equiden-altersstruktur

title_for_slug: equiden-altersstruktur

fr: animaux-de-rente animaux-de-compagnie evolution-de-la-population equides age

de: alter heimtiere populationsentwicklung equiden nutztiere

en: livestock population-trends companion-animals age equids

it: animale-da-reddito eta evoluzione-della-popolazione animale-da-compagnia equidi

identifier: equids-pyr

fr: Total des équidés enregistrés et vivants par catégorie d’âge et par sexe mâle et femelle (poulains, juments, étalons et hongres).

de: Verteilung der registrierten, lebenden Equiden nach Altersklassen und Geschlechtern männlich und weiblich (Fohlen, Stuten, Hengste und Wallache) im Zeitverlauf.

en: Number of registered and living equids by age category and sexes male and female (foals, mares, stallions et geldings).

it: Totale dei equidi registrati e vivi per categoria di età e per sesso maschile e femminile (puledro, giumenta, stallone e castrone).

age

display_name: age

alter

display_name: alter

animale-da-compagnia

display_name: animale-da-compagnia

animale-da-reddito

display_name: animale-da-reddito

animaux-de-compagnie

display_name: animaux-de-compagnie

animaux-de-rente

display_name: animaux-de-rente

companion-animals

display_name: companion-animals

equiden

display_name: equiden

equides

display_name: equides

equidi

display_name: equidi

equids

display_name: equids

eta

display_name: eta

heimtiere

display_name: heimtiere

livestock

display_name: livestock

identitas

fr: Identitas AG développe et exploite des applications pour l'identification, l'enregistrement et la surveillance des animaux de rente et de compagnie. En tant que prestataire leader de services IT en Suisse pour les données en rapport avec les animaux et la production végétale, nous contribuons à la création de valeur ajoutée pour la santé et le bien-être animale et pour la sécurité des denrées alimentaires. A notre siège à Berne, nous employons environ 110 collaborateurs.

it: Identitas AG sviluppa e gestisce applicazioni per l'identificazione, la registrazione e il monitoraggio degli animali da reddito e da compagnia. In qualità di fornitore di servizi informatici leader in Svizzera per i dati relativi agli animali e alla produzione vegetale, contribuiamo alla creazione di valore aggiunto per la salute e il benessere degli animali e la sicurezza alimentare. Nella nostra sede centrale di Berna abbiamo circa 110 dipendenti.

fr: Équidés - Pyramide d'âges

de: Equiden - Altersstruktur

en: Equids - Age pyramid

it: Equidi - Piramide delle età


nutzungsplanung-glarus-sud

regi

fr: Régions et villes

de: Regionen und Städte

en: Regions and cities

it: Regioni e città

envi

fr: Environnement

de: Umwelt

en: Environment

it: Ambiente

title_for_slug: nutzungsplanung-glarus-sud

de: raumentwicklung nutzungsplanung nutzungsplan zonenplan oreb larmempfindlichkeitsstufe

author: Departement Hochbau und Liegenschaften (GLS)

fr: geocat.ch permalien

de: geocat.ch Permalink

en: geocat.ch permalink

it: geocat.ch link permanente

state: active

type: dataset

media_type: application/zip

fr: Nutzungsplanung Glarus Süd

de: Nutzungsplanung Glarus Süd

en: Nutzungsplanung Glarus Süd

it: Nutzungsplanung Glarus Süd

format: ZIP

de: Kommunale Nutzungsplanung (Zonenplan), Datensatz der Gemeinde Glarus Süd (1631). Altes Modell, rechtskräftig. Hinweis: Die Nutzungsplanungs-Geodaten (NUP und LES) wurden aus einem alten, kantonalen Datenmodell in das neue Datenmodell transformiert. Die Geodaten müssen mit der Steuerungsdatei «no_area_validation.toml» ohne Area-Überschneidungsprüfung validiert werden.

spatial: Kanton Glarus

fr: Kanton Glarus

de: Kanton Glarus

en: Kanton Glarus

it: Kanton Glarus

en: Office of Geoinformation, Canton of Glarus

de: Fachstelle Geoinformation des Kantons Glarus. Zuständig für die kantonale Geodaten-Infrastruktur inklusive Geoportal und Datenbereitstellung.

political_level: canton

kanton-glarus

capacity: public

type: organization

approval_status: approved

value: Glarus-kt - Geocat Harvester


fruchtfolgeflachen-fff

title_for_slug: fruchtfolgeflachen-fff

de: fruchtfolgeflachen-fff kulturflache landwirtschaftliche-bewirtschaftung raumentwicklung richtplanung

author: Fachstelle Raumentwicklung (DBU)

fr: Fruchtfolgeflächen FFF

de: Fruchtfolgeflächen FFF

en: Fruchtfolgeflächen FFF

it: Fruchtfolgeflächen FFF

de: Sachplan Fruchtfolgeflächen (FFF). Fruchtfolgeflächen sind die wertvollsten Landwirtschaftsflächen der Schweiz. Mit dem Sachplan FFF werden die besten Ackerflächen geschützt.

de: Fachstelle Geoinformation des Kantons Glarus


amtliche-vermessung-glarus-nord

title_for_slug: amtliche-vermessung-glarus-nord

de: amtliche-vermessung-av bodenbedeckung einzelobjekt grundstuck fixpunkt grenze hoheitsgrenze grenzlinie gebaudeadresse strasse strassenachse liegenschaft grenzen flurname ortsname toponymie

author: Fachstelle Vermessung (DBU)

fr: Amtliche Vermessung Glarus Nord

de: Amtliche Vermessung Glarus Nord

en: Amtliche Vermessung Glarus Nord

it: Amtliche Vermessung Glarus Nord

de: Amtliche Vermessung, Publikationsdaten aus dem Bundesdatenmodell DM.01-AV-CH Version 24D, LV95 hergeleitet. Datensatz/Operat der Gemeinde Glarus Nord (1630). Enthält Fixpunkte, Grundstücke, Bodenbedeckung + Einzelobjekte ("Situation"), Strassen und Adressen, Flurnamen und Lokalisationen.


nutzungsplanung-glarus-nord

title_for_slug: nutzungsplanung-glarus-nord

de: raumentwicklung nutzungsplanung nutzungsplan zonenplan oreb waldabstand larmempfindlichkeitsstufe

author: Ressort Bau und Umwelt (GLN)

fr: Nutzungsplanung Glarus Nord

de: Nutzungsplanung Glarus Nord

en: Nutzungsplanung Glarus Nord

it: Nutzungsplanung Glarus Nord

de: Kommunale Nutzungsplanung (Zonenplan), Datensatz der Gemeinde Glarus Nord (1630), rechtskräftig.


regi

de: Regionen und Städte

envi

de: Umwelt

author: Abteilung Wald und Naturgefahren (DBU)

fr: Gefahrenkartierung

de: Gefahrenkartierung

en: Gefahrenkartierung

it: Gefahrenkartierung

de: Naturgefahren, Gefahrenkartierung. Gefahrenstufen, Intensitäten, Prozessquellen für die Hauptprozessarten Wasser (Überschwemmung), Lawine, Rutschung und Sturz.


baugrundklassen

title_for_slug: baugrundklassen

de: bodeneigenschaften baugrund mikrozonierung naturgefahr

de: geocat.ch Permalink

fr: Baugrundklassen

de: Baugrundklassen

en: Baugrundklassen

it: Baugrundklassen

de: Baugrundklassen der seismischen Mikrozonierung. Berücksichtigung der lokalen Geologie bei der Bestimmung der Erdbebeneinwirkungen für die Bemessung und Überprüfung von Bauwerken. Die Intensität und die Dauer seismischer Erschütterungen hängen in sehr hohem Masse von den geotechnischen Eigenschaften des lokalen Baugrunds ab. Diese Eigenschaften können ein Erdbebensignal unter Umständen bereits auf kurze Distanz erheblich verändern. Deshalb ist es wichtig, dass bei der Bestimmung der seismischen Einwirkung, die als Basis für die Bemessung oder Überprüfung von Bauwerken dient, die verschiedenen Phänomene, welche eine Verstärkung des Erdbebensignals bewirken, quantifiziert werden können. Diese Phänomene werden auch als Standorteffekte bezeichnet.


wfs-kanton-glarus

title_for_slug: wfs-kanton-glarus

author: Fachstelle Geoinformation (DBU)

fr: WFS Kanton Glarus

de: WFS Kanton Glarus

en: WFS Kanton Glarus

it: WFS Kanton Glarus

format: WFS

de: Web Feature Service der Geodaten des Kantons Glarus.

de: Kanton Glarus

de: Fachstelle Geoinformation des Kantons Glarus. Zuständig für die kantonale Geodaten-Infrastruktur inklusive Geoportal und Datenbereitstellung.


flussordnungszahl-nach-strahler

title_for_slug: flussordnungszahl-nach-strahler

author: Bundesamt für Umwelt

fr: Map (Preview) Aperçu map.geo.admin.ch

de: Map (Preview) Vorschau map.geo.admin.ch

en: Map (Preview)  map.geo.admin.ch

it: Map (Preview) Previsione map.geo.admin.ch

fr: Aperçu map.geo.admin.ch

de: Vorschau map.geo.admin.ch

en: Preview map.geo.admin.ch

it: Previsione map.geo.admin.ch

format: SERVICE

fr: Service WMS-IFDG, couche "Ordre des cours d`eau"

de: WMS-BGDI Dienst, Layer "Flussordnung"

en: WMS-FSDI service, layer "Stream order"

it: Servizio WMS-IFDG, strato "Numerazione dei segmenti fluviali"

format: WMS

fr: Service WMTS-IFDG, couche , Layer "Ordre des cours d’eau"

de: WMTS-BGDI Dienst, Layer "Flussordnung"

en: WMTS-FSDI service, layer "Stream order"

it: Servizio WMTS-IFDG, strato "Numerazione dei segmenti fluviali"

format: WMTS

fr: Lien vers la distribution des données

de: Link zum Datenbezug

en: Link for download

it: Link per le fonti dei dati

fr: RESTful API de geo.admin.ch

de: RESTful API von geo.admin.ch

en: RESTful API from geo.admin.ch

it: RESTful API da geo.admin.ch

format: API

fr: L’ordre des cours d’eau selon Strahler indique le degré de ramification du réseau hydrographique. Il est une base pour les analyses et descriptions morphologiques d'un cours d'eau. --- Selon Strahler l’ordre 1 est attribué au sources. Si deux cours d’eau de même ordre se rejoignent, l’ordre augmente de 1. Ainsi le Rhin et l’Aar sont d’ordre 9, le Rhône est d’ordre 7. --- Le reseau hydrographique issu de VECTOR25 a servi comme base de calcul pour l'ordre des cours d'eau.

de: Die Flussordnung nach Strahler gibt den Grad der Verzweigung eines Gewässernetzes an. Sie ist die Grundlage für morphologische Untersuchungen und Beschreibungen eines Gewässers. --- Nach Strahler weisen Quellen eine Flussordnungszahl von 1 auf. Fliessen zwei Nebengewässer derselben Flussordnung zusammen, nimmt die Ordnungszahl um 1 zu. Der Rhein und die Aare haben erhalten eine Ordnung von 9, die Rhone erhält eine Flussordnungszahl von 7. --- Die Flussordnung wurde auf dem Gewässernetz VECTOR25 gwn berechnet.

en: The stream order according to Strahler specifies the degree of branching of a hydrographic network. It forms the basis for morphological studies and descriptions of a watercourse. --- According to Strahler, watercourse sources are assigned a stream order of 1. If two tributaries of the same stream order join, the order increases by 1. The Rhine and the River Aare have a stream order of 9, whereas the Rhône has a stream order of 7. --- The stream order was calculated using the VECTOR25 hydrographic network.

it: La numerazione dei segmenti fluviali secondo Strahler indica il grado di ramificazione di una rete di corsi d’acqua e costituisce la base per analisi e descrizioni morfologiche di un corso d’acqua. --- Secondo il metodo Strahler, alle sorgenti di corsi d’acqua viene attribuito il numero 1. Alla confluenza di due affluenti che presentano lo stesso ordine, il numero aumenta di 1. A Reno e Aare è stato attribuito l’ordine 9, mentre al Rodano l’ordine 7. --- La nu-merazione dei segmenti fluviali è stata calcolata sulla rete di corsi d’acqua VECTOR25.

spatial: Schweiz

fr: Office fédéral de l'environnement

de: Bundesamt für Umwelt / Abteilung Wasser

en: Federal Office for the Environment

it: Ufficio federale dell'ambiente

bundesamt-fur-umwelt-bafu

fr: Office fédéral de l'environnement OFEV

de: Bundesamt für Umwelt BAFU

en: Federal Office for the Environment FOEN

it: Ufficio federale dell'ambiente UFAM

fr: L’autorité compétente pour les questions environnementales. Il est à ce titre chargé de gérer les ressources naturelles selon les principes du développement durable et de protéger l’homme contre les dangers naturels et l’environnement contre les pollutions.

de: Die Fachbehörde für die Umwelt. Es ist zuständig für die nachhaltige Nutzung der natürlichen Ressourcen sowie für den Schutz des Menschen vor Naturgefahren und den Schutz der Umwelt vor übermässigen Belastungen.

en: The federal environmental authority. It is responsible for ensuring that natural resources are used sustainably, that the public is protected against natural hazards, and that the environment is protected from unacceptable adverse impacts.

it: Il servizio specializzato in materia ambientale, è competente per quanto riguarda l'utilizzazione sostenibile delle risorse naturali, la protezione delle persone dai pericoli naturali e dell'ambiente da un inquinamento eccessivo.

political_level: confederation

value: BAFU - Geocat Harvester

fr: Ordre des cours d’eau selon Strahler

de: Flussordnungszahl nach Strahler

en: Strahler stream order

it: Numerazione dei segmenti fluviali secondo Strahler


maintainer: Bundesamt für Gesundheit, Sektion Datenmanagement und Statistik

title_for_slug: Qualitätsindikatoren der Schweizer Akutspitäler 2023

fr: lamal statistique-medicale taux-de-mortalite nombres-de-cas indicateurs-de-qualite ofsp office-federal-de-la-sante-publique ch-iqi hopitaux-de-soins-aigus

de: bundesamt-fuer-gesundheit fallzahlen mortalitaetsraten kvg bag medizinische-statistik qualitaetsindikatoren akutspitaler ch-iqi

en: numbers-of-cases mortality-rates federal-office-of-public-health health-insurance-act ch-iqi medical-statistics acute-care-hospitals foph quality-indicators

it: ch-iqi statistica-medica ospedali-per-cure-acute tassi-di-mortalita ufficio-federale-della-sanita-pubblica ufsp lamal numero-di-casi indicatori-di-qualita

author: Bundesamt für Gesundheit

media_type: text/csv

format: CSV

media_type: text/plain

format: TXT

media_type: application/pdf

format: PDF

format: XLS

fr:
Les indicateurs de qualité des hôpitaux suisses de soins aigus contiennent des données relatives aux traitements appliqués dans les hôpitaux suisses en 2023. Les nombres de cas, divers pourcentages (p. ex., taux de césarienne), les taux de mortalité pour certaines pathologies et interventions ainsi que des durées de séjour sélectionnées, des transferts et traitements avec nombre minimal de cas y sont notamment présentés.
Ces indicateurs permettent tout d'abord au grand public d'avoir un aperçu de l'activité des établissements hospitaliers, lesquels sont financés en grande partie par les primes des caisses-maladie et les impôts. Il est donc possible de choisir son hôpital en toute connaissance de cause. Par ailleurs, les comparaisons entre hôpitaux devraient alimenter les discussions sur la qualité menées par les spécialistes au sein des organisations professionnelles.

en:
The quality indicators of the Swiss acute care hospitals include details of treatments provided at Swiss hospitals in 2023. Given values include the number of cases, figures for subcategories (e.g. rates of caesarean section), mortality rates for specific clinical patterns and procedures as well as selected lengths of stay, transfer rates and minimum case numbers.
The indicators are intended to give the interested public an overview of activities conducted at hospitals largely financed by health insurance premiums and taxes, and to allow patients to make their choice of hospital based on facts. It is also intended to provide specialists at the hospitals concerned and outside with a basis for comparison between hospitals and inject a new aspect into discussions on quality.

de:
Die Qualitätsindikatoren der Schweizer Akutspitäler beinhalten Angaben zu den Behandlungen in den Schweizer Spitälern 2023. Ausgewiesen werden Fallzahlen, Anteilswerte (z. B. Kaiserschnittrate), die Mortalität bei bestimmten Krankheitsbildern und Eingriffen sowie ausgewählte Aufenthaltsdauern, Verlegungsraten und Behandlungen mit Mindesfallzahlen.
Die Indikatoren bezwecken einerseits, der interessierten Öffentlichkeit einen Einblick in die Tätigkeit der Spitäler zu ermöglichen, die zu einem grossen Teil mittels Krankenkassenprämien sowie Steuergeldern finanziert sind, und dem Patienten die Möglichkeit zu geben, die Spitalwahl faktenbasiert zu treffen. Andererseits sollen Fachleute in- und ausserhalb der Spitäler die Möglichkeit erhalten, basierend auf Vergleichen zwischen Spitälern einen weiteren Aspekt in die Qualitätsdiskussion in Fachgesellschaften einzubringen.

it:
Gli indicatori di qualità degli ospedali per cure acute svizzeri comprendono dati relativi ai trattamenti effettuati nei nosocomi svizzeri nel 2023: numero di casi, percentuali (p. es. di parti cesarei), mortalità per determinati quadri clinici e interventi, durate delle degenze, trasferimenti e trattamenti con mumero minimo di casi particolari.
Gli indicatori sono stati introdotti, da un lato, per consentire al pubblico interessato di farsi un'idea dell'attività degli ospedali, finanziati in gran parte con i premi delle casse malati e le entrate fiscali, e per permettere al paziente di scegliere con cognizione di causa l'ospedale che fa al caso suo. Dall'altro i professionisti, all'interno come all'esterno degli ospedali, devono avere la possibilità d'introdurre, sulla base di un confronto tra i diversi istituti, un aspetto supplementare nella discussione sulla qualità condotta in seno alle associazioni di specialisti.

acute-care-hospitals

display_name: acute-care-hospitals

akutspitaler

display_name: akutspitaler

bag

display_name: bag

bundesamt-fuer-gesundheit

display_name: bundesamt-fuer-gesundheit

ch-iqi

display_name: ch-iqi

fallzahlen

display_name: fallzahlen

federal-office-of-public-health

display_name: federal-office-of-public-health

foph

display_name: foph

health-insurance-act

display_name: health-insurance-act

hopitaux-de-soins-aigus

display_name: hopitaux-de-soins-aigus

indicateurs-de-qualite

display_name: indicateurs-de-qualite

indicatori-di-qualita

display_name: indicatori-di-qualita

kvg

display_name: kvg

lamal

display_name: lamal

medical-statistics

display_name: medical-statistics

medizinische-statistik

display_name: medizinische-statistik

mortalitaetsraten

display_name: mortalitaetsraten

mortality-rates

display_name: mortality-rates

nombres-de-cas

display_name: nombres-de-cas

numbers-of-cases

display_name: numbers-of-cases

numero-di-casi

display_name: numero-di-casi

office-federal-de-la-sante-publique

display_name: office-federal-de-la-sante-publique

ofsp

display_name: ofsp

ospedali-per-cure-acute

display_name: ospedali-per-cure-acute

qualitaetsindikatoren

display_name: qualitaetsindikatoren

quality-indicators

display_name: quality-indicators

statistica-medica

display_name: statistica-medica

statistique-medicale

display_name: statistique-medicale

tassi-di-mortalita

display_name: tassi-di-mortalita

taux-de-mortalite

display_name: taux-de-mortalite

ufficio-federale-della-sanita-pubblica

display_name: ufficio-federale-della-sanita-pubblica

ufsp

display_name: ufsp

fr: Indicateurs de qualité des hôpitaux suisses de soins aigus 2023

en: Quality indicators of the Swiss acute care hospitals 2023

it: Indicatori di qualità degli ospedali per cure acute svizzeri 2023

heal

fr: Santé

de: Gesundheit

en: Health

it: Salute

bundesamt-fur-gesundheit-bag

fr: Office fédéral de la santé publique OFSP

it: Ufficio federale della sanità pubblica UFSP

fr: L'Office fédéral de la santé publique (OFSP) fait partie du Département fédéral de l'intérieur. De concert avec les cantons, il assume la responsabilité des domaines touchant à la santé publique ainsi que la mise en œuvre de la politique sanitaire. Autorité à vocation nationale, il représente les intérêts sanitaires de la Suisse dans les organisations internationales et auprès d'autres Etats.

de: Das Bundesamt für Gesundheit (BAG) ist Teil des Eidgenössischen Departements des Innern. Es ist - zusammen mit den Kantonen - verantwortlich für die Gesundheit der Schweizer Bevölkerung und für die Entwicklung der nationalen Gesundheitspolitik. Zudem vertritt das BAG als nationale Behörde die Schweiz in Gesundheitsbelangen in internationalen Organisationen und gegenüber anderen Staaten.

en: The Federal Office of Public Health (FOPH) is part of the Federal Department of Home Affairs. Along with the cantons it is responsible for public health in Switzerland and for developing national health policy. As the national health authority, the FOPH also represents Switzerland's interests in the field of health in international organisations and with respect to other countries.

it: L'Ufficio federale della sanità pubblica (UFSP) è incorporato nel Dipartimento federale dell'interno. Unitamente ai Cantoni, è responsabile della salute della popolazione svizzera e dello sviluppo della politica nazionale in materia di salute. In qualità di autorità nazionale rappresenta inoltre gli interessi della Svizzera in materia di sanità in seno a organizzazioni internazionali e nei rapporti con altri Stati.


elektro-leitungen

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

ener

fr: Énergie

de: Energie

en: Energy

it: Energia

regi

fr: Régions et villes

de: Regionen und Städte

en: Regions and cities

it: Regioni e città

envi

fr: Environnement

de: Umwelt

en: Environment

it: Ambiente

title_for_slug: elektro-leitungen

author: Elektrizitätswerke

fr: geocat.ch permalien

de: geocat.ch Permalink

en: geocat.ch permalink

it: geocat.ch link permanente

fr: WMS-Dienst des Kanton Uri

de: WMS-Dienst des Kanton Uri

en: WMS-Dienst des Kanton Uri

it: WMS-Dienst des Kanton Uri

fr: WFS-Dienst des Kanton Uri

de: WFS-Dienst des Kanton Uri

en: WFS-Dienst des Kanton Uri

it: WFS-Dienst des Kanton Uri

fr: Elektro Trasse, Bauwerke und Trassepunkte.

spatial: Canton of Uri (UR)

fr: Elektrizitätswerke

de: Elektrizitätswerke

en: Elektrizitätswerke

it: Elektrizitätswerke

geoinformation-kanton-uri

de: Lisag AG, Geoinformation Kanton Uri

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

value: Uri-Kt - Geocat Harvester

fr: Elektro Leitungen

de: Elektro Leitungen

en: Elektro Leitungen

it: Elektro Leitungen


wasser-leitungen-darstellung-eigentum

title_for_slug: wasser-leitungen-darstellung-eigentum

url: https://geo.ur.ch/?layers=Wasser Leitungen Darstellung Eigentum

fr: Dieser Datensatz hat den gleichen Inhalt wie der Datensatz "Wasser Leitung öffentlich". Die Wasserleitungen sind gemäss dem Attribut "Eigentum" verschiedenfarbig dargestellt.

fr: Gemeinden des Kt. Uri

de: Gemeinden des Kt. Uri

en: Gemeinden des Kt. Uri

it: Gemeinden des Kt. Uri

fr: Wasser Leitungen Darstellung Eigentum

de: Wasser Leitungen Darstellung Eigentum

en: Wasser Leitungen Darstellung Eigentum

it: Wasser Leitungen Darstellung Eigentum


wasser-leitungen-offentlich

title_for_slug: wasser-leitungen-offentlich

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

fr: Wasser Leitungen öffentlich

de: Wasser Leitungen öffentlich

en: Wasser Leitungen öffentlich

it: Wasser Leitungen öffentlich


fernwarme-leitungen

title_for_slug: fernwarme-leitungen

fr: Fernwärme Trasse und Leitungspunkt.

de: Fernwärme Trasse und Leitungspunkt.

en: Fernwärme Trasse und Leitungspunkt.

it: Fernwärme Trasse und Leitungspunkt.

fr: Oeko Energie AG

de: Oeko Energie AG

en: Oeko Energie AG

it: Oeko Energie AG

fr: Fernwärme Leitungen

de: Fernwärme Leitungen

en: Fernwärme Leitungen

it: Fernwärme Leitungen


windenergie

title_for_slug: windenergie

de: elektrizitat energie gebaude-und-anlagen landschaftsschutz naturschutz raumplanung richtplanung ver--und-entsorgung

protocol: https

de: Datenshop

de: Produktansicht Geodatenshop

de: Windenergiestandorte und Eignungs-, Ausschluss-, Interessens- und Vorbehaltsgebiete für potentielle Standorte für Windkraftanlagen aus den kantonalen Windkonzepten 2011 und 2020.

spatial: Kanton Luzern

de: Kanton Luzern, Abteilung Geoinformation

fr: Géoinformation du canton de Lucerne

de: Geoinformation Kanton Luzern

en: Geoinformation Canton of Lucerne

it: Geoinformazione del Cantone di Lucerna

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

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

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

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

value: LU-Kt - Geocat Harvester

de: Windenergie


regi

fr: Régions et villes

de: Regionen und Städte

envi

fr: Environnement

de: Umwelt

de: artenschutz biodiversitat lebensraum naturschutz

author: Kanton Luzern, Abteilung Geoinformation

de: geocat.ch Permalink

de: Landwirtschaftliche Vernetzungsprojekte zur Erhaltung und Förderung der natürlichen Artenvielfalt auf der landwirtschaftlichen Nutzfläche.

de: Ökologische Vernetzungsprojekte


soci

fr: Population et société

de: Bevölkerung und Gesellschaft

en: Population and society

it: Popolazione e società

de: adressen bevolkerung gebaude-und-anlagen statistik

fr: geocat.ch permalien

de: Gebäude- und Wohnungsinformationen basierend auf den Registerinformationen des kantonalen Gebäude- und Wohnungsregisters (kGWR). Enthält alle öffentlich zugänglichen Informationen zu Gebäuden und Wohnungen.

de: Kantonales Gebäude- und Wohnungsregister (kGWR), öffentlich


statische-waldgrenzen-glarus-sud

agri

fr: Agriculture, pêche, sylviculture et alimentation

de: Landwirtschaft, Fischerei, Forstwirtschaft und Nahrungsmittel

en: Agriculture, fisheries, forestry and food

it: Agricoltura, pesca, silvicoltura e prodotti alimentari

title_for_slug: statische-waldgrenzen-glarus-sud

de: raumentwicklung nutzungsplanung waldfeststellung oreb

fr: Statische Waldgrenzen Glarus Süd

de: Statische Waldgrenzen Glarus Süd

en: Statische Waldgrenzen Glarus Süd

it: Statische Waldgrenzen Glarus Süd

de: Statische Waldgrenzen in Bauzonen, Datensatz der Gemeinde Glarus Süd (1631). Statische Waldgrenzen werden vom Kanton auf der Grundlage eines Waldfeststellungsverfahrens festgelegt.

fr: Kanton Glarus

de: Kanton Glarus

en: Kanton Glarus

it: Kanton Glarus

en: Office of Geoinformation, Canton of Glarus

de: Fachstelle Geoinformation des Kantons Glarus. Zuständig für die kantonale Geodaten-Infrastruktur inklusive Geoportal und Datenbereitstellung.


regi

en: Regions and cities

it: Regioni e città

envi

en: Environment

it: Ambiente

de: lebensraum biotop umweltschutz

en: geocat.ch permalink

it: geocat.ch link permanente

media_type: application/json

de: Biotope als json-Datei

format: JSON

de: Biotope als Geopackage (.gpkg)

format: GPKG

de: Biotope als dxf-Datei

format: DXF

de: Informationen und Standorte der Biotope. Bei einem Biotop handelt es sich um einen abgrenzbaren Lebensraum, in welchem Pflanzen und seltene Tiere leben. Oft werden Biotope aus Umweltschutzgründen errichtet.

spatial: Stadt Luzern

de: Geoinformationszentrum (GIS) Stadt Luzern

stadt-luzern

de: Stadt Luzern

de:
Die Stadt Luzern veröffentlicht hier schrittweise OGD-Datensätze der städtischen Dienstabteilungen. Mehr über «Open Government Data» bei der Stadt Luzern ist über den Link <www.ogd.stadtluzern.ch> zu erfahren.

Die Geometadaten zu den einzelnen OGD-Daten sind unter "Zusätzliche Informationen" als Link aufgeführt.
Zu beachten sind weitere Bestimmungen zum Datenschutz und Nutzungsbestimmungen gemäss OGD-Internetauftritt der Stadt Luzern.

political_level: commune

value: Stadt Luzern - Geocat Harvester

de: Biotope


de: schule musik point-of-interest-poi

de: geocat.ch Permalink

de: WFS GetCapabilities Musikschule

de: Standorte Musikschulen

de: Musikschulen - Standort und Informationen (WFS)


wasserrechte

regi

de: Regionen und Städte

envi

de: Umwelt

title_for_slug: wasserrechte

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

author: Kanton Zürich

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

fr: Öffentliche Oberflächengewässer, Gewässerraum und Wasserrechte

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

spatial: Kanton Zürich

fr: Kanton Zürich

de: Kanton Zürich

en: Kanton Zürich

it: Kanton Zürich

geoinformation-kanton-zuerich

fr: Département de Géoinformation du Canton de Zurich

de: Geoinformation Kanton Zürich

en: Department of Geoinformation of the Canton of Zurich

it: Dipartimento della geoinformazione del Cantone di Zurigo

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

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

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

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

value: ZH - Geocat Harvester

fr: Wasserrechte

de: Wasserrechte

en: Wasserrechte

it: Wasserrechte


alpen-inkl-weideperimeter

agri

de: Landwirtschaft, Fischerei, Forstwirtschaft und Nahrungsmittel

title_for_slug: alpen-inkl-weideperimeter

author: Amt für Landwirtschaft Kt. Uri

fr: WMS-Dienst des Kanton Uri

de: WMS-Dienst des Kanton Uri

en: WMS-Dienst des Kanton Uri

it: WMS-Dienst des Kanton Uri

fr: WFS-Dienst des Kanton Uri

de: WFS-Dienst des Kanton Uri

en: WFS-Dienst des Kanton Uri

it: WFS-Dienst des Kanton Uri

fr: In diesem Layer sind die Alpgrenzen und innerhalb dieser die beitragsberechtigten Landwirtschaftsflächen dargestellt. Grundsätzlich basieren die Grenzen und Flächen auf digitalisierten Plan-Einzeichnungen (Massstab 1:5'000) der verschiedenen Allmendaufseher. Bei denjenigen Alpen, wo im Geodatensatz "BFF im Sömmerungsgebiet" beurteilte Flächen vorhanden sind, wurden die Flächen in Feldbegehungen überprüft (sofern die betroffenen Flächen nicht über 2'200 m liegen, bzw. über 2'400 m im Urserental).

fr: Amt für Landwirtschaft Kt. Uri

de: Amt für Landwirtschaft Kt. Uri

en: Amt für Landwirtschaft Kt. Uri

it: Amt für Landwirtschaft Kt. Uri

geoinformation-kanton-uri

de: Lisag AG, Geoinformation Kanton Uri

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

fr: Alpen inkl. Weideperimeter

de: Alpen inkl. Weideperimeter

en: Alpen inkl. Weideperimeter

it: Alpen inkl. Weideperimeter


perimeter-ln-und-sommerungsflachen-ur

regi

fr: Régions et villes

title_for_slug: perimeter-ln-und-sommerungsflachen-ur

fr: perimetre geodonnees-de-base

de: perimeter geobasisdaten

en: perimeter official-geodata

it: perimetro geodati-di-base

url: https://geo.ur.ch/?layers=Perimeter LN- und Sömmerungsflächen

de: geocat.ch Permalink

fr: Der Perimeter LN stellt die potenziell zu bewirtschaftende Landwirtschaftsfläche, ohne die landwirtschaftlich unproduktiven Flächen dar. Der Perimeter LN wird jährlich anhand der Bodenbedeckung der Amtlichen Vermessung aktualisiert. Im Kanton Uri werden die Sömmerungsflächen in diesem Geobasisdatensatz nicht ausgeschieden. Die Artenreichen Streueflächen im Sömmerungsgebiet sind jedoch in einem eigenen Layer aufrufbar (Layer BFF im Sömmerungsgebiet).

fr: Perimeter LN- und Sömmerungsflächen (UR)


bff-qualitatsstufe-ii-ur

agri

fr: Agriculture, pêche, sylviculture et alimentation

title_for_slug: bff-qualitatsstufe-ii-ur

fr: estivage geodonnees-de-base

de: sommerung geobasisdaten

en: summering official-geodata

it: estivazione geodati-di-base

fr: geocat.ch permalien

fr: In diesem Layer sind Flächen enthalten, welche die Anforderungen an die Qualitätsstufe II erfüllen und die erforderliche botanische Qualität oder Strukturen zur Förderung der Biodiversität aufweisen. Die gelb eingefärbte Fläche ist die effektive Fläche, die über Q II verfügt. Gelb schraffiert ist die ganze Biodiversitätsförderfläche BFF mit dem Q II-Anteil in Prozenten. Die Bewirtschafter melden die Flächen an, wenn sie vermuten, dass eine BFF I die Kriterien für die Qualitätsstufe II erfüllen könnte. Eine Fachperson überprüft anschliessend die Fläche. Die minimale Vertragsdauer ist 8 Jahre.

fr: BFF Qualitätsstufe II (UR)

de: BFF Qualitätsstufe II (UR)

en: BFF Qualitätsstufe II (UR)

it: BFF Qualitätsstufe II (UR)


nutzungsflachen-inkl-bff-i-ur

agri

en: Agriculture, fisheries, forestry and food

it: Agricoltura, pesca, silvicoltura e prodotti alimentari

regi

de: Regionen und Städte

title_for_slug: nutzungsflachen-inkl-bff-i-ur

url: https://geo.ur.ch/?layers=Nutzungsflächen inkl. BFF I

fr: Der Geobasisdatensatz 153.1 Nutzungsflächen (inkl. Biodiversitätsförderflächen Qualitätsstufe I) beinhaltet die landwirtschaftlich genutzten Flächen gemäss landwirtschaftlicher Begriffsverordnung (LBV), Direktzahlungsverordnung (DZV) und der daraus abgeleiteten Definitionen (inkl. Codierung) der Nutzungen durch den Bund und die Kantone. Die Biodiversitätsförderflächen (BFF) Qualitätsstufe I sind zusätzlich auch noch als separater Layer verfügbar.

fr: Nutzungsflächen inkl. BFF I (UR)

de: Nutzungsflächen inkl. BFF I (UR)

en: Nutzungsflächen inkl. BFF I (UR)

it: Nutzungsflächen inkl. BFF I (UR)


bff-vernetzung-ur

title_for_slug: bff-vernetzung-ur

fr: Vernetzungsprojekte haben zum Ziel, die natürliche Artenvielfalt zu erhalten und zu fördern, indem Biodiversitätsförderflächen (BFF) zu Gunsten ausgewählter Arten angelegt, aufgewertet und gepflegt werden. Vernetzungsprojekte behandeln schwerpunktmässig die landwirtschaftliche Nutzfläche und ihre biologische Vielfalt. Es werden aber auch andere Nutzungen in die Überlegungen mit einbezogen. Vernetzungsbeiträge können jedoch nur für Elemente der landwirtschaftlichen Nutzfläche geltend gemacht werden.

fr: BFF Vernetzung (UR)

de: BFF Vernetzung (UR)

en: BFF Vernetzung (UR)

it: BFF Vernetzung (UR)


bewirtschaftungseinheit-ur

regi

en: Regions and cities

it: Regioni e città

title_for_slug: bewirtschaftungseinheit-ur

fr: WMS-Dienst des Kanton Uri

fr: WFS-Dienst des Kanton Uri

fr: Die Bewirtschaftungseinheiten zeigen auf, durch welchen Betrieb die entsprechende Fläche bewirtschaftet wird. Die Betriebsstandorte sind ebenfalls ersichtlich.

fr: Bewirtschaftungseinheit (UR)

de: Bewirtschaftungseinheit (UR)

en: Bewirtschaftungseinheit (UR)

it: Bewirtschaftungseinheit (UR)


bff-qualitatsstufe-i

regi

fr: Régions et villes

title_for_slug: bff-qualitatsstufe-i

en: geocat.ch permalink

it: geocat.ch link permanente

fr: In diesem Datensatz sind sämtliche BFF der Qualitätsstufe I abgebildet. Die früheren Beiträge für den ökologischen Ausgleich, die biologische Qualität und die Vernetzung wurden im Rahmen der Agrarpolitik 2014-2017 zu den Biodiversitätsbeiträgen fusioniert. Mit diesen Beiträgen fördert der Bund die Arten- und Lebensraumvielfalt. Der Anteil an Biodiversitätsförderflächen muss mindestens 7 Prozent der landwirtschaftlichen Nutzfläche ohne Spezialkulturen und 3,5 Prozent der mit Spezialkulturen belegten landwirtschaftlichen Nutzfläche betragen. Der Anteil der Hochstamm-Feldobstbäume und der einheimischen, standortgerechten Einzelbäume und Alleen darf maximal die Hälfte der verlangten Biodiversitätsförderfläche betragen.

fr: Amt für Landwirtschaft Kt. Uri

fr: BFF Qualitätsstufe I

de: BFF Qualitätsstufe I

en: BFF Qualitätsstufe I

it: BFF Qualitätsstufe I


soci

fr: Population et société

de: Bevölkerung und Gesellschaft

en: Population and society

it: Popolazione e società

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

de: Datenshop

de: Produktansicht Geodatenshop

de: Inventar der schützenswerten Ortsbilder der Schweiz (ISOS)

de: Kanton Luzern, Abteilung Geoinformation

fr: Géoinformation du canton de Lucerne

de: Geoinformation Kanton Luzern

en: Geoinformation Canton of Lucerne

it: Geoinformazione del Cantone di Lucerna

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

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

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

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


unuberbaute-bauzonen-in-kraft-genehmigt

envi

fr: Environnement

de: Umwelt

en: Environment

it: Ambiente

title_for_slug: unuberbaute-bauzonen-in-kraft-genehmigt

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

fr: geocat.ch permalien

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

de: Unüberbaute Bauzonen, in Kraft (genehmigt)


maintainer: Thorsten Bartels-Rausch

en: chemistry arctic brine snow solution antarctica metamorphism ozone laboratory-study alpine interface heterogeneous reaction

en: data list

en: Lists file names, snow sample properties, and flow settings for each data-set.

en: Matlab Data

en: All data as Matlab mat file

en: Data Figure 1

en: Data of published Figure

en: Readme file

en: This file shows data, code, and plots.

mimetype: application/pdf

en: Acetone data

en:
intensity of the acetone-water-cluster and water-cluster (as derived by a chemical ionisation mass spectrometer) with time in the carrier gas downstream of packed bed flow tube holding the snow sample or the by-pass, respectively. Time "0" denotes the time when the mass spectrometer data acquisition was started. When the carrier
gas with acetone was passed via the packed bed flow tube, that is switched from by-pass to pass the snow, acetone concentration in the carrier gas rapidly dropped due to surface adsorption and slowly recovered as the adsorption sites saturate. From this, the surface area can be obtained.

en: Matlab Script to view and plot data

en: Ozone data

en: Trend of ozone mixing ratio ( _O3mixingRatio_ppb_ with time ( _time_s_ ) as measured downstream of the snow sample during the experiments. Time "0" denotes the time when data acquisition with ozone analyser was started. And, ozone (_nOzone_molec_per_s_ ) loss for each experiment with duration of ozone exposure ( _duration_s_ ) of ozone exposure, as derived from the ozone trends shown above. Time "0" denotes the time when the carrier gas with ozone was passed via the snow sample.

en: Earth’s snow cover is very dynamic on diurnal time scales. The changes to the snow structure during this metamorphism have wide ranging impacts such as on avalanche formation and on the capacity of surface snow to exchange trace gases with the atmosphere. Here, we investigate the influence of dry metamorphism, which involves fluxes of water vapor, on the chemical reactivity of bromide in the snow. For this, the heterogeneous reactive loss of ozone at a concentration of 5-6E12 molecules cm-3 is investigated in artificial, shock-frozen snow samples doped with 6.2 uM sodium bromide and with varying metamorphism history. The oxidation of bromide in snow is one reaction initiating polar bromine releases and ozone depletions.

alpine

display_name: alpine

antarctica

display_name: antarctica

arctic

display_name: arctic

brine

display_name: brine

chemistry

display_name: chemistry

heterogeneous

display_name: heterogeneous

interface

display_name: interface

laboratory-study

display_name: laboratory-study

metamorphism

display_name: metamorphism

ozone

display_name: ozone

reaction

display_name: reaction

snow

display_name: snow

solution

display_name: solution

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

key: guid

key: uri

value: Envidat Harvester

en: Data set on bromide oxidation by ozone in snow during metamorphism from laboratory study


maintainer: Matthias Haeni

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

en: Site photo of the forest station (JUB)

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

mimetype: image/jpeg

en: Site aerial photo (Jussy)

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

en: Data plot of the field station (JUF)

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

en: Forest station data (JUB)

en: Data file.

mimetype: text/csv

en: Data plot of the forest station (JUB)

en: Site photo of the field station (JUF)

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

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Jussy in Switzerland where one station is located within a natural broad-leaved forest stand (JUB) with sessile oak (_Quercus petrea_; 90 yrs), aspen (_Populus tremula_; 60 yrs) and European hornbeam (_Carpinus betulus_; 60 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, JUF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Jussy 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

educ

en: Education, culture and sport

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


maintainer: Rebecca Mott

en: tls terrestrial-laser-scanning patchy-snow-covers snow-ablation-rates riegl-vz6000

en: Snowmask 2014

mimetype: application/zip

en: Digital Elevation Modell 2016

en: Digital Elevation Modell 2014

en: Gletschboden test site

en: Digital Elevation Modell 2015

en: Snowmask 2016

en: Snowmask 2015

en: We recorded snow ablation maps with a terrestrial laser scanner (TLS, Riegl-VZ6000) at the Gletschboden area. The TLS position is located approximately 30 vertical meters above the Gletschboden area at a northerly exposed slope. In total 44 TLS measurement sets have been conducted in three consecutive years 2014-2016 (2014: 13 measurements; 2015: 17 measurements; 2016: 14 measurements). The TLS system has a single-point measurement frequency of 300 kHz and a beam divergence of 0.007°. This set-up allows a horizontal resolution of approximately 0.01 m in 100 m distance to the TLS position. One scan of the Gletschboden area lasts approximately 15 minutes. The travel time from the laser scanner towards the surface is recorded and afterwards converted into a point cloud of distances. 5 reflectors located at the Gletschboden area and in the closer surroundings were additionally scanned during each measurement to transform the point cloud from the scanner own coordinate system into Swiss coordinates. Additionally, orthophotos have been created by using pictures recorded from the TLS in order to provide snow mask maps. Snow and bare ground can be distinguished by the RGB color information of the orthophoto. Cells with blue band information greater than 175 were categorized as snow and all cells with values smaller or equal 175 were categorized as bare ground.

patchy-snow-covers

display_name: patchy-snow-covers

snow-ablation-rates

display_name: snow-ablation-rates

terrestrial-laser-scanning

display_name: terrestrial-laser-scanning

tls

display_name: tls

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: DISCHMEX - High-resolution daily snow ablation rates in an Alpine environment


en: alpine climate imis snowmelt phenology long-term snow ultrasonic grassland monitoring

en: Contains daily climate station data including daily median temperatures, snow depth, plant height, and annual melt-out timings.

en: Contains unfiltered annual phenological, temperature and snow-climatic data for all IMIS stations.

en: implements the phenofit method. Note: This script is computationally intensive and may take a while to run. We recommend using the temporary data created from it (i.e. starting from 2_temperature.R). For a better description of the phenofit R package and the different methods to determine the SOG, see Kong et al. (2022): https://doi.org/10.1111/2041-210X.13870

format: OCTET

en: Contains IMIS station location data

en: Contains custom functions required for the scripts; this file must be sourced before running other scripts.

en: Documents and reproduces the statistical models used in the analysis.

en: Contains annual phenological variables and phenofit outputs, sorted, ordered and renamed according with the readme in this repository

en: Generates the methods-related figures used in the publication.

media_type: image/png

en: Snow3.png and grass.png are needed for plotting the methods plot in 5_methods_plots.Rmd

format: PNG

en: Temporary data. Contains annual unfiltered output of phenofit (i.e. the variable names are in line with the phenofit documentation), including all curve parametrization variables for plotting and additional variables, which are not necessarily used in the analysis.

en: Contains filtered annual phenological data from 40 IMIS stations suitable and used for phenological analysis

en: Is the readme that documents all data and code in this repository

en:
Long-term phenological data in alpine regions are often limited to a few locations and thus, little is known about climate-change induced plant phenological shifts above the treeline. Because plant growth initiation in seasonally snow-covered regions is largely driven by snowmelt timing and local temperature, it is essential to simultaneously track phenological shifts, snowmelt, and near-ground temperatures. In this study, we make use of ultrasonic snow height sensors installed at climate stations in the Swiss Alps to reveal phenological advance of grassland ecosystems and relate them to climatic changes over 25 years (1998 – 2023). When snow is absent, these snow height sensors additionally provide information on plant growth at a uniquely fine temporal scale. Daily temperatures and snow- / plant height values, annual phenological- and melt-out dates were derived from IMIS climate station data (doi.org/10.16904/envidat.406). We observed an advance of green-up by -2.4 days/decade coinciding with strong warming of up to +0.8°C/decade. Although the timing of melt-out has not changed significantly over the study period in this focal region, phenological responses to early melt-out years varied due to differing influences of photoperiodic and thermal constraints, which were not equally important across elevations and communities. Phenological shifts of alpine grasslands are thus likely to become even more pronounced if melt-out timing advances in the future as predicted.
This repository relates to the publication Zehnder et al. (2025). Snow height sensors reveal phenological advance in alpine grasslands. Global Change Biology. accepted. The model data and code that support our findings are deposited in this repository.

alpine

display_name: alpine

climate

display_name: climate

grassland

display_name: grassland

imis

display_name: imis

monitoring

display_name: monitoring

phenology

display_name: phenology

snowmelt

display_name: snowmelt

ultrasonic

display_name: ultrasonic

en: IMIS Phenology Data Since 1998


maintainer: Maria Elvira Murazzi

en: 13c-labelling polystyrene forest-trees uptake nanoplastic

en: Nanoplastic in forests experiment

en:
Data embargoed until August 2022, and it will be provided upon request

-  excel file containing: two working sheets (leaf supply and root supply experiment) with a table with the values 13C-nanopolystyrene in the different tissues of the two forest tree species; graph showing the differences between controls and treatments of 13C-nanopolystyrene enrichment for each tissue of each forest tree species; ANOVA table showing significant values after having exposed the tree species to 13C-nanopolystyrene

en:
The fate of plastic in the environment is of global concern, because its production recently has
increased strongly and it accumulates in terrestrial and aquatic ecosystems. Although some knowledge on
its role in aquatic and terrestrial ecosystems was gained in the recent decade, hitherto very little is known
about the impact of micro and nanoplastics in forest ecosystems.

The aim of this pioneering project was to explore if nanoplastics are taken up by forest trees species
through leaves or roots. In greenhouse experiments, we exposed leaves or roots of seedling of two forest
trees species to solutions with highly 13C-labelled polystyrene nanoparticles (13C-nPS, 99 atom%) and
examined if they were incorporated in different above- and belowground tissues.

Treated part of the trees for both species showed significant 13C-enrichment, indicating that trees take up nanoparticles. However, the overall 13C signal strength in tissues that were not exposed to the 13C label remained low (Δδ13C<1‰) and was confined to a few seedlings, leaving it ambiguous whether nanoplastic transport occurs or not.

We acknowledge that the new method developed might be not sensitive enough to unequivocally detect
mechanisms of nanoplastic uptake and transport at environmentally realistic concentrations.

forest-trees

display_name: forest-trees

nanoplastic

display_name: nanoplastic

polystyrene

display_name: polystyrene

uptake

display_name: uptake

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Nanoplastics in forests: Exploring the effects of nanoplastics on forest soils and tree physiology (NanoPlast)


maintainer: EnviDat Support

en: meteo-station snow-height longwave-radiation shortwave-radiation gemstock

en: DATA ACCESS

en:
Meteo data at Gemsstock [Level 2 dataset](http://models.slf.ch/p/dataset-processing/).

fields      = timestamp TSS HS VW DW VW_MAX OSWR ISWR ILWR RECORD AUX_VOLTAGE LOGGER_VOLTAGE LW_SENSOR_TEMP VENTILATION_FLAG OLWR

en:
Meteorological station at Gemstock (3021 m asl) in Canton Uri. The station includes in/out LW/SW and a snow height sensor.

Data from this station is managed by the permos.ch project. More information: https://www.permos.ch/permafrost-monitoring/field-sites

gemstock

display_name: gemstock

longwave-radiation

display_name: longwave-radiation

meteo-station

display_name: meteo-station

shortwave-radiation

display_name: shortwave-radiation

snow-height

display_name: snow-height

en: GEM2: Meteorological and snow station at Gemsstock (3021 m asl), Canton Uri, Switzerland


maintainer: Justine Berg

en: fsm2 snowpack dischma snow-hydrology fsm2trans hicar

en: Input data April 2017 HICAR 50m resolution

en: Input data February 2017 HICAR 100m resolution

en: Input data November 2016 HICAR 50m resolution

en: Input data February 2017 HICAR 250m resolution

en: Input data April 2017 COSD

en: Input data December 2016 HICAR 250m resolution

en: input data january 2016 COSD

en: Input data January 2017 COSD

en: Available input files for running with FSM2trans

en: Input data December 2016 HICAR 100m resolution

en: Input data March 2017 HICAR 100m resolution

en: Input data March 2017 COSD

en: Input data June 2017 HICAR 50m resolution

en: Input data May 2017 COSD

en: Input data November 2016 HICAR 250m resolution

en: Input data March 2017 HICAR 50m resolution

en: input data october 2016 COSD

en: Input data July 2017 HICAR 250m resolution

en: Input data october 2016 HICAR 100m resolution

en: input data november 2016 COSD

en: Input data october 2016 HICAR 50m resolution

en: Input data May 2017 HICAR 100m resolution

en: Input data July 2017 COSD

en: Input data May 2017 HICAR 50m resolution

en: Input data February 2017 HICAR 50m resolution

en: Input data April 2017 HICAR 250m resolution

en: Input data January 2017 HICAR 50m resolution

en: Input data April 2017 HICAR 100m resolution

en: Input data July 2017 HICAR 50m resolution

en: Input data november 2016 HICAR 100m resolution

en:
time series of daily averages of the whole domain for each simulation run
- rainfall
- snowfall
- runoff
- snowmelt
- temperature

en: Input data May 2017 HICAR 250m resolution

en: Input data July 2017 HICAR 100m resolution

en: Input data March 2017 HICAR 250m resolution

en: Input data January 2017 HICAR 100m resolution

en: Input data June 2017 HICAR 100m resolution

en: Input data January 2017 HICAR 250m resolution

en:
snowheight output of validation days (LiDAR days) for all simulation runs
- 20.03.2027
- 31.03.2017
- 17.05.2017

en: Input data June 2017 HICAR 250m resolution

en: Input data October 2016 HICAR 250m resolution

en: Input data December 2016 HICAR 50m resolution

en:
 DESCRIPTION
The dataset is used to run snowpack simulations with various forcing data at different resolutions with the Flexible Snow Model (FSM2oshd). A dynamical downscaling model (HICAR) and a semi-statistical downscaling approach (COSD) are used to downscale COSMO data to resolutions of 250 m, 100 m and 50 m. Simulations are run with the operational snow cover model and a model extension including wind- and gravity-induced snow redistribution (FSM2trans).
 Input Data
This paragraph describes the input data used to run simulations with FSM2trans
 HICAR
The HICAR input data is created by the High-resolution Intermediate Complexity Atmospheric Research model. The input data is available for different resolutions (250m, 100m and 50m) for a domain covering complex terrain in the Swiss Alps (Dischma)
 COSD
The COSD data was statistically downscaled within the OSHD framework to different resolutions (250m, 100m, 50m) for a domain covering complex terrain in the Swiss Alps (Dischma)
 Simulation files
For the Simulations, a landuse file and a namelist file with the parametrizations need to be supplied to the model
 simulation output
This simulation output that is used for the analysis of the study is provided.

en: FSM2trans snowpack simulations with HICAR input


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

en: Site photo of the field station (CIF)

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

en: Forest station data (CIB)

en: Data file.

en: Site aerial photo (Chironico)

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

en: Data plot of the forest station (CIB)

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

en: Site photo of the forest station (CIB)

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

en: Data plot of the field station (CIF)

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 Chironico in Switzerland where one station is located within a natural coniferous forest stand (CIB) with Norway spruce (_Picea abies_; 160-180 yrs) and European silver fir (_Abies alba_; 140-160 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, CIF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Chironico 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

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Chironico, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 2000 onwards


en: historical-carthography environmental--policy ecology switzerland environmental---history wetlands

en: Wetlands 1850 - 2010 (shapefiles)

en:
 History of wetlands - GIS-Layers
 Location of wetlands based on signatures in topographic maps
* Basis 1850: original measuring table sheets Dufour created between the years 1834 and 1863.
* Basis 1900/ Basis 1950: The Siegfried maps were updated and published at regular intervals between 1870 and 1949. This long period of time made it possible to reconstruct wetlands for two periods based on the Siegfried Maps: the first edition (hereinafter referred to as "1900") and the last edition ("1950").
* Basis 2010: Map sheets from the National Map from 2006 to 2011 were used for the most recent time period of the reconstruction.
 Location of wetlands due to reconstructions
The reconstruction **must not** be interpreted in the sense of a surface-accurate separation, but shows without claim to location accuracy the approximate extent of the areas that would have corresponded to the mapping criteria for wetlands used in today's topographic maps around 1850, 1900 and 1950. The basics can be found in Stuber & Bürgi 2019, chapter 5.4.

en:
Naturally, large parts of the Swiss Plateau are characterised by wetlands
and meandering rivers. That this is no longer the case today is the result
of centuries of efforts to obtain dry land. But how did this process take
place? What were the relevant actors and what were their motivations? And
what can be said about the ecological consequences of this development?

In a research project on the history of wetlands in Switzerland since 1700,
we conducted (a) a historical analysis of the development of land use in
wetlands and the actors involved, (b) a historical-cartographic
reconstruction of wetland extent since 1850 and (c) an evaluation of
ecological effects of changes in wetlands on various organisms groups.

The series of GIS layers on wetland history stem from the second part of
the project. The area reconstruction is based on digitized and homogenized
signatures from national map series, as they have been available since
about 1850. Details about the digitalization process and the homogenization
procedures applied ("Rekonstruktionen") are included in Stuber & Bürgi
2019.

Book Citation:
 > Stuber M, Bürgi M (2019) Vom «eroberten Land» zum Renaturierungsprojekt. Geschichte der Feuchtgebiete in der Schweiz seit 1700. "Bristol Schriftenreihe", Band 59. Haupt Verlag, Bern, Stuttgart, Wien. 262 Seiten.

ecology

display_name: ecology

environmental---history

display_name: environmental---history

environmental--policy

display_name: environmental--policy

historical-carthography

display_name: historical-carthography

switzerland

display_name: switzerland

wetlands

display_name: wetlands

en: History of wetlands in Switzerland since 1850


maintainer: Achille Capelli

en: snow gps liquid-water-content swe gnss lwc snow-water-equivalent

en: Daily observer data from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-2020.

en: Daily data from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-2020. These data were processed to daily values from the other data files.

en: Daily data for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020. These data were processed to daily values from the other data files.

en: Snow and meteo data from automatic sensors for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winter 2018-2020.

en: Manual profiles

en: Data of the traditional manual profiles for the locations Weissfluhjoch, Klosters and Küblis. For the location of Küblis there are no traditional manual profile data. We provided the data as both PDF and .CAAML format (See https://niviz.org/ or http://caaml.org/Schemas/V5.0/Profiles/SnowProfileIACS/CAAMLv5_SnowProfileIACS.pdf). Moreover, we added LWC and density profiles when available (all locations).

en: GNSS derived SWE, HS, and LWC for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

en: GNSS derived data for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winter 2018-2020.

en: Daily data for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winters 2018-2020. These data were processed to daily values from the other data files.

en: Daily data from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-2020. These data were processed to daily values from the other data files.

en: Snow and meteo data from automatic sensors for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

en: Snow profiles data from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-2020.

en: Daily observer data for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

en: GNSS derived SWE, LWC and HS from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-20

en: Snow profiles data for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

en: Webcam images

en: Webcam images showing some typical differences in the snow conditions at the four sites (Küblis, Klosters, Laret and Weissfluhjoch) for two time periods (14-18.3.2019 and 27.1-11.2.2020). Both time periods included a precipitation event with partial rain-on–snow occurring at the lower locations.

en: GNSS derived SWE, LWC and HS from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-20

en: Daily observer data from automatic sensors from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-2020.

en: Snow and meteo data from automatic sensors from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-2020.

en: Snow profiles data from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-2020.

en: Profile data for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winter 2018-2020.

en: Snow and meteo data from automatic sensors from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-2020.

en: This database contains GNSS derived snow water equivalent (SWE), liquid water content (LWC), and snow height (HS) and reference data collected during the two winter 2018-2020 at 4 sites Weissfluhjoch (2540 m asl, 46°49’47’’ N, 9°48’34’’E), Laret (1515 m asl, . 46°50’2’’N, 9°52’17’’E), Klosters (1200 m asl, 46°51’49’’N, 9°53’17’’E), and Küblis (815 m asl, 46°54’48’’N, 9°46’54’’E).

gnss

display_name: gnss

gps

display_name: gps

liquid-water-content

display_name: liquid-water-content

lwc

display_name: lwc

snow

display_name: snow

snow-water-equivalent

display_name: snow-water-equivalent

en: Snow water equivalent measurements with low-cost GNSS receivers along a steep elevation gradient in the East-ern Swiss Alps


data-interactive-avalanche-segmentation

maintainer: Elisabeth D. Hafner

title_for_slug: data-interactive-avalanche-segmentation

en: snow avalanche interactive deep-learning segmentation

en:
Images and corresponding avalanche annotations used for interactive avalanche segmentation:
SLF train, vali and test
WebNew
GroundPic
UserPic
UIBK dataset: List of images used
Pictures are from SLF (mostly from the Dischma Cams) if not otherwise indicated. If used, the data needs to be properly cited!

avalanche

display_name: avalanche

deep-learning

display_name: deep-learning

interactive

display_name: interactive

segmentation

display_name: segmentation

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Data interactive avalanche segmentation


automatic-classification-of-avalanches

maintainer: Alec van Herwijnen

title_for_slug: automatic-classification-of-avalanches

en: avalanche localization seismic machine-learning monitoring

en:
This tar file contains classification results for two seismic geophone arrays deployed above Davos, Switzerland. The of the first array located at the Dischma Valley was used to automatically identify all seismic events produced by avalanches, whereas the classification perfomed at the second array located at the Wannengrat field site was used to identify airplanes and earthquakes.
Furthermore, this tar file also contains the median back-azimuth of all classified events at the Dischma field site.

mimetype: application/x-tar

en: This dataset contains the classification and localization results obtained during the automatic classification of avalanches during the winter season 2017.

localization

display_name: localization

machine-learning

display_name: machine-learning

monitoring

display_name: monitoring

seismic

display_name: seismic

en: Automatic Classification of Avalanches


vegetation-height-model-sentinel-nfi

title_for_slug: vegetation-height-model-sentinel-nfi

en: nfi remote-sensing forest cnn sentinel-2 switzerland vegetation-height

media_type: image/tiff

format: TIFF

en: Countrywide vegetation height models (VHM) were generated for Switzerland based on Copernicus Sentinel-2 imagery and the digital terrain model (DTM) swissALTI3D from the Swiss Federal Office of Topography swisstopo. A Convolutional Neural Network (CNN) model was trained to estimate the maximum vegetation height at the spatial resolution of the Sentinel-2 pixel of 10 m. Vegetation heights from the spatially higher-resolved VHM Lidar NFI were used as reference data for the CNN training. Within the framework of the Swiss National Forest Inventory (NFI), the VHMs were modelled annually based on available Sentinel-2 imagery from May – September of the respective year. Further details on the creation of the VHM Sentinel NFI can be found in the paper Jiang et al. (2023, https://doi.org/10.1016/j.srs.2023.100099). Contains modified Copernicus Sentinel data.

cnn

display_name: cnn

forest

display_name: forest

nfi

display_name: nfi

remote-sensing

display_name: remote-sensing

vegetation-height

display_name: vegetation-height

en: Vegetation Height Model Sentinel NFI


en: lidar single-tree tree-volume forest-inventory biomass terrestiral-laserscanning

en: Individual tree parameters and volume estimates based on TLS point clouds and allometric models

en:
"ASM" denotes volume estimates calculated by allometric scaling models (as used in Swiss NFI), based on tree height, DBH and D7.
"QSM" denotes volume estimates derived from TLS point clouds by quantitative structure models (QSM).
The remaining variables are geometrical features directly calculated from single tree TLS point clouds.
For more detailed variable descriptions see related publication.

en:
 Dataset
This dataset is based on terrestrial laser scanning (TLS) data acquired during winter 2020/2021 in leaf-off conditions, with a Leica BLK 360 instrument following a tree-centric scanning pattern.
The data was acquired on two sites (47.42°N 8.49°E and 47.504°N, 7.78°E), both of which were managed mixed temperate forest stands.
Individual trees were semi-automatically segmented from the co-registered TLS point clouds.

 Background
Accurate estimates of individual tree volume or biomass within forest inventories are essential for calibration and validation of biomass mapping products based on Earth observation data.
Terrestrial laser scanning (TLS) enables detailed and non-destructive volume estimation of individual trees, with existing approaches ranging from simple geometrical features to virtual 3D reconstruction of entire trees. Validating such approaches with weight measurements is a key step before the integration of TLS or other close-range technologies into operational applications such as forest inventories.
In this study, we firstly evaluate individual tree volume estimation approaches based on 3D reconstruction through quantitative structure models (QSM) against destructive reference data of 60 trees and compare them to operational allometric scaling models (ASM).
Secondly, we determine the explanatory power of TLS-derived geometric parameters regarding total tree, stem, coarse wood and fine branch volume.

biomass

display_name: biomass

forest-inventory

display_name: forest-inventory

lidar

display_name: lidar

single-tree

display_name: single-tree

terrestiral-laserscanning

display_name: terrestiral-laserscanning

tree-volume

display_name: tree-volume

en: Individual tree TLS point clouds for tree volume estimation


en: beech network-analysis dendrochronology tree-ring dendroecology fagus-sylvatica

en:
This dataset contains the raw ring-width and climate data (1952-2020) used in the research article "No future growth enhancement expected at the northern edge for European beech due to continued water limitation".
RW= ring width
Year= Calendar year
CN = core number
ID3= plot identifier
longitude = longitude (°)
latitude = latitude (°)
elevation = elevation (m)
age= estimated age in calendar year
radius= estimated radius in calendar year (mm)
MAT= mean annual temperature (°C)
MAP= mean annual precipitation (mm)
MCWB= mean climatic water balance (P-PET; mm)
amplitude= simple continentality measure (July - January mean maximum temperatures)
MATspline1 and 2, MCWBspline1 and 2, radiusspline4 and 5= natural spline transformed values of MAT, MCWB and radius ns(x,2)
CWD_pMay_Jun= Climatic Water Balance of previous year May to June !!!#please note CWD here is NOT Climatic Water Deficit, this was a labeling error in the creation of the dataset headers
CWD_May_Jun= Climatic Water Balance of current year May to June
Tmax_pMay_Jun= mean monthly maximum temperature of previous year May to June
Tmax_May_Jun= mean monthly maximum temperature of current year May to June
weight = site replication-based weighting input for model
SOM= membership of regional cluster 1-8 based on Supervised Organized Mapping

en:
R code to run the model behind the results of the research article "No future growth enhancement expected at the northern edge for European beech due to continued water limitation".
Requires beech_rawtrw.txt.

en: Model output

en: Output of the Mixed-effects model

en: This dataset contains the raw ring-width and climate data (1952-2020), as well as the R code to run the model and the model output used in the research article "No future growth enhancement expected at the northern edge for European beech due to continued water limitation".

beech

display_name: beech

dendrochronology

display_name: dendrochronology

dendroecology

display_name: dendroecology

fagus-sylvatica

display_name: fagus-sylvatica

network-analysis

display_name: network-analysis

tree-ring

display_name: tree-ring

en: European Beech tree-ring dataset 1952-2020


en: sense-of-place place-attachment landscape electrodermal-activity urban-ecology

en: 03. Publication Ready Data

en: 01. EDA Processed Data

en: 04. Scripts

en: 00. EDA Raw Data

en: 02. Questionnaire Data

en: 05. Visuals

en: This repository contains data on EDA measurements of visitors with different cultural backgrounds in virtual urban park settings. The parks are a Persian garden (Shiraz, Iran) and a historical park in Zurich, Switzerland. The cultural background of the visitors is Persian and Central European. The repository contains raw data from EDA, processed time series and statistical procedures.

electrodermal-activity

display_name: electrodermal-activity

landscape

display_name: landscape

place-attachment

display_name: place-attachment

sense-of-place

display_name: sense-of-place

urban-ecology

display_name: urban-ecology

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Electrodermal Activity (EDA) of Bi-cultural Visitors In Virtual Park Settings


en: face co2 nitrogen picea-abies soil forest

en: Water analyses

en: Analyses of water, especially soil solution

en: Collection points

en: Collection points under the trees

en: Collection of soil solution

en: Soil solution collected by suction cups

en: Gross N mineralisation and nitrification

en: Gross rates of N mineralisation and nitrification (15N dilution)

en: Soil C and N

en: Analyses of C, N and their stable isotopes in the soil, and soil pH

en: Resin bags

en: Ammonium, nitrate and their 15N in resin bags

en: Data obtained in the free-air CO2 enrichment (FACE) experiment at Hofstetten, NW Switzerland, between 2009 and 2016. This dataset contains analyses of the soil solution throughout the experiment, especially for nitrate, as well as different analyses done at the end of the experiment: ammonium and nitrate captured by ion-exchange resin bags and extracted from soil cores, gross N mineralisation and nitrification measured by isotope dilution.

face

display_name: face

forest

display_name: forest

nitrogen

display_name: nitrogen

picea-abies

display_name: picea-abies

soil

display_name: soil

en: Nitrogen availability under trees exposed to CO2 enrichment (FACE)


maintainer: Dirk Nikolaus Karger

en: precipitation climate climatology climate-layers free-climate-data temperature bioclim climate-change

en: CHELSA V1.2 (deprecated)

en: CHELSA Version 1.2 based on ERA-Interim forcings and CMIP5

format: GeoTIFF

en: CHELSA V2.1 (current)

en: CHELSA Version 2.1 based on ERA5 forcings and CMIP6

en: CHELSA Technical Documentation

en: Technical documentation document including the variable names, units, scaling, offset, and background.

en:
High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present the CHELSA (Climatologies at high resolution for the earth’s land surface areas) data of downscaled temperature and precipitation to a high resolution of 30 arc sec. The temperature algorithm is based on statistical downscaling of atmospheric temperatures. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height, with a subsequent bias correction.

CHELSA data published in EnviDat includes the deprecated version 1.2 (originally published under 10.5061/dryad.kd1d4). Please use the current 2.1 version.

Paper Citation:
 > _Karger DN. et al. Climatologies at high resolution for the earth’s land surface areas, Scientific Data, 4, 170122 (2017) [doi: 10.1038/sdata.2017.122](https://doi.org/10.1038/sdata.2017.122)._

bioclim

display_name: bioclim

climate

display_name: climate

climate-change

display_name: climate-change

climate-layers

display_name: climate-layers

climatology

display_name: climatology

free-climate-data

display_name: free-climate-data

precipitation

display_name: precipitation

temperature

display_name: temperature

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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


greenland-shrubs-and-microclimate

maintainer: Nathalie Chardon

title_for_slug: greenland-shrubs-and-microclimate

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

en: Kangerluarsunnguaq

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

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

 COLUMN ABBREVIATIONS

 Metadata for 90 m spatial grid

id: sampling zone and unique 90 m grid identifier

long: WGS84 longitude

lat: WGS84 latitude

x_stere: longitude in Polar Stereographic (EPSG 4326)

y_stere: latitude in Polar Stereographic (EPSG 4326)

date: sampling day.month.year

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

alcr: _Alnus crispa_

bena: _Betula nana_

dila: _Diapensia lapponica_

emni: _Empetrum nigrum_

hahy: _Harimanella hypoides_

lopr: _Loiseleuria procumbens_

oxpa: _Oxycoccus palustre_

phca: _Phyllodoce caerulea_

rhod: _Rhododendron_ sp.

saar: _Salix arctica_

sagl: _Salix glauca_

sahe: _Salix herbacea_

vaul: _Vaccinium uliginosum_

drin: _Dryas integrifolia_

thpr: _Thymus praecox_

 Habitat characteristics [% cover]

shrub: all shrub species

gram: graminoids

forb: forbs

bryo: bryophytes

lich: lichens

litt: litter

bare: bare ground

stone: stones (> 2 cm)

org: organic crust

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

alcr_c: _Alnus crispa_ cover

alcr_h: _Alnus crispa_ height

bena_c: _Betula nana_ cover

bena_h: _Betula nana_ height

dila_c: _Diapensia lapponica_ cover

dila_h: _Diapensia lapponica_ height

emni_c: _Empetrum nigrum_ cover

emni_h: _Empetrum nigrum_ height

hahy_c: _Harimanella hypoides_ cover

hahy_h: _Harimanella hypoides_ height

lopr_c: _Loiseleuria procumbens_ cover

lopr_h: _Loiseleuria procumbens_ height

oxpa_c: _Oxycoccus palustre_ cover

oxpa_h: _Oxycoccus palustre_ height

phca_c: _Phyllodoce caerulea_ cover

phca_height: _Phyllodoce caerulea_ height

rhod_c: _Rhododendron_ sp. cover

rhod_h: _Rhododendron_ sp. height

saar_c: _Salix arctica_ cover

saar_h: _Salix arctica_ height

sagl_c: _Salix glauca_ cover

sagl_h: _Salix glauca_ height

sahe_c: _Salix herbacea_ cover

sahe_h: _Salix herbacea_ height

vaul_c: _Vaccinium uliginosum_ cover

vaul_h: _Vaccinium uliginosum_ height

drin_c: _Dryas integrifolia_ cover

drin_h: _Dryas integrifolia_ height

 Community characteristics

rich_pa: shrub species richness at grid level

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

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

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

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

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

 Topography and terrain wetness (calculated from)

tcws: Tasseled Cap Wetness component (Sentinal images)

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

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

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

 Downscaled climate (time series 1984 – 2013)

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

precipmam: cumulative March-May precipitation [mm]

tempcont: yearly maximum – minimum temperature [ºC]

tempmin: yearly minimum temperature [ºC]

precipjja: cumulative June-August precipitation [mm]

tempjja: average maximum June-August temperature [ºC]

tempmax: yearly maximum temperature [ºC]

en: Nuup Kangerlua

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

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

 COLUMN ABBREVIATIONS

 Metadata for 90 m spatial grid

year: sampling year

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

long: WGS84 longitude

lat: WGS84 latitude

x_stere: longitude in Polar Stereographic (EPSG 4326)

y_stere: latitude in Polar Stereographic (EPSG 4326)

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

pa_bena: _Betula nana_

pa_sagl: _Salix glauca_

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

cov_bena: _Betula nana_ cover

cov_sagl: _Salix glauca_ cover

 Downscaled climate (time series 1984 – 2013)

tempjja: average maximum June-August temperature [ºC]

tempmax: yearly maximum temperature [ºC]

tempmin: yearly minimum temperature [ºC]

tempcont: yearly maximum – minimum temperature [ºC]

precipmam: cumulative March-May precipitation [mm]

precipjja: cumulative June-August precipitation [mm]

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

 Topography and terrain wetness (calculated from)

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

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

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

tcws: Tasseled Cap Wetness component (Sentinal images)

 Community characteristics

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

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

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

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

en: GitLab Repository

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

en:
 Study Aim

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

 Data Description

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

 Nuup Kangerlua (Godthåbsfjord)

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

 Kangerluarsunnguaq (Kobbefjord)

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

 Methods

 Field survey in Nuup Kangerlua

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

 Field survey in Kangerluarsunnguaq

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

 Biotic variables

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

 Climate variables

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

 Topography and terrain wetness indicator variables

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

 Computer code

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

 Third-party data

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

accessible-shrub-forest

display_name: accessible-shrub-forest

arctic

display_name: arctic

field-data

display_name: field-data

sdm

display_name: sdm

validation

display_name: validation

en: Greenland shrubs and microclimate


data-konzeptionelles-systemmodell

maintainer: Matthias Bürgi

title_for_slug: data-konzeptionelles-systemmodell

en: qualitative-research scenarios climate-change landscape-change policy-action

en:
This folder contains documents relevant to the project step Konzeptionelles Systemmodell. It specifically contains the following documents:

1. 0201_Konzeptionelles Systemmodell_20221027: This document is the final version of the Konzeptionelles Systemmodell. The underlying structure of the Konzeptionelles Systemmodell is explained in chapter 4.4 of the document 0202_Masterarbeit Elena Siegrist_UniBern_Herbstsemester 2022 also deposited in this folder. The document contains 3 sheets. The sheet "Bausteine" shows the different possible options for table columns of the type Bausteine (see chapter 4.4 of Masterarbeit), the sheet "Direkte Effekte" contains identified direct impact pathways and the sheet "Indirekte Effekte" contains identified indirect impact pathways.

2. 0202_Masterarbeit Elena Siegrist_UniBern_Herbstsemester 2022: This document is the Master Thesis of Elena Grace Siegrist submitted to and accepted by the University of Bern in the Autumn Semester 2022. Chapter 4.4 of the document explains the underlying structure of the Konzeptionelles Systemmodell.

3. 0203_Literaturverzeichnis_konzeptionelles Systemmodell_20221026: This document lists all literature and documents cited in the Konzeptionelle Systemmodell.

landscape-change

display_name: landscape-change

policy-action

display_name: policy-action

qualitative-research

display_name: qualitative-research

scenarios

display_name: scenarios

en: Data Konzeptionelles Systemmodell


en: natural-enemies cities hymennoptera performance-traits urbanization wasps life-history-traits hymenoptera bees urban-ecology cavity-nesting richness

en: Performance traits and life-history traits

en:
It contains the data of two performance traits (survival and parasitism) and two life-history traits (sex ratio and number of offspring per nest for the hosts) at the nest level.

We assessed performance traits, i.e. survival and parasitism, at the individual cell level. Specifically, for each cell within a nest, we noted whether the cell was alive or dead (survival), and whether it had a parasite or not (parasitism). Further, we calculated the parasitism rate as the number of cells with a parasite divided by the total number of cells. Concerning life-history traits, we calculated the total number of brood cells per nest as the total number of brood cells within a single nest, regardless of whether the larvae were dead, alive or parasitized. Finally, we used the probability that a femalehost emerged from a given cell as a proxy for the sex ratio. To do so, we first identified the sex of the individual hosts. Then, we noted it in a binary way to indicate whether the host was a female or not. We could identify the sex of all hatched hosts. Further, because CNBW first lay females and then males, on some occasions we could also identify the host sex in dead and parasitized cells, that is, when we had already identified the sex of the preceding and the following cells.

en: Information on the study sites

en: For each site, the IDs, city, coordinates, structure where the trapnest was attached and height is provided

en: Taxonomic metrics on the natural enemies

en: It contains the species richness and the abundance of natural enemies per site.

en: Predictors of the study

en: Taxonomic metrics on CNBW

en:
The data contains at the site level the species richness of cavity-nesting bees and wasps. It also contains site level estimates of the life-history traits and the performance traits.

We calculated species richness, abundance, and number of nests of the hatched hosts separately for bees and wasps. We calculated the abundance of hatched hosts as the number of brood cells where an individual emerged, i.e. the total number of brood cells minus the number of parasitized cells and the number of cells with no emergence for reasons other than parasitism.

City codes: An =Antwerp, Pa = Paris; Po = Poznan; Ta =Tartu; Zu= Zurich

en:
 Background
Urban ecosystems are associated with socio-ecological conditions that can filter and promote taxa. However, the strength of the effect of ecological filtering on biodiversity could vary among biotic and abiotic factors. Here, we provide the data used to investigate the effects of habitat amount, temperature, and host-enemy biotic interactions in shaping communities of cavity-nesting bees and wasps (CNBW) and their natural enemies. To do so, we installed trap-nests in 80 sites distributed along urban intensity gradients in 5 European cities (Antwerp, Paris, Poznan, Tartu and Zurich). We quantified the species richness and abundance of CNBW hosts and their natural enemies, as well as two performance traits (survival and parasitism) and two life-history traits (sex ratio and number of offspring per nest for the hosts).
The dataset contains:
* The taxonomic metrics on CNBW
* The taxonomic metrics on the natural enemies from CNBW
* The life-history traits and performance traits

bees

display_name: bees

cavity-nesting

display_name: cavity-nesting

cities

display_name: cities

hymennoptera

display_name: hymennoptera

hymenoptera

display_name: hymenoptera

life-history-traits

display_name: life-history-traits

natural-enemies

display_name: natural-enemies

performance-traits

display_name: performance-traits

richness

display_name: richness

urban-ecology

display_name: urban-ecology

urbanization

display_name: urbanization

wasps

display_name: wasps

en: Community structure, life-history traits and performance traits of urban cavity-nesting bees annd wasps