global-cryosphere-watch-data-survey

maintainer: Mathias Bavay

title_for_slug: global-cryosphere-watch-data-survey

en: data-portal data-usage automatic-publication gcw data-exchange

en: Data interoperability survey

en: Third party data usage

en: Two surveys on the topic of data usage where conducted for the Global Cryosphere Watch data portal. The first one focused on the data provider point of view while the second one focused on the data user point of view. 37 data providers (ie institutions) worldwide provided their answers for the first survey (from fall 2017 until summer 2018) while 54 users (contacted through various mailing list such as the Cryolist) answered the questions on their third party data usage (fall 2019 until January 2020).

automatic-publication

display_name: automatic-publication

data-exchange

display_name: data-exchange

data-portal

display_name: data-portal

data-usage

display_name: data-usage

gcw

display_name: gcw

en: Global Cryosphere Watch data survey


maintainer: EnviDat Support

en: occupancy-models wetlands conservation amphibians landscape

state: active

type: dataset

en: Original raw data

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

format: XLS

en: Model code, description

media_type: text/html

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

format: HTML

en: Covariate data 2

media_type: text/csv

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

format: CSV

en: Readme

media_type: application/pdf

en: Description of methods and datasets.

format: PDF

en: Covariate data 1

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

en: Observation data

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

en: Model code

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

format: SERVICE

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

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

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

amphibians

display_name: amphibians

conservation

display_name: conservation

landscape

display_name: landscape

occupancy-models

display_name: occupancy-models

wetlands

display_name: wetlands

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: Amphibian observation and pond data (Aargau, Switzerland)


maintainer: Markus Schlegel

en: metabarcoding edna spore-dispersal monitoring air-sampling

en: Data files and analysis scripts (Version 1)

media_type: application/zip

en: This ZIP archive contains the data and source code necessary to reproduce the analyses for the manuscript "A case study on the application of spore sampling for the monitoring of fungi". Please refer to README.pdf inside the archive for more information.

format: ZIP

mimetype: application/zip

en:
Resources associated with the manuscript *A case study on the application of spore sampling for the monitoring of fungi* (Schlegel et al. 2023, submitted).
Airborne environmental DNA (eDNA) monitoring of fungi was evaluated on a species-rich grassland site. Extensive fruiting body surveys yielded 29 waxcap and clavarioid species, 19 of which were also detectable in air eDNA. The analysis indicates that spores of locally occurring fruiting bodies were detected, in addition to a large diversity of common fungal species, while rare and threatened species were underrepresented in the air.
The contents of the dataset (ZIP archive):
* All analysis scripts in [R-Markdown](https://rmarkdown.rstudio.com) format, including the rendered PDF output with figures and tables (*analysis* directory)
* eDNA sample metadata (*amplicon_meta*) and results of the amplicon clustering (*amplicon_data*) and the source code of the pipeline (*amplicon_pipeline*)
* positions of all fruiting body groups found in the surveys (3838 GPS points) (*surveys*)
* positions of the spore traps, taxonomy of studied grassland fungi and red-list species, and many more resources
Please refer to `README.pdf` inside the archive for a complete description of the file structure and information on how to reproduce the analyses.

air-sampling

display_name: air-sampling

edna

display_name: edna

metabarcoding

display_name: metabarcoding

monitoring

display_name: monitoring

spore-dispersal

display_name: spore-dispersal

en: Supporting information for case study on the application of spore sampling for the monitoring of fungi


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

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

en: Seilaplan Tutorial: Merge DTM tiles


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

en: Data plot of the forest station (NOB)

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

mimetype: application/pdf

en: Data plot of the field station (NOF)

en: Site aerial photo (Novaggio)

en: Site aerial photo from both Novaggio stations. Source: Google. 2016. Site aerial photo from both Novaggio stations. Retrieved November 15, 2016, from https://www.google.ch/maps

mimetype: image/jpeg

en: Site photo of the forest station (NOB)

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

en: Field station data (NOF)

en: Data file.

mimetype: text/csv

en: Site photo of the field station (NOF)

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

en: Forest station data (NOB)

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Novaggio in Switzerland where one station is located within a natural deciduous forest stand (NOB) with Turkey oak (_Quercus cerris_; 70 yrs), sweet chestnut (_Castanea sativa_; 90 yrs) and silver birch (_Betula pendula_; 70 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, NOF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Novaggio is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

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


radiosondes

maintainer: Jörg Wieder

title_for_slug: radiosondes

en: pressure raclets relative-humidity wind radiosonde wind-direction temperature holoballoon

en: Radiosond Template Files

en:
The "template" files contain standard parameters, i.e. ambient pressure (mbar and Pa), temperature (°C), relative humidity (%), wind speed (m s‾ ¹) and wind direction (true deg), as well as additional information, e.g. time (UTC and local time), dew point (°C), mixing ration (g/kg), density (kg m³), altitude (m MSL), height above ground (ft and m AGL), and geographic coordinates (longitude, latitude). If the wind speed was zero, no wind direction is given.
The zip files contain ascents on the following days:
February 10 (morning and afternoon),
February 11 (morning),
February 16 (afternoon),
February 22 (morning),
March 1 (morning),
March 5 (morning),
March 11 (morning),
March 12 (morning),
March 13 (morning),
March 14 (morning),
March 17 (afternoon), and
March 18.
The data was managed with Windsond Receiver Application Demo Version 2.92, (C) Sparv Embedded AB 2016.

en: Radiosond Sounding Files

en:
The "soundings" radiosond data contains information on ambient pressure (mbar), temperature (C), relative humidity (%), wind speed (m s‾ ¹) and wind direction (true deg), and are ordered with respect to the height above ground (m AGL) with a step size of 20 m. The first line of each file idicates the geographic location and the start time (in UTC) of the ascent. If the wind speed was zero, no wind direction is given.
The zip files contain ascents on the following days:
February 10 (morning and afternoon),
February 11 (morning),
February 16 (afternoon),
February 22 (morning),
March 1 (morning),
March 5 (morning),
March 11 (morning),
March 12 (morning),
March 13 (morning),
March 14 (morning),
March 17 (afternoon), and
March 18.
The data was managed with Windsond Receiver Application Demo Version 2.92, (C) Sparv Embedded AB 2016.

en: Radiosondes (Windsond, Sparv Embedded AB) were started in Davos Wolfgang (LON: 9.853594, LAT: 46.835577) to report height profiles of pressure, relative humidity and temperature at specific days. In addition to regular launches of radiosondes, sondes were attached to [HoloBalloon](https://www.envidat.ch/group/clouds-in-situ-raclets) to report the ambient conditions of the in-situ measurements. Further profiles of meteorological measures were recorded at [HoloGondel](https://www.envidat.ch/group/clouds-in-situ-raclets) which was installed at the gondola moving between Gotschnaboden and Gotschnagrat at 2285 m a.s.l.

holoballoon

display_name: holoballoon

pressure

display_name: pressure

raclets

display_name: raclets

radiosonde

display_name: radiosonde

wind

display_name: wind

wind-direction

display_name: wind-direction

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Radiosondes


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

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

media_type: text/plain

format: TXT

mimetype: text/plain

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

catchment-data

display_name: catchment-data

meteorological-data

display_name: meteorological-data

rain-on-snow

display_name: rain-on-snow

snow-hydrology

display_name: snow-hydrology

snowpack

display_name: snowpack

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


en: stable-water-isotopes streamflow alptal runoff groundwater

en: Precipitation and discharge event B

en:
This data is collected at the Studibach catchment (Alptal, Switzerland) by Leonie Kiewiet (University of Zürich)
With any questions about the data, please contact leonie.kiewiet@geo.uzh.ch or leoniekiewiet@gmail.com

datetime: Date (year-month-day) and time (hours:minutes:seconds) in UTC
Q: Discharge in mm per hour at catchment outlet (697234,210472)
P: Rainfall in mm per hour averaged for two rain gauges (697494,210675) and (698004, 210680)

en: Deuterium rainfall event B

en:
This data is collected at the Studibach catchment (Alptal, Switzerland) by Leonie Kiewiet (University of Zürich)
For any questions about the data, please contact leonie.kiewiet@geo.uzh.ch or leoniekiewiet@gmail.com

datetime: Date (year-month-day) and time (hours:minutes:seconds) in UTC
P d.D: Deuterium composition of the rainfall samples (per mil)
X: X coordinate (CH1903 LV03)
Y: Y coordinate (CH1903 LV03)

en: Deuterium streamwater event C

en:
This data is collected at the Studibach catchment (Alptal, Switzerland) by Leonie Kiewiet (University of Zürich)
For any questions about the data, please contact leonie.kiewiet@geo.uzh.ch or leoniekiewiet@gmail.com

datetime: Date and time of sample collection (UTC; year-month-day, hours:minutes)
Q d.D: Deuterium composition (per mil) of streamwater sampled at the catchment outlet (697234,210472); the first sample is used as pre-event streamflow sample

en: Deuterium streamwater event A

en: Deuterium rainfall event A

en: Deuterium groundwater and baseflow August 2017

en:
This data is collected at the Studibach catchment (Alptal, Switzerland) by Leonie Kiewiet (University of Zürich)
For any questions about the data, please contact leonie.kiewiet@geo.uzh.ch or leoniekiewiet@gmail.com

Date: Date (year-month-day)
Location: Well number or outlet
X: X coordinate (CH1903 LV03)
Y: Y coordinate (CH1903 LV03)
riparian: riparian wells (i.e. from the riparian-like cluster as presented in Kiewiet et al. (2019)) are marked a one, all other wells are marked a zero
d.D (per mil): Deuterium composition (per mil)

en: Precipitation and discharge event A

en: Deuterium rainfall event C

en: Deuterium rainfall extra event

en: Precipitation and discharge extra event

en: Deuterium streamwater event B

en: Deuterium groundwater and baseflow October 2016

en: Deuterium streamwater extra event

en: Precipitation and discharge event C

en: Deuterium groundwater and baseflow October 2017

en:
This dataset includes discharge and rainfall measurements and deuterium compositions of streamflow, rainfall and groundwater, for four rainfall events and three baseflow snapshot campaigns in the Studibach (Alptal, Switzerland). More specifically, we present the following data:

- Specific discharge at the catchment outlet at 5-minute resolution (mm per hour);
- Rainfall at 5-minute resolution (mm per hour);
- Rainfall deuterium composition (‰);
- Stormflow deuterium composition (‰);
- Groundwater and baseflow deuterium compositions (‰).

For the files containing rainfall and discharge timeseries (QP), and rainfall and streamwater deuterium compositions ("Deuterium_Rainfall" and "Deuterium_Streamwater"), we added the corresponding event identifier (A, B, C or extra) to the file names. For the files containing the groundwater and baseflow deuterium values ("Deuterium_Snapshot") we added the sample collection date to the file name. We included the X and Y coordinates for each data point (coordinate system: CH1903 LV3) as well as the date and time (UTC).

More information on the data collection and preparation can be found in Kiewiet et al. (in review). A detailed description of the baseflow snapshot campaigns can also be found in Kiewiet et al., 2019.

en: Discharge, rainfall, and deuterium compositions of streamwater, rainwater and groundwater, for four rainfall events in the Studibach, Alptal, Switzerland


maintainer: Nander Wever

en: microstructure snow-density-profile antarctica snowmicropen snow-density

en: List of SnowMicroPen measurements

en: List of all SnowMicroPen measurements, including date and location, and processing status. This list has been generated by the python code (see other resources in this dataset).

en: Documentation SnowMicroPen measurements

en: This *.zip archive contains all available documentation of the SnowMicroPen measurements, including the mapping of the SMP measurements in the terrestrial laser scan field at PEA (see documentation for 20161220) and at the Hammarryggen Ice Rise (see documentation for 20190104 and 20190111).

en: Manual snow pits and other snow density measurements

en: SnowMicroPen pts files and processed txt files

en: Zip archive containing the acquired *.pts files (binary files recorded by the SnowMicroPen), and the converted *.txt files using the SMP.ipynb jupyter notebook (see the python code in the other resources in this dataset)

en: Python code exported from the Jupyter notebook for processing the SnowMicroPen files

en: Jupyter notebook for processing the SnowMicroPen files.

en: SnowMicroPen (SMP) measurements and manual snowpits from Dronning Maud Land, East Antarctica. Measurements were taken in the vicinity of the Belgium Princess Elisabeth Station (PEA), in a transect towards the coast, and on the Lokeryggen and Hammarryggen Ice Rises near the coast. Measurements were taken during 3 individual campaigns in the 2016-2017, 2018-2019 and 2019-2020 field seasons.

antarctica

display_name: antarctica

microstructure

display_name: microstructure

snow-density

display_name: snow-density

snow-density-profile

display_name: snow-density-profile

snowmicropen

display_name: snowmicropen

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: SnowMicroPen measurements and manual snowpits from Dronning Maud Land, East Antarctica


maintainer: Sofia Pagliarin

en: barcelona soil-sealing imperviousness urban-atlas copernicus milan

en: Shareable project in Esri ArcGIS format including the HRL IMD data in raster format for each of the territorial boundaries as specified in letter a)-h), see Dataset description

en: Information on data sources

en:
Dataset description

This dataset is a recalculation of the Copernicus 2015 high resolution layer (HRL) of imperviousness density data (IMD) at different spatial/territorial scales for the case studies of Barcelona and Milan. The selected spatial/territorial scales are the following:

 * a)	Barcelona city boundaries
 * b)	Barcelona metropolitan area, Àrea Metropolitana de Barcelona (AMB)
 * c)	Barcelona greater city (Urban Atlas)
 * d)	Barcelona functional urban area (Urban Atlas)
 * e)	Milan city boundaries
 * f)	Milan metropolitan area, Piano Intercomunale Milanese (PIM)
 * g)	Milan greater city (Urban Atlas)
 * h)	Milan functional urban area (Urban Atlas)

In each of the spatial/territorial scales listed above, the number of 20x20mt cells corresponding to each of the 101 values of imperviousness (0-100% soil sealing: 0% means fully non-sealed area; 100% means fully sealed area) is provided, as well as the converted measure into squared kilometres (km2).

 Dataset composition

The dataset is provided in .csv format and is composed of:

_IMD15_BCN_MI_Sources.csv_: Information on data sources

 _IMD15_BCN.csv_: This file refers to the 2015 high resolution layer of imperviousness density (IMD) for the selected territorial/spatial scales in Barcelona:
 * a)	Barcelona city boundaries (label: bcn_city)
 * b)	Barcelona metropolitan area, Àrea metropolitana de Barcelona (AMB) (label: bcn_amb)
 * c)	Barcelona greater city (Urban Atlas) (label: bcn_grc)
 * d)	Barcelona functional urban area (Urban Atlas) (label: bcn_fua)

 _IMD15_MI.csv_: This file refers to the 2015 high resolution layer of imperviousness density (IMD) for the selected territorial/spatial scales in Milan:
 * e)	Milan city boundaries (label: mi_city)
 * f)	Milan metropolitan area, Piano intercomunale milanese (PIM) (label: mi_pim)
 * g)	Milan greater city (Urban Atlas) (label: mi_grc)
 * h)	Milan functional urban area (Urban Atlas) (label: mi_fua)

_IMD15_BCN_MI.mpk_: the shareable project in Esri ArcGIS format including the HRL IMD data in raster format for each of the territorial boundaries as specified in letter a)-h).

Regarding the territorial scale as per letter f), the list of municipalities included in the Milan metropolitan area in 2016 was provided to me in 2016 from a person working at the PIM.

In the IMD15_BCN.csv and IMD15_MI.csv, the following columns are included:

 * Level: the territorial level as defined above (a)-d) for Barcelona and e)-h) for Milan);
 * Value: the 101 values of imperviousness density expressed as a percentage of soil sealing (0-100%: 0% means fully non-sealed area; 100% means fully sealed area);
 * Count: the number of 20x20mt cells corresponding to a certain percentage of soil sealing or imperviousness;
 * Km2: the conversion of the 20x20mt cells into squared kilometres (km2) to facilitate the use of the dataset.

 Further information on the Dataset

This dataset is the result of a combination between different databases of different types and that have been downloaded from different sources. Below, I describe the main steps in data management that resulted in the production of the dataset in an Esri ArcGIS (ArcMap, Version 10.7) project.

 1. The high resolution layer (HRL) of the imperviousness density data (IMD) for 2015 has been downloaded from the official website of Copernicus. At the time of producing the dataset (April/May 2021), the 2018 version of the IMD HRL database was not yet validated, so the 2015 version was chosen instead. The type of this dataset is raster.

 2. For both Barcelona and Milan, shapefiles of their administrative boundaries have been downloaded from official sources, i.e. the ISTAT (Italian National Statistical Institute) and the ICGC (Catalan Institute for Cartography and Geology). These files have been reprojected to match the IMD HRL projection, i.e. ETRS 1989 LAEA.

 3. Urban Atlas (UA) boundaries for the Greater Cities (GRC) and Functional Urban Areas (FUA) of Barcelona and Milan have been checked and reconstructed in Esri ArcGIS from the administrative boundaries files by using a Eurostat correspondence table. This is because at the time of the dataset creation (April/May 2021), the 2018 Urban Atlas shapefiles for these two cities were not fully updated or validated on the Copernicus Urban Atlas website. Therefore, I had to re-create the GRC and FUA boundaries by using the Eurostat correspondence table as an alternative (but still official) data source. The use of the Eurostat correspondence table with the codes and names of municipalities was also useful to detect discrepancies, basically stemming from changes in municipality names and codes and that created inconsistent spatial features. When detected, these discrepancies have been checked with the ISTAT and ICGC offices in charge of producing Urban Atlas data before the final GRC and FUA boundaries were defined.

Steps 2) and 3) were the most time consuming, because they required other tools to be used in Esri ArcGIS, like spatial joins and geoprocessing tools for shapefiles (in particular dissolve and area re-calculator in editing sessions) for each of the spatial/territorial scales as indicated in letters a)-h).

Once the databases for both Barcelona and Milan as described in points 2) and 3) were ready (uploaded in Esri ArcGIS, reprojected and their correctness checked), they have been ‘crossed’ (i.e. clipped) with the IMD HRL as described in point 1) and a specific raster for each territorial level has been calculated. The procedure in Esri ArcGIS was the following:

 * Clipping: Arctoolbox > Data management tools > Raster > Raster Processing > Clip. The ‘input’ file is the HRL IMD raster file as described in point 1) and the ‘output’ file is each of the spatial/territorial files. The option "Use Input Features for Clipping Geometry (optional)” was selected for each of the clipping.
 * Delete and create raster attribute table: Once the clipping has been done, the raster has to be recalculated first through Arctoolbox > Data management tools > Raster > Raster properties > Delete Raster Attribute Table and then through Arctoolbox > Data management tools > Raster > Raster properties > Build Raster Attribute Table; the "overwrite" option has been selected.

 Other tools used for the raster files in Esri ArcGIS have been the spatial analyst tools (in particular, Zonal > Zonal Statistics). As an additional check, the colour scheme of each of the newly created raster for each of the spatial/territorial attributes as per letters a)-h) above has been changed to check the consistency of its overlay with the original HRL IMD file. However, a perfect match between the shapefiles as per letters a)-h) and the raster files could not be achieved since the raster files are composed of 20x20mt cells.

The newly created attribute tables of each of the raster files have been exported and saved as .txt files. These .txt files have then been copied in the excel corresponding to the final published dataset.

barcelona

display_name: barcelona

copernicus

display_name: copernicus

imperviousness

display_name: imperviousness

milan

display_name: milan

soil-sealing

display_name: soil-sealing

urban-atlas

display_name: urban-atlas

en: Soil sealing Barcelona and Milan different territorial levels


maintainer: Stephanie Mayer

en: rutschblock failure-layer snowpack snow-stratigraphy snowpack-simulation snow-instability snow-profiles

en: Observed snow profiles

en: Observed snow stratigraphy in form of one *.csv-file per snow profile from the DAV and SWISS data sets (in respective subfolders). Each file is named after the profile ID which allows identifying the corresponding simulated profile in the "simulated_profiles" folder. One row corresponds to one observed snow layer. The content of the columns is described in Table 2 of the ReadMe file.

en: SWISS data set: Properties of observed snow profiles and simulated failure layers

en: For each profile pair from the SWISS data set, this file contains one row with information on the observed snow stability and all extracted features describing the simulated failure layers. Please note that only those profile pairs which fulfilled the similarity criteria (column similarity_obs_sim = 1, 121 profiles) were used to validate the RF model. The content of the columns is explained in Table 1 of the ReadMe file.

en: Simulated snow profiles

en: Simulated snow stratigraphy in form of one *.csv-file per snow profile from the DAV and SWISS data sets (in respective subfolders). Each file is named after the profile ID which allows identifying the corresponding observed profile in the "observed_profiles" folder. One row corresponds to one simulated snow layer. The content of the columns 1-7 is described in Table 3 of the ReadMe file. The remaining columns (8-31) contain all features that were used in the development of the RF model and are described in Table 1 of the ReadMe file.

en: ReadMe

en: ReadMe file explaining data structure and content

en: DAV data set: Properties of observed snow profiles and simulated failure layers

en:
For each profile pair from the DAV data set, this file contains one row with information on the observed snow stability and all extracted features describing the simulated failure layers. The content of the columns is explained in Table 1 of the ReadMe file. Only those profile pairs which fulfilled predefined similarity criteria (column
"similarity_obs_sim" = 1) and which belonged to the stability classes "stable" and "unstable" (column "stability_class"
= 0 or 1) were used to train the RF model. Please note that for each of the two snow profiles with RB score 7 (profile
IDs 7228 and 9526), three additional layers were included in this data set to obtain a balanced set of training data (73
unstable and 73 stable profiles).

en:
This data set includes information on all observed and simulated snow profiles that were used to train and validate the
random forest model described in Mayer et al. (2022). The RF model was trained to assess snow instability from simulated snow stratigraphy. The data set contains observed snow profiles from the region of Davos (DAV subset, 512 profiles) and from all over Switzerland (SWISS subset, 230 profiles). For each observed snow profile, there is a corresponding simulated profile which was obtained using meteorological input data for the numerical snow cover model SNOWPACK. The information on the observed snow profile contains a Rutschblock test result including the depth of the failure interface. As part of the study described in Mayer et al. (2022), each observed snow profile was manually compared to its simulated counterpart and the simulated layer corresponding to the Rutschblock failure layer was identified.

The data are provided in the following form: one file each per observed and simulated snow profile (2x512 files DAV, 2x230 files SWISS), two files (1 file DAV, 1 file SWISS) containing the observed information on snow instability, the allocation between observed and simulated failure layer, and all features extracted from the simulated weak layers that were used to develop the RF model.

failure-layer

display_name: failure-layer

rutschblock

display_name: rutschblock

snow-instability

display_name: snow-instability

snow-profiles

display_name: snow-profiles

snow-stratigraphy

display_name: snow-stratigraphy

snowpack-simulation

display_name: snowpack-simulation

en: Observed and simulated snow profile data


maintainer: Matthias Haeni

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

en: Site aerial photo (Jussy)

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

en: Site photo of the field station (JUF)

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

en: Data plot of the field station (JUF)

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

en: Data file.

en: Forest station data (JUB)

en: Site photo of the forest station (JUB)

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

en: Data plot of the forest station (JUB)

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Jussy in Switzerland where one station is located within a natural broad-leaved forest stand (JUB) with sessile oak (_Quercus petrea_; 90 yrs), aspen (_Populus tremula_; 60 yrs) and European hornbeam (_Carpinus betulus_; 60 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, JUF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Jussy is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

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


maintainer: Mahdi Jafari

en: numerical-simulation openfoam snow-density-profile convection-in-snow vapor-transport

en: snowpackBuoyantPimpleFoam

en:
This zipped folder includes (1) the source code files, (2) an example case for simulation, and (3) an instruction how to compile and use the new solver.

Note that the most updated version of snowpackBuoyantPimpleFoam can be found in the WSL/SLF GitLab repository: https://gitlabext.wsl.ch/snow-models/snowpackbuoyantpimplefoam

en: snowpackBuoyantPimpleFoam is a two-phase solver implemented to model convection of water vapor with phase change in snowpacks. This new solver is based on the standard solver of buoyantPimpleFoam in the open-source fluid dynamics software, OpenFOAM 5.0 (www.openfoam.org).

convection-in-snow

display_name: convection-in-snow

numerical-simulation

display_name: numerical-simulation

openfoam

display_name: openfoam

snow-density-profile

display_name: snow-density-profile

vapor-transport

display_name: vapor-transport

en: snowpackBuoyantPimpleFoam: an OpenFOAM Eulerian–Eulerian two-phase solver for modelling convection of water vapor in snowpacks


stadtplan-darstellung-farbig

regi

fr: Régions et villes

de: Regionen und Städte

en: Regions and cities

it: Regioni e città

envi

fr: Environnement

de: Umwelt

en: Environment

it: Ambiente

title_for_slug: stadtplan-darstellung-farbig

de: av grundkarte hintergrundkarte

label: geocat.ch permalink

fr: Map (Preview)

de: Map (Preview) Stadtplan

en: Map (Preview)

it: Map (Preview)

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

de: WMS Stadtplan_farbig

de: Web Map Services WMS

format: WMS

de: WMTS Stadtplan_farbig

de: Web Map Tile Services WMTS

format: WMTS

de: WFS Stadtplan_farbig

de: Web Feature Services WFS

format: WFS

de: Farbige, einfache Darstellung basierend auf den Daten der amtlichen Vermessung.

spatial: Stadt Bern

geoinformation-der-stadt-bern

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

de: Geoinformation Stadt Bern

en: The Land Surveying Office of the City of Bern

it: L'ufficio del catasto nella Città di Berna

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

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

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

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

political_level: commune

value: Geoinformation Stadt Bern - Geocat Harvester

de: Stadtplan (Darstellung farbig)


stadtplan-darstellung-schwarz-weiss

title_for_slug: stadtplan-darstellung-schwarz-weiss

de: WMS Stadtplan_schwarzweiss

de: WMTS Stadtplan_schwarzweiss

de: WFS Stadtplan_schwarzweiss

de: Schwarz/weisse, einfache Darstellung basierend auf den Daten der amtlichen Vermessung.

de: Stadtplan (Darstellung schwarz/weiss)


velopumpenstandorte

regi

de: Regionen und Städte

tran

fr: Transports

de: Verkehr

en: Transport

it: Trasporti

title_for_slug: velopumpenstandorte

de: fahrrad poi pumpe velo

de: Map (Preview) Stadtplan

de: WMS Velopumpenstandorte

de: Standorte der öffentlichen Velopumpen der Stadt Bern.

de: Velopumpenstandorte


en: bacteria biogeochemical-cycling wolf yellowstone soil-biodiversity fungi carrion detritus carcass decomposition

en:
Site	ID for different carcasses across the Yellowstone Landscape
Location	Sampling location, either under carcass or control plot
Species	Indicating whether carcass was from bison or elk
Gender	Gener of carcass; F = female, M = male, ? = unknown
Lat	Latitude
Long	Longitude
DSK	Days since kill; in days
Soil.N	Soil N concentration (%)
Soil.C	Soil C concentration (%)
Veg.C	Vegetation C concentration (%); dominant grasses sampled
Veg.N	Vegetation N concentration (%); dominant grasses sampled
Veg.CtoN	Vegetation C to N ratio; dominant grasses sampled
Soil.resp	Soil respiration (g m-2 h-1)
soil.temp	Soil temperature (°C)
Soil.moist	Soil moisture (volumentric; %)
CRR	Cotton rotting rate
bacteria.richness	Bacteria richness
bacteria.diversity	Bacteria diverstiy
fungi.richness	Fungi richness
fungi.diversity	Fungi diversity
bacteria.abundance	Bacteria abundance
fungi.abundance	Fungi abundance

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

Paper Citation:

Risch, A.C., Frossard, A., Schütz, M., Frey, B., Morris, A.W., Bump, J.K. (accepted) Effects of elk and bison carcasses on soil microbial communities and ecosystem functions in Yellowstone, USA. (accepted). Functional Ecology
doi: ...

Methods
Study area and study sites
This study was conducted in YNP’s Northern Range (NR), located in north-western Wyoming and south-western Montana, USA (~44.9163° N, 110.4169° W). The NR expands over ~1000 km2 and features long cold winters and short dry summers. Grasslands and shrublands dominate the NR that is the home of large migratory herds of bison (winter counts 2017: ~3919 individuals; Geremia, Wallen, & White, 2017) and elk (~5349 individuals) as well as their main predators, approximately five packs of wolves with a total of 33 individuals (Smith et al., 2017). As part of a long-term research program within YNP, wolf predation has been studied since their reintroduction in 1995.
For our study, we received ground-truthed coordinates of bison and elk carcasses from winter 2016/17 (November 2016 through April 2017) from the YNP Wolf Project. Between June 20 and July 1, 2017, we visited 24 carcasses in total. At five sites, we could not sample as the carcasses were no longer found. In total we located remains (hairmats, rumen content, bones, teeth) of 19 adult male and female carcasses (7 bison, 12 elk; Supplementary Table 1). Live body weights of adult bison and elk are approximately 730 kg (male bison), 450 kg (female bison), 330 kg (male elk), and 235 kg (female elk, Meagher, 1973; Quimby & Johnson, 1951).
The kills and subsequent consumption happened between 34 and 173 days prior to our sampling (hereafter “days since kill”, DSK), for which we accounted in our statistics. Note that wolves and other scavengers consumed the soft tissue of the carcasses quickly, hence, there is close to no soft tissue left for decomposition as compared to an intact body left on the soil surface. The 19 carcass sites covered the extent of YNP’s NR, with both bison and elk carcasses showing similar distributions; elevation ranged from 1703 to 2884 m a.s.l. (Supplementary Fig 1 & Supplementary Table 1). The carcasses were all located in grassland or sage-brush shrubland, with or without sparsely scattered trees, and both bison and elk carcasses showed the same distribution of DSK. At each study site, we selected a reference plot (hereafter “control”) that was of comparable size, slope aspect and vegetation to the carcass location (hereafter “carcass”). The control was at least 10 m away (Danell, Berteaux, & Brathen, 2002; Melis et al., 2007) from the carcass itself to ensure the absence of potential direct and indirect carcass effects (paired design; (Bump, Webster, et al., 2009; Bump, Peterson, et al., 2009).

Ecosystem functions and soil properties
We randomly collected 50 g of mineral soil from three locations on both control and carcass plots to a depth of 5 cm with sterile techniques and gently mixed the material to obtain a composite sample. Half the soil sample was immediately bagged in plastic bags (whirl packs), stored in a cooler with ice packs (~5 ºC), sieved (2-mm) and frozen within 4-6 hours of collection to assess soil microbial communities. For this purpose, we extracted total genomic DNA from 0.5 g soil using the PowerSoil DNA Isolation Kit (Qiagen, Hilden, Germany). DNA concentrations were measured using PicoGreen (Molecular Probes, Eugene, OR, USA). PCR amplifications of partial bacterial small-subunit ribosomal RNA genes (region V3–V4 of 16S rRNA) and fungal ribosomal internal transcribed spacers (region ITS2) were performed as described previously (Frey et al., 2016). Each sample consisting of 40 ng DNA was amplified in triplicate and pooled before purification with Agencourt AMPure XP beads (Beckman Colter, Berea, CA, USA) and quantified with the Qubit 2.0 fluorometric system (Life Technologies, Paisley, UK). Amplicons were sent to the Genome Quebec Innovation Center (Montreal, Canada) for barcoding using the Fluidigm Access Array technology and paired-end sequencing on the Illumina MiSeq v3 platform (Illumina Inc., San Diego, CA, USA).
Quality control of bacterial and fungal reads was performed using a customized pipeline (Supplementary Table 2; Frey et al., 2016). Paired-ends reads were matched with USEARCH (Edgar & Flyvbjerg, 2015), substitution errors were corrected using Bayeshammer (Nikolenko, Korobeynikov, & Alekseyev, 2013) and PCR primers were trimmed (allowing for 1 mismatch, read length >300 bp for 16S and >200 bp for ITS primers) using Cutadapt (M. Martin, 2011). Sequences were dereplicated and singleton reads removed prior to clustering into operational taxonomic units (OTUs) at 97% identity using USEARCH (Edgar, 2013). The remaining centroid sequences were tested for the presence of ribosomal signatures using Metaxa2 (Bengtsson-Palme et al., 2015) or ITSx (Bengtsson-Palme et al., 2013). Taxonomic assignments of the OTUs were obtained using Bayesian classifier (Wang, Garrity, Tiedje, & Cole, 2007) with a minimum bootstrap support of 60% implemented in mothur (Schloss et al., 2009) by querying the bacterial and fungal reads against the SILVA Release 128 (Quast et al., 2013) and UNITE 8.0 (Abarenkov et al., 2010) reference databases for 16S and ITS OTUs, respectively.
Abundances of the bacterial 16S rRNA gene and fungal ITS amplicon were determined by quantitative real-time PCR (qPCR) on an ABI7500 Fast Real-Time PCR system (Applied Biosystems, Foster City, CA, USA) as described previously (Frossard et al., 2018). The same primers (without barcodes) and cycling conditions as for the sequencing approach were used for the 16S and ITS qPCR. Three standard curves per target region were obtained using tenfold serial dilutions of plasmids generated from cloned targets (Frey, Niklaus, Kremer, Lüscher, & Zimmermann, 2011). Data were converted to represent mean copy number of targets per gram of soil (dry weight).
The other half of the soil sample was bagged in paper, dried to constant weight at 60°C, passed through a 2 mm sieve and analyzed for total C and N concentration with a CE Instruments NC 2100 soil analyzer (CE Elantech Inc., Lakewood NJ, USA). We also collected 20 mature and undamaged leaves of the dominant grass species growing on control and carcass sites, but taxa were not recorded. The plant material was dried at 60°C, finely ground till homogenized and also analyzed to obtain total C and N concentrations. Soil temperature (10 cm depth) was measured with a waterproof digital thermometer (Barnstead International, Dubuque IA, USA) at three locations each at the control and carcass site. Soil moisture (0 – 10 cm depth) was measured with time domain reflectometry (Field-Scout TDR-100; Spectrum Technologies, Plainfield IL, USA) at five randomly chosen points on control and carcass sites. We measured soil respiration at five randomly chosen points at both control and carcass sites with a PP-Systems SRC-1 soil respiration chamber (closed circuit) attached to a PP-Systems EGM-4 infrared gas analyzer (PP-Systems, Amesbury, MA, USA). For each measurement the soil chamber (15 cm high; 10 cm diameter) was tightly placed on the soil surface, after clipping plants to avoid measuring plant respiration or photosynthesis. Measurements were conducted over 120 s.
In addition, we assessed the decomposition rates of standardized OM using the cotton strip assay (Latter & Howson, 1977; Latter & Walton, 1988). Cotton cloth tensile strength loss (CTSL) is a measure of decomposition, and an index to express the combined effect of soil microclimatic, physical, chemical and biological properties on decomposition while accounting for OM quality (Latter & Walton, 1988; Risch, Jurgensen, & Frank, 2007; Withington & Sanford Jr., 2007). We placed five 20 cm wide x 13 cm long sheets of 100% unbleached cotton cloth (American Type SM 1/18’’, Warp: 34/1, Weft: 20/1, Weave plain, 29.5 picks/cm warp, 22 picks/cm weft, 237 g/m2; Daniel Jenny & Co., Switzerland;) at each carcass and control site vertically into the soil by making slits with a flat spade to a depth of 12 cm. We inserted each cloth with the spade, and then pushed the slit closed to assure tight contact with the soil. The cloths were retrieved after 18 to 27 days. After retrieval, the cloths were air-dried, remaining soil gently removed by hand, and 1.5 cm wide strips were cut at the 3.5-5.0 cm (top) and the 9-10.5 cm (bottom) soil depth. The strips were equilibrated at 50 % relative humidity and 20°C for 48 hours (climate chamber) prior to strength testing (Scanpro Awetron TH-1 tensile strength tester; AB Lorentzen and Wettre, Kista, Sweden). Cotton rotting rate (CRR) = (CTScontrol - CTSfinal/CTSfinal)1/3 * (365/t), where CTScontrol is the cotton tensile strength of a control cloth and CTSfinal the cotton tensile strength of the incubated sample, t is the incubation period in days. Control cloths were inserted into the ground and immediately retrieved to account for tensile strength loss associated with cloth insertion. We averaged the CRR of top and bottom strips for further analyses as no difference was found between the two. All sampling and cloth insertion took place between June 20 and July 1, 2017, cloths were retrieved between July 17 and 20, 2017. Soil respiration, average CRR, vegetation N concentration and vegetation C:N ratio are defined as ecosystem functions, soil C and N concentration, soil temperature and moisture as soil abiotic properties, and bacterial and fungal richness (number of taxa), diversity (Shannon) and abundance as soil biotic properties.

Statistical analyses
Univariate analyses for ecosystem functions, soil biotic and abiotic properties
We tested whether individual ecosystem functions, soil biotic and abiotic properties differed between carcass and control (“Location”), bison and elk (“Species”) and days since kill (“DSK”). For this purpose, we used linear mixed effect models (LMM, “nlme” package v 3.1 – 131.1 in R v 3.4.4; Pinheiro, Bates, DebRoy, & Sarkar, 2018; R Core Team, 2019) with Location, Species, Location x Species and DSK as fixed effects. Site was included as random effect to account for the paired design. We developed a separate model for all dependent variables. All but bacterial richness, fungal richness, fungal diversity and vegetation N concentration were natural-log transformed to meet model assumptions. For each LMM, we calculated contrasts to assess the specific comparisons we were interested in with the “lsmeans” package v 2.27-62 (Lenth & Love, 2018): 1) carcass vs control, 2) carcass bison vs control bison, and 3) carcass elk vs control elk. We also tested whether we had differences between bison and elk carcasses or the sites where bison and elk were killed and included contrasts 4) carcass bison vs carcass elk and 5) control bison vs control elk.
We calculated the log response ratio (LRR = ln[carcass/control]) to obtain carcass effects for all variables for both species separately. LRR < 0 indicates higher value at control compared to carcass, LRR > 0 indicates higher values at carcass compared control. We used LRRs for visualization and to assess spatial patterns in carcass effects across YNP. For this purpose we calculated the Moran’s I statistic for each ecosystem function, soil biotic and abiotic property based on a latitude-longitude matrix with the “moran.test” function in the “spdep” package version 1.1-3 (Bivand et al., 2019).
Multivariate analyses
Rare OTUs, defined as OTUs with a low abundance of reads, were retained in multivariate methods because they only marginally influence these analyses (Gobet, Quince, & Ramette, 2010). Bray–Curtis dissimilarity matrices were generated based on square-root-transformed matrices. We used Principal Coordinate Analyses (PCoA) to assess how soil bacterial and fungal communities differed between control and carcass of bison and elk (“vegan” package v 2.5-4, Oksanen et al., 2019). We then extracted PCoA axes scores 1 and 2 and used LMM (“nlme” package) with Location, Species, Location x Species and DSK as fixed effects. Site was, again, included as random effect. We again calculated the contrasts as described above using the “lsmeans” package. We also assessed how ecosystem functions, and soil abiotic and biotic properties were related to the soil bacteria and fungi community structure associated with bison and elk control and carcasses using the “envfit” function in the “vegan” package (Oksanen et al., 2019).
Indicator species analyses were performed using the multipatt function implemented in the “indicspecies” package version 1.7.6 with 100000 permutations (De Caceres & Jansen, 2016). This step allowed to identify OTUs that led to changes in multivariate patterns between control and carcass of both bison and elk separately (De Cáceres, Legendre, & Moretti, 2010). The multipatt function uses a point biserial correlation coefficient statistical test. Indicator OTUs were defined as bacterial and fungal OTUs with more than 50 sequences, i.e., removing rare taxa and taxa with low abundances containing little indicator information (Rime et al., 2015) and that were significantly correlated with Location (p < 0.05, correlation coefficient > 0.3). A heatmap of these OTUs were generated with the vegan and ggplot2 packages. The indicator analyses were performed in R version 3.3.3 (R Core Team, 2017).

References
Abarenkov, K., Henrik Nilsson, R., Larsson, K.-H., Alexander, I. J., Eberhardt, U., Erland, S., … Kõljalg, U. (2010). The UNITE database for molecular identification of fungi – recent updates and future perspectives. New Phytologist, 186(2), 281–285. doi:10.1111/j.1469-8137.2009.03160.x
Bengtsson-Palme, J., Hartmann, M., Eriksson, K. M., Pal, C., Thorell, K., Larsson, D. G. J., & Nilsson, R. H. (2015). metaxa2: improved identification and taxonomic classification of small and large subunit rRNA in metagenomic data. Molecular Ecology Resources, 15(6), 1403–1414. doi:10.1111/1755-0998.12399
Bengtsson-Palme, J., Ryberg, M., Hartmann, M., Branco, S., Wang, Z., Godhe, A., … Nilsson, R. H. (2013). Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for analysis of environmental sequencing data. Methods in Ecology and Evolution, 4(10), 914–919. doi:10.1111/2041-210X.12073
Bivand, R., Altman, M., Anselin, L., Assuncao, R., Berke, O., Blanchet, G., … Yu, D. (2019). spdep: Spatial dependence, weighthing schemes, statistics. R package version 1.1-3.
Bump, J. K., Peterson, R. O., & Vucetich, J. A. (2009). Wolves modulate soil nutrient heterogeneity and foliar nitrogen by configuring the distribution of ungulate carcasses. Ecology, 90(11), 3159–3167.
Bump, J. K., Webster, C. R., Vucetich, J. A., Peterson, R. O., Shields, J. M., & Powers, M. D. (2009). Ungulate carcasses perforate ecological filters and create biogeochemical hotspots in forest herbaceous layers allowing trees a competitive advantage. Ecosystems, 12(6), 996–1007. doi:10.1007/s10021-009-9274-0
Danell, K., Berteaux, D., & Brathen, K. A. (2002). Effect of muskox carcasses on nitrogen concentration in tundra vegetation. Arctic, 55(4), 389392.
De Caceres, M., & Jansen, F. (2016). indicspecies: relationship between species and groups of species. R package version 1.7.6.
De Cáceres, M., Legendre, P., & Moretti, M. (2010). Improving indicator species analysis by combining groups of sites. Oikos, 119(10), 1674–1684. doi:10.1111/j.1600-0706.2010.18334.x
Edgar, R. C. (2013). UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature Methods, 10, 996.
Edgar, R. C., & Flyvbjerg, H. (2015). Error filtering, pair assembly and error correction for next-generation sequencing reads. Bioinformatics, 31(21), 3476–3482. doi:10.1093/bioinformatics/btv401
Frey, B., Niklaus, P. A., Kremer, J., Lüscher, P., & Zimmermann, S. (2011). Heavy-machinery traffic impacts methane emissions as well as methanogen abundance and community structure in oxic forest soils. Applied and Environmental Microbiology, 77(17), 6060–6068. doi:10.1128/AEM.05206-11
Frey, B., Rime, T., Phillips, M., Stierli, B., Hajdas, I., Widmer, F., & Hartmann, M. (2016). Microbial diversity in European alpine permafrost and active layers. FEMS Microbial Ecology, 92(3), fiw018.
Frossard, A., Donhauser, J., Mestrot, A., Gygax, S., Bååth, E., & Frey, B. (2018). Long- and short-term effects of mercury pollution on the soil microbiome. Soil Biology and Biochemistry, 120, 191–199. doi:https://doi.org/10.1016/j.soilbio.2018.01.028
Geremia, C., Wallen, R., & White, P. J. (2017). Status report of the Yellowstone bison population, September 2017. Yellowstone National Park, Mammoth, WY, USA: National Park Service, Yellowstone Center for Resources.
Gobet, A., Quince, C., & Ramette, A. (2010). Multivariate cutoff level analysis (MultiCoLA) of large community data sets. Nucleic Acids Research, 38(15), e155–e155. doi:10.1093/nar/gkq545
Latter, P., & Howson, G. (1977). The use of cotton strips to indicate cellulose decomposition in the field. Pedobiologia, (17), 145–155.
Latter, P., & Walton, D. (1988). The cotton strip assay for cellulose decomposition studies in soil: history of the assay and development. In Cotton strip assay: an index for decomposition in soils (pp. 7–9). ITE Symposium, Institute of Terrestrial Ecology, Natural Environment Research Council, UK.
Lenth, R., & Love, J. (2018). lsmeans: least-squares means. R package version 2.27-62.
Martin, M. (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.Journal, 17(1), 10–12.
Meagher, M. M. (1973). The bison of Yellowstone National Park. NPS Scientific Monograph (Vol. 1). National Park Service, Yellowstone Center for Resources.
Melis, C., Selva, N., Teurlings, I., Skarpe, C., Linnell, J. D. C., & Andersen, R. (2007). Soil and vegetation nutrient response to bison carcasses in Białowieża Primeval Forest, Poland. Ecological Research, 22(5), 807–813. doi:10.1007/s11284-006-0321-4
Nikolenko, S. I., Korobeynikov, A. I., & Alekseyev, M. A. (2013). BayesHammer: Bayesian clustering for error correction in single-cell sequencing. BMC Genomics, 14(1), S7. doi:10.1186/1471-2164-14-S1-S7
Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., … Wagner, H. H. (2019). vegan: community ecology package. R package version 2.5-4.
Pinheiro, J., Bates, D., DebRoy, S., & Sarkar, D. (2018). nlme: Linear and nonlinear mixed effect models. R package version 3.1-131.1.
Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., … Glöckner, F. O. (2013). The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Research, 41(Database issue), D590–D596. doi:10.1093/nar/gks1219
Quimby, D. C., & Johnson, D. E. (1951). Weights and measurements of Rocky Mountain elk. Journal of Wildlife Management, 15, 57–62.
R Core Team. (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Zurich, Switzerland.
R Core Team. (2019). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
Rime, T., Hartmann, M., Brunner, I., Widmer, F., Zeyer, J., & Frey, B. (2015). Vertical distribution of the soil microbiota along a successional gradient in a glacier forefield. Molecular Ecology, 24(5), 1091–1108. doi:10.1111/mec.13051
Risch, A. C., Jurgensen, M. F., & Frank, D. A. (2007). Effects of grazing and soil micro-climate on decomposition rates in a spatio-temporally heterogeneous grassland. Plant and Soil, 298(1–2), 191–201. doi:10.1007/s11104-007-9354-x
Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., … Weber, C. F. (2009). Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and Environmental Microbiology, 75(23), 7537–7541. doi:10.1128/AEM.01541-09
Smith, D., Stahler, D., Cassidy, K., Stahler, E., Metz, M., Cassidy, B., … Cato, E. (2018). Yellowstone National Park wolf project annual report 2017. Yellowstone National Park, Mammoth, WY, USA: National Park Service, Yellowstone Center of Resources.
Wang, Q., Garrity, G. M., Tiedje, J. M., & Cole, J. R. (2007). Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Applied and Environmental Microbiology, 73(16), 5261–5267. doi:10.1128/AEM.00062-07
Withington, C., & Sanford Jr., R. (2007). Decomposition rates of buried substances increase with altitude in a forest-alpine tundra ecotone. Soil Biology and Biochemistry, (39), 68–75.

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

bacteria

display_name: bacteria

biogeochemical-cycling

display_name: biogeochemical-cycling

carcass

display_name: carcass

carrion

display_name: carrion

decomposition

display_name: decomposition

detritus

display_name: detritus

fungi

display_name: fungi

soil-biodiversity

display_name: soil-biodiversity

wolf

display_name: wolf

yellowstone

display_name: yellowstone

educ

de: Bildung, Kultur und Sport

envidat

de: EnviDat: das Umwelt-Datenportal

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

en: Elk and bison carcasses in Yellowstone, USA


schwarzplan

envi

de: Umwelt

title_for_slug: schwarzplan

de: av grundkarte hintergrundkarte

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

de: WMS Schwarzplan

de: Web Map Services WMS

de: WMTS Schwarzplan

de: Web Map Tile Services WMTS

de: Die Gebäudeflächen aus der Bodenbedeckung der Amtlichen Vermessung schwarz eingefärbt bilden eine einfache Grundkarte "Schwarzplan".

geoinformation-der-stadt-bern

de: Geoinformation Stadt Bern

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

de: Schwarzplan


maintainer: Ionuț Iosifescu Enescu

en: map 2GB

media_type: image/tiff

format: TIFF

mimetype: image/tiff

en: map 0.2GB

en: Bulk Data Access

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

en: map 0.5GB

en: large map 10GB

en: huge map 100GB

en: map preview 0.5MB

en: map 1GB

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

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

gis

display_name: gis

large-files

display_name: large-files

natural-earth

display_name: natural-earth

qgis

display_name: qgis

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

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


regi

de: Regionen und Städte

en: Regions and cities

envi

en: Environment

tran

de: Verkehr

en: Transport

label: Sachplanseite des Bundes

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

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

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

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

fr: Aperçu map.geo.admin.ch

de: Vorschau map.geo.admin.ch

en: Preview map.geo.admin.ch

it: Previsione map.geo.admin.ch

fr: RESTful API de geo.admin.ch

de: RESTful API von geo.admin.ch

en: RESTful API from geo.admin.ch

it: RESTful API da geo.admin.ch

format: API

fr: Service WMS-IFDG, couche "PS Transp. souterrain de marchandises"

de: WMS-BGDI Dienst, Layer "SP Unterirdischer Gütertransport"

en: WMS-FSDI service, layer "SP Underground freight transport"

it: Servizio WMS-IFDG, strato "PS Trasporto merci sotterraneo"

fr: Téléchargement des géodonnées de geo.admin.ch

de: Download der Geodaten von geo.admin.ch

en: Download server from geo.admin.ch

it: Download dei geodati da geo.admin.ch

fr: Le plan sectoriel des transports, partie Transport souterrain de marchandises (SUG), représente les périmètres adéquats pour les emplacements du projet Cargo sous terrain.

de: Im Sachplan Verkehr, Teil Unterirdischer Gütertransport (SUG), werden die geeigneten Räume für das Vorhaben des Projekts Cargo sous terrain bezeichnet.

en: In the sectoral plan for transport, part underground freight transport (SUG), the suitable areas for the Cargo sous terrain project are designated.

it: Nel piano settoriale dei trasporti, parte trasporto merci in sotterraneo (SUG), sono indicate le aree idonee per il progetto Cargo sous terrain

spatial: Schweiz

bundesamt-fur-verkehr-bav

fr: Office fédéral des transports OFT

de: Bundesamt für Verkehr BAV

en: Federal Office of Transport FOT

it: Ufficio federale dei trasporti UFT

fr: L'Office fédéral des transports (OFT) veille journellement à concevoir et à mettre en œuvre la politique suisse en matière de transports publics, réalisant ainsi des parties essentielles de la politique des transports décidée par le peuple, le Parlement et le Conseil fédéral. Les spécialistes de l'OFT sont compétents pour le transport de voyageurs et de marchandises sur le rail, pour les téléphériques et les télésièges, les bus et les bateaux.

de: Das Bundesamt für Verkehr (BAV) ist täglich dafür besorgt, die schweizerische Politik im Bereich des öffentlichen Verkehrs umzusetzen und mitzugestalten. Es realisiert damit wesentliche Teile der von Volk, Parlament und Bundesrat beschlossenen Verkehrspolitik. Die Fachleute des BAV sind zuständig für den Personen- und den Güterverkehr auf der Schiene, für die Seilbahnen und Sessellifte, die Busse und die Schiffe.

it: L'ufficio federale dei trasporti (UFT) si occupa quotidianamente di attuare e pianificare la politica svizzera nell'ambito dei trasporti pubblici, realizzando così i principi chiave della politica dei trasporti decisa dal Popolo, dal Parlamento e dal Consiglio federale. Gli esperti dell'UFT sono competenti per il trasporto di viaggiatori e di merci con la ferrovia, per gli impianti a fune, gli autobus e i battelli.

political_level: confederation

fr: Plan sectoriel des transports, partie Transport souterrain de marchandises (SUG)

en: Sectoral plan on transport, underground freight transport section (SUG)

it: Piano settoriale dei trasporti, parte Trasporto merci sotterraneo (SUG)


kunsteisbahnen

soci

fr: Population et société

de: Bevölkerung und Gesellschaft

en: Population and society

it: Popolazione e società

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

it: Istruzione, cultura e sport

heal

fr: Santé

de: Gesundheit

en: Health

it: Salute

title_for_slug: kunsteisbahnen

de: eisbahn eisfeld eishockey schlittschubahn

de: Map (Preview) Stadtplan

en: Map (Preview)

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

de: WMS Kunsteisbahnen

de: Web Map Services WMS

de: WFS Kunsteisbahnen

de: Web Feature Services WFS

de: Standorte von Kunsteisbahnen in der Stadt Bern.

geoinformation-der-stadt-bern

en: The Land Surveying Office of the City of Bern

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

de: Kunsteisbahnen


fr: oreb zonenplan bauzone grundplan nutzungsplan flachennutzungsplan

fr: WFS-Dienst des Kantons Schwyz

de: WFS-Dienst des Kantons Schwyz

en: WFS-Dienst des Kantons Schwyz

it: WFS-Dienst des Kantons Schwyz

fr: ÖREB: Nutzungsplanung kommunal, Altendorf, Sondernutzungsplanung

fr: ÖREB Sondernutzungsplanung Gemeinde Altendorf

fr: Die Gemeinden können Sondernutzungspläne erlassen und damit die allgemeine Nutzungsplanung in einem konkreten, klar umgrenzten Areal weiter präzisieren.

spatial: Altendorf

amt-geoinformation-sz

de: Amt für Geoinformation Kanton Schwyz

de: Das Amt für Geoinformation verwaltet und veröffentlicht die Geodaten des Kantons Schwyz als OGC-Geodienste und als Datei-Download. Alle aktuell verfügbaren Geodaten, welche dem OGD unterliegen, sind hier aufgeführt.

political_level: canton

value: SZ-Kt Geoinformation - Geocat Harvester


regi

fr: Régions et villes

de: Regionen und Städte

en: Regions and cities

it: Regioni e città

envi

fr: Environnement

de: Umwelt

it: Ambiente

de: gelandedarstellung skitour landeskarte verkehrsnetz geobasisdienst grenze vegetation gewasser relief kartographie geografische-bezeichnungen bgdi-bundesgeodaten-infrastruktur topografie karte

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

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

de: Vorschau map.geo.admin.ch

fr: Swiss Map Raster 50 Hiver

de: Swiss Map Raster 50 Winter

en: Swiss Map Raster 50 Winter

it: Swiss Map Raster 50 Inverno

fr: Informations sur le produit et téléchargement

de: Produktinformation und Download

en: Product information and download

it: Informazioni sul prodotto e download

fr: La carte nationale 1:50'000 en représentation hivernale est un dérivé de la carte nationale topographique 1:50'000 avec la même représentation détaillée du réseau routier, des localités, du terrain, de l'hydrographie et de la végétation. La Suisse est intégralement représentée en version hivernale. La carte nationale 1:50'000 hiver n'est disponible qu'en version numérique (Swiss Map Raster, API, WMTS).

de: Die Landeskarte 1:50'000 Winter ist eine Ableitung der topografischen Landeskarte 1:50'000 der Schweiz mit detaillierter Verkehrs-, Siedlungs-, Gelände-, Gewässer- und Vegetationsdarstellung. Die Schweiz wird mittels einer winterlichen Darstellung flächendeckend abgebildet. Die Landeskarte 1:50'000 Winter ist ausschliesslich in digitaler Form (Swiss Map Raster, API, WMTS) verfügbar.

en: The winter national map 1:50'000 is a derivation of the topographical national map 1:50'000 of Switzerland with detailed depictions of traffic, settlement areas, terrain, bodies of water and vegetation. Switzerland is comprehensively mapped in a winter representation. The winter national map 1:50'000 is only available in a digital format (Swiss Map Raster, API, WMTS).

it: La carta nazionale 1:50'000 inverno è una derivazione della carta nazionale topografica 1:50'000 della Svizzera con una rappresentazione dettagliata della rete viaria, degli insediamenti, del terreno, dei corsi d'acqua e della vegetazione. La Svizzera viene riprodotta in modo capillare attraverso una rappresentazione invernale. La carta nazionale 1:50'000 inverno è disponibile esclusivamente in formato digitale (Swiss Map Raster, API, WMTS).

spatial: Landeskarte 1:50'000

name: Bundesamt für Landestopografie swisstopo

fr: Office fédéral de topographie swisstopo

en: Federal Office of Topography swisstopo

it: Ufficio federale di topografia swisstopo

fr: Le centre de compétence de la Confédération suisse pour les informations géographiques, c'est-à-dire pour la description, la représentation et l’archivage de données à référence spatiale.

de: Das Kompetenzzentrum der Schweizerischen Eidgenossenschaft für Geoinformation, d.h. für die Beschreibung, Darstellung und Archivierung von raumbezogenen Geodaten.

en: The centre of competence for the Swiss Confederation responsible for geographical reference data, for instance the description, representation and archiving of geographic spatial data.

it: Il centro d’eccellenza della Confederazione Elvetica per geoinformazione, cioè per la descrizione, rappresentazione e archiviazione dei dati georeferenziati (geodati).

value: Swisstopo - Geocat Harvester

fr: Carte nationale 1:50'000 en représentation hivernale

de: Landeskarte 1:50'000 Winterdarstellung

en: National Map 1:50'000, winter representation

it: Carta nazionale 1:50'000 - rappresentazione invernale


en: Environment

de: gelandedarstellung skitour landeskarte verkehrsnetz geobasisdienst relief vegetation gewasser kartographie grenze geografische-bezeichnungen bgdi-bundesgeodaten-infrastruktur topografie karte

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

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

fr: Aperçu map.geo.admin.ch

en: Preview map.geo.admin.ch

it: Previsione map.geo.admin.ch

fr: Swiss Map Raster 100 Hiver

de: Swiss Map Raster 100 Winter

en: Swiss Map Raster 100 Winter

it: Swiss Map Raster 100 Inverno

fr: La carte nationale 1:100'000 en représentation hivernale est un dérivé de la carte nationale topographique 1:100'000 avec la même représentation détaillée du réseau routier, des localités, du terrain, de l'hydrographie et de la végétation. La Suisse est intégralement représentée en version hivernale. La carte nationale 1:100'000 hiver n'est disponible qu'en version numérique (Swiss Map Raster, API, WMTS).

de: Die Landeskarte 1:100'000 Winter ist eine Ableitung der topografischen Landeskarte 1:100'000 der Schweiz mit detaillierter Verkehrs-, Siedlungs-, Gelände-, Gewässer- und Vegetationsdarstellung. Die Schweiz wird mittels einer winterlichen Darstellung flächendeckend abgebildet. Die Landeskarte 1:100'000 Winter ist ausschliesslich in digitaler Form (Swiss Map Raster, API, WMTS) verfügbar.

en: The winter national map 1:100'000 is a derivation of the topographical national map 1:100'000 of Switzerland with detailed depictions of traffic, settlement areas, terrain, bodies of water and vegetation. Switzerland is comprehensively mapped in a winter representation. The winter national map 1:100'000 is only available in a digital format (Swiss Map Raster, API, WMTS).

it: La carta nazionale 1:100'000 inverno è una derivazione della carta nazionale topografica 1:100'000 della Svizzera con una rappresentazione dettagliata della rete viaria, degli insediamenti, del terreno, dei corsi d'acqua e della vegetazione. La Svizzera viene riprodotta in modo capillare attraverso una rappresentazione invernale. La carta nazionale 1:100'000 inverno è disponibile esclusivamente in formato digitale (Swiss Map Raster, API, WMTS).

spatial: Landeskarte 1:100'000

de: Bundesamt für Landestopografie swisstopo

fr: Carte nationale 1:100'000 en représentation hivernale

de: Landeskarte 1:100'000 Winterdarstellung

en: National Map 1:100'000, winter representation

it: Carta nazionale 1:100'000 - rappresentazione invernale


maintainer: Bundesamt für Energie BFE

fr: bilan-electricite ogd51-bfe production-nette production-d-electricite statistique-electricite energie-hydraulique consommation-de-courant saldo-import energies-renouvelables consommation-nationale pompes-d-accumulation energie-atomique production-d-electricite-thermique

de: stromverbrauch thermische-stromproduktion landesverbrauch elektrizitaetsbilanz nettoerzeugung wasserkraft kernkraft ogd51-bfe elektrizitaetsstatistik importsaldo speicherpumpen stromproduktion erneuerbare-energien

it: produzione-netta produzione-d-elettricita energie-rinnovabili saldo-importazioni pompe-d-accumulo consumo-nazionale bilancio-elettricita energia-idrica statistica-elettricita produzione-d-elettricita-termica ogd51-bfe energia-atomica consumo-di-corrente

fr: Statistiques heddomadaires: production et consommation d'énergie électrique en Suisse

it: Bilancio dell'elettricità: statistica settimanale - produzione e consumo di energia elettrica in Svizzera

author: Bundesamt für Energie BFE

label: BFE - Elektrizitätsstatistik

mimetype: SERVICE

fr: Cette statistique, élaborée chaque mercredi, recense la production, l'importation, l'exportation et la consommation d'électricité en Suisse. Elle présente la production hydraulique (centrales au fil de l'eau/centrales à accumulation), la production des centrales nucléaires ainsi que la production thermique classique et d'autres modes de production. Elle indique aussi la consommation des installations de pompage d'accumulation. Elle est établie dans le cadre de la Statistique de l'électricité, qui fait partie de la statistique publique de la Suisse (base légale: LSF).

de: Die Wochenstatistik Elektrizitätsbilanz - Erzeugung und Abgabe elektrischer Energie in der Schweiz gibt Auskunft über Produktion, Import / Export und Verbrauch von Elektrizität in der Schweiz jeweils am Mittwoch auf wöchentlicher Basis. Bei der Produktion werden die Hydrologische Erzeugung (Lauf- und Speicherwerke), die Erzeugung der Kernkraftwerke sowie die Konv.-therm. und andere Erzeugung unterschieden. Der Verbrauch der Speicherpumpen wird ausgewiesen. Die Wochenstatistik Elektrizitätsbilanz wird im Rahmen der Elektrizitätsstatistik erhoben, welche Teil der öffentlichen Statistik der Schweiz ist (gesetzliche Grundlage: BStatG).

bilan-electricite

display_name: bilan-electricite

bilancio-elettricita

display_name: bilancio-elettricita

consommation-de-courant

display_name: consommation-de-courant

consommation-nationale

display_name: consommation-nationale

consumo-di-corrente

display_name: consumo-di-corrente

consumo-nazionale

display_name: consumo-nazionale

elektrizitaetsbilanz

display_name: elektrizitaetsbilanz

elektrizitaetsstatistik

display_name: elektrizitaetsstatistik

energia-atomica

display_name: energia-atomica

energia-idrica

display_name: energia-idrica

energie-atomique

display_name: energie-atomique

energie-hydraulique

display_name: energie-hydraulique

energie-rinnovabili

display_name: energie-rinnovabili

energies-renouvelables

display_name: energies-renouvelables

erneuerbare-energien

display_name: erneuerbare-energien

importsaldo

display_name: importsaldo

kernkraft

display_name: kernkraft

landesverbrauch

display_name: landesverbrauch

nettoerzeugung

display_name: nettoerzeugung

pompe-d-accumulo

display_name: pompe-d-accumulo

pompes-d-accumulation

display_name: pompes-d-accumulation

production-d-electricite

display_name: production-d-electricite

production-d-electricite-thermique

display_name: production-d-electricite-thermique

production-nette

display_name: production-nette

produzione-d-elettricita

display_name: produzione-d-elettricita

produzione-d-elettricita-termica

display_name: produzione-d-elettricita-termica

produzione-netta

display_name: produzione-netta

saldo-import

display_name: saldo-import

saldo-importazioni

display_name: saldo-importazioni

speicherpumpen

display_name: speicherpumpen

statistica-elettricita

display_name: statistica-elettricita

statistique-electricite

display_name: statistique-electricite

stromproduktion

display_name: stromproduktion

stromverbrauch

display_name: stromverbrauch

thermische-stromproduktion

display_name: thermische-stromproduktion

wasserkraft

display_name: wasserkraft

ener

fr: Énergie

de: Energie

en: Energy

it: Energia

gove

fr: Gouvernement et secteur public

de: Regierung und öffentlicher Sektor

en: Government and public sector

it: Governo e settore pubblico

econ

fr: Économie et finances

bundesamt-fur-energie-bfe

fr: L'Office fédéral de l'énergie (OFEN) est le centre de compétences du Département fédéral de l'environnement, des transports, de l'énergie et de la communication (DETEC) pour les questions liées à l'approvisionnement en énergie et à son utilisation.

de: Das Bundesamt für Energie (BFE) ist das Kompetenzzentrum für Fragen der Energieversorgung und der Energienutzung im Eidgenössischen Departement für Umwelt, Verkehr, Energie und Kommunikation (UVEK).

en: The Swiss Federal Office of Energy (SFOE) is the country's competence centre for issues relating to energy supply and energy use at the Federal Department of the Environment, Transport, Energy and Communications (DETEC).

it: L'Ufficio federale dell'energia (UFE) è il centro di competenza in materia di approvvigionamento e di impiego dell'energia in seno al Dipartimento federale dell'ambiente, dei trasporti, dell'energia e delle comunicazioni (DATEC).


regi

fr: Régions et villes

de: Regionen und Städte

it: Regioni e città

envi

fr: Environnement

de: Umwelt

it: Ambiente

de: gelandedarstellung skitour landeskarte verkehrsnetz geobasisdienst grenze relief kartographie vegetation gewasser geografische-bezeichnungen bgdi-bundesgeodaten-infrastruktur topografie karte

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

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

fr: Swiss Map Raster 10 Hiver

de: Swiss Map Raster 10 Winter

en: Swiss Map Raster 10 Winter

it: Swiss Map Raster 10 Invernoi

fr: La carte nationale 1:10'000 en représentation hivernale est un dérivé de la carte nationale topographique 1:10'000 avec la même représentation détaillée du réseau routier, des localités, du terrain, de l'hydrographie et de la végétation. La Suisse est intégralement représentée en version hivernale. La carte nationale 1:10'000 hiver n'est disponible qu'en version numérique (Swiss Map Raster, API, WMTS).

de: Die Landeskarte 1:10'000 Winter ist eine Ableitung der topografischen Landeskarte 1:10'000 der Schweiz mit detaillierter Verkehrs-, Siedlungs-, Gelände-, Gewässer- und Vegetationsdarstellung. Die Schweiz wird mittels einer winterlichen Darstellung flächendeckend abgebildet. Die Landeskarte 1:10'000 Winter ist ausschliesslich in digitaler Form (Swiss Map Raster, API, WMTS) verfügbar.

en: The winter national map 1:10'000 is a derivation of the topographical national map 1:10'000 of Switzerland with detailed depictions of traffic, settlement areas, terrain, bodies of water and vegetation. Switzerland is comprehensively mapped in a winter representation. The winter national map 1:10'000 is only available in a digital format (Swiss Map Raster, API, WMTS).

it: La carta nazionale 1:10'000 inverno è una derivazione della carta nazionale topografica 1:10'000 della Svizzera con una rappresentazione dettagliata della rete viaria, degli insediamenti, del terreno, dei corsi d'acqua e della vegetazione. La Svizzera viene riprodotta in modo capillare attraverso una rappresentazione invernale. La carta nazionale 1:10'000 inverno è disponibile esclusivamente in formato digitale (Swiss Map Raster, API, WMTS).

spatial: Landeskarte 1:10'000

fr: Carte nationale 1:10'000 en représentation hivernale

de: Landeskarte 1:10'000 Winterdarstellung

en: National Map 1:10'000, winter representation

it: Carta nazionale 1:10'000 - rappresentazione invernale


regi

en: Regions and cities

de: gelandedarstellung skitour landeskarte verkehrsnetz geobasisdienst grenze relief gewasser kartographie vegetation geografische-bezeichnungen bgdi-bundesgeodaten-infrastruktur topografie karte

fr: Aperçu map.geo.admin.ch

de: Vorschau map.geo.admin.ch

fr: Swiss Map Raster 200 Hiver

de: Swiss Map Raster 200 Winter

en: Swiss Map Raster 200 Winter

it: Swiss Map Raster 200 Inverno

fr: Informations sur le produit et téléchargement

de: Produktinformation und Download

en: Product information and download

it: Informazioni sul prodotto e download

fr: La carte nationale 1:200'000 en représentation hivernale est un dérivé de la carte nationale topographique 1:200'000 avec la même représentation détaillée du réseau routier, des localités, du terrain, de l'hydrographie et de la végétation. La Suisse est intégralement représentée en version hivernale. La carte nationale 1:200'000 hiver n'est disponible qu'en version numérique (Swiss Map Raster, API, WMTS).

de: Die Landeskarte 1:200'000 Winter ist eine Ableitung der topografischen Landeskarte 1:200'000 der Schweiz mit detaillierter Verkehrs-, Siedlungs-, Gelände-, Gewässer- und Vegetationsdarstellung. Die Schweiz wird mittels einer winterlichen Darstellung flächendeckend abgebildet. Die Landeskarte 1:200'000 Winter ist ausschliesslich in digitaler Form (Swiss Map Raster, API, WMTS) verfügbar.

en: The winter national map 1:200'000 is a derivation of the topographical national map 1:200'000 of Switzerland with detailed depictions of traffic, settlement areas, terrain, bodies of water and vegetation. Switzerland is comprehensively mapped in a winter representation. The winter national map 1:200'000 is only available in a digital format (Swiss Map Raster, API, WMTS).

it: La carta nazionale 1:200'000 inverno è una derivazione della carta nazionale topografica 1:200'000 della Svizzera con una rappresentazione dettagliata della rete viaria, degli insediamenti, del terreno, dei corsi d'acqua e della vegetazione. La Svizzera viene riprodotta in modo capillare attraverso una rappresentazione invernale. La carta nazionale 1:200'000 inverno è disponibile esclusivamente in formato digitale (Swiss Map Raster, API, WMTS).

spatial: Landeskarte 1:200'000

fr: Office fédéral de topographie swisstopo

it: Ufficio federale di topografia swisstopo

fr: Le centre de compétence de la Confédération suisse pour les informations géographiques, c'est-à-dire pour la description, la représentation et l’archivage de données à référence spatiale.

de: Das Kompetenzzentrum der Schweizerischen Eidgenossenschaft für Geoinformation, d.h. für die Beschreibung, Darstellung und Archivierung von raumbezogenen Geodaten.

it: Il centro d’eccellenza della Confederazione Elvetica per geoinformazione, cioè per la descrizione, rappresentazione e archiviazione dei dati georeferenziati (geodati).

fr: Carte nationale 1:200'000 en représentation hivernale

de: Landeskarte 1:200'000 Winterdarstellung

en: National Map 1:200'000, winter representation

it: Carta nazionale 1:200'000 - rappresentazione invernale


envi

en: Environment

de: gelandedarstellung skitour landeskarte verkehrsnetz geobasisdienst grenze vegetation relief gewasser kartographie geografische-bezeichnungen bgdi-bundesgeodaten-infrastruktur topografie karte

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

it: Previsione map.geo.admin.ch

fr: Swiss Map Raster 25 Hiver

de: Swiss Map Raster 25 Winter

en: Swiss Map Raster 25 Winter

it: Swiss Map Raster 25 Inverno

fr: La carte nationale 1:25'000 en représentation hivernale est un dérivé de la carte nationale topographique 1:25'000 avec la même représentation détaillée du réseau routier, des localités, du terrain, de l'hydrographie et de la végétation. La Suisse est intégralement représentée en version hivernale. La carte nationale 1:25'000 hiver n'est disponible qu'en version numérique (Swiss Map Raster, API, WMTS).

de: Die Landeskarte 1:25'000 Winter ist eine Ableitung der topografischen Landeskarte 1:25'000 der Schweiz mit detaillierter Verkehrs-, Siedlungs-, Gelände-, Gewässer- und Vegetationsdarstellung. Die Schweiz wird mittels einer winterlichen Darstellung flächendeckend abgebildet. Die Landeskarte 1:25'000 Winter ist ausschliesslich in digitaler Form (Swiss Map Raster, API, WMTS) verfügbar.

en: The winter national map 1:25'000 is a derivation of the topographical national map 1:25'000 of Switzerland with detailed depictions of traffic, settlement areas, terrain, bodies of water and vegetation. Switzerland is comprehensively mapped in a winter representation. The winter national map 1:25'000 is only available in a digital format (Swiss Map Raster, API, WMTS).

it: La carta nazionale 1:25'000 inverno è una derivazione della carta nazionale topografica 1:25'000 della Svizzera con una rappresentazione dettagliata della rete viaria, degli insediamenti, del terreno, dei corsi d'acqua e della vegetazione. La Svizzera viene riprodotta in modo capillare attraverso una rappresentazione invernale. La carta nazionale 1:25'000 inverno è disponibile esclusivamente in formato digitale (Swiss Map Raster, API, WMTS).

spatial: Landeskarte 1:25'000

de: Bundesamt für Landestopografie swisstopo

en: Federal Office of Topography swisstopo

en: The centre of competence for the Swiss Confederation responsible for geographical reference data, for instance the description, representation and archiving of geographic spatial data.

fr: Carte nationale 1:25'000 en représentation hivernale

de: Landeskarte 1:25'000 Winterdarstellung

en: National Map 1:25'000, winter representation

it: Carta nazionale 1:25'000 - rappresentazione invernale


regi

de: Regionen und Städte

de: Umwelt

fr: Lien vers le téléchargement de données (Zip)

de: Link zum Daten-Download (Zip)

fr: Ce géoproduit représente le maillage triangulaire BEENyx15, qui a été élaboré dans le cadre du projet 'Nouvelles coordonnées MN95'. Avec BEENyx15, des géodonnées peuvent être transformées de manière précise de MN03 vers MN95 et inversement. Les maillages triangulaires sont définis à partir de points d'appui pour la transformation (PAT). BEENyx15 repose sur le maillage triangulaire national CHENyx06. Ce dernier a été densifié localement de manière à ce que les données puissent être redressées lors de la transformation. D'autre part, une autre couche montre sous forme de vecteurs la différence planimétrique entre des points transformés avec BEENyx15 et ces mêmes points transformés avec CHENyx06.

de: Dargestellt wird die kantonale Dreiecksvermaschung BEENyx15, die speziell für das Projekt 'Neue Koordinaten LV95' entwickelt wurde. Mit BEENyx15 können Geodaten präzise von LV03 nach LV95 und umgekehrt transformiert werden. Bei den Eckpunkten der einzelnen Dreiecke handelt es sich um sogenannte Transformationsstützpunkte (TSP). BEENyx15 beruht auf der nationalen Dreiecksvermaschung CHENyx06. Um gleichzeitig mit der Transformation lokale Spannungen zu beseitigen wurde letzterer lokal verdichtet. In einer zusätzlichen Ebene wird mittels Differenzvektoren aufgezeigt, wie sich die Lage von Punkten unterscheidet, wenn diese einmal mit BEENyx15 und einmal mit CHENyx06 transformiert worden sind.

spatial: Kanton Bern (erweitert)

fr: Office de l'information géographique du canton de Berne

de: Amt für Geoinformation des Kantons Bern

fr: L'Office de la géoinformation est le centre de compétence pour l'information spatiale dans le canton de Berne. Les géodonnées cantonales peuvent être utilisées gratuitement, sous réserve des conditions d'utilisation. Le Géoportail du canton de Berne est le point d'entrée central pour les cartes interactives, les géodonnées et les géoservices de l'administration cantonale.

de: Das Amt für Geoinformation ist das Kompetenzzentrum für räumliche Informationen im Kanton Bern. Die kantonalen Geodaten sind unter Einhaltung der Nutzungsbedingungen kostenlos nutzbar. Das Geoportal des Kantons Bern ist der zentrale Einstiegspunkt für interaktive Karten, Geodaten und Geodienste der kantonalen Verwaltung.

value: Bern-Kt - Geocat Harvester

fr: Nouvelles coordonnées MN95

de: Neue Koordinaten LV95


ov-linien

fr: Régions et villes

tran

fr: Transports

de: Verkehr

en: Transport

it: Trasporti

title_for_slug: ov-linien

de: bus poi tram zug ov offentlicher-verkehr

fr: Map (Preview)

de: Map (Preview) Stadtplan

en: Map (Preview)

it: Map (Preview)

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

de: WMS OeV_Linien

de: Web Map Services WMS

de: WFS OeV_Linien

de: Web Feature Services WFS

de: ÖV-Linien in der Stadt Bern und Agglomeration.

spatial: Agglomeration Bern

geoinformation-der-stadt-bern

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

de: Geoinformation Stadt Bern

en: The Land Surveying Office of the City of Bern

it: L'ufficio del catasto nella Città di Berna

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

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

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

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

de: ÖV-Linien


wildhuteraufsichtskreise

regi

de: Regionen und Städte

it: Regioni e città

envi

fr: Environnement

de: Umwelt

it: Ambiente

title_for_slug: wildhuteraufsichtskreise

fr: chasse animal-sauvage limite-administrative bordure

de: jagd wildtier verwaltungsgrenze grenze

de: Link zum Daten-Download (Zip)

fr: Représentation des limites des arrondissements de surveillance de l'inspection de la chasse attribués au garde-faune. Ces limites sont d'abord construites sur les limites politiques et adaptées ensuite, si nécessaire, aux circonstances locales. Le garde-faune d'un arrondissement surveille à la fois la chasse et la gestion de la faune sauvage.

de: Darstellung der Grenzen der Wildhüteraufsichtskreise des Jagdinspektorates. Die Grenzen orientieren sich primär an den politischen Grenzen und sekundär an den örtlichen Gegebenheiten. Der für einen Kreis zuständige Wildhüter übt die Jagd- und Wildtieraufsicht aus.

spatial: Kanton Bern

fr: Secteurs de surveillance de l'insp. de la chasse

de: Wildhüteraufsichtskreise


gewasserraum-differenz-ur

title_for_slug: gewasserraum-differenz-ur

label: Web GIS Client des Kanton Uri. Die Daten können direkt via dem Portal in den unterschiedlichsten Datenformaten heruntergeladen werden.

fr: WMS-Dienst des Kanton Uri

de: WMS-Dienst des Kanton Uri

en: WMS-Dienst des Kanton Uri

it: WMS-Dienst des Kanton Uri

fr: WFS-Dienst des Kanton Uri

de: WFS-Dienst des Kanton Uri

en: WFS-Dienst des Kanton Uri

it: WFS-Dienst des Kanton Uri

fr: Ab der öffentlichen Auflage werden folgende Änderungen des Gewässerraums (abgeleitet aus der Nutzungsplanung) und übergangsweise die Gewässerbaulinien (aus der Nutzungsplanung) dargestellt: Neu erfasste Objekte / Gelöschte Objekte

spatial: Canton of Uri (UR)

geoinformation-kanton-uri

de: Lisag AG, Geoinformation Kanton Uri

de: Die Lisag AG bewirtschaftet das geografische Informationssystem des Kantons Uri (GIS Uri), ist die verantwortliche Stelle für den ÖREB-Kataster Uri und zudem zuständig für die Durchführung der amtlichen Vermessung auf Urner Kantonsgebiet.

value: Uri-Kt - Geocat Harvester

fr: Gewässerraum Differenz (UR)

de: Gewässerraum Differenz (UR)

en: Gewässerraum Differenz (UR)

it: Gewässerraum Differenz (UR)


digitales-hohenmodell-lidar-zusatzdaten

regi

en: Regions and cities

title_for_slug: digitales-hohenmodell-lidar-zusatzdaten

fr: laser altitude bois

de: laser hohe wald

fr: Lien vers le téléchargement de données (Zip)

fr: Données supplémentaires sur les trajectoires de vol et sur les densités de points des mesures LIDAR (LBEROH) (https://www.agi.dij.be.ch/fr/start/geoportal/geodaten/detail.html?type=geoproduct&code=LBEROH) pour la création des modèles d'altitude Modèle numérique de terrain LIDAR 50cm (https://www.agi.dij.be.ch/fr/start/geoportal/geodaten/detail.html?type=geoproduct&code=LDTM50CM) et Modèle numérique de surface LIDAR 50cm (https://www.agi.dij.be.ch/de/start/geoportal/geodaten/detail.html?type=geoproduct&code=LDOM50CM).

de: Zusätzliche Daten zu den Flugwegen und zu den Punktdichten der LIDAR-Messungen (LBEROH) (https://www.agi.dij.be.ch/de/start/geoportal/geodaten/detail.html?type=geoproduct&code=LBEROH) für die Erstellung der Höhenmodelle Digitales Terrainmodell LIDAR 50cm (https://www.agi.dij.be.ch/de/start/geoportal/geodaten/detail.html?type=geoproduct&code=LDTM50CM) und Digitales Oberflächenmodell LIDAR 50cm (https://www.agi.dij.be.ch/de/start/geoportal/geodaten/detail.html?type=geoproduct&code=LDOM50CM).

de: Amt für Geoinformation des Kantons Bern

de: Das Amt für Geoinformation ist das Kompetenzzentrum für räumliche Informationen im Kanton Bern. Die kantonalen Geodaten sind unter Einhaltung der Nutzungsbedingungen kostenlos nutzbar. Das Geoportal des Kantons Bern ist der zentrale Einstiegspunkt für interaktive Karten, Geodaten und Geodienste der kantonalen Verwaltung.

fr: Modèle altimétrique numér. LIDAR données supplémentaires

de: Digitales Höhenmodell LIDAR Zusatzdaten