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: wohnungen wohnung heizung gebaeude gebaeudeart heizungsart wohnungsbestand strassen gemeinden

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

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

fr: Merkmalsbeschreibung des eidg. Gebäude- und Wohnungsregisters

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: Wohnungen

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.

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 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


name: Fachstelle Open Government Data des Kantons Zürich

de: corona gesundheit kantone covid-19 krankheiten schweiz

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

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

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

state: active

type: dataset

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

media_type: text/csv

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".

format: CSV

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.

corona

display_name: corona

gesundheit

display_name: gesundheit

kantone

display_name: kantone

krankheiten

display_name: krankheiten

schweiz

display_name: schweiz

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


fr: Fachausschuss für offene Behördendaten

de: Fachausschuss für offene Behördendaten

en: Fachausschuss für offene Behördendaten

it: Fachausschuss für offene Behördendaten

de: OGD FA Q1/2025 - Protokoll

media_type: application/pdf

format: PDF

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

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

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

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

de: OGD FA Q3/2021 - Folien

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

de: OGD FA Q4/2020 - Folien

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

de: OGD FA Q4/2021 - Protokoll

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

de: OGD FA Q1/2023 - Protokoll

de: OGD FA Q4/2024 - Protokoll

de: OGD FA Q4/2023 - Protokoll

de: OGD FA Q2/2023 - Protokoll

de: OGD FA Q1/2020 - Folien

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

de: OGD FA Q2/2024 - Protokoll

de: OGD FA Q3/2019 - Folien

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

de: OGD FA Q1/2021 - Folien

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

de: OGD FA Q3/2024 - Protokoll

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

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.

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


de: kantonaleverwaltung kantonzuerich

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

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.

kantonaleverwaltung

display_name: kantonaleverwaltung

kantonzuerich

display_name: kantonzuerich

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: Verzeichnis der Personenstammdaten der zentralen Verwaltung des Kantons Zürich


de: kantonzuerich kantonaleverwaltung

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

de: ZH Datasets Visits - 2020 November

de: Ausgenommen sind die Datensätze, die von der Geoinformation des Kantons Zürich auf opendata.swiss verwaltet werden.

de: ZH Datasets Visits - 2018 Mai

de: ZH Datasets Visits - 2018 Dezember

de: ZH Datasets Visits - 2019 September

de: ZH Datasets Visits - 2019 August

de: ZH Datasets Visits - 2019 Oktober

de: ZH Datasets Visits - 2019 Mai

de: ZH Datasets Visits - 2020 Februar

de: ZH Datasets Visits - 2018 März

de: ZH Datasets Visits - 2018 August

de: ZH Datasets Visits - 2018 Juli

de: ZH Datasets Visits - 2019 Juli

de: ZH Datasets Visits - 2020 April

de: ZH Datasets Visits - 2019 Dezember

de: ZH Datasets Visits - 2020 Oktober

de: ZH Datasets Visits - 2019 März

de: ZH Datasets Visits - 2020 Juni

de: Ausgenommen sind die Datensätze, die von der Geoinformation des Kanton Zürich auf opendata.swiss verwaltet werden.

de: Monatliche Nutzungsstatistiken (Anzahl Besuche) der Open Government Data (OGD) Metadatensätze von Verwaltungseinheiten und Organen des Kantons Zürich, die auf dem zentralen Katalog für offene Behördendaten opendata.swiss findbar sind. Hinweise: Ab Januar 2019 sind die Web Analytics um weitere Metadateninformationen erweitert bzw. wurden Metadatenanpassungen vorgenommen. Ab März 2021 sind die monatlichen Aktualisierungen aufgrund technischer Herausforderungen pausiert. Variabelndefinitionen: Column 'name' = dataset slug; 'issued' = first publication of dataset; 'organization_name' = publisher slug; 'organization_url' = publisher URL; 'E' up to 'AB' = thematic categories according to DCAT AP Switzerland.

de: Web Analytics der Open Government Data des Kantons Zürich auf opendata.swiss von Februar 2018 bis Februar 2021


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

fr: Zentraler Firmenindex Zefix (Linked Data Service LINDAS)

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

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

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 Rechtseinheiten haben unterschiedliche ID-Typen (EHRAID, CHID, UID) und erscheinen deshalb mehrmals in den Daten. Die Abfrage kann für die eigenen Bedürfnisse angepasst werden. Als Ausgangslage eignet sich 'company_uri'.

format: JSON-LD

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

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


name: Direktion der Justiz und des Innern, Fachstelle Kultur

de: kulturfinanzierung corona covid-19 kultur

fr: Medienmitteilung (04.03.2021)

de: Medienmitteilung (04.03.2021)

en: Medienmitteilung (04.03.2021)

it: Medienmitteilung (04.03.2021)

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

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


designprinzipien-fur-behorden

title_for_slug: designprinzipien-fur-behorden

fr: UK government's design principles

de: UK government's design principles

en: UK government's design principles

it: UK government's design principles

de: 3 Gestalte mit Daten

de: 1 Beginne mit den Bedürfnissen der Nutzenden

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

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

de: 2 Mach weniger

de: 7 Verstehe den Kontext

de: 8 Erstelle digitale Dienste, nicht Websites

de: 5 Iteriere. Dann iteriere erneut

de: 9 Sei konsistent, nicht gleichförmig

de: 6 Es ist für alle

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.

de: Designprinzipien für Behörden


name: Baurekursgericht des Kantons Zürich, Gerichtskanzlei

de: bauvorhaben gerichte

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

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

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: pflanzenschutzmittel wasserversorgung gesundheit lebensmittel pestizide trinkwasser wasser schwellenwert gemeinden verunreinigung

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

fr: 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).

gemeinden

display_name: gemeinden

gesundheit

display_name: gesundheit

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

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


de: politischeparteien politik exekutive kantonsrat

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

de: Mitglieder des Kantonsrats

de: Mitglieder des Kantonsrats inkl. deren Wahlkreis, Parteizugehörigkeit, Amtsantritt, Kommission sowie Kontaktdaten. Zu finden sind nur Mitglieder des Kantonsrats, die aktuell im Amt sind.

de: Geschäfte im Kantonsrat

de: Geschäfte des Kantonsrat ab 1987. Filter: 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: Wahlkreise des Kantons Zürich

de: Kommissionen des Kantonsrats

de: Zu finden sind aktuell im Kantonsrat bestehende Kommissionen

de: Direktionen und Organe, die dem Kantonsrat unterstehen

de: Agenda der Sitzungen im Kantonsrat

de: Geplante Themen der Sitzungen des Kantonsrats und seiner Gremien ab 1995.

de: Sitzungs- und Geschäftsdokumente des Kantonsrats

de: Sitzungs- und Geschäftsdokumente ab 1995. Abgelegt in drei Publikatonsbereichen: 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: Kommissionen des Kantonsrats inkl. ihrer Mitglieder

de: Zu finden sind aktuell im Kantonsrat aktive Kommissionen und ihre Mitglieder. Die Beispielabfrage ist gefiltert nach der Kommission 'KBIK' = {Kommissionskurzname; Beispiel: Kommission für Bildung und Kultur}

de: Kantonsratsfraktionen und ihre Mitglieder

de: Zu finden sind aktuell im Kantonsrat vertretene Fraktionen und Mitglieder. Die Beispielabfrage ist gefiltert nach der Fraktion AL ('FRAAL' = {Fraktionskurzname; Beispiel: Fraktion Alternative Liste AL})

de: Inhalte des Kantonsratsversands

de: Die Mitglieder des Kantonsrats erhalten regelmässig Unterlagen zu anstehenden Geschäften. Die Inhalte dieses sogenannten Kantonsratsversand sind ab November 2018 abrufbar.

de: Fraktionen im Kantonsrat

de: Angezeigt werden die Fraktionen, die es aktuell im Kantonsrat gibt

de: Fraktionsdetails des Kantonsrats

de: Zu finden sind aktuell im Kantonsrat vertretene Fraktionen. Die Beispielabfrage ist gefiltert nach der Fraktion AL ('FRAAL' = {Fraktionskurzname; Beispiel: Fraktion Alternative Liste AL})

de: Parteien im Kantonsrat

de: Zu finden sind die aktuell im Kantonsrat vertretenen Parteien

de: Gremientypen im Kantonsrat

de: Geschäftstypen im Kantonsrat

de: Medienmitteilungen des Kantonsrats

de: Der Kantonsrat ist das kantonale Parlament von Zürich, die gesetzgebende und oberste aufsichtführende Behörde des Kantons. Alle Geschäfte des kantonalen Parlaments werden in einem System geführt und dokumentiert, dieses System bietet eine teilweise öffentliche Schnittstelle. Darüber können die auf der Webseite (siehe landing page) publizierten Inhalte als XML bezogen werden. Folgende Daten sind verfügbar: Geschäfte; Agenda der Sitzungen; (aktuelle) Mitglieder nach Partei, Fraktion und Kommission; Dokumente des Kantonsratsversands; Kantonsrats-Protokolle und die aktuellen Gremien, Kommissionen und Direktionen.  Wir haben für jeden Datenbereich Beispielabfragen hinterlegt, die Sie in der URL anpassen können.

exekutive

display_name: exekutive

kantonsrat

display_name: kantonsrat

politik

display_name: politik

politischeparteien

display_name: politischeparteien

de: Organisation und Geschäfte des Zürcher Kantonsrats


uebersichtsplan

title_for_slug: uebersichtsplan

de: Uebersichtsplan

de: WMTS Uebersichtsplan 5000 (LV95)

de:
Der Übersichtsplan ist seit Mitte 2008 kein Produkt mehr der amtlichen Vermessung.
Der Übersichtsplan wird durch den "Basisplan-AV" abgelöst.

name: Amt für Grundbuch und Geoinformation des Kantons Zug

kanton-zug

fr: Canton de Zoug

de: Kanton Zug

en: Canton of Zug

it: Cantone di Zugo

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

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

value: ZG-Kt - Geocat Harvester


jagdbezirke

title_for_slug: jagdbezirke

de: Jagdbezirke

format: WMS

format: WFS

de: Die Jagdbezirke unterteilen das Jagdgebiet. Pro Jagdbezirk wird jährlich die Jagdplanung ermittelt.

jagd

display_name: jagd


abfallanlagen-und-sammelstellen

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

ener

fr: Énergie

de: Energie

en: Energy

it: Energia

regi

fr: Régions et villes

de: Regionen und Städte

en: Regions and cities

it: Regioni e città

envi

fr: Environnement

de: Umwelt

en: Environment

it: Ambiente

title_for_slug: abfallanlagen-und-sammelstellen

de: abfall-sammelstelle abfallanlage

author: Geoinformation Stadt Bern

fr: geocat.ch permalien

de: geocat.ch Permalink

en: geocat.ch permalink

it: geocat.ch link permanente

fr: Map (Preview)

de: Map (Preview) Stadtplan

en: Map (Preview)

it: Map (Preview)

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

format: SERVICE

de: Abfallsammelstellen

de: Web Map Services WMS

de: WFS Abfallsammelstellen

de: Web Feature Services WFS

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

spatial: Stadt Bern

de: Geoinformation Stadt Bern

geoinformation-der-stadt-bern

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

en: The Land Surveying Office of the City of Bern

it: L'ufficio del catasto nella Città di Berna

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

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

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

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

political_level: commune

value: Geoinformation Stadt Bern - Geocat Harvester

de: Abfallanlagen und Sammelstellen


okoinfomobil

title_for_slug: okoinfomobil

de: oekomobil okomobil

de: OekoInfoMobil

de: WFS OekoInfoMobil

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

de: ÖkoInfoMobil


maintainer: Volksschulamt des Kantons Zürich

de: schulen bildung volksschule primarschule schulgemeinden sekundarschule

author: Volksschulamt des Kantons Zürich

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

fr: Informationen für die Volksschule: Kontaktpersonen für die Gemeinden (Karte)

de: Tabelle der Kontaktpersonen des Volksschulamts des Kantons Zürich nach Schulgemeinde und Themen

de: Variabeln: 'Code_P' = Primarstufe, 'Name_P' = Name der Primarschulgemeinde, 'Personal_P' = Name der Kontaktperson für das Thema 'Personal', 'Lohn_P' = Name der Kontaktperson für das Thema 'Lohn', 'Beratung_P' = Name der Kontaktperson für das Thema 'Beratung', 'Sonderpädagogik_P' = Name der Kontaktperson für das Thema 'Sonderpädagogik', 'Code_O' = Oberstufe, 'Name_O' = Name der Oberstufenschulgemeinde, 'Personal_O' = Name der Kontaktperson für das Thema 'Personal', 'Lohn_O' = Name der Kontaktperson für das Thema 'Lohn', 'Beratung_O' = Name der Kontaktperson für das Thema 'Beratung', 'Sonderpädagogik_O' = Name der Kontaktperson für das Thema 'Sonderpädagogik'

mimetype: text/csv

de: Für Schulbehörden, Schulleitungen, Schulverwaltungen und Lehrpersonen zusammengestellte Kontaktpersonen des Volksschulamts zu Primar- und Sekundarschulen und den Themen 'Personal', 'Lohn', 'Beratung' und 'Sonderpädagogik'.

bildung

display_name: bildung

primarschule

display_name: primarschule

schulen

display_name: schulen

schulgemeinden

display_name: schulgemeinden

sekundarschule

display_name: sekundarschule

volksschule

display_name: volksschule

volksschulamt-kanton-zuerich

de: Volksschulamt Kanton Zürich

de: Das Volksschulamt ist zuständig für schulorganisatorische Aufgaben und die Verwaltung des Lehrpersonals. Die Abteilungen unterstützen die Schulen in der Personaladministration, in Fragen zu Unterricht, Lehrplan und Lehrmitteln. Sie übernehmen Koordinations-, Aufsichts- und Beratungsaufgaben für den Sonderschulbereich. Sie beraten Behörden, Schulleitungen und Schulverwaltungen.

value: ZH-Kt Volksschulamt - DCAT Harvester


asiatische-hornisse-ag

agri

fr: Agriculture, pêche, sylviculture et alimentation

de: Landwirtschaft, Fischerei, Forstwirtschaft und Nahrungsmittel

en: Agriculture, fisheries, forestry and food

it: Agricoltura, pesca, silvicoltura e prodotti alimentari

regi

de: Regionen und Städte

title_for_slug: asiatische-hornisse-ag

author: Landwirtschaft Aargau

de: geocat.ch Permalink

de: AGIS Direktdownload

de: Der Datensatz zeigt eine Übersicht der Funde der invasiven Asiatischen Hornisse im Kanton Aargau. Es sind sowohl Einzelfunde als auch die Standorte der gefundenen Nester aufgeführt.

de: Landwirtschaft Aargau

de: AGIS Service Center Kanton Aargau

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

value: Aargau-Kt - Geocat Harvester

de: Asiatische Hornisse AG


envi

de: Umwelt

fr: inventaire-environnement source eau espace-naturel-sensible

de: inventar quelle wasser empfindliche-naturlandschaft

en: inventory spring-hydrology-land water sensitive-natural-area

it: inventario sorgente-idrologia-territorio acqua area-naturale-sensibile

fr: Accès aux données (fr) : téléchargement et services web (Géoportail cantonal)

fr: Datenzugriff (de): Download und Webservices (Kantonales Geoportal)

fr: Inventaire cantonal des sources naturelles

fr: Map (Preview) Visualisation des données

de: Map (Preview) Visualisierung der Daten

en: Map (Preview) Visualization of the data

it: Map (Preview) Visualizzazione dei dati

fr: Visualisation des données

de: Visualisierung der Daten

en: Visualization of the data

it: Visualizzazione dei dati

fr: Les sources naturelles sont des milieux de vie dignes de protection selon la Loi fédérale sur la protection de la nature et du paysage (LPN). Ils abritent une faune et flore très spécialisées et souvent menacées. Les données contiennent l'emplacement de sources naturelles dans le canton, leur importance pour la protection de la nature et leur état, ainsi que des zones tampon autour des sources. L'inventaire n'est pas exhaustif.

spatial: Canton de Fribourg (FR)

fr: Service des forêts et de la nature

de: Service des forêts et de la nature

en: Service des forêts et de la nature

it: Service des forêts et de la nature

fr: Geoinformation Canton Fribourg

de: Geoinformation Kanton Freiburg

en: Geoinformation Canton Fribourg

it: Geoinformazione Canton Friburgo

fr: Les géodonnées publiques du Canton Fribourg sont gérées par le Centre de compétence SIT du Service de la géoinformation SGéo et mises à disposition sous forme de services web et de téléchargement via le géoportail cantonal.

en: The public geodata of the Canton of Fribourg are managed by the GIS Competence Center of the Geoinformation Office GeoA and made available as web services and for download via the cantonal geoportal.

de: Die öffentlichen Geodaten des Kantons Freiburg werden durch das GIS-Kompetenzzentrum des Amtes für Geoinformation GeoA verwaltet und über das kantonale Geoportal als Webservices und zum Download bereitgestellt.

it: I geodati pubblici del Cantone di Friburgo sono gestiti dal Centro di Competenza GIS dell'Ufficio di Geoinformazione GeoA e resi disponibili come servizi web e per il download attraverso il geoportale cantonale.

value: Fribourg-Kt - Geocat Harvester


regi

fr: Régions et villes

fr: Environnement

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

author: Kanton Luzern, Abteilung Geoinformation

fr: geocat.ch permalien

protocol: https

de: Datenshop

de: Produktansicht Geodatenshop

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

spatial: Kanton Luzern

de: Kanton Luzern, Abteilung Geoinformation

fr: Géoinformation du canton de Lucerne

de: Geoinformation Kanton Luzern

en: Geoinformation Canton of Lucerne

it: Geoinformazione del Cantone di Lucerna

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

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

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

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

value: LU-Kt - Geocat Harvester

de: Kataster der belasteten Standorte (KbS)


maintainer: Frank Techel

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

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

en: Data for the 2023/2024 forecasting season. It was added on 26 Aug 2024 by F. Techel.

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

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

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

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

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

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

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

avalanche-bulletin

display_name: avalanche-bulletin

avalanche-danger-level

display_name: avalanche-danger-level

avalanche-forecast

display_name: avalanche-forecast

avalanche-forecasting

display_name: avalanche-forecasting

verification

display_name: verification

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

value: Envidat Harvester

en: Re-analyzed regional avalanche danger levels in Switzerland


maintainer: EnviDat Support

en: laboratory-study snow-atmosphere-interactions chemistry ice atmosphere

en: Processed data as shown in Figure 4 of publication. X-ray energy in eV in column one, electron count intensity in arb. units in column 2.

en:
The table lists all NEXAFS raw data with the following columns:

* name:						raw file name
* xRayEkinRange_eV: 		Smallest and largest X-ray photon energy in eV
* scientaPassEnergy_eV: 	Pass energy of electron analyzer in eV
* scientaDwellTime_ms: 		Dwell time of electron analyzer in ms
* regionName:				O K-edge for all data
* scientaAcquisitionMode: 	Fixed energy (snapshot) mode
* scientaEkinRange_eV: 		Smallest and largest energy of the kinetic energy window used for detection in this work
* scientaEkinStep_eV		Not defined in fixed energy mode
* scientaLensMode:			Tune table
* scientaRegionIterations: 	Iterations (sweeps)
* scientaSequenceIterations: Iterations (sweeps)
* sampleTemp_dC: 			Temperature as monitored at the sample holder in °C
* cellPressure_mbar: 		Total pressure in measurement cell in mbar
* iceTemp_dC: 				Temperature of ice sample calculated based on vapour pressure (assuming total pressure equals partial pressure of water) in °C
* smplX_mm: 				positon of measurement spot, x dimension in mm
* smplY_mm: 				positon of measurement spot, y dimension in mm
* smplZ_mm: 				positon of measurement spot, z dimension in mm
* sealingTemp:				temperature at base of sample manipulator
* lastModifiedDatestr: 		time and date when data aquisition was finished
* sample: 					sample description

en: Near Ambient Pressure partial Auger-Meitner electron yield X-ray absorption spectra and photon flux data. X-ray energy in eV in column 1, analyzer electron count intensity in arb. units in column 2.

en: Data are compiled that have been used to demonstrate the impact of high water partial pressure on X-ray absorption spectra of ice.

atmosphere

display_name: atmosphere

chemistry

display_name: chemistry

ice

display_name: ice

laboratory-study

display_name: laboratory-study

snow-atmosphere-interactions

display_name: snow-atmosphere-interactions

en: An ice oxygen K-edge NEXAFS spectroscopy data set on gas-phase processing


maintainer: Irene Cordero

en: heatwave pesticide drought soil soil-biodiversity soil-nutrients land-use

en: All soil data

en: Soil physicochemical characteristics

en:
Soil chemical and biological properties of soils affected by 10 different perturbations related with global change, applied individually or in combination. Greenhouse experiment. Perturbations applied to intact soil cores (15 cm diameter, 15 cm deep) collected in an extensively managed grassland on the WSL grounds in June 2023.
This dataset contains all data on which the publication below was based:
"Fioratti Junod M, Gombeer S, Holmes J, Zimmerman S, Rillig M, Risch AC and Cordero I. Soil functionality declines under multiple superimposed global change perturbations. XXXX "
Please, cite this publication together with the citation of the datafile.
Database includes:
Pot_ID: pot or soil core identifier.
Batch: numeric, 1 or 2.
Enzyme_plate: numeric, from 1 to 6.
Number_of_perturbations: numeric, from 0 to 10.
N_addition: binary. Yes = 1, No = 0.
P_addition: binary. Yes = 1, No = 0.
Defoliation: binary. Yes = 1, No = 0.
Trampling: binary. Yes = 1, No = 0.
Insecticide: binary. Yes = 1, No = 0.
Fungicide: binary. Yes = 1, No = 0.
Herbicide: binary. Yes = 1, No = 0.
Antibiotic: binary. Yes = 1, No = 0.
Drought: binary. Yes = 1, No = 0.
Heat_wave: binary. Yes = 1, No = 0.
Treatment: character. Description of the treatment, 15 levels.
SWC: soil water content. Numeric, unitless.
Green_biomass: green plant biomass. Numeric, g.
Brown_biomass: brown or dead plant biomass. Numeric, g.
Biomass_cut: plant biomass cut during defoliation treatments. Numeric, g.
CO2_flux_light: net CO2 flux under ambient light. Numeric, mg CO2 m-2 h-1.
CO2_flux_dark: ecosystem respiration. Numeric, mg CO2 m-2 h-1.
S: organic matter stabilisation factor (tea bag index). Numeric, unitless.
k: organic matter decomposition rate (tea bag index). Numeric, unitless.
DOC: dissolved organic carbon, mg Kg-1 dry soil.
IC: inorganic carbon, mg Kg-1 dry soil.
Ammonium_KCl: plant available ammonium, mg Kg-1 dry soil.
Nitrate_KCl: plant available nitrate, mg Kg-1 dry soil.
Phosphate: phosphate, mg Kg-1 dry soil.
MBC: microbial biomass carbon, mg Kg-1 dry soil.
MBN: microbial biomass nitrogen, mg Kg-1 dry soil.
PHO: phosphatase activity, nmol h-1 g-1 dry soil.
BG: β-glucosidase, nmol h-1 g-1 dry soil.
XYL: xylosidase, nmol h-1 g-1 dry soil.
CBH: cellobiohydrolase, nmol h-1 g-1 dry soil.
NAG: N-acetylglucosaminidase, nmol h-1 g-1 dry soil.
LAP: leucine aminopeptidase, nmol h-1 g-1 dry soil.
POX: phenoloxidase, nmol h-1 g-1 dry soil.
PER: peroxidase, nmol h-1 g-1 dry soil.
pH: soil pH, unitless
Water_stable_aggregates: water stable aggregates, %
Surface_infiltration_time: water surface infiltration time, s.

drought

display_name: drought

heatwave

display_name: heatwave

land-use

display_name: land-use

pesticide

display_name: pesticide

soil

display_name: soil

soil-biodiversity

display_name: soil-biodiversity

soil-nutrients

display_name: soil-nutrients

en: Escalating effects of multiple perturbations on soil functionality


maintainer: Virginie Marques

en: marine coral-reef fish-composition teleo indian-ocean edna laperouse environmental-dna

en: Data access

en: Please use cyberduck to download for the moment

en:
Fish environmental DNA data set collected in the shallow seamount of La Pérouse located between Madagascar and Réunion Island

In 2019, we sampled the shallow seamount of La Pérouse, located between Madagascar and Réunion Island in the WIO. La Pérouse is an extinct volcano with a summit depth at 60 m below the sea surface. The plateau of the summit has a maximum length of 12 km and a maximum width of 4 km. One side of the seamount might have collapsed in the past, leading to a less common crescent-shaped summit. The abyssal plains that surround this isolated pinnacle are at a depth of 5000 m. Despite its proximity to Réunion Island (160 km northwest), La Pérouse and its surroundings are still poorly studied.
To sample eDNA in this location we used a submersible pump (Subspace, Geneva, Switzerland; nominal flow of ~ 1.0 L/min) to collect samples at various depths during close circuit rebreather dives. The pump has an internal battery and can be activated underwater allowing in situ filtration directly at the targeted habitat and depth. The filtration lasted for 30 min to collect a total water volume of ~ 30 L. During close circuit rebreather dives, water samples from various depths, ranging from 60 to 140 m below the sea surface, were collected using only the Submersible method (eight stations: no replicates). Out of the eight water samples, six were collected during the day, while two were collected at night (IS2_1 at 62 m and IS7_1 at 100–125 m). We applied this filtration protocols to collect and filter water throughout a VigiDNA® 0.2 μM cross-flow filtration capsule (SPYGEN, le Bourget du Lac, France) using disposable sterile tubing for each filtration capsule. After the filtration process, the remaining water in the capsules was emptied. The capsules were then filled with 80 ml of lysis conservation buffer (CL1 buffer SPYGEN, le Bourget du Lac, France) and stored at room temperature in the dark. At La Pérouse, a total of eight water samples were collected during the MONT LA PÉROUSE expedition (https://gombessa-expeditions.com/mont-la-perouse/) on board the ship La Curieuse to the seamount La Pérouse from October 27th to November 5th 2019. We followed a strict contamination control protocol in both field and laboratory stages. Each water sample processing included the use of disposable gloves and single-use filtration equipment to avoid any risk of contamination. Libraries were prepared with ligation using the MetaFast protocol (Fasteris).
Data content:
	* rawdata/: contains the raw reads for each individual sample. One archive contains the paired-end reads specified by the _R1 or _R2 suffix as well as individually tagged PCR replicates (if available) together with an archive containing all extraction and PCR blank samples of the library. Reads have been demultiplexed using cutadapt but not trimmed, individual demultiplexing tags and primers remain present in the sequences.
	* taxadata/: contains the table with all detected taxonomy for each sample after bioinformatic processing (see Polanco et al. 2020 for details; https://doi.org/10.1002/edn3.140) and associated field metadata.
	* metadata/: contains two metadata files, one related to the data collected in the field for each filter, and the second related to the sequencing process in the lab (including the tag sequence, library name, and marker information for each sample)

coral-reef

display_name: coral-reef

edna

display_name: edna

environmental-dna

display_name: environmental-dna

fish-composition

display_name: fish-composition

indian-ocean

display_name: indian-ocean

laperouse

display_name: laperouse

marine

display_name: marine

teleo

display_name: teleo

en: Environmental DNA Marine France LaPerouse 2019


maintainer: Ionuț Iosifescu Enescu

en: Bulk Data Access

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

format: GeoTIFF

en: map preview 0.5MB

mimetype: image/jpeg

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

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

gis

display_name: gis

large-files

display_name: large-files

natural-earth

display_name: natural-earth

qgis

display_name: qgis

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

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


en: avalanche lidar snow wind-drifted-snow low-cost monitoring

media_type: application/zip

format: ZIP

en:
We developed a monitoring system using low-cost lidar (Livox Avia) and optical sensors, to measure snow depth variations in an avalanche release area at a high spatiotemporal scale (centimeter to low decimeter spatial resolution and hourly temporal resolution). A detailed description of the setup and a first data analysis is published in:
[Ruttner-Jansen, P., Voordendag, A., Hartmann, T., Glaus, J., Wieser, A., & Bühler, Y. (2024). Monitoring snow depth variations in an avalanche release area using low cost lidar and optical sensors. EGUsphere, 2024, 1-20.](https://nhess.copernicus.org/articles/25/1315/2025/)
This dataset contains hourly 3D lidar data of the snow surface at the test site Braemabuel in Davos, Switzerland from the winter season 2023-2024 (23.11.2023 - 14.04.2024). Braema1 is installed at 2191 m a.s.l. in the slope, looking up into the avalache release area, and Braema2 is installed at 2255 m a.s.l at the ridge, looking down. Both scans together cover an area of about 15000 m².
The data is in format ".las" and contains the points in XYZ [m], in the scanner own coordinate system
- scalar fields: intensity, return number, timestamp, tag, label
-- tag: binary code from Livox (see [Livox Avia Manual](https://terra-1-g.djicdn.com/65c028cd298f4669a7f0e40e50ba1131/Download/Avia/Livox%20Avia%20User%20Manual%20202204.pdf))
-- label: marking outliers (filtered by distance and SOR to get snow particles in the air)

avalanche

display_name: avalanche

lidar

display_name: lidar

low-cost

display_name: low-cost

monitoring

display_name: monitoring

snow

display_name: snow

wind-drifted-snow

display_name: wind-drifted-snow

en: Snow depth mapping by lidar station Braemabuel


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

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

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

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

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

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

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

dsm

display_name: dsm

forest

display_name: forest

soc

display_name: soc

soil

display_name: soil

soil-depth

display_name: soil-depth

soil-ph

display_name: soil-ph

soil-properties

display_name: soil-properties

soil-texture

display_name: soil-texture

switzerland

display_name: switzerland

en: Soil property maps for the Swiss forest


maintainer: Elisabeth Hafner

en: validation satellite snow avalanche-hazard-mapping avalanche

en: Satellite Avalanche Mapping Validation data and data description

en: 14 shapefiles to be used only together with the data description also provided as a pdf.

en:
Validation points, validation area, ground truth coverage, SPOT 6 avalanche outlines, Sentinel-1 avalanche outlines, Sentinel-2 avalanche outlines, Davos avalanche mapping (DAvalMap) avalanche outlines as shapefiles and a detailed attribute description (DataDescription_EvalSatMappingMethods.pdf).

Coordinate system: CH1903+_LV95

The generation of this dataset is described in detail in:

Hafner, E. D., Techel, F., Leinss, S., and Bühler, Y.: Mapping avalanches with satellites – evaluation of performance and completeness, The Cryosphere, https://doi.org/10.5194/tc-2020-272, 2021.

avalanche-hazard-mapping

display_name: avalanche-hazard-mapping

satellite

display_name: satellite

validation

display_name: validation

en: Satellite avalanche mapping validation data


en: saltation-parameterizations aerodynamic-entrainment large-eddy-simulation drifting-snow snow-saltation splash

en: Code and input files to reproduce the simulations in Melo et al. (2024), as well as the post-processing scripts used in the analysis.

en:
This dataset includes the necessary files to reproduce the results presented in Melo et al. (2024):
- code version used in the simulations (also available at the institutional [GitLab repository](https://gitlabext.wsl.ch/atmospheric-models/les-lsm) - tag 'r2024')
- input files to run the simulations
- post-processing scripts (MATLAB and FORTRAN)
- selected raw output files
- post-processed results (MATLAB data files).
 	**Note:** Direct model outputs are not provided because of their size, but they can be shared upon request. They can be reproduced by running the code. The post-processed results allow you to reproduce all figures in Melo et al. (2024).

aerodynamic-entrainment

display_name: aerodynamic-entrainment

drifting-snow

display_name: drifting-snow

large-eddy-simulation

display_name: large-eddy-simulation

saltation-parameterizations

display_name: saltation-parameterizations

snow-saltation

display_name: snow-saltation

splash

display_name: splash

en: Understanding snow saltation parameterizations - Reproducibility


maintainer: Lyu

en: species-occurrence species-richness species-range-map pinales species-distribution-model fagales

en: Species range maps of species in Fagales and Pinales (with enough occurrences)

en: species_map.zip includes species 1690 range maps generated based on the integrated method described in Lyu et al., (2022). File names with (polygon) indicate that this map is only generated by polygon mapping, as there are not enough occurrences for SDMs.

en: Occurrence maps for species of Fagales and Pinales with few records

en: Point_buffer.zip includes 267 occurrence maps of species that do not have enough occurrence data for polygon mapping.

en:
This database contains 1957 distribution maps of species from Fagales and Pinales constructed based on a method integrating polygon mapping and SDMs (Lyu et al., 2022).
To construct the maps, we first collected occurrence data from 48 different sources. According to the number of occurrences after data cleaning, three kinds of maps are constructed: (1) For species with more than 20 occurrences, we performed SDM and polygon mapping described in Lyu et al. (2022) and select the integration of the two layers as the distribution range; (2) For species with more than 4 but less than 20 occurrences, we only use polygon mapping to draw the distribution range; (3) For species with less than 4 occurrences, a 20-km buffer was generated around the occurrences as the distribution range. The maps were manually checked and evaluated (see Note S3 and Table S9 in Lyu et al., 2022 for details).

fagales

display_name: fagales

pinales

display_name: pinales

species-distribution-model

display_name: species-distribution-model

species-occurrence

display_name: species-occurrence

species-range-map

display_name: species-range-map

species-richness

display_name: species-richness

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Species distribution maps of Fagales and Pinales (GDPlants)


aerosol-data-weissfluhjoch

title_for_slug: aerosol-data-weissfluhjoch

en: aerosol raclets particles ice-nucleation inp linda

en: APS Dataset Weissfluhjoch

en: The APS file contains aerosol data (total number concentration, median, mean, geometric mean, mode and geometric standard deviation of the size distribution) for aerodynamic diameters between 0.5 μm and ~20 μm as well as instrument characteristics, in a temporal resolution of 20 seconds, with time in UTC, measured at Weissfluhjoch.

mimetype: application/x-netcdf

en: OPC-GT526S Dataset Weissfluhjoch

en: Particle counts were recorded in six size channels (0.3, 0.5, 0.8, 1.5, 2.5 and 5 μm) in a resolution of 20 seconds (time in UTC) by a particle counter (GT-526S, Met One Instruments, Inc.).

en: OPC-N3 Dataset Weissfluhjoch

en: Particle concentrations in a size range of 0.35 μm to 40 μm were measured with an OPC-N3 (Alphasense, Inc.) in a resolution of 20 seconds (time in UTC). PM₁, PM₂,₅ and PM₁₀ were directly derived.

en: LINDA Dataset Weissfluhjoch

en: The LINDA file contains information on temperature (in °C) dependent frozen fractions, number of Ice Nuleating Particles (INP, per standard liter) and differential INP concentrations (per standard liter and Kelvin).

en: SMPS Dataset Weissfluhjoch

en: The SMPS file contains aerosol data (particle concentration, total number concentration, median, mean, geometric mean, mode and geometric standard deviation of the size distribution) in a size range between 12 and 460 nm (electrical mobility diameter) as well as instrument characteristics (sample temperature, pressure, relative humidity, mean free path and gas viscosity) in a temporal resolution of 2 minutes, with time in UTC, measured at Weissfluhjoch.

en:
Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Weissfluhjoch (LON: 9.806475, LAT: 46.832964). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs), and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room.

Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 – 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0).

Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle’s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³).

Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (SMPS 3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3787 TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³).

Coriolis μ and LINDA: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l min‾¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the LED based Ice Nucleation Detection Apparatus (LINDA, University of Basel) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles.

aerosol

display_name: aerosol

ice-nucleation

display_name: ice-nucleation

inp

display_name: inp

linda

display_name: linda

particles

display_name: particles

raclets

display_name: raclets

en: Aerosol Data Weissfluhjoch


en: european-summer-drought forests growth-reaction-to-drought-stress drought-2018 fagus-sylvatica leaf-senescence

en: Drought and Beech: 1000 beeches data set

en: The data set comprises data from the Drought and Beech 1000 beeches project. The actual data file with monitoring data of 963 beech trees in northern Switzerland over four years (Drought_and_Beech_1000beeches_Data_v1.0.csv), a metadata file with column descriptions (Drought_and_Beech_1000beeches_Legend_v1.0.csv), and a file defining the different survey periods (Drought_and_Beech_1000beeches_Survey_periods_v1.0.csv).

en: This study investigated multi-year drought impacts on beech forests through a unique large-scale monitoring of 963 individual beech trees, which showed either premature leaf discoloration during the drought in summer 2018 or no visible damage. We conducted the study in two highly drought-affected regions in northern Switzerland and one less drought-affected region located further south. We quantified the development of crown dieback and tree mortality as well as secondary drought damage, i.e. the presence of bleeding cankers and bark beetle infestations, in these trees for three consecutive years. We also determined the impact of several potential climate- and stand-related (predisposing) factors on mortality and drought legacy processes.

european-summer-drought

display_name: european-summer-drought

fagus-sylvatica

display_name: fagus-sylvatica

forests

display_name: forests

growth-reaction-to-drought-stress

display_name: growth-reaction-to-drought-stress

leaf-senescence

display_name: leaf-senescence

educ

en: Education, culture and sport

en: Data on multi-year drought impacts on European beech in northern Switzerland


maintainer: Marco Moretti

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

en: Dataset 2

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

en: Not used:

en: Not used for the analyses and not link to the paper.

en: Dataset 1

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

en: Dataset 3

en: Dataset on the the number of beehives and apiaries on the years 2012, 2013, 2014, 2015, 2016, 2017, 2018 in several Swiss cities.

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

honeybees

display_name: honeybees

pollination

display_name: pollination

remote-sensing

display_name: remote-sensing

sustainability

display_name: sustainability

urban-biodiversity

display_name: urban-biodiversity

urbanization

display_name: urbanization

envidat

en: EnviDat: the Environmental Data Portal

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

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


maintainer: Matthias Haeni

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

en: Forest station data (ALB)

en: Data plot of the forest station (ALB)

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

mimetype: application/pdf

en: Site aerial photo (Alpthal)

en: Site aerial photo from the Alpthal forest station. Source: Google. 2016. Site aerial photo from the Alpthal forest station. Retrieved November 11, 2016, from https://www.google.ch/maps

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for one meteorological station in Alpthal in Switzerland which is located within a natural coniferous forest (ALB) with Norway spruce (_Picea abies_; 180-230 yrs) as dominant tree species. The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Alpthal is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

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


photogrammetric-drone-data-gruenbodeli

title_for_slug: photogrammetric-drone-data-gruenbodeli

en: dsm orthophoto wingtra drone-data snow-depth-mapping uav photogrammetry

en: Data Access

en:
- ortho (3 cm/25 mm resolution)
- DSM (10 cm resolution)
- HS (10 cm resolution)
- RGB images
- Agisoft Metashape Project Report

All products are also available as a cloud optimized geotiff.

en:
We conducted various drone flights at Grüenbödeli near Davos with the Sony RX1R II mounted on a Wingtra drone during 2020/21/22. The data was processed with the Agisoft Metashape Professional Software.
The following products are available for download:
- DSM 10cm resolution
- Orthomosaic 3cm/25mm resolution
- Snow Raster 10cm resolution
- original RGB images

drone-data

display_name: drone-data

dsm

display_name: dsm

orthophoto

display_name: orthophoto

photogrammetry

display_name: photogrammetry

snow-depth-mapping

display_name: snow-depth-mapping

uav

display_name: uav

wingtra

display_name: wingtra

educ

en: Education, culture and sport

en: Photogrammetric Drone Data Grüenbödeli


maintainer: Varun Sharma

en: blowing-snow snow-atmosphere-interaction mesoscale-modelling antarctica alps

en:
REPRODUCIBILITY_POSTPROCESSING.zip contains data and scripts used to produce all figures from for the associated manuscript submitted for peer review ( see section on Citations below )

The folders stand for the three case studies presented in the manuscript. Two case studies (case Ia and case Ib) are on simulations in Antarctica and one case study
case II is for simulations in the Alps.

The associated data in the form of netcdf files or text files were produced using CRYOWRF v1.0

Apart from reproducibility, the intention of sharing these scripts is also to make it easy for new users to adopt CRYOWRF and be efficient, in particular in processing snowpack outputs.

The scripts were tested on multiple machines (different OSes) with the attached YAML file (wrf_analysis.yml) to setup the conda environment. All scripts were run using spyder as the IDE.

en: REPRODUCIBILITY_SIMULATION.zip contains all the extraneous files required to perform the simulations in associated manuscript (see Citations 3 in README.txt). The purpose of sharing these files apart from purposes of reproducibility is to also serve as templates for users to learn the workflow of CRYOWRF and perform simulations for their own purposes.

en:
This repository contains data required for reproducibility of the results to be published in the associated manuscript.

Apart from reproducibility, the attached datasets also serve as templates for new users to adopt CRYOWRF in their research.

The datasets consist of two folders organized in zip format:

1. REPRODUCIBILITY_SIMULATION:
  Consists of namelists for WPS, WRF and SNOWPACK to reproduce simulations published in the manuscript
  Additional files include datasets (from IMAU-FDM / RACMO, see "credits" below ) as well as helper python scripts to
  produce *.sno files
  which are used as initial conditions for SNOWPACK in CRYOWRF.

2. REPRODUCIBILITY_POSTPROCESSING:
   Includes outputs of CRYOWRF and python scripts used to prepare figures in the manuscript.

Each of the folders have their own readme files for more details.

 Code
citation: Varun Sharma. (2021, July 2). vsharma-next/CRYOWRF: CRYOWRF v1.0 (Version v1.0). Zenodo. http://doi.org/10.5281/zenodo.5060165

location: https://gitlabext.wsl.ch/atmospheric-models/CRYOWRF   (stable releases / institutional repo)
        https://github.com/vsharma-next/CRYOWRF            (dev branches / developer repo)

 Publication
**Introducing CRYOWRF v1.0: Multiscale atmospheric flow simulations with advanced snow cover modelling.**
Varun Sharma, Fraziska Gerber and Michael Lehning, Submitted to Geoscientific Model Development

 Acknowledgements
We thank Peter Kuipers Munneke (P.KuipersMunneke@uu.nl) for preparing and sharing outputs of IMAU-FDM and RACMO used for initial conditions for case Ia. The relevant citations for the methods through which these datasets were generated are:

* Kuipers Munneke, P., S. R. M. Ligtenberg, B. P. Y. Noël, I. M. Howat, J. E. Box, E. Mosley-Thompson, J. R. McConnell, K. Steffen, J. T. Harper, S. B. Das and M. R. van den Broeke. 2015. Elevation change of the Greenland ice sheet due to surface mass balance and firn processes, 1960-2014. The Cryosphere, 9, 2009-2025. doi:10.5194/tc-9-2009-2015

 * Ligtenberg, S. R. M., P. Kuipers Munneke, B. P. Y. Noël, and M. R. van den Broeke. 2018. Brief communication: Improved simulation of the present-day Greenland firn layer (1960-2016). The Cryosphere, 12, doi:10.5194/tc-12-1643-2018

alps

display_name: alps

antarctica

display_name: antarctica

blowing-snow

display_name: blowing-snow

mesoscale-modelling

display_name: mesoscale-modelling

snow-atmosphere-interaction

display_name: snow-atmosphere-interaction

envidat

en: EnviDat: the Environmental Data Portal

en: Reproducibility Dataset for CRYOWRF v1.0


maintainer: Anita C. Risch

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

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

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


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

en: Site photo of the field station (CIF)

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

en: Data plot of the field station (CIF)

en: Data file.

en: Data plot of the forest station (CIB)

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

en: Site photo of the forest station (CIB)

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

en: Site aerial photo (Chironico)

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

en: Forest station data (CIB)

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Chironico in Switzerland where one station is located within a natural coniferous forest stand (CIB) with Norway spruce (_Picea abies_; 160-180 yrs) and European silver fir (_Abies alba_; 140-160 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, CIF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Chironico is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

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


tree-species-map-of-switzerland

maintainer: Tiziana Li Koch

title_for_slug: tree-species-map-of-switzerland

en: map forest tree-species remote-sensing switzerland

media_type: image/png

en: Image of the tree species map

format: PNG

en:
Dominant tree species map of Switzerland
We created a tree species map of Switzerland for the dominant tree species in the forested areas. The spatial resolution of the map is 10 m and the coordinate system is ETRS89-extended / LAEA Europe (EPSG 3035). The map comprises Sentinel-2 index time series from the year 2020, a digital elevation model and species reference data from the Swiss National Forest Inventory. The map is available as raster (.tif) or vector dataset (.gpkg).
**Access will be granted upon request.**
In total, the following 15 species were mapped:
*Abies alba*, *Acer pseudoplatanus*, *Alnus glutinosa*, *Alnus incana*, *Betula pendula*, *Castanea sativa*, *Fagus sylvatica*, *Fraxinus excelsior*, *Picea abies*, *Pinus cembra*, *Pinus mugo arborea*, *Pinus sylvestris*, *Quercus petraea*, *Quercus robur*, *Sorbus aucuparia*.

 Approach

 Data
- Swiss National Forest Inventory Data (stand species with > 60 % dominance in upper canopy; on at least more than 9 plots dominant)
- Sentinel-2 time series (2020, Indices: CCI, CIRE, NDMI, EVI, NDVI)
- Digital elevation model (DEM) (swissalti3d, 5 m)
- Biogeographical regions (Federal Office for the Environment FOEN)
- Forest mask 2017 (Approach: Waser et al., 2015)

 Modeling approach
We identified the most meaningful variables that led to separation of the respective groups by using random forest models with a forward feature selection (Meyer et al., 2018; Ververidis & Kotropoulos, 2005). In this approach, the final random forest model is solely built from the selected meaningful variables. By identifying meaningful variables, we can determine which variables might influence the grouping. Further, to avoid overfitting and overly optimistic results, we applied 10-fold spatial cross-validation and put all pixels from a plot in the same spatial fold.
The modeling was realized using the CAST package in R (Meyer et al., 2022), based on the well-known caret package (Kuhn, 2022). We used the ranger package in R (Wright & Ziegler, 2017) to implement the random forest models, due to its short computation time.

 Training data for modeling
- 295 Sentinel-2, DEM & Biogeographical variables
- 10525 tree species pixels

 Selected variables for final model
1. EVI of 2020.05.16
2. NDMI of 2020.03.12
3. CIRE of 2020.04.16
4. NDMI of 2020.07.05
5. CCI of 2020.05.11
6. dem
7. CCI of 2020.08.14
8. NDMI of 2020.08.24
9. CCI of 2020.12.22
10. NDMI of 2020.04.21
11. NDMI of 2020.11.17
12. NDMI of 2020.08.09
13. CIRE of 2020.03.22
14. CIRE of 2020.08.09
14. CCI of 2020.11.02
15. CIRE of 2020.06.10

 Overall Accuracy of final model
- 0.759

 Nationwide prediction
- Predicted throughout forest mask 2017 (Approach: Waser et al., 2015)
- Not applied on incomplete Sentinel-2 time series (own category in final map: incomplete_ts)
- Applied the Area of Applicability (Meyer 2022) to sort out pixels outside of the feature space; basically where the model had not the same values for pixels as in the available training data

 *Be aware that the map is only validated with the training data itself, an independent validation with other data sources remains missing*

 References
- Kuhn, M. (2022). Classification and Regression Training. 6.0-93.
- Meyer, H., Reudenbach, C., Hengl, T., Katurji, M., & Nauss, T. (2018). *Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation*. Environmental Modelling and Software, 101, 1-9. https://doi.org/10.1016/j.envsoft.2017.12.001
- Meyer, H., Milà, C., & Ludwig, M. (2022). *CAST: 'caret' Applications for Spatial-Temporal Models*. 0.7.0.
- Ververidis, D., & Kotropoulos, C. (2005). *Sequential forward feature selection with low computational cost*. 2005 13th European Signal Processing Conference.
- Waser, L., Fischer, C.,Wang, Z., & Ginzler, C. (2015). *Wall-to-Wall Forest Mapping Based on Digital Surface Models from Image-Based Point Clouds and a NFI Forest Definition*. Forests, 6, 12, 4510–4528.
- Wright, M. N., & Ziegler, A. (2017). *ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R*. Journal of Statistical Software, 77(1), 1-17. https://doi.org/doi:10.18637/jss.v077.i01

forest

display_name: forest

map

display_name: map

remote-sensing

display_name: remote-sensing

switzerland

display_name: switzerland

tree-species

display_name: tree-species

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Tree species map of Switzerland


maintainer: Michael Lehning

en: snowpack terrestrial-laser-scanning snow-conservation snow-farming snow winter-tourism

en: Martell .smet

en: Meteodata used as input for SNOWAPCK simulations, orginating from Station Hintermartell.

en: HS Martell autumn 2015

media_type: text/plain

en:
Snow depth/ height of snow heap in autumn 2015 obtained by terrestrial laserscanning
cell size: 10 cm
spatial reference: European Datum 1950 UTM
Nodata: -9999

format: TXT

mimetype: text/plain

en: HS Fluela spring 2015

en:
Snow depth/ height of snow heap in spring 2015 obtained by terrrestrial laserscanning
cell size: 10 cm
spatial reference: CH1903

en: HS Fluela autumn 2015

en:
Snow depth/ height of snow heap in autumn 2015 obtained by terrestrial laserscanning
cell size: 10 cm
spatial reference: CH1903
Nodata: -9999

en: HS Martell spring 2015

en:
Snow depth/ height of snow heap in spring 2015 obtained by terrestrial laserscanning
cell size: 10 cm
spatial reference: European Datum 1950 UTM
Nodata: -9999

en: Martell .sno

en: Input snow profile (.sno file) used for SNOWPACK simulations of the Martell heap

en: Fluela .smet

en: Meteodata used as input for SNOWAPCK simulations, orginating from Station Windtunnel and DAV.

en: Fluela .sno

en: Input snow profile (.sno file) used for SNOWPACK simulations of the Flüela heap

en:
Two data sets obtained for snow farming projects (Fluela, Davos, CH and Martell, IT) in 2015.

The data set contains for each site:

* 10 cm GIS raster of snow depth calculated from terrestrial laserscanning surveys (TLS) in the end of winter season (April/May)

* 10 cm GIS raster of snow depth calculated from TLS in the end of summer season (October)

Input files for SNOWPACK model:

* .sno: snow profile at the end of winter

* .smet: meteorological data measured by weather stations in the area

For more details see Grünewald, T., Lehning, M., and Wolfsperger, F.: Snow farming: Conserving snow over the summer season, The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-93, in review, 2017.

snow

display_name: snow

snow-conservation

display_name: snow-conservation

snow-farming

display_name: snow-farming

snowpack

display_name: snowpack

terrestrial-laser-scanning

display_name: terrestrial-laser-scanning

winter-tourism

display_name: winter-tourism

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: Snowfarming data set Davos and Martell 2015


en: meteorological-data snowpack rain-on-snow catchment-data snow-hydrology

en: The file contains all data needed to run simulations with the snow cover model SNOWPACK.

mimetype: application/zip

en: Meteorological data used to run SNOWPACK for 58 catchments in the Swiss Alps. The data consists of a 2 km grid of "virtual meteorological stations" for each catchment. It was used to simulate snow cover processes during rain-on-snow events, therefore meteorological data of each catchment contains at least one rain-on-snow event. Further information can be found in the attached readme.txt and in Würzer & Jonas et al. (2017), currently under review in Hydrological Processes.

catchment-data

display_name: catchment-data

meteorological-data

display_name: meteorological-data

rain-on-snow

display_name: rain-on-snow

snow-hydrology

display_name: snow-hydrology

educ

en: Education, culture and sport

en: EnviDat: the Environmental Data Portal

en: Meteorological data for investigation of rain-on-snow events in 58 catchments in Switzerland


en: photovoltaic winter-energy-production snow shortwave-radiation albedo

en: Data Forward Scattering

en: GROUNDEYE model for multiple forward scattering in snow-covered terrain. Meteorological and topographical input data set for the Meierhofertäli near Davos, Switzerland, September 2017 - August 2018. Output data for September 2017 - August 2018 at hourly resolution and every pixel. Post-processing scripts for plotting and analysis.

en:
 Overview
We present GROUNDEYE, a new model of radiative transfer over mountainous terrain, which considers for the first time the forward scattering properties of snow.
Embedded in the surface process model Alpine3D, the new terrain radiation model GROUNDEYE receives interpolated real weather data with diffuse and direct broadband shortwave radiation for each pixel as well as a spatially variable plane albedo from the module SNOWPACK.

 Format
The GROUNDEYE model is written in c++, as is the entire environment of Alpine3d. The input and output data sets are .xlsx or .txt format, pre- and postprocessing including the generation of all figures is in .R format.

 Structure
In Data_Forward_Scattering.zip you will find all necessary data and model details to
reproduce the results of the JGR publication "How forward-scattering snow and terrain change the Alpine radiation balance with application to solar panels"

 - 	Model Input Data contains the meteorological and topographic input data sets, the BRDF, and preprocessing scripts.
 - Model Code contains the full model Alpine3d including the radiative transfer module GROUNDEYE.
 - Model Output Data contains the results of the simulation of terrain irradiance and irradiance of solar panels; hourly resolution, 1. Sptember 2017 - 31. August 2018.
 - Measurements Solar Testsite contains information and measurements of the solar testsite at the Totalp near Davos, Switzerland.
 - Postprocessing contains all R-Scripts used for the analysis and
plotting of the corresponding data.

In each of these folders you will find detailed information in
the file 'About this Folder.txt'.

albedo

display_name: albedo

photovoltaic

display_name: photovoltaic

shortwave-radiation

display_name: shortwave-radiation

winter-energy-production

display_name: winter-energy-production

en: Data, Code Link and Metadata on Forward Scattering of Snow at Totalp


en: functional-diversity spatial-factors metacommunity beta-diversity environmental-factors water-filled-tree-holes community-assembly taxonomic-diversity

en: This dataset contains trait information used to characterize the macroinvertebrates in our study, for the Indian forest plot. The dataset includes seven trait groups: aquatic developmental stage, maximum body size, locomotion, dispersal mode, respiration mode, feeding group, and cohort production interval. These trait groups are further divided into 32 distinct categories.

en: This dataset includes physical and environmental characteristics of water-filled tree holes (WTHs), such as water depth, height, opening size, hole origin and orientation, circumference at breast height, WTH type, and water volume. It also contains physico-chemical parameters, nutrient concentrations (ammonium, nitrogen, phosphate, sulfate, chloride), CDOM, Chlorophyll-A, turbidity, and dry organic matter (FPOM, CPOM, total).

en: Code to do the analysis and figures of the paper.

format: OCTET

en: This dataset describes the animal community composition sampled from 35 water-filled tree holes (WTHs) per site. It includes abundance and taxonomic identification (to morphospecies level) of aquatic macroinvertebrates and Chordata.

en: This dataset provides the geographic coordinates (latitude and longitude) of each sampled water-filled tree hole (WTH) for the Brazilian forest plot.

en: This dataset includes results from variation partitioning analysis used to assess the relative contributions of environmental and spatial factors to taxonomic and functional β-diversity and their components across biogeographic regions. It contains site-level values for pure and shared effects of predictors. Used to generate Fig_2.

en: This dataset provides the geographic coordinates (latitude and longitude) of each sampled water-filled tree hole (WTH) for the Indian forest plot.

en: This dataset provides the geographic coordinates (latitude and longitude) of each sampled water-filled tree hole (WTH) for the French forest plot.

en: This dataset contains trait information used to characterize the macroinvertebrates in our study, for the Brazilian forest plot. The dataset includes seven trait groups: aquatic developmental stage, maximum body size, locomotion, dispersal mode, respiration mode, feeding group, and cohort production interval. These trait groups are further divided into 32 distinct categories.

en: This Excel file contains metadata that provides detailed information about all other files included here. It describes the ten accompanying datasets, outlining the type of data each contains, the variables used, their descriptions, and the units of measurement (where applicable).

en: This dataset contains trait information used to characterize the macroinvertebrates in our study, for the French forest plot. The dataset includes seven trait groups: aquatic developmental stage, maximum body size, locomotion, dispersal mode, respiration mode, feeding group, and cohort production interval. These trait groups are further divided into 32 distinct categories.

en: This dataset contains values of taxonomic and functional β-diversity for each study site, along with their respective components: species richness difference vs. turnover (taxonomic), and functional richness difference vs. functional turnover (functional). Used to generate Fig. 1.

en: These datasets and code support research on the community assemblage of aquatic macroinvertebrates in ephemeral microhabitats, specifically water-filled tree holes (WTH), in three biogeographically and climatically distinct old-growth forests: Temperate-Mediterranean (France), Neotropical (Brazil) and Palaeotropical (India). By integrating standardised biological sampling, detailed environmental measurements and spatial analysis, the dataset allows for an in-depth examination of how local habitat conditions and spatial configuration influence taxonomic and functional beta-diversity patterns. The data capture the variation in species composition and ecological traits in 35 WTH per forest site and are particularly important for understanding how environmental filtering and spatial processes shape beta-diversity in natural forest ecosystems.

beta-diversity

display_name: beta-diversity

community-assembly

display_name: community-assembly

environmental-factors

display_name: environmental-factors

functional-diversity

display_name: functional-diversity

metacommunity

display_name: metacommunity

spatial-factors

display_name: spatial-factors

taxonomic-diversity

display_name: taxonomic-diversity

water-filled-tree-holes

display_name: water-filled-tree-holes

en: Spatial factors shape taxonomic and functional beta-diversity in water-filled tree holes in different biogeographical regions


climarctic-seasons

maintainer: Aline Frossard

title_for_slug: climarctic-seasons

en: microbial-functions greenhouse-gases soil arctic microbial-biodiversity

en: Climarctic seasons dataset Envidat.xlsx

en:
Samples description
•	sample number: sample list number (S1 to S126)
•	season: season during which the samples were collected
•	location: one of the two location where the samples were collected in the vicinity of Ny Alesund on Svalbard
•	slope site: sites where the samples were collected along the toposequence (upslope = top of the slope, midslope = middle of the slope, downslope = bottom of the slope)
•	soil layer: depth layer where the sample were collected (biocrust = 0-2 cm and mineral soil = 5-10 cm)
•	replicate: replicated sampled (3 replicates per site)
Environmental variables
•	pH = pH value of the soil
•	sand = proportion of sand of the soil [%]
•	silt = proportion of silt of the soil [%]
•	clay = proportion of clay of the soil [%]
•	GWC = Gravimetric Water content in the soil [%]
•	OM = Organic Matter content in the soil [g]
•	N = Total Nitrogen content in soil [mg/g]
•	C = Total Carbon content in soil [mg/g]
•	C/N = ration of C content over N content [ratio]
•	NO3 = Nitrate content in soil [mg/kg]
•	NO2 = Nitrite content in soil [mg/kg]
•	NH4 = Ammonium content in soil [mg/kg]
•	Ptot = Total Phosphorus content in soil [mg/kg]
•	Pavail = Available Phosphorus content in soil [mg/kg]
•	PO4 = Phsphate content in soil [mg/kg]
•	CO2 = CO2 fluxes from soil [mmol CO2 m-2 d-1]
•	CH4 = CH4 fluxes from soil [umol CH4 m-2 d-1]
•	N2O = N2O fluxes from soil [umol CH4 m-2 d-1]
•	GPP = Gross Primary Production [mmol CO2 m-2 d-1]
•	ER = Ecosystem respiration [mmol CO2 m-2 d-1]
•	NEE = Net Ecosystem Exchange [mmol CO2 m-2 d-1]
•	glucoOM = glucosidase enzyme activity [mmol h-1 gOM-1]
•	23 xyloOM = xylosidase enzyme activity [mmol h-1 gOM-1]
•	chitOM = N-acetyl-glucosaminidase (chitinase) enzyme activity [mmol h-1 gOM-1]
•	phosOM = phosphatase enzyme activity [mmol h-1 gOM-1]
•	leuOM = leucine-aminopeptidase enzyme activity [mmol h-1 gOM-1]
•	PPOM = Polyphenol Peroxidase enzyme activity [mmol h-1 gOM-1]
•	POOM = Polyphenol Oxidase enzyme activity [mmol h-1 gOM-1]
•	30. Bac16SOM = Bacterial 16S gene abundance [copies gOM-1]
•	31. BacamoAOM = Bacterial amoA gene abundance [copies gOM-1]
•	32. ArchamoAOM = Archaeal 16S gene abundance [copies gOM-1]
•	33. nifHOM = nifH gene abundance [copies gOM-1]
•	34. nirSOM = nirS gene abundance [copies gOM-1]
•	35. nosZOM = nosZ gene abundance [copies gOM-1]
•	36. nxrBOM = nxrB gene abundance [copies gOM-1]
•	37. mcrAOM = mcrA gene abundance [copies gOM-1]
•	38. pmoAOM = pmoA gene abundance [copies gOM-1]

en: This dataset includes measurements of potential microbial functions (potential extracellular enzyme activities and microbial functional genes abundance) and soil physico-chemical properties (pH, nutrients, texture, etc) in biocrusts and underlying mineral soils in High-Arctic tundra near Ny-Ålesund (Svalbard) during each season along a toposequence.

arctic

display_name: arctic

greenhouse-gases

display_name: greenhouse-gases

microbial-biodiversity

display_name: microbial-biodiversity

microbial-functions

display_name: microbial-functions

soil

display_name: soil

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Climarctic seasons