name: Fachstelle Open Government Data des Kantons Zürich

label: Repository: Formale Leitlinien zur Datenpublikation des Kantons Zürich

label: Fachausschuss für offene Behördendaten: Standards

de: Leitlinien des Kantons Zürich für einfach nutzbare Behördendaten (Version 1.0)

de: Version 1.0 in Kraft seit dem 16. Dezember 2021.

de: Handlungsleitende Normen, um offene Behördendaten ('Open Government Data', OGD) bereitzustellen, die einfach nutzbar sind. Der Fachausschuss Open Government Data des Kantons Zürich verwaltet die Normen gemäss der Maxime, den Bedürfnissen der Nutzenden zweckmässig und wirtschaftlich zu entsprechen. Die Fach- und Koordinationsstelle Open Government Data nimmt Feedback (Hinweise, Wünsche und Fragen) zu den Leitlinien entgegen. Entweder als 'Issue' im Repository (verlinkt unter 'Weitere Informationen') oder via E-Mail (verlinkt unter 'Kontakt'). Im Austausch mit den Nutzenden klärt sie deren Bedürfnisse, berät mit den Datenverwaltenden ('Data Custodians') mögliche Lösungen und schlägt diese vierteljährlich dem Fachausschuss zur Inkraftsetzung vor.


name: Baurekursgericht des Kantons Zürich, Gerichtskanzlei

de: gerichte bauvorhaben

state: active

type: dataset

de: Aktuelle Entscheide von öffentlichem Interesse

media_type: application/xml

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

format: XML

mimetype: application/xml

de: Aktuelle Leitentscheide

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

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

bauvorhaben

display_name: bauvorhaben

gerichte

display_name: gerichte

construction

fr: Construction, logement

de: Bau- und Wohnungswesen

en: Construction and housing

it: Costruzione e abitazione

territory

fr: Territoire et environnement

de: Raum und Umwelt

en: Territory and environment

it: Territorio e ambiente

fachstelle-ogd-kanton-zuerich

de: Fach- und Koordinationsstelle Open Government Data Kanton Zürich

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

political_level: canton

kanton-zuerich

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: ZH-Kt Fachstelle OGD - DCAT Harvester


gebaude-und-wohnungen-im-kanton-zurich

title_for_slug: gebaude-und-wohnungen-im-kanton-zurich

name: Amt für Raumentwicklung des Kantons Zürich, Fachstelle Datenlogistik, Koordinationsstelle GWR-ZH

de: heizung heizungsart wohnungsbestand wohnung wohnungen gebaeudeart gebaeude gemeinden strassen

label: Bezug tagesaktueller Daten des eidg. GWR über die Schnittstelle des Bundes

label: Merkmalsbeschreibung des eidg. Gebäude- und Wohnungsregisters

label: Vorbereiteter Code in R oder Python für jedes Dataset mit csv-Datei(en)

de: Wohnungen

media_type: application/octet-stream

de: Daten des eidg. GWR mit Bezug zu den einzelnen Wohnungen in einem Gebäude. Aus den Daten lassen sich beispielsweise Alter, Fläche, Lage im Gebäude und Ausstattungsangaben herauslesen. Die Merkmale entsprechen denen im Merkmalskatalogs des eidg. GWR (siehe Link unter "weitere Informationen" im Datensatzbeschrieb) und sind dort beschrieben. Leerwerte wurden in den Gemeinden nicht ausgefüllt, ihr Wert ist unbekannt.

format: CSV

mimetype: application/octet-stream

de: Gebäudeeingänge bzw. Adressen pro Gebäude

de: Daten des eidg. GWR mit Bezug zu den einzelnen Eingängen zu einem Gebäude. Adressen beziehen sich auf einzelne Gebäudeeingänge und sind deswegen pro Eingang in diesem File zu finden. Die Merkmale entsprechen denen im Merkmalskatalogs des eidg. GWR (siehe Link unter 'weitere Informationen' im Datensatzbeschrieb) und sind dort beschrieben. Leerwerte wurden in den Gemeinden nicht ausgefüllt, ihr Wert ist unbekannt.

de: Gebäude

de: Daten des eidg. GWR mit Bezug zu ganzen Gebäuden und ihrer Haustechnik. Aus den Daten lassen sich umfassende Angaben zum Gebäudebestand ablesen, beispielsweise zum Gebäudealter oder den Wärme- und Energiequellen. Die Merkmale entsprechen denen im Merkmalskatalogs des eidg. GWR (siehe Link unter "weitere Informationen" im Datensatzbeschrieb) und sind dort beschrieben. Leerwerte wurden in den Gemeinden nicht ausgefüllt, ihr Wert ist unbekannt. Für die einfachere Nutzung fügt die Fach- und Koordinationsstelle OGD vor der Publikation folgende Merkmale hinzu: Für das Merkmal 'Kanton' wird für alle Beobachtungen ZH abgefüllt. Das Merkmal 'Gemeindename' wird mittels BFS-Nummer entsprechend der Gemeindetabelle des Statistischen Amts ausgegeben. Das Merkmal 'Anzahl_Wohnungen' wird anhand der Ressource Wohnungen berechnet.

de: Gebäude und Wohnungen im Kanton Zürich gemäss eidgenössischem Gebäude- und Wohnungsregister (eidg. GWR). Es verzeichnet die Gebäude und jede Wohnung im Kanton umfassend  in Merkmalen gemäss Merkmalsbeschreibung (siehe Link). Die Ressourcen lassen sich über die EGID verknüpfen. Das eidg. GWR wird durch die Gemeinden des Kantons Zürich geführt, die kantonale Koordinationsstelle GWR-ZH begleitet und berät sie dabei. Die Koordinationsstelle GWR-ZH stellt die Daten alle 3 Monate zur Verfügung und werden zum Stichtag vollständig ersetzt. Ein Zeitverlauf ist in den Daten nicht ersichtlich. Datenquelle bildet das eidg. GWR des Bundesamts für Statistik (BFS). Die Daten bilden den Stand des eidg. GWR ab, wie er durch die Gemeinden erfasst wird. Abweichungen vom tatsächlichen Zustand der Gebäude und Wohnungen sind möglich und im Bereich Energiequellen (Solaranlagen) sowie Wärmequellen (Heizungen) wahrscheinlich. Hinweise auf Abweichungen senden Sie bitte direkt an die Gemeinde, auf deren Boden sich das Objekt befindet, respektive an Statistik Stadt Zürich für Daten auf dem Stadtgebiet Zürich.

gebaeude

display_name: gebaeude

gebaeudeart

display_name: gebaeudeart

gemeinden

display_name: gemeinden

heizung

display_name: heizung

heizungsart

display_name: heizungsart

strassen

display_name: strassen

wohnung

display_name: wohnung

wohnungen

display_name: wohnungen

wohnungsbestand

display_name: wohnungsbestand

de: Gebäude und Wohnungen im Kanton Zürich


de: kantonaleverwaltung kantonzuerich

label: Datenkatalog

de: Suchbegriffe 2022, 1. Quartal

media_type: text/csv

de: Maximal 1000 häufgste Suchbegriffe, die von Nutzenden im Quartal verwendet worden sind. Personennamen, Beleidigungen oder diskriminierenden Äusserungen wurden entfernt.

mimetype: text/csv

de: Suchbegriffe 2021, 4. Quartal

de: Suchbegriffe 2021, 3. Quartal

de: Suchbegriffe 2021, 2. Quartal

de: Nutzungsstatistiken der Metadatensätze von Verwaltungseinheiten und Organen des Kantons Zürich, die im Daten- und Publikationskatalogs des Kantons Zürich findbar sind. Verfügbar sind: von Nutzenden verwendete Suchbegriffe. Hinweise: Die Ressourcen zum Jahr 2020 beginnen ab dem Zeitpunkt der Einführung des neuen kantonalen Webauftritts per Anfang Juli 2020. Die Ressourcen zum Jahr 2021 umfassen alle vier Quartale. Die Ressourcen zum Jahr 2022 umfassen nur das erste Quartal. Ab April 2022 ist die Aktualisierung aufgrund der Einführung einer neuen Version des Katalogs pausiert.

kantonaleverwaltung

display_name: kantonaleverwaltung

kantonzuerich

display_name: kantonzuerich

national-economy

fr: Economie nationale

de: Volkswirtschaft

en: National economy

it: Economia

de: Web Analytics des Daten- und Publikationskatalogs des Kantons Zürich ab April 2021 bis März 2022


maintainer: Direktion der Justiz und des Innern, Fachstelle Kultur

de: kulturfinanzierung kultur covid-19 corona

label: Medienmitteilung (04.03.2021)

de: Corona-Hilfen im Kulturbereich im Kanton Zürich

de: Die Direktion der Justiz und des Innern des Kantons Zürich, Fachstelle Kultur, veröffentlicht ab dem 04.03.2021 anonymisierte Daten zu den nachgefragten, beschlossenen und ausbezahlten Ausfallentschädigungen. Die Zeitreihe beginnt am 06.04.2020 mit dem ersten eingegangenen Gesuch. Spaltenüberschriften: 'Kategorie' = Antragstellende Kategorie ('Kulturschaffende', 'Kulturunternehmen gemeinnützig' oder 'Kulturunternehmen gewinnorientiert'); 'Sparte' = Kultursparte ('Bereichsübergreifend', 'Bildende Kunst', 'Design', 'Film', 'Kino', 'Klubs und Konzertlokale', 'Literatur', 'Museen', 'Musik' oder 'Tanz/Theater'); 'Nachgefragt' = Nachgefragter Betrag in CHF (gerundet auf 100 CHF); 'Beschluss' = Beschlossener Betrag in CHF (gerundet auf 100 CHF, 'NA' bei Ablehnung); 'Status' = Status des Antrags ('Ablehnung', 'Ausbezahlt' oder 'Saldo offen'); 'Eingangsdatum' = Datum des Eingangs des Gesuchs; 'Beschlussdatum' = Datum des Beschlusses über das Gesuch; 'ID' = anonymisierter Identifikator der antragstellenden natürlichen oder juristischen Person.

format: XLS

de: Um die wirtschaftlichen Auswirkungen der Massnahmen zur Pandemiebekämpfung abzufedern und die kulturelle Vielfalt zu erhalten, können Kulturschaffende, nicht-gewinnorientierte sowie gewinnorientierte Kulturunternehmen finanzielle Unterstützung anfordern. Bei den Ausfallentschädigungen handelt es sich um nicht-rückzahlbare Beiträge für im Rahmen der COVID-19-Pandemie abgesagte Veranstaltungen oder Projekte und Betriebsschliessungen. Die Daten werden von der Direktion der Justiz und des Innern des Kantons Zürich, Fachstelle Kultur, ab dem 04.03.2021 in unregelmässiger Frequenz aktualisiert, sofern weitere Ausfallentschädigungsgesuche bewilligt werden können. Nachgefragte wie beschlossene Beträge sind gerundet auf 100 CHF; antragstellende natürliche oder juristische Personen sind anonymisiert.

corona

display_name: corona

kultur

display_name: kultur

kulturfinanzierung

display_name: kulturfinanzierung

culture

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

de: Kultur, Medien, Informationsgesellschaft, Sport

en: Culture, media, information society, sport

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

fachstelle-ogd-kanton-zuerich

de: Fach- und Koordinationsstelle Open Government Data Kanton Zürich

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


maintainer: Fachstelle Open Government Data des Kantons Zürich

de: gesundheit krankheiten sars-cov-2 covid-19 kantone schweiz corona

label: Update Status Dashboard: Aktuelle SARS-CoV-2 Fallzahlen der Kantone und Fürstentum Liechtenstein

label: Readme: SARS-CoV-2 Cases communicated by Swiss Cantons and Principality of Liechtenstein

de: COVID_19 open government data reported by Swiss Cantons and Principality of Liechtenstein from January 5, 2020

de: Case numbers include persons tested in Switzerland per Canton (26 Cantons) plus Principality of Liechtenstein. For definition of variables see Readme listed below "Further information / Weitere Informationen".

de: Data is generated and validated daily using manual and automated procedures. Note that we only publish data that are reported by the Swiss Cantons and the Principality of Liechtenstein. Thus, gaps result if Swiss Cantons or the Principality of Liechtenstein do not report data for a specific date. Updates of cantonal case numbers during weekends are infrequent.

gesundheit

display_name: gesundheit

kantone

display_name: kantone

krankheiten

display_name: krankheiten

schweiz

display_name: schweiz

health

fr: Santé

de: Gesundheit

en: Health

it: Sanità


de: lebensmittel wasser trinkwasser gemeinden pestizide wasserversorgung schwellenwert pflanzenschutzmittel gesundheit verunreinigung

label: Trinkwasserqualität der Wohngemeinden (Schweizerischer Verein des Gas- und Wasserfaches SVGW)

de: Netzproben der Trinkwasserqualität

de: Konzentrationsbereiche zur Trinkwasserqualität für verschiedene Analyten aus den im Auftrag der Wasserversorgungen durchgeführten ergänzenden Selbstkontrolluntersuchungen in den Gemeinden des Kantons Zürich - im regulären Trinkwassernetz (ohne Notversorgung, Laufbrunnen und Ressourcen). Die Konzentrationsbereiche zur Trinkwasserqualität aus den Kontrolluntersuchungen durchgeführt durch das Kantonale Labor Kanton Zürich sind im Datensatz "Qualität des Trinkwassers im Kanton Zürich ab 2020" der Gesundheitsdirektion enthalten (siehe Verwandte Datensätze).

lebensmittel

display_name: lebensmittel

pestizide

display_name: pestizide

pflanzenschutzmittel

display_name: pflanzenschutzmittel

schwellenwert

display_name: schwellenwert

trinkwasser

display_name: trinkwasser

verunreinigung

display_name: verunreinigung

wasser

display_name: wasser

wasserversorgung

display_name: wasserversorgung

agriculture

fr: Agriculture, sylviculture

de: Land- und Forstwirtschaft

en: Agriculture, forestry

it: Agricoltura, selvicoltura

territory

de: Raum und Umwelt

de: Trinkwasserqualität im Kanton Zürich nach Gemeinden und Verteilzonen (Selbstkontrolle der Wasserversorgungen)


designprinzipien-fur-behorden

title_for_slug: designprinzipien-fur-behorden

label: UK government's design principles

de: 2 Mach weniger

media_type: application/pdf

format: PDF

mimetype: application/pdf

de: 5 Iteriere. Dann iteriere erneut

de: 8 Erstelle digitale Dienste, nicht Websites

de: 10 Mach die Sachen offen: Es macht die Sachen besser

de: 9 Sei konsistent, nicht gleichförmig

de: 1 Beginne mit den Bedürfnissen der Nutzenden

de: 3 Gestalte mit Daten

de: 6 Es ist für alle

de: 4 Leiste die harte Arbeit, die Sachen einfach zu machen

de: 7 Verstehe den Kontext

de: 2012 hat der britische Government Digital Service (GDS) seine Designprinzipien für Behörden veröffentlicht. 2019 haben Designerinnen und Designer der GDS-Community für nutzerzentriertes Design für jedes Prinzip ein neues Poster gestaltet. Für unsere eigene Arbeit sind die Prinzipien so zentral, dass wir sie auf Deutsch übersetzt haben. Die Gestaltung unserer britischen Kolleginnen und Kollegen haben wir weitgehend übernommen. Unsere Poster stellen wir hier unter einer Creative Commons CC-BY Lizenz zur freien Wiederverwendung zum Download bereit. Auf die Originale des GDS verweisen wir unter «Weitere Informationen». Über unsere praktischen Erfahrungen mit der Anwendung der Prinzipien berichten wir laufend ebenda. Wir freuen uns auf den Erfahrungsaustausch mit anderen Anwenderinnen und Anwendern.

national-economy

de: Volkswirtschaft

de: Designprinzipien für Behörden


label: DCAT-AP CH – The Swiss Application Profile for Data Portals and Catalogues

de: API-Endpoint für Datensätze im Metadatenkatalog des Kantons Zürich

media_type: application/json

de: Enthält alle aktuell im Metadatenkatalog des Kantons Zürich enthaltenen Datensätze mit ihren Metadaten: (1) häufig nachgefragte Excel-Tabellen, (2) Indikatoren aus dem Gemeindeporträt, (3) Publikationen des Statistischen Amts sowie (4) offene Behördendaten des Kantons Zürich (ausgenommen offene Geodaten).

format: JSON

mimetype: application/json

de: Enthält alle aktuell im Metadatenkatalog des Kantons Zürich enthaltenen Datensätze mit ihren Metadaten. Folgt dem Metadaten-Standard 'DCAT-AP CH v1' (eCH-0200).

fachstelle-ogd-kanton-zuerich

de: Fach- und Koordinationsstelle Open Government Data Kanton Zürich

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

de: Web Service des Metadatenkatalogs des Kantons Zürich


de: vereine handelsregister stiftungen unternehmen gmbh genossenschaften einzelfirmen unternehmensdaten shab firmen wirtschaft ehra zefix

label: Zentraler Firmenindex Zefix (Eidgenössisches Amt für das Handelsregister)

label: Zentraler Firmenindex Zefix (Linked Data Service LINDAS)

de: Bestand im Handelsregister eingetragener Rechtseinheiten mit Sitz im Kanton Zürich, täglich aktualisiert

media_type: application/ld+json

de: SPARQL-Suchanfrage, um die einmal täglich aktualisierten Daten im RDF-Format via den Linked Data Service LINDAS aus dem zentralen Firmenindex Zefix® abzufragen. Zum Download der Daten (im Format CSV) auf das Download-Icon in der Menu-Leiste oberhalb der Resultate-Tabelle klicken. Die Informationen aller Entitäten beginnend mit "https://register.ld.admin.ch/zefix/company/[ID]..." können im Format JSON-LD bezogen werden: 'legalName' = Name der Rechtseinheit; 'description' = Zweck der Rechtseinheit.

format: JSON-LD

mimetype: application/ld+json

de: Täglich aktualisierte Informationen zu den im Firmenzentralregister des Eidgenössischen Amts für das Handelsregister eingetragenen Rechtseinheiten (Firma / Name) mit Sitz (Domiziladresse) im Kanton Zürich.

ehra

display_name: ehra

einzelfirmen

display_name: einzelfirmen

firmen

display_name: firmen

genossenschaften

display_name: genossenschaften

gmbh

display_name: gmbh

handelsregister

display_name: handelsregister

shab

display_name: shab

stiftungen

display_name: stiftungen

unternehmen

display_name: unternehmen

unternehmensdaten

display_name: unternehmensdaten

vereine

display_name: vereine

wirtschaft

display_name: wirtschaft

zefix

display_name: zefix

industry

fr: Industrie, services

de: Industrie, Dienstleistungen

en: Industry and services

it: Industria, servizi

work

fr: Travail, rémunération

de: Arbeit, Erwerb

en: Work and income

it: Lavoro e reddito

de: Bestandsdaten zum Kanton Zürich aus dem Zentralen Firmenindex (Zefix) des Bundes


en: soil-water-balance growing-degree-days temperature growing-season-length precipitation relative-humidity solar-radiation

en: CHELSA-BIOCLIM+ Technical Documentation

media_type: PDF

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

en: CHELSA-BIOCLIM+

media_type: geotiff

en: Global extented set of bioclimatic variables at different temporal (e.g. monthly, annual, long term normals) resolution in Geotiff format.

format: GeoTIFF

en: A multitude of physical and biological processes on which ecosystems and human societies depend are governed by climatic conditions. Understanding how these processes are altered by climate change is central to mitigation efforts. Based on mechanistically downscaled climate data, we developed a set of climate-related variables at yet unprecedented spatiotemporal detail as a basis for environmental and ecological analyses. We created gridded data for near-surface relative humidity (hurs), cloud area fraction (clt), near-surface wind speed (sfcWind), vapour pressure deficit (vpd), surface downwelling shortwave radiation (rsds), potential evapotranspiration (pet), climate moisture index (cmi), and site water balance (swb), at a monthly temporal and 30 arcsec spatial resolution globally starting 1980 until 2018. At the same spatial resolution, we further estimated climatological normals of frost change frequency (fcf), snow cover days (scd), potential net primary productivity (npp), growing degree days (gdd), and growing season characteristics for the periods 1981-2010, 2011-2040, 2041-2070, and 2071-2100, considering three shared socioeconomic pathways (SSP126, SSP370, SSP585) and five Earth system models. Time-series variables showed high accuracy when validated against observations from meteorological stations. Climatological normals were also highly correlated to observations although some variables showed notable biases, e.g., snow cover days (scd). Together, the data sets presented here allow improving our understanding of patterns and processes that are governed by climate, including the impact of recent and future climate changes on the world’s ecosystems and associated services to societies.

growing-degree-days

display_name: growing-degree-days

growing-season-length

display_name: growing-season-length

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

soil-water-balance

display_name: soil-water-balance

solar-radiation

display_name: solar-radiation

temperature

display_name: temperature

education

fr: Education, science

de: Bildung, Wissenschaft

en: Education and science

it: Formazione e scienza

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

value: Envidat Harvester

en: CHELSA-BIOCLIM+ A novel set of global climate-related predictors at kilometre-resolution


de: Verzeichnis der Personenstammdaten ZH - Merkmale zu Registern und Systemen

de: Abkürzungen und Definitionen sind in der Ressource "Verzeichnis der Personenstammdaten ZH - Abkürzungen und Definitionen" erklärt.

de: Verzeichnis der Personenstammdaten ZH - Merkmale zu Verwaltungseinheiten

de: Im Rahmen des Impulsprojekts 3.1 "Strategie Datenmanagement und Data-Governance" wurde ein - nicht abschliessendes - Verzeichnis der Personenstammdaten erarbeitet, die von Einheiten der zentralen Verwaltung des Kantons Zürich zu ihrer fachspezifischen Aufgabenerfüllung verwaltet und/oder genutzt werden. Das Verzeichnis ist eine Grundlage für die Einführung eines koordinierten Stammdatenmanagements. Es wird in maschinenlesbarer Form zur Verfügung gestellt, um zweckmässige Anwendungen zu ermöglichen, mit denen es verwaltet und/oder genutzt werden kann. Das Verzeichnis wird unregelmässig aktualisiert. Die Verwaltungseinheiten sind für ihre Angaben verantwortlich. Bei Fragen, Korrekturen und Ergänzungen wenden Sie sich bitte an die Kontaktstelle.

direktionen

display_name: direktionen

kantonaleverwaltung

display_name: kantonaleverwaltung

kantonzuerich

display_name: kantonzuerich

national-economy

de: Volkswirtschaft

de: Verzeichnis der Personenstammdaten der zentralen Verwaltung des Kantons Zürich


maintainer: EnviDat Support

en: urbanization modeling statistical-model land-use switzerland land-use-change landscape-services

en: Scripts for Assessing potential landscape service trade-offs driven by urbanization in Switzerland

media_type: application/zip

en:
Scripts ordered by their main purpose within sequential folders in this ReadMe marked with

Within the folders the capital letters refer to the parallel or sequential use of the scripts; scripts here in ReadMe are marked with

 00_Function Scripts
 Functions.r
Script containing several functions for analyzing statistical models: cohen.kappa, meva.table, D2, max-Tss, plot-TSS

 Service_functions.R
Script containing functions for calculating all services and returning sums (and median for bd and rec) as well as rasters

 01_Data Preparation
 A_AS_raster_preparation.R
Preparation of the land use rasters for the years 1985, 1997, and 2009 based on the original Arealstatistik (AS/ASCH); Assigning our categories and assining new values to all "construction sites" for calibrating the models
For model projection starting point (2009) and for service calculations (all ASCH years) the "other" categories were assigned to the land use categories based on dominant neighbor, so that there were no empty cells
Creation of water & mountainous area raster that stayed stable throughout the projections
Creation of MFH raster (2009), to have as base for all new projection years, as this category never becames any other

 B_Factors_preparation.R
Preparation of the factor rasters that were used to calculate service values per cell (see Table 2): LWfactor, Transportation, Vol, Rivers, Lakes, slope, TWW, Hazard_risk

 B_Predictors_preparation.R
Preparation of the raster of the predictors used for calibrating (ASCH years) and projecting (stable) the transition models that do not depend on the land use categories: Temperature (ASCH years), solar radiation (stable), Public transportation access (stable), Elevation (stable), Workers density (ASCH years), Population change (ASCH transitions)

 B_Regions_preparation.R
Preparation of bioregions as raster to be used to mask asxxxx later to calculated services and land use area just for one region

 C_Pred_proj.R
Projecting the dynamic predictors into the future for projecting the land use transitions with the model: Linear extrapolations for workers densities and population; more detailed extrapolation of temperature (based on data for all stations that matched between the temp table we used for the calibration period and the projections from CS2M, and then making a spatial interpolation for all years based on the interpolation of temperature for all years per station)

 02_Model calibration
 A_Bioreg_pred_corr.R
Calculating correlation between the predictors for each bioregion and land use category, Output are those with too high correlation so that one predictor can be removed from transitions were both predictors occur

 A_trans_numbers_bioreg.R
Calculate transition numbers for each bioregion to decide which transitions to primarily consider

 B_Bioreg_ext_eval.R
Calculating TSS based on the external evaluation for the bioregion transitions that happened often enough to try to model them (calibration data: 85-97, evaluation data: 97-09)

 B_Bioreg_int_eval.R
Calculating TSS based on the internal evaluation for the bioregion transitions that happened often enough to try to model them (calibration data: 70%, evaluation data: 30%)

 C_Bioreg_TSS_analysis.R
Extracting TSS numbers from internal and external evaluation, calculating mean and median for each transition for each region for each evaluation method and saving as csv per region to be able to select only the transitions with high enough model performance

 D_Bioreg_Models_for_prediction.R
Calibration of the models that were then used to project land use transitions into the future; calibrated separately for each bioregion

 E_Residuals_trans_models.R
Calculation of Moran's I statistics to analyze the residuals of the land use transition models

 03_Projections
 A_Bioreg_Projections_run.R
Master script for: Extrapolaitng into the future with the regional models and then assembling for each time step to calculate new AS predictors; Several Outputs: txt file to test for NAs in Predictors, txt file with random seed number for sampling, txt file with times for calculation steps (check progress of script while running), csv sheet with projected transition frequencies, tif-rasters for every region and time step, pdf with raster plot for every region and time step

 04_Service calculation
 A_Bioreg_as_services.R
Calculate services per bioregion for years 1985, 1997, 2009

 A_Service_calculation_bioregions.R
Extracting service numbers(sums) per bioregions for all projection runs and storing them in a list of matrixes to be available for further plotting; can be run for swiss wide projections or regional ones

 05_Analysis
 A_Bioreg_ASCH_development.R
Extracting the occurrence of each category per region for all runs (first part), then for ASCH years, and then plotting them nicely; only works for regional projections

 A_Bioreg_Predictors_importanceD2.R
Calculating D2 for each transition and each considered predictor from bivariate models to get value of importance

 B_Bioreg_Pred_imp_analysis.R
Using calculated D2 to sort Predictors according to their importance for each transition

 A_Bioreg_Spatial_results_analysis.R
Plotting randomly sampled runs for ASCH and services to analyses their spatial development

 A_Bioreg_Transition_numbers_check_v1.R
Analyzing the frequency for all transitions for all regions and producing the point plots for each transition separately

 A_Bioreg_Transition_numbers_check_v2.R
Analyzing percentage and numbers of all transitions but plotting them per region and originating ASCH category

 B_Transition_Analysis.R
Further Analysis of transition numbers

 A_Service_analysis.R
Plotting of the service development for bioregions in several ways for analyzing

format: ZIP

mimetype: application/zip

en: Data for Assessing potential landscape service trade-offs driven by urbanization in Switzerland

en:
The provided folder structure follows the set up of the scripts.
Data is only provided when it has been fully produced by us, otherwise dummies are given.
For data that will be produced by one of the scripts, no dummies are given.
The folders may contain sub-folders, which would be filled if the scripts were all run

 00_Analysis_Data
Data produced throughout the process of the calibration and projection that is later needed for analysis

 01_Raw_Data
Primary data from external sources that were used to compile the rasters and numbers used during the calibration and projection of landscape transitions as well as service calculation

 02_Prepared_Data
Data that will be prepared for further use by scripts are put in here
Additionally it contains the parameter sheets for the transition predictors that we compiled

 03_Model_Data
 Model outputs
Folder were the projection models statistics will be saved
 Models
Folder into which the actual transition models will be saved
 Residuals
Folder into which data on models residuals will be saved for future use

 04_Projected_Data
The output of the projections will be saved into this folder

 05_Service_Data
Any service calculations will be saved in here

 06_Results_Analysis
Any Analysis plots and tables as well as intermediate data will be saved here in the respective sub-folder

en:
Data and scripts of publication:

Madleina Gerecke, Oskar Hagen, Janine Bolliger, Anna M. Hersperger, Felix Kienast, Bronwyn Price, Loïc Pellissier (2019) Assessing potential landscape service trade-offs driven by urbanization in Switzerland. Palgrave communications.

Contains land use projections for Switzerland and scripts and data for these projections as well as the calculation of landscape services.

Data Folder:
Contains sub-folder with the data necessary for this study (provided were no copyright issues, otherwise placeholders with descriptions), and folders where produced data may be stored

Scripts Folder:
Contains scripts organized into subfolders depending on their purpose

Note: Some abbreviations within the scripts and data are derived from German words and not English.

land-use

display_name: land-use

land-use-change

display_name: land-use-change

landscape-services

display_name: landscape-services

modeling

display_name: modeling

statistical-model

display_name: statistical-model

switzerland

display_name: switzerland

urbanization

display_name: urbanization

en: Land use projections and services for Switzerland


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

en: Dataset

media_type: ZIP

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

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

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

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

lee-side-flow-field

display_name: lee-side-flow-field

lidar

display_name: lidar

preferential-deposition

display_name: preferential-deposition

tls

display_name: tls

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


maintainer: Christoph Marty

en: Graubunden input

media_type: application/x-tar

en: Grid input data for the 'Graubunden' CH2014 simulation.

format: SERVICE

mimetype: application/x-tar

en: Aare input

en: Grid input data for the 'Aare' CH2014 simulation.

en: Datasets are grouped under experiment "Aare" and "Graubunden".Data is also made available in netCDF format. A range of tools are available to [read netCDF](http://www.unidata.ucar.edu/software/netcdf/software.html).

en: CH2014 REPORT

en:
CH2014-Impacts: Toward Quantitative Scenarios of Climate Change Impacts in Switzerland

The CH2014-Impacts report presents the results of a concerted effort of 15 Swiss research institutions to describe impacts of climate change in Switzerland quantitatively. This dataset contributes to the Crosphere/Snowcover section.

en:
 Overview

The CH2014-Impacts initiative is a concerted national effort to describe impacts of climate change in Switzerland quantitatively, drawing on the scientific resources available in Switzerland today. The initiative links the recently developed Swiss Climate Change Scenarios CH2011 with an evolving base of quantitative impact models. The use of a common climate data set across disciplines and research groups sets a high standard of consistency and comparability of results. Impact studies explore the wide range of climatic changes in temperature and precipitation projected in CH2011 for the 21st century, which vary with the assumed global level of greenhouse gases, the time horizon, the underlying climate model, and the geographical region within Switzerland. The differences among climate projections are considered using three greenhouse gas scenarios, three future time periods in the 21st century, and three climate uncertainty levels (Figure 1). Impacts are shown with respect to the reference period 1980-2009 of CH2011, and add to any impacts that have already emerged as a result of earlier climate change.

 Experimental Setup

Future snow cover changes are simulated with the physics-based model Alpine3D (Lehning et al., 2006). It is applied to two regions: The canton of Graubünden and the Aare catchment. These domains are modeled with a Digital Elevation Model (DEM) with a resolution of 200 m × 200 m. This defines the simulation grid that has to be filled with land cover data and downscaled meteorological input data for each cell for the time period of interest at hourly resolution. The reference data set consists of automatic weather station data. All meteorological input parameters are spatially interpolated to the simulation grid. The reference period comprises only thirteen years (1999–2012), because the number of available high elevation weather stations for earlier times is not sufficient to achieve unbiased distribution of the observations with elevation. The model uses projected temperature and precipitation changes for all greenhouse gas scenarios (A1B, A2, and RCP3PD) and CH2011 time periods (2035, 2060, and 2085).

 Data

Snow cover changes are projected to be relatively small in the near term (2035) (Figure 5.1 top), in particular at higher elevations above 2000 m asl. As shown by Bavay et al. (2013) the spread in projected snow cover for this period is greater between different climate model chains (Chapter 3) than between the reference period and the model chain exhibiting the most moderate change. In the 2085 period much larger changes with the potential to fundamentally transform the snow dominated alpine area become apparent (Figure 5.1 bottom). These changes include a shortening of the snow season by 5–9 weeks for the A1B scenario. This is roughly equivalent to an elevation shift of 400–800 m. The slight increase of winter precipitation and therefore snow fall projected in the CH2011 scenarios (with high associated uncertainty) can no longer compensate for the effect of increasing winter temperatures even at high elevations. In terms of Snow Water Equivalents (SWE), the projected reduction is up to two thirds toward the end of the century (2085). A continuous snow cover will be restricted to a shorter time period and/or to regions at increasingly high elevation. In Bern, for example, the number of days per year with at least 5 cm snow depth will decrease by 90% from now 20 days to only 2 days on average.

climate-change

display_name: climate-change

snow-depth

display_name: snow-depth

snow-water-equivalent

display_name: snow-water-equivalent

en: Alpine3D simulations of future climate scenarios CH2014


maintainer: Anita C. Risch

en: soil-biology global nutrient-network soil-chemistry worldwide grassland mineralization soil

en:
site.code: Site code

Continent: Continent

sampleID: Sample ID

nn.latitude: Lattitude

nn.longitude: Longitude

bioclim.T.ann: Mean annual temperature (°C)

bioclim.T.var: Temperature variability (°C)

bioclim.T.q.wet: Temperature of the wettest quarter (°C)

bioclim.T.q.dry: Temperature of the driest quarter (°C)

bioclim.T.q.warm: Temperature of the warmest quarter (°C)

bioclim.P.ann: Mean annual preciptiation (mm)

bioclim.P.q.wet: Precipitation of the wettest quarter (mm)

ch.pH: pH

ch.WSK.vol: Water holding capacity (vol%)

ch.bulkdensity.gcm3: Bulk density (g cm-3)

ch.porespace: Pore space (vol%)

ch.sand.perc: Soil texture: sand (%)

ch.silt.perc: Soil texture: silt (%)

ch.clay.perc: Soil texture: clay (%)

ch.NO3start.mgg: NO3 at the beginning of the incubation (mgkg)

ch.Ntot.perc: Total soil N (%)

ch.Ctot.perc: Total soil C (%)

ch.Corg.perc: Soil organic C (%)

ch.netNminfield.mgkgd: Realised soil net N mineralisation (mg kg-1 d-1)

ch.netNminlab.mgkgd: Potential soil ne N mineralisation (mg kg-1 d-1)

archaea.cpg.log: Ammonia oxidising archaea (archaeal amoA gene copies g-1 soil)

bacteria.cpg.log: Ammonia oxidising bacteria (bacterial amoA gene copies g-1 soil)

micr.biom: Microbial biomass (ug g-1 soil)

ch.CtoN: C to N ratio

ch.netNnitriffield.mgkgd.sqrt: Realised soil net nitrification, transformed (mg kg-1 d-1)

ch.netNammoniffield.mgkgd.sqrt: Realised soil net ammonification, transformed (mg kg-1 d-1)

ch.netNnitriflab.mgkgd.sqrt: Potential soil net nitrification, transformed (mg kg-1 d-1)

ch.netNammoniflab.mgkgd.sqrt: Potential soil net ammonifiation, transformed (mg kg-1 d-1)

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

Paper Citation:

Risch, A. C.; Zimmermann, S.; Ochoa-Hueso, R.; Schütz, M.; Frey, B.; Firn, J. L.; Fay, P. A.; Hagedorn, F.; Borer, E. T.; Seabloom, E. W.; et al. Soil net nitrogen mineralisation across global grasslands. Nat. Commun. 2019, 10 (1), 4981 (10 pp.). doi.org/10.1038/s41467-019-12948-2

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

We conducted coordinated measurements of realised and potential soil net Nmin, and assessed water holding capacity, bulk density, C and N content, texture, pH, pore space, microbial biomass, and archaeal (AOA) and bacterial (AOB) ammonia oxidiser abundance using identical materials and methods across 30 grasslands on six continents. The sites covered a globally relevant range of climatic and edaphic conditions. Climate data was obtained from worldclim - Global climate data https://www.worldclim.org/

global

display_name: global

grassland

display_name: grassland

mineralization

display_name: mineralization

nutrient-network

display_name: nutrient-network

soil

display_name: soil

soil-biology

display_name: soil-biology

soil-chemistry

display_name: soil-chemistry

worldwide

display_name: worldwide

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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


maintainer: Oberjugendanwaltschaft des Kantons Zürich

de: kantonzuerich verurteilungen strafdelikte jugendkriminalitaet

label: Zahlen & Fakten zum Jugendstrafrecht

de: Finanzieller Aufwand für Schutzmassnahmen

de: Anzahl Delikte bei den Verurteilungen

de: Spaltenüberschriften: 'uebrige StGB'=übrige Delikte gemäss Schweizerischem Strafgesetzbuch; 'SVG'=Strassenverkehrsgesetz; 'BetmG'=Betäubungsmittelgesetz

de: Anzahl Personen mit mindestens einer Untersuchungshaft

de: Anzahl verzeigte Jugendliche

de: Anzahl verzeigte Jugendliche insgesamt und wegen Gewaltdelikten seit 2008.

de: Anzahl Verurteilungen nach Geschlecht

de: Alter bei Eröffnung der Strafuntersuchung

de: Art der Sanktion

de: Anzahl Jugendliche in einer Massnahme per Jahresende

de: 'per Jahresende' bedeutet am 31. Dezember des entsprechenden Jahres

de: Begehen Jugendliche zwischen dem 10. und 18. Lebensjahr ein Delikt, gilt für sie das Jugendstrafrecht. Die Jugendanwaltschaften des Kantons Zürich untersuchen und beurteilen sämtliche Straftaten von Jugendlichen, die im Kanton Zürich wohnhaft sind. Ausserdem sind sie für den Vollzug jugendstrafrechtlicher Schutzmassnahmen und Strafen verantwortlich.

jugendkriminalitaet

display_name: jugendkriminalitaet

kantonzuerich

display_name: kantonzuerich

strafdelikte

display_name: strafdelikte

verurteilungen

display_name: verurteilungen

crime

fr: Criminalité, droit pénal

de: Kriminalität, Strafrecht

en: Crime, criminal justice

it: Criminalità, diritto penale

oberjugendanwaltschaft-kanton-zuerich

de: Oberjugendanwaltschaft Kanton Zürich

de: Die Jugendstrafrechtspflege des Kantons Zürich besteht aus der Oberjugendanwaltschaft, fünf Jugendanwaltschaften sowie der Stelle für Mediation. Die Jugendanwaltschaften untersuchen und beurteilen sämtliche von Jugendlichen zwischen zehn und achtzehn Jahren begangenen Straftaten und sind für den Vollzug jugendstrafrechtlicher Schutzmassnahmen und Strafen verantwortlich.

value: ZH-Kt Oberjugendanwaltschaft - DCAT Harvester

de: Statistik der Jugendstrafrechtspflege des Kantons Zürich


en: cosmo-wrf complex-terrain precipitation-variability snow-precipitation orographic-precipitation

en: Snow depth maps over Dischma valley for 26 January 2016 and 9 March 2016 and snow depth differences between the two dates.

en: StationData

en:
The data presented here corresponds to the publication "Spatial variability in snow precipitation and accumulation in COSMO-WRF simulations and radar estimations over complex terrain" (Gerber et al., 2018a), which investigates the precipitation variability of snow precipitation in the central northern part of the Grisons (CH) and the publication "The importance of near-surface winter precipitation processes in complex alpine terrain" (Gerber et al., 2018b). The dataset contains:

* WRFsimulations: WRF simulation output for simulations with 4x (14x) terrain smoothing with an output timestep of 30 min/5 min and horizontal grid spacings of 1350 m, 450 m, 150 m and 50 m (currently: data available upon request).
* StationData: Meteorological station data of 18 meteorological stations in the central northern part of the Grisons with 30 minute resolution for the period 1 January 2016 till 1 May 2016.
* ADS80data: Photogrammetrically determined snow depth distribution data over the Dischma valley for the 26 January 2016 and 9 March 2016. Snow heights are corrected for buildings, vegetation (> 1m), outliers, and pixles, which are obivously snow-free on the pictures (Bühler et al., 2015). In addition the snow depth differences (snow depth on 9 March 2016 minus snow depth on 26 January 2016) are provided.

For more details about the simulation and observation data, see Gerber et al., 2018 and Gerber and Sharma (2018).

Publications:

Bühler, Y., Marty, M., Egli, L., Veitinger, J., Jonas, T., Thee, P., and Ginzler, C.: Snow depth mapping in high-alpine catchments using digital photogrammetry. Cryosphere, 9, 229–243, doi:10.5194/tc-9-229-2015, 2015.

Gerber, F., Besic, N., Sharma, V., Mott, R., Daniels, M., Gabella, M., Berne, A., Germann, U., and Lehning, M.: Spatial variability in snow precipitation and accumulation in COSMO-WRF simulations and radar estimations over complex terrain, The Cryosphere, 12, 3137–3160, doi:10.5194/tc-12-3137-2018, 2018.

Gerber, F., Mott, R. and Lehning, M.: The importance of near-surface winter precipitation processes in complex alpine terrain, Journal of Hydrometeorology, accepted, 2018.

Gerber, F., and Sharma, V.: Running COSMO-WRF on very-high resolution over complex terrain. Laboratory of Cryospheric Sciences CRYOS, École Polytechnique Fédérale de Lausanne EPFL, Lausanne, Switzerland. doi:10.16904/envidat.35, 2018.

complex-terrain

display_name: complex-terrain

cosmo-wrf

display_name: cosmo-wrf

orographic-precipitation

display_name: orographic-precipitation

precipitation-variability

display_name: precipitation-variability

snow-precipitation

display_name: snow-precipitation

education

de: Bildung, Wissenschaft

envidat

de: EnviDat: das Umwelt-Datenportal

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

en: DISCHMEX - High-resolution WRF simulations in complex alpine terrain and station measurements


maintainer: Thorsten Bartels-Rausch

en: laboratory-study sea-salt-aerosol cryosphere snow antartic polar arctic

en: Experimental results of uptake coefficient vs. temperature and ozone concentration

en:
Data to Figure 3: Measured uptake coefficients as a function of temperature at 5E12 molecules per cm3 ozone concentration.

Data to Figure 6: Measured uptake coefficients at different temperatures as a function of gas- phase ozone concentration.

en: Experimental Results: Uptake coefficients vs. temperature and ozone concentration

en:
* Final Data to Figure 3: Uptake coefficients as a function of temperature at 5E12 molecules per cm3 ozone concentration.
* Final Data to Figure 6: Uptake coefficients at different temperatures as a function of gas- phase ozone concentration.

The average ozone uptake coefficients of the three cycles between the bypass and the flow tube per repeat is taken as the mean uptake coefficient for each experiment. Gamma was corrected for gas phase diffusion limitation following the Cooney–Kim– Davis (CKD) method52 (see supporting material of publication for details).

en: The reaction of ozone with bromide in polar regions results in the formation of reactive bromide species with impacts on ozone budget and the oxidative capacity of the lower atmosphere. Here, we present a data investigating the temperature dependence of bromide oxidation by ozone using a coated wall flow tube reactor coated with an aqueous mixture of citric acid and sodium bromide, a proxy for sea salt aerosol in snow or the free troposphere. Thus study shows the effect of of organic species at relatively mild temperatures between the freezing point and eutectic temperature as typical for Earth's cryosphere.

antartic

display_name: antartic

arctic

display_name: arctic

cryosphere

display_name: cryosphere

laboratory-study

display_name: laboratory-study

polar

display_name: polar

sea-salt-aerosol

display_name: sea-salt-aerosol

snow

display_name: snow

en: Kinetic experiments on the oxidation of bromide by ozone from 289-245 K.


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

en: Daily simulated data for the future period

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

en: Daily simulated data for the reference period

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

en: Shapefile of 307 catchments in Switzerland

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

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

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

*References*:

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

climate-change

display_name: climate-change

discharge

display_name: discharge

hydrology

display_name: hydrology

precipitation

display_name: precipitation

snowmelt

display_name: snowmelt

soil-moisture

display_name: soil-moisture

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


maintainer: Lisa Hülsmann

en: forest-inventory forest-reserves mortality-models tree-mortality primeval-forests fagus-sylvatica

en: German data

en:
READ ME for

Hülsmann L, Bugmann H, Commarmot B, Meyer P, Zimmermann S, Brang P
Data from: Does one model fit all? Patterns of beech mortality in natural forests of three European regions.
Ecological Applications http://dx.doi.org/10.5061/dryad.h4s6t

Forest inventory data of beech for the development of individual-tree mortality models from strict forests reserves in Switzerland, Germany and Ukraine.

The data package provides the files
CH_reduced: inventory data of the reduced dataset from Switzerland that was used to assess the influence of additional environmental and stand characteristics on beech mortality
CH: inventory data from strict forest reserves in Switzerland
DE: inventory data from strict forest reserves in Germany
UA: inventory data from a primeval beech forest in Ukraine

A set of three consecutive inventories was used to generate records for the calibration of mortality models based on trees that were alive in the first and second inventory and either dead or alive in the third inventory.
Diameter at breast height: dbh (mm)
Annual relative basal area increment: relBAI (), calculated based on the first and the second dbh measurement as the compound annual growth rate of the trees basal area (Bigler and Bugmann 2004)
Status of the tree: y, 0 = alive, 1 = dead
Length of the mortality period: dt (years)

Additional environmental and stand characteristics were derived for the reduced Swiss dataset.
Note the varying seasonal level of the covariates considering the entire year, the growing season from April to September (G) and the winter month January to March (W).
All climatic covariates were calculated for both the ‘growth’ (2) and the ‘mortality’ period (3). The stand covariates were derived for the second inventory of each record (2).

Mean diameter at breast height: dbh.mean.2 (mm)
Interquartile range of diameter at breast height: dbh.IQR.2 (mm)
Median diameter at breast height: dbh.median.2 (mm)
Basal area per hectare: BA.2 (m2 ha-1)
Number of trees per hectare: N.2 (ha-1)
pH-value: pH ()
Available water capacity in the soil: AWC (mm)
Mean annual precipitation sum: P.2, P.3 (mm)
Mean precipitation sum during growing season: PG.2, PG.3 (mm)
Mean annual air temperature: mT.2, mT.3 (°C)
Mean air temperature during growing season: mTG.2, mTG.3 (°C)
Mean air temperature during winter months: mTW.2, mTW.3 (°C)
Mean growing degree-days: GDD.2, GDD.3 ()
Mean number of months during growing season with water deficit: mDEFdurG.2, mDEFdurG.3 ()
Mean annual maximum water deficit: mDEFmax.2, mDEFmax.3 (mm)
Overall maximum of the annual maximum water deficit: maxDEFmax.2, maxDEFmax.3 (mm)
Mean of cumulative water deficit during growing season: mDEFcumG.2, mDEFcumG.3 (mm)
Overall maximum of cumulative water deficitduring growing season: maxDEFcumG.2, maxDEFcumG.3 (mm)
Percentage of years with at least one month water deficit: percDEF.2, percDEF.3 (%)

en: Swiss data full

en: Ukrainian data

en: Swiss data reduced

en:
The datasets comprise nearly 19’000 trees of European beech (_Fagus sylvatica_ L.) from unmanaged forests in Switzerland, Germany / Lower Saxony and Ukraine. Tree death was modelled as a function of size and growth, i.e., stem diameter (DBH) and relative basal area increment (relBAI). To explain the spatial and temporal variability in mortality patterns, we considered a large set of environmental and stand characteristics.

 Inventory data
The strict forest reserves in Switzerland and Germany had been established in the period of 1961-1975 and 1971-1974, respectively. Every reserve included up to 10 permanent plots ranging from 0.09 to 1.8 ha in size, with slightly irregular re-measurement intervals. Permanent plots with pure or mixed beech stands were selected from the reserves of both networks. Reserves with considerable wind disturbance during the monitored intervals were excluded from the analysis. In addition to data from the Swiss and German reserves, data from a 10 ha plot in the primeval beech forest Uholka in Western Ukraine including three remeasurements were used. The inventory data provide diameter measurements at breast height (dbh) for revisited trees with a diameter of more than 4, 7 and 6 cm for Switzerland, Germany and Ukraine, respectively.

 Mortality predictors
A set of three consecutive inventories was used to generate records for the calibration of mortality models based on trees that were alive in the first and second inventory and either dead or alive in the third inventory. As an explanatory variable, the annual relative basal area increment (relBAI) was calculated based on the first and the second dbh measurement as the compound annual growth rate of the trees basal area. Tree dbh in the second inventory was used in addition to relBAI to model tree status (alive or dead) of the third inventory.

To increase the generality of the mortality models, we selected environmental variables that are known to have a considerable influence on growth and mortality of beech. We emphasized the effects of water availability using a large set of drought characteristics that were calculated based on the local site water balance. We also related beech mortality to soil pH, temperature, precipitation and growing degree-days. Additionally, we considered stand characteristics that reflect the development stage, competition and structure of the forests.

 Further information
For further information, refer to Hülsmann _et al_. (2016) Does one model fit all? patterns of beech mortality in natural forests of three European regions. _Ecological Applications_.

fagus-sylvatica

display_name: fagus-sylvatica

forest-inventory

display_name: forest-inventory

forest-reserves

display_name: forest-reserves

mortality-models

display_name: mortality-models

primeval-forests

display_name: primeval-forests

tree-mortality

display_name: tree-mortality

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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

en: Data from: Does one model fit all? patterns of beech mortality in natural forests of three European regions


de: regierungsrat staat kantonaleverwaltung kantonzuerich

label: Fachausschuss für offene Behördendaten

de: OGD FA Q4/2021 - Protokoll

de: Protokoll des Fachausschusses für offene Behördendaten des Kantons Zürich vom 29. November 2021.

de: OGD FA Q2/2021 - Folien

media_type: text/html

de: Folien des Fachausschusses für offene Behördendaten des Kantons Zürich vom 31. Mai 2021.

format: HTML

mimetype: text/html

de: OGD FA Q1/2021 - Folien

de: Folien des Fachausschusses für offene Behördendaten des Kantons Zürich vom 30. März 2021.

de: Protokoll OGD FA Q1/2022, 21. März 2022

de: OGD FA Q4/2020 - Folien

de: Folien des Fachausschusses für offene Behördendaten des Kantons Zürich vom 9. Dezember 2020.

de: OGD FA Q4/2019 - Folien

de: Folien des Fachausschusses für offene Behördendaten des Kantons Zürich vom 5. November 2019.

de: OGD FA Q3/2021 - Folien

de: Folien des Fachausschusses für offene Behördendaten des Kantons Zürich vom 20. September 2021.

de: OGD FA Q1/2020 - Folien

de: Folien des Fachausschusses für offene Behördendaten des Kantons Zürich vom 4. Februar 2020.

de: Protokoll OGD FA Q3/2022, 01. September 2022

de: OGD FA Q1/2023 - Protokoll

de: OGD FA Q3/2019 - Folien

de: Folien des Fachausschusses für offene Behördendaten des Kantons Zürich vom 20. August 2019.

de: Protokoll OGD FA Q4/2022, 10. November 2022

de: Der OGD-Fachausschuss ist das vom Regierungsrat eingesetzte Koordinationsgremium, das für die einheitliche Publikation offener Behördendaten sorgt. Er betreut die dazu notwendigen Standards, Vorgaben, Empfehlungen und sorgt für ihre bedarfsgerechte Weiterentwicklung. Er trifft sich mindestens dreimal jährlich. In ihm sind mindestens alle Verwaltungseinheiten und Organe des Kantons Zürich vertreten, die offene Daten bereitstellen.

kantonaleverwaltung

display_name: kantonaleverwaltung

kantonzuerich

display_name: kantonzuerich

regierungsrat

display_name: regierungsrat

staat

display_name: staat

national-economy

de: Volkswirtschaft

en: National economy

fachstelle-ogd-kanton-zuerich

de: Fach- und Koordinationsstelle Open Government Data Kanton Zürich

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

de: Unterlagen und Protokolle des Fachausschusses für offene Behördendaten des Kantons Zürich


digitizing-historical-plague

maintainer: Christian Ginzler

title_for_slug: digitizing-historical-plague

en: palaeo-epidemiology black-death bubonic-plague plague-inventory historical-plague plague-outbreaks plague-mapping yersinia-pestis

en: We present newly digitized data on 6,929 plague outbreaks that occurred between 1347 and 1900 AD across Europe. The data base on an inventory initially published 1976. For georeferencing the information of Tele Atlas 2009 was used. The coordinates are in the reference systems ETRS89 and WGS84.

black-death

display_name: black-death

bubonic-plague

display_name: bubonic-plague

historical-plague

display_name: historical-plague

palaeo-epidemiology

display_name: palaeo-epidemiology

plague-inventory

display_name: plague-inventory

plague-mapping

display_name: plague-mapping

plague-outbreaks

display_name: plague-outbreaks

yersinia-pestis

display_name: yersinia-pestis

education

de: Bildung, Wissenschaft

envidat

de: EnviDat: das Umwelt-Datenportal

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

en: Digitizing historical plague


maintainer: Avalanche Warning Service SLF

en: Avalanche fatalities per year in Switzerland since 1936/1937

media_type: CSV

en:
This dataset contains the statistics on the number of avalanche fatalities per hydrological year in Switzerland. The data set commences with the beginning of the hydrological year 1936/37 on 01/10/1936. After the completion of a hydrological year, the new data is appended to the existing dataset.

The following information is contained (by column and column title):
 - hydrological year
 - number of fatalities in the backcountry (=tour)
 - number of fatalities in terrain close to ski areas (=offpiste)
 - number of fatalities on transportation corridors including ski runs, roads, railway lines (=transportation.corridors)
 - number of fatalities in or around buildings or in settlements (= buildings)
 - sum (of all four categories)

The definition for these four categories as described in the guidelines to the avalanche accident database:

**tour**:
activities include back-country ski, snowboard or snow-shoe touring

**offpiste**:
access from ski area, generally from the top of a skilift with short hiking distances

**transportation.corridors** ([Techel et al., 2016](http://www.geogr-helv.net/71/147/2016/ )):
people travelling or recreating on open or temporarily closed
transportation corridors (e.g. a road user or a skier on a ski run) and people working on open or closed transportation corridors (e.g. maintenance crews on roads, professional rescue teams)

**buildings** ([Techel et al., 2016](http://www.geogr-helv.net/71/147/2016/ )):
people inside or just outside buildings, and workers on high alpine building
sites

avalanche-accident-statistics

display_name: avalanche-accident-statistics

avalanche-accidents

display_name: avalanche-accidents

avalanche-fatalities

display_name: avalanche-fatalities

fatalities-in-or-around-buildings

display_name: fatalities-in-or-around-buildings

fatalities-in-settlements

display_name: fatalities-in-settlements

fatalities-in-the-backcountry

display_name: fatalities-in-the-backcountry

fatalities-on-transportation-corridors

display_name: fatalities-on-transportation-corridors

en: Number of avalanche fatalities per hydrological year in Switzerland since 1936-1937


de: politischeparteien kantonsrat politik

label: Dokumentation zur Nutzung des Web Service des Geschäftsverwaltungssystems des Kantonsrates des Kantons Zürich

de: Geschäftsarten des Kantonsrates des Kantons Zürich

de: Kantonsratsversand des Kantonsrates des Kantons Zürich

de: https://parlzhcdws.cmicloud.ch/parlzh1/cdws/Index/KRVERSAND/searchdetails?q=datum_start < "2020-11-19 00:00:00" sortBy datum_start/sort.descending&m=8&l=de-CH

de: Wahlkreise im Gever des Kantonsrates des Kantons Zürich

de: Kommissionen des Kantonsrates des Kantons Zürich

de: Medienmitteilungen des Kantonsrates des Kantons Zürich

de: Beispielabfrage: Fraktionsdetails des Kantonsrates des Kantons Zürich

de: https://parlzhcdws.cmicloud.ch/parlzh2/cdws/Index/BEHOERDEN/searchdetails?l=de-CH&q=kurzname adj FRAAL - 'FRAAL' = {Fraktionskurzname; Beispiel: Fraktion Alternative Liste AL}

de: Kommissionsmitglieder des Kantonsrates des Kantons Zürich

de: https://parlzhcdws.cmicloud.ch/parlzh2/cdws/Index/MITGLIEDER/searchdetails?l=de-CH&q=seq>0 and dauer_end >= "2020-11-19 00:00:00" and dauer_start <= "2020-11-19 00:00:00" and gremium adj KBIK sortBy name/sort.ascending vorname/sort.ascending - 'KBIK' = {Kommissionskurzname; Beispiel: Kommission für Bildung und Kultur}

de: Fraktionen im Gever des Kantonsrates des Kantons Zürich

de: Beispielabfrage: Sitzungs- und Geschäftsdokumente des Kantonsrates des Kantons Zürich

de: Sitzungs- und Geschäftsdokumente sind in drei Publikatonsbereichen abgelegt: https://parlzhcdws.cmicloud.ch/parlzh3/cdws/Files, https://parlzhcdws.cmicloud.ch/parlzh5/cdws/Files und https://parlzhcdws.cmicloud.ch/parlzh2/cdws/Files - effektive Enpoints werden gebildet durch Ergänzung um '/{id}/{rendition}/{fileType}'

de: Beispielabfrage: Fraktionsmitglieder des Kantonsrates des Kantons Zürich

de: https://parlzhcdws.cmicloud.ch/parlzh2/cdws/Index/MITGLIEDER/searchdetails?l=de-CH&q=seq>0 and dauer_end >= "2020-11-19 00:00:00" and dauer_start <= "2020-11-19 00:00:00" and gremium adj FRAAL sortBy name/sort.ascending vorname/sort.ascending - 'FRAAL' = {Fraktionskurzname; Beispiel: Fraktion Alternative Liste AL}

de: Direktionen und Organe im Gever des Kantonsrates des Kantons Zürich

de: Parteien im Gever des Kantonsrates des Kantons Zürich

de: Geschäfte des Kantonsrates des Kantons Zürich

de: https://parlzhcdws.cmicloud.ch/parlzh5/cdws/Index/GESCHAEFT/searchdetails?q=krnr any * sortBy beginn_start/sort.descending&l=de-CH&s=1&m=50 - Language: 'l=de-CH'; Query: 'q={searchparams}'; Anzahl Einträge: 'm=50'; Seite: 's=1' (somit erhält man mit 'm=50' die ersten 50 Einträge)

de: Gremientypen im Gever des Kantonsrates des Kantons Zürich

de: Sitzungen (Agenda) des Kantonsrates des Kantons Zürich und seiner Organe

de: https://parlzhcdws.cmicloud.ch/parlzh3/cdws/Index/SITZUNGENDETAIL/searchdetails?q=datum_start >= "2020-11-19 00:00:00" sortBy datum_end/sort.ascending uhrzeit/sort.ascending&l=de-CH

de: Mitglieder des Kantonsrates des Kantons Zürich

de: https://parlzhcdws.cmicloud.ch/parlzh2/cdws/Index/MITGLIEDER/searchdetails?q=seq>0 and dauer_end >= "2020-11-19 00:00:00" and dauer_start <= "2020-11-19 00:00:00" and gremium all KR and funktion any "Mitglied Präsidium Vizepräsidium" sortBy name/sort.ascending vorname/sort.ascending&l=de-CH

de: Im Geschäftsverwaltungssystem (Gever) des Kantonsrates des Kantons Zürich werden sämtliche Geschäfte des Kantonsrates bewirtschaftet und dokumentiert. Über den Web Service können die im Internet publizierten Daten zu Geschäften, Sitzungen (Agenda) und (aktuellen) Mitgliedern nach Parteien, Fraktionen und Kommissionen abgerufen werden. Ebenso können Dokumente des Kantonsratsversands (jeweils die letzten 8 Versände) und die Kantonsrats-Protokolle (ab Mai 1995) bezogen werden. Die entsprechenden Endpunkte stehen als Ressourcen im Format XML zur Verfügung. Bei den Ressourcen handelt es sich um Endpunkte (teilweise als parametrisierte Beispielabfragen). Für Fragen und Rückmeldungen zu den Schnittstellen konsultieren Sie die Dokumentation unter "Weitere Informationen" oder wenden Sie sich per E-Mail an die bezeichnete Kontaktstelle.

kantonsrat

display_name: kantonsrat

politik

display_name: politik

politischeparteien

display_name: politischeparteien

politics

fr: Politique

de: Politik

en: Politics

it: Politica


weissfluhjoch-dataset-for-esm-snowmip

maintainer: Nander Wever

title_for_slug: weissfluhjoch-dataset-for-esm-snowmip

en: snow-depth esm-snowmip weissfluhjoch snow-models snow

en: Conversion script

media_type: text/x-sh

en:
Use this script to convert WFJ_optimaldataset_v6.smet to wfj.smet, the file that is used for the ESM-SnowMIP project. It also requires the file: WFJ_AirPressure_Data.zip

Use: bash create_wfj_dataset.sh > input/wfj.smet

mimetype: text/x-sh

en: ini file

en: This ini file is required by data-converter from MeteoIO version 1887 (http://models.slf.ch/p/meteoio/)

en: Dataset Weissfluhjoch for ESM-SnowMIP

en: This Weissfluhjoch dataset is a processed version of the Weissfluhjoch dataset version 6 from https://doi.org/10.16904/6. This dataset was specially created for the ESM-SnowMIP project. Here it is documented how this dataset has been created.

esm-snowmip

display_name: esm-snowmip

snow

display_name: snow

snow-depth

display_name: snow-depth

snow-models

display_name: snow-models

weissfluhjoch

display_name: weissfluhjoch

education

de: Bildung, Wissenschaft

envidat

de: EnviDat: das Umwelt-Datenportal

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

en: Weissfluhjoch dataset for ESM-SnowMIP


maintainer: Henning Löwe

en: unconfined-compression-experiments spherical-model-snow microstructure discrete-element-modelling 3d-ct-images snow-mechanics

en: Data Structure (Python Notebook - view online)

media_type: ipynb

media_type: text/plain

format: TXT

mimetype: text/plain

en: Data Access

media_type: HTTPS

en: Data Structure (Python Notebook)

en:
For the investigation of microstructural and mechanical properties of snow unconfined compression experiments and 3D computed tomography (CT) imaging were performed on sintered rounded grain snow and spherical model snow. The spherical model snow was generated to create geometrically simplified, well-defined microstructures for calibration of numerical models, such as discrete element models (DEM) in which the microstructure is represented by spherical particles.
In the experiments, microstructural variation was created by varying the sintering time (contact size) and the density of the ice sphere samples (number of contacts). The 3D CT images allow for a complete reconstruction of the entire experimental sample (cylindrical sample dimension: diameter = 33.6 mm; height = 14 mm).

discrete-element-modelling

display_name: discrete-element-modelling

microstructure

display_name: microstructure

snow-mechanics

display_name: snow-mechanics

spherical-model-snow

display_name: spherical-model-snow

unconfined-compression-experiments

display_name: unconfined-compression-experiments

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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

en: Unconfined compression experiments and 3D CT images of spherical model snow and RG snow samples


maintainer: Christian G. Sommer

en: kinect snow wind-crust wind-packing wind-tunnel

en: Kinect data

media_type: Link

en: Links to the dataset

en: README

en: Describes the dataset in detail

en: Processing scripts

en: Contains the Matlab- and R-scripts used to process the Kinect data

en:
This data sets contains the Microsoft Kinect data from 15 wind tunnel experiments on wind-packing / wind crust formation. These experiments were performed in the winter 2016/17. The Kinect measures distributed snow depth.

The Kinect data is organized per experiment. Each experiment subfolder contains the processed Kinect depth images and some additional files.

Please refer to the README for more details on the data. The processing scripts are available for download as well. The scripts are mainly provided as documentation and would need to be adjusted to be used.

This dataset is the basis of the following publication:

Sommer C.G., Lehning M. & Fierz C. (2018). Wind Tunnel Experiments: Influence of Erosion and Deposition on Wind-Packing of New Snow. Front. Earth Sci. 6:4. doi: 10.3389/feart.2018.00004

kinect

display_name: kinect

wind-crust

display_name: wind-crust

wind-packing

display_name: wind-packing

wind-tunnel

display_name: wind-tunnel

en: Wind crust formation: Microsoft Kinect data


envidat-supports-open-science

maintainer: Ionut Iosifescu Enescu

title_for_slug: envidat-supports-open-science

en: research-data-management juypter-notebooks envidat open-science wsl

en: Article as PDF

en:
The article "EnviDat Supports Open Science" originally appeared in WSLintern No. 3 (2020), page 14-15 and it is republished here with permission from the WSLintern editorial team. It contains guidelines for WSL scientists about the main issues behind Open Science and how to pragmatically approach the complexities of doing Open Science with EnviDat’s support.

License: This article is released by WSL and the EnviDat team to the public domain under a Creative Commons 4.0 CC0 "No Rights Reserved" international license. You can reuse the information contained herein in any way you want, for any purposes and without restrictions.

envidat

display_name: envidat

juypter-notebooks

display_name: juypter-notebooks

open-science

display_name: open-science

research-data-management

display_name: research-data-management

wsl

display_name: wsl

en: EnviDat Supports Open Science


maintainer: Marcia Phillips

en: borehole permos permafrost meteo-station ritigraben

en: DATA ACCESS

media_type: SMET

en: Meterological station at the [Ritigraben permafrost borehole](http://www.envidat.ch/dataset/rit1) (RIT_0102) in canton Valais, Switzerland. The station is located at 2690 m asl on a flat site.

borehole

display_name: borehole

meteo-station

display_name: meteo-station

permafrost

display_name: permafrost

permos

display_name: permos

ritigraben

display_name: ritigraben

education

en: Education and science

en: RIT2: Meteorological station at Ritigraben borehole site


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

en: Stability tests

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

en: README file explaining data structure and content

en: Extracted data

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

en: SNOWPACK simulations

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

en: Manually observed snow profiles

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

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

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

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

avalanche

display_name: avalanche

avalanche-forecast

display_name: avalanche-forecast

snow

display_name: snow

snow-stability-evaluation

display_name: snow-stability-evaluation

snowpack

display_name: snowpack

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: Validating and improving the critical crack length in SNOWPACK


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

en: Acetone data

media_type: dat

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: Readme file

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

en: Data Figure 1

en: Data of published Figure

en: Matlab Data

media_type: mat

en: All data as Matlab mat file

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: Matlab Script to view and plot data

media_type: .m

en: data list

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

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

solution

display_name: solution

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


en: wetlands occupancy-models conservation landscape amphibians

en: Covariate data 2

en: This file contains covariate data that changes over time for 856 ponds in 21 years (long format): whether or not a pond exists in year _t_, the age of constructed ponds in year _t_, structural connectivity of the pond in year _t_.

en: Model code

media_type: Rmd

en: This R markdown file contains all code and descriptions necessary to load and format the provided data and to fit the dynamic occupancy models described in the associated publication, including JAGS code for the fitted models.

en: Covariate data 1

en: This file contains covariate data that does not change over time for 856 ponds: the region the pond is in, whether the pond is old or new (constructed since 1991), elevation, water surface area, fluctuations of the water table (yes, no), the percent forest cover in 100m radius around the pond, the area or large (>6m wide) roads in 1km radius around the pond.

en: Model code, description

media_type: HTML

en: This is the output of the R markdown file that provides JAGS model code and describes how to format the provided data and fit the dynamic occupancy models described in the associated publication, for a quick overview.

en: Original raw data

media_type: XLSX

en: This file contains the original raw monitoring data as provided by the canton of Aargau. Access is restricted and only considered upon request and in consultation with the canton of Aargau.

en: Observation data

en: This file contains detection/non-detection data for the 12 amphibian species between 1999 and 2019 for 3 annual visits. Detectable denotes whether or not a visit j was made to pond i in year t. ObservedID gives a unique ID for the observer collecting the data during visit j to pond i in year t.

en: Readme

en: Description of methods and datasets.

en:
In the canton of Aargau, hundreds of new ponds have been constructed since the 1990s to benefit declining amphibian populations. This dataset consists of monitoring data for all 12 pond-breeding amphibian species in the canton of Aargau from 1999 to 2019 in 856 ponds, and environmental variables that describe the ponds and the landscape surrounding the ponds. Species observation data is detection/non-detection data from repeat visits during survey years, during which all potentially suitable ponds in an area were surveyed. Environmental variables describing the ponds are whether the pond has been newly constructed since 1991 or not, pond age (if constructed), elevation a.s.l., the water surface area, and whether the water table fluctuates or not. Environmental variables describing the surroundings of the ponds are the percent area of forest within a circular buffer of radius 100m around the pond, the area of large (width ≥6m) roads within a circular buffer of radius 1km around the pond, and structural connectivity.

The canton of Aargau is the owner of the monitoring data; the original datafile is only disclosed upon request and in consultation with the canton of Aargau.

The edited dataset contains cleaned observation data for the 12 amphibian species, as well as compiled and edited covariate data and code to fit dynamic occupancy models.

amphibians

display_name: amphibians

conservation

display_name: conservation

landscape

display_name: landscape

occupancy-models

display_name: occupancy-models

wetlands

display_name: wetlands

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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

en: Amphibian observation and pond data (Aargau, Switzerland)


root-traits

maintainer: Ivano Brunner

title_for_slug: root-traits

en: ingrowth-cores fine-root-traits soil-coring belowground-biomass drought

en: Root-traits

media_type: application/zip

en:
Fine-root traits of Scots pine in response to enhanced soil water availability deriving from long-term irrigation in the Pfynwald

Data_Fig.1.xlsx
Fine-root biomass of the topsoil (0-10 cm) in the dry and irrigated treatment of the Scots pine forest of the years 2003 to 2016 recorded by soil coring

Data_Tab1+2_2005.xlsx
Fine-root traits from roots of ingrowth cores from 2005 after two years of growth in the dry and irrigated treatment of the Scots pine forest

Data_Tab1+2_2016.xlsx
Fine-root traits from roots of ingrowth cores from 2016 after two years of growth, and from roots of the soil-coring sampling from 2016 in the dry and irrigated treatment of the Scots pine forest

belowground-biomass

display_name: belowground-biomass

drought

display_name: drought

fine-root-traits

display_name: fine-root-traits

ingrowth-cores

display_name: ingrowth-cores

soil-coring

display_name: soil-coring


maintainer: Jonas Stillhard

en: long-term-data-sets forest-structure forest-dynamics stand-inventory-data primeval-beech-forest dem forest permanent-forest-plot-data carpathian-biosphere-reserve fagus-sylvatica

en: Data description

media_type: application/pdf

en: Available online on publishers website

en: Dataset

en:
In 2000, a permanent forest plot of 10 ha has been established in the core zone of the primeval beech forest of Uholka.
 All living and dead trees with a diameter at breast height (DBH) ≥ 60 mm were identified to species, DBH measured, stems tagged and mapped.
Since then, the plot has been remeasured in 2005, 2010, and 2015. In total, 4,820 individual trees were measured with 14,116 individual measurements throughout all four inventories.
In spring 2018, an Airborne Laser Scan was carried out, covering the Uholka‐Shyrokyi Luh forest. This data set allows us to derive a high‐resolution digital elevation model (DEM) of the plot area.
The data set allows for important insights into the development and the spatial and temporal dynamics of primeval beech forests.

carpathian-biosphere-reserve

display_name: carpathian-biosphere-reserve

dem

display_name: dem

fagus-sylvatica

display_name: fagus-sylvatica

forest

display_name: forest

forest-dynamics

display_name: forest-dynamics

forest-structure

display_name: forest-structure

long-term-data-sets

display_name: long-term-data-sets

permanent-forest-plot-data

display_name: permanent-forest-plot-data

primeval-beech-forest

display_name: primeval-beech-forest

stand-inventory-data

display_name: stand-inventory-data

en: Stand inventory data from the 10‐ha forest research plot in Uholka, Ukraine


radar-wind-profiler-davos-wolfgang

maintainer: Maxim Hervo

title_for_slug: radar-wind-profiler-davos-wolfgang

en: radar wind-speed raclets wolfgangpass wind-direction

en: Radar Wind Profiler Data

en: A Vaisala Lap3000 was installed at Davos Wolfgang to record wind profiles. The time format is number of days fom January 0, 0000 in UTC with a 10 minute resolution.

en: The RADAR wind profiler from Meteoswiss was installed at Davos Wolfgang (LON: 9.853594, LAT: 46.835577) and measured from 2171 m above sea level to 11079 m, with a temporal resolution of 10 minutes.

raclets

display_name: raclets

radar

display_name: radar

wind-direction

display_name: wind-direction

wind-speed

display_name: wind-speed

wolfgangpass

display_name: wolfgangpass

en: RADAR Wind profiler Davos Wolfgang


backward-trajectories

maintainer: Michael Sprenger

title_for_slug: backward-trajectories

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

en: Backward Trajectories

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

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

alps

display_name: alps

atmospheric-dynamics

display_name: atmospheric-dynamics

backward-trajectories

display_name: backward-trajectories

complex-topopgraphy

display_name: complex-topopgraphy

cosmo

display_name: cosmo

lagrangian-analysis

display_name: lagrangian-analysis

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

en: Backward  Trajectories


maintainer: Leo Gallus Bont

en: seilaplan qgis accessible-forest openstreetmap forest geoprocessing

en: Digitised sample perimeter

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

en: Juypter Notebook for Data Preparation

en: Preview

media_type: image/png

format: PNG

mimetype: image/png

en: Juypter Notebook for Data Preparation (online)

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

media_type: image/tiff

format: TIFF

mimetype: image/tiff

en: Forest vector data (for sample forest range)

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

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

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

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

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

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

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

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

accessible-forest

display_name: accessible-forest

forest

display_name: forest

geoprocessing

display_name: geoprocessing

openstreetmap

display_name: openstreetmap

qgis

display_name: qgis

seilaplan

display_name: seilaplan

envidat

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

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