en: species-richness bryophytes nature-conservation vascular-plants habitat-area extinction-debt habitat-fragmentation wetlands

en: Wetlands of the canton of Zürich (Switzerland): Data on species richness and recent and historic area and connectivity of 55 fens.

en:
The data provided in the Excel sheet contain the following information for 55 wetlands of the canton of Zürich (Switzerland):
-Species richness data for vascular plants and bryophytes classified as follows: (1) all vascular plant species; (2) wetlands specialists among vascular plants; (3) generalists, which were all non-specialist vascular plant species; (4) short-lived vascular plant specialists; (5) long-lived vascular plant specialists; (6) short-lived vascular plant generalists; (7) long-lived vascular plant generalists; (8) bryophyte species.
-Area of all wetlands in 1850, 1900, 1950 and 2000
-Wetland area within buffers with 2km radius with the center of the wetland as starting point
-Mean indicator values according to Landolt et al. (2010) and the standard deviation of these indicator values based on presence-absence data of vascular plants. Indicator values for temperature, light availability, moisture, acidity, nutrients, amount of humus and soil aeration were considered.

en:
This dataset includes data on species richness of vascular plants and bryophytes in 55 wetlands of the canton of Zürich (Switzerland) as well as recent and historic data on the area and connectivity of these 55 wetlands and was used for the paper Jamin A., Peintinger M., Gimmi U., Holderegger R., Bergamini A. (2020) Evidence for a possible extinction debt in Swiss wetland specialist plants. Ecology and Evolution.
Species richness data are available for vascular plants and bryophytes. The field survey was carried out between June 5 and August 10, 2012. The survey covered all wetland (fen) types in the canton of Zürich. For data collection, at least half a day per wetland was spent searching for species. Within each wetland all different vegetation types were covered until no new species were found to get as complete species lists as possible. In the Excel file information on species richness of the following groups is provided: (1) all vascular plant species; (2) wetlands specialists among vascular plants; (3) generalists, which were all non-specialist vascular plant species; (4) short-lived vascular plant specialists; (5) long-lived vascular plant specialists; (6) short-lived vascular plant generalists; (7) long-lived vascular plant generalists; (8) bryophyte species. Specialist vascular plant species included all characteristic species listed in Appendix 1a of the wetland inventory of Switzerland (BUWAL, 1990).
Based on the data of Gimmi et al. (2011), the area of all wetlands in 1850, 1900, 1950 and 2000 were determined as well as the wetland area within buffers 2km in radius with the center of the wetland as starting point. These data are also provided in the Excel sheet.
Moreover, for each wetland mean indicator values according to Landolt et al. (2010) and the standard deviation of these indicator values based on presence-absence data of vascular plants were calculated and are provided in Excel sheet. Indicator values for temperature, light availability, moisture, acidity, nutrients, amount of humus and soil aeration were considered.

bryophytes

display_name: bryophytes

extinction-debt

display_name: extinction-debt

habitat-area

display_name: habitat-area

habitat-fragmentation

display_name: habitat-fragmentation

nature-conservation

display_name: nature-conservation

species-richness

display_name: species-richness

vascular-plants

display_name: vascular-plants

wetlands

display_name: wetlands


en: meteo-station ground-temperature depth-of-snowfall swiss-alps climate-change-impact

state: active

type: dataset

media_type: text/plain

format: TXT

en: GenPSUm

media_type: application/zip

en:
GenPSUm contains Files to get the complete precipitation set:

1. MeteoData.ini: Ini file for MeteoIO to get the met. input data. It has to be run two times, to get prec. Data from Imis 	stations and from ANETZ regressions.

2 GenPSUM.ini: Inifile for Snowpack (in research mode), to get winter precipitation calculated by snowpack out of the snow hight measurements.
	In:_sno contain the soil profils for the station
	In_met contains the meterological input data. It's from the data base and MeteoData.ini is used to get it.

3. The folder R_programms contains the R sripts to generate the precipitation data set, it reads in IMIS, ANETZ and SNOWPACK precipi and makes one complet data set out of it. (it contains the function- script plus a script to use the function)

format: ZIP

en: SNOWPACK climate change simulation files

en: This folder contains SNOWPACK INI-files for snowpack, soil profiles, and input data for climate change modeling.

en: R-Script

en: The Folder R- Script contains, the R files to create the Climate Change input and the Data for the WFJ- Modeling.

en: Precipitation datasets

en:
This folder contains 16 ideal precipitation data sets for IMIS stations.

*Updated documentation 24 August 2020:*

1. Datasets have been filtered for unphysical values using a MeteoIO min/max tool, however spikes still exist that pass this filter yet appear to be outliers, no further manual filtering was applied.
2. Data prior to 2009 can have large data gaps in certain stations. This is documented in the accompanying publication and was not used for simulations.
3. GUT2 was erroneously filtered during summer periods. This does not affect published snow modelling results but summer (Jun-Oct) values of 8.25 should not be used and filtered prior to use. This affects 4759 summer values or 1.2% of the time-series.

en:
This dataset includes all data and simulation files presented in the publication: Bender et al. 2020. Changes in climatology, snow cover and ground temperatures at high alpine locations, DOI: 10.3389/feart.2020.00100.

This includes:

* meteorological forcing,
* climate change timeries and
* simulation files together with
* snow depth
* ground temperature

Please refer to the following publication for further details which should be cited when using this dataset:

Bender et al. 2020. Changes in climatology, snow cover and ground temperatures at high alpine locations, DOI: 10.3389/feart.2020.00100.

climate-change-impact

display_name: climate-change-impact

depth-of-snowfall

display_name: depth-of-snowfall

ground-temperature

display_name: ground-temperature

meteo-station

display_name: meteo-station

swiss-alps

display_name: swiss-alps

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: Changes in climatology, snow cover and ground temperatures at high alpine locations in Switzerland (Bender et al. 2020)


ect-and-rb-data-switzerland

title_for_slug: ect-and-rb-data-switzerland

en: slope-stability stability-test ect snow-stability rutschblock

en: data set (update)

media_type: text/csv

en:
This is the same data set as uploaded originally, except for the following three additional columns, which are required to replicate the respective classification of ECT results: *depthCriteriaDecisive_ectStabOrig*, *depthCriteriaDecisive_ectStabWinkler*, *depthCriteriaDecisive_ectStabNew*. These columns have the following values:
- 1 if the depth criteria as defined in the paper was decisive for rating the ECT result with stability class 4
- 0 if the depth of the fracture plane was not decisive for the stability classification

In addition, the stability class has been recoded for:
- *ectStabOrig* from [1, 2] to [1, 4]
- *ectStabWinkler* from [1, 2, 3] --> [1, 2.5, 4]
to be fully in line with the stability classes used in the paper.

format: CSV

en: data set

en:
The data set contains the following information. For details, refer to the publication:
Variables
- unique identifier for the snow pit location (profileID)
- unique identifier for the ECT stability test (stabTestID)
- ECT stability classifications
-- orginal (ectStabOrig)
-- Winkler and Schweizer (ectStabWinkler)
-- new (ectStabNew)
- ECT results
-- lowest number of taps (ectScoreMin) for the decisive propagation
-- propagation
- slope stability information (slopeStability 0 = stable, 1 = unstable)
- RB stability class
- forecast avalanche danger level (forecastDangerLevel)

en: The data set contains the data used in the publication "On snow stability interpretation of Extended Column Test results" by Techel et. al. (2020), published in Natural Hazards Earth System Sciences.

ect

display_name: ect

rutschblock

display_name: rutschblock

slope-stability

display_name: slope-stability

snow-stability

display_name: snow-stability

stability-test

display_name: stability-test

en: ECT and RB data Switzerland


high-resolution-climate-data-for-europe

maintainer: EnviDat Support

title_for_slug: high-resolution-climate-data-for-europe

en: cordex daily-climate maximum-temperature eur11 minimum-temperature precipitation air-temperature europe

en: Data Access

format: SERVICE

en: CHELSA EUR11 Technical Documentation

media_type: application/pdf

en: As the CHELSA project is continuously expanded, certain new parameters, time scales etc. are not included anymore in the original publication (Karger et al. 2017). We are therefore provide this document as a guideline of the current climatic parameters available and will expand it as new parameters or time periods will become available. Please refer to the version history for changes made in the document.

format: PDF

en:
High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present downscaled climate data for the CORDEX EUR11 domain at a high resolution of 30 arc sec. The temperature algorithm is based on statistical downscaling of atmospheric temperature lapse rates. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height. The resulting data consist of a daily temperature and precipitation timeseries.

The data is distributed under a: Creative Commons: Attribution 4.0 International (CC BY 4.0) license.

air-temperature

display_name: air-temperature

cordex

display_name: cordex

daily-climate

display_name: daily-climate

europe

display_name: europe

maximum-temperature

display_name: maximum-temperature

minimum-temperature

display_name: minimum-temperature

precipitation

display_name: precipitation

en: High resolution climate data for Europe


maintainer: Axel Volkwein

en: mass-movements experiments field-tests trajectory field-test natural-hazards rockfall experiment field-data

en: Jumps and impacts

en: This table contains data of more than 2200 of each jumps and impacts of the test blocks on a grassy slope. It gives there durations in combination with acting accerlartions and the rotational speed of the blocks. The data were retrieved using the block internal accelerometer and rotation sensors. The combination of these data with the measurement from the local positioning system allows the deduction of jump lengths, slope gradient etc.

en: These data contain the point clouds obtained from laser scanning the test slope and the six test blocks used for the experiments. The coordinates of the slope are given in CH-1903 format. The coordinates of the block point clouds are kept in a lokal x/y/z coordinate system.

mimetype: application/zip

en: Overview of all tests

en:
This table gives an overview on all tests performed including
- block used
- start & end position
- number of jumps
- etc.
-

en: Block trajectories recorded from local positioning system

en: This file contains more than 80 rockfall trajectories projected onto the terrain, i.e. the positions of the single blocks over time during the single tests that were recorded by the local positioning system with about 10Hz. It contains the detected positions as well as a smoothened path (see Volkwein et al., Geosciences, 2018). The table also contains the derived velocities and accelerations along the slope (attention: these accelerations are the once that occur during an impact).

en:
In summer 2014, 6 rock blocks between 20 and 80kg have been thrown in total 111 times down a slope at the Swiss Oberalppass close to the village Tschamut. The slope was mainly covered by grass and its lower part was flat and large enough to provide natural runouts of the single trajectories.

An extensive measurement program has been set up to measure the block trajectories: With surveyor's instruments the slope and the six used rock blocks were scanned and the start and end positions of each test were recorded. During the single events two cameras filmed the trajectories. A special sensor device located within the blocks recorded the acting accelerations and rotational speeds over time. Further, the device emitted a Wifi signal that got detected from eight receivers around the slope. Based on this signal the block position has been recorded over time.

The dataset contains all data that were gathered through above field campaign.

experiment

display_name: experiment

experiments

display_name: experiments

field-data

display_name: field-data

field-test

display_name: field-test

field-tests

display_name: field-tests

mass-movements

display_name: mass-movements

natural-hazards

display_name: natural-hazards

rockfall

display_name: rockfall

trajectory

display_name: trajectory

educ

en: Education, culture and sport

en: Repetitive trajectory testing in Tschamut 2014


swiss-canopy-crane-ii-research-site

title_for_slug: swiss-canopy-crane-ii-research-site

en: swiss-forest-lab climate-change drought forest ecosystem-functions infrastructure damage-of-tree

en: Link to website

en: Research site metadata

format: XLS

en: Swiss Canopy Crane II

en:
![alt text](https://www.envidat.ch/dataset/fc69e369-eee9-42ab-8486-d2c38cff317d/resource/68fa7065-de32-4343-aa26-71094c5254ae/download/sccii.jpg "Swiss Canopy Crane II")

This research site is located near Hölstein in Canton Basel-Landschaft in a mature temperate forest that harbours more than 400 trees from 14 different species. The 1.6 ha site is equipped with the latest infrastructure, including 60 automated point dendrometers, automated soil respiration chambers, 72 ceramic suction cups at various locations and depths across the site, and a range of automated environmental sensors in the soil, the forest floor and in the canopy. A key piece of infrastructure is the new Swiss Canopy Crane II (SCC II), a 50 m tall crane with a 50 m jib that provides canopy access to 250 trees from 12 different species.

climate-change

display_name: climate-change

damage-of-tree

display_name: damage-of-tree

drought

display_name: drought

ecosystem-functions

display_name: ecosystem-functions

forest

display_name: forest

infrastructure

display_name: infrastructure

swiss-forest-lab

display_name: swiss-forest-lab

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: Swiss Canopy Crane II research site


maintainer: Rebecca Mott

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

en: Snowmask 2014

en: Digital Elevation Modell 2014

en: Snowmask 2016

en: Digital Elevation Modell 2015

en: Snowmask 2015

en: Gletschboden test site

mimetype: application/pdf

en: Digital Elevation Modell 2016

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

patchy-snow-covers

display_name: patchy-snow-covers

snow-ablation-rates

display_name: snow-ablation-rates

terrestrial-laser-scanning

display_name: terrestrial-laser-scanning

tls

display_name: tls

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


en: strategic-planning governance planning-efficacy land-use-planning analytical-hierarchy-process

en: The present content is part of the published paper Palka, G., Oliveira, E., Pagliarin, S., & Hersperger, A. M. (2021). Strategic spatial planning and efficacy: an analytic hierarchy process (AHP) approach in Lyon and Copenhagen. European planning studies, 29(6), 1174-1192. It contains the jupyter notebook and sample data to compute Analytical Hierarchy Process, and a report on its use.

analytical-hierarchy-process

display_name: analytical-hierarchy-process

governance

display_name: governance

land-use-planning

display_name: land-use-planning

planning-efficacy

display_name: planning-efficacy

strategic-planning

display_name: strategic-planning

educ

en: Education, culture and sport

en: Planning efficacy computation based on AHP


maintainer: Jana von Freyberg

en: snowmelt-lysimeter solutes snowmelt-rate snowpack-outflow stable-water-isotopes

en: The dataset contains volumes, solutes and stable water isotopes of snowpack outflow at daily resolution. 10-minute data are provided of the measurements taken by three snowmelt lysimeter systems at spatially-distributed locations.

en:
This data contain volumes, solutes and isotopes of snowpack outflow measured by a snowmelt lysimeter system at three locations in the southern Alp catchment, situated Central Switzerland. The river Alp is a snow-dominated catchment situated in Central Switzerland characterized by an elevation range from 840 to 1898 m a.s.l.
The dataset provides solutes (major anions and cations, trace metals) and stable water isotopes and water fluxes (snowpack outflow volumes) at daily intervals from several sampling locations. Additionally, the data measured by the snowmelt lysimeter system are provided in 10-minute resolution.

snowmelt-lysimeter

display_name: snowmelt-lysimeter

snowmelt-rate

display_name: snowmelt-rate

snowpack-outflow

display_name: snowpack-outflow

solutes

display_name: solutes

stable-water-isotopes

display_name: stable-water-isotopes

envidat

en: EnviDat: the Environmental Data Portal

en: Daily data of the volumes, solutes and isotopes in snowpack outflow measured at three locations in the southern Alp catchment


en: avalanche-fatalities number-of-dead-persons number-of-caught-persons avalanche-accidents avalanche-accident-statistics

en: Fatal avalanche accidents in Switzerland since 1995-1996

mimetype: text/csv

en:
Attention: this data is not updated after 2022 anymore.
This data collection contains information concerning all accidents by snow avalanches causing at least one fatality in Switzerland. The data set commences on 01/10/1995. After the completion of a hydrological year, the new data is added.
The following information is provided:
 * avalanche identifier
 * date of the accident
 * accuracy of the date in range of days before and after
 * canton
 * name of the locality
 * start zone of the avalanche
  * coordinates (Swiss coordinate system, approximately in middle of start zone)
  * accuracy of the coordinates in meters
 * elevation (in meteres above sea level, app. in middle of start zone)
 * slope aspect (main orientation of start zone)
 * slope inclination (in degree, steepest point within start zone)
 * number of dead persons
 * number of caught persons
 * number of fully buried persons
 * forecasted avalanche danger level
 * activity/location of the accident party at the time of the incident

avalanche-accident-statistics

display_name: avalanche-accident-statistics

avalanche-accidents

display_name: avalanche-accidents

avalanche-fatalities

display_name: avalanche-fatalities

number-of-caught-persons

display_name: number-of-caught-persons

number-of-dead-persons

display_name: number-of-dead-persons

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: snow-height snow-depth parallel-measurements snow-onset-and-disappearance-dates climate-indicators

en: ReadMe

en: File containing information about variables used in this data set

en: snow climate indicators

en: Snow climate indicators derived from parallel manual snow measurements

en: Sdur

en: Potential sunshine duration during MA and mean snow disappearance dates for a selection of station pairs.

en: Data set consisting of snow climate indicators derived from parallel manual snow measurements in Switzerland.

climate-indicators

display_name: climate-indicators

parallel-measurements

display_name: parallel-measurements

snow-depth

display_name: snow-depth

snow-height

display_name: snow-height

snow-onset-and-disappearance-dates

display_name: snow-onset-and-disappearance-dates


irkis-soil-moisture-measurements-davos

maintainer: Nander Wever

title_for_slug: irkis-soil-moisture-measurements-davos

en: davos meteo-station soil-moisture dischma-catchment meteorological-measurements

en: IRKIS soil moisture data (zip file)

en: Compressed archive (zip) containing soil moisture measurements, as well as meteorological measurements and interpolated meteorological time series.

en:
Meteorological and soil moisture measurements from soil moisture stations installed from October 2010 - October 2013 in the area surrounding Davos, in particular in the Dischma catchment.

There are in total 7 stations: 1202, 1203, 1204, 1205, 222, 333 and SLF2. For each of the stations, there is a:

* vwc_[stn].smet: containing the soil moisture measurements

* station_[stn].smet: in-situ measured meteorlogical parameters. Note, the quality of these measurements for stations 1202, 1203, 1204 and 1205 is very low, with data gaps. Use this data with care. For stations 222, 333 and SLF2, data quality is high and only the default cautiousness should be applied.

* interpolatedmeteo_[stn].smet contains per stations a dataset derived by interpolating from several stations in the Davos area to the stations location. This dataset was generated from the output of the Alpine3D model, of which simulations are presented in the Wever et al. (2017) manuscript.

At the soil moisture measurement sites, Decagon 10HS sensors were installed, at 10, 30, 50, 80 and 120 cm depth. Per depth 2 sensors were installed, labelled A and B in the datafiles. Note that at stations 1203, 1204 and 1205, sensors were only installed at 10, 30 and 50 cm depth.

The files follow the SMET format: https://models.slf.ch/docserver/meteoio/SMET_specifications.pdf and metadata for the stations can be found in the header of the smet files.

Please cite the Wever et al. (2017) reference when using this data in publications.

For a more detailed description, please refer to: Wever, N., Comola, F., Bavay, M., and Lehning, M.: Simulating the influence of snow surface processes on soil moisture dynamics and streamflow generation in an alpine catchment, Hydrol. Earth Syst. Sci., 21, 4053-4071, https://doi.org/10.5194/hess-21-4053-2017, 2017.

davos

display_name: davos

dischma-catchment

display_name: dischma-catchment

meteo-station

display_name: meteo-station

meteorological-measurements

display_name: meteorological-measurements

soil-moisture

display_name: soil-moisture

educ

en: Education, culture and sport

en: IRKIS Soil moisture measurements Davos


en: raster-processing qgis-plugin swiss-geodata open-source seilaplan cable-yarding digital-planning digital-elevation-model geoinformation-system geodata-processing

en: DHM-Kacheln zusammenfügen

en: View video on youtube!

en:
In order to use the QGIS plugin ‘Seilaplan’ for digital cable line planning, a digital terrain model (DTM) is required. In this tutorial video, we show how to merge multiple DTM raster tiles into one file, using the QGIS tool ‘Virtual Raster’. This simplifies the digital planning of a cable line using the QGIS plugin ‘Seilaplan’. Please note that the tutorial language is German!

Link to Seilaplan website: https://seilaplan.wsl.ch

***************************

Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung ist ein digitales Höhenmodell (DHM) nötig. In diesem Tutorialvideo zeigen wir, wie man mit dem QGIS-Plugin Virtuelles Raster mehrere DHM-Kacheln zu einem einzigen Rasterfile zusammenfügen und abspeichern kann. Für die Seillinienplanung mit Seilaplan muss nun nur noch eine Datei, mein neues virtuelles Raster, ausgewählt werden.

Link zur Seilaplan-Website: https://seilaplan.wsl.ch

cable-yarding

display_name: cable-yarding

digital-elevation-model

display_name: digital-elevation-model

digital-planning

display_name: digital-planning

geodata-processing

display_name: geodata-processing

geoinformation-system

display_name: geoinformation-system

open-source

display_name: open-source

qgis-plugin

display_name: qgis-plugin

raster-processing

display_name: raster-processing

seilaplan

display_name: seilaplan

swiss-geodata

display_name: swiss-geodata

envidat

en: EnviDat: the Environmental Data Portal

en: Seilaplan Tutorial: Merge DTM tiles


lwf-chironico-long-term-research-site

title_for_slug: lwf-chironico-long-term-research-site

en: meteorological-data infrastructure forest plot swiss-forest-lab meteorological-measurements lwf

en: LWF plot Chironico. Photo: Anne Thimonier Rickenmann, 2019

en: Chironico Meteorological Data LWF

en:
![alt text](https://www.envidat.ch/dataset/67e14643-c7f2-4190-ae6e-c8c94f1c5f01/resource/1d23d53f-8572-47eb-b466-88dd8ad244c9/download/lwf_chironico.jpg "LWF Chironico")

LWF Plot Chironico

- Community: Chironico / canton TI
- Date of installation: 29 August 1995
- Size of the plot: 2 ha
- Altitude: 1342-1387 m
- Mean slope: 35%
- Geology (in German): Untergrund: Penninikum; Paragneisse u. Glimmerschiefer - Oberfläche: Quartär; karbonatfreie Moräne, Hängeschutt
- Provisional soil type (WSL) : Distric Cambisol
- Woodland association after EK72: 47: Calamagrostio villosae-Abietetum
- Main tree species: Picea abies
- Management system: high forest
- Silvicultural system: group selection
- Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 54.1 cm
- Number of trees BHD >= 12 cm (2011): 750
- Maximum tree age: Picea abies: 160-180 yr - Abies alba: 140-160 yr

More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/chironico.html

forest

display_name: forest

infrastructure

display_name: infrastructure

lwf

display_name: lwf

meteorological-data

display_name: meteorological-data

plot

display_name: plot

swiss-forest-lab

display_name: swiss-forest-lab

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: LWF Chironico long-term research site


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

en: Data plot of the forest station (LEB)

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

en: Site photo of the forest station (LEB)

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

mimetype: image/jpeg

en: Forest station data (LEB)

en: Data file.

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for one meteorological station in Lens in Switzerland which is located within a natural coniferous forest with Scots pine (_Pinus sylvestris_; 150-170 yrs)) as dominant tree species. The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Lens is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

temperature

display_name: temperature

wind-speed

display_name: wind-speed

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


tree-ring-laser-ablation-data

maintainer: Paula Ballikaya

title_for_slug: tree-ring-laser-ablation-data

en: dendrochronology dendrochemistry tree-rings air-pollution laser-ablation

en: Laser ablation data

en:
This dataset contains the values of several chemical elements (Mg, Al, Si, S, K, Ca, Ti, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Sr, Tl, Pb, Bi) measured in the latewood of tree rings of Mongolian oak from Harbin, China, at a 5-year resolution. Due to the lack of a suitable reference material for wood, absolute concentration was not calculated, and the ratio between the chemical element and 13C was taken as proxy for the element signal.

In Harbin, one of the largest cities and most important industrial centers in northeastern China, air quality monitoring systems were built only by the end of 2015 to meet the national requirements. Thus, dendrochemical analyses could be used as a tool to complement for the lack of air quality data over longer periods of time, allowing for the reconstruction of the temporal trend of trace metals. Our main scopes were to: (a) assess the chemical composition of Quercus mongolica Fisch. ex Ledeb. tree rings from Harbin using a recently developed system of laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS), (b) identify the main chemical elements which
derived from air pollution and may be used as indicators over the period 1965–2020 in Harbin, while excluding those that were controlled by physiological processes in the tree, and (c) reconstruct the history of pollution in Harbin by comparing the tree-ring chemical composition of recent decades with that of previous decades, in trees growing in the highly polluted city of Harbin and in trees growing in a control site 90 km away from major pollution sources.

Briefly, the temporal trend of some elements was influenced by physiological factors, by environmental factors such as pollution, or influenced by both. Mg, K, Zn, Cu, Ni, Pb, As, Sr and Tl showed changes in pollution levels over time.

air-pollution

display_name: air-pollution

dendrochemistry

display_name: dendrochemistry

dendrochronology

display_name: dendrochronology

laser-ablation

display_name: laser-ablation

tree-rings

display_name: tree-rings

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Tree-ring laser ablation data


en:
Sum of stem cross-section areas of all dead trees in a stand at a height of 1.3 m (dbh measurement height) recorded according to the NFI1 method.

Citation:
 > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_

accessible-forest-without-shrub-forest

display_name: accessible-forest-without-shrub-forest

altitudinal-vegetation-zone

display_name: altitudinal-vegetation-zone

biogeographical-region

display_name: biogeographical-region

canton

display_name: canton

conifers-broadleaves

display_name: conifers-broadleaves

economic-region

display_name: economic-region

lower-higher-altitudinal-zones

display_name: lower-higher-altitudinal-zones

main-tree-species

display_name: main-tree-species

ownership

display_name: ownership

production-region

display_name: production-region

protection-forest-region

display_name: protection-forest-region

tree-state-standing-lying

display_name: tree-state-standing-lying

envidat

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

en: Basal area of dead wood NFI1


maintainer: Ueli Reber

en: policy-analysis text-corpus bgb biodiversity environmental--policy policy-documents switzerland

en: Replication files

en:
 Introduction

The ZIP file contains all data and code to replicate the analyses reported in the following paper.

Reber, U., Fischer, M., Ingold, K., Kienast, F., Hersperger, A. M., Grütter, R., & Benz, R. (2022). Integrating biodiversity: A longitudinal and cross-sectoral analysis of Swiss politics. *Policy Sciences*. [https://doi.org/10.1007/s11077-022-09456-4](https://doi.org/10.1007/s11077-022-09456-4)

If you use any of the material included in this repository, please refer to the paper. If you use (parts of) the text corpus, please also refer to the sources used for its compilation listed below. The content of the texts may not be changed.

 Data folder

The data folder contains the following files.

* _corpus.parquet_: Text corpus of Swiss policy documents
* _dict_de.csv_: Biodiversity dictionary (German)
* _dict_fr.csv_: Biodiversity dictionary (French)
* _dict_it.csv_: Biodiversity dictionary (Italian)
* _topic_labels.csv_: labels/codes for policy sectors
* _topics.csv_: labels/codes for policy sectors

The corpus and the dictionary were compiled by the authors specifically for this project. The labels/codes for policy sectors are based on the [coding scheme](http://ws-old.parlament.ch/affairs/topics) of the Swiss Parliament.

 Text corpus

The text corpus consists of 439,984 Swiss policy documents in German, French, and Italian from 1999 to 2018. The corpus was compiled from the following source between 2020-10-01 and 2021-01-31.

* Transcripts and parliamentary businesses (e.g. questions, motions, parliamentary initiatives) via the [Web Services (WS)](https://www.parlament.ch/de/%C3%BCber-das-parlament/fakten-und-zahlen/open-data-web-services) provided by the Swiss Parliament
* The official compilation of federal legislation ("Amtliche Sammlung", AS) via [opendata.swiss](https://opendata.swiss/de/dataset/official-compilation-of-federal-legislation-bs-as-1947-2018) provided by the Swiss Federal Archives (SFA)
* The federal gazette ("Bundesblatt") via [fedlex.admin.ch](https://www.fedlex.admin.ch/de/fga/index)
* Decisions of federal courts via [entscheidsuche.ch (ES)](https://entscheidsuche.ch/)

The corpus is stored in a single data frame to use with R saved as [PARQUET](https://parquet.apache.org/) file (corpus.parquet). The data frame has the following structure.

* _text_id_: Unique identifier for each text (source information as prefix, e.g. "t_")
* _doc_type_: Document type (see coding scheme below)
* _branch_: Government branche (1 legislative, 2 executive, 3 judicative)
* _stage_: Stage of policy process (1 drafting, 2 introduction, 3 interpretation)
* _year_: Year of publication
* _topic_: Policy sector (coding scheme in separate file in data folder)
* _lang_: Language (de, fr, it)
* _text_: Text

The following list contains the coding scheme for the doc_type variable.

* 101: Federal gazette // Draft for public consultation ("Vernehmlassungsverfahren")
* 102: Federal gazette // Explanation of draft for parliament ("Botschaft")
* 103: Federal gazette // Strategy, action plan
* 104: Federal gazette // Federal council decree ("Bundesratsbeschluss")
* 105: Federal gazette // (Simple) Federal decree ("(Einfacher) Bundesbeschluss")
* 106: Federal gazette // General decree ("Allgemeinverfügung")
* 107: Federal gazette // Treaty ("Übereinkommen")
* 108: Federal gazette // Treaty ("Abkommen")
* 109: Federal gazette // Draft for parliament ("Entwurf")
* 110: Federal gazette // Report ("Bericht")
* 111: Federal gazette // Report of parliamentary comission ("Bericht")
* 112: Federal gazette // Report of federal council ("Bericht")
* 201: Parl. businesses // Submitted text
* 202: Parl. businesses // Reason text
* 203: Parl. businesses // Federal council response
* 204: Parl. businesses // Initial situation
* 205: Parl. businesses // Proceedings
* 301: Parl. transcripts // Speech of MP
* 302: Parl. transcripts // Speech of federal council
* 401: Federal legislation // Legal text of the official compilation (law, ordinances, etc.)
* 501: Court decisions // Federal Supreme Court
* 502: Court decisions // Federal Criminal Court
* 503: Court decisions // Federal Administrative Court

 Code folder

The code folder contains all R code for the analyses. The files are numbered chronologically.

* _1_classifier_training.R_: Training of classifiers for classification of policy sectors
* _2_classifier_application.R_: Classification of documents in corpus
* _3_dictionary_application.R_: Biodiversity indexing of documents in corpus
* _4_stm_truncation.R_: Truncation of indexed documents to keep only relevant parts
* _5_stm_translation.R_: Translation of FR and IT documents to DE
* _6_stm_model.R_: Preprocesssing and structural topic model
* _7_plots.R_: Plots and numbers as included in the paper

The code/functions folder contains custom functions used in the scripts, e.g. to support topic model interpretation.

Package versions and setup details are noted in the code files.

 Contact

Please direct any questions to Ueli Reber (ueli.reber@eawag.ch).

bgb

display_name: bgb

biodiversity

display_name: biodiversity

environmental--policy

display_name: environmental--policy

policy-analysis

display_name: policy-analysis

policy-documents

display_name: policy-documents

switzerland

display_name: switzerland

text-corpus

display_name: text-corpus

en: Replication files for "Integrating biodiversity: A longitudinal and cross-sectoral analysis of Swiss politics"


automatic-detection-of-avalanches

maintainer: Matthias Heck

title_for_slug: automatic-detection-of-avalanches

en: snow avalanche-activity monitoring hidden-markov-model seismic

en: Results and Scripts

en: This zip package contains the classification results, coherence results, reference data set, python scripts used for the classification and the start and end times for the pre-processed continuous seismic data. A further readme file is also include for more detailed information.

en: This dataset contains the results obtained by an automatic classification using hidden Markov models of a continuous seismic dataset. To avoid long computational times, we reduced the seismic data using pre-processing step. The start and end times of the windows used for the classification are also included in this dataset. Furthermore, an avalanche reference data set is included and the python scripts used to perform the processing steps and the classification.

avalanche-activity

display_name: avalanche-activity

hidden-markov-model

display_name: hidden-markov-model

monitoring

display_name: monitoring

seismic

display_name: seismic

snow

display_name: snow

en: Automatic detection of avalanches


automatic-classification-of-avalanches

maintainer: Alec van Herwijnen

title_for_slug: automatic-classification-of-avalanches

en: machine-learning monitoring avalanche seismic localization

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

mimetype: application/x-tar

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

avalanche

display_name: avalanche

localization

display_name: localization

machine-learning

display_name: machine-learning

en: Automatic Classification of Avalanches


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

en: Manually observed snow profiles

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

en: Extracted data

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

en: SNOWPACK simulations

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

en: README

en: README file explaining data structure and content

mimetype: text/plain

en: Stability tests

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

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

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

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

avalanche-forecast

display_name: avalanche-forecast

snow-stability-evaluation

display_name: snow-stability-evaluation

snowpack

display_name: snowpack

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Validating and improving the critical crack length in SNOWPACK


en:
Sum of stem cross-section areas at a height of 1.3 m (dbh measurement height) of all living and dead trees and shrubs starting at 12 cm dbh recorded according to the NFI1 method. In NFI1 only those dead trees were recorded whose wood could still be exploited.

Citation:
 > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_

accessible-forest-without-shrub-forest

display_name: accessible-forest-without-shrub-forest

altitudinal-vegetation-zone

display_name: altitudinal-vegetation-zone

biogeographical-region

display_name: biogeographical-region

canton

display_name: canton

economic-region

display_name: economic-region

main-tree-species

display_name: main-tree-species

production-region

display_name: production-region

protection-forest-region

display_name: protection-forest-region

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: Total basal area NFI1


en: switzerland nuclear-microsatellite-markers trichopria-drosophilae multilocus-genotypes drosophila-suzukii population-genetics biological-control simple-sequence-repeats

en: Multilocus genotypes (biallelic coding for 21 nuclear microsatellites) of Trichopria drosophilae described in Gugerli et al. Agrarforschung Schweiz 2019. Missing data denoted as '999'. Population codes (PopNo) are the following: 1, Vezia; 3, Contone ; 5, Davesco; 6, Corteglia; 7, Mezzana; 8, Stabio; 9, Gordola; 10, Sementina; 12, Brusata di Novazzano; 14, Monteggio; 16, Giubiasco; 17, ComoBP; 18, VD; 19, Weil_D; 20, Dossenheim; 21, BioPlanet_Como. Coordinates (CoordCH_E, CoordCH_N) refer to the Swiss coordinates system CH1903.

en: Table comprising all pertinent information for multiplex PCRs of species-specific nuclear microsatellite makers for Trichopria drosophilae, comprising all 21 newly developed markers, details on full sequences with GenBank accession numbers, primer sequences (including labeling and pig-tails), and repeat motives. PCRs with respective annealing temperatures (AnnealingTemp) refer to Type-It Multiplex PCR Kit (Qiagen, Hilden, Germany).

en:
 Nuclear microsatellite markers and genotype data for _Trichopria ddrosophilae_

This data set comprises (i) the characteristics of a set of 21 species-specific nuclear microsatellites for PCR amplification in _Trichopria drosophilae_ (ii) and genotype data for samples collected in southern Switzerland (Canton of Ticino), with few reference samples from Canton of Vaud, southern Germany, and northern Italy (lab-reared population).
Markers were developed by Ecogenics GmbH, Balgach (Switzerland), using MiSeq Nano 2x250 v2 format (on a mix of 10 individuals). Multiplex PCR assays for multilocus genotyping were established by Ecological Genetics (WSL Birmensdorf), and population genetic analyses are found in Gugerli et al., Agrarforschung Schweiz 2019.

biological-control

display_name: biological-control

drosophila-suzukii

display_name: drosophila-suzukii

multilocus-genotypes

display_name: multilocus-genotypes

nuclear-microsatellite-markers

display_name: nuclear-microsatellite-markers

population-genetics

display_name: population-genetics

simple-sequence-repeats

display_name: simple-sequence-repeats

switzerland

display_name: switzerland

trichopria-drosophilae

display_name: trichopria-drosophilae

en: Nuclear microsatellite markers for Trichopria drosophilae, parasitoid wasp on Drosophila suzukii


en: debris-flow illgraben magnitude monitoring-station force-plate volume

en: Illgraben debris-flow bulk volumes from 2000 to 2017.

en:
Debris-flow bulk volumes from the WSL monitoring station.
More information can be found in McArdell et al. (2007) and Schlunegger et al. (2009).

debris-flow

display_name: debris-flow

force-plate

display_name: force-plate

illgraben

display_name: illgraben

magnitude

display_name: magnitude

monitoring-station

display_name: monitoring-station

volume

display_name: volume

en: Debris-flow volumes at the Illgraben 2000-2017


phenological-observations-lwf

title_for_slug: phenological-observations-lwf

en: icp-forests long-term-forest-ecosystem-research precipitation tree-phenology swiss-forest-lab

en: Data Request

en:
Phenological observations are recorded every 14 days on LWF plots where throughfall and bulk precipitation are sampled. The percentage of foliage in reference to its maximum potential development in summer, the percentage of foliage with autumnal discoloration and the percentage of fallen leaves (broadleaved stands) are estimated at the plot level. At two LWF plots (Othmarsingen, Vordemwald), phenological stages are documented on individual trees

 Purpose:

To document the seasonal development of the canopy of trees and shrubs at the plot level

icp-forests

display_name: icp-forests

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

precipitation

display_name: precipitation

swiss-forest-lab

display_name: swiss-forest-lab

tree-phenology

display_name: tree-phenology

en: Phenological observations LWF


lwf-neunkirch-long-term-research-site

title_for_slug: lwf-neunkirch-long-term-research-site

en: forest meteorological-data lwf meteorological-measurements swiss-forest-lab plot infrastructure

en: LWF plot Neunkirch. Photo: Anne Thimonier Rickenmann, 2011

en: Neunkirch Meteorological Data LWF

en:
![alt text](https://www.envidat.ch/dataset/86603cf3-979c-4b28-a63e-9852bfb969bd/resource/69a58300-d372-4487-8596-5403254d539d/download/lwf_neunkirch.jpg "LWF Neunkirch")

LWF Plot Neunkirch

- Community: Neunkirch / canton SH
- Date of installation: 14 July 1995
- Size of the plot: 2 ha
- Altitude (m): 554-609
- Mean slope: 58%
- Geology (in German): Tafeljura, oberer Malmkalk; Malmhängeschutt
- Soil types (WSL) : Rendzic Leptosols
- Woodland association after EK72: 13: Cardamino-Fagetum tilietosum
- Main tree species: Fagus sylvatica
- Management system: former coppices w. standards
- Silvicultural system: reserve
- Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 56.5 cm
- Number of trees BHD >= 12 cm (2011): 442
- Maximum tree age: Fagus sylvatica 160 yr - Acer pseudoplatanus 160 yr - Tilia sp. 110 yr

More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/neunkirch.html

forest

display_name: forest

infrastructure

display_name: infrastructure

lwf

display_name: lwf

meteorological-data

display_name: meteorological-data

meteorological-measurements

display_name: meteorological-measurements

plot

display_name: plot

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: LWF Neunkirch long-term research site


en:
Volume of stemwood with bark of all trees and shrubs starting at 12 cm dbh removed from the forest as a result of damage occurring between two inventories, in this case insects, and not because of management planning. This feature is derived on the level of a sample plot from the cutting of the sample trees and the salvage cut proportion (according to information from the forester). *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted.

Citation:
 > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_

altitudinal-vegetation-zone

display_name: altitudinal-vegetation-zone

canton

display_name: canton

salvage-logging-due-to-insects-star

display_name: salvage-logging-due-to-insects-star

envidat

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

en: Salvage logging due to insects*


maintainer: Paraskevi Georgakaki

en: liquid-water-content wrf ice-water-content swiss-alps jungfraujoch blowing-snow ice-crystal-number-concentration secondary-ice-production

en: WRF outputs

en:
The .zip file contains all the NetCDF outputs produced by the mesoscale Weather Research and Forecasting (WRF) model (version: 4.0.1). Two case studies were simulated in this study, between 25-28 January 2014 (00:00 UTC) and 29 January - 1 February 2014 (00:00 UTC), respectively. The former is denoted as "NW" and the latter as "SE", owing to the prevailing wind conditions at the Jungfraujoch (JFJ) station.

The outputs come from the innermost domain of the simulations with a spatial resolution of 1 km and are extracted from the nearest to JFJ model grid point (46.55°N 7.98°E). The dimensions of each model variable is [145,100], which corresponds to time and height, respectively. The frequency of the outputs is 30 min. Note that the first 24 hours of each simulation (i.e., the first 48 outputs) were considered spin up time.

The following sets of sensitivity simulations were presented in this study:

* CNTRL_NW.nc, CNTRL_SE.nc : Sensitivity simulations performed with the default version of WRF
* DS_PHILL_NW.nc, DS_PHILL_SE.nc : Sensitivity simulations including a description of the droplet shattering process following Phillips et al. (2018)
* DS_SULL_NW.nc, DS_SULL_SE.nc : Sensitivity simulations including a description of the droplet shattering process following Sullivan et al. (2018)
* PHILL_NW.nc, PHILL_SE.nc : Sensitivity simulations including a description of the ice-ice collisional break-up process following Phillips et al. (2017)
* TAKAH_NW.nc, TAKAH_SE.nc : Sensitivity simulations including a description of the ice-ice collisional break-up process following Sullivan et al. (2018) based on Takahashi et al. (1995)
* TAKAHrim_NW.nc, TAKAHrim_SE.nc : Same set-up as before but applied only to collisions between rimed ice particles
* BIPS_Xk0_DY_NW.nc, BIPS_Xk0_DY_SE.nc : Sensitivity simulations including a source of Xk0 (either 10.0 or 100.0 L⁻¹) ice crystals with sizes of DY (either 10 or 100 μm) in the first atmospheric level of the model
* BIPS100_PHILL_NW.nc, BIPS100_PHILL_SE.nc : Sensitivity simulations where the combined effect of blowing snow and ice-ice collisional break-up is examined

en: Observational data

en:
The .zip file contains 6 .csv files including all microphysical measurements taken at the Jungfraujoch station during the two periods of interest:

* 25-28 January 2014 : northwesterly-winds (NW)
* 29 January - 1 February 2014 : southeasterly-winds (SE)

The total Ice Crystal Number Concentration (ICNC; reported in L⁻¹) is measured by the two-dimensional stereo hydrometeor spectrometer (2D-S; Lawson et al., 2006) over the size range 10-1280 μm. The total ice water content (IWC; reported in gm⁻³) is also derived by the 2D-S data using the Brown and Francis (1995) mass-diameter relationship. Finally, the quantification of the liquid water content (LWC; reported in gm⁻³) is based on the liquid droplet size distribution data derived from a DMT cloud droplet probe (CDP; Lance et al., 2010) over the size range 2-50 μm. The frequency of the measurements in the .csv files is 30 min, to match the output frequency of the model. Datetime is also reported in the files (DD/MM/YYYY h:mm) in UTC.

Acknowledgment: Collection and pre-processing of the microphysical measurements has been performed by Gary Lloyd (gary.lloyd@manchester.ac.uk). More details about the instrumentation during the CLACE2014 campaign can be found in Lloyd et al. (2015).

en:
This repository contains all WRF model outputs and observational data sets used for the paper:
Georgakaki, P., Sotiropoulou, G., Vignon, É., Billault-Roux, A.-C., Berne, A., and Nenes, A.: Secondary ice production processes in wintertime alpine mixed-phase clouds, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-760, in review, 2021.

blowing-snow

display_name: blowing-snow

ice-crystal-number-concentration

display_name: ice-crystal-number-concentration

ice-water-content

display_name: ice-water-content

jungfraujoch

display_name: jungfraujoch

liquid-water-content

display_name: liquid-water-content

secondary-ice-production

display_name: secondary-ice-production

swiss-alps

display_name: swiss-alps

wrf

display_name: wrf

en: Data for the publication "Secondary ice production processes in wintertime alpine mixed-phase clouds"


en: point-cloud photogrammety image-matching 3d digital-surface-model

en: 2016 Hillshade DSM for Vegetation Heigth Model (current)

en: Thumbnail (JPG)

en: Hillshade of the digital surface model (DSM), calculated from digital aerial stereo images. The image data was acquired by the Federal Office of Topography swisstopo. The resolution of the DSM is 1 m x 1 m.

digital-surface-model

display_name: digital-surface-model

image-matching

display_name: image-matching

photogrammety

display_name: photogrammety

point-cloud

display_name: point-cloud

en: Hillshade for Vegetation Height Model NFI


en: swiss-forest-lab long-term-forest-ecosystem-research soil-water-matrix-potential water-cycle icp-forests

en: Data Request

en:
Continuous measurement of soil matrix potential at 15, 50 and 80 cm depth with Decagon MPS-2 sensors

 Purpose:

Improve the available data for the calibration or validation of the water cycle modells, i.e. the determination of the water flux needed for calculating the leaching fluxes.

icp-forests

display_name: icp-forests

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

soil-water-matrix-potential

display_name: soil-water-matrix-potential

swiss-forest-lab

display_name: swiss-forest-lab

water-cycle

display_name: water-cycle

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: MPS-2 soil water matric potential LWF


en: nature-based-solutions ecosystem-services discrete-choice climate-change-adaptation resilience

en: DIVES questionnaire

en: English translation of the original survey questionnaire

en: DIVES CE raw data

en: Raw data obtained from a choice experiment on the insurance value of forests

en: DIVES data description

en: Description of the raw data variables

en: We present stated preference data for improved forest management measures from seven Swiss municipalities in the Cantons of Grisons and Valais. The data was collected between October 2019 and February 2020 using an online questionnaire. We invited 10289 households to participate and received 939 responses. The online questionnaire consisted of two main parts: (i) an online choice experiment and (ii) questions on the sociodemographic characteristics of the responding households. The choice experiment confronted households with twelve consecutive choice tasks. Each choice task consisted of three options with a varying degree of avalanche and rock fall risk reduction due to improved forest management. The options further differed with respect to the way the costs for the improved forest management are allocated and the way they are calculated. We additionally provided each of the options with a cost attribute, allowing for the calculation of willingness to pay measures. At the end of the choice experiment we asked five de-briefing questions and eight attitudinal questions. Additionally, we asked the responding households to state their willingness to take risks. The sociodemographic characteristics collected in the second part of the questionnaire allow for an analysis of the impact they have on the choices we observed in the first part of the questionnaire.

climate-change-adaptation

display_name: climate-change-adaptation

discrete-choice

display_name: discrete-choice

ecosystem-services

display_name: ecosystem-services

nature-based-solutions

display_name: nature-based-solutions

resilience

display_name: resilience

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: Stated preference data on the insurance value of forests in Switzerland


en: danger-level avalanche snow-instability avalanche-forecasting avalanche-danger snow

en: Read-me file

en: This read-me file contains the descriptions of the variables.

en: Field observations of snow instabilities

en:
This data set includes 589 snow profile observations including a rutschblock test, observations of signs of instability and an assessment of the local avalanche danger level, mainly recorded in the region of Davos (eastern Swiss Alps) during the winter seasons 2001-2002 to 2018-2019.

These data were analyzed and results published by Schweizer et al. (2021). They characterized the avalanche danger levels based on signs of instability (whumpfs, shooting cracks, recent avalanches), snow stability class and new snow height.

The data are provided in a csv file (589 records); the variables are described in the corresponding read-me file.

These data are the basis of the following publication: Schweizer, J., Mitterer, C., Reuter, B., and Techel, F.: Avalanche danger level characteristics from field observations of snow instability, Cryosphere, 15, 3293-3315, https://doi.org/10.5194/tc-15-3293-2021, 2021.

 Acknowlegements
Many of the data were recorded by SLF observers and staff members, among those Roland Meister, Stephan Harvey, Lukas Dürr, Marcia Phillips, Christine Pielmeier and Thomas Stucki. Their contribution is gratefully acknowledged.

avalanche

display_name: avalanche

avalanche-danger

display_name: avalanche-danger

avalanche-forecasting

display_name: avalanche-forecasting

danger-level

display_name: danger-level

snow

display_name: snow

snow-instability

display_name: snow-instability


maintainer: Leo Gallus Bont

en: accessible-forest openstreetmap geoprocessing seilaplan qgis forest

en: Digitised sample perimeter

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

media_type: image/tiff

format: TIFF

mimetype: image/tiff

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

en: Forest vector data (for sample forest range)

en: Preview

media_type: image/png

format: PNG

mimetype: image/png

en: Juypter Notebook for Data Preparation (online)

en: Juypter Notebook for Data Preparation

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

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

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

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

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

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

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

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

accessible-forest

display_name: accessible-forest

forest

display_name: forest

geoprocessing

display_name: geoprocessing

openstreetmap

display_name: openstreetmap

qgis

display_name: qgis

seilaplan

display_name: seilaplan

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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


en: stonenode induced-rockfall rockfall-runout stonenodedata rockfall-experiments natural-hazard rockfall earth-surface-dynamics dem

en: Experimental Runs Archive

en: Compressed archive with the StoneNode experimental runs and the respective figures. Matlab format.

en: Input Archive

en: Video Archive

en: EOTA

en: Cubic EOTA(111) and platy EOTA(221) point cloud.

en: Output Archive

en: Data under embargo: Trajectory reconstruction tables

en:
The data archive contains site specific geographical data such as DEM and orthophoto as well as the deposition points of manually induced rockfall by releasing differently shaped boulders with 46, 200, 800 and 2670 kg of mass.

Additionally available are all the reconstructed data sets for all trajectories with videogrammetry installed comprising StoneNode data streams for rocks equipped with a sensor. The data set consists of:

 Resources (individual zip-archives)

ExperimentalRuns: Archive with all available StoneNode data streams and its respective figure (.mat files)

Input: Archive containing folders

* GNSS: 182 Deposition points of all different weight and shape classes, shape files for release point, cliff and scree line,
* UAS: UAS generated pre- and post-experimental digital surface models and orthophoto of the four most important experimental days and
* VG_Coord: Reconstruction input: Videogrammetry based coordinate list along side with the corresponding sensor/video times

EOTA: Point cloud of cubic EOTA(111) and platy EOTA(221) rock as input for RAMMS::ROCKFALL or other suitable rockfall simulation codes incorporating complex shape files.

Output: Reconstruced trajectory information for all 82 reconstructed trajectories

Video: available video streams for all runs

 Further information
Preceeding publications concering the deployed sensors and the reconstruction methods are found in the subsequent references:

A. Caviezel et al., Design and Evaluation of a Low-Power Sensor Device for Induced Rockfall Experiments, IEEE Transactions on Instrumentation and Measurement, 2018, 67, 767-779, http://ieeexplore.ieee.org/document/8122020/
P. Niklaus et al., StoneNode: A low-power sensor device for induced rockfall experiments, 2017 IEEE Sensors Applications Symposium (SAS), 2017, 1-6, http://ieeexplore.ieee.org/document/7894081/

Caviezel, A., Demmel, S. E., Ringenbach, A., Bühler, Y., Lu, G., Christen, M., Dinneen, C. E., Eberhard, L. A., von Rickenbach, D., and Bartelt, P.: Reconstruction of four-dimensional rockfall trajectories using remote sensing and rock-based accelerometers and gyroscopes, Earth Surf. Dynam., 7, 199–210, https://doi.org/10.5194/esurf-7-199-2019, 2019

dem

display_name: dem

earth-surface-dynamics

display_name: earth-surface-dynamics

induced-rockfall

display_name: induced-rockfall

natural-hazard

display_name: natural-hazard

rockfall

display_name: rockfall

rockfall-experiments

display_name: rockfall-experiments

rockfall-runout

display_name: rockfall-runout

stonenode

display_name: stonenode

stonenodedata

display_name: stonenodedata

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: Induced Rockfall Dataset #2 (Chant Sura Experimental Campaign), Flüelapass, Grisons, Switzerland


en: alpine3d-simulations climate-change snow graubunden switzerland

en: Configuration file

en: Simulation data

en: Reference data as well as the 2050 and 2099 scenarios for ARPEGE and ETHZ other RCMs available on demand)

en:
This is the simulation dataset from _"Response of snow cover and runoff to climate change in high Alpine catchments of Eastern Switzerland"_, M. Bavay, T. Grünewald, M. Lehning, Advances in Water Resources 55, 4-16, 2013

A model study on the impact of climate change on snow cover and runoff has been conducted for the Swiss Canton of Graubünden. The model Alpine3D has been forced with the data from 35 Automatic Weather Stations in order to investigate snow and runoff dynamics for the current climate. The data set has then been modified to reflect climate change as predicted for the 2021-2050 and 2070-2095 periods from an ensemble of regional climate models.

The predicted changes in snow cover will be moderate for 2021-2050 and become drastic in the second half of the century. Towards the end of the century the snow cover changes will roughly be equivalent to an elevation shift of 800 m. Seasonal snow water equivalents will decrease by one to two thirds and snow seasons will be shortened by five to nine weeks in 2095.

Small, higher elevation catchments will show more winter runoff, earlier spring melt peaks and reduced summer runoff. Where glacierized areas exist, the transitional increase in glacier melt will initially offset losses from snow melt. Larger catchments, which reach lower elevations will show much smaller changes since they are already dominated by summer precipitation.

climate-change

display_name: climate-change

graubunden

display_name: graubunden

snow

display_name: snow

switzerland

display_name: switzerland

en: Alpine3D simulations of future climate scenarios for Graubunden


yield-of-merchantable-branches

title_for_slug: yield-of-merchantable-branches

en:
Wood volume of branches with bark at least 7 cm in diameter (limit for coarse wood) of all living trees and shrubs starting at 12 cm dbh that were present in the pre-inventory and cut meanwhile.

Citation:
 > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_

biogeographical-region

display_name: biogeographical-region

conifers-broadleaves

display_name: conifers-broadleaves

economic-region

display_name: economic-region

ownership

display_name: ownership

production-region

display_name: production-region

protection-forest-region

display_name: protection-forest-region

yield-of-merchantable-branches

display_name: yield-of-merchantable-branches

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Yield of merchantable branches


en: spectroscopy assisted-gene-flow traits assisted-migration species-classification predictive-provenancing fagus-sylvatica-l fagus-sylvatica-subsp-orientalis

en: Wäldi spectral and traits data

en: Orietnal and European beech leaf spectral and trait data for top and bottom of canopy leaves

en: Allenwiller spectral and traits data

en: Wäldi genetic data

en: Allenwiller genetic data

en: The dataset includes leaf spectroscopy, leaf traits and genetic data for oriental and european beech trees at two mature forest sites (Allenwiller in France and Wäldi in Switzerland) sampled in summer 2021 and 2022 for top and bottom of canopy leaves.

assisted-gene-flow

display_name: assisted-gene-flow

assisted-migration

display_name: assisted-migration

fagus-sylvatica-l

display_name: fagus-sylvatica-l

fagus-sylvatica-subsp-orientalis

display_name: fagus-sylvatica-subsp-orientalis

predictive-provenancing

display_name: predictive-provenancing

species-classification

display_name: species-classification

spectroscopy

display_name: spectroscopy

traits

display_name: traits

en: Oriental and European beech spectral, traits and genetics data


de: haushalt dienststellen direktionen rechnung budget

de: fgb

de: Budget 2021 der Stadt St.Gallen nach Institutioneller Gliederung (English Data) (fgb)

en: Budget 2020 of the City of St.Gallen in Institutional Structure (English Data) (fgb)

format: octet-stream

license: CC BY

de: Budget 2021 der Stadt St.Gallen nach Institutioneller Gliederung (English Data) (n3)

en: Budget 2020 of the City of St.Gallen in Institutional Structure (English Data) (n3)

de: jsonl

media_type: application/json

de: Budget 2021 der Stadt St.Gallen nach Institutioneller Gliederung (English Data) (jsonl)

en: Budget 2020 of the City of St.Gallen in Institutional Structure (English Data) (jsonl)

format: JSON

de: turtle

media_type: text/turtle

de: Budget 2021 der Stadt St.Gallen nach Institutioneller Gliederung (English Data) (turtle)

en: Budget 2020 of the City of St.Gallen in Institutional Structure (English Data) (turtle)

format: RDF Turtle

de: parquet

de: Budget 2021 der Stadt St.Gallen nach Institutioneller Gliederung (English Data) (parquet)

en: Budget 2020 of the City of St.Gallen in Institutional Structure (English Data) (parquet)

de: json

de: Budget 2021 der Stadt St.Gallen nach Institutioneller Gliederung (English Data) (json)

en: Budget 2020 of the City of St.Gallen in Institutional Structure (English Data) (json)

de: csv

de: Budget 2021 der Stadt St.Gallen nach Institutioneller Gliederung (English Data) (csv)

en: Budget 2020 of the City of St.Gallen in Institutional Structure (English Data) (csv)

de: jsonld

de: Budget 2021 der Stadt St.Gallen nach Institutioneller Gliederung (English Data) (jsonld)

en: Budget 2020 of the City of St.Gallen in Institutional Structure (English Data) (jsonld)

format: JSON-LD

de: rdfxml

media_type: application/rdf+xml

de: Budget 2021 der Stadt St.Gallen nach Institutioneller Gliederung (English Data) (rdfxml)

en: Budget 2020 of the City of St.Gallen in Institutional Structure (English Data) (rdfxml)

format: RDF XML

de: xls

de: Budget 2021 der Stadt St.Gallen nach Institutioneller Gliederung (English Data) (xls)

en: Budget 2020 of the City of St.Gallen in Institutional Structure (English Data) (xls)

de: Der Datensatz zeigt das Budget 2021 der Stadt St.Gallen nach Institutioneller Gliederung. Ebenfalls enthalten sind die Rechnung 2019, das Budget 2020, sowie die Abweichung zum Budget 2021. Eine klickbare Visualisierung des Budgets finden Sie im Tab "Klickbares Budget".

en: The dataset shows the Budget 2021 of the city of St.Gallen in institutional structure. It also contains the Invoice 2019, the Budget 2020 and the deviation to the Budget 2020. You find a clickable version of the Budget in the Tab "Clickable Budget"

budget

display_name: budget

dienststellen

display_name: dienststellen

direktionen

display_name: direktionen

haushalt

display_name: haushalt

rechnung

display_name: rechnung

econ

fr: Économie et finances

de: Wirtschaft und Finanzen

en: Economy and finance

it: Economia e finanze

stadt-st-gallen

fr: Ville de Saint-Gall

de: Stadt St.Gallen

en: City of St.Gallen

it: Città di San Gallo

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

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

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

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

political_level: commune

value: DCAT Harvester Stadt St. Gallen


de: betreibung schulden pfandung armut

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

de: turtle

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

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

de: jsonl

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

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

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

de: fgb

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

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

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

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

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

armut

display_name: armut

betreibung

display_name: betreibung

pfandung

display_name: pfandung

schulden

display_name: schulden

de: Pfändungen Stadt St.Gallen (2005-2018)


fernwarmestrategie-hestia

title_for_slug: fernwarmestrategie-hestia

de: warmeversorgung gasversorgung sgsw energy-hackdays

de: xls

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

de: json

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

de: rdfxml

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

de: csv

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

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

de: kml

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

format: KML

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

de: gpx

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

format: gpx

de: shp

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

de: parquet

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

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

de: jsonld

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

de: geojson

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

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

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

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

energy-hackdays

display_name: energy-hackdays

gasversorgung

display_name: gasversorgung

sgsw

display_name: sgsw

stadt-st-gallen

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


de: fahrrad zahlstellen velodaten velo

de: kml

de: Geokoordinaten Velo Zählstellen Stadt St.Gallen (kml)

de: Geokoordinaten Velo Zählstellen Stadt St.Gallen (parquet)

de: Geokoordinaten Velo Zählstellen Stadt St.Gallen (geojson)

de: csv

de: Geokoordinaten Velo Zählstellen Stadt St.Gallen (csv)

de: Geokoordinaten Velo Zählstellen Stadt St.Gallen (shp)

de: turtle

de: Geokoordinaten Velo Zählstellen Stadt St.Gallen (turtle)

de: Geokoordinaten Velo Zählstellen Stadt St.Gallen (ov2)

de: json

de: Geokoordinaten Velo Zählstellen Stadt St.Gallen (json)

de: Geokoordinaten Velo Zählstellen Stadt St.Gallen (n3)

de: Geokoordinaten Velo Zählstellen Stadt St.Gallen (jsonld)

de: xls

de: Geokoordinaten Velo Zählstellen Stadt St.Gallen (xls)

de: Geokoordinaten Velo Zählstellen Stadt St.Gallen (gpx)

de: fgb

de: Geokoordinaten Velo Zählstellen Stadt St.Gallen (fgb)

de: jsonl

de: Geokoordinaten Velo Zählstellen Stadt St.Gallen (jsonl)

de: rdfxml

de: Geokoordinaten Velo Zählstellen Stadt St.Gallen (rdfxml)

de: Der Datensatz zeigt die Geokoordinaten der Velozählstellen der Stadt St.Gallen. Die Zählungsdaten der Zählstellen finden Sie unter: https://daten.stadt.sg.ch/explore/dataset/velozahlungen-stadt-stgallen

fahrrad

display_name: fahrrad

velo

display_name: velo

velodaten

display_name: velodaten


trottoirs-in-der-stadt-st-gallen

title_for_slug: trottoirs-in-der-stadt-st-gallen

de: gehweg fussweg strassen fussganger burgersteig

de: Trottoirs in der Stadt St.Gallen (rdfxml)

de: jsonld

de: Trottoirs in der Stadt St.Gallen (jsonld)

de: shp

de: Trottoirs in der Stadt St.Gallen (shp)

de: Trottoirs in der Stadt St.Gallen (xls)

de: turtle

de: Trottoirs in der Stadt St.Gallen (turtle)

de: parquet

de: Trottoirs in der Stadt St.Gallen (parquet)

de: Trottoirs in der Stadt St.Gallen (fgb)

de: gpx

de: Trottoirs in der Stadt St.Gallen (gpx)

de: Trottoirs in der Stadt St.Gallen (json)

de: kml

de: Trottoirs in der Stadt St.Gallen (kml)

de: Trottoirs in der Stadt St.Gallen (ov2)

de: geojson

de: Trottoirs in der Stadt St.Gallen (geojson)

de: Trottoirs in der Stadt St.Gallen (n3)

de: csv

de: Trottoirs in der Stadt St.Gallen (csv)

de: Trottoirs in der Stadt St.Gallen (jsonl)

de: Der Datensatz zeigt die Trottoirs der Stadt St.Gallen aus dem Grundbuchplan amtliche Vermessung

stadt-st-gallen

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


de: strassen algorithmus foto sicherheit wahrnehmung

de: Wahrgenommene Sicherheit der Strassen in der Stadt St.Gallen (Streetwise) (n3)

de: jsonl

de: Wahrgenommene Sicherheit der Strassen in der Stadt St.Gallen (Streetwise) (jsonl)

de: Wahrgenommene Sicherheit der Strassen in der Stadt St.Gallen (Streetwise) (kml)

de: rdfxml

de: Wahrgenommene Sicherheit der Strassen in der Stadt St.Gallen (Streetwise) (rdfxml)

de: Wahrgenommene Sicherheit der Strassen in der Stadt St.Gallen (Streetwise) (csv)

de: parquet

de: Wahrgenommene Sicherheit der Strassen in der Stadt St.Gallen (Streetwise) (parquet)

de: shp

de: Wahrgenommene Sicherheit der Strassen in der Stadt St.Gallen (Streetwise) (shp)

de: jsonld

de: Wahrgenommene Sicherheit der Strassen in der Stadt St.Gallen (Streetwise) (jsonld)

de: turtle

de: Wahrgenommene Sicherheit der Strassen in der Stadt St.Gallen (Streetwise) (turtle)

de: Wahrgenommene Sicherheit der Strassen in der Stadt St.Gallen (Streetwise) (geojson)

de: Wahrgenommene Sicherheit der Strassen in der Stadt St.Gallen (Streetwise) (ov2)

de: Wahrgenommene Sicherheit der Strassen in der Stadt St.Gallen (Streetwise) (gpx)

de: json

de: Wahrgenommene Sicherheit der Strassen in der Stadt St.Gallen (Streetwise) (json)

de: xls

de: Wahrgenommene Sicherheit der Strassen in der Stadt St.Gallen (Streetwise) (xls)

de: fgb

de: Wahrgenommene Sicherheit der Strassen in der Stadt St.Gallen (Streetwise) (fgb)

de: Der Datensatz zeigt die wahrgenommene Sicherheit der Strassen in der Stadt St.Gallen. Die Daten basieren auf einer Analyse von Streetwise, welche mittels Algorithmus Fotos der Strassenabschnitte nach ihrer Sicherheitswahrnehmung analysieren. Mehr Informationen unter: https://streetwise.space/

algorithmus

display_name: algorithmus

foto

display_name: foto

sicherheit

display_name: sicherheit

strassen

display_name: strassen


de: fussganger miv auto open-data-hack-stgallen erreichbarkeit

de: Erreichbarkeitsanalyse Areal Bach, Stadt St.Gallen (ov2)

de: rdfxml

de: Erreichbarkeitsanalyse Areal Bach, Stadt St.Gallen (rdfxml)

de: jsonl

de: Erreichbarkeitsanalyse Areal Bach, Stadt St.Gallen (jsonl)

de: Erreichbarkeitsanalyse Areal Bach, Stadt St.Gallen (n3)

de: kml

de: Erreichbarkeitsanalyse Areal Bach, Stadt St.Gallen (kml)

de: Erreichbarkeitsanalyse Areal Bach, Stadt St.Gallen (json)

de: geojson

de: Erreichbarkeitsanalyse Areal Bach, Stadt St.Gallen (geojson)

de: gpx

de: Erreichbarkeitsanalyse Areal Bach, Stadt St.Gallen (gpx)

de: Erreichbarkeitsanalyse Areal Bach, Stadt St.Gallen (jsonld)

de: Erreichbarkeitsanalyse Areal Bach, Stadt St.Gallen (shp)

de: Erreichbarkeitsanalyse Areal Bach, Stadt St.Gallen (turtle)

de: csv

de: Erreichbarkeitsanalyse Areal Bach, Stadt St.Gallen (csv)

de: parquet

de: Erreichbarkeitsanalyse Areal Bach, Stadt St.Gallen (parquet)

de: Erreichbarkeitsanalyse Areal Bach, Stadt St.Gallen (fgb)

de: Erreichbarkeitsanalyse Areal Bach, Stadt St.Gallen (xls)

de: Die Geomatik und Vermessung hat eine Erreichbarkeitsanalyse des Areal Bach in St.Gallen für Fussgängerinnen und Fussgänger, sowie den motorisierten Individualverkehr durchgeführt. Der Datensatz zeigt die Gehdauer, bzw. Fahrdauer bis zu einer Stunde in 5-Minuten Schritten.

auto

display_name: auto

erreichbarkeit

display_name: erreichbarkeit

fussganger

display_name: fussganger

miv

display_name: miv

stadt-st-gallen

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


de: bahnhof fussganger open-data-hack-stgallen erreichbarkeit

de: turtle

de: Erreichbarkeitsanalyse Bahnhof Bruggen, Stadt St.Gallen (turtle)

de: fgb

de: Erreichbarkeitsanalyse Bahnhof Bruggen, Stadt St.Gallen (fgb)

de: Erreichbarkeitsanalyse Bahnhof Bruggen, Stadt St.Gallen (ov2)

de: kml

de: Erreichbarkeitsanalyse Bahnhof Bruggen, Stadt St.Gallen (kml)

de: rdfxml

de: Erreichbarkeitsanalyse Bahnhof Bruggen, Stadt St.Gallen (rdfxml)

de: xls

de: Erreichbarkeitsanalyse Bahnhof Bruggen, Stadt St.Gallen (xls)

de: jsonl

de: Erreichbarkeitsanalyse Bahnhof Bruggen, Stadt St.Gallen (jsonl)

de: Erreichbarkeitsanalyse Bahnhof Bruggen, Stadt St.Gallen (geojson)

de: jsonld

de: Erreichbarkeitsanalyse Bahnhof Bruggen, Stadt St.Gallen (jsonld)

de: shp

de: Erreichbarkeitsanalyse Bahnhof Bruggen, Stadt St.Gallen (shp)

de: json

de: Erreichbarkeitsanalyse Bahnhof Bruggen, Stadt St.Gallen (json)

de: Erreichbarkeitsanalyse Bahnhof Bruggen, Stadt St.Gallen (csv)

de: Erreichbarkeitsanalyse Bahnhof Bruggen, Stadt St.Gallen (gpx)

de: Erreichbarkeitsanalyse Bahnhof Bruggen, Stadt St.Gallen (parquet)

de: Erreichbarkeitsanalyse Bahnhof Bruggen, Stadt St.Gallen (n3)

de: Die Geomatik und Vermessung hat eine Erreichbarkeitsanalyse des Bahnhofs Bruggen für Fussgängerinnen und Fussgänger durchgeführt. Der Datensatz zeigt die Gehdauer bis zu 30 Minuten in 5-Minuten Schritten.

bahnhof

display_name: bahnhof

open-data-hack-stgallen

display_name: open-data-hack-stgallen


de: verkehr miv zahlstellen zahlung auto

de: Verkehrszählung MIV Stadt St.Gallen (2018-2023) (shp)

de: Verkehrszählung MIV Stadt St.Gallen (2018-2023) (jsonl)

de: Verkehrszählung MIV Stadt St.Gallen (2018-2023) (ov2)

de: gpx

de: Verkehrszählung MIV Stadt St.Gallen (2018-2023) (gpx)

de: Verkehrszählung MIV Stadt St.Gallen (2018-2023) (json)

de: Verkehrszählung MIV Stadt St.Gallen (2018-2023) (jsonld)

de: turtle

de: Verkehrszählung MIV Stadt St.Gallen (2018-2023) (turtle)

de: kml

de: Verkehrszählung MIV Stadt St.Gallen (2018-2023) (kml)

de: parquet

de: Verkehrszählung MIV Stadt St.Gallen (2018-2023) (parquet)

de: Verkehrszählung MIV Stadt St.Gallen (2018-2023) (n3)

de: Verkehrszählung MIV Stadt St.Gallen (2018-2023) (xls)

de: rdfxml

de: Verkehrszählung MIV Stadt St.Gallen (2018-2023) (rdfxml)

de: csv

de: Verkehrszählung MIV Stadt St.Gallen (2018-2023) (csv)

de: geojson

de: Verkehrszählung MIV Stadt St.Gallen (2018-2023) (geojson)

de: fgb

de: Verkehrszählung MIV Stadt St.Gallen (2018-2023) (fgb)

de: Das Tiefbauamt der Stadt St.Gallen zählt den Motorisierten Individualverkehr (MIV) an verschiedenen Orten der Stadt. Dieser Datensatz zeigt die MIV-Verkehrsdaten aus sämtlichen städtischen Zählstellen und wird jährlich aktualisiert.Die Daten der Zählstellen wurden vom Tiefbauamt bereits plausibilisiert. Die Richtungsnummer (RI) kann aus den Detailplänen der Zählstellen herausgelesen werden: https://daten.stadt.sg.ch/explore/dataset/detailplane-und-geokoordinaten-miv-zahlstellen-stadt-stgallenUnter folgendem Link finden Sie eine Visualisierung der Verkehrszähldaten inkl. Visualisierung der Detailpläne: https://www.stadt.sg.ch/home/mobilitaet-verkehr/verkehrspolitik/verkehrszaehlung/opendata_miv.htmlDatenstruktur:ORT-ID: ID der ZählstelleBEZEICHNUNG: Name/Ort der ZählstelleDATUMWOCHENTAGRI: Richtungsnummer der Messung. Die Daten zeigen eine Zeile pro Richtungsnummer. Die Bedeutung der Richtungsnummer kann den Zählstellen-Detailplänen entnommen werden.1 - 24: Uhrzeit (Stunde), wann die Anzahl Fahrzeuge gemessen wurde.Anmerkung: Durchgehende Nullen für eine spezifische Richtungsnummer bedeuten, dass diese Richtungsnummer für die entsprechende Zählstelle nicht gebraucht wird.TAGESTOTAL: Anzahl gemessene Fahrzeuge pro Richtung (=Summe 1-24)

verkehr

display_name: verkehr

stadt-st-gallen

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


de: real-time news polizei medienmitteilung newsfeed

de: json

de: Newsfeed Medienmitteilungen der Stadtpolizei St.Gallen (json)

de: Newsfeed Medienmitteilungen der Stadtpolizei St.Gallen (rdfxml)

de: jsonld

de: Newsfeed Medienmitteilungen der Stadtpolizei St.Gallen (jsonld)

de: Newsfeed Medienmitteilungen der Stadtpolizei St.Gallen (fgb)

de: Newsfeed Medienmitteilungen der Stadtpolizei St.Gallen (n3)

de: xls

de: Newsfeed Medienmitteilungen der Stadtpolizei St.Gallen (xls)

de: Newsfeed Medienmitteilungen der Stadtpolizei St.Gallen (csv)

de: jsonl

de: Newsfeed Medienmitteilungen der Stadtpolizei St.Gallen (jsonl)

de: Newsfeed Medienmitteilungen der Stadtpolizei St.Gallen (parquet)

de: turtle

de: Newsfeed Medienmitteilungen der Stadtpolizei St.Gallen (turtle)

de: Der Datensatz bildet eine Schnittstelle zum Newsfeed der Medienmitteilungen der Stadtpolizei St.Gallen. Der Link zum Newsfeed befindet sich unter "Referenzen".

medienmitteilung

display_name: medienmitteilung

news

display_name: news

newsfeed

display_name: newsfeed

polizei

display_name: polizei

real-time

display_name: real-time

gove

fr: Gouvernement et secteur public

de: Regierung und öffentlicher Sektor

en: Government and public sector

it: Governo e settore pubblico

de: Stadt St.Gallen


de: verlust finanzen betreibung

de: fgb

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

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

de: json

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

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

de: rdfxml

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

de: csv

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

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

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

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

de: parquet

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

de: Anzahl ausgestellter Verlustscheine der Stadt St.Gallen (Anzahl und in Mio., Jahre 2005-2018)

betreibung

display_name: betreibung

finanzen

display_name: finanzen

verlust

display_name: verlust

econ

de: Wirtschaft und Finanzen

stadt-st-gallen

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


de: velo verkehr velodaten

de: Velozählungen Stadt St.Gallen - Archiv (2011 - 2016) (parquet)

de: xls

de: Velozählungen Stadt St.Gallen - Archiv (2011 - 2016) (xls)

de: Velozählungen Stadt St.Gallen - Archiv (2011 - 2016) (csv)

de: Velozählungen Stadt St.Gallen - Archiv (2011 - 2016) (rdfxml)

de: jsonl

de: Velozählungen Stadt St.Gallen - Archiv (2011 - 2016) (jsonl)

de: geojson

de: Velozählungen Stadt St.Gallen - Archiv (2011 - 2016) (geojson)

de: fgb

de: Velozählungen Stadt St.Gallen - Archiv (2011 - 2016) (fgb)

de: Velozählungen Stadt St.Gallen - Archiv (2011 - 2016) (json)

de: Velozählungen Stadt St.Gallen - Archiv (2011 - 2016) (ov2)

de: shp

de: Velozählungen Stadt St.Gallen - Archiv (2011 - 2016) (shp)

de: kml

de: Velozählungen Stadt St.Gallen - Archiv (2011 - 2016) (kml)

de: turtle

de: Velozählungen Stadt St.Gallen - Archiv (2011 - 2016) (turtle)

de: gpx

de: Velozählungen Stadt St.Gallen - Archiv (2011 - 2016) (gpx)

de: jsonld

de: Velozählungen Stadt St.Gallen - Archiv (2011 - 2016) (jsonld)

de: Velozählungen Stadt St.Gallen - Archiv (2011 - 2016) (n3)

de: Seit dem Jahr 2011 zählt die Stadt St.Gallen Velofahrer an verschiedenen Zählstellen in der Stadt. Die Zahlen beziehen sich auf die Anzahl Velofahrer pro Stunde in beide Richtungen.

velo

display_name: velo

velodaten

display_name: velodaten

tran

fr: Transports