissfluhjoch versuchsfeld.

en: Metadata

en: Processing doc

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

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

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

longwave-radiation

display_name: longwave-radiation

radiation

display_name: radiation

shortwave-radiation

display_name: shortwave-radiation

weisfluhjoch

display_name: weisfluhjoch

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


disdrometer-data-laret

maintainer: Daniel Wolfensberger

title_for_slug: disdrometer-data-laret

en: disdrometer visibility hydrometeors raclets precipitation

en: Disdrometer Data Laret

en: The zip folder contains one netCDF file per day for Laret from February 7 to March 29 2019 with a temporal resolution of 30 seconds (time in UTC). The naming convention for the files is Laret_YYYYMMDD.nc and the files include the following: characteristics of hydrometeors (e.g. diameter, fall velocity, amount per diameter class,...), precipitation rate, radar reflectivity, visibility range, weather codes and instrument performance.

en:
A laser optical disdrometer (Parsivel² , OTT Hydromet) was used to measure hydrometeors by extinction when passing a laser beam. The instrument can classify eight different kinds of precipitation, including rain, hail, snow, drizzle, and hybrid forms.
The dataset contains information on precipitation amount and type for the period of February 7 to March 29 2019 in Laret.

disdrometer

display_name: disdrometer

hydrometeors

display_name: hydrometeors

visibility

display_name: visibility

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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


maintainer: Anita C. Risch

en: grassland herbivory native-plant weed-management non-native-plant

en: Correction: Data file native and non-native plant interactions in Australian grasslands

en:
taxa tab:	list and overview of all the recorded species

* family:                 family of the plant

* genus:                genus of the plant

* species:                plant species name

* origin:                indicates whether the plant is native to Australia or not (non-native)

* common name:	        common name of the plant species

* functional group:	        functional group of the plant species: forb, graminoid, fern, tree

* functional group detailed:	gives more detail about the type of graminoid and forb: legume, forb (non-leguminous),
                        grass, sedge, rush, fern, tree

* lifeform:                annual or perennial lifeform (species which can have both of these lifeforms where
                        considered as annuals)

plot tab:	list of species found in each plot and their cover (%)

* sampling.session:        provides information about the sampling time:
                        May2020 = data that was collected in May 2020
                        Nov2020 = data that was collected in November 2020

* farm.name:	            name of the farms where the sampling took place:
                        SF = farm of the Southern group
                        NF = farm of the Northern group
                        numbers 1-3 = randomly allocated to the Southern group of farms
                        numbers 4-6 = randomly allocated to the Northern group of farms

* site.type:	                KG: Kangaroo grass dominated site
                        ALG: ALG+KG site (Kangarooo grass and African lovegrass co-dominated site)

* plot.no:                plot number: 9-12

* scientific.name:	        scientific name of the plant species found in a certain plot

* functional group:	        indicates whether the species found is a tree, forb, graminoid or fern

* functional group detailed:	indicates whether the species found is a tree, legume, forb (non-leguminous),
                        grass, sedge, rush, fern

* lifeform:                specifies the lifeform of the species: annual or perennial lifeform
                        (species which can have both of these lifeforms where considered as annuals)

corrections tab:	Detailed description of corrections made in plot.data tab

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

Paper Citation:

_Schlierenzauer, C., Risch, A.C., Schütz, M., Firn, J. 2021. Non-native Eragrostis curvula reduces plant species diversity in pastures of South-eastern Australia even when native Themeda triandra remains co-dominant. Plants 10, 596._

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

Study area
The study was conducted in the lowland grassy woodlands of the Bega Valley Region, which is located in the south-east corner of New South Wales, Australia. Embedded between the Pacific Ocean and the Australian Alps, the lowland grassy woodlands are mostly located on granitic substrates and reach elevations of roughly 500 m above sea level. Typically, these grassy woodlands receive less precipitation (mean annual precipitation between 700-1100 mm) compared to the more elevated areas that surround them (NSW Government - Office of Environment and Heritage 2017).
The vegetation is dominated by an open tree canopy layer consisting of Eucalyptus tereticornis Sm, Angophora floribunda Sm. (Sweet) and a range of other eucalypt species. Sometimes shrub or small trees are also present, whereas grasses and forbs form the ground-cover. In areas without intensive agricultural history, this layer is dominated by perennial, tussock grasses such as Themeda triandra Forssk, Microlaena stipoides R.Br (Weeping Grass), Eragrostis leptostachya Steud. (Paddock Lovegrass) and Echinopogon ovatus P.Beauv (Forest Hedgehog Grass). The remaining inter-tussock spaces are occupied by a diversity of growth-restricted grasses and herbaceous forbs (NSW - Department of Planing, Industries and Environment 2019; NSW Government - Office of Environment and Heritage 2017).
Clearing, pasture sowing, fertilizer application and livestock grazing resulted in a dramatic decrease in the extent of these natural woodlands, with less than five percent within conservation reserves and overall, with only about 20% of their original extent in New South Wales still existing (Tozer et al. 2010). The remaining areas outside of reserves are threatened by altered fire frequencies, habitat clearing, livestock grazing and especially by non-native plant invasion, particularly Eragrostis curvula (Schrad.) Nees. For this reason, the grassy woodlands are listed as an endangered ecological community in the NSW state legislation. Additionally, they are considered as critically endangered by the Commonwealth of Australia (Threatened Species Scientific Committee (TSSC) 2013).

Experimental design and sampling
The study was conducted on six farms and in each of them two sites were chosen, representing a paired design. One of the sites at each farm is dominated by native Themeda triandra, the other one co-dominated by non-native Eragrostis curvula and Themeda triandra. All farms are within a radius of approximately 10 km from the town Candelo. Three of the farms are located North (36°40’ to 36°42’ S and 149°38’ to 149°42’ E) and three of them are located South (36°51’ to 36°49’ S and 149°38’ to 149°42’ E) of Candelo.
Non-native herbivores (mainly cattle, sheep and rabbits) and native herbivorous marsupials (mainly kangaroos, wallabies and wombats) are present in the area of these sites. On each site, data was collected within four plots (each 1 x 1 m) in May and November 2020. All plant species found within a plot were recorded and their relative abundance was estimated.

References
NSW - Department of Planing, Industries and Environment. 2019. “Lowland Grassy Woodland in the South East Corner Bioregion - Endangered Ecological Community Listing.” https://www.environment.nsw.gov.au/topics/animals-and-plants/threatened-species/nsw-threatened-species-scientific-committee/determinations/final-determinations/2004-2007/lowland-grassy-woodland-south-east-corner-bioregion-endangered-ecological-community-l (February 18, 2021).
NSW Government - Office of Environment and Heritage. 2017. “Lowland Grassy Woodland in the South East Corner Bioregion - Profile.” https://www.environment.nsw.gov.au/threatenedSpeciesApp/profile.aspx?id=20070 (January 31, 2021).
Threatened Species Scientific Committee (TSSC). 2013. Environment Protection and Biodiversity Conservation Act 1999 (EPBC Act) Conservation Advice for Lowland Grassy Woodland in the South East Corner Bioregion. http://www.environment.gov.au/biodiversity/threatened/communities/pubs/82-conservation-advice.pdf.
Tozer, Mark et al. 2010. “Native Vegetation of Southeast NSW: A Revised Classification and Map for the Coast and Eastern Tablelands.” Cunninghamia : a journal of plant ecology for eastern Australia 11(3): 359–406.

grassland

display_name: grassland

herbivory

display_name: herbivory

native-plant

display_name: native-plant

non-native-plant

display_name: non-native-plant

weed-management

display_name: weed-management

en: Native and no-native plant interactions in Australian grasslands


maintainer: Matthias Haeni

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

en: Data plot of the forest station (NAB)

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

en: Data plot of the field station (NAF)

en: Site photo of the forest station (NAB)

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

en: Forest station data (NAB)

en: Data file.

en: Field station data (NAF)

en: Site photo of the field station (NAF)

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

en: Site aerial photo (Nationalpark)

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

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 Nationalpark in Switzerland where one station is located within a natural coniferous forest stand (NAB) with mountain pine (_Pinus mugo_; 210 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, NAF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Nationalpark 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: Nationalpark, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards


maintainer: Michael Lehning

en: snow-distribution snow alpine3d alpine-precipitation snow-cover alpine3d-simulations snow-hydrology

en: ADS datasets and simulation inputs

en: The file contains the ADS datasets of snow depth and input data used for simulations.

en:
Dataset (Model input, snow distribution and validation) for the precipitation scaling paper, which should be cited along with the data set citation.

This data is useful for distributed hydrological modelling or other tasks that involve the study of snow distribution and precipitation in the high Alpine.

The format of the data is for Alpine3D (models.slf.ch) model runs but other models could be used, too.

Please cite:

_Vögeli, C., Lehning, M., Wever, N., Bavay M., 2016: Scaling Precipitation Input to Spatially Distributed Hydrological Models by Measured Snow Distribution., Front. Earth Sci. 4: 108. doi: 10.3389/feart.2016.00108._

Dataset is provided as a single zip file.
The archive contains two directories, the valuable distributed snow depth maps for the landscape Davos and the simulation input.
The archive also contains the file: "ReadMeMetadataDataSetPrecipitationScaling" which explains the data structure.

alpine-precipitation

display_name: alpine-precipitation

snow

display_name: snow

snow-cover

display_name: snow-cover

snow-distribution

display_name: snow-distribution

snow-hydrology

display_name: snow-hydrology

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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

en: Precipitation Scaling Data Set (Vögeli et al., Frontiers)


maintainer: Nander Wever

en: air-temperature precipitation-amount shortwave-radiation snow-ice-temperature wind-direction snow-depth humidity longwave-radiation wind-speed

en: Version 3

en:
Deprecated version. Release notes:

Bug fix: in version 2, the undercatch correction was not applied correctly

Bug fix: in version 2, snow lysimeter data was missing for extended periods of time

Update: dataset extended to 2016-01-02T00:30

Update: refreshing data originating from MeteoSwiss (particularly precipitation)

en: Version 6

en:
Deprecated version.

Release notes:

- Missing ISWR for 2009-09-30 and 2010-04-30 has been replaced by data from WFJ Gipfel

This dataset is the basis for the Weissfluhjoch dataset in ESM-SnowMIP (doi: http://dx.doi.org/10.16904/16).

en: Version 7 (current)

en:
Release notes:

Bug fix: in previous versions, snow lysimeter data from September 2010 onwards was erroneously reported for UTC+2, instead of UTC+1 (i.e., reported one hour later than should have been).

Update: dataset extended to 2017-07-11T00:30.

en: Undercatch correction

en: This document describes how the undercatch correction for the Weissfluhjoch measurement site was determined.

en: Version 1

en:
Deprecated version. Release notes:

First release of dataset.

en: Version 5

en:
Deprecated version. Release notes:

Update: using the incoming and outgoing short- and longwave radiation data from the new processing chain from 2010 onwards, filling also data gaps in pre-2010 data.

Update: dataset extended to 2016-10-04T00:30

Update: refreshing data originating from MeteoSwiss (particularly precipitation)

en: Version 4

en:
Deprecated version. Release notes:

Update: dataset extended to 2016-09-02T00:30

Update: refreshing data originating from MeteoSwiss (particularly precipitation)

en: SNOWPACK ini file (new syntax)

en: This ini-file can be used to run SNOWPACK simulations with the WFJ data set, using the new MeteoIO Filter, Generator and Interpolation syntax.

en: All versions

en: SNOWPACK sno file

en: This sno-file can be used to run SNOWPACK simulations with the WFJ data set, using version 3.4.0 or higher.

en: Version 2

en:
Deprecated version. Release notes:

Bug fix: in v1, incoming radiation components were taken from WFJ_Gipfel station, instead of WFJ Versuchsfeld.

en: SNOWPACK ini file (old syntax)

en: This ini-file can be used to run SNOWPACK simulations with the WFJ data set, using version 3.4.0 or higher.

en:
Dataset of meteorological and snowpack measurements from the automatic weather station at Weissfluhjoch, Davos, Switzerland, suitable for driving snowpack models. The dataset contains standard meteorological measurements, and additionally snowpack runoff data from a snow lysimeter. Where possible, data is quality checked and missing data are replaced from backup sensors from the measurement site itself, or (in only a few cases) from the MeteoSwiss weather station at 470 m distance and 150 m above the measurement site.

Publication
Wever, N., Schmid, L., Heilig, A., Eisen, O., Fierz, C., and Lehning, M. Verification of the multi-layer SNOWPACK model with different water transport schemes. 2015. The Cryosphere. Volume 9. 2271-2293. http://dx.doi.org/10.5194/tc-9-2271-2015.

air-temperature

display_name: air-temperature

humidity

display_name: humidity

longwave-radiation

display_name: longwave-radiation

precipitation-amount

display_name: precipitation-amount

shortwave-radiation

display_name: shortwave-radiation

snow-depth

display_name: snow-depth

snow-ice-temperature

display_name: snow-ice-temperature

wind-direction

display_name: wind-direction

en: WFJ_MOD: Meteorological and snowpack measurements from Weissfluhjoch, Davos, Switzerland


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

en: Sonic wind component w

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

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

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

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

en: Sonic temperature

media_type: npy

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

en: Sonic time

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

en: Sonic wind component u

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

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

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

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

en: Sonic wind component v

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

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

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

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

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

anemometer

display_name: anemometer

raclets

display_name: raclets

wind

display_name: wind

wind-velocity

display_name: wind-velocity

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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

en: Snow Drift Station - 3D Ultrasonic


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

en: Data plot of the field station (CLF)

en: Data file.

en: Site photo of the forest station (CLB)

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

en: Site photo of the field station (CLF)

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

en: Data plot of the forest station (CLB)

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

en: Site aerial photo (Celerina)

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

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Celerina in Switzerland where one station is located within a natural coniferous forest stand (CLB) with Swiss pine (_Pinus cembra_; 210-250 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, CLF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Celerina is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

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


en: landsca ecoregions synonyms species-distribution hard-limit gbif occurrence-records species-observations large-scale filtering range-maps conservation taxonomy accepted-name flags r-package

en: gbif.range R package version 0.2 (current)

en: GitHub URL

media_type: R

en: wsl.gbif R package version 0.1 (deprecated)

en: Test version, not to be used.

en: Although species range may be obtained using expert maps or modeling methods, expert data is often species-limited and statistical models need more technical expertise as well as many species observations. When unavailable, such information may be extracted from the Global Biodiversity Information facility (GBIF), the largest public data repository inventorying georeferenced species observations worldwide. However, retrieving GBIF records at large scale may be tedious if users are unaware of specific tools and functions that need to be employed. Here we present *gbif.range*, a R library that contains automated methods to generate species range maps from scratch using in-house ecoregions shapefiles and an easy-to-use GBIF download wrapper. Finally, this library also offers a set of additional very useful parameters and functions for large GBIF datasets (generate doi, extract GBIF taxonomy, records filtering...). [gbif.range R project](https://github.com/8Ginette8/gbif.range)

accepted-name

display_name: accepted-name

conservation

display_name: conservation

ecoregions

display_name: ecoregions

filtering

display_name: filtering

flags

display_name: flags

gbif

display_name: gbif

hard-limit

display_name: hard-limit

landsca

display_name: landsca

large-scale

display_name: large-scale

occurrence-records

display_name: occurrence-records

r-package

display_name: r-package

range-maps

display_name: range-maps

species-distribution

display_name: species-distribution

species-observations

display_name: species-observations

synonyms

display_name: synonyms

taxonomy

display_name: taxonomy

education

en: Education and science

en: gbif.range - An R package to generate species range maps based on ecoregions and a user-friendly GBIF wrapper


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

en: Data access

media_type: .zip

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

climate-change-impact

display_name: climate-change-impact

discharge

display_name: discharge

hydrology

display_name: hydrology

model

display_name: model

water-temperature

display_name: water-temperature

envidat

en: EnviDat: the Environmental Data Portal

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

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


en: planning-outcome nature-conservation spatial-modelling topography forest-gain forest-persistence urban-morphology urban-sprawl urban-compactness basic-services land-use-and-cover-change built-up-classification

en: Increase in Normalized Difference Vegetation Index

en: Variables description

media_type: PDF

en: Table of content. Variables description, source, and other important information

en: Urban persistence: Built-up 2005 = Built-up 2015

media_type: SHP

en: Ecological-Economic Zoning (EEZ)

format: SHP

media_type: .shp

en: Master plan Ubatuba (zones)

en: Topographic Index Position (TPI)

en: Increase in waste collection service

en: Axial

en: New built-up pixels classified as axial

en: Population density per pixel (change per year)

en: Master plan Ihabela (zones)

en: Native forest cover gains from 2005 to 2015

en: Mean income in Reais (R$) per census sector (change per year)

en: slope

en: Slope °

en: Descricao dos dados em portiuguês

en: Portugues description

en: Native forest cover persistence from 2005 to 2015

en: Human Development Index (HDI) per municipality (change per year)

en: isolated

en: New built-up pixels classified as isolated

en: Permanent housing density increase

en: Increase in clean water provision service

en: Increase in sanitation service

en:
Basic education in % per census sector (change per
year)

en: infill

en: New built-up pixels classified as infill

en:
The present dataset is part of the published scientific paper entitled “The role of spatial planning in land change: An assessment of urban planning and nature conservation efficiency at the southeastern coast of Brazil” (Pierri Daunt, Inostroza and Hersperger, 2021). In this work, we evaluated the conformance of stated spatial planning goals and the outcomes in terms of urban compactness, basic services and housing provision, and nature conservation for different land-use strategies. We evaluate the 2005 Ecological-Economic Zoning (EEZ) and two municipal master plans from 2006 in a coastal region in São Paulo State, Brazil. We used Partial Least Squares Path Modelling (PLS-PM) to explain the relationship between the plan strategies and land-use change ten years after implementation in terms of urban compactness, basic services and housing increase, and nature conservation.

We acquired the data for the explanatory variables from different sources listed on Table 1. Since the model is spatially explicit, all input data were transformed to a 30 m resolution raster. Regarding the evaluated spatial plans, we acquired the zones limits from the São Paulo State Environmental Planning Division (CPLA-SP), Ilhabela and Ubatuba municipality.

1)	Land use and cover data: Urban persistence, Urban axial, Urban infill, Urban Isolates, Forest cover persistence, Forest cover gain, NDVI increase

We acquired two Landsat Collection 1 Higher-Level Surface Reflectance images distributed by the U.S. Geological Survey (USGS), covering the entire study area (paths 76 and 77, row 220, WRS-2 reference system, https://earthexplorer.usgs.gov/). We classified one image acquired by the Landsat 5 Thematic Mapper (TM) sensor on 2005-05-150, and one image from the Landsat 8 Operational Land Imager (OLI) sensor from 2015-08-15. We collected 100 samples for forest cover, 100 samples for built-up cover and 100 samples for other classes. We then classified these three classes of land cover at each image date using the Support Vector Machine (SVM) supervised algorithm (Hsu et al., 2003), using ENVI 5.0 software.

Land-use and land-cover changes from 2005 to 2015 were quantified using map algebra, by mathematically adding them together in pairs (10*LULC2015 + LULC2005). We reclassified the LULC data into forest gain (conversion of any 2005 LULC to forest cover in 2015); forest persistence (2005 forested pixels that remained forested in 2015); new built-up area (conversion of any 2005 LULC to built-up in 2015); and urban maintenance (2005 built-up pixels that remained built-up in 2015).

To describe the spatial configuration of the urban expansion, we classified the new built-up areas into axial, infill and isolated, following Inostroza et al. (2013) (For details, please refer to Supplementary Material I at the original publication).

The NDVI was obtained from the same source used for the LULC data. With the Google Engine platform, we used an annual average for the best pixels (without clouds) for 2005 and 2015, and we calculated the changes between dates. We used increases of > 0.2 NDVI to represent an improvement in forest quality.

2)	Federal Census data organization: Urban Basic Services and Housing indicator, socioeconomic and population:

The data used to infer the values of basic services provision, socioeconomic and population drivers was derived from the Brazilian National Census data (IBGE, 2000 and 2010). Population density, permanent housing unit density, mean income, basic education, and the percentage of houses receiving waste collection, sanitation and water provision services, called basic services in the context of this study, were calculated per 30 m pixel. The Human Development Index is only available at the municipality level. We attributed the HDI for the vector file with the municipality border, and we rasterized (30 m resolution) this file in QGIS. Annual rates of change were then calculated to allow comparability between LULC periods. To infer the BSH, we used only areas with an increase in permanent housing density and basic services provision (See Supplementary Material I at the original publication).

3)	Topographic drivers

To infer the values of the topographic driver, we used the slope data and the Topographic Index Position (TPI) based on the digital elevation model from SRTM (30 m resolution) produced by ALOS (freely available at eorc.jaxa.jp/ALOS/en/about/about_index.htm), and both variables were considered constant from 2005 to 2015 (See Supplementary Material I at the original publication).

basic-services

display_name: basic-services

en: Spatially explicit data to evaluate spatial planning outcomes in a coastal region in São Paulo State, Brazil


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

en: Codebook

en: Description of variable attributes and valid n

en: Raw date (SPSS)

media_type: SAV

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

en: Raw data (.csv)

media_type: 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:
 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

switzerland

display_name: switzerland

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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

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


maintainer: Stephan Zimmermann

en: biogeochemical-cycling magnesium potassium calcium fagus-orientalis-lipsky

en: Concentrations of Ca, Mg, K, and Na in throughfall, litterflow and soil solution of a Hyrcanian forst in Northern Iran

en:
Throughfall, litterflow and soil solution were sampled during one whole year under five Oriental beech trees in a mixed Hyrcanian beech forest. The amounts of Ca2+, Mg2+, K+ and Na+ in these fluxes were calculated based on their concentrations and the sampled volumes, and subsequently compared with the respective fluxes in the rainfall and soil solution of an adjacent forest gap.
In addition six soil profiles, one close to every single tree and one in the forest gap, were analyzed for pH, CaCO3, organic matter and texture.

biogeochemical-cycling

display_name: biogeochemical-cycling

calcium

display_name: calcium

fagus-orientalis-lipsky

display_name: fagus-orientalis-lipsky

magnesium

display_name: magnesium

potassium

display_name: potassium

en: Base cation dynamics in an Oriental beech forest


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

en: Forest station data (OTB)

media_type: text/csv

en: Data file.

en: Site aerial photo (Othmarsingen)

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

en: Field station data (OTF)

en: Data plot of the field station (OTF)

media_type: application/pdf

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

en: Site photo of the forest station (OTB)

en: Site photo of the forest station from above (viewing downwards the slope).

en: Data plot of the forest station (OTB)

en: Site photo of the field station (OTF)

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

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 Othmarsingen in Switzerland where one station is located within a natural deciduous forest stand (OTB) with European beech (_Fagus sylvatica_; 120-140 yrs) and lime trees (_Tilia sp._; 120-140 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, OTF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Othmarsingen 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: Othmarsingen, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1996 onwards


causal-effect-of-lup

title_for_slug: causal-effect-of-lup

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

en: causal-effect-of-lup

media_type: ZIP

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

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

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

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

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

annual-effect

display_name: annual-effect

land-use-change

display_name: land-use-change

land-use-planning

display_name: land-use-planning

plan-evaluation

display_name: plan-evaluation

psm-did

display_name: psm-did

education

en: Education and science

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: Causal effect of LUP


en: sintering snow-avalanche snow acoustic-emissions snow-failure snow-mechanics fiber-bundle-model viscous-deformation

en: fiber-bundle-model-for-snow-failure

en: Data underlying Figures in publication "Modelling Snow Failure Behavior and Concurrent Acoustic Emissions Signatures with a Fiber Bundle Model" submitted to "Geophysical Research Letters"

en:
This dataset contains modeled and experimental results for laboratory snow failure experiments and the concurrent acoustic emissions signatures for different loading rates. For modelling the snow failure we used a fiber bundle model that includes sintering and viscous deformation.

The data underlay the figures in the publication "Modelling Snow Failure Behavior and Concurrent Acoustic Emissions Signatures with a Fiber Bundle Model" submitted for publication to "Geophysical Research Letters".

acoustic-emissions

display_name: acoustic-emissions

fiber-bundle-model

display_name: fiber-bundle-model

sintering

display_name: sintering

snow

display_name: snow

snow-avalanche

display_name: snow-avalanche

snow-failure

display_name: snow-failure

snow-mechanics

display_name: snow-mechanics

viscous-deformation

display_name: viscous-deformation

en: Fiber Bundle Model for snow failure and concurrent Acoustic Emissions


en: avalanche-forecast snowpack avalanche snow-stability-evaluation snow

en: Manually observed snow profiles

en: Manually observed snow profiles as csv-files containing depth [cm], thickness [cm], density [kg m-3], hand hardness, first grain type, second grain type, first grain size [mm], and second grain size [mm] of each layer.

en: README

media_type: text/plain

en: README file explaining data structure and content

en: SNOWPACK simulations

en: This dataset contains all SNOWPACK simulations, including input files, setup and output files.

en: Stability tests

en: Stability tests including csv-files with CT, ECT and PST experiments for each year and each field site.

en: Extracted data

en: Extracted data contain validation and results published in Richter et al. (2019).

en:
To validate the critical crack length as implemented in the snow cover model SNOWPACK, PST experiments were conducted for three winter seasons (2015-2017) at two field site above Davos, Switzerland. This dataset contains manually observed snow profiles and stability tests. Furthermore, corresponding SNOWPACK simulations are included.
These data were analyzed and results were published in Richter et al. (2019).

Please refer to the Readme file for further details on the data.

These data are the basis of the following publication:
Richter, B., Schweizer, J., Rotach, M. W., and van Herwijnen, A.: Validating modeled critical crack length for crack propagation in the snow cover model SNOWPACK, The Cryosphere, 13, 3353–3366, https://doi.org/10.5194/tc-13-3353-2019, 2019.

avalanche

display_name: avalanche

avalanche-forecast

display_name: avalanche-forecast

snow-stability-evaluation

display_name: snow-stability-evaluation

snowpack

display_name: snowpack

en: Validating and improving the critical crack length in SNOWPACK


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

en: Dataset 2

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

en: Dataset 3

media_type: .csv

en: Aggregated data on number hives, beekeeping locations and ratios

en: Dataset 1

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

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

honeybees

display_name: honeybees

pollination

display_name: pollination

remote-sensing

display_name: remote-sensing

sustainability

display_name: sustainability

urban-biodiversity

display_name: urban-biodiversity

urbanization

display_name: urbanization

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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

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