maintainer: M. Carol Resch

en: long-term-experiment restoration grassland nematodes diversity vegetation topsoil-removal

en: Nematode families

en:
Plot: Plot ID combining treatment code and plot number (in total 55 plots)

Treatment: Three restoration treatments and two reference treatments. Initial=intensively managed grasslands; Harvest only= multiple hay harvest (2-3 times) per years; Topsoil= Topsoil removal (upper 10-30 cm); Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Target= species-rich ancient grasslands

column 3-41: Nematode abundance (individuals per 100g dry soil) per family; nomenclature according to Bongers 1988

en: Coordinates of one research plot were wrong. This was corrected in the current version of the file.

en: Nemadotes and vegetation

en:
Plot: Plot ID combining treatment code and plot number (in total 55 plots)

Treatment: Three restoration treatments and two reference treatments. Initial=intensively managed grasslands; Harvest only= multiple hay harvest (2-3 times) per years; Topsoil= Topsoil removal (upper 10-30 cm); Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Target= species-rich ancient grasslands

X: X-Coordinate

Y: Y-Coordinate

Ri.veg: Total number of plant species

Div.veg: Shannon diversity of plant species

Ri.nem: Total number of nematode families

Div.nem: Shannon diversity of nematode families

abund: Total nematode abundance (individuals per 100g dry soil)

MI: Maturity index according to Bongers 1990; calculations done in online tool NINJA (Sieriebriennikov, Ferris, & de Goede, 2014)

PPI: Plant parasite index according to Bongers 1990; calculations done in online tool NINJA (Sieriebriennikov, Ferris, & de Goede, 2014)

PPI.MI: Plant parasite to Maturity index according to Bongers, van der Meulen, & Korthals 1997; calculations done in online tool NINJA (Sieriebriennikov, Ferris, & de Goede, 2014)

CI: Channel index according to Ferris, Bongers, & de Goede 2001 (%); calculations done in online tool NINJA (Sieriebriennikov, Ferris, & de Goede, 2014)

EI: Enrichment index according to Ferris, Bongers, & de Goede 2001 (%); calculations done in online tool NINJA (Sieriebriennikov, Ferris, & de Goede, 2014)

SI: Structure index according to Ferris, Bongers, & de Goede 2001 (%); calculations done in online tool NINJA (Sieriebriennikov, Ferris, & de Goede, 2014)

He.a: Herbivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

He.ecto.a: Herbivorous ectoparasite nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

He.endo.a: Herbivorous endoparasite nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Ba.a: Bacterivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Fu.a: Fungivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Om.a: Omnivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Ca.a: Carnivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Om.Ca.a	Sum of omnivorous and carnivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014
CP1.a: C-P (colonizer-persister) class 1 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990; equivalent to enrichment indicators according to Ferris, Bongers, & de Goede 2001

CP2.a: C-P (colonizer-persister) class 2 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990; equivalent to stress indicators according to Ferris, Bongers, & de Goede 2001

CP3.a: C-P (colonizer-persister) class 3 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990

CP4.a: C-P (colonizer-persister) class 4 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990

CP5.a: C-P (colonizer-persister) class 5 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990

CP3-5.a: Sum of C-P3, C-P4 and C-P5 nematode abundance (individuals per 100g dry soil) equivalent to structure indicators according to Ferris, Bongers, & de Goede 2001

protect.sp.pro: plant species categorized into species of concern based on their red list status in Switzerland as well as their protection status in Switzerland and the Canton Zurich (Moser, Gygax, Bäumler, Wyler, & Palese, 2002)

tar.sp: Number of target species (all species recorded in the eleven target plots plus propagules of species applied by hand, resulting in a total of 143 species)

Indi.moist: Plant indicator values for soil moisture according to Landolt et al. 2010

Indi.nutri: Plant indicator values for soil nutrients according to Landolt et al. 2010

Management: level of mowing and manuring frequency (35-40 kg N ha-1 application-1); intensity1 = mowing once and no manure; intensity2 = 2-3 times mowing and no manure; intensity3 = 2-3 times mowing and manuring; intensity4 = 4-5 times mowing and manuring

Tillage: last time of tillage

Land.use.history: for Initial plots = recent agricultural use; for all restored sites = agricultural use before restoration
Mowing	earliest date of biomass harvesting (excluding “Initial”) based on contracts between farmers and the Nature Protection Agency of Zurich

Soil.type: based on soil mapping survey 1991 (before restoration)

Soil.texture: determined by the sediment method (Gee & Bauder 1986) and classified into soil textural classes according to FAO (1990)

pH: soil pH (CaCl2)

Slope: slope gradient classes according to FAO (1990; %)

en: Nematode colonizer-persister classes

en:
Plot: Plot ID combining treatment code and plot number (in total 55 plots)

Treatment: Three restoration treatments and two reference treatments. Initial=intensively managed grasslands; Harvest only= multiple hay harvest (2-3 times) per years; Topsoil= Topsoil removal (upper 10-30 cm); Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Target= species-rich ancient grasslands

CP1.a: C-P (colonizer-persister) class 1 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990; equivalent to enrichment indicators according to Ferris, Bongers, & de Goede 2001
CP2.a: C-P (colonizer-persister) class 2 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990; equivalent to stress indicators according to Ferris, Bongers, & de Goede 2001
CP3.a: C-P (colonizer-persister) class 3 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990
CP4.a: C-P (colonizer-persister) class 4 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990
CP5.a: C-P (colonizer-persister) class 5 nematode abundance (individuals per 100g dry soil); C-P classification according to Bongers 1990

en: Nematode feeding types

en:
Plot: Plot ID combining treatment code and plot number (in total 55 plots)

Treatment: Three restoration treatments and two reference treatments. Initial=intensively managed grasslands; Harvest only= multiple hay harvest (2-3 times) per years; Topsoil= Topsoil removal (upper 10-30 cm); Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Target= species-rich ancient grasslands

He.ecto.a: Herbivorous ectoparasite nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

He.endo.a: Herbivorous endoparasite nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Ba.a: Bacterivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Fu.a: Fungivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Om.a: Omnivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

Ca.a: Carnivorous nematode abundance (individuals per 100g dry soil); classification according to Sieriebriennikov, Ferris, & de Goede, 2014

en: Plant community composition (plant species)

en:
Plot: Plot ID combining treatment code and plot number (in total 55 plots)

Treatment: Three restoration treatments and two reference treatments. Initial=intensively managed grasslands; Harvest only= multiple hay harvest (2-3 times) per years; Topsoil= Topsoil removal (upper 10-30 cm); Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Target= species-rich ancient grasslands

column 3-252: Plant abundance per species (in total 252 plant species) expressed as cover percentage estimation according to van der Maarel (vdM 1979; 7 classes: 1-7); nomenclature according to Lauber & Wagner (1996)

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

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

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

 Methods
 Study area and experimental settings
The study was conducted in a nature reserve (Eigental: 47° 27’ to 47° 29’ N, 8° 37’ E, 461 to 507 m a.s.l.) that is located on the Swiss Central plateau close to Zurich airport (Canton Zurich, Switzerland). The mean annual temperature in this area ranges from 8.9 to 10.6 °C, mean annual precipitation from 910 to 1260 mm [10-year average (2007-2017); MeteoSchweiz, 2018]. The main soil types are calcaric to gleyic Cambisol and Gleysols. The reserve was established in 1967 to protect small remnants of oligotrophic semi-natural grasslands (roughly 12 ha). The plant community can be characterized as Molinion and Mesobromion (semi-wet to semi-dry), depending on the site-specific groundwater level and slope inclination (Delarze, Gonseth, Eggenberg, & Vust, 2015). These remnants represent species-rich islands in an otherwise intensively managed agricultural landscape. Semi-natural grasslands covered an area of 60,000 ha in the Canton Zurich in 1939, however, by 2005 only roughly 600 ha remained (Baudirektion Kanton Zürich, 2007). In 1990, the government of Canton Zurich decided to enlarge the nature reserve Eigental. The goal was to incorporate eleven patches of 20 ha adjacent intensively farmed land and transform these patches into semi-natural grasslands. The patches had a different agricultural history, ranging from permanent (no tillage for >50 years) to temporary grassland (as part of crop rotation; last tillage <5 years). On all freshly integrated patches fertilization was stopped in 1992 and from then on biomass was harvested three times a year and removed. After 5 years without noticeable effects on vegetation composition, the Nature Conservation Agency of Canton Zurich decided to increase the restoration efforts. In 1995, a large-scale experiment was initialized to evaluate if certain treatments can facilitate restoration within a reasonable timeframe of 5 to 10 years after treatment implementation.
The three restoration treatments used were:
i. “Harvest only”: Plots are being mowed two to three times a year and the biomass is removed.
ii. “Topsoil”: Topsoil was removed to a depth of 10 to 20 cm, depending on the depth of the O and A horizon, in four randomly selected areas within each of the eleven patches in late autumn 1995. The size of each topsoil removal area depended on individual patch size and was between 2700 and 7000 m2.
iii. “Topsoil+Propagules”: Propagules from target vegetation were added on half of the area where topsoil was removed, using fresh, seed-containing hay originating from a mixture of semi-dry to semi-wet species-rich grasslands of local provenance (within a radius of 7 km). Hay applications were conducted twice in 1995 and 1996. Repeated applications were chosen to account for the low quantity of available plant material per transfer, since area ratio between receptor and donor sites was roughly 1:1. In addition, hand-collected propagules from 15 selected target species of regional provenance (within a radius of 30 km) were equally applied in 1996 and 1997.
“Topsoil” and “Topsoil+Propagules” plots are mowed once a year, and the biomass is removed. Mowing on these plots started five years after the treatment was implemented.
Eleven permanent plots of 5 m x 5 m were randomly established in each treatment to monitor the vegetation development. The experiment was complemented with 11 control plots that represent the initial state of intensively managed grasslands, further referred to as “Initial”, and 11 control plots that represent the targeted state of donor sites for “Topsoil+Propagules”, further referred to as “Target”. Consequently, the experiment consists of 55 plots (5 treatments x 11 replicates). Management of intensively used grasslands includes mowing and fertilizing (manure) between two to five times a year, as well as different tillage regimes (no tillage for >50 years; last time of tillage <5 years).

 Nematode and plant sampling
Soil nematodes were sampled in 2 m x 2 m plots, randomly established at least 2 m away from the vegetation plots. We collected eight soil cores with a 2.2 cm diameter soil core sampler (Giddings Machine Company, Windsor, CO, USA) to a depth of 12 cm (representing the majority of the plant rooting system) in each plot at the beginning of July 2017. The eight cores within each replicate plot were combined, gently homogenized, placed in coolers and transported to the laboratory of NIOO in Wageningen, the Netherlands, within one week. Free-living nematodes were extracted from 200 g of fresh soil using Oostenbrink elutriator (Oostenbrink, 1960) and concentrated, resulting in 6 mL nematode solution. The nematode solution was subdivided into three subsamples, two for morphological identification and quantification, and one for molecular work (not used in this study). For morphological identification and quantification, nematodes were heat-killed at 90 °C and fixed in 4 % formaldehyde solution (final volume 10 mL per subsample). All nematodes in 1 mL of formaldehyde solution were counted, and a minimum of 150 individuals per 1 mL sample (or all if less nematodes were present) were identified to family level using Bongers (1988). We then extrapolated the numbers of each nematode taxa identified to the entire sample and expressed them per 100 g dry soil for further analyses.
We calculated number of nematode taxa and Shannon diversity and assessed nematode community composition. In addition, we classified the nematode taxa into feeding types (herbivores, bacterivores, fungivores, omni-carnivores), structural and functional guilds (Table S4). Structural guilds assign nematode taxa according to life-history traits into five colonizer-persister (C-P) classes, ranging from one (early colonizers of new resources) to five (persisters in undisturbed habitats; Bongers 1990). C-P classes can be categorized as indicators for nutrient-enriched (C-P1), stressed (C-P2) and structured (C-P3 + C-P4 + C-P5) soil conditions (Ferris, Bongers, & de Goede, 2001). Functional guilds assign nematode taxa according to their C-P classification combined with their feeding habits (Ferris, Bongers, & de Goede, 2001). Based on the structural and functional guild classification we calculated five additional indices to assess soil nutrient status, disturbance and food web characteristics using NINJA (Sieriebriennikov, Ferris, & de Goede, 2014). 1) The Maturity index indicates the degree of different environmental perturbations (e.g., tillage, nutrient enrichment, pollution) and is used to monitor colonization and subsequent succession after disturbances (Bongers, 1990). 2) The ratio between the Plant Parasite (C-P of herbivorous nematodes only) to Maturity index is used to monitor the recovery of disturbed habitats incorporating information of life-history traits for all feeding types (Bongers, van der Meulen, & Korthals, 1997). 3) The Enrichment index indicates nutrient-enriched soils and agricultural management practices (Ferris, Bongers, & de Goede, 2001). 4) The Structure index provides information about the succession stage of the soil food web and therefore correlates with the degree of maturity of an ecosystem (Ferris, Bongers, & de Goede, 2001). 5) The Channel index provides information about the predominant decomposition pathways, where higher values stand for a higher proportion of energy transformed through the slow fungal decomposition channel (Ferris, Bongers, & de Goede, 2001). In addition, the Structure and Enrichment indices can be displayed in a biplot where nematode assemblages are plotted along a structure (x-axis) and enrichment (y-axis) trajectory (increasing index values). Each biplot quadrat reflects different levels of disturbance, soil nutrient pools and decomposition pathways (Ferris, Bongers, & de Goede, 2001).
The plant surveys were conducted on the 25 m2 permanent plots in June 2017. Plant species cover was visually assessed according to the semi-quantitative cover-abundance scale of Braun-Blanquet (1964; nomenclature: Lauber & Wagner, 1996). We calculated number of species and Shannon diversity, and assessed plant community composition. We also counted the number of target species (all species recorded in the eleven target plots plus propagules of species applied by hand, resulting in a total of 143 species) and categorized plant species into species of concern based on their red list status in Switzerland as well as their protection status in Switzerland and the Canton Zurich (Moser, Gygax, Bäumler, Wyler, & Palese, 2002). Furthermore, we calculated indicator values for soil moisture and soil nutrients for each species according to Landolt et al. (2010).

 References
Baudirektion Kanton Zürich (2007). 10 Jahre Naturschutz-Gesamtkonzept für den Kanton Zürich 1995-2005 – Stand der Umsetzung. Zürich: Baudirektion Kanton Zürich.
Bongers, T. (1988). De nematoden van Nederland. Utrecht: Stichting Uitgeverij Koninklijke Nederlandse Natuurhistorische Vereniging.
Bongers, T. (1990). The maturity index: an ecological measure of environmental disturbance based on nematode species composition. Oecologia, 83, 14-19. doi:10.1007/BF00324627
Bongers, T., van der Meulen, H., & Korthals, G. (1997). Inverse relationship between the nematode maturity index and plant parasite index under enriched nutrient conditions. Applied Soil Ecology, 6, 195-199. doi:10.1016/S0929-1393(96)00136-9
Braun-Blanquet, J. (1964). Pflanzensoziologie, Grundzüge der Vegetationskunde (3rd ed.). Wien: Springer.
Delarze, R., Gonseth, Y., Eggenberg, S., & Vust, M. (2015). Lebensräume der Schweiz: Ökologie - Gefährdung - Kennarten (3rd ed.). Bern: Ott.
Ferris, H., Bongers, T., & de Goede, R.G.M. (2001). A framework for soil food web diagnostics: extension of the nematode faunal analysis concept. Applied Soil Ecology, 18, 13-29. doi:10.1016/S0929-1393(01)00152-4
Landolt, E., Bäumler, B., Erhardt, A., Hegg, O., Klötzli, F., Lämmler, W., … Wohlgemuth, T. (2010). Flora indicativa. Ecological indicator values and biological attributes of the Flora of Switzerland and the Alps (2nd ed.). Bern: Haupt.
Lauber, K., & Wagner, G. (1996). Flora Helvetica. Flora der Schweiz. Bern: Haupt.
MeteoSchweiz (2018). Klimabulletin Jahr 2017, Zürich: MeteoSchweiz.
Moser, D., Gygax, A., Bäumler, B., Wyler, N., & Palese, R. (2002) Rote Liste der gefährteten Farn- und Blütenpflanzen der Schweiz. Bern: BUWAL.
Oostenbrink, M. (1960). Estimating nematode populations by some selected methods. In N.J. Sasser & W.R. Jenkins (Eds.), Nematology (pp. 85-101). Chapel Hill: University of North Carolina Press.
Sieriebriennikov, B., Ferris, H., & de Goede, R.G.M (2014). NINJA: An automated calculation system for nematode-based biological monitoring. European Journal of Soil Biology, 61, 90-93. doi:10.1016/j.ejsobi.2014.02.004

diversity

display_name: diversity

long-term-experiment

display_name: long-term-experiment

nematodes

display_name: nematodes

restoration

display_name: restoration

topsoil-removal

display_name: topsoil-removal

vegetation

display_name: vegetation

en: Grassland restoration: nematodes and plant communities


maintainer: EnviDat Support

state: active

type: dataset

en: European Snow Booklet unpublished metadata

media_type: XLSX

en: The entire station metadata of all European institutions that contributed data is stored in METADATA_unpublished.xlsx. This file, however, cannot be accessed, since not all institutions gave permission to publish their station metadata. If you wish to access the metadata, please send a request to marty@slf.ch stating clearly why you want to access these metadata. NOTE: in this file ONLY station metadata (Lat/Lon/Altitude) is given, NO measured snow depth data.

format: XLS

en: COST European Snow Booklet (ESB)

media_type: application/pdf

en: Please find here the web version of the COST European Snow Booklet (ESB).

format: PDF

mimetype: application/pdf

en: COST European Snow Booklet (ESB) - DORA Link

media_type: HTML

en: DORA Link of the COST European Snow Booklet (ESB).

format: HTML

en: List of authors and list of the editorial board of the European Snow Booklet

en:
Here you can find the list of all lead authors of the different chapters of the European Snow Booklet (ESB).
In addition a list of the editorial board that reviewed the ESB is given.

en: European Snow Booklet public metadata

en:
The core of the European Snow Booklet (ESB), the country reports of 38 European countries, are based on information and metadata on snow measurements collected through a comprehensive survey, the ESB Questionnaire, between August 2017 and March 2018. The station metadata (for example location, elevation, measurement start date) from each country was provided by one or several institutions responsible for snow observations. By answering the questionnaire the contacts agreed that the information and metadata provided could be presented in the ESB. For cases where institutions indicated that information on station locations could be made publicly available, the metadata for the variables snow depth (HS), depth of snowfall (HN) and water equivalent of the snow cover (SWE), is additionally provided here for download (METADATA.xlsx).
Please note that not all institutions gave permission to publish their station metadata. This metadata has to be requested from the non-personal institution contacts provided in the METADATA.xlsx file. In some cases, however, no institutional contact is available and thus the contact of the country representative who filled in the questionnaire is used.
For current, updated station metadata we refer to the (non-personal) institutional contacts provided in METADATA.xlsx and/or the institution webpages. Note, however, that we cannot guarantee for completeness of the dataset for all countries because operational networks are prone to changes.

en:
The European Snow Booklet (ESB) is a book of reference for snow measurements that has been produced through collaboration with many European snow practitioners and snow scientists in the framework of the European Cooperation in Science and Technology (COST) Action ES1404 “A European network for a harmonised monitoring of snow for the benefit of climate change scenarios, hydrology and numerical weather prediction (HarmoSnow)”. The ESB provides a unique collection of information about operational snow observations in the European countries and the methods used to perform basic measurements of snow on the ground: snow depth (HS), depth of snowfall (HN), water equivalent of the snow cover (SWE) and presence of snow on the ground (PSG). Information and station metadata (for example location, elevation) for these basic snow variables were collected through a comprehensive survey, the ESB questionnaire between August 2017 and March 2018. Numerous institutions of 38 European countries provided detailed information describing the status of the operational snow observations and the methods used at the time of the survey. Based on the information provided, a country report was written for each European country. Similarities and differences among the countries, that is, the choice of snow variables to be measured, the measurement principles applied, the number of stations, or the spatial and elevational station distribution are pointed out. Thus the collection of country reports demonstrates the relevance of snow measurements for each country. Thus, the intention of the ESB is to foster better knowledge transfer regarding snow measurements between the snow science and operational communities and to improve the communication of information to the general public.

For detailed information on the European countries, we refer to the ESB, which can be downloaded here (envidat.ch). Please note that the ESB is not a living document and information and station metadata are from August 2017 till March 2018, except for Latvia (metadata updated in December 2018).

depth-of-snowfall

display_name: depth-of-snowfall

harmonisation-of-snow-measurements

display_name: harmonisation-of-snow-measurements

snow-depth

display_name: snow-depth

snow-measurement-methods

display_name: snow-measurement-methods

water-equivalent-of-the-snow-cover

display_name: water-equivalent-of-the-snow-cover

education

fr: Education, science

de: Bildung, Wissenschaft

en: Education and science

it: Formazione e scienza

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: European Snow Booklet – an Inventory of Snow Measurements in Europe


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

en: DATA ACCESS

media_type: SMET

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

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

format: SERVICE

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

gemstock

display_name: gemstock

longwave-radiation

display_name: longwave-radiation

meteo-station

display_name: meteo-station

shortwave-radiation

display_name: shortwave-radiation

snow-height

display_name: snow-height

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


en: mountain-forest foresty treeline-ecosystems afforestation stillberg davos

en: Documentation

media_type: application/msword

mimetype: application/msword

en: Dataset

media_type: GDB

en: This archive contains the following files:

format: GDB

en: Site photo

media_type: JPEG

en:
#Background

A history of many centuries of seasonal livestock grazing of alpine pastures has significantly depressed the treeline in many European mountain ranges. Therefore, observations of treeline patterns are more likely to be obscured by historical land use than in regions with less human influence and might not provide relevant information about environmental factors that influence mortality and growth patterns at the natural high-elevation limit of tree existence. To overcome this, we used a large afforestation experiment in the Swiss Alps, "Stillberg", to address the following questions:

Can trees survive above the current treeline in the European Alps? Are environmental factors that influence spatial patterns of mortality the same as those that influence height growth? Does the relative importance of these environmental variables change over the first 40 years after planting as seedlings?

#Dataset
The 5 ha Stillberg long-term afforestation research area is located near Davos (Central Alps, Switzerland). The bottom of the afforestation is approximately at the current treeline and it covers an altitudinal gradient of more than 150 m (2075 to 2230 m a.s.l.; see picture above). Approximately 92'000 seedlings of three treeline species (Larix decidua, Pinus mugo ssp. uncinata and Pinus cembra) were systematically planted in 1975, and mortality and height growth were closely monitored during the following 40 years.

Publications
Barbeito, I., Dawes, M. A., Rixen, C., Senn, J. and Bebi, P. (2012), Factors driving mortality and growth at treeline: a 30-year experiment of 92 000 conifers. Ecology, 93: 389–401. http://dx.doi.org/10.1890/11-0384.1

afforestation

display_name: afforestation

davos

display_name: davos

foresty

display_name: foresty

mountain-forest

display_name: mountain-forest

stillberg

display_name: stillberg

treeline-ecosystems

display_name: treeline-ecosystems

en: Long-term treeline research dataset at Stillberg, Davos


aerosol-data-weissfluhjoch

title_for_slug: aerosol-data-weissfluhjoch

en: aerosol ice-nucleation raclets inp particles linda

en: OPC-GT526S Dataset Weissfluhjoch

media_type: text/csv

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.).

format: CSV

mimetype: text/csv

en: SMPS Dataset Weissfluhjoch

media_type: application/x-netcdf

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

mimetype: application/x-netcdf

en: OPC-N3 Dataset Weissfluhjoch

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

en: APS Dataset Weissfluhjoch

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

en: LINDA Dataset Weissfluhjoch

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

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

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

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

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

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

aerosol

display_name: aerosol

ice-nucleation

display_name: ice-nucleation

inp

display_name: inp

linda

display_name: linda

particles

display_name: particles

raclets

display_name: raclets

en: Aerosol Data Weissfluhjoch


en: frost-change-velocity aridity sdms ground-temperature precipitation soil

media_type: TIFF

en: Soil organic carbon content modified from SoilGrids

format: TIFF

media_type: tif

en: Mean annual temperature modified from CHELSA

en: Mean diurnal range modified from CHELSA

en: Soil pH modified from SoilGrids

en: Precipitation seasonality from CHELSA

en: Soil clay content modified from SoilGrids

en: Precipitation of driest quarter modified from CHELSA

en: Frost change frequency modified from CHELSA

en: The dataset contains seven environmental layers (average annual temperature, aridity [annual precipitation divided by annual potential evapotranspiration], frost change frequency, precipitation in the driest quarter, mean diurnal temperature range, and precipitation seasonality) modified from CHELSA (https://chelsa-climate.org/) and three soil layers (soil organic matter content, pH water, and clay content) modified from SoilGrids (https://soilgrids.org/).

aridity

display_name: aridity

frost-change-velocity

display_name: frost-change-velocity

ground-temperature

display_name: ground-temperature

precipitation

display_name: precipitation

sdms

display_name: sdms

soil

display_name: soil

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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

en: Environmental layers for SDM simulations (GDPlants)


maintainer: Christian G. Sommer

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

en: README

media_type: text/plain

en: Describes the dataset in detail

format: TXT

mimetype: text/plain

en: Processing scripts

media_type: application/zip

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

format: ZIP

mimetype: application/zip

en: Kinect data

media_type: Link

en: Links to the dataset

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

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

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

This dataset is the basis of the following publication:

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

kinect

display_name: kinect

snow

display_name: snow

wind-crust

display_name: wind-crust

wind-packing

display_name: wind-packing

wind-tunnel

display_name: wind-tunnel

en: Wind crust formation: Microsoft Kinect data


en: numerical-model snow vapor-transport python pattern-formation

media_type: ZIP

en:
Data and code v1.0 downloaded from the git.

Running the code requires an installation of FEniCS via docker.

en:
This python code uses the Finite Element library FEniCS (via docker) to solve the one dimensional partial differential equations for heat and mass transfer in snow. The results are written in vtk format.

The dataset contains the code and the output data to reproduce the key Figure 5 from the related publication:

_Schürholt, K., Kowalski, J., Löwe, H.; Elements of future snowpack modeling - Part 1: A physical
instability arising from the non-linear coupling of transport and phase changes, The Cryosphere, 2022_

The code and potential updates can be accessed directly through git via:

https://gitlabext.wsl.ch/snow-physics/snowmodel_fenics

numerical-model

display_name: numerical-model

pattern-formation

display_name: pattern-formation

python

display_name: python

vapor-transport

display_name: vapor-transport

en: A numerical solver for heat and mass transport in snow based on FEniCS


en: high-resolution cosmo-wrf complex-terrain meteoswiss numerical-simulation

en: WPSrun

en: Scripts to run the WRF pre-processing (WPS) for COSMO-WRF simulation (see Section 5 in COSMOWRFdocumentation.pdf)

en: COSMO-WRF documentation v1.0

media_type: PDF

en:
Gerber, Franziska; Sharma, Varun (2018). Running COSMO-WRF on very-high resolution over complex terrain. EnviDat. doi:10.16904/envidat.35

Documentation about the procedure to run COSMO-WRF

en: MeteorologicalData

en: Scripts to process meteorological data (see Section 2 in COSMOWRFdocumentation.pdf).

en: StaticData

en:
Scripts to process geographical/static data (see Section 3 in COSMOWRFdocumentation.pdf).
- ascii_to_bin_landuse.f90
- ascii_to_bin_soiltype.f90
- ascii_to_bin_topo_wgs84_ws2sf1.f90
- see_slopes.ncl
- soiltype_writing.py
- toposmooth_wgs84_cosmobound.ncl
- write_geogrid.o

en: WPSSetup

en: Scripts that need to be changed to compile WPS (see COSMOWRFdocumentation.pdf, Section 4)

en: Postprocessing

en: Postprocessing scripts to map WRF output on swiss coordinates (see Section 7 in COSMOWRFdocumentation.pdf)

en: WRFrun

en: Scripts to run COSMO-WRF simulation (see Section 6 in COSMOWRFdocumentation.pdf)

en: This is a technical documentation of the procedure to run the Weather Research and Forecasting (WRF) model over complex alpine terrain using Consortium for Small-Scale Modeling (COSMO) reanalysis by the Federal Office of Meteorology and Climatology (MeteoSwiss) as initial and boundary conditions (COMSO-WRF). The setup is adapted for very high resolution simulations based on COSMO-2 (2.2 km resolution) reanalysis. This document gives an overview over steps to setup COSMO-WRF and adaptations needed to run COSMO-WRF. Additionally, the calculation of precipitation rate at a horizontal plane and remapping COSMO-WRF output on Swiss coordinates are documented.

complex-terrain

display_name: complex-terrain

cosmo-wrf

display_name: cosmo-wrf

high-resolution

display_name: high-resolution

meteoswiss

display_name: meteoswiss

numerical-simulation

display_name: numerical-simulation

education

en: Education and science


staatsquote-des-kantons-zurich

title_for_slug: staatsquote-des-kantons-zurich

name: Finanzverwaltung des Kantons Zürich, Steuerung Finanzhaushalt

label: Regierungsratsbeschluss Nr. 373/2022

label: Geschäftsbericht & Rechnung (Teil III: Finanzbericht)

de: Staatsquote des Kantons Zürich ab 2008

de: Variabeln: 'gesamtausgaben' und 'bruttoinlandprodukt' in Tausend CHF.

de: Die Staatsquote entspricht den «konsolidierten Gesamtausgaben» in Prozent des kantonalen Bruttoinlandprodukts (BIP). Die «konsolidierten Gesamtausgaben» umfassen die Ausgaben (Aufwand ohne Abschreibungen, Einlagen in Bestandeskonti von Fonds im Eigen- und Fremdkapital, durchlaufende Beiträge und interne Verrechnungen) plus Bruttoinvestitionen. Die konsolidierten Gesamtausgaben werden durch die Eidgenössischen Finanzverwaltung (EFV) berechnet. Das BIP des Kantons Zürich wird vom Bundesamt für Statistik (BFS) berechnet. Beide Wirtschaftsindikatoren werden mit einer zeitlichen Verzögerung von zwei Jahren veröffentlicht. Vorliegend wird die Staatsquote des Kantons ohne Gemeinden ausgewiesen. Für die interkantonale Vergleichbarkeit müsste die Staatsquote des Kantons und seiner Gemeinden betrachtet werden, aufgrund der je nach Kanton unterschiedlichen Aufgabenteilungen zwischen Kanton und Gemeinden.

finanzkennzahlen

display_name: finanzkennzahlen

oeffentlichefinanzen

display_name: oeffentlichefinanzen

staatsfinanzen

display_name: staatsfinanzen

national-economy

fr: Economie nationale

de: Volkswirtschaft

en: National economy

it: Economia

finanzverwaltung-kanton-zuerich

de: Finanzverwaltung Kanton Zürich

de: Der Kanton Zürich hat, nach dem Bund, den zweitgrössten öffentlichen Haushalt der Schweiz. Die Finanzverwaltung ist verantwortlich für die Planung, Führung und Überwachung dieses Finanzhaushalts. Sie erstellt das Budget und den Jahresabschluss der kantonalen Verwaltung und publiziert den Geschäftsbericht. Zudem übernimmt sie das operative Rechnungswesen und sichert mit der Tresorie die Zahlungsbereitschaft des Kantons. Die Finanzverwaltung bildet die finanzielle Schnittstelle zum Bund und den anderen Kantonen, beispielsweise für den nationalen Finanzausgleich.

political_level: canton

value: ZH-Kt Finanzverwaltung - DCAT Harvester

de: Staatsquote des Kantons Zürich


en: biodiversity topsoil-removal soil-chemistry bacteria soil-biodiversity fungi soil-biology nematodes grassland plants soil restoration

en: Vegetation data

en:
Plot.ID	consecutive numbers for plots (in total 36 plots)
Treatment	One restoration treatment and two reference treatments. Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Initial=intensively managed grasslands; Target= species-rich semi-wet to semi-dry grasslands
treat.yr	Treatment abbreviation. For restored grasslands: Treatment abbreviation + time since restoration; Initial + Target = NA
yr.trt	year when topsoil was removed; Initial + Target = NA
Age	time since restoration (sampling year 2018 - yr.trt); Initial + Target = NA
Age.class	restored sites of similar age grouped into four age classes. Y.4 = Year 3+4 (recently restored); Y.18 = Year 17+19 (mid-term); Y.24 = Year 23+25 (long-term); Y.30 = Year 27+32 (long-term); Initial + Target = NA
Cluster	clustered spatial arrangment of sites in the field (see Supporting Information Fig. S1)
Site	clustered spatial arrangment (replicates) of plots per site (see Supporting Information Fig. S1)
column I-LW	Plant abundance per species (in total 327 plant species) expressed as cover percentage estimation according to van der Maarel (vdM 1979; 7 classes: 1-7); nomenclature according to Lauber & Wagner (1996)

en: Nematode data

en:
Sample.data
Plot.ID	consecutive numbers for plots (in total 36 plots)
Treatment	One restoration treatment and two reference treatments. Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Initial=intensively managed grasslands; Target= species-rich semi-wet to semi-dry grasslands
treat.yr	Treatment abbreviation. For restored grasslands: Treatment abbreviation + time since restoration; Initial + Target = NA
yr.trt	year when topsoil was removed; Initial + Target = NA
Age	time since restoration (sampling year 2018 - yr.trt); Initial + Target = NA
Age.class	restored sites of similar age grouped into four age classes. Y.4 = Year 3+4 (recently restored); Y.18 = Year 17+19 (mid-term); Y.24 = Year 23+25 (long-term); Y.30 = Year 27+32 (long-term); Initial + Target = NA
Cluster	clustered spatial arrangment of sites in the field (see Supporting Information Fig. S1)
Site	clustered spatial arrangment (replicates) of plots per site (see Supporting Information Fig. S1)

Taxonomy
ASV.ID	unit number.letter code per amplicon sequence variant (in total 904)
Taxonomic rank	Phylum, Class, Order, Family, Genus, Species

Abundance
columns	Plot.ID (see Sample.data)
rows	ASV.ID (see Taxonomy)

en: Distance to target results

en:
Community	biotic community (vegetation, nematoda, fungi, prokaryota)
Treatment	Treatment groups; Y.4-30= restored grasslands (time since restoration); Initial=intensively managed grasslands (I)
Comparison	within- or between-community Bray-Curtis dissimilarity distance comparison
Label	within-community distances of initial/restored grasslands: treatment abbreviation; between-communities distances of initial/restored-to-target grasslands: treatment abbreviation plus Target (T; species-rich grasslands)
mean	Mean distance to Target (6 plots per treatment group)
sd	Standard deviation of distances to Target (6 plots per treatment group)

en: Networks

en:
Treatment	Treatment groups; Y.4-18 = restored grasslands; Initial = intensively managed grasslands; Target = species-rich semi-wet to semi-dry grasslands
Correlation	non-parametric Spearman rank correlations
Vertex.1+2	Comparison between network components; N.pool = total soil nitrogen pool [kg ha-1 12 cm-1]; Corg.pool = soil organic carbon pool [kg ha-1 12 cm-1], pH = soil pH [CaCl2]; BD = bulk density [g cm-3]; FC = field capacity [%]
	PCoA 1 + 2 biotic communities [Veg = vegetation; Nem = nematoda; Fun = fungi; Prok = prokaryota]
Type	Interactions type; A.A = abiotic.abiotic; A.B = abiotic.biotic; B.B = biotic.biotic

en: Ecosystem coupling_interactions

en:
Treatment	Treatment groups; Y.4-18 = restored grasslands; Initial = intensively managed grasslands; Target = species-rich semi-wet to semi-dry grasslands
Type	Interactions type; A.A = abiotic.abiotic; A.B = abiotic.biotic; B.B = biotic.biotic
Correlation	non-parametric Spearman rank correlations between biotic and abiotic parameters (ecological interactions)

en: Fungi data

en:
Sample.data
Plot.ID	consecutive numbers for plots (in total 36 plots)
Treatment	One restoration treatment and two reference treatments. Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Initial=intensively managed grasslands; Target= species-rich semi-wet to semi-dry grasslands
treat.yr	Treatment abbreviation. For restored grasslands: Treatment abbreviation + time since restoration; Initial + Target = NA
yr.trt	year when topsoil was removed; Initial + Target = NA
Age	time since restoration (sampling year 2018 - yr.trt); Initial + Target = NA
Age.class	restored sites of similar age grouped into four age classes. Y.4 = Year 3+4 (recently restored); Y.18 = Year 17+19 (mid-term); Y.24 = Year 23+25 (long-term); Y.30 = Year 27+32 (long-term); Initial + Target = NA
Cluster	clustered spatial arrangment of sites in the field (see Supporting Information Fig. S1)
Site	clustered spatial arrangment (replicates) of plots per site (see Supporting Information Fig. S1)

Taxonomy
OTU.ID	consecutive numbers for operational taxonomic units (OTUe 1-7794)
Taxonomic rank	Domain, Phylum, Class, Order, Family, Genus, Species

Abundance
columns	Plot.ID (see Sample.data)
rows	OTU.ID (see Taxonomy)

en: Soil properties

en:
Plot.ID	consecutive numbers for plots (in total 36 plots)
Treatment	One restoration treatment and two reference treatments. Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Initial=intensively managed grasslands; Target= species-rich semi-wet to semi-dry grasslands
treat.yr	Treatment abbreviation. For restored grasslands: Treatment abbreviation + time since restoration; Initial + Target = NA
yr.trt	year when topsoil was removed; Initial + Target = NA
Age	time since restoration (sampling year 2018 - yr.trt); Initial + Target = NA
Age.class	restored sites of similar age grouped into four age classes. Y.4 = Year 3+4 (recently restored); Y.18 = Year 17+19 (mid-term); Y.24 = Year 23+25 (long-term); Y.30 = Year 27+32 (long-term); Initial + Target = NA
Cluster	clustered spatial arrangment of sites in the field (see Supporting Information Fig. S1)
Site	clustered spatial arrangment (replicates) of plots per site (see Supporting Information Fig. S1)
N.pool	Soil nitrogen pool (June 2017); corrected for bulk density and 12 cm sampling depth [kg ha-1 12cm-1]
Corg.pool	Soil organic carbon pool (June 2017); corrected for bulk density and 12 cm sampling depth [kg ha-1 12cm-1]
pH	soil pH [CaCl2]
BD	Bulk density [g cm-3]
FC	Field capacity [%]
Veg.1	principle coordinate axis 1 (vegetation)
Veg.1	principle coordinate axis 2 (vegetation)
Nem.1	principle coordinate axis 1 (nematoda)
Nem.2	principle coordinate axis 2 (nematoda)
Fun.1	principle coordinate axis 1 (fungi)
Fun.2	principle coordinate axis 2 (fungi)
Prok.1	principle coordinate axis 1 (prokaryota)
Prok.2	principle coordinate axis 2 (prokaryota)
Veg.H	Shannon diversity (vegetation)
Nem.H	Shannon diversity (nematoda)
Fun.H	Shannon diversity (fungi)
Prok.H	Shannon diversity (prokaryota)

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

Paper Citation:

_Resch, M.C., Schütz, M., Ochoa-Hueso, R., Buchmann, N., Frey, B., Graf, U., van der Putten, W.H., Zimmermann, S., Risch, A.C. (in review). Long-term recovery of above- and belowground interactions in restored grassland after topsoil removal and seed addition. Journal of Applied Ecology_

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

Study area and experimental design
The study was conducted in and around two nature reserves, Eigental and Altläufe der Glatt, which were located approximately 5 km apart (47°27´ to 47°29´ N, 8°37´ to 8°32´ E, 417 to 572 m a.s.l., Canton of Zurich, Switzerland; Figure S1 and S2, Table S1). Mean annual temperature and precipitation are 9.8 ± 0.6 °C and 990 ± 168 mm (Kloten climate station 1988-2018; MeteoSchweiz, 2019). TFor this study, we used a space-for-time approach based on eight restoration sites that were between 3 and 32 years old. We measured recovery and restoration success by comparing the restored grasslands with intensively managed and semi-natural grasslands. Using a space-for-time approach requires high similarities in historical properties of the site, such as soil conditions and management regimes, to assure that temporal processes are appropriately represented by spatial patterns (Walker et al., 2010). This was the case in our study. The restored sites had similar soil conditions (i.e., soil type, structure, water availability) as the targeted semi-natural grasslands, while they shared the same agricultural legacy with intensively managed grasslands, i.e., biomass harvest and fertilization (manure and/or slurry) three to five times a year as well as tillage. We randomly established three 5 m x 5 m (25-m2) plots for plant identification and three 2 m x 2 m (4-m2) subplots for soil biotic and abiotic data collection at least 2 m away from the 25-m2 plots in each restoration site. Sites of similar age were grouped into four age classes: Y.4 (3 & 4 years after restoration), Y.18 (17 & 19 years), Y.24 (23 & 25 years), and Y.30 (27 & 32 years). Six intensively managed (Initial) and six semi-natural grassland (Target) sites complemented the experimental set-up, for a total of 36 plots. All plots were sampled under similar conditions, i.e., day of the year, air temperature, soil moisture, and time since last rain event, in June/July 2017 (intensively managed and semi-natural plots) and 2018 (restored plots).

Collection of plants and selected soil biota data
Plant species cover (in %) was visually estimated in each 25-m2 plot in mid-June (Braun-Blanquet, 1964; nomenclature: Lauber & Wagner, 1996). We calculated Shannon diversity and assessed plant community structure. We included soil microbial (fungi, procaryotes) and nematodes in our study as they represent the majority of soil biotic diversity and abundance (Bardgett & van der Putten, 2014), cover various trophic levels of the soil food web (Bongers & Ferris, 1999), and play key roles in soil functioning and ecosystem processes (Bardgett & van der Putten, 2014). In particular, soil nematodes were found to be well suited belowground indicators to evaluate recovery/development after restoration (e.g. Frouz, et al. 2008; Kardol et al., 2009; Resch et al., 2019). We randomly collected ten soil cores (2.2 cm diameter x 12 cm depths; sampler from Giddings Machine Company, Windsor, USA) in the 4-m2 subplots to assess soil nematode and microbial (fungal, prokaryotic) diversities and community structures. For soil nematodes, eight of the soil cores were combined and gently homogenized, placed in coolers and stored at 4 °C and transported to the laboratory (Netherlands Institute of Ecology, NIOO, Wageningen, Netherlands) within three days after collection. Free-living nematodes were extracted from 200 g of fresh soil using Oostenbrink elutriators (Oostenbrink, 1960). After extraction, each sample was divided into three subsamples, two for molecular identification and one to determine nematode abundance (see Resch et al., 2019). For the molecular work, two subsamples were stored in 70% ethanol (final volume 10 mL each) and transported to the laboratory at the Swiss Federal Research Institute WSL (Birmensdorf, Switzerland). Each subsample was reduced to roughly 200 μL by centrifugation and removal of the supernatant. The remaining ethanol was vaporized (65 °C for 3 h). Thereafter, 180 μL ATL buffer solution (Qiagen, Hilden, Germany) was immediately added and samples were stored at 4 °C until further processing. From these samples, nematode metagenomic DNA was extracted using the DNeasy Blood & Tissue Kit (Qiagen, Hilden, Germany) according to the manufacturer`s protocol, except for the incubation step which was run at 56 °C for 4 h. PCR amplification of the V6-V8 region of the eukaryotic small-subunit (18S) was performed with 7.5 μL of genomic DNA template (ca. 1 ng/μL) in 25 μL reactions containing 5 μL PCR reaction buffer, 2.5 mM MgCL2, 0.2 mM dNTPs, 0.8 μM of each primer (NemF: Sapkota & Nicolaisen, 2015; 18Sr2b: Porazinska et al., 2009), 0.5 μL BSA, and 0.25 μL GoTaq G2 Hot Start Polymerase (Promega Corporation, Madison, USA). Amplification was using an initial DNA denaturation step of 95 °C for 2 min, followed by 35 cycles at 94 °C for 40 sec, 58 °C for 40 sec, 72 °C for 1 min, and a final elongation step at 72 °C for 10 min. Filtering, dereplication, sample inference, chimera identification, and merging of paired-end reads was implemented using the DADA2 pipeline (v.1.12; Callahan et al., 2016) to finally assign amplicon sequence variants (ASVs) as taxonomic units.
We combined and homogenized the remaining two soil cores to assess soil microbes, placed them in coolers (4 °C) and transported them to the laboratory at WSL. Metagenomic DNA was extracted from 8 g sieved soil (2 mm) using the DNAeasy PowerMax Soil Kit (Qiagen, Hilden, Germany) according to the manufacturer´s protocol. PCR amplification of the V3-V4 region of the small-subunit (16S) of prokaryotes (i.e., bacteria and archaea) and the ribosomal internal transcribed spacer region (ITS2) of fungi was performed with 1 ng of template DNA using PCR primers and conditions as previously described (Frey et al., 2016). PCRs were run in triplicates, pooled and 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, USA). Quality filtering, clustering into operational taxonomic units (OTUs, 97% similarity cutoffs) and taxonomic assignment were performed as previously described (Resch et al., 2021).Taxonomic classification of nematode, prokaryotic and fungal sequences was conducted querying against the most recent versions of PR2 (v.4.11.1; Guillou et al., 2013), SILVA (v.132; Quast et al., 2013), and UNITE (v.8; Nilsson et al., 2019) reference sequence databases. Taxonomic assignment cutoffs were set to confidence rankings ≥ 0.8 (below ranked as unclassified). Prokaryotic OTUs assigned to mitochondria or chloroplasts as well as OTUs or ASVs assigned to other than Fungi or Nematoda were manually removed prior to data analysis. The three datasets were filtered to discard singletons and doubletons. Taxonomic abundance matrices were rarefied to the lowest number of sequences per community to achieve parity of the total number of reads between samples (Prokaryotes: 10,929 reads; Fungi: 18,337 reads; Nematodes: 6,662 reads). We calculated Shannon diversity and assessed community structures for soil nematodes, prokaryotes and fungi based on their relative abundances of ASV or OTU at the taxon level.

Collection of soil physical and chemical properties
We randomly collected one undisturbed soil core (5 cm diameter, 12 cm depth) per 4-m2 subplot using a steel cylinder that fit into the soil corer. The cylinders were capped to avoid disturbance during transport and used to measure field capacity, rock content and fine earth density as previously described (Resch et al., 2021). We randomly collected another three soil cores (5 cm diameter, 12 cm depths) in each 4-m2 subplot to determine soil chemical properties. The cores were pooled, dried at 60 °C for 48 h and passed through a 2 mm sieve. We measured soil pH (CaCl2) on dried samples, total nitrogen (N) and organic carbon (C) concentration on dried and fine-ground samples (≤ 0.5 mm; for details see Resch et al., 2021). We calculated total N and organic C pools after correcting its concentration for soil depth, rock content and fine earth density.

bacteria

display_name: bacteria

biodiversity

display_name: biodiversity

fungi

display_name: fungi

grassland

display_name: grassland

nematodes

display_name: nematodes

plants

display_name: plants

restoration

display_name: restoration

soil

display_name: soil

soil-biodiversity

display_name: soil-biodiversity

soil-biology

display_name: soil-biology

soil-chemistry

display_name: soil-chemistry

topsoil-removal

display_name: topsoil-removal

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: Long-term recovery of above-and belowground interactions in restored grasslands


maintainer: Maria Elvira Murazzi

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

en: Nanoplastic in forests experiment

media_type: XLS

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

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

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

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

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

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

forest-trees

display_name: forest-trees

nanoplastic

display_name: nanoplastic

polystyrene

display_name: polystyrene

uptake

display_name: uptake

education

en: Education and science

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


schwermetallbelastung-des-bodens

title_for_slug: schwermetallbelastung-des-bodens

media_type: text/html

mimetype: text/html

fr: SPARQL Endpoint avec présélection du graphe

en: SPARQL Endpoint with graph preselection

de: SPARQL Endpoint mit Vorauswahl des Graph

media_type: application/sparql-query

format: SPARQL

mimetype: application/sparql-query

fr: Les données mesurées sont des moyennes des concentrations de métaux lourds relevées sur les 20 cm supérieurs du sol sur 100 sites d'observation à long terme de l'Observatoire national des sols (NABO).

en: The measurement data are the surveyed mean values of heavy metal concentrations recorded in the top 20 cm of the soil in around 100 permanent monitoring sites operated by the Swiss National Soil Monitoring Network (NABO).

de: Bei den Messdaten handelt es sich um Erhebungsmittelwerte von Schwermetallkonzentrationen in den obersten 20 cm des Bodens von rund 100 Dauerbeobachtungsstandorten der Nationalen Bodenbeobachtung NABO.

it: I dati misurati sono valori medi delle concentrazioni di metalli pesanti rilevate nei 20 cm dello strato superiore del suolo presso circa 100 siti di osservazione a lungo termine dell'Osservatorio nazionale dei suoli (NABO).

agriculture

fr: Agriculture, sylviculture

de: Land- und Forstwirtschaft

en: Agriculture, forestry

it: Agricoltura, selvicoltura

territory

fr: Territoire et environnement

de: Raum und Umwelt

en: Territory and environment

it: Territorio e ambiente

bundesamt-fur-umwelt-bafu

fr: Office fédéral de l'environnement OFEV

de: Bundesamt für Umwelt BAFU

en: Federal Office for the Environment FOEN

it: Ufficio federale dell'ambiente UFAM

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

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

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

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

political_level: confederation

value: LINDAS DCAT Harvester BAFU

fr: Pollution du sol par les métaux lourds

de: Schwermetallbelastung des Bodens

en: Heavy Metal Soil Contamination

it: Carico di metalli pesanti nel suolo


maintainer: Nander Wever

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

en: Undercatch correction

media_type: application/pdf

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

en: SNOWPACK ini file (new syntax)

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

en: SNOWPACK ini file (old syntax)

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

en: Version 7 (current)

en:
Release notes:

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

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

en: All versions

en: Version 5

en:
Deprecated version. Release notes:

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

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

Update: refreshing data originating from MeteoSwiss (particularly precipitation)

en: Version 3

en:
Deprecated version. Release notes:

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

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

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

Update: refreshing data originating from MeteoSwiss (particularly precipitation)

en: SNOWPACK sno file

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

en: Version 6

en:
Deprecated version.

Release notes:

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

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

en: Version 1

en:
Deprecated version. Release notes:

First release of dataset.

en: Version 2

en:
Deprecated version. Release notes:

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

en: Version 4

en:
Deprecated version. Release notes:

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

Update: refreshing data originating from MeteoSwiss (particularly precipitation)

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

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

air-temperature

display_name: air-temperature

humidity

display_name: humidity

longwave-radiation

display_name: longwave-radiation

precipitation-amount

display_name: precipitation-amount

shortwave-radiation

display_name: shortwave-radiation

snow-depth

display_name: snow-depth

snow-ice-temperature

display_name: snow-ice-temperature

wind-direction

display_name: wind-direction

wind-speed

display_name: wind-speed

education

de: Bildung, Wissenschaft

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


qualitat-der-badegewasser

title_for_slug: qualitat-der-badegewasser

name: Païvi Rinta, Sektion Hydr. Grundlagen Gewässerzustand

en: SPARQL Endpoint with graph preselection

fr: Les relevés de la qualité des eaux de baignade permettent d’évaluer la qualité hygiénique des eaux à l’aide des indicateurs que sont E. coli et les entérocoques intestinaux. Les informations fournies servent à déterminer l’efficacité des mesures de protection des eaux, notamment si les stations d’épuration remplissent leur fonction.

de: Die Erhebungen zur Badegewässerqualität beurteilen die hygienische Qualität der Gewässer anhand der E.Coli und intestinalen Enterokokken. Sie geben Hinweise darauf, wie gut Gewässerschutzmassnahmen greifen, und insbesondere wieweit die Abwasserreinigungsanlagen ihre Funktion erfüllen.

en: The assessment of bathing waters is made on the basis of hygienic quality using E.coli and intestinal enterococci. They provide indications on the effectiveness of water protection measures and in particular the extent to which waste water treatment plants are fulfilling their function.

it: I rilevamenti per la determinazione della qualità delle acque di balneazione valutano la qualità igienica delle acque in base all’E. coli e agli enterococchi intestinali. Forniscono informazioni sull’incisività delle misure di protezione delle acque, in particolare sull’efficienza degli impianti di depurazione delle acque di scarico.

territory

en: Territory and environment

bundesamt-fur-umwelt-bafu

en: Federal Office for the Environment FOEN

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

fr: Qualité des eaux de baignade

de: Qualität der Badegewässer

en: Bathing water quality

it: Qualità delle acque di balneazione


fr: Pourcentage de la population, des appartements et des bâtiments exposés au bruit de la circulation routière, ferroviaire et aérienne de jour et de nuit. Les données sont issues d'une modélisation effectuée à l'échelle nationale utilisant le modèle sonBASE.

de: Anteil der Bevölkerung, Wohnungen und Gebäude, die tagsüber und nachts durch Straßen-, Schienen- und Fluglärmbelastung ausgesetzt sind. Die Daten basieren auf flächendeckenden Modellberechnungen (sonBASE).

en: Percentage of the population, flats and buildings exposed to road, rail and air traffic noise during the day and night. The data is based on area-wide model calculations (sonBASE).

it: Percentuale della popolazione, degli appartamenti e degli edifici esposti al rumore del traffico stradale, ferroviario e aereo diurno e notturno. I dati sono derivati da un esercizio di modellazione nazionale utilizzando il modello sonBASE.

air-traffic-noise

display_name: air-traffic-noise

bruit-du-trafic-aerien

display_name: bruit-du-trafic-aerien

bruit-du-trafic-ferroviaire

display_name: bruit-du-trafic-ferroviaire

bruit-du-trafic-routier

display_name: bruit-du-trafic-routier

eisenbahnlarm

display_name: eisenbahnlarm

fluglarm

display_name: fluglarm

rail-traffic-noise

display_name: rail-traffic-noise

road-traffic-noise

display_name: road-traffic-noise

rumore-del-traffico-aero

display_name: rumore-del-traffico-aero

rumore-del-traffico-ferroviaro

display_name: rumore-del-traffico-ferroviaro

rumore-del-traffico-stradale

display_name: rumore-del-traffico-stradale

strassenverkehrslarm

display_name: strassenverkehrslarm

health

fr: Santé

de: Gesundheit

en: Health

it: Sanità

fr: Exposition au bruit du trafic

de: Lärmbelastung durch Verkehr

en: Traffic noise pollution

it: Esposizione al rumore del traffico


globale-konjunkturbarometer

maintainer: KOF Datenservice

title_for_slug: globale-konjunkturbarometer

en: Data description

de: Datenbeschreibung

fr: Baromètres conjoncturels mondiaux

de: Globale Konjunkturbarometer

en: Global Economic Barometers

fr: Les Baromètres conjoncturels mondiaux sont un système d’indicateurs destiné à l’analyse en temps réel de l’évolution conjoncturelle mondiale. Il s’agit d’un projet de coopération du KOF Centre de recherches conjoncturelles de l’ETH Zürich et de la Fundação Getúlio Vargas (FGV) de Rio de Janeiro (Brésil). Le système comprend deux sous- indicateurs : un indicateur coïncident et un indicateur avancé.

en: The Global Economic Barometers are a system of indicators that enables a timely analysis of global economic development. They represent a collaboration between the KOF Swiss Economic Institute and Fundação Getúlio Vargas (FGV) based in Rio de Janeiro, Brazil. The system consists of two indicators: a coincident indicator and a leading indicator.

de: Die globalen Konjunkturbarometer sind ein Indikatorensystem für die zeitnahe Analyse der globalen Konjunkturentwicklung. Sie sind ein Kooperationsprojekt der KOF Konjunkturforschungsstelle der ETH Zürich und der Fundação Getúlio Vargas (FGV) in Rio de Janeiro, Brasilien. Das System besteht aus zwei Teilindikatoren: einem Koinzidenz- und einem Frühindikator.

fr: Description des données

economie-mondiale

display_name: economie-mondiale

global-economy

display_name: global-economy

weltwirtschaft

display_name: weltwirtschaft

national-economy

de: Volkswirtschaft

en: National economy

kof-konjunkturforschungsstelle

de: KOF Konjunkturforschungsstelle

en: KOF Swiss Economic Institute

it: KOF Centro di ricerca congiunturale

fr: Le KOF, Centre de recherches conjoncturelles, fournit des informations tangibles dans le domaine de la recherche économique et conjoncturelle. Il établit une multitude de prévisions et d’indicateurs utiles à l’observation de la conjoncture. Sa base de données, issue d’enquêtes menées auprès des entreprises, est unique en Suisse. Ces données permettent au KOF de générer divers indicateurs (baromètre, indicateur de l’emploi, indicateur bancaire), qui reflètent le climat de l’économie suisse. Les résultats de ces enquêtes sont évalués chaque trimestre et communiqués au public, tout comme les prévisions conjoncturelles. Le KOF analyse également l’activité d’innovation des entreprises suisses, effectue des études sur les dépenses de santé et commente les derniers développements de l’économie. Il participe aussi (en collaboration avec le centre Ifo de Munich) à l’élaboration des prévisions conjoncturelles allemandes (« Gemeinschaftsdiagnose »), lesquelles servent de base au gouvernement allemand en ce qui concerne l’évolution future de l’économie.

en: KOF delivers profund insight into the field of Economic Research. Next to conducting surveys in all branches of the Swiss economy, it also creates forecasts and indicators for business cycle analysis. KOF's data pool of business surveys is unique in Switzerland. KOF uses these data sets to mirror the economic situations and moods of Swiss companies through the generation of indicators. The KOF Economic Barometer, KOF Employment Indicator, KOF Business Situation, etc., are crucial for assessing economic developments in Switzerland. Many of these surveys are carried out monthly and results are evaluated quarterly to be presented to the public. KOF also analyzes the innovation of Swiss companies, provides studies on health issues and comments on current economic developments. KOF is also (together with the Ifo Institute in Munich) involved in the joint economic forecast in Germany, which is used by the German Government as a directional measure of future economic development.

de: Das KOF Institut liefert fundierte Informationen im Bereich der Wirtschafts- und Konjunkturforschung. Sie erstellt eine Vielzahl von Prognosen und Indikatoren zur Konjunkturbeobachtung. Ihr Datenpool aus Unternehmensbefragungen ist einmalig in der Schweiz. Aus diesen Daten generiert die KOF verschiedene Indikatoren (KOF Konjunkturbarometer, KOF Beschäftigungsindikator, KOF Geschäftslageindikator, u.a.), die ein Stimmungsbild der Schweizer Wirtschaft wiedergeben. Ihre Umfrageresultate wertet sie vierteljährlich aus und stellt sie der Öffentlichkeit vor, ebenso ihre Konjunkturprognosen. Sie analysiert aber auch die Innovationstätigkeit Schweizer Unternehmen, liefert Studien zum Arbeitsmarkt, den Gesundheitsausgaben und kommentiert aktuelle wirtschaftliche Entwicklungen. So ist die KOF auch (zusammen mit dem ifo Institut in München) an der Gemeinschaftsdiagnose in Deutschland beteiligt, die der deutschen Regierung als Richtmass für die zukünftige wirtschaftliche Entwicklung dient.

it: l KOF fornisce informazioni approfondite in materia di ricerca economica e congiunturale, producendo svariati indicatori e previsioni per il monitoraggio congiunturale. Il pool di dati derivante da indagini tra le aziende è unico in Svizzera. A partire da questi dati, il KOF genera diversi indicatori (barometro congiunturale, indicatore di occupazione, situazione commerciale, e così via) che riflettono la realtà dell'economia svizzera. Valuta trimestralmente i risultati delle indagini e li presenta al pubblico, unitamente alle previsioni congiunturali. Ma il KOF analizza anche l'attività di innovazione delle imprese svizzere, svolge studi sulla spesa sanitaria o commenta gli ultimi sviluppi economici. Il KOF, inoltre (insieme all'Istituto ifo di Monaco di Baviera) partecipa alla previsione congiunta in Germania, che il governo tedesco utilizza come riferimento per gli sviluppi economici futuri.

value: KOF Harvester


fr: pollution-atmospherique accord-sur-les-transports-terrestres trafic transit air

de: transitverkehr landverkehrsabkommen luft verkehr luftbelastung

de: SPARQL Endpoint mit Vorauswahl des Graph

fr: Le projet "Suivi des mesures d’accompagnement Environnement" (SMA-E) mesure la pollution atmosphérique et sonore le long des axes de transit A2 et A13. Il doit permettre d'évaluer l'impact environnemental du trafic de marchandises sur les axes nord-sud. Dans le domaine de l'environnement, cette mesure permet de contrôler la mise en œuvre de la loi sur le transfert du trafic et l'efficacité des mesures d'accompagnement dans le cadre de la politique de transfert de la Confédération.

de: Das Projekt "Monitoring flankierende Massnahmen Umwelt" (MFM-U) misst die Luft- und Lärmbelastung entlang der Transitachsen A2 und A13. Damit soll die Umweltbelastung des Güterverkehrs auf den Nord-Süd-Achsen aufgezeigt werden. Damit wird im Bereich Umwelt die Umsetzung des Verlagerungsgesetzes und die Wirksamkeit der flankierenden Massnahmen im Rahmen der Verlagerungspolitik des Bundes überprüft.

en: The project "Monitoring flankierende Massnahmen Umwelt" (MFM-U) measures air and noise pollution along the A2 and A13 transit axes. This is intended to show the environmental impact of freight traffic on the north-south axes. In this way, the implementation of the Transfer of Traffic Act and the effectiveness of the accompanying measures within the framework of the federal government's transfer policy are examined in the area of the environment.

it: Il progetto "Monitoraggio delle misure di accompagnamento” (MMA-A) misura l'inquinamento atmosferico e acustico lungo gli assi di transito A2 e A13. L'obiettivo è quello di quantificare l'impatto ambientale del traffico merci sugli assi nord-sud, per verificare l’efficacia ambientale dell'attuazione della legge sul trasferimento del traffico e delle misure di accompagnamento nell'ambito della politica di trasferimento della Confederazione.

accord-sur-les-transports-terrestres

display_name: accord-sur-les-transports-terrestres

air

display_name: air

landverkehrsabkommen

display_name: landverkehrsabkommen

luft

display_name: luft

luftbelastung

display_name: luftbelastung

pollution-atmospherique

display_name: pollution-atmospherique

trafic

display_name: trafic

transit

display_name: transit

transitverkehr

display_name: transitverkehr

verkehr

display_name: verkehr

mobility

fr: Mobilité et transports

de: Mobilität und Verkehr

en: Mobility and Transport

it: Mobilità e trasporti

fr: Pollution atmosphérique  le long de l'A2 et de l'A13 - Moyennes annuelles - NO et NO2

de: Luftbelastung entlang der A2 und der A13 - Jahresmittelwerte - NO und NO2

en: Air pollution along the A2 and A13 - annual averages - NO and NO2

it: Inquinamento atmosferico lungo la A2 e la A13 - Medie annuali - NO e NO2


kof-economic-sentiment-indicator

title_for_slug: kof-economic-sentiment-indicator

fr: KOF Indice du climat économique

en: KOF Economic Sentiment Indicator

de: KOF Economic Sentiment Indicator

fr: Le KOF Indice du climat économique pour la Suisse (KOF Economic Sentiment Indicator) se base sur les résultats d’enquêtes conjoncturelles du KOF et de celle menées auprès des consommateurs par le Secrétariat d’État à l’économie (SECO). La méthode de calcul des indices pour la Suisse s’appuie sur la méthodologie de la Commission européenne. La Commission européenne calcule de cette manière les indices pour tous les pays membres de l’UE ainsi que pour différents agrégats, tels que l’UE dans son ensemble ou la zone euro.

de: Der KOF Economic Sentiment Indicator für die Schweiz basiert auf den Konjunkturumfragen der KOF und der Konsumentenbefragung des Staatssekretariats für Wirtschaft (SECO). Die Berechnungsmethode der Indikatoren für die Schweiz lehnt sich an die Methodik der EU-Kommission an. Die EU-Kommission berechnet auf diese Weise Indikatoren für alle Mitgliedsländer der EU sowie für verschiedene Aggregate, etwa der EU insgesamt oder den Euro-Raum.

en: The KOF Economic Sentiment Indicator for Switzerland is based on the results of the KOF Business Tendency Surveys and the Consumer Confidence Survey conducted by the State Secretariat for Economic Affairs (SECO). The calculation method used for the Swiss indicators follows the method employed by the EU Commission to calculate indicators for all EU member states as well as various aggregates, for instance total EU or Euro area.

climat-economique

display_name: climat-economique

economic-sentiment

display_name: economic-sentiment

okonomische-stimmung

display_name: okonomische-stimmung

national-economy

fr: Economie nationale

it: Economia

kof-konjunkturforschungsstelle

fr: KOF Centre de recherches conjoncturelles


maintainer: OGD-Koordinationsstelle St.Gallen

de: wahlen abstimmungen real-time resultate api

de: csv

de: Schnittstelle zum Portal "Wahlen & Abstimmungen" des Kantons St.Gallen (wab.sg.ch) (csv)

license: CC BY

de: json

media_type: application/json

de: Schnittstelle zum Portal "Wahlen & Abstimmungen" des Kantons St.Gallen (wab.sg.ch) (json)

format: JSON

mimetype: application/json

de: Das Portal "Wahlen & Abstimmungen" des Kantons St.Gallen (wab.sg.ch) bietet eine Schnittstelle an, um auf die verfügbaren Abstimmungs- und Wahlresultate zugreifen zu können. Damit kann das gesamte Archiv oder auch die aktuellen Abstimmungs- oder Wahlresultate in Echtzeit abgefragt und genutzt werden.Unter folgendem Link ist die Nutzung der API-Schnittstelle ausführlich erklärt:https://github.com/OneGov/onegov-cloud/blob/master/docs/api/election_day/open_data_de.md

abstimmungen

display_name: abstimmungen

api

display_name: api

real-time

display_name: real-time

resultate

display_name: resultate

wahlen

display_name: wahlen

politics

fr: Politique

de: Politik

en: Politics

it: Politica

staatskanzlei-kanton-st-gallen

de: Staatskanzlei Kanton St.Gallen

de:
Die Staatskanzlei ist als Stabsstelle von Kantonsrat und Regierung die zentrale Schaltstelle in der kantonalen Staatsverwaltung. Die Staatskanzlei nimmt vielfältige Unterstützungs- und Koordinationsaufgaben wahr und verbindet die verschiedenen Akteure miteinander. Ob beratend, koordinierend oder federführend in gewissen Sachbereichen, die Staatskanzlei muss in ihrer Rolle immer durch Qualität, Verlässlichkeit und dienstleistungsorientierte Zusammenarbeit überzeugen. Dies bringt die Vision der Staatskanzlei auf den Punkt:

«Wir verbinden und überzeugen. Ihre Staatskanzlei.»

value: DCAT Staatskanzlei Kanton St.Gallen


kof-konjunkturbarometer

title_for_slug: kof-konjunkturbarometer

fr: suisse conjoncture

de: konjunktur vorlaufindikator schweiz

en: switzerland fli business-cycle leading-indicator

fr: KOF Baromètre conjoncturel

de: KOF Konjunkturbarometer

en: KOF Economic Barometer

fr: Depuis les années 70, le KOF publie un indicateur phare pour l’économie suisse, le baromètre conjoncturel, lequel prévoit l’évolution de la conjoncture suisse dans un avenir proche. La méthode de calcul a été révisée en 1998 puis en 2006. La dernière révision a eu lieu en 2014.

de: Seit den 1970er Jahren veröffentlicht die KOF einen vorlaufenden Sammelindikator, das Konjunkturbarometer, welches anzeigt, wie sich die Schweizer Konjunktur in der nahen Zukunft entwickeln dürfte. Die Methodik wurde 1998 revidiert, dann nochmals 2006. Die letzte umfassende Revision erfolgte im Jahr 2014.

en: Since the 1970s, the KOF Swiss Economic Institute is publishing a leading composite indicator, the KOF Economic Barometer, predicting how the Swiss economy should perform in the near future. The methodology was overhauled in 1998 and again in 2006. The last thorough revision was conducted in 2014.

de: Datenbeschreibung

business-cycle

display_name: business-cycle

conjoncture

display_name: conjoncture

fli

display_name: fli

konjunktur

display_name: konjunktur

leading-indicator

display_name: leading-indicator

schweiz

display_name: schweiz

suisse

display_name: suisse

switzerland

display_name: switzerland

vorlaufindikator

display_name: vorlaufindikator

national-economy

de: Volkswirtschaft

kof-konjunkturforschungsstelle

de: KOF Konjunkturforschungsstelle

de: Das KOF Institut liefert fundierte Informationen im Bereich der Wirtschafts- und Konjunkturforschung. Sie erstellt eine Vielzahl von Prognosen und Indikatoren zur Konjunkturbeobachtung. Ihr Datenpool aus Unternehmensbefragungen ist einmalig in der Schweiz. Aus diesen Daten generiert die KOF verschiedene Indikatoren (KOF Konjunkturbarometer, KOF Beschäftigungsindikator, KOF Geschäftslageindikator, u.a.), die ein Stimmungsbild der Schweizer Wirtschaft wiedergeben. Ihre Umfrageresultate wertet sie vierteljährlich aus und stellt sie der Öffentlichkeit vor, ebenso ihre Konjunkturprognosen. Sie analysiert aber auch die Innovationstätigkeit Schweizer Unternehmen, liefert Studien zum Arbeitsmarkt, den Gesundheitsausgaben und kommentiert aktuelle wirtschaftliche Entwicklungen. So ist die KOF auch (zusammen mit dem ifo Institut in München) an der Gemeinschaftsdiagnose in Deutschland beteiligt, die der deutschen Regierung als Richtmass für die zukünftige wirtschaftliche Entwicklung dient.


de: auszahlungen wahlen real-time abstimmungen

de: Aktuelle Wahl- bzw. Abstimmungsresultate Kanton St.Gallen (csv)

de: Aktuelle Wahl- bzw. Abstimmungsresultate Kanton St.Gallen (json)

de: Der Datensatz bildet eine Schnittstelle zu wab.sg.ch und liefert während des Wahl- oder Abstimmungssonntags die Auszählungen der Wahl- und Abstimmungsresultate des Kantons St.Gallen in Echtzeit. Der Datensatz wird alle 15 Minuten aktualisiert.Anmerkung: Zwischen Wahlen und Abstimmungen kann es sein, dass die Schnittstelle keine Daten liefert, da die neuen Wahlen und Abstimmungen bereits aufgeschaltet werden.

auszahlungen

display_name: auszahlungen


kof-business-situation-indicator

title_for_slug: kof-business-situation-indicator

fr: suisse situation-des-affaires

de: schweiz geschaftslageindikator

fr: KOF Indicateur de la situation des affaires

de: KOF Business Situation Indicator

en: KOF Geschäftslageindikator

fr: The KOF Business Situation Indicator is based on over 4,500 Swiss enterprise respondents. Each month, a survey is conducted among companies active in industry, retail, construction, project engineering as well as financial and insurance services. Companies in hotel and catering, wholesale and other services are interviewed in the first month of every quarter.

en: L’indicateur de la situation des affaires du KOF se fonde sur plus de 4500 informations d’entreprises de Suisse. Chaque mois, les entreprises des secteurs de l’industrie, du commerce de détail, du bâtiment, des bureaux d’études et des services financiers et assurantiels sont interrogées. Les entreprises de l’hôtellerie, du commerce de gros et des autres services font l’objet d’une enquête trimestrielle, à chaque fois le premier mois d’un nouveau trimestre.

de: Die KOF Geschäftslage basiert auf mehr als 4500 Meldungen von Unternehmen in der Schweiz. Monatlich werden Unternehmen in den Wirtschaftsbereichen Industrie, Detailhandel, Baugewerbe, Projektierung sowie Finanz- und Versicherungsdienstleistungen befragt. Unternehmen im Gastgewerbe, im Grosshandel und in den übrigen Dienstleistungen werden vierteljährlich, jeweils im ersten Monat eines Quartals, befragt.

bussiness-situation

display_name: bussiness-situation

economics

display_name: economics

geschaftslageindikator

display_name: geschaftslageindikator

situation-des-affaires

display_name: situation-des-affaires


geography

fr: Géographie

de: Geographie

en: Geography

it: Geografia

health

fr: Santé

de: Gesundheit

en: Health

label: Link zum Geoportal

label: geocat.ch permalink

spatial: Schweiz-BAG-Faeller

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

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

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

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

fr: Aperçu map.geo.admin.ch

de: Vorschau map.geo.admin.ch

en: Preview map.geo.admin.ch

it: Previsione map.geo.admin.ch

fr: Serveur de téléchargement de geo.admin.ch

de: Download Server von geo.admin.ch

en: Download server from geo.admin.ch

it: Server di download di geo.admin.ch

fr: RESTful API de geo.admin.ch

de: RESTful API von geo.admin.ch

en: RESTful API from geo.admin.ch

it: RESTful API da geo.admin.ch

format: API

fr: La méningo-encéphalite à tiques (FSME) est une infection transmise par les tiques, soumise à l'obligation légale de déclarer. Entre 2012 et 2022, au moins un cas de FSME a été déclaré dans les communes figurant en orange sur la carte.

de: Die Frühsommer-Meningoenzephalitis (FSME) ist eine durch Zecken übertragene Erkrankung, für die eine gesetzlich verankerte Meldepflicht besteht. Diese Karte zeigt in Orange die Gemeinden auf, in denen im Zeitraum 2012-2022 mindestens 1 Fall von FSME gemeldet wurden.

en: Tick-borne encephalitis (TBE) is an infectious viral disease transmitted by ticks, which is subject to a statutory duty to report. This map shows in orange the communes in which at least one case of TBE was reported in the period 2012-2022.

it: La meningoencefalite primaverile-estiva (FSME) è una malattia trasmessa dalle zecche, per la quale è stato sancito per legge l'obbligo di dichiarazione. La presente cartina mostra in arancio i Comuni in cui nel periodo 2012-2022 è stato dichiarato almeno un caso di FSME.

bundesamt-fur-gesundheit-bag

fr: Office fédéral de la santé publique OFSP

de: Bundesamt für Gesundheit BAG

en: Federal Office of Public Health FOPH

it: Ufficio federale della sanità pubblica UFSP

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

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

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

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

value: BAG - Geocat Harvester

fr: Lieux déclarés de piqûre pour la méningo-encéphalite à tiques (FSME), Suisse, 2012-2022

de: Gemeldete Stichorte der Frühsommer-Meningoenzephalitis (FSME), Schweiz, 2012-2022

en: Site of tick bite reported for tick-borne encephalitis (TBE), Switzerland, 2012-2022

it: Luoghi in cui sono stati dichiarati casi di meningoencefalite primaverile-estiva (FSME), Svizzera, 2012-2022