en: rural bumblebees macronutrients nutrient-intake bern dietary-patterns pollen-metabarcoding foraging-behaviour bombus-pasquorum zurich lad-use-chages urban bombus-lapidarius bees basel

en:
The dataset contains the information on the 16 study sites:
- Location: the three Swiss cantons studied. Levels: Bern =BE; Basel = BS; Zurich =ZH
- Landscape: the two typea of human-dominated ecosystems, nested to the Swiss canton. Levels: Urban = U; Rural = R
- Replicate: sampling place (800-m buffer) nested with the location and the landscape. Levels: A-E.
- Site_ID: combination of location, landscape and replicate
- X: X coordinate, WGS84
- Y: Y coordinate WGS84
- Plant S in landscape: Plant species richness within the 800 m calculated using both information from the Global Biodiversity Information. Facility (GBIF) and InfoFlora (www.infoflora.ch).
- % Buildings: the proportion of buildings and other impervious surfaces within 800 m radius.
- % Agricultural: the proportion of agricultural land and other impervious surfaces within 800 m radius.
- % Water: the proportion of water and other impervious surfaces within 800 m radius.
- % Meadows & grasslands: the proportion of meadows and grasslands and other impervious surfaces within 800 m radius.
The raw metabarcoding data is archived in NCBI project PRJNA1137320.

en:
The dataset contains the fatty acid content and ratios for the two bumblebees. Measures are taken only from pollen from the leg-baskets. SampleID is not the same as in the metabarcoding, several samples from different bumblebees were merged within a Location ,Landscape and Replica. Not for all the samples there is information on both amino acids and fatty acids. The columns are:
- SampleID_chemical: The ID for chemical analyses
- Location: the three Swiss cantons studied. Levels: Bern =BE; Basel = BS; Zurich =ZH
- Landscape: the two typea of human-dominated ecosystems, nested to the Swiss canton. Levels: Urban = U; Rural = R
- Replicate: sampling place (800-m buffer) nested with the location and the landscape. Levels: A-E.
- Bumblebee_species: the two studied species, levels: B.pasquorum; B. lapidarius
- LocationLandscape: combination between location and landscape
- Caprylic, Pelargonic, ..., Lacceroic: Content of individual fatty acids acids.
- sum: total sum of fatty acid content (mg)
- unsaturatedsum: total sum unsaturated fatty acids (mg)
- saturatedsum: total sum saturated fatty acids (mg)
- omega3sum: total sum Omega 3 fatty acids (mg)
- omega6sum: total sum Omega 6 fatty acids (mg)
- omega9sum: total sum Omega 9 fatty acids (mg)
- omega6to3ratio: ratio Omega 6 to Omega3 fatty acids
-saturatedtounsaturatedration: ratio between saturated and unsaturated fatty acids

en:
The dataset contains the filtered plant composition1 (removing species with too low relative abundances) and the relative abundance obtained from DNA metabarcoding on pollen samples for both the leg-baskets and the body. The dataset contains the following columns:
- SampleID: Combining bumblebee ID, location, landscape and replica
- Abundance: numeric, relative abundance
- BumblebeeID: numerical ID for each bumblebee individual within a location, landscape and replicate sampling place.
- Location: canton, levels: Bern =BE; Basel = BS; Zurich =ZH
- Landscape: landscape type, levels: Urban = U; Rural = R
- Replicate: sampling place (800-m buffer) within location and landscape. Levels: A-E.
- LocationLandscape:
- Bumblebee_species: the two studied species, levels: B.pasquorum; B. lapidarius
- Transportation_structure: bumblebee pollen transportation structure, levels: B = Body; L = leg-baskets (i.e. corbicula)
- Kingddom: taxonomic
- Phylum: taxonomic
- Order: taxonomic
- Family: taxonomic
- Genus: taxonomic
- Species: plant sicentific name
-Binomial_abundance: Logic, indicating if the plant species is included (1).
The raw metabarcoding data is archived in NCBI project PRJNA1137320

en:
The dataset contains the plant trait values obtined from published sources. It contains:
- Kingddom: taxonomic
- Phylum: taxonomic
- Order: taxonomic
- Family: taxonomic
- Genus: taxonomic
- Species: species name
- plant.species: full scientific name
- native_exotic: origin status. As presented in https://www.doi.org/10.16904/envidat.210
- pollination mode: vector of pollination. As presented in https://www.doi.org/10.16904/envidat.210
- Flowering_months_duration: Number of flowering months. As presented in https://www.doi.org/10.16904/envidat.210
- start_flowering: month of the starting of the flowering. As presented in https://www.doi.org/10.16904/envidat.210
- growth_form_category: plant growth form. As presented in https://www.doi.org/10.16904/envidat.210
- plant-height: plant heght in meters. As presented in https://www.doi.org/10.16904/envidat.210
- inflorescence: Logic, plant with single flowers (0) or inflorescences (1). As presented in https://www.doi.org/10.16904/envidat.210
- structural_blossom_class: depicting the accessibility of the flowers. As presented in https://www.doi.org/10.16904/envidat.210
- symmetry: floral symmetry. As presented in https://www.doi.org/10.16904/envidat.210
- nectar: logic, nectar present. As presented in https://www.doi.org/10.16904/envidat.210
- pollen: logic, pollen rewards present. As presented in https://www.doi.org/10.16904/envidat.210
- oil: logic. oil present. As presented in https://www.doi.org/10.16904/envidat.210
- sugar.concentration: the amount of sugar present in the nectar, in mg.
Additional information can be found in https://doi.org/10.1016/j.dib.2021.107243

en:
Processed data. The dataset contains the phylogeneitc diversity metrics based on the plant composition from the leg-baskets per bumblebee individual. It contains:
- SampleID: Combining bumblebee ID, location, landscape and replica
- Location: canton, levels: Bern =BE; Basel = BS; Zurich =ZH
- Landscape: landscape type, levels: Urban = U; Rural = R
- Replicate: sampling place (800-m buffer) within location and landscape. Levels: A-E.
- LocationLandscape: combining location and landscape
- Bumblebee_species: the two studied species, levels: B.pasquorum; B. lapidarius
- PVar: phylogenetic variance
- PRic: Phylogenetic richness
- PEve: Phylogenetic evenness
- PClu: Phylogenetic clustering

en:
The dataset contains the amino acid content and ratios for the two bumblebees. Measures are taken only from pollen from the leg-baskets. SampleID is not the same as in the metabarcoding, several samples from different bumblebees were merged within a Location ,Landscape and Replica. Not for all the samples there is information on both amino acids and fatty acids. The columns are:
- SampleID_chemical: The ID for chemical analyses
- Location: the three Swiss cantons studied. Levels: Bern =BE; Basel = BS; Zurich =ZH
- Landscape: the two typea of human-dominated ecosystems, nested to the Swiss canton. Levels: Urban = U; Rural = R
- Replicate: sampling place (800-m buffer) nested with the location and the landscape. Levels: A-E.
- Bumblebee_species: the two studied species, levels: B.pasquorum; B. lapidarius
- LocationLandscape: Combining location and landscape
- Asp, Thr, ..., Arg: Content of individual amino acids. Amino acids are written as abbreviated names. FUll names be checked here: https://en.wikipedia.org/wiki/Amino_acid
- sum: total sum of amino acid content (mg)
- sumessential: total sum essential amino acids (mg)
-sumnon-essential: total sum non-essential amino acids (mg)
- proportionnonessentialessential: proportion between essential and non-essential amino acids.

en:
Processed data. The dataset contains the functional diversity metrics based on the plant composition from the leg-baskets per bumblebee individual. It contains:
- SampleID: Combining bumblebee ID, location, landscape and replica
- Location: canton, levels: Bern =BE; Basel = BS; Zurich =ZH
- Landscape: landscape type, levels: Urban = U; Rural = R
- Replicate: sampling place (800-m buffer) within location and landscape. Levels: A-E.
- LocationLandscape: combining location and landscape
- Bumblebee_species: the two studied species, levels: B.pasquorum; B. lapidarius
- Fric.w: Weighted functional richness
-Fric: functional richness
-FEve.w: Weighted functional evenness
- FEve: functional evenness
- FDiv.w: weighted functional divergence
- FDiv: functional divergence

en:
Processed data. The dataset contains the plant species richness in the leg-baskets per bumblebee individual. It contains:
- SampleID: Combining bumblebee ID, location, landscape and replica
- Location: canton, levels: Bern =BE; Basel = BS; Zurich =ZH
- Landscape: landscape type, levels: Urban = U; Rural = R
- Replicate: sampling place (800-m buffer) within location and landscape. Levels: A-E.
- LocationLandscape: combining location and landscape
- Bumblebee_species: the two studied species, levels: B.pasquorum; B. lapidarius
- Plant species richness: the number of plant species found in the pollen of the leg-baskets.

en:
Data on the diet of urban and rural bumblebees individuals, Bombus lapidarius and Bombus pascuorum, collected in Switzerland (cantons Bern, Basel and Zürich) obtained using metabarconding and chemical analyses from two pollen transportation structures, that is, the body and the leg-baskets (i.e., corbicula). The bumblebees were collected by Egenberger et al., 2019 (https://doi.org/10.5061/dryad.qt8mr4v)
The data contains:
- The composition and relartive abundance of plant species in the body and the leg-baskets, separately.
- The content and ratios of two groups of macronutrients, that is, amino acids and fatty acids.
The dataset contains the data used in the paper "Urban bumblebees diversify their foraging strategy to maintain nutrient intake" as well as unpublished data.

basel

display_name: basel

bees

display_name: bees

bern

display_name: bern

bombus-lapidarius

display_name: bombus-lapidarius

bombus-pasquorum

display_name: bombus-pasquorum

bumblebees

display_name: bumblebees

dietary-patterns

display_name: dietary-patterns

foraging-behaviour

display_name: foraging-behaviour

lad-use-chages

display_name: lad-use-chages

macronutrients

display_name: macronutrients

nutrient-intake

display_name: nutrient-intake

pollen-metabarcoding

display_name: pollen-metabarcoding

rural

display_name: rural

urban

display_name: urban

zurich

display_name: zurich


en: snowpack snow-cover snow-models snow snow-temperatures

state: active

type: dataset

en: Metadata 1991

media_type: text/plain

format: TXT

en: Dataset 1991

en:
During the development of DAISY, the snowpack model we
realised that we did not have enough accurate calibration measurements. We
needed more reliable measurements of snow temperatures and settlements
within the snow cover.

Therefore, from winter 1990/91 the thermal development of the season snow
cover in the test field with self-developed temperature harps was measured.
These temperature harps can move freely with the snow cover, in contrast to
the usually fixed temperature profiles.

With these harps, it became possible to monitor the temperatures and
settlements of the individual layers throughout the winter.

Additionally, the surface temperature, snow level and the usual
meteorological parameters such as air temperature, humidity, wind speed and
the radiation in various wavelength ranges were measured.

Furthermore, conventional snow profiles were recorded with measurements of
densities, hardness, grain sizes and grain shapes.

During three winters, this facility was intensively used for monitoring
purposes. The support and monitoring of these measurements and the
accompanying, very time-intensive manual measurements were carried out by
Peter Weilenmann and Franz Herzog.

The results of these measurements in winter 1990/91, 1991/92 and 1992/93
are given in the internal report No. 723.

The use of these measurements for the validation of DAISY and MiniDAISY are
gathered internally in report No. 724..726.

snow

display_name: snow

snow-cover

display_name: snow-cover

snow-models

display_name: snow-models

snow-temperatures

display_name: snow-temperatures

snowpack

display_name: snowpack

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

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: WFJ_CAL: Calibration dataset for snow models


maintainer: Matthias Haeni

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

en: Site photo of the forest station (BTB)

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

format: SERVICE

mimetype: image/jpeg

en: Field station data (BTF)

media_type: text/csv

en: Data file.

format: CSV

mimetype: text/csv

en: Site photo of the field station (BTF)

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

en: Data plot of the field station (BTF)

media_type: application/pdf

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

format: PDF

mimetype: application/pdf

en: Data plot of the forest station (BTB)

en: Site aerial photo (Bettlachstock)

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

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Bettlachstock in Switzerland where one station is located within a natural mixed forest stand (BTB) with European beech (_Fagus sylvatica_; 170-190 yrs), European silver fir (_Abies alba_; 190 yrs) and Norway spruce (_Picea abies_; 200 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, BTF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Bettlachstock 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: Bettlachstock, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards


restoring-grassland-multifunctionality

maintainer: EnviDat Support

title_for_slug: restoring-grassland-multifunctionality

en: soil-biodiversity plants fungi ecosystem-multifunctionality nematodes grassland bacteria restoration biodiversity mineralization

format: XLS

en: Please note that EMF, MF.below, MF.abio and Soil.hetero have been corrected.

en: Please note that C.sto and Corg have been corrected.

en: Functional diversity above_belowground fauna

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

Resch, M. C., Schütz, M., Buchmann, N., Frey, B., Graf, U., van der Putten, W. H., Zimmermann, S., Risch, A. C. 2021. Evaluating long-term success in grassland restoration – an ecosystem multifunctionality approach. Ecological Applications 31, e02271.

 Study area

The study was conducted in the Canton of Zurich, Switzerland, in and around two nature reserves Eigental and Altläufe der Glatt (47°27’ to 47°29’ N, 8°37’ to 8°32’ E, 417 to 572 m a.s.l.). All studied grasslands were located with a radius of approximately 4 km. Average monthly temperatures range from 0.7 ± 2.0 °C (January) to 19.0 ± 1.5 °C (July), and monthly precipitation range from 60 ± 42 mm (January) to 118 ± 46 mm (July [maxima]; 1989-2017; MeteoSchweiz 2018). In our study, we focused on semi-dry and semi-wet oligo- to mesotrophic grasslands characterized by high plant species richness and groundwater fluctuations throughout the year (Delarze et al. 2015, see also Resch et al. 2019).

 Experimental design and sampling

A large-scale restoration experiment to expand and reconnect isolated remnants of species-rich grasslands was initiated in the nature reserve Eigental in 1990. Twenty hectares of adjacent intensive grasslands were chosen for restoration. In 1995, three restoration methods of increasing intervention intensities were implemented. The goal of all three methods was to lower the availability of soil nutrients and hence, facilitate ecosystem development towards the targeted nutrient-poor grasslands. These methods were: Harvest only (hay harvest twice a year), Topsoil (removal of the nutrient-rich topsoil), and Topsoil+Propagules (topsoil removal combined with the application of hay from target vegetation). Plant biomass harvest (once a year in late summer/early autumn) commenced in Topsoil and Topsoil+Propagules five years after the soils were removed and is still ongoing today. We measured restoration success by comparing the three restoration methods with intensively managed (Initial) and semi-natural grasslands (Target) 22 years after restoration. Initial grassland sites share the same agricultural history as the restored sites: mowing and subsequent fertilizing (manure) up to five times a year, as well as different tillage regimes (Resch et al. 2019). Target sites were the sites from which hay for seeding the Topsoil+Propagules sites was collected. Soil conditions (i.e., soil types, soil texture) were comparable to those found in the restored grasslands (Resch et al. 2019). Additionally, Target sites were selected to represent a variety of semi-natural grasslands, including semi-dry to semi-wet conditions. In Target grasslands, biomass is harvested once a year in late summer or early autumn. Eleven 5 m x 5 m (25 m2) plots were randomly established in each of the five treatments (in total 55 plots; for a detailed map see Neff et al. 2020). An additional 2 m x 2 m (4 m2) subplot was randomly established at least 2 m away from each 25 m2 plot for destructive sampling. Data sampling took place between June and September 2017.

Vegetation properties

All plant species were identified within the 25 m2 plots (nomenclature: Lauber and Wagner 1996) in mid-June 2017 (in total 250 species). Vegetation structure and plant biomass were assessed diagonally on a transect of 2 m x 10 cm within the 25 m2 plot in early July 2017. We measured the maximum and mean height of the vegetation at the start, middle and end of the transect and calculated the standard deviation of these measures to describe vegetation structural heterogeneity (Schuldt et al. 2019). Thereafter, biomass was clipped on the entire transect to 1 cm height, sorted into five functional groups (graminoids, forbs, legumes, litter, and woody species), dried at 60 °C for 48 h, and weighed (Meyer et al. 2015).

 Aboveground arthropods

Aboveground arthropods were sampled at two locations in each 25 m2 plot in early July 2017 (see also Neff et al. 2020). Briefly, two cylindrical baskets (50 cm diameter, 67 cm height; woven fabric) were thrown simultaneously from outside the plot into two opposite corners. A closable mosquito mesh sleeve was mounted to the top of the baskets and an integrated metal ring at the bottom was fixed to the ground with metal stakes to assure that insects could not escape. A suction sampler (Vortis, Burkhard Manufacturing Co. Ltd., Hertfordshire, England) was then inserted into one of the baskets through the opening of the sleeve and the plot was “vacuumed" twice for 105 seconds with a 30 seconds break. The collected animals were immediately transferred into 70% ethanol. Arthropods were sorted and assigned to 23 taxonomic groups. Holometabolic larvae were lumped into one category while hemimetabolic larvae were grouped separately from adults in the respective taxonomic rank. We used mean values of individuals per plot for total abundance. Aboveground arthropod richness was defined by the number of different taxa to lowest taxonomic level (in total 23 taxa). All taxa were assigned to one of five trophic levels: 1) primary producers, 2) primary consumers, 3) secondary consumers, 4) tertiary consumers, and 5) quaternary consumers.

 Belowground fauna

Sampling of all belowground fauna took place in mid-July 2017. Earthworms were sampled in two 30 cm x 30 cm x 20 cm soil monoliths at two opposite corners of the 25 m2 plot (opposite to aboveground arthropod sampling). The excavated soil monolith was broken by hand, all earthworms collected and immediately transferred in a 4% formaldehyde solution. Thereafter, earthworm individuals were identified to species level (in total 10 taxa; Christian and Zicsi 1999) and species assigned to three functional groups (Bouché 1977).
To assess soil arthropod communities, we randomly collected one undisturbed soil core (5 cm diameter, 12 cm depth) in each 4 m2 subplot with a slide hammer corer lined with a plastic sleeve (AMS Samplers, American Falls, Idaho, USA). Soil arthropods were extracted using Berlese-Tullgren funnels (3 mm mesh), starting the day of sampling and lasting 14 days. Individuals were stored in 70% ethanol. Soil arthropods were assigned to 41 taxonomic groups and 4 feeding types. Holometabolic and hemimetabolic larvae were treated as previously described for aboveground arthropods. Belowground arthropod richness refers to the 41 taxonomic groups.

For soil nematode sampling, we randomly collected eight soil cores of 2.2 cm diameter (Giddings Machine Company, Windsor, CO, USA) within each 4 m2 subplot to a depth of 12 cm. The eight cores were combined, gently homogenized, placed in coolers, kept at 4 °C and transported to the laboratory at NIOO in Wageningen (NL) within one week after collection. Free-living nematodes were extracted from 200 g of fresh soil using Oostenbrink elutriator (Oostenbrink 1960) and prepared for morphological identification and quantification as described by Resch et al. (2019). Nematodes were identified to family level (39 taxa) according to Bongers (1988), assigned to 17 functional groups, 5 feeding types and 5 colonizer-persister (C-P) classes (Yeates et al. 1993, Bongers 1990, Resch et al. 2019).

We randomly collected two more soil cores (2.2 cm diameter x 12 cm depth) within each 4 m2 subplot to determine soil microbial communities. Again, the soil cores were combined, homogenized, placed in coolers and transported to the laboratory at WSL in Birmensdorf (Switzerland) where the metagenomic DNA was extracted from 8 g sieved soil (2 mm) using the DNeasy PowerMax Soil Kit (Quiagen, Hilden, NRW, GER) according to the manufacturer`s instructions. PCR amplification of the V3-V4 region of the prokaryotic small-subunit (16S) and the ribosomal internal transcribed spacer region (ITS2) of eukaryotes was performed with 1 ng of template DNA utilizing PCR primers and conditions as previously described (Frey et al., 2016). PCRs were run in triplicates and pooled. The pooled amplicons were sent to the Genome Quebec Innovation Centre (Montreal, QC, Canada) for barcoding using the Fluidigm Access Array technology (Fluidigm) and paired-end sequencing on the Illumina MiSeq v3 platform (Illumina Inc., San Diego, CA, USA). Quality filtering, clustering into operational taxonomic units (OTUs) and taxonomic assignment were performed as described by Frey et al. (2016) and Adamczyk et al. (2019). We used a customised pipeline largely based on UPARSE (Edgar 2013) implemented in USEARCH v. 9.2 (Edgar 2010). After discarding singletons of dereplicated sequences, clustering into OTUs with 97% sequence similarity was performed (Edgar 2013). Quality-filtered reads were mapped on the filtered set of centroid sequences. Taxonomic classification of prokaryotic and fungal sequences was conducted querying against most recent versions of SILVA (v.132, Quast et al. 2013) and UNITE (v.8, Nilsson et al. 2018). Only taxonomic assignments with confidence rankings equal or higher than 0.8 were accepted (assignments below 0.8 set to unclassified). Prokaryotic OTUs assigned to mitochondria or chloroplasts as well as eukaryotic OTUs assigned other than fungi were removed prior to data analysis. In addition, prokaryotic and fungal datasets were filtered to discard singletons and doubletons. Thereafter, OTU abundance matrices were rarefied to the lowest number of sequences per community, to normalize the total number of reads and achieve parity between samples (Prokaryota: 29,843 reads; Fungi: 26,690 reads). Finally, prokaryotic and fungal observed richness (number of OTUs) were estimated (Prokaryota: 14,010 OTUs; Fungi: 5,813 OTUs). For prokaryotes, we distinguished five and for fungi six functional types based on lowest taxonomic resolution (Nguyen et al. 2016, Tedersoo et al. 2014). Belowground taxon richness was defined by the total number of earthworm, arthropod, nematode, fungi, and prokaryote taxa assigned to lowest taxonomic level. Finally, all belowground taxa were assigned to the same five trophic levels as the aboveground arthropods.

 Soil chemical and physical properties, soil nitrogen mineralization

We randomly collected three 5 cm diameter x 12 cm depth soil samples in each 4 m2 subplot with a slide hammer corer (AMS Samplers, American Falls, Idaho, USA), pooled them and then made two subsamples. One was field-fresh and stored at 3 °C until analysis, the other was dried for 48 h at 60 °C and passed through a 4 mm mesh. From the dried sample, we measured soil pH potentiometrically in 0.01 M CaCl2 (soil:solution ratio=1:2; 30 minutes equilibration time). Total and organic carbon content were measured on fine-ground samples (≤ 0.5 mm) by dry combustion using a CN analyzer NC 2500 (CE Instruments, Wigan, United Kingdom). Inorganic carbon of samples with a pH > 6.5 was removed with acid vapor prior to analysis of organic carbon (Walthert et al. 2010). We calculated total soil carbon (C) storage after correcting its content for soil depth, stone content and density of fine earth (see below). Exchangeable cations were determined on another 5 g dry soil sample with 50 mL unbuffered 1 M NH4Cl solution (soil:solution ratio=1:10, end-over-end shaker for 1.5 hours) and measured by an ICP-OES (Optima 7300 DV, Perkin-Elmer, Waltham, Massachusetts, USA). Thereafter, cation exchange capacity (CEC) was calculated as the sum of exchangeable cations and protons (and expressed as mmolc per 1 kg soil) and used to describe nutrient retention capacity in our plots. Concentrations of exchangeable protons were calculated as the difference between total and Al-induced exchangeable acidity as determined by the KCl-method (Thomas 1982).

Ammonium (NH4+) and nitrate (NO3−) were extracted from a 20 g fresh subsample with 80 mL 1M KCl for 1.5 hours on an end-over-end shaker and filtered through ashless folded filter paper (DF 5895 150, ALBET LabScience, Hahnemühle FineArt GmbH, Dassel, Germany). NH4+ concentrations were determined colorimetrically by automated flow injection analysis (FIAS 300, Perkin-Elmer, Waltham, Massachusetts, USA). NO3− concentrations were measured colorimetrically according to Norman and Stucki (1981). Potential soil net nitrogen (N) mineralization was assessed during an 8-week incubation period under controlled moisture (60% of field capacity), temperature (20 °C) and light conditions (dark) in the laboratory. We weighed duplicate samples of fresh soil equivalent to 8 g dry soil (24 h at 104 °C) into 50 mL Falcon tubes. Soil samples were extracted for NH4+ and NO3− at the beginning and after eight weeks as described above. Soil net N mineralization was calculated as the difference between the inorganic nitrogen (NH4+ and NO3−) before and after the incubation (Hart et al. 1994), corrected for the total incubation time and represented per day values expressed as mg N kg-1 soil d-1.

To assess soil physical properties, we randomly collected one undisturbed soil core per 4 m2 subplot (5 cm diameter, 12 cm depth) in a steel cylinder that fit into the slide hammer (AMS Samplers, American Falls, Idaho, USA). The cylinder was capped in the field to avoid disturbance. We then measured field capacity in the laboratory. For this purpose, the cylinder and soil therein were saturated in a water bath and drained on a sand/silt-bed with a suction corresponding to 60 cm hydrostatic head. The moist soil was dried at 105 °C to constant weight. Field capacity was calculated by dividing the mass of water by the total mass of wet soil contained at 60 cm hydrostatic head and used to describe water holding capacity. Thereafter, samples were passed through a 4 mm mesh. Fine-earth and skeleton fractions were weighed separately to assess bulk soil density (fine-earth plus skeleton), density of fine earth, and proportion of skeleton. Particle density was determined with the pycnometer method (Blake and Hartge 1986), and total porosity and proportion of fine pores were calculated (Danielson and Sutherland 1986). Clay, silt, and sand contents were quantified with the sediment method (Gee and Bauder 1986).

Surface and soil temperature (12 cm depth, water-resistant digital pocket thermometer; IP65, H-B Instrument, Trappe, Pennsylvania, USA) as well as volumetric soil moisture content (12 cm depth, time domain reflectometry; Field-Scout TDR 300, Spectrum Technologies, Aurora, Illinois, USA) were measured at five random locations within the 4 m2 subplots every month from June to September. We calculated the standard deviation of each temperature and moisture measure over four months to describe seasonal variations. Slope inclination was determined at plot-level via GPS measurements (GPS 1200, Leica Geosystem, Heerbrugg, Switzerland) and categorized into slope gradient classes according to FAO standards (1990). Thickness of the topsoil horizon (equivalent to Ah or Aa horizon) was determined at one soil monolith (30 x 30 x 30 cm3) per 4 m2 subplot in cm and rounded to next integer.

 References

Adamczyk, M., F. Hagedorn, S. Wipf, J. Donhauser, P. Vittoz, C. Rixen, A. Frossard, J. Theurillat, and B. Frey. 2019. The soil microbiome of GLORIA mountain summits in the Swiss Alps. Frontiers in Microbiology 10:1080-1101.

Blake, G.R., and K. H. Hartge. 1986. Particle Density. Pages 377-382 in A. Klute, editor. Methods of soil analysis: Part 1—Physical and mineralogical methods. Soil Science Society of America (SSSA) Inc., Madison.

Bongers, T. 1988. De nematoden van Nederland. Stichting Uitgeverij van de Koniklijke Nederlandse Natuurhistorische Verenigung (KNNV), Utrecht.

Bongers, T. 1990. The maturity index: an ecological measure of environmental disturbance based on nematode species composition. Oecologia 83:14-19.

Bouché, M. B. 1977. Strategies lombriciennes. Ecological Bulletins 25:122-132.

Christian, E., and A. Zicsi. 1999. Ein synoptischer Bestimmungsschlüssel der Regenwürmer Österreichs (Oligochaeta: Lumbricidae). Die Bodenkultur 50:121-131.

Danielson, R. E., and P. L. Sutherland. 1986. Porosity. Pages 443-461 in A. Klute, editor. Methods of soil analysis: Part 1—Physical and mineralogical methods. Soil Science Society of America (SSSA) Inc., Madison.

Delarze, R., Y. Gonseth, S. Eggenberg, and M. Vust. 2015. Lebensräume der Schweiz: Ökologie ‐ Gefährdung ‐ Kennarten, Ott Verlag, Bern.

Edgar, R. C. 2010. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26:2460–2461.

Edgar, R. C. 2013. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature Methods 10:996–998.

FAO. 1990. Guidelines for soil description, third ed. Land and Water Development Division at the Food and Agriculture Organization of the United Nations (FAO), Rome.

Frey, B., T. Rime, M. Phillips, B. Stierli, I. Hajdas, F. Widmer, and M. Hartmann. 2016. Microbial diversity in European alpine permafrost and active layers. FEMS Microbiology Ecology 92:fiw018.

Gee, G.W., and J. W. Bauder. 1986. Particle-size analysis. Pages 383-411 in A. Klute, editor. Methods of soil analysis: Part 1—Physical and mineralogical methods. Soil Science Society of America (SSSA) Inc., Madison.

Hart, S. C, J. M. Stark, E. A. Davidson, and M. K. Firestone. 1994. Nitrogen mineralization, immobilization, and nitrification. Pages 985-1016 in R. W. Weaver, S. Angle, P. Bottomley, D. Bezdicek, S. Smith, A. Tabatabai, and A. Wollum, editors. Methods of soil analysis: Part 2—Microbiological and biochemical properties. Soil Science Society of America (SSSA) Inc., Madison.

Lauber, K., Wagner, G., 1996. Flora Helvetica. Flora der Schweiz. Haupt Verlag, Bern.

MeteoSchweiz, 2018. Klimabulletin Jahr 2017. MeteoSchweiz, Zürich.

Meyer, S. T., C. Koch, and W. W. Weisser. 2015. Towards a standardized rapid ecosystem function assessment (REFA). Trends in Ecology and Evolution 30:390-397.

Neff, F., M. C. Resch, A. Marty, J. Rolley, M. Schütz, A. C. Risch, and M. M. Gossner. 2020. Long-term restoration success of insect herbivore communities in semi-natural grasslands – a functional approach. Ecological Applications 0:e02133

Nguyen, N.H., Z. W. Song, S. T. Bates, S. Branco, L. Tedersoo, J. Menke, J. S. Schilling, and P. G. Kennedy. 2016. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecology 20:241–248.

Nilsson, R. H., K.-H. Larsson, A. F. S. Taylor, J. Bengtsson-Palme, T. S. Jeppesen, D. Schigel, P. G. Kennedy, K. Picard, F. O. Glöckner, L. Tedersoo, I. Saar, U. Kõljalg, and K. Abarenkov. 2018. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Research 47:259–264.

Norman, R. J., and J. W. Stucki. 1981. The Determination of Nitrate and Nitrite in Soil Extracts by Ultraviolet Spectrophotometry. Soil Science Society of America Journal 45:347-353.

Oostenbrink, M. 1960. Estimating nematode populations by some selected methods. Pages 81-101 in J. J. Sasser, and W. R. Jenkins, editors. Nematology. Univ. of North Carolina Press, Chapel Hill.

Quast, C., E. Pruesse, P. Yilmaz, J. Gerken, T. Schweer, P. Yarza, J. Peplies, and F. O. Glöckner. 2013. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Research 41:590-596.

Resch, M. C., M. Schütz, U. Graf, R. Wagenaar, W.H. van der Putten, and A. C. Risch. 2019. Does topsoil removal in grassland restoration benefit both soil nematode and plant communities? Journal of Applied Ecology 56:1782-1793.

Schuldt, A., A. Ebeling, M. Kunz, M. Staab, C. Guimarães-Steinicke, D. Bachmann, N. Buchmann, W. Durka, A. Fichtner, F. Fornoff, W. Härdtle, L. R. Hertzog, A-N. Klein, C. Roscher, J. Schaller, von G. Oheimb, A. Weigelt, W. Weisser, C.Wirth, J. Zhang, H. Bruelheide, and N. Eisenhauer. 2019. Multiple plant diversity components drive consumer communities across ecosystems. Nature Communications 10:1460.

Tedersoo, L., M. Bahram, S. Põlme, U. Kõljalg, N. S. Yorou, R. Wijesundera, L. Villarreal Ruiz, A. M. Vasco-Palacios, P. Q. Thu, A. Suija, M. E. Smith, C. Sharp, E. Saluveer, A. Saitta, M. Rosas, T. Riit, D. Ratkowsky, K. Pritsch, K. Põldmaa, M. Piepenbring, C. Phosri, M. Peterson, K. Parts, K. Pärtel, E. Otsing, E. Nouhra, A. L. Njouonkou, R. H. Nilsson, L. N. Morgado, J. Mayor, T. W. May, L. Majuakim, D. J. Lodge, S. See Lee, K.-H. Larsson, P. Kohout, K. Hosaka, I. Hiiesalu, T. W. Henkel, H. Harend, L.-D. Guo, A. Greslebin, G. Grelet, J. Geml, G. Gates, W. Dunstan, C. Dunk, R. Frenkhan, L. Dearnaley, A. De Kesel, T. Dang, X. Chen, F. Buegger, F. Q. Brearley, G. Bonito, S. Anslan, S. Abell, and K. Abarenkov. 2014. Global diversity and geography of soil fungi. Science 346:1256688.

Thomas, G.W. 1982. Exchangeable cations. Pages 159-165 in A. L. Page, R. H. Miller, and D. R. Keeney, editors. Methods of Soil Analysis: Part 2—Chemical and microbiological properties. Soil Science Society of America (SSSA) Inc., Madison.

Walthert, L., U. Graf, A. Kammer, J. Luster, D. Pezzotta, S. Zimmermann, and F. Hagedorn. 2010. Determination of organic and inorganic carbon, δ13C, and nitrogen in soils containing carbonates after acid fumigation with HCl. Journal of Plant Nutrition and Soil Sciences 173:207-216.

Yeates, G. W., T. Bongers, R. G. M. de Goede, D. W. Freckman, and S. S. Georgieva. 1993. Feeding habits in soil nematode families and genera – an outline for soil ecologists. Journal of Nematology 25:315-331.

bacteria

display_name: bacteria

biodiversity

display_name: biodiversity

ecosystem-multifunctionality

display_name: ecosystem-multifunctionality

fungi

display_name: fungi

grassland

display_name: grassland

mineralization

display_name: mineralization

nematodes

display_name: nematodes

plants

display_name: plants

restoration

display_name: restoration

soil-biodiversity

display_name: soil-biodiversity

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Restoring grassland multifunctionality


maintainer: Mountain Hydrology and Mass Movements

en: rockfall-fatalities earthquake-fatalities windstorm-fatalities landslide-fatalities flood-fatalities debris-flow-fatalities natural-hazard-fatalities lightning-fatalities ice-avalanche-fatalities

en: Natural hazard fatalities per year in Swizterland since 1946.

en:
This dataset contains the number of fatalities due to flood, debris flow, landslide, rockfall, windstorm, lightning, ice avalanche, earthquake and other processes like roof avalanche or lacustrine tsunami for each year since 1946.
The following information is contained (by column and column title):

* year
* total number of hazard fatalities
* number of fatalities by flood (German: Hochwasser, Überschwemmung). Flood includes people drowned in flooded or inundated areas or carried away in streams under high-water conditions.
* number of fatalities by debris flow (German: Murgang).
* number of fatalities by landslide (German: Erdrutsch). Landslide includes people killed by landslides and hillslope debris flows (German: Hangmure).
* number of fatalities by rockfall (German: Steinschlag, Fels- und Bergsturz).
* number of fatalities by windstorm (German: Sturm). Windstorm includes people killed by falling objects or trees during very strong wind conditions and people who drowned in lakes because their boat capsized during such conditions.
* number of fatalities by lightning (German: Blitz).
* number of fatalities by ice avalanche (German: Eislawine).
* number of fatalities by earthquake (German: Erdbeben).
* number of fatalities by other processes like roof avalanche, lacustrine tsunami (German: andere Prozesse wie Dachlawine, Tsunami im See).

The data was collected based on newspaper research. For more information please refer to

_Badoux, A., Andres, N., Techel, F., and Hegg, C.: Natural hazard fatalities in Switzerland from 1946 to 2015, Nat. Hazards Earth Syst. Sci., 16, 2747-2768, https://doi.org/10.5194/nhess-16-2747-2016, 2016._

The data collection is financed by the FOEN (with exception of the collection of the avalanche fatalities). The data contains the official statistics of the FOEN on fatalities due to flood, debris flow, landslide, rock fall and avalanche.

Restrictions: The data set is not complete. Only fatalities in or around settlements and on open transportation routes are included. More precisely, fatalities were not collected, when persons exposed themselves to a great danger on purpose. Or fatalities during leisure activities which are connected to a higher risk were not included (this includes e.g. canoeing or river surfing during flood, canyoning, mountaineering, climbing, walking or driving on a closed road).

Fatalities by avalanches are collected at the WSL Institute for Snow and Avalanche Research SLF. You can download the avalanche fatalities per hydrological year [here](https://www.envidat.ch/dataset/avalanche-fatalities-switzerland-1936) and per calendar year [here](https://www.envidat.ch/dataset/avalanche-fatalities-per-calendar-year-since-1936). For a direct comparison with the fatalities presented here, please download the data set with the calendar years and do not consider fatalities in the backcountry (tour) or in terrain close to ski areas (offpiste).

debris-flow-fatalities

display_name: debris-flow-fatalities

earthquake-fatalities

display_name: earthquake-fatalities

flood-fatalities

display_name: flood-fatalities

ice-avalanche-fatalities

display_name: ice-avalanche-fatalities

landslide-fatalities

display_name: landslide-fatalities

lightning-fatalities

display_name: lightning-fatalities

natural-hazard-fatalities

display_name: natural-hazard-fatalities

rockfall-fatalities

display_name: rockfall-fatalities

windstorm-fatalities

display_name: windstorm-fatalities

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: Number of natural hazard fatalities per year in Switzerland since 1946


maintainer: Marco Moretti

en: wild-bees zurich urban-ecosystems gardens urbanization urban-biodiversity resource-availability honeybees bettergardens city4bees beekeeping pollinator-experiment

en:
Mean, standard deviation , minimum and maximum values of six functional traits. The data has the following structure:
- Bee_species: bee scientific name
- mean_trait: mean value
- sd_trait: standard deviation
_min_trait: minimum value
- max_trait: maximum value
- trait: the six traits used. ITD = Intertegular distance (mm); Pheno.duration = duration of the phenology; Pheno.start = start of the phenology; Pheno.end = end of the phenology; Rel.tongue.length= relative tongue length (mm); Daytime.activity = daily activity (h)
DistNichePart: functional dissimilarity in a multidimentional trait space defined by five traits between wild bee individuals of a wild bee species and the honeybee individuals. Values are at the species level.

en:
The data contains the following columns:

  SiteID: numeric, the identity of the garden where the bee was sampled.
  Taxonname: character, species name
  Lecty: numeric discreet, a mesure of specialisation degree. Levels: 1 = monolectic; 2=oliolectic; 3=polilectic
  Pheno.start= numeric discreet, the starting week of the flying activity.
  Pheno.end= numeric discreet, the ending week of the flying activity.
  Pheno.duration=numeric discreet, Pheno.end - Pheno.start
  Daytime.activity = numeric continuous, individual-level measure of the day hour when the bee was sampled
  ITD = numeric continuous, individual-level measure of the InterTegular Distance, a metric of mobility in mm.
  Rel.tongue.length =numeric continuous, individual-level measure of the relative tongue length (Tongue length / ITD), a metric of specilaisation degree, in mm.

en: Response and predictors used for the SEMs

en: Metadata for the predictor and response dataset

en: Metadata for the predictors and response dataset

en:
Cities are socio-ecological systems that filter and select species, thus establishing unique species assemblages and biotic interactions. Urban ecosystems can host richer wild bee communities than highly intensified agricultural areas, specifically in resource-rich urban green spaces such as allotment and family gardens. At the same time, urban beekeeping has boomed in many European cities, raising concerns that the fast addition of a large number of managed bees could deplete the existing floral resources, triggering competition between wild bees and honeybees. The data has been used to investigated the interplay between resource availability and the number of honeybees at local and landscape scales and how this relationship influences wild bee diversity.

This dataset contains the raw and processed data supporting the findings from the paper: "Low resource availability drives feeding niche partitioning between wild bees and honeybees in a European city".

The data contains:
1. Bee trait measurements at the species and individual-level of five functional traits.
2. The values of the feeding niche partitioning (functional dissimilarity to honeybees)
3. The predictors of resource availability and beekeeping intensity at local and landscape scales used in the modelling of the paper for the 23 experimental sites.

beekeeping

display_name: beekeeping

bettergardens

display_name: bettergardens

gardens

display_name: gardens

honeybees

display_name: honeybees

pollinator-experiment

display_name: pollinator-experiment

resource-availability

display_name: resource-availability

urban-biodiversity

display_name: urban-biodiversity

urban-ecosystems

display_name: urban-ecosystems

urbanization

display_name: urbanization

wild-bees

display_name: wild-bees

zurich

display_name: zurich

en: Niche partitioning between wild bees and honeybees


en: edna african-manatee biodiversity-monitoring environmental-dna crispr-dx crispr-cas threatened-species qpcr field-based-protocol

media_type: application/zip

en:
Folder containing the data files with Cp values (qPCR) and the CRISPR-Dx classifications. CRISPR_field_based_classifications.csv file contains the classifications per pool per extract (0: no detection, 1: detection) for the African manatee (Trichechus senegalensis).
CRISPR_lab_based_classifications.csv file contains the classifications per pool per extract (0: no detection, 1: detection).
qPCR_Cp_2nd_derivative.csv file contains the Cp value of the 8 replicates of each extract.

format: ZIP

en: File containing overview of site names and extract number (field-based or lab-based extraction). The extract_number_field_based corresponds to the identically called column in CRISPR_field_based_classifications.csv file in the data folder, as the extract_number_lab_based to the column in the file CRISPR_lab_based_classifications.csv file and the qPCR_Cp_2nd_derivative.csv file (also in the data folder). The file includes the GPS coordinates of the sampling location (transect start point).

en: Description of project and folders and files

en:
Data associated to the publication "Rapid field-based detection of a threatened and elusive species with environmental DNA and CRISPR-Dx"

 Authors:

Flurin Leugger, Martina Lüthi, Michel Schmidlin, Zacharias Kontarakis and Loïc Pellissier

 Contact:

flurinleugger@gmail.com
loic.pellissier@usys.ethz.ch

 Project description:

We collected eDNA samples in the Conkouati-Douli National Park in the Republic of Congo in 2023 in collaboration with Beauval Nature and HELP Congo. We analyzed the samples on site in the national park using CRISPR-Dx assays to detect the African manatee (*Trichechus senegalensis*). Additionally, we transported the remaining samples (buffer) to Switzerland and used a high-sensitivity protocol with CRISPR-Dx assays and a qPCR assay to verify the field-based detections. The qPCR of Hunter et al. (2018) detects the genus manatee (Trichechus). See publication for more details (doi will be added after publication).

 Folders:

 lateral_flow_tests_images\:

Images of the lateral flow tests used to analyze the eDNA extracts in the national park. File names correspond to the site names. At each site, we took two filter replicates. We ran 8 PCRs per filter/extract and pooled two PCRs together, resulting in 4 CRISPR-Dx assays per extract. The EVE number on top of the tests indicates the environmental extract number we use to track the extracts in our data base. It corresponds to the extract_number_field_based in the metadata and data folder. The negative extraction control with the strongest "band" was placed right to the eDNA extract on each image (labelled hand written with NC x.y).

 metadata\:

File containing overview of site names and extract number (field-based or lab-based extraction). The extract_number_field_based corresponds to the identically called column in CRISPR_field_based_classifications.csv file, as the extract_number_lab_based to the column in the file CRISPR_lab_based_classifications.csv file and the qPCR_Cp_2nd_derivative.csv file. The file includes the GPS coordinates of the sampling location (transect start point).

 data\:

Folder containing the files with Cp values (qPCR) and the CRISPR-Dx classifications. CRISPR_field_based_classifications.csv file contains the classifications per pool per extract (0: no detection, 1: detection) for the African manatee (Trichechus senegalensis).
CRISPR_lab_based_classifications.csv file contains the classifications per pool per extract (0: no detection, 1: detection).
qPCR_Cp_2nd_derivative.csv file contains the Cp value of the 8 replicates of each extract.

african-manatee

display_name: african-manatee

biodiversity-monitoring

display_name: biodiversity-monitoring

crispr-cas

display_name: crispr-cas

crispr-dx

display_name: crispr-dx

edna

display_name: edna

environmental-dna

display_name: environmental-dna

field-based-protocol

display_name: field-based-protocol

qpcr

display_name: qpcr

threatened-species

display_name: threatened-species

en: Field-based eDNA analysis to detect the threatened and elusive African manatee


en: climate-change scenario landscape-change visualization policy-action

en: Entremon subtitles FR

en: Scenarios of landscape change under climate change in the Swiss mountains. Trailer in French with subtitles.

en: Entremont subtitles DE

en:
Scenarios of landscape change under climate change in the Swiss mountains.
Trailer in German with written subtitles.

en: Seeland subtitles FR

en:
Scenarios of landscape change under climate change in the Swiss lowlands.
Trailer in French with subtitles.

en: Seeland subtitles DE

en:
Scenarios of landscape change under climate change in the Swiss lowlands.
Trailer in German with subtitles.

en:
Short videos showing scenarios of potential landscape development in a 4°C warmer climate for a Swiss mountain region and a region on the Swiss Plateau. The videos are in German or French with subtitles.
The videos' key message is: Climate change will have an impact on the Swiss landscapes. However, according to the strategy chosen to adapt to climate change, society and politics will also have a strong influence on the future landscapes and the services they will provide. If no action is taken to adapt to climate change today, then society will have to react in a hurry at a later point in time. This can often result in resource-intensive technical solutions. Conversely, if society takes action in an anticipatory way already today, nature-based solutions can be implemented more successfully and humans can benefit more from the landscape services..
These short videos are teasers that should make the viewers curious to visit the panorama pictures (https://viergrad.envidat.ch/).

climate-change

display_name: climate-change

landscape-change

display_name: landscape-change

policy-action

display_name: policy-action

scenario

display_name: scenario

visualization

display_name: visualization

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Teaser videos Swiss landscapes under climate change


maintainer: Franziska Gerber

en: wrf landuse static-data topography cryowrf cosmo-wrf

en: Static data for WRF - Soiltype bottom

en: Documentation

en: Documentation of the dataset

en: Example namelist.wps to use with the 1s static data

en: Static data for WRF - Topography

en:
Example GEOGRID.TBL to use with the 1s static data;
Note about naming:
* 121 -> 1-2-1 topography smoothing applied to dataset
* 1 -> one smoothing cycle applied to the data (needs to be changed depending on the chosen resolution to avoid too steep slope angles)

en: Static data for WRF - Soiltype top

en: Static data for WRF - Landuse

en: Static input data (topography, landuse and soiltype) for the WRF preprocessing system WPS is provided for Switzerland and its neighboring countries between 45-49 N and 4-12 E. The data is provided at a resolution of 1 s. Topography is based on the Aster dataset, while landuse is extracted from the Corine landuse dataset. Soil type is set to silty clay loam for the entire domain. This static input data is valid for WRF and CRYOWRF.

cosmo-wrf

display_name: cosmo-wrf

cryowrf

display_name: cryowrf

landuse

display_name: landuse

static-data

display_name: static-data

topography

display_name: topography

wrf

display_name: wrf

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: High resolution static data for WRF over Switzerland


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

en: Site photo of the forest station (VOB)

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

en: Field station data (VOF)

en: Data file.

en: Forest station data (VOB)

en: Data plot of the forest station (VOB)

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

en: Site aerial photo (Vordemwald)

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

en: Site photo of the field station (VOF)

en: Site photo of the field stationfrom the side.

en: Data plot of the field station (VOF)

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 Vordemwald in Switzerland where one station is located within a natural mixed forest stand (VOB) with European silver fir (_Abies alba_; 110 yrs) and oak trees (_Quercus sp._; 190-210 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, VOF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Vordemwald is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

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


en: climate-change european-alps fire mountains meteorological-data europe climate indicator-values climate-indicators forest-fire

en: Data access

en: This dataset contains the Fire Weather Index for Hydrological Bavaria from 1980 - 2099 as stated in the paper "Climate change impacts on regional fire weather in heterogeneous landscapes of Central Europe" published in Natural Hazards and Earth System Sciens (NHESS) 2023. The dataset contains daily Fire Weather Index values for all 50 members (subfolders of the dataset) of the CRCM5-LE (11 km spatial resolution) from 1980 to 2099 over the domain of Hydrological Bavaria. Please cite this dataset as the publication: Miller, J., Böhnisch, A., Ludwig, R., & Brunner, M. I. (2023). Climate change impacts on regional fire weather in heterogeneous landscapes of Central Europe. Natural Hazards and Earth System Sciences Discussions, 1-25. doi: 10.5194/nhess-2023-51.

climate

display_name: climate

climate-change

display_name: climate-change

climate-indicators

display_name: climate-indicators

europe

display_name: europe

european-alps

display_name: european-alps

fire

display_name: fire

forest-fire

display_name: forest-fire

indicator-values

display_name: indicator-values

meteorological-data

display_name: meteorological-data

mountains

display_name: mountains

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: Fire Weather Index for Hydrological Bavaria from 1980-2099 derived from the 50 member CRCM5-LE


en: grassland-management biodiversity disturbance land-use-abandonment vascular-plant

en: Boch et al. - Vegetation records Alpengarten Schynige Platte 2010-2015.xlsx

en: Vegetation records (species lists, percentage cover of species) of a six-year experiment to test whether different land-use treatments affect species diversity and composition in alpine to subalpine graslands.

en: This dataset was used for the publication "Boch, Blaser, Föhr, Küchler, Fischer (2024) Haymaking complemented by moderate disturbances can sustain and restore species-rich alpine to subalpine grasslands" in Alpine Botany. It contains vegetation records (species lists, percentage cover of species) of six grassland sites with each five 2x2 m treatment plots established in the Schynige Platte Alpine Botanical Garden. In a six-year experiment, we tested the response of plant species richness and mean ecological indicator values that we calculated from this dataset, to management treatments ranging from combined haymaking and scraping, to haymaking or scraping alone, to complete abandonment. We further tested whether the treatments led to changes in species composition, i.e. shifts to different vegetation types.

biodiversity

display_name: biodiversity

disturbance

display_name: disturbance

grassland-management

display_name: grassland-management

land-use-abandonment

display_name: land-use-abandonment

vascular-plant

display_name: vascular-plant

en: Schynige Platte Alpine Botanical Garden land-use experiment


aerosol-data-davos-wolfgang

title_for_slug: aerosol-data-davos-wolfgang

en: inp raclets drincz ice-nucleation aerosol particles

en: OPC-N3 Dataset Davos Wolfgang

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: APS Dataset Davos Wolfgang

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 Davos Wolfgang.

mimetype: application/x-netcdf

en: OPC GT-526S Dataset Davos Wolfgang

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: SMPS Dataset Davos Wolfgang

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 Davos Wolfgang.

en: DRINCZ Dataset Davos Wolfgang

en: The DRINCZ 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:
Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Davos Wolfgang (LON: 9.853594, LAT: 46.835577). 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 (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 (3788 , 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 DRINCZ: 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 minˉ¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the DRoplet Ice Nuclei Counter Zurich (DRINCZ, ETH Zurich) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles.

aerosol

display_name: aerosol

drincz

display_name: drincz

ice-nucleation

display_name: ice-nucleation

inp

display_name: inp

particles

display_name: particles

raclets

display_name: raclets

en: Aerosol Data Davos Wolfgang


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

en: Predictors

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

mimetype: application/zip

en: Description

en: Description of the predictors provided in this dataset

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

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

Paper Citation:

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

ecological-indicator-values

display_name: ecological-indicator-values

ecological-maps

display_name: ecological-maps

landolt

display_name: landolt

plants

display_name: plants

soil-mapping

display_name: soil-mapping

soil-moisture

display_name: soil-moisture

soil-ph

display_name: soil-ph

switzerland

display_name: switzerland

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.


fr: Annuaire statistique de la Suisse 1899

media_type: text/html

format: HTML

mimetype: text/html

de: Statistisches Jahrbuch der Schweiz 1899

basas-statisticas-e-survistas

display_name: basas-statisticas-e-survistas

bases-statistiques-et-generalites

display_name: bases-statistiques-et-generalites

grundlagen-und-ubersichten

display_name: grundlagen-und-ubersichten

statistical-basis-and-overviews

display_name: statistical-basis-and-overviews

spatial: Schweiz

gove

fr: Gouvernement et secteur public

de: Regierung und öffentlicher Sektor

en: Government and public sector

it: Governo e settore pubblico

bundesamt-fur-statistik-bfs

fr: Office fédéral de la statistique OFS

de: Bundesamt für Statistik BFS

en: Federal Statistical Office FSO

it: Ufficio federale di statistica UST

fr: L'OFS est le centre de compétence de la statistique publique suisse. Il produit des informations statistiques sur la population, l'économie, la société, la formation et la recherche, le territoire et l'environnement. Ces informations servent à la formation de l’opinion publique. Elles sont un outil de planification et de gouvernance dans tous les domaines de l’action politique et contribuent au bon fonctionnement de notre État moderne et démocratique.

de: Das BFS ist das nationale Kompetenzzentrum der öffentlichen Statistik der Schweiz. Es produziert und publiziert statistische Informationen über den Stand und die Entwicklung von Bevölkerung, Wirtschaft, Gesellschaft, Bildung, Forschung, Raum und Umwelt. Diese Informationen dienen der Meinungsbildung in der Bevölkerung sowie der Planung und Steuerung von zentralen Politikbereichen. Sie leisten einen wichtigen Beitrag für einen modernen, demokratischen Staat.

en: The FSO is Switzerland's national competence centre for official statistics. It produces and publishes statistical information on the status and development of the population, economy, society, education, research, territory and the environment. This information is used for opinion building among the population and for the planning and management of key policy areas. They make an important contribution to a modern, democratic state.

it: L’UST è il centro di competenza nazionale per la statistica pubblica della Svizzera. Produce e pubblica informazioni statistiche sullo stato e sull'evoluzione della popolazione, dell'economia, della società, della formazione, della ricerca, del territorio e dell'ambiente. Queste informazioni servono alla formazione dell'opinione pubblica nonché alla pianificazione e alla gestione in ambiti politici di centrale importanza. Forniscono un importante contributo per uno Stato moderno e democratico.

political_level: confederation

value: BFS – DCAT Harvester


fr: Annuaire statistique de la Suisse 1913

de: Statistisches Jahrbuch der Schweiz 1913


fr: Annuaire statistique de la Suisse 1903

de: Statistisches Jahrbuch der Schweiz 1903


fr: Annuaire statistique de la Suisse 1909

de: Statistisches Jahrbuch der Schweiz 1909


fr: Annuaire statistique de la Suisse 1935

de: Statistisches Jahrbuch der Schweiz 1935


de: Statistisches Jahrbuch der Schweiz 1959-60

fr: Annuaire statistique de la Suisse 1959-60


fr: Annuaire statistique de la Suisse 1972

de: Statistisches Jahrbuch der Schweiz 1972


fr: Annuaire statistique de la Suisse 1936

de: Statistisches Jahrbuch der Schweiz 1936


de: Statistisches Jahrbuch der Schweiz 1975

fr: Annuaire statistique de la Suisse 1975


fr: Annuaire statistique de la Suisse 1987-88

de: Statistisches Jahrbuch der Schweiz 1987-88


fr: Annuaire statistique de la Suisse 1998

de: Statistisches Jahrbuch der Schweiz 1998


fr: Annuaire statistique de la Suisse 1982

de: Statistisches Jahrbuch der Schweiz 1982


fr: R-D en Suisse: financement, exécution et personnel, selon le secteur d'activité

de: F+E in der Schweiz: Finanzierung, Durchführung und Personal, nach Sektor

bildung-und-wissenschaft

display_name: bildung-und-wissenschaft

education-and-science

display_name: education-and-science

education-et-science

display_name: education-et-science

formazione-e-scienza

display_name: formazione-e-scienza

furmaziun-e-scienza

display_name: furmaziun-e-scienza

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport


de: Harmonisierter Verbraucherpreisindex (HVPI) im internationalen Vergleich

fr: Indice des prix à la consommation harmonisée (IPCH) en comparaison internationale

preise

display_name: preise

pretschs

display_name: pretschs

prezzi

display_name: prezzi

prices

display_name: prices

prix

display_name: prix

econ

fr: Économie et finances

de: Wirtschaft und Finanzen

en: Economy and finance

it: Economia e finanze


fr: Statistique de l'aviation civile suisse 2020 - 5. Passagers

de: Schweizerische Zivilluftfahrtstatistik 2020 - 5. Passagiere

mobilita-e-trasporti

display_name: mobilita-e-trasporti

mobilitad-e-traffic

display_name: mobilitad-e-traffic

mobilitat-und-verkehr

display_name: mobilitat-und-verkehr

mobilite-et-transports

display_name: mobilite-et-transports

mobility-and-transport

display_name: mobility-and-transport

tran

fr: Transports

de: Verkehr

en: Transport

it: Trasporti

bundesamt-fur-statistik-bfs

fr: Office fédéral de la statistique OFS

de: Bundesamt für Statistik BFS


fr: Portrait des PME suisses, 2011-2019

de: Porträt der Schweizer KMU, 2011-2019

industria-e-servetschs

display_name: industria-e-servetschs

industria-e-servizi

display_name: industria-e-servizi

industrie-et-services

display_name: industrie-et-services

industrie-und-dienstleistungen

display_name: industrie-und-dienstleistungen

industry-and-services

display_name: industry-and-services

fr: L'OFS est le centre de compétence de la statistique publique suisse. Il produit des informations statistiques sur la population, l'économie, la société, la formation et la recherche, le territoire et l'environnement. Ces informations servent à la formation de l’opinion publique. Elles sont un outil de planification et de gouvernance dans tous les domaines de l’action politique et contribuent au bon fonctionnement de notre État moderne et démocratique.

de: Das BFS ist das nationale Kompetenzzentrum der öffentlichen Statistik der Schweiz. Es produziert und publiziert statistische Informationen über den Stand und die Entwicklung von Bevölkerung, Wirtschaft, Gesellschaft, Bildung, Forschung, Raum und Umwelt. Diese Informationen dienen der Meinungsbildung in der Bevölkerung sowie der Planung und Steuerung von zentralen Politikbereichen. Sie leisten einen wichtigen Beitrag für einen modernen, demokratischen Staat.


fr: Sentiment d'insécurité professionnelle des personnes diplômées des hautes écoles - situation cinq ans après l'obtention du diplôme en 2016

de: Subjektive Beschäftigungsunsicherheit von Hochschulabsolventinnen und -absolventen - Stand fünf Jahre nach Abschluss im Jahr 2016

bildung-und-wissenschaft

display_name: bildung-und-wissenschaft

education-and-science

display_name: education-and-science

education-et-science

display_name: education-et-science

formazione-e-scienza

display_name: formazione-e-scienza


fr: Statistique des caisses de pensions 2020

fr: La publication de la statistique des caisses de pensions 2020 rend compte des résultats agrégés de l’enquête annuelle portant sur l’exercice 2020. Outre les éléments relatifs à la conception de l’enquête, elle donne une idée globale du paysage suisse et de l’évolution des institutions de prévoyance avec assurés actifs et prestations réglementaires. Les résultats commentés apportent des chiffres et des informations sur la structure des institutions de prévoyance, la situation de leurs assurés et prestations ainsi que sur la partie comptable (bilan et compte d’exploitation) et technique. Un glossaire de termes théoriques et spécifiques à l’enquête complète cette publication.

de: Pensionskassenstatistik 2020

de: Die Publikation zur Pensionskassenstatistik 2020 basiert auf den aggregierten Ergebnissen der für das Geschäftsjahr 2020 durchgeführten Erhebung. Sie erläutert das Konzept der Erhebung und orientiert über die Struktur und die Entwicklung der Schweizer Vorsorgeeinrichtungen mit aktiven Versicherten und reglementarischen Leistungen. Die kommentierten Ergebnisse liefern Kennzahlen und Informationen zur Struktur der Vorsorgeeinrichtungen, zur Situation ihrer Versicherten und Leistungen sowie zur Bilanz, zur Betriebsrechnung und zu den technischen Angaben. Abgeschlossen wird die Publikation mit einem Glossar zu spezifischen, in der Publikation verwendeten Fachbegriffen.

securite-sociale

display_name: securite-sociale

segirezza-sociala

display_name: segirezza-sociala

sicurezza-sociale

display_name: sicurezza-sociale

social-security

display_name: social-security

soziale-sicherheit

display_name: soziale-sicherheit

soci

fr: Population et société

de: Bevölkerung und Gesellschaft

en: Population and society

it: Popolazione e società


de: Einkommen nach der gymnasialen Maturität und der Fachmaturität

fr: Les revenus après la maturité gymnasiale et la maturité spécialisée

furmaziun-e-scienza

display_name: furmaziun-e-scienza


de: Fleischbilanz

fr: Bilan de la viande

agricoltura-e-selvicoltura

display_name: agricoltura-e-selvicoltura

agricultura-ed-economia-forestala

display_name: agricultura-ed-economia-forestala

agriculture-and-forestry

display_name: agriculture-and-forestry

agriculture-et-sylviculture

display_name: agriculture-et-sylviculture

land--und-forstwirtschaft

display_name: land--und-forstwirtschaft

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


de: Sonderabfälle nach Abfallgruppen

fr: Déchets spéciaux par groupes de déchets

espace-et-environnement

display_name: espace-et-environnement

raum-und-umwelt

display_name: raum-und-umwelt

territori-ed-ambient

display_name: territori-ed-ambient

territorio-e-ambiente

display_name: territorio-e-ambiente

territory-and-environment

display_name: territory-and-environment

envi

fr: Environnement

de: Umwelt

en: Environment

it: Ambiente


fr: Limites communales généralisées: géodonnées

basas-statisticas-e-survistas

display_name: basas-statisticas-e-survistas

bases-statistiques-et-generalites

display_name: bases-statistiques-et-generalites

grundlagen-und-ubersichten

display_name: grundlagen-und-ubersichten

statistical-basis-and-overviews

display_name: statistical-basis-and-overviews

spatial: Gemeinden

gove

fr: Gouvernement et secteur public

de: Regierung und öffentlicher Sektor


de: Mobilität und Verkehr

de: Dieses Dataset präsentiert die jährlichen Zahlen der Statistik der Schweizer Städte 2024 - Mobilität und Verkehr: Strassenfahrzeuge; Weg- und Binnenpendler nach Hauptverkehrsmittel. Die Beschreibungen (allgemeine Informationen, Legende, Erklärungen zur Codes, Liste der Variablen, Erklärungen zur Variablen, Quellen, Status der Daten) der Variablen in der CSV-Datei sind im Anhang verügbar.

fr: Mobilité et transports

fr: Ce dataset présente les chiffres annuels des Statistiques des villes suisses 2024 - Mobilité et transports: Véhicules routiers; Pendulaires (sortants et intracommunaux) selon le principal moyen de transport. Les descriptions (informations générales, légende, explication des codes, liste variables, explications variables, source, status de la donnée) des variables du fichier CSV sont disponibles dans l’annexe.

format: ODS

mobilita-e-trasporti

display_name: mobilita-e-trasporti

mobilitad-e-traffic

display_name: mobilitad-e-traffic

mobilitat-und-verkehr

display_name: mobilitat-und-verkehr

mobilite-et-transports

display_name: mobilite-et-transports

mobility-and-transport

display_name: mobility-and-transport


fr: Consultations de victimes selon la relation auteur-victime

crime-and-criminal-justice

display_name: crime-and-criminal-justice

criminalita-e-diritto-penale

display_name: criminalita-e-diritto-penale

criminalitad-e-dretg-penal

display_name: criminalitad-e-dretg-penal

criminalite-et-droit-penal

display_name: criminalite-et-droit-penal

kriminalitat-und-strafrecht

display_name: kriminalitat-und-strafrecht

just

fr: Justice, système juridique et sécurité publique

de: Justiz, Rechtssystem und öffentliche Sicherheit

en: Justice, legal system and public safety

it: Giustizia, sistema giuridico e sicurezza pubblica


de: Radio: Die wichtigsten Sender in der Schweiz

fr: Radio: Principales chaînes en Suisse

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

bundesamt-fur-statistik-bfs

fr: Office fédéral de la statistique OFS


fr: Stagiaires rémunérés

de: Bezahlte Praktikanten

arbeit-und-erwerb

display_name: arbeit-und-erwerb

lavoro-e-reddito

display_name: lavoro-e-reddito

lavur-e-gudogn

display_name: lavur-e-gudogn

travail-et-remuneration

display_name: travail-et-remuneration

work-and-income

display_name: work-and-income

econ

fr: Économie et finances

de: Wirtschaft und Finanzen

de: Bundesamt für Statistik BFS

fr: L'OFS est le centre de compétence de la statistique publique suisse. Il produit des informations statistiques sur la population, l'économie, la société, la formation et la recherche, le territoire et l'environnement. Ces informations servent à la formation de l’opinion publique. Elles sont un outil de planification et de gouvernance dans tous les domaines de l’action politique et contribuent au bon fonctionnement de notre État moderne et démocratique.


fr: Personnel des hautes écoles universitaires selon la haute école et le domaine d'enseignement

de: Personal an universitären Hochschulen nach Hochschule und Fachbereich

bildung-und-wissenschaft

display_name: bildung-und-wissenschaft

education-and-science

display_name: education-and-science

education-et-science

display_name: education-et-science

formazione-e-scienza

display_name: formazione-e-scienza

furmaziun-e-scienza

display_name: furmaziun-e-scienza

bundesamt-fur-statistik-bfs

de: Das BFS ist das nationale Kompetenzzentrum der öffentlichen Statistik der Schweiz. Es produziert und publiziert statistische Informationen über den Stand und die Entwicklung von Bevölkerung, Wirtschaft, Gesellschaft, Bildung, Forschung, Raum und Umwelt. Diese Informationen dienen der Meinungsbildung in der Bevölkerung sowie der Planung und Steuerung von zentralen Politikbereichen. Sie leisten einen wichtigen Beitrag für einen modernen, demokratischen Staat.


fr: Bourses: montant et bénéficiaires selon les catégories d'étrangers et le canton

de: Stipendien: Betrag, Bezügerinnen und Bezüger nach Ausländerkategorie und Kanton


fr: Dynamique du marché du travail: transitions brutes entre activité professionnelle, chômage au sens du BIT et non-activité, migrations de main-d'oeuvre et mouvements naturels

fr: Ce dataset présente les transitions annuelles brutes entre activité professionnelle, chômage au sens du BIT et non-activité, les migrations de main-d'oeuvre et mouvements naturels, depuis 1991. Les descriptions des variables du fichier CSV sont disponibles dans l’annexe.

de: Dynamik des Arbeitsmarkts: Bruttoübergänge zwischen Erwerbstätigkeit, Erwerbslosigkeit gemäss ILO und Nichterwerbstätigkeit, Arbeitskräftewanderungen und natürliche Bewegungen

de: Dieses Dataset präsentiert die jährlichen Bruttoübergänge zwischen Erwerbstätigkeit, Erwerbslosigkeit gemäss ILO und Nichterwerbstätigkeit, Arbeitskräftewanderungen und natürliche Bewegungen, seit 1991. Die Beschreibungen der Variablen in der CSV-Datei sind im Anhang verfügbar.


fr: SH-AsylStat: Bénéficiaires de l'aide sociale dans le domaine de l'asile dès 18 ans selon l'état civil

de: SH-AsylStat: Sozialhilfebeziehende im Asylbereich ab 18 Jahren nach Zivilstand

securite-sociale

display_name: securite-sociale

segirezza-sociala

display_name: segirezza-sociala

sicurezza-sociale

display_name: sicurezza-sociale

social-security

display_name: social-security

soziale-sicherheit

display_name: soziale-sicherheit

soci

fr: Population et société

de: Bevölkerung und Gesellschaft


fr: ASE: Unités d'assistance, bénéficiaires et taux d'aide sociale économique par canton

de: WSH: Unterstützungseinheiten, Sozialhilfebeziehende und Sozialhilfequote der wirtschaftlichen Sozialhilfe nach Kanton

spatial: Kantone


de: resultate abstimmungen

de: fgb

de: Abstimmungsresultate Stadt St.Gallen 2017-2019 (fgb)

format: OCTET

de: jsonl

media_type: application/json

de: Abstimmungsresultate Stadt St.Gallen 2017-2019 (jsonl)

format: JSON

de: json

de: Abstimmungsresultate Stadt St.Gallen 2017-2019 (json)

de: jsonld

de: Abstimmungsresultate Stadt St.Gallen 2017-2019 (jsonld)

format: JSON-LD

de: parquet

de: Abstimmungsresultate Stadt St.Gallen 2017-2019 (parquet)

format: PARQUET

de: xls

de: Abstimmungsresultate Stadt St.Gallen 2017-2019 (xls)

de: turtle

media_type: text/turtle

de: Abstimmungsresultate Stadt St.Gallen 2017-2019 (turtle)

format: RDF Turtle

de: rdfxml

media_type: application/rdf+xml

de: Abstimmungsresultate Stadt St.Gallen 2017-2019 (rdfxml)

format: RDF XML

de: Abstimmungsresultate Stadt St.Gallen 2017-2019 (n3)

de: csv

de: Abstimmungsresultate Stadt St.Gallen 2017-2019 (csv)

de: Der Datensatz zeigt die Anzahl Stimmen (Ja, Nein, leer, gültig, ungültig), sowie die Stimmbeteiligung der Abstimmungen der Jahre 2017 bis 2019 in der Stadt St.Gallen.

abstimmungen

display_name: abstimmungen

resultate

display_name: resultate

gove

fr: Gouvernement et secteur public

de: Regierung und öffentlicher Sektor

stadt-st-gallen

fr: Ville de Saint-Gall

de: Stadt St.Gallen

en: City of St.Gallen

it: Città di San Gallo

fr: Avec une population de 80 000 habitants, Saint-Gall, huitième ville de Suisse, est la métropole économique, politique, sociale et culturelle de la Suisse orientale. Ses 1400 ans d'histoire se mêlent à la créativité urbaine, à une économie innovante et à un paysage préservé.

en: With a population of 80,000, St.Gallen, Switzerland’s eighth largest city, is the economic, political, social and cultural metropolis of eastern Switzerland. Its 1400 years of history blend with urban creativity, an innovative economy and an unspoilt landscape.

de: St.Gallen, die achtgrösste Stadt der Schweiz, ist mit 80'000 Einwohnern die wirtschaftliche, politische, soziale und kulturelle Metropole der Ostschweiz. Ihre 1400-jährige Geschichte verbindet sich mit urbaner Kreativität, einer innovativen Wirtschaft und einer intakten Landschaft.

it: Con una popolazione di 80.000 abitanti, San Gallo, l'ottava città più grande della Svizzera, è la metropoli economica, politica, sociale e culturale della Svizzera orientale. I suoi 1400 anni di storia si fondono con la creatività urbana, un'economia innovativa e un paesaggio incontaminato.

political_level: commune

value: DCAT Harvester Stadt St. Gallen


de: photovoltaik strom pv sonnenenergie sonne energy-hackdays erneuerbare-energien solar

de: Stromproduktion der Solaranlagen der St.Galler Stadtwerke ab 2023 (parquet)

de: Stromproduktion der Solaranlagen der St.Galler Stadtwerke ab 2023 (xls)

de: fgb

de: Stromproduktion der Solaranlagen der St.Galler Stadtwerke ab 2023 (fgb)

de: Stromproduktion der Solaranlagen der St.Galler Stadtwerke ab 2023 (ov2)

de: jsonl

de: Stromproduktion der Solaranlagen der St.Galler Stadtwerke ab 2023 (jsonl)

de: Stromproduktion der Solaranlagen der St.Galler Stadtwerke ab 2023 (rdfxml)

de: jsonld

de: Stromproduktion der Solaranlagen der St.Galler Stadtwerke ab 2023 (jsonld)

de: kml

de: Stromproduktion der Solaranlagen der St.Galler Stadtwerke ab 2023 (kml)

format: KML

de: Stromproduktion der Solaranlagen der St.Galler Stadtwerke ab 2023 (csv)

de: Stromproduktion der Solaranlagen der St.Galler Stadtwerke ab 2023 (n3)

de: gpx

de: Stromproduktion der Solaranlagen der St.Galler Stadtwerke ab 2023 (gpx)

format: GPX

de: json

de: Stromproduktion der Solaranlagen der St.Galler Stadtwerke ab 2023 (json)

de: geojson

de: Stromproduktion der Solaranlagen der St.Galler Stadtwerke ab 2023 (geojson)

de: Stromproduktion der Solaranlagen der St.Galler Stadtwerke ab 2023 (turtle)

de: shp

de: Stromproduktion der Solaranlagen der St.Galler Stadtwerke ab 2023 (shp)

de: Der Datensatz zeigt die vergangene Stromproduktion der Solaranlagen der St.Galler Stadtwerke (sgsw) seit 2023, welche im Rahmen des Pilotprojekts "Kraftwerk St.Gallen" mit Stromzähler ausgerüstet wurden. Nur ein Teil der Solaranlagen, vorwiegend die Kleineren, wurden mit Smart-Me Zählern ausgerüstet.Die Daten der Jahre 2021 und 2022 finden Sie hier:[Stromproduktion der Solaranlagen der St.Galler Stadtwerke - 2021-2022 — St.Gallen (sg.ch)](https://daten.stadt.sg.ch/explore/dataset/stromproduktion-der-solaranlagen-der-stgaller-stadtwerke-2021-2022/table/?disjunctive.name&disjunctive.name_des_smart_me_zahlers&disjunctive.modultyp&disjunctive.leistung_modul_in_wp&sort=-additional_energy_import)Echtzeitdaten zu denselben Zählern finden Sie hier: [https://daten.stadt.sg.ch/explore/dataset/aktuelle-stromproduktion-der-solaranlagen-der-stgaller-stadtwerke](https://daten.stadt.sg.ch/explore/dataset/aktuelle-stromproduktion-der-solaranlagen-der-stgaller-stadtwerke)Alle Solaranlagen der St.Galler Stadtwerke finden Sie in folgendem Datensatz:[https://daten.stadt.sg.ch/explore/dataset/solaranlagen-der-stgaller-stadtwerke](https://daten.stadt.sg.ch/explore/dataset/solaranlagen-der-stgaller-stadtwerke)

energy-hackdays

display_name: energy-hackdays

erneuerbare-energien

display_name: erneuerbare-energien

photovoltaik

display_name: photovoltaik

pv

display_name: pv

solar

display_name: solar

sonne

display_name: sonne

sonnenenergie

display_name: sonnenenergie

strom

display_name: strom

ener

fr: Énergie

de: Energie

en: Energy

it: Energia


ortsbildinventar-nach-isos

title_for_slug: ortsbildinventar-nach-isos

de: entsieglungspotential ortsbildinventar

de: parquet

de: Ortsbildinventar nach ISOS (parquet)

de: xls

de: Ortsbildinventar nach ISOS (xls)

de: Ortsbildinventar nach ISOS (shp)

de: jsonld

de: Ortsbildinventar nach ISOS (jsonld)

de: Ortsbildinventar nach ISOS (geojson)

de: Ortsbildinventar nach ISOS (ov2)

de: fgb

de: Ortsbildinventar nach ISOS (fgb)

de: rdfxml

de: Ortsbildinventar nach ISOS (rdfxml)

de: Ortsbildinventar nach ISOS (gpx)

de: Ortsbildinventar nach ISOS (n3)

de: Ortsbildinventar nach ISOS (json)

de: Ortsbildinventar nach ISOS (kml)

de: jsonl

de: Ortsbildinventar nach ISOS (jsonl)

de: turtle

de: Ortsbildinventar nach ISOS (turtle)

de: csv

de: Ortsbildinventar nach ISOS (csv)

de: Der Datensatz zeigt das Ortsbildinventar nach ISOS mit den Gebiets- und Umgebungszonen.

entsieglungspotential

display_name: entsieglungspotential

ortsbildinventar

display_name: ortsbildinventar

stadt-st-gallen

de: St.Gallen, die achtgrösste Stadt der Schweiz, ist mit 80'000 Einwohnern die wirtschaftliche, politische, soziale und kulturelle Metropole der Ostschweiz. Ihre 1400-jährige Geschichte verbindet sich mit urbaner Kreativität, einer innovativen Wirtschaft und einer intakten Landschaft.


de: parkhaus verkehr parkplatz auto real-time

de: Freie Parkplätze in der Stadt St.Gallen (PLS) (csv)

de: Freie Parkplätze in der Stadt St.Gallen (PLS) (fgb)

de: Freie Parkplätze in der Stadt St.Gallen (PLS) (ov2)

de: jsonld

de: Freie Parkplätze in der Stadt St.Gallen (PLS) (jsonld)

de: Freie Parkplätze in der Stadt St.Gallen (PLS) (n3)

de: Freie Parkplätze in der Stadt St.Gallen (PLS) (jsonl)

de: xls

de: Freie Parkplätze in der Stadt St.Gallen (PLS) (xls)

de: shp

de: Freie Parkplätze in der Stadt St.Gallen (PLS) (shp)

de: Freie Parkplätze in der Stadt St.Gallen (PLS) (rdfxml)

de: gpx

de: Freie Parkplätze in der Stadt St.Gallen (PLS) (gpx)

de: kml

de: Freie Parkplätze in der Stadt St.Gallen (PLS) (kml)

de: parquet

de: Freie Parkplätze in der Stadt St.Gallen (PLS) (parquet)

de: geojson

de: Freie Parkplätze in der Stadt St.Gallen (PLS) (geojson)

de: json

de: Freie Parkplätze in der Stadt St.Gallen (PLS) (json)

de: Freie Parkplätze in der Stadt St.Gallen (PLS) (turtle)

de: Die PLS Parkleitsystem St.Gallen AG misst die freien Parkplätze in den Parkhäusern der Stadt St.Gallen. Dieser Datensatz zeigt die freien Parkplätze in Echtzeit ([https://www.pls-sg.ch/parkraeume/](https://www.pls-sg.ch/parkraeume/)).Zu statistischen Auswertungen der Parkhausbelegung können die CityParking St. Gallen AG ([www.cityparking.ch](http://www.cityparking.ch)) oder die PLS Parkleitsystem St.Gallen AG Auskunft geben.

auto

display_name: auto

parkhaus

display_name: parkhaus

parkplatz

display_name: parkplatz

real-time

display_name: real-time

verkehr

display_name: verkehr

tran

fr: Transports

de: Verkehr

stadt-st-gallen

de: Stadt St.Gallen


fernwarmestrategie-hestia

title_for_slug: fernwarmestrategie-hestia

de: sgsw energy-hackdays warmeversorgung gasversorgung

de: fgb

de: Fernwärmestrategie der Stadt St.Gallen (Hestia) (fgb)

de: Fernwärmestrategie der Stadt St.Gallen (Hestia) (gpx)

de: xls

de: Fernwärmestrategie der Stadt St.Gallen (Hestia) (xls)

de: rdfxml

de: Fernwärmestrategie der Stadt St.Gallen (Hestia) (rdfxml)

de: Fernwärmestrategie der Stadt St.Gallen (Hestia) (geojson)

de: Fernwärmestrategie der Stadt St.Gallen (Hestia) (parquet)

de: Fernwärmestrategie der Stadt St.Gallen (Hestia) (json)

de: shp

de: Fernwärmestrategie der Stadt St.Gallen (Hestia) (shp)

de: jsonld

de: Fernwärmestrategie der Stadt St.Gallen (Hestia) (jsonld)

de: Fernwärmestrategie der Stadt St.Gallen (Hestia) (ov2)

de: csv

de: Fernwärmestrategie der Stadt St.Gallen (Hestia) (csv)

de: jsonl

de: Fernwärmestrategie der Stadt St.Gallen (Hestia) (jsonl)

de: Fernwärmestrategie der Stadt St.Gallen (Hestia) (kml)

de: Fernwärmestrategie der Stadt St.Gallen (Hestia) (n3)

de: turtle

de: Fernwärmestrategie der Stadt St.Gallen (Hestia) (turtle)

de: Mit diesem Datenset ist es möglich, für jede Adresse das entsprechende Gebiet der städtischen Wärmeversorgungsstrategie sowie den voraussichtlichen Zeitpunkt der Einführung von Fernwärme (falls geplant) einzusehen. Die Daten umfassen die geplanten Ausbauphasen 3 und 4 der Fernwärme, über die voraussichtlich im November 2023 die städtische Stimmbevölkerung abstimmen wird.

gasversorgung

display_name: gasversorgung

sgsw

display_name: sgsw

warmeversorgung

display_name: warmeversorgung

envi

fr: Environnement

de: Umwelt

de: St.Gallen, die achtgrösste Stadt der Schweiz, ist mit 80'000 Einwohnern die wirtschaftliche, politische, soziale und kulturelle Metropole der Ostschweiz. Ihre 1400-jährige Geschichte verbindet sich mit urbaner Kreativität, einer innovativen Wirtschaft und einer intakten Landschaft.


de: armut betreibung rechnung schulden

de: xls

de: Betreibungsfälle Stadt St.Gallen (2005-2018) (xls)

de: Betreibungsfälle Stadt St.Gallen (2005-2018) (jsonl)

de: json

de: Betreibungsfälle Stadt St.Gallen (2005-2018) (json)

de: parquet

de: Betreibungsfälle Stadt St.Gallen (2005-2018) (parquet)

de: Betreibungsfälle Stadt St.Gallen (2005-2018) (turtle)

de: Betreibungsfälle Stadt St.Gallen (2005-2018) (jsonld)

de: Betreibungsfälle Stadt St.Gallen (2005-2018) (csv)

de: fgb

de: Betreibungsfälle Stadt St.Gallen (2005-2018) (fgb)

de: rdfxml

de: Betreibungsfälle Stadt St.Gallen (2005-2018) (rdfxml)

de: Betreibungsfälle Stadt St.Gallen (2005-2018) (n3)

de: Anzahl Betreibungsfälle in der Stadt St.Gallen (Jahre 2005-2018)

armut

display_name: armut

betreibung

display_name: betreibung

rechnung

display_name: rechnung

schulden

display_name: schulden

econ

de: Wirtschaft und Finanzen

stadt-st-gallen

de: Stadt St.Gallen


de: lkw miv zahlstellen swiss10 motorrader bus lieferwagen zahlung verkehr auto fahrzeugklasse

de: Verkehrszählung MIV Stadt St.Gallen (nach Fahrzeugkategorien SWISS10, 2019-2022) (rdfxml)

de: gpx

de: Verkehrszählung MIV Stadt St.Gallen (nach Fahrzeugkategorien SWISS10, 2019-2022) (gpx)

de: kml

de: Verkehrszählung MIV Stadt St.Gallen (nach Fahrzeugkategorien SWISS10, 2019-2022) (kml)

de: jsonl

de: Verkehrszählung MIV Stadt St.Gallen (nach Fahrzeugkategorien SWISS10, 2019-2022) (jsonl)

de: csv

de: Verkehrszählung MIV Stadt St.Gallen (nach Fahrzeugkategorien SWISS10, 2019-2022) (csv)

de: jsonld

de: Verkehrszählung MIV Stadt St.Gallen (nach Fahrzeugkategorien SWISS10, 2019-2022) (jsonld)

de: shp

de: Verkehrszählung MIV Stadt St.Gallen (nach Fahrzeugkategorien SWISS10, 2019-2022) (shp)

de: turtle

de: Verkehrszählung MIV Stadt St.Gallen (nach Fahrzeugkategorien SWISS10, 2019-2022) (turtle)

de: Verkehrszählung MIV Stadt St.Gallen (nach Fahrzeugkategorien SWISS10, 2019-2022) (fgb)

de: geojson

de: Verkehrszählung MIV Stadt St.Gallen (nach Fahrzeugkategorien SWISS10, 2019-2022) (geojson)

de: Verkehrszählung MIV Stadt St.Gallen (nach Fahrzeugkategorien SWISS10, 2019-2022) (n3)

de: Verkehrszählung MIV Stadt St.Gallen (nach Fahrzeugkategorien SWISS10, 2019-2022) (json)

de: Verkehrszählung MIV Stadt St.Gallen (nach Fahrzeugkategorien SWISS10, 2019-2022) (parquet)

de: Verkehrszählung MIV Stadt St.Gallen (nach Fahrzeugkategorien SWISS10, 2019-2022) (ov2)

de: xls

de: Verkehrszählung MIV Stadt St.Gallen (nach Fahrzeugkategorien SWISS10, 2019-2022) (xls)

de: Das Tiefbauamt der Stadt St.Gallen zählt den Motorisierten Individualverkehr (MIV) an verschiedenen Standorten der Stadt St.Gallen und ihrer Umgebung. Dieser Datensatz zeigt die Daten dieser Verkehrszählungen aufgeschlüsselt nach den unterschiedlichen Fahrzeugkategorien gemäss SWISS10.Datenstruktur:ORT-ID: ID der ZählstelleBEZEICHNUNG: Name/Ort der ZählstelleDATUMWOCHENTAGRI: Richtungsnummer der Messung. Die Daten zeigen eine Zeile pro Richtungsnummer.[](https://github.com/CityofStGallen/Motorisierter-Individualverkehr-MIV/tree/master/2_Zaehlstellen_Details)KLASSE: Fahrzeugkategorie nach SWISS10 mit entsprechendem NAME_D auf Deutsch und der Zuordnung zu den entsprechenden Kategorien nach SWISS7 und SWISS61 - 24: Uhrzeit (Stunde), wann die Anzahl Fahrzeuge gemessen wurde.Anmerkung: Durchgehende Nullen für eine spezifische Richtungsnummer bedeuten, dass diese Richtungsnummer für die entsprechende Zählstelle nicht gebraucht wird.TAGESTOTAL: Anzahl gemessene Fahrzeuge pro Richtung (=Summe 1-24)

bus

display_name: bus

fahrzeugklasse

display_name: fahrzeugklasse

lieferwagen

display_name: lieferwagen

lkw

display_name: lkw

miv

display_name: miv

motorrader

display_name: motorrader

zahlstellen

display_name: zahlstellen

zahlung

display_name: zahlung

de: St.Gallen, die achtgrösste Stadt der Schweiz, ist mit 80'000 Einwohnern die wirtschaftliche, politische, soziale und kulturelle Metropole der Ostschweiz. Ihre 1400-jährige Geschichte verbindet sich mit urbaner Kreativität, einer innovativen Wirtschaft und einer intakten Landschaft.


entsorgungsstatistik-stadt-st-gallen

title_for_slug: entsorgungsstatistik-stadt-st-gallen

de: abfall recycling abfuhr entsorgung

de: jsonld

de: Entsorgungsstatistik Stadt St.Gallen (jsonld)

de: Entsorgungsstatistik Stadt St.Gallen (turtle)

de: rdfxml

de: Entsorgungsstatistik Stadt St.Gallen (rdfxml)

de: Entsorgungsstatistik Stadt St.Gallen (n3)

de: parquet

de: Entsorgungsstatistik Stadt St.Gallen (parquet)

de: jsonl

de: Entsorgungsstatistik Stadt St.Gallen (jsonl)

de: csv

de: Entsorgungsstatistik Stadt St.Gallen (csv)

de: fgb

de: Entsorgungsstatistik Stadt St.Gallen (fgb)

de: json

de: Entsorgungsstatistik Stadt St.Gallen (json)

de: Entsorgungsstatistik Stadt St.Gallen (xls)

de: Die Stadt St.Gallen stellt ihre Entsorgungsstatistik als Open Data zur Verfügung. In diesem Datensatz enthalten sind die Mengen der verschiedenen Abfallfraktionen Kehrricht, Karton, Papier, Glas, Dosen / Alu, Altmetall, Sonderabfall und Altöl, falls vorhanden aufgeteilt nach Unterkategorie, Entsorgungsart (Strassensammlung/Annahmestelle) und Abfuhrgebiet. Der Datensatz zeigt die Statistik seit dem Jahr 2015 und wird laufend aktualisiert.

abfall

display_name: abfall

abfuhr

display_name: abfuhr

entsorgung

display_name: entsorgung

recycling

display_name: recycling

envi

de: Umwelt

stadt-st-gallen

de: Stadt St.Gallen

de: Entsorgungsstatistik Stadt St.Gallen