en:
License: GPL-v2
The R script presents an advanced sampling approach for monitoring biodiversity on agricultural land by combining multiple objectives and integrating environmental and geographic space.
The example demonstrates the first-stage selection of squares (km2) in the ALL-EMA sampling design using modern sampling techniques such as unequal probability sampling with fixed sample size, balanced sampling, stratified balancing and geographic spreading. Sampling is done with unequal probabilities and weights defined by power allocation to give equal weight to extrapolations to the total agricultural area of Switzerland and two stratifications of predefined interest (regions and agricultural production zones). Calibration is used to limit the distribution of the sampling weights. The sample sizes are almost fixed within the strata and evenly distributed across the years of a temporal rotation plan, which is favourable for the organisation of the field survey. Sampling also ensures an optimal (annual) distribution across geographic space, including altitude. Despite the complexity of the sampling, estimation based on probability theory is straightforward.
Ecker, K.T., Meier, E.S. & Tillé, Y. 2023. Integrating spatial and ecological information into comprehensive biodiversity monitoring on agricultural land. Environmental Monitoring and Assessment 195.

en: multi-stage unequal-probability habitat balanced-sampling vegetation

envidat

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

en: R script and input data for "ALL-EMA sampling design"

envi

en: Environment

en:
Resource Title: data
    Size: 22605 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/0b303fb7-17fc-4910-af8f-dbee54c0d093/resource/89264e93-545c-4794-b6eb-46247ec92310/download/s0.csv
    Description:

en:
Resource Title: R script
    Size: 10670 bytes
    Format: text/x-r
    URL: https://www.envidat.ch/dataset/0b303fb7-17fc-4910-af8f-dbee54c0d093/resource/2fa8e0c9-acbb-4a25-bb8e-79c76908a3b7/download/sample_selection_s1.r
    Description:

balanced-sampling

display_name: balanced-sampling

habitat

display_name: habitat

multi-stage

display_name: multi-stage

unequal-probability

display_name: unequal-probability

vegetation

display_name: vegetation


$oid: OBSOLETE

en:
This dataset contains the raw environmental DNA data associated with the publication *Environmental drivers of eukaryotic plankton and fish biodiversity in an Arctic fjord* in the journal Polar Biology (2023).
 Methods

**Sampling**
We sampled the Lilliehöök fjord on the west coast of Spitsbergen (Svalbard, Norway) over 3 days from 3 to 5 of August 2021. Samples were taken from the glacier front up to the fjord mouth of the Krossfjorden system, around 30 km long, after the Lilliehöök fjord merged with the mouth of Möller fjord. The fjord’s maximum depth has been recorded at 373 m (Svendsen et al. 2002) and has no sill at its entrance, thereby facilitating water exchange with the open ocean of the West Spitsbergen Current. We used a research vessel to sample 5 sites for a total of 15 samples, sampling 3 depths per site (3-m, chlorophyll a maximum and 85-m, unless sea floor was shallower). Shallow and intermediate samples between 3-m and 12-m represent ~35-L of water filtered in-situ using long tubing and a peristaltic pump, and all other deeper samples were taken from a total of 3 Niskin bottles (General Oceanics), representing 22-L of water sampled per sample. Water was filtered through a VigiDNA filtration capsule (SPYGEN) with a 0.20-µm pore size using an Athena peristaltic pump (Proactive Environmental Products, Bradenton, Florida) with a flow rate of ~1-L/min. Each sample was handled with single use tubing and gloves.
**Molecular**
To perform the amplification, we used two sets of primers: teleo (forward: ACACCGCCCGTCACTCT, reverse: CTTCCGGTACACTTACCATG; Valentini et al. 2016) and the universal eukaryotic 1389F/1510R primer pair, amplifying the V9-18S rDNA gene (Amaral-Zettler et al. 2009) (forward: TTGTACACACCGCCC, reverse: CCTTCYGCAGGTTCACCTAC).
 Data content:
+ Metabarcoding data: This zip file contains the 2 sequencing libraries filtered to only retain the samples used in the present study.
+ Code, data and figure: This zip file contains all data and code to reproduce the figures and the analysis in the study, with an associated README explaining the content of each folder.
 Additional informations
For more details, please see the Methods in the associated publication: DOI: 10.1007/s00300-023-03187-9.

en: biodiversity marine plankton fish environmental-dna fjord dna-metabarcoding

envidat

approval_status: approved

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.

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.

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

fr: EnviDat: le portail de données environnementales

it: EnviDat: il portale dei dati ambientali

political_level: other

state: active

type: organization

wsl

capacity: public

de: EnviDat

en: Data: Environmental drivers of eukaryotic plankton and fish biodiversity in an Arctic fjord

type: dataset

key: guid

key: uri

value: Envidat Harvester

envi

de: Umwelt

fr: Environnement

it: Ambiente

en:
Resource Title: Code, data and figures
    Size: 63570143 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/2a7f749a-430f-443b-bbac-073b166c2361/resource/ce6111f5-09b4-4d7a-8a5d-21529c2da40b/download/code_data_figures.zip
    Description: Here there are all the data and code to reproduce the figures and analysis presented in the paper, with a README.md file

format: ZIP

media_type: application/zip

en:
Resource Title: Metabarcoding data
    Size: 621901827 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/2a7f749a-430f-443b-bbac-073b166c2361/resource/1e6638c8-fb7e-4049-8fee-effa8d64177b/download/metabarcoding-data.zip
    Description: Raw metabarcoding data for both primers (teleo and 18S) and corresponding file to perform the demultiplexing and primer trimming

biodiversity

display_name: biodiversity

dna-metabarcoding

display_name: dna-metabarcoding

environmental-dna

display_name: environmental-dna

fish

display_name: fish

fjord

display_name: fjord

marine

display_name: marine

plankton

display_name: plankton


en:
The data set contained in this repository was used in the analysis by Mayer et al. (2023):
Mayer, S. I., Techel, F., Schweizer, J., and van Herwijnen, A.: Prediction of natural dry-snow avalanche activity using physics-based snowpack simulations, EGUsphere, https://doi.org/10.5194/egusphere-2023-646, 2023.

en: avalanche machine-learning avalanche-size snowpack-model natural-avalanches

en: Data-set for prediction of natural dry-snow avalanche activity and avalanche size using physics-based snowpack simulations

en:
Resource Title: dataForValidationDavos.csv
    Size: 74355 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/28660c6d-68ce-484d-a7d7-24d7a612f392/resource/38836706-8b98-47a5-9f94-a3b5e88553e2/download/davos_validation.csv
    Description: Data set AV3 was used for validation of the avalanche-day classifier (Sections 2.1.3 and 3.3(1) in Mayer et al.).

format: XLS

en:
Resource Title: dataForValidationWithDangerLevels.csv
    Size: 34614625 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/28660c6d-68ce-484d-a7d7-24d7a612f392/resource/fac61ade-ba4c-40bc-bc90-f47c763e153c/download/dataforvalidationwithdangerlevels.csv
    Description: Data set DL was used for validation of the avalanche-day classifier and the avalanche-size estimator. The data set contains the quality-checked danger levels merged with the respective SNOWPACK simulations (Sections 2.3 and 3.3(2) in Mayer et al.).

en:
Resource Title: dataForAvalancheDayModelDevelopment.csv
    Size: 1584694 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/28660c6d-68ce-484d-a7d7-24d7a612f392/resource/961c07d9-5f7e-4c7b-9073-e2b31b6dab28/download/dataforavalanchedaymodeldevelopment.csv
    Description: Data set AV1 was used for training the avalanche-day classifier (Sections 2.1.1, 2.2, and 3.1 in Mayer et al.).

en:
Resource Title: dataForAvalancheSizeModelDevelopment.csv
    Size: 423741 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/28660c6d-68ce-484d-a7d7-24d7a612f392/resource/49e9e29b-683a-4784-a3ab-0c02d2e0902b/download/dataforavalanchesizemodeldevelopment.csv
    Description: Data set AV2 used for training the avalanche-size estimator (Sections 2.1.2 and 3.2 in Mayer et al.)

avalanche

display_name: avalanche

avalanche-size

display_name: avalanche-size

machine-learning

display_name: machine-learning

natural-avalanches

display_name: natural-avalanches

snowpack-model

display_name: snowpack-model


en:
Summary
* Dataset of daily inflow to Luzzone reservoir in Ticino, Switzerland
* R scripts used to generate return levels for low reservoir inflow, low precipitation, high inflow, and extreme high precipitation based on various methods from extreme value analysis
 Data
The dataset included here is the "natural" reservoir inflow for the Luzzone reservoir.
Additional analyses were conducted on daily total precipitation of 6 meteorological stations (abbreviations: TIOLI, TIOLV, COM, VRN, VLS, ZEV). These precipitation data are freely available for teaching and research from the MeteoSwiss IDAweb portal (https://www.meteoswiss.admin.ch/services-and-publications/service/weather-and-climate-products/data-portal-for-teaching-and-research.html).
 Codes
R scripts used to determine return levels of the data set are included for both extreme high events and low events. The scripts include the following methods for calculating return levels:
* GEV (Generalized Extreme Value)
* GPD and GPDd (Generalized Pareto Distribution including declustered version)
* eGPD (extended Generalized Pareto Distribution)
* MEV (Metastatistical Extreme Value)

en: return-periods extreme-values low-flow reservoir-inflow alpine-precipitation

envidat

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

en: Data and Code on Extreme Inflow and Lowflow Analysis for Alpine Reservoirs

en:
Resource Title: R codes for Extreme Values Analysis
    Size: 35946 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/b138fae4-b287-4af6-b666-ef860ac593ff/resource/6647b3fe-62a5-461b-8b22-be7bcdfb51be/download/r_codes.zip
    Description: Zip file containing R codes for running extreme value analyses for high precipitation and inflow events as well as code for determining return levels of low inflow and low precipitation events

en:
Resource Title: Daily Luzzone Reservoir Inflow 01-01-2000 to 31-12-2019
    Size: 261834 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/b138fae4-b287-4af6-b666-ef860ac593ff/resource/f3b117de-ba26-42dd-b958-e46e826222e4/download/luz.csv
    Description: Daily total water inflow to Luzzone reservoir. Natural inflow (LUZ_inflow_raw) is back-calculated based on daily water level in reservoir (removing hydropower inputs and spills). Linear interpolation between daily values is used to bring all inflow values into positive space (LUZ_inflow_intrp).

format: CSV

media_type: text/csv

alpine-precipitation

display_name: alpine-precipitation

extreme-values

display_name: extreme-values

low-flow

display_name: low-flow

reservoir-inflow

display_name: reservoir-inflow

return-periods

display_name: return-periods


en:
The files correspond to the data and R-script used for the analyses of the following paper "Feasting on the ordinary or starving for the exceptional: phenological synchrony between spongy moth and budburst of European trees in a warmer climate" published in Ecology and Evolution by Vitasse et al. 2023.
There are three zip files corresponding to the Temperature data, phenology/preference/performance tests and R-Scripts for the analyses.
Input data:
'Synchrony_Cuttings_Pheno.txt':

en: budburst chilling hatch spongy-month phenology

envidat

en: EnviDat: the Environmental Data Portal

en: Synchrony between spongy moth hatching and leaf phenology of temperate trees

envi

en: Environment

en:
Resource Title: Phenology and performance/preference tests Data
    Size: 7058 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/31166a90-ab95-46c4-b651-4f7d474e66a7/resource/464b7325-15da-49ed-8e50-9c1be08a3d20/download/phenology_performance_preference-tests_data.zip
    Description: -"Synchronex_Cuttings_Pheno.txt" provides the budburst date ('Stage 2') of each twig of the six studies species in the four different temperature treatments ("ambient", "a+5"=Ambient +5°C, 'Greenhouse-5'= Greenhouse at temperature >5°C, 'Greenhouse_10' = Greenhouse at temperature >10°C); 'Success' give the percentage of buds within the twig that succeeded to reach at least stage 2 during the experiment.

'Synchronex-Gypsy.txt' provides the number of eggs that have hatched at each monitoring date (Day of the year in columns); "DOY_hatch_1" corresponds to the first DOY when at least one egg has hatched; "Replicate_egg_mass" correspond to the egg mass replicate (1 to 5)

'PerformanceTest_GypsyMoth.txt' provides the biomass in mg of the caterpillars fed with fresh leaves of each tested tree species during one week. "Fresh_alive" is the biomass of only the living caterpillars after 1 week and "Freshpermoth" corresponds the weight per living caterpillar (Fresh alive/Nalive)

'PreferenceTest_All_species.txt' provides the total surface of leaf disc of each tested tree species remaining after exposing 5 caterpillars to these leaves during 24H. "Total_area" is the surface of the disc of each species measured after 24h exposed to the spongy moth first instars. For each species 'control' means the surface of the disc after 24H without any exposure to the larvae. 'EatenPercent correspond to the percentage surface loss compared to the control.

en:
Resource Title: Temperature data Spongy moth experiment
    Size: 84293 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/31166a90-ab95-46c4-b651-4f7d474e66a7/resource/3891fb6e-eb1d-4835-80bf-e8ae0c85164c/download/temperature.zip
    Description: 4 files with hourly temperature recorded at the height of buds in each of the four temperature treatments:
- Winter6: greenhouse where temperature was kept >6°C over winter
- Winter10: greenhouse where temperature was kept >10°C over winter
- OTC_Ambient: Open Top Chamber at Ambient temperature
- OTC_Warm: Open Top Chamber at +3.5°C warmer temperature

en:
Resource Title:
    Size: 19723 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/31166a90-ab95-46c4-b651-4f7d474e66a7/resource/6680e96d-e720-4e07-b844-ba19e2de693e/download/r-scripts.zip
    Description: 3 R-Scripts used for the analyses of the temperature, phenology and performance/preference tests

budburst

display_name: budburst

chilling

display_name: chilling

hatch

display_name: hatch

phenology

display_name: phenology

spongy-month

display_name: spongy-month


en:
Summary
This data set contains Python programming code and modeled data discussed in a related research article. We developed a simple isotope model to study the drivers of the particularly depleted vapour isotopic composition measured on the ship of the Antarctic Circumnavigation Expedition close to the outlet of the Mertz glacier, East Antarctica, in the 6-day period from 27 January 2017 to 1 February 2017. The model considers the stable water isotopologues H2(16O), H2(18O), and HD(16O). It uses data from the ERA5 reanalysis product with a spatial resolution of 0.25° x 0.25° (Hersbach et al., 2018) and 10-day backward trajectories for the location of the ship, published by Thurnherr et al. (2020a). Our data set includes the model code, Python scripts for visualizing the results, and data produced by the model including the results shown in the figures of the related research article. Here, we summarize the most important model characteristics while further details can be found in the readme.txt file and the related research article including its supporting information.
 Main model characteristics
The modeling approach consists of two steps called *Model Sublimation* and *Model Air Parcel*. The former estimates the isotopic compositions of the snow and sublimation flux across the Antarctic Ice Sheet using an Eulerian frame of reference while the latter models the vapour isotopic composition and specific humidity along air parcel trajectories using a Lagrangian frame of reference. The isotope effects of most phase changes are represented by equilibrium fractionation. Only for ocean evaporation, kinetic fractionation is additionally taken into account (original Craig-Gordon formula). For snow sublimation, two assumptions are tested: *Run E* assumes that sublimation is associated with equilibrium fractionation while *Run N* assumes that sublimation occurs without isotopic fractionation.
 Model Sublimation
Model Sublimation uses a simple one-dimensional mass-balance approach in each grid cell, considering snow accumulation due to snowfall and vapour deposition and snow ablation due to sublimation. The snowpack is represented by 100 layers of equal thickness (e.g., 1 cm) and density (350 kg m-3). The isotopic composition of snowfall is parameterized by generalizing a site-specific, empirical relationship between the daily mean air temperature and snowfall isotopic composition. In the case of vapour deposition, Model Sublimation assumes equilibrium fractionation and estimates the isotopic composition of the atmospheric vapour as the average value for two idealized situations: (i) locally sourced vapour which has the same isotopic composition as the sublimation flux; (ii) non-locally sourced vapour in isotopic equilibrium with snowfall. Model Sublimation is run with a time step of 1 h, independently of Model Air Parcel.
 Model Air Parcel
Every hour, an ensemble of trajectories arrives at different heights in the ABL above the ship. For each of these trajectories, we consider an air parcel with a constant volume of 1 x 1 x 1 m3. The air parcels are initialized at the first suitable time when the trajectories are located in the ABL, either over the ice-free ocean in conditions of evaporation or over snow (Antarctic Ice Sheet or sea ice). Subsequently, the masses of the water isotopologues in the air parcels are simulated with a time step of 3 h, considering vapour uptake or removal due to the moisture flux at the snow or liquid ocean surface (only if the parcel is in the ABL) and cloud/precipitation formation (if the saturation specific humidity is reached). Sea ice is taken into account in a very simplified way. We represent the sea ice by grid cells with a sea-ice cover of more than 90% and assume the isotopic composition of the sublimation flux to be identical to that in the nearest grid cell of the Antarctic Ice Sheet. The isotopic composition of the sublimation flux is taken from Model Sublimation whereas the isotopic composition of the vapour deposition flux (over snow) and condensation flux (over ice-free ocean) is simulated assuming an isotopic equilibrium with the air parcel. Isotope effects of cloud/precipitation formation are represented using the classic Rayleigh distillation model with equilibrium fractionation, where the cloud water is assumed to precipitate immediately after formation.
 References
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horanyi, A., Munoz Sabater, J.,... others (2018). *ERA5 hourly data on single levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS)*. doi: 10.24381/cds.bd0915c6
Thurnherr, I., Wernli, H., & Aemisegger, F. (2020a). *10-day backward trajectories from ECMWF analysis data along the ship track of the Antarctic Circumnavigation Expedition in austral summer 2016/2017*. Zenodo. doi: 10.5281/zenodo.4031705

en: snow-sublimation stable-water-isotopes isotopic-distillation backward-trajectories antarctica lagrangian-analysis evaporation

en: Modeled Isotopic Composition of Water Vapour Along Air Parcel Trajectories in the Antarctic

en:
Resource Title: Readme
    Size: 23594 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/99ad307f-be6f-48d3-ac80-387bcb0aded7/resource/8be31ec0-9d80-4995-be16-c6e1704144fb/download/readme.txt
    Description: Meta data

format: TXT

media_type: text/plain

en:
Resource Title: Python scripts for data visualization
    Size: 21542 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/99ad307f-be6f-48d3-ac80-387bcb0aded7/resource/acb8a9ee-2499-4892-a6ea-79d7381019d4/download/python_scripts_plotting.zip
    Description: Scripts for generating the figures of the related research article and generating the data files (csv) from the model output

en:
Resource Title: Model code
    Size: 38463 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/99ad307f-be6f-48d3-ac80-387bcb0aded7/resource/9d6a4ef1-c0fa-46bf-9479-43bbf1d13964/download/python_scripts_model.zip
    Description: Model Sublimation and Model Air Parcel (python scripts)

en:
Resource Title: Data
    Size: 2993639 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/99ad307f-be6f-48d3-ac80-387bcb0aded7/resource/6d387739-0552-425f-90ac-584c2f8330d0/download/data_processed.zip
    Description: Modeled data including the data shown in the figures of the related research article

antarctica

display_name: antarctica

backward-trajectories

display_name: backward-trajectories

evaporation

display_name: evaporation

isotopic-distillation

display_name: isotopic-distillation

lagrangian-analysis

display_name: lagrangian-analysis

snow-sublimation

display_name: snow-sublimation

stable-water-isotopes

display_name: stable-water-isotopes


en:
The fate of plastic in the environment is of global concern, because its production recently has
increased strongly and it accumulates in terrestrial and aquatic ecosystems. Although some knowledge on
its role in aquatic and terrestrial ecosystems was gained in the recent decade, hitherto very little is known
about the impact of micro and nanoplastics in forest ecosystems.
The aim of this pioneering project was to explore if nanoplastics are taken up by forest trees species
through leaves or roots. In greenhouse experiments, we exposed leaves or roots of seedling of two forest
trees species to solutions with highly 13C-labelled polystyrene nanoparticles (13C-nPS, 99 atom%) and
examined if they were incorporated in different above- and belowground tissues.
Treated part of the trees for both species showed significant 13C-enrichment, indicating that trees take up nanoparticles. However, the overall 13C signal strength in tissues that were not exposed to the 13C label remained low (Δδ13C-1‰) and was confined to a few seedlings, leaving it ambiguous whether nanoplastic transport occurs or not.
We acknowledge that the new method developed might be not sensitive enough to unequivocally detect
mechanisms of nanoplastic uptake and transport at environmentally realistic concentrations.

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

envidat

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

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

forest-trees

display_name: forest-trees

nanoplastic

display_name: nanoplastic

polystyrene

display_name: polystyrene

uptake

display_name: uptake


en:
The R script eemma.R, which implements Ensemble End-Member Mixing Analysis (EEMMA) to estimate source fractions in mixtures, exploiting information contained in time-series correlations among tracer time series.
A brief user's guide, a demonstration script, and a demonstration data set are also provided, to accompany Kirchner, J.W., Mixing models with multiple, overlapping, or incomplete end-members, quantified using time series of a single tracer, Geophysical Research Letters, 2023.
The user's guide is available for public use under Creative Commons CC-BY-SA.  Public use of the scripts is permitted under GNU General Public License 3 (GPL3); for details see https://www.gnu.org/licenses/

en: stable-isotopes eemma emma tracers end-member-mixing-analysis mixing-models

envidat

en: EnviDat: the Environmental Data Portal

en: eemma.R, an R script for Ensemble End-Member Mixing Analysis

en:
Resource Title: eemma.R
    Size: 46043 bytes
    Format: text/x-r
    URL: https://www.envidat.ch/dataset/48dff6cd-bfcb-4c5e-a30d-f909dc8d36d3/resource/af04514c-75bb-4fbb-a5d2-4b25fcadf32f/download/eemma.r
    Description: An R script that performs ensemble end-member mixing analysis

en:
Resource Title: eemma user's guide
    Size: 501164 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/48dff6cd-bfcb-4c5e-a30d-f909dc8d36d3/resource/8df18f5d-c902-4acc-a8ee-b94324a043a1/download/eemma-users-guide.pdf
    Description: A brief user's guide to EEMMA, with illustrations using a simple data set

format: PDF

media_type: application/pdf

en:
Resource Title: vignette data
    Size: 1659 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/48dff6cd-bfcb-4c5e-a30d-f909dc8d36d3/resource/f72873bb-b4f2-4cce-a408-8d53f54bdbec/download/vignette_data.txt
    Description: A simple data set for the vignette script

en:
Resource Title: eemma vignette script
    Size: 4457 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/48dff6cd-bfcb-4c5e-a30d-f909dc8d36d3/resource/a96e9b19-9fee-4b21-b36c-5f7f84a4fb37/download/eemma_vignette_script.r
    Description: An R script for the vignettes (simple data analysis demonstrations) shown in the user's guide

format: OCTET

eemma

display_name: eemma

emma

display_name: emma

end-member-mixing-analysis

display_name: end-member-mixing-analysis

mixing-models

display_name: mixing-models

stable-isotopes

display_name: stable-isotopes

tracers

display_name: tracers


en:
What are the research data files about: Raw data on perfomance (dry weight, development and mortality) of emerald ash borer larvae used in published bioassays. Raw data on ash dieback leasion lenghts. Raw data on untargeted and targeted specialized ash metabolites.
Which methods were used: Bioassays in greenhouses and climate chambers to collect data on emerald ash borer and ash dieback perfomance. Phytochemical analyses on ash phloem for quantifiying specialized metabolites.
When and where was the data created / extracted: Summer 2020-2021

en: defense-chemistry invasive-species cross-resistance fraxinus intraspecific-variation ash-dieback emerald-ash-borer

en: Raw data-Publication cross-resistance in ash - New Phytologist

envi

en: Environment

ash-dieback

display_name: ash-dieback

cross-resistance

display_name: cross-resistance

defense-chemistry

display_name: defense-chemistry

emerald-ash-borer

display_name: emerald-ash-borer

fraxinus

display_name: fraxinus

intraspecific-variation

display_name: intraspecific-variation

invasive-species

display_name: invasive-species


en:
The data in this repository was used for the calibration and validation of the
SWE2HS model in the following publication:
Aschauer, J., Michel, A., Jonas, T., & Marty, C. (2023). An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0. Geoscientific Model Development Discussions, 1-19. https://doi.org/10.5194/gmd-2022-258
Contains daily snow water equivalent and snow depth timeseries from stations in the European Alps.

en: model-calibration-data model-validation-data snow-density snow-water-equivalent snow-depth

en: SWE2HS model calibration and validation data

model-calibration-data

display_name: model-calibration-data

model-validation-data

display_name: model-validation-data

snow-density

display_name: snow-density

snow-depth

display_name: snow-depth

snow-water-equivalent

display_name: snow-water-equivalent


en:
This dataset includes the processed data of the glide-snow avalanche activity and dynamics on Dorfberg (Davos, Switzerland) covering seasons 2008/09 to 2021/22. This dataset was described in the research article:
Fees, A., van Herwijnen A., Altenbach, M., Lombardo, M., Schweizer, J.: Glide-snow avalanche characteristics at different time-scales extracted from time-lapse photography, Annals of Glaciology, 91
We extracted the dynamics of opening glide-cracks and the glide-snow avalanche activity from time-lapse photographs. Glide-snow avalanches were separated into surface and interface events using the liquid water content which was simulated with SNOWPACK at 10 virtual stations on Dorfberg.

en: glide-snow-avalanche snowpack-simulation avalanche-activity time-lapse-photography avalanche

envidat

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

en: Glide-snow avalanche activity on Dorfberg, Davos, Switzerland

en:
Resource Title: Glide distances for opening glide-cracks
    Size: 512266 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/d6c7a578-6317-49f3-8ba5-71f62b5b6610/resource/7ee75315-9399-48fe-b5b1-e3ef1d2f0774/download/metadata.zip
    Description: This folder contains all glide-cracks where glide-distances were extracted from time-lapse photographs. Glide-distances were calculated in x-direction (width) and y-direction (downslope, length) by georeferencing the images.

en:
Resource Title: Glide-snow avalanche activity
    Size: 88583 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/d6c7a578-6317-49f3-8ba5-71f62b5b6610/resource/0aca99e1-7b3d-492f-a7bb-2756e5b74bbd/download/events.csv
    Description: Table that contains all glide-snow avalanche events that were detected in time-lapse photographs (season 2008/09 to 2021/22). The columns are explained in the README.txt

en:
Resource Title: SNOWPACK simulations
    Size: 719769784 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/d6c7a578-6317-49f3-8ba5-71f62b5b6610/resource/73dee7b2-a419-4fd2-ac0e-585bc0138d90/download/snowpack.zip
    Description: SNOWPACK simulations (.pro and .smet) covering season 2008/09 to 2021/22 at 10 virtual locations on Dorfberg. The locations correspond to the most common glide-snow avalanche paths.

en:
Resource Title: README
    Size: 1996 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/d6c7a578-6317-49f3-8ba5-71f62b5b6610/resource/26a702a9-37e6-4dc9-9ce9-e7a223d73bff/download/readme.txt
    Description: README.txt explaining the dataset and columns of tables in more detail.

avalanche

display_name: avalanche

avalanche-activity

display_name: avalanche-activity

glide-snow-avalanche

display_name: glide-snow-avalanche

snowpack-simulation

display_name: snowpack-simulation

time-lapse-photography

display_name: time-lapse-photography


tree-ring-laser-ablation-data

en:
This dataset contains the values of several chemical elements (Mg, Al, Si, S, K, Ca, Ti, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Sr, Tl, Pb, Bi) measured in the latewood of tree rings of Mongolian oak from Harbin, China, at a 5-year resolution. Due to the lack of a suitable reference material for wood, absolute concentration was not calculated, and the ratio between the chemical element and 13C was taken as proxy for the element signal.
In Harbin, one of the largest cities and most important industrial centers in northeastern China, air quality monitoring systems were built only by the end of 2015 to meet the national requirements. Thus, dendrochemical analyses could be used as a tool to complement for the lack of air quality data over longer periods of time, allowing for the reconstruction of the temporal trend of trace metals. Our main scopes were to: (a) assess the chemical composition of Quercus mongolica Fisch. ex Ledeb. tree rings from Harbin using a recently developed system of laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS), (b) identify the main chemical elements which
derived from air pollution and may be used as indicators over the period 1965–2020 in Harbin, while excluding those that were controlled by physiological processes in the tree, and (c) reconstruct the history of pollution in Harbin by comparing the tree-ring chemical composition of recent decades with that of previous decades, in trees growing in the highly polluted city of Harbin and in trees growing in a control site 90 km away from major pollution sources.
Briefly, the temporal trend of some elements was influenced by physiological factors, by environmental factors such as pollution, or influenced by both. Mg, K, Zn, Cu, Ni, Pb, As, Sr and Tl showed changes in pollution levels over time.

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

envidat

en: EnviDat: the Environmental Data Portal

en: Tree-ring laser ablation data

title_for_slug: tree-ring-laser-ablation-data

air-pollution

display_name: air-pollution

dendrochemistry

display_name: dendrochemistry

dendrochronology

display_name: dendrochronology

laser-ablation

display_name: laser-ablation

tree-rings

display_name: tree-rings


en: This data includes (1) Scripts to aggregate landscape resistance layers into squared and hexagonal grids (i.e., different representations and resolutions), (2) Input resistance layers and focal nodes in .txt format to run in Circuitscape (Python implementation v4.0.5). Circuitscape is a software tool for modeling and analyzing landscape connectivity, which simulates movement of organisms across landscapes by estimating resistance to movement across each point of the landscape. (3) Scripts for the ecological network analysis, and (4) environmental predictors for amphibian whole-life cycle habitats used to describe the local environment for BGI design (i.e. topographic, hydrologic, edaphic, vegetation, land-use derived, movement-ecology related).

en: ecosystem-services circuit-theory network-analysis urban-planning ecological-corridors biodiversity

en: Multi-Scale Prioritization framework for Urban Blue-Green Infrastructure Planning to Support Biodiversity: Data & Codes

en:
Resource Title:  Amphibian movement resistance layers and scripts for connectivity modelling - part 2
    Size: 20335272 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/dd7b7d6e-6fe8-455e-9743-adf791914a17/resource/1283e651-6999-46ad-b23d-0e81c593a90e/download/2_network-analysis.zip
    Description: The zipped folder includes the script to create and analyse the networks from Circuitscape outputs. The subfolders “Boundaries”, “Layers”, and “Reference_net” contain files either called by the code or created in the process. The subfolder “Outputs_from_Circuitscape” contains the outputs from Circuitscape including node and branch cumulative currents in a network format.

en:
Resource Title:  Amphibian movement resistance layers and scripts for connectivity modeling - part 1
    Size: 247430561 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/dd7b7d6e-6fe8-455e-9743-adf791914a17/resource/e364cf62-5561-4b4d-8909-0977b3da60f3/download/1_resistance_layers.zip
    Description: The zipped folder includes the script to aggregate the resistance layers into squared and hexagonal grids. The subfolders “Boundaries”, “Layers”, “Reference_grids”, and “Resistance_from_SDMs” contain files either called by the code or created in the process.The subfolder “Inputs_for_Circuitscape” contains the inputs to run Circuitscape, after doing some subsequent processing steps in QGIS (listed in the R script) with the files created with the script.

biodiversity

display_name: biodiversity

circuit-theory

display_name: circuit-theory

ecological-corridors

display_name: ecological-corridors

ecosystem-services

display_name: ecosystem-services

network-analysis

display_name: network-analysis

urban-planning

display_name: urban-planning


en:
Topic of Survey
The data at hand on energy cooperatives in Switzerland were collected in 2016 as part of the project "Collective financing of renewable energy projects in Switzerland and Germany" of the National Research Programme 71 "Managing Energy Consumption". The cooperatives were surveyed on their organizational structure, their activities in electricity and heat generation, their finances, the political context and their assessments of the future.
 Survey Method
The survey was targeted at all energy cooperatives in Switzerland (this is the basic population). The Swiss Commercial Register was searched for cooperatives and specific keywords in order to determine this basic population and collect addresses. This search in May 2016 resulted in a total of 304 energy cooperatives, to which a questionnaire was sent in July 2016. A pre-test with 8 persons had been carried out before the questionnaire was sent out. The questionnaire was provided in German and French. It was sent by mail and an attached letter referred to a link for the digital version if preferred. The online version was designed with the software "Sawtooth". After three weeks, a first, and after six weeks a second reminder letter was sent to those cooperatives that had not yet completed the questionnaire. The returned hardcopy questionnaires were manually entered into the database and then combined with the electronic data from the online survey. In the course of the survey, the total population was reduced from 304 to 289: in 4 cases the survey was not deliverable, 4 cooperatives had dissolved, 6 were not actually energy cooperatives, 1 case had recently changed its legal form. With a response rate of 47%, the final data set comprises 136 responses (from 77 digital and 59 hardcopy questionnaires). However, not all 136 of the returned questionnaires were filled out completely. We checked for answers that seemed contradictory or incomprehensible. If an error could be clearly identified and the correct answer derived, the answer was adjusted, otherwise the answer was replaced by "missing data".
 Anonymization
Participating cooperatives have been assured that their information will be kept confidential and will only be made public anonymously. For this reason, the data have been anonymized in in order to prevent any identification of individual cooperatives.
 How to Use the Data
* The data are available in CSV and SPSS (sav.) format.
* A codebook and a modified version of the used questionnaire are provided to illustrate the data and variable structure. In the questionnaire, the variable names are assigned to the corresponding questions. In the codebook, further information on these variables (valid n, answer categories) can be found. This information (of the codebook) is already integrated in the SPSS file.
 Current Embargo on Data
These data are currently under embargo and will only be released when the project is completed (not before 2020).
Additional Information
* The used questionnaire is provided in German and French.
* Descriptive results of the survey were published in a WSL report: https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:18943

en: switzerland cooperatives community-energy renewable-energy energy-cooperatives

en: Energy Cooperatives in Switzerland: Survey Results // Energiegenossenschaften in der Schweiz: Befragungsergebnisse

envi

en: Environment

en:
Resource Title: Raw date (SPSS)
    Size: 323839 bytes
    Format: application/x-spss-sav
    URL: https://www.envidat.ch/dataset/ec4125f8-d549-452f-a134-b53439f3b6e5/resource/1319f978-1d65-498c-8bfd-589f1b89e6a7/download/energy-cooperatives-switzerland_data.sav
    Description: Raw data in sav. format (SPSS). For comprehension of the data and variable structure, please consult the modified questionnaire with variable names.

en:
Resource Title: Raw data (.csv)
    Size: 196544 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/ec4125f8-d549-452f-a134-b53439f3b6e5/resource/7f6c3275-0b87-410f-882e-4496a5705a08/download/energy-cooperatives-switzerland_data.csv
    Description: Raw data in csv. format. For comprehension of the data and variable structure, please consult the codebook and the modified questionnaire with variable names.

en:
Resource Title: Codebook
    Size: 2433879 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/ec4125f8-d549-452f-a134-b53439f3b6e5/resource/30440e19-9f40-4bd5-a779-af1640db1891/download/energy-cooperatives-switzerland_codebook.pdf
    Description: Description of variable attributes and valid n

en:
Resource Title: Questionnaire with variable names
    Size: 3397996 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/ec4125f8-d549-452f-a134-b53439f3b6e5/resource/806cb4ee-b4b7-4b77-9bbd-c014ae28bc76/download/energy-cooperatives-switzerland_questionnaire-with-variable-names.pdf
    Description: Modified questionnaire with variable names for each item. Should be used together with the data files (.csv or .sav) to reconstruct the data and variable structures.

community-energy

display_name: community-energy

cooperatives

display_name: cooperatives

energy-cooperatives

display_name: energy-cooperatives

renewable-energy

display_name: renewable-energy

switzerland

display_name: switzerland


bryophytes-of-europe-traits-bet-dataset

en: The Bryophytes of Europe Traits (BET) dataset includes values for 65 biological and ecological traits and 25 bioclimatic variables for all 1816 bryophytes included in the European Red List (Hodgetts et al. 2019). The traits are compiled from several regional trait datasets and manually complemented using Floras, species-specific literature and expert knowledge. The bioclimatic variables are calculated using the European range of each species. Details regarding the trait compilation and extraction of bioclimatic variables can be found in the corresponding data paper (Van Zuijlen et al. 2023).

en: red-list hornworts mosses bioclimatic-variables indicator-values liverworts life-history-traits iucn

envidat

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

title_for_slug: bryophytes-of-europe-traits-bet-dataset

en:
Resource Title: BET_data
    Size: 1072488 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/4865a082-169d-40d1-920b-fc20ad0acad2/resource/d2d2f958-051c-4638-a808-88547cc64d92/download/betdata.txt
    Description: Text file including the BET dataset.

en:
Resource Title: BET_explanations
    Size: 249953 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4865a082-169d-40d1-920b-fc20ad0acad2/resource/9ac4a865-5bb2-479b-93f4-b3bfd99c7ada/download/bet_explanations.pdf
    Description: PDF including the (trait) explanations.

en:
Resource Title: BET_data_explanations
    Size: 981939 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/4865a082-169d-40d1-920b-fc20ad0acad2/resource/6367f57f-1894-4f1b-997d-068d4d7511cc/download/betdata.xlsx
    Description: Excel file including the BET dataset and (trait) explanations.

bioclimatic-variables

display_name: bioclimatic-variables

hornworts

display_name: hornworts

indicator-values

display_name: indicator-values

iucn

display_name: iucn

life-history-traits

display_name: life-history-traits

liverworts

display_name: liverworts

mosses

display_name: mosses

red-list

display_name: red-list


en: We conducted four drone flights in Davos Wolfgang Arelen, in 2020/21 and 2022 to obtain data for the generation of DSMs and orthomosaics at a high resolution. The data was processed with the Agisoft Metashape Professional Software.

en: uav photogrammetry dsm wingtra drone-data

envidat

en: EnviDat: the Environmental Data Portal

en: Photogrammetric Drone Data Wolfgang Arelen

drone-data

display_name: drone-data

dsm

display_name: dsm

photogrammetry

display_name: photogrammetry

uav

display_name: uav

wingtra

display_name: wingtra


photogrammetric-drone-data-schurlialp

en: The data was collected on 16.04.2021 and on 28.05.2021 with a Wingtra Gen II and a Sony RX1 II RGB sensor to obtain snow depth and distribution data. Following the data collection, the data was processed with Agisoft Metashape. A 10cm DSM, a 10cm snow depth raster, a 3mm orthophoto and the original drone images are available for download.

en: drone-data orthophoto uav photogrammetry dsm wingtra

en: Photogrammetric Drone Data Schürlialp

title_for_slug: photogrammetric-drone-data-schurlialp

orthophoto

display_name: orthophoto


en:
This dataset contains all data on which the following publication below is based.
Paper Citation:
Risch Anita C., Zimmermann, Stefan, Schütz, Martin, Borer, Elizabeth T., Broadbent, Arthur A.D., Caldeira, Maria C., Davies, Kendi F., Eisenhauer, Nico, Eskelinen, Anu, Fay, Philip A., Hagedorn, Frank, Knops, Johannes M.H., Lembrechts, Jonas, J., MacDougall, Andrew S., McCulley, Rebecca L., Melbourne, Brett A., Moore, Joslin L., Power, Sally A., Seabloom, Eric W., Silveira, Maria L., Virtanen, Risto, Yahdjian, Laura, Ochoa-Hueso, Raul (accepted). Drivers of the microbial metabolic quotient across global grasslands. Global Ecology and Biogeography
Please cite this paper together with the citation for the datafile.
The microbial metabolic quotient (MMQ; mg CO2-C mg MBC-1 h-1), defined as the amount of microbial CO2 respired (MR; mg CO2-C kg soil-1 h-1) per unit of microbial biomass C (MBC; mg C kg soil-1), is a key parameter for understanding the microbial regulation of the carbon (C) cycle, including soil C sequestration. Here, we experimentally tested hypotheses about the individual and interactive effects of multiple nutrient addition (NPK+micronutrients) and herbivore exclusion on MR, MBC, and MMQ across 23 sites (5 continents). Our sites encompassed a wide range of edaphoclimatic conditions, thus we assessed which edaphoclimatic variables affected MMQ the most and how they interacted with our treatments. Soils were collected in plots with established experimental treatments. MR was assessed in a five-week laboratory incubation without glucose addition, MBC via substrate-induced respiration. MMQ was calculated as MR/MBC and corrected for soil temperatures (MMQsoil). Using LMMs and SEMs, we analysed how edaphoclimatic characteristics and treatments interactively affected MMQsoil. MMQsoil was higher in locations with higher mean annual temperature, lower water holding capacity, and soil organic C concentration, but did not respond to our treatments across sites as neither MR nor MBC changed. We attributed this relative homeostasis to our treatments to the modulating influence of edaphoclimatic variables. For example, herbivore exclusion, regardless of fertilization, led to greater MMQsoil only at sites with lower soil organic C (-1.7%). Our results pinpoint the main variables related to MMQsoil across grasslands and emphasize the importance of the local edaphoclimatic conditions in controlling the response of the C cycle to anthropogenic stressors. By testing hypotheses about MMQsoil across global edaphoclimatic gradients, this work also helps to align the conflicting results of prior studies.

en: anthropogenic-management soil-properties microbial-respiration microbial-biomass-carbon herbivore-exclusion climate nutrient-addition

envi

en: Environment

anthropogenic-management

display_name: anthropogenic-management

climate

display_name: climate

herbivore-exclusion

display_name: herbivore-exclusion

microbial-biomass-carbon

display_name: microbial-biomass-carbon

microbial-respiration

display_name: microbial-respiration

nutrient-addition

display_name: nutrient-addition

soil-properties

display_name: soil-properties


en: The specific surface area (SSA) of different snow types were measured with the IceCube instrument and the Scanco Medical microCT 40. In addition, the snow particles created during the preparation of IceCube samples were counted. The difference in SSA between these instruments is explained by the formation of the surface particles. A numerical simulation using TARTES simulation support the observations.

en: microct specific-surface-area snow icecube intercomparison

en:
Resource Title: TARTES simulations
    Size: 233652 bytes
    Format: application/x-ipynb+json
    URL: https://www.envidat.ch/dataset/2b433431-cf23-409d-a87a-0388bdd87ba1/resource/da0c5370-5987-4849-b776-3a32928722f3/download/tartes_simulations_icvsct.ipynb
    Description: Jupyter notebook with TARTES simulations.

en:
Resource Title: Statistics of samples
    Size: 11192 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/2b433431-cf23-409d-a87a-0388bdd87ba1/resource/b9c6ddcf-e197-4c42-9715-c18dbf6cfba4/download/final_relativepercentagedifference.xlsx
    Description:

en:
Resource Title: greenland_summit_density_ssa_data
    Size: 60342 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/2b433431-cf23-409d-a87a-0388bdd87ba1/resource/9e8148c1-be7d-4f71-b356-2afa52cff363/download/all_data_greenland_2015.xlsx
    Description: The file contains all data for the plot in figure 1 of the co-located density and SSA measurement at Summit, Greenland in May 2015, conducted by Schneebeli, Proksch, Schmid.

en:
Resource Title: Grain survey of snow particles
    Size: 1135587 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/2b433431-cf23-409d-a87a-0388bdd87ba1/resource/b54b0c06-c651-4687-b156-67f0e5136b31/download/final_grainsurvey_data_weight.xlsx
    Description: The file contains all measurements of the artificial particles for snow types A, B, C, D and E, including the artificial particle mass in the Petri dish.

en:
Resource Title: artificial_snow_data set
    Size: 10754 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/2b433431-cf23-409d-a87a-0388bdd87ba1/resource/1da22e66-c2d4-4e51-86b8-d429fd9a8a9f/download/artificial_snow_data.xlsx
    Description: The file contains all micro-CT and IC measurements of the artificial snow data set. The data set was originally produced to investigate the influence of black carbon on SSA measurements.

en:
Resource Title: SMP_measurements
    Size: 485022 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/2b433431-cf23-409d-a87a-0388bdd87ba1/resource/8bf8eb60-0a76-4e41-a564-6ffd62bc0bd1/download/smp.zip
    Description: SMP measurement files for snow types A, B, C and D.

en:
Resource Title: Influence of black carbon on the measurement of the specific surface of snow with the near infrared method - Philipp Baumann
    Size: 7484371 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/2b433431-cf23-409d-a87a-0388bdd87ba1/resource/ea53a98b-702f-4557-89ae-736616525fbf/download/semesterarbeit_philipp_baumann_v3.pdf
    Description: These experiments investigate different possible errors by measuring the specific surface area (SSA) of snow with the near infrared method. The influence of black carbon on this measurement was disproved, the theory was therefore validated. It shows that sunlight has no influence on the measurement with the IceCube. Wind drift snow as thin layer on the sample can increase the measured value of the specific surface by a few percent. This could be part of the error, observed in former measurements at SLF. As reference for the measured SSA values with the near infrared method, computer tomography was used to verify the SSA values.

en:
Resource Title: IceCube and MicroCT data
    Size: 25666 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/2b433431-cf23-409d-a87a-0388bdd87ba1/resource/fa34731a-ab3c-4400-8a6b-0e059e268dca/download/final_ic_and_microct_data.xlsx
    Description: The file contains all micro-CT and IC measurements for snow types A, B, C, D and E used in our study. All samples were processed (and stored)  in a cold laboratory at -15°C.

icecube

display_name: icecube

intercomparison

display_name: intercomparison

microct

display_name: microct

snow

display_name: snow

specific-surface-area

display_name: specific-surface-area


en: Data of a survey of flowering plants in 80 sites in five European cities and urban agglomerations (Antwerp, Belgium; greater Paris, France; Poznan, Poland; Tartu, Estonia; and Zurich, Switzerland) sampled between April and July 2018.

en: tartu zurich urban antwerp plants floral-inventories paris floral-resources angiospermae poznan

envidat

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

en: EnviDat: the Environmental Data Portal

en: Flowering Plants (Angiospermae) in Urban Green Areas in five European Cities

en:
Resource Title: Floral_1_occurrence.csv
    Size: 1535390 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/cf6885b4-0705-415b-983b-13b118c3cc95/resource/3c30a1f0-d0c3-4323-891b-646f3d94378f/download/floral_1_occurrence.csv
    Description: List of plant species sampled in the five cities (Antwerp, Paris, Poznan, Tartu and Zurich) during the different sampling perionds (four for Zurich, three for the other cities) with their frequency (oc1-oc16) within a 100 m radius

en:
Resource Title: Floral_2_counts.csv
    Size: 260871 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/cf6885b4-0705-415b-983b-13b118c3cc95/resource/380c0645-2665-4f9e-a8a1-bcc75bc714b8/download/floral_2_counts.csv
    Description: Floral units, mean number of flowers per floral unit and floral abundance of the different plants counted in quadrats in the study sites in Zurich during the four sampling periods

en:
Resource Title: Floral_Traits.csv
    Size: 248377 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/cf6885b4-0705-415b-983b-13b118c3cc95/resource/31270de2-4fe9-416f-b0c0-5d871505aec9/download/floral_traits.csv
    Description: Trait values for the sampled plants

angiospermae

display_name: angiospermae

antwerp

display_name: antwerp

floral-inventories

display_name: floral-inventories

floral-resources

display_name: floral-resources

paris

display_name: paris

plants

display_name: plants

poznan

display_name: poznan

tartu

display_name: tartu

urban

display_name: urban

zurich

display_name: zurich


en:
This dataset comprises a large array of ecological data for the European Alps:
    (1) Current soil and climate predictors at various resolutions.
    (2) GBIF observations of the European Alps Flora (~4,000 species).
    (3) Species habitat suitability maps (1,109 species; based on species observations filtered at 40x40-km)
    at various resolutions used in the study to generate (4); except 'expert'...
    (4) Expert, Taxonomic, phylogenetic and functional diversity of the study region at various resolutions
    (from 100-m to 40-km --- 100-m aggregated & mean to km + non-aggregated/predicted) for CLIM,
    SOIL and CLIM-SOIL models.
    (5) Ecological and altitudinal preferences of the European Alps Flora.
    (6) Data outputs of the related published article.
    (7) All scripts used for analyses.
    (8) Additional files used for analyses.
    (9) Improved set of species habitat suitability maps (~2,600 species; based on species observations
    filtered at 1x1-km) and related taxonomic diversity at 100-m resolution (aggregated to km +
    non-aggregated/predicted) for CLIM, SOIL and CLIM-SOIL models ---- not incorporated in the study.

en: grain-size soil climate taxonomic-diversity ecological-indicator-values functional-diversity phylogenetic-diversity

en: Resolution in species distribution models shapes spatial patterns of plant multifaceted diversity

envi

en: Environment

climate

display_name: climate

ecological-indicator-values

display_name: ecological-indicator-values

functional-diversity

display_name: functional-diversity

grain-size

display_name: grain-size

phylogenetic-diversity

display_name: phylogenetic-diversity

soil

display_name: soil

taxonomic-diversity

display_name: taxonomic-diversity


en:
Cities are socio-ecological systems that filter and select species, thus establishing unique species assemblages and biotic interactions. Urban ecosystems can host richer wild bee communities than highly intensified agricultural areas, specifically in resource-rich urban green spaces such as allotment and family gardens. At the same time, urban beekeeping has boomed in many European cities, raising concerns that the fast addition of a large number of managed bees could deplete the existing floral resources, triggering competition between wild bees and honeybees. The data has been used to investigated the interplay between resource availability and the number of honeybees at local and landscape scales and how this relationship influences wild bee diversity.
This dataset contains the raw and processed data supporting the findings from the paper: "Low resource availability drives feeding niche partitioning between wild bees and honeybees in a European city".
The data contains:
1. Bee trait measurements at the species and individual-level  of five functional traits.
2. The values of the feeding niche partitioning (functional dissimilarity to honeybees)
3. The predictors of resource availability and beekeeping intensity at local and landscape scales used in the modelling of the paper for the 23 experimental sites.

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

en: Niche partitioning between wild bees and honeybees

en:
Resource Title:
    Size: 4090 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/4e058b7f-925c-4187-8d58-28fba31ed64d/resource/285d199d-bef4-4d18-b01f-1e95eccfd6ad/download/readme.csv
    Description: Metadata for the predictor and response dataset

en:
Resource Title:
    Size: 2345 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/4e058b7f-925c-4187-8d58-28fba31ed64d/resource/f173b2b5-9d37-4836-95e3-c397f2687101/download/metadata.csv
    Description: Metadata for the predictors and response dataset

en:
Resource Title:
    Size: 200011 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/4e058b7f-925c-4187-8d58-28fba31ed64d/resource/43152ded-fa38-4a75-938c-bf63d7018ad1/download/city4bees_traits.exp.csv
    Description: The data contains the following columns:

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

en:
Resource Title:
    Size: 10415 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/4e058b7f-925c-4187-8d58-28fba31ed64d/resource/24ac489e-5de8-4c4f-ae58-74dddd5e08bb/download/response_predictors_modelling.csv
    Description: Response and predictors used for the SEMs

en:
Resource Title:
    Size: 31326 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/4e058b7f-925c-4187-8d58-28fba31ed64d/resource/63ee6a25-0fef-4f42-95de-e835d7fb0c94/download/data_s1.traits_bees.csv
    Description: Mean, standard deviation , minimum  and maximum values of six functional traits. The data has the following structure:
- Bee_species: bee scientific name
- mean_trait: mean value
- sd_trait: standard deviation
_min_trait: minimum value
- max_trait: maximum value
- trait: the six traits used. ITD = Intertegular distance (mm); Pheno.duration = duration of the phenology; Pheno.start = start of the phenology; Pheno.end = end of the phenology; Rel.tongue.length= relative tongue length  (mm); Daytime.activity = daily activity (h)
DistNichePart: functional dissimilarity in a multidimentional trait space defined by five traits between wild bee individuals of a wild bee species and the honeybee individuals. Values are at the species level.

beekeeping

display_name: beekeeping

bettergardens

display_name: bettergardens

gardens

display_name: gardens

honeybees

display_name: honeybees

pollinator-experiment

display_name: pollinator-experiment

resource-availability

display_name: resource-availability

urban-biodiversity

display_name: urban-biodiversity

urban-ecosystems

display_name: urban-ecosystems

urbanization

display_name: urbanization

wild-bees

display_name: wild-bees


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

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

envidat

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

en: EnviDat: the Environmental Data Portal

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

en:
Resource Title:
    Size: 3057 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/316d5fcf-76f0-4921-807f-2ea1d3e9f7bc/resource/e654832d-1c8e-4d9c-a140-e19a29dbebea/download/processeddata.zip
    Description: Processed data as shown in Figure 4 of publication. X-ray energy in eV in column one, electron count intensity in arb. units in column 2.

en:
Resource Title:
    Size: 434478 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/316d5fcf-76f0-4921-807f-2ea1d3e9f7bc/resource/bb9c5895-b2f0-4451-9869-b869dc1e081e/download/spectraandphotonfluxdata.zip
    Description: Near Ambient Pressure partial Auger-Meitner electron yield X-ray absorption spectra and photon flux data. X-ray energy in eV in column 1, analyzer electron count intensity in arb. units in column 2.

en:
Resource Title:
    Size: 3858 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/316d5fcf-76f0-4921-807f-2ea1d3e9f7bc/resource/4523fcaa-78bf-456c-8c56-f60196724043/download/datalist.csv
    Description: The table lists all NEXAFS raw data with the following columns:

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

atmosphere

display_name: atmosphere

chemistry

display_name: chemistry

ice

display_name: ice

laboratory-study

display_name: laboratory-study

snow-atmosphere-interactions

display_name: snow-atmosphere-interactions


en: The dataset contains several snow models in the Standard Tesselated Geometry File Format (stl) for 3D visualization, printing and additive manufacturing. Different snow types are available (new snow, rounded snow, depth hoar, buried surface hoar, graupel).

en: 3d-print weak-layer rounded-snow snow 3d-model depth-hoar snow-cast new-snow

envi

en: Environment

en:
Resource Title: Description of snowtypes, filenames and other metadata
    Size: 2170683 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/f5163bdf-657d-43f8-a907-5a87e440f06a/resource/2092babe-4033-4cd3-bacc-857bce65cbb1/download/3d_snow_models_metadata.xlsx
    Description: This file contains information about the stl-files of the different snow models. Snowtype (Schneeart), International Snow Classification, preview picture, notes about the origin of the snow, filename of the stl-data, and physical size of the model are given.

en:
Resource Title: Snow models in STL-format
    Size: 382570967 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f5163bdf-657d-43f8-a907-5a87e440f06a/resource/59fbf858-ae63-4067-9c51-4cd21817e22c/download/3d_snow_stl.zip
    Description: The zipped file contains all data described in the file 3d_snow_models_metadata. The stl-files should be ready for additive manufacturing.

depth-hoar

display_name: depth-hoar

new-snow

display_name: new-snow

rounded-snow

display_name: rounded-snow

snow

display_name: snow

snow-cast

display_name: snow-cast

weak-layer

display_name: weak-layer


en: This repository contains data on EDA measurements of visitors with different cultural backgrounds in virtual urban park settings. The parks are a Persian garden (Shiraz, Iran) and a historical park in Zurich, Switzerland. The cultural background of the visitors is Persian and Central European. The repository contains raw data from EDA, processed time series and statistical procedures.

en: landscape sense-of-place electrodermal-activity urban-ecology place-attachment

en: Electrodermal Activity (EDA) of Bi-cultural Visitors In Virtual Park Settings

en:
Resource Title: 00. EDA Raw Data
    Size: 61199195 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/5f6276bf-6e08-4432-bf3b-9c77672976ba/resource/543ea671-79be-49bd-9b5f-b39526ffc271/download/00_eda_raw_data.zip
    Description:

en:
Resource Title: 01. EDA Processed Data
    Size: 59757431 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/5f6276bf-6e08-4432-bf3b-9c77672976ba/resource/758e86b4-fa9a-469f-8c19-6bc0210799e6/download/01_eda_processed_data.zip
    Description:

en:
Resource Title: 04. Scripts
    Size: 6091 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/5f6276bf-6e08-4432-bf3b-9c77672976ba/resource/27eb8bfa-9dac-4705-b616-c2a077623c82/download/04_scripts.zip
    Description:

en:
Resource Title: 02. Questionnaire Data
    Size: 31084 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/5f6276bf-6e08-4432-bf3b-9c77672976ba/resource/64311577-59d4-49a6-85a2-dcd6a0f4a5f9/download/02_questionnaire_data.zip
    Description:

en:
Resource Title: 05. Visuals
    Size: 16568187 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/5f6276bf-6e08-4432-bf3b-9c77672976ba/resource/fb5ff1a3-68da-4a4e-ab0e-d4675fbf33ca/download/05_visuals.zip
    Description:

en:
Resource Title: 03. Publication Ready Data
    Size: 979015 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/5f6276bf-6e08-4432-bf3b-9c77672976ba/resource/d10a79a8-363f-4b20-8240-3e3e3e16b863/download/03_publication_ready_data.zip
    Description:

electrodermal-activity

display_name: electrodermal-activity

landscape

display_name: landscape

place-attachment

display_name: place-attachment

sense-of-place

display_name: sense-of-place

urban-ecology

display_name: urban-ecology


en:
Recent publications about declines in arthropod biomass, abundance and species diversity
raise concerns and call for measures. Agricultural intensification is likely one cause for the negative trends. But rare long-term arthropod surveys conceal trends in arthropod communities in agricultural land. Here, we report about a standardized sampling of arthropod fauna in a Swiss agricultural landscape, repeated over 32 years (1987, 1997 and 2019). We sampled 8 sites covering 4 semi-natural and agricultural habitat types. Four trap types  were used to capture a wide range of flying and ground dwelling arthropods between May and July. Over the three sampling periods, 58’255 specimens of 1’343 species were analysed. Mean arthropod biomass, abundance and species richness per trap was significantly higher in 2019 than in prior years and gamma diversity of the study area was highest in 2019. Biomass and abundance increased stronger in the flight traps than in the pitfall traps. The implementation of agri-environmental schemes has improved habitat quality since 1993, while landscape composition and pesticide and fertilizer use remained stable over the study period, both contributing to the findings. The results of this study contrast with outcomes of comparable investigations and highlight the importance of further long-term investigations on arthropod dynamics.
Data are provided on request to contact person against bilateral agreement.

en: switzerland time-series biodiversity insect-decline long-term

envidat

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

en: EnviDat: the Environmental Data Portal

en: Arthropod biomass, abundance and species richness trends over 32 years in the agricultural Limpach valley, Switzerland

en:
Resource Title: Body size lookup-table
    Size: 47769 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/91417163-72ac-4f71-9c80-b6fed1f39218/download/koerpergroessen_araneae_edjf.csv
    Description: Araneae

en:
Resource Title: Limpach1987+1997+2019_ArtInd.sql
    Size: 4269 bytes
    Format: application/x-sql
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/38baef03-5b46-4894-ba32-e0f6307c7b62/download/limpach198719972019_artind.sql
    Description: SQL query executed on WSL Fauna Database to retrieve the data

en:
Resource Title: Species incidence per year
    Size: 30771 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/96edac02-f93b-44e3-95e0-b9e7c2dcddf1/download/incidence_freq_20210221.csv
    Description: Total abundance of species per year

en:
Resource Title: Body size lookup-table
    Size: 6456 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/5ed0e1e9-bd33-4c8b-ac7e-8c4ec47deafc/download/koerpergroessen_heteroptera_edjf.csv
    Description: Heteroptera

en:
Resource Title: Master thesis of Regula Gygax, 1999, at University of Zürich
    Size: 5380429 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/bc0a4717-49ee-4120-aa6c-8c7dec773577/download/regula-gygax-1999-diplomarbeit-uni-zh.pdf
    Description: The study is comparing in 1997 the trapping efficiency of a newly developed combination trap with traditional window traps and yellow pan traps. Study locations in the Limpach valley matched with a subset from a larger study, which was conducted in 1987.

en:
Resource Title: Body size lookup-table
    Size: 617 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/bb63365b-4971-4992-af92-cdcfbf49e7c8/download/koerpergroessen_neuropteroidea_edjf.csv
    Description: Neuropteroidae

en:
Resource Title: Body size lookup-table
    Size: 2057 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/4f8eb204-a9be-41d5-86a4-a2bb2c394169/download/koerpergroessen_syrphidae_edjf.csv
    Description: Syrphidae

en:
Resource Title: Body size lookup-table
    Size: 17437 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/56e5590a-b1aa-4ba5-8ad2-6f839c6b3b85/download/koerpergroessen_carabidae_edjf.csv
    Description: Carabidae

en:
Resource Title: Climate data
    Size: 44128 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/e1d5afa9-530a-4b4e-99e3-d43b6ca613cc/download/climate_data_879719_jan_july.csv
    Description: Climate data of the respective sampling years

en:
Resource Title: Body size lookup-table
    Size: 44183 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/2a8f6d75-ff31-41cc-bed4-7b90b6d579f0/download/koerpergroessen_staphylinidae_edjf.csv
    Description: Staphylinidae

en:
Resource Title: Input data - species list and trophic guilds
    Size: 163154 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/44cb422b-d09d-49b7-94fd-3b3648ad4fce/download/artliste_mittrophicguild.csv
    Description: List of found species and associated trophic guild

en:
Resource Title: Body size lookup-table
    Size: 16314 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/ba8c3d6b-b809-4e42-a0e4-6a858a9c7636/download/koerpergroessen_coleoptera_edjf.csv
    Description: Coleoptera

en:
Resource Title: Limpach1987+1997+2019_ArtInd.csv
    Size: 5148268 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/a0d555bb-1dc3-4c39-884c-a6366438809a/download/limpach198719972019_artind.csv
    Description: Data extract from WSL Fauna Database containing arthropod species and abundance data from 1987, 1997, 2019

en:
Resource Title: Sites and traps
    Size: 8117 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/325305be-ff93-48c4-8c16-a3a4e37663ab/download/limp_sites_lookup.csv
    Description: Lookup table of trap and site locations

en:
Resource Title: Climate data
    Size: 267 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/b53622ce-6ad7-4def-b009-e5d21ceeae5a/download/climaaddition.csv
    Description: Additional climate data on climate trend

en:
Resource Title: R code
    Size: 38710 bytes
    Format: text/x-r
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/888eb6a0-e541-4296-ba26-b43a87a6a938/download/20220818_dataanalysisdef.r
    Description: R code for data analyses

en:
Resource Title: Fused catching data 1987, 1997, 2019
    Size: 5216910 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/373100d8-43f4-476b-9678-d40cc4fa47b0/download/limpach198719972019_artind.csv
    Description: Complete dataset of species and abundances per year an trap

en:
Resource Title: Capture data with size and mass
    Size: 2804030 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/310ccd7a-b1d0-4ab1-99e4-1ba70e552fc5/download/data_publication_20210221.csv
    Description: Complete capture data including body sizes and biomass values

en:
Resource Title: Body size lookup-table
    Size: 1782 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/290e0cfa-857f-4ddd-940d-96076d736419/download/koerpergroessen_formicidae_edjf.csv
    Description: Formicidae

en:
Resource Title: Species number and abundances per trap
    Size: 56123 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/1f32e64c-1d0f-44ac-a584-7c62c053d185/download/trap_data_publication20210221.csv
    Description: Sum of species and abundances listed per trap

en:
Resource Title: R code
    Size: 14746 bytes
    Format: text/x-r
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/0e13f98d-7529-451b-80fc-72d6a2ecf76b/download/20201111_dataediting.r
    Description: R code for data editing

en:
Resource Title: Body size lookup-table
    Size: 7775 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e6e78c74-8f63-4fc9-a651-80b47db27354/resource/71e46f93-be9a-4805-b83a-3033447ff616/download/koerpergroessen_aculeata_edjf.csv
    Description: Aculeata

biodiversity

display_name: biodiversity

insect-decline

display_name: insect-decline

long-term

display_name: long-term

switzerland

display_name: switzerland

time-series

display_name: time-series


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.

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

envidat

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

en: EnviDat: the Environmental Data Portal

en: Observed and simulated snow profile data

envi

en: Environment

en:
Resource Title: Observed snow profiles
    Size: 261432 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f109f639-c6c8-46d6-82fd-6a5dbc2bb114/resource/89fd369e-f7d9-49b1-8d24-f9fe3b15cd08/download/observed_snow_profiles.zip
    Description: 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:
Resource Title: DAV data set: Properties of observed snow profiles and simulated failure layers
    Size: 228035 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/f109f639-c6c8-46d6-82fd-6a5dbc2bb114/resource/98972dcd-f6bb-4391-8dfa-1ce22cd3d7cc/download/properties_observed_and_simulated_dav.csv
    Description: 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:
Resource Title: SWISS data set: Properties of observed snow profiles and simulated failure layers
    Size: 105662 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/f109f639-c6c8-46d6-82fd-6a5dbc2bb114/resource/8307de98-3d32-40de-97e8-e938a026cf2b/download/properties_observed_and_simulated_swiss.csv
    Description: 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:
Resource Title: ReadMe
    Size: 140674 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/f109f639-c6c8-46d6-82fd-6a5dbc2bb114/resource/2156a195-28ea-418e-ad7e-4af7b5337d7a/download/readme.pdf
    Description: ReadMe file explaining data structure and content

en:
Resource Title: Simulated snow profiles
    Size: 8635143 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f109f639-c6c8-46d6-82fd-6a5dbc2bb114/resource/adaf78bb-27a6-4ab9-ae40-ff114e683496/download/simulated_snow_profiles.zip
    Description: 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.

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

display_name: snowpack

snowpack-simulation

display_name: snowpack-simulation


en: Compiled data on the impacts of seven important NNTs (Acacia dealbata, Ailanthus altissima, Eucalyptus globulus, Prunus serotina, Pseudotsuga menziesii, Quercus rubra, Robinia pseudoacacia) on physical and chemical soil and biodiversity in Europe, and summarise commonalities and differences. A total of 107 publications considered, studies referred to biodiversity attributes and soil properties: 2804 lines and 30 rows.

en: soil-impacts pairwise-stand-comparisons forest-management biogeography biodiversity

en: Impact of non-native tree species in Europe on soil properties and biodiversity: a review

en:
Resource Title: references
    Size: 24932 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/74062fb9-588e-462b-9125-26bc8cb7d1e6/resource/f55b90cb-3baa-4557-82e9-d870a1d02655/download/references.xlsx
    Description: Bibliography of all references cited in data_wohlgemuth_etal_Neobiota_2022.

en:
Resource Title: headers
    Size: 10213 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/74062fb9-588e-462b-9125-26bc8cb7d1e6/resource/4c8d584a-399a-48a9-a20a-c42f71b08dbf/download/headers.xlsx
    Description: Description of headers for data_wohlgemuth_etal_Neobiota_2022

en:
Resource Title: data_wohlgemuth_neobiota_2022
    Size: 729955 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/74062fb9-588e-462b-9125-26bc8cb7d1e6/resource/84c48309-eef7-4b07-baa2-59a0a1d75015/download/wohlgemuth_etal_neobiota_2022.csv
    Description: Baseline data

biogeography

display_name: biogeography

forest-management

display_name: forest-management

pairwise-stand-comparisons

display_name: pairwise-stand-comparisons

soil-impacts

display_name: soil-impacts


intratrait

en:
This data set was used to test whether species specialized to high elevations or with narrow elevational ranges show more conservative (i.e. less variable) trait responses across their elevational distribution, or in response to neighbours, than species from lower elevations or with wider elevational ranges. We did so by studying intraspecific trait variation of 66 species along 40 elevational gradients in four countries (Switzerland, Australia, New Zealand, China) in both hemispheres. As an indication of potential neighbour interactions that could drive trait variation, we also analysed plant species’ height ratio, its height relative to its nearest neighbour.
The following traits and parameters were measured and are available in this data set:
As an indication of plant stature, we measured vegetative and generative height, where vegetative height was distance from soil to highest vegetative leaf and generative height was distance to the highest point on the reproductive shoot. As a measure of reproductive investment, we noted the presence of flowers on the randomly chosen individuals (see below). As a measure of individual and genet basal area, we measured individual plant and patch diameters, in two dimensions (along the largest diameter and perpendicular to it). In clonal plant species, plant diameter was equivalent to an individual rosette, whereas patch diameter referred to the whole genet and could represent the size of a tuft, tussock or cushion. For genera with more singular growth forms (e.g., some Gentiana species) plant and patch diameter were the same. The two diameter measurements were made at right angles, allowing estimates of patch and plant areas to be calculated as an ellipse (i.e., area = 0.5 a 0.5 b Π). All traits were measured on ten randomly selected individuals per site. Flower count data was considered in a binary fashion on a per individual basis (because for some species individuals only produce one flower when flowering) so that the presence or absence of flower(s) was a nominal value between 0 and 10 for each species at each site. We then collected at least three leaves (up to 30 for small and light leaves) from each of the first three individuals selected from each species for determination of leaf dry matter content (LDMC) and specific leaf area (SLA). For calculations of LDMC and SLA, fresh leaves were scanned on a flatbed scanner to determine leaf area. Leaves were then weighed on a balance to a precision of +/- 0.001g, prior to being air dried and reweighed with a balance to a precision of +/- 0.0001g. LDMC was calculated by dividing dry leaf mass by fresh leaf mass. SLA was calculated by dividing leaf area by dry leaf mass. Additionally, within an area of 10 cm diameter around the target individual, we determined the tallest neighbouring species and measured its vegetative and generative height, and estimated the percent cover of the target species, other vegetation, rock, and bare soil.
For more details see Rixen et al. 2022, Journal of Ecology.

en: elevation-gradient climate-change cold-adapted-plants plant-traits elevation-range neighbour-interactions

en: intratrait

title_for_slug: intratrait

en:
Resource Title: R code Rixen_etal_2022_JEcol_AllTraitData
    Size: 74099 bytes
    Format: text/x-r
    URL: https://www.envidat.ch/dataset/58a5f3e7-89e3-41c9-a880-5971ecaceab5/resource/bcff4cc4-e77a-4018-8a88-7b0f38d295e6/download/final_code_lme4_supplement.r
    Description:

en:
Resource Title: Rixen_etal_2022_JEcol_AllTraitData
    Size: 3679833 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/58a5f3e7-89e3-41c9-a880-5971ecaceab5/resource/d6845438-a41c-49a6-be0a-254e258a9acd/download/rixen_etal_2022_jecol_alltraitdata.csv
    Description: Plant trait data along elevation gradients on mountains

climate-change

display_name: climate-change

cold-adapted-plants

display_name: cold-adapted-plants

elevation-gradient

display_name: elevation-gradient

elevation-range

display_name: elevation-range

neighbour-interactions

display_name: neighbour-interactions

plant-traits

display_name: plant-traits


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

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

envidat

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

en: EnviDat: the Environmental Data Portal

envi

en: Environment

en:
Resource Title: WPSSetup
    Size: 30162 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/30c985c3-4a14-4cd5-936e-9eb848175812/resource/a4f6bae9-dee7-4359-8c07-527ebbc6040f/download/wpssetup.zip
    Description: Scripts that need to be changed to compile WPS (see COSMOWRFdocumentation.pdf, Section 4)

en:
Resource Title: COSMO-WRF documentation v1.0
    Size: 275721 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/30c985c3-4a14-4cd5-936e-9eb848175812/resource/3a038630-044e-4769-9dd4-9032d7f337eb/download/cosmowrfdocumentation.pdf
    Description: Gerber, Franziska; Sharma, Varun (2018). Running COSMO-WRF on very-high resolution over complex terrain. EnviDat. doi:10.16904/envidat.35

Documentation about the procedure to run COSMO-WRF

en:
Resource Title: Postprocessing
    Size: 3782 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/30c985c3-4a14-4cd5-936e-9eb848175812/resource/316468d8-3fe9-4a1c-95e0-6595a4862396/download/postprocessing.zip
    Description: Postprocessing scripts to map WRF output on swiss coordinates (see Section 7 in COSMOWRFdocumentation.pdf)

en:
Resource Title: WPSrun
    Size: 6718 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/30c985c3-4a14-4cd5-936e-9eb848175812/resource/47a716bd-a1e5-4220-b9cc-53ef80089c7b/download/wpsrun.zip
    Description: Scripts to run the WRF pre-processing (WPS) for COSMO-WRF simulation (see Section 5 in COSMOWRFdocumentation.pdf)

en:
Resource Title: StaticData
    Size: 28859 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/30c985c3-4a14-4cd5-936e-9eb848175812/resource/86b121da-35f3-4096-88cd-1324ee511ac8/download/staticdata.zip
    Description: Scripts to process geographical/static data (see Section 3 in COSMOWRFdocumentation.pdf).
- ascii_to_bin_landuse.f90
- ascii_to_bin_soiltype.f90
- ascii_to_bin_topo_wgs84_ws2sf1.f90
- see_slopes.ncl
- soiltype_writing.py
- toposmooth_wgs84_cosmobound.ncl
- write_geogrid.o

en:
Resource Title: WRFrun
    Size: 8787 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/30c985c3-4a14-4cd5-936e-9eb848175812/resource/168aba72-d098-4d3b-a4d8-445ad91d5d10/download/wrfrun.zip
    Description: Scripts to run COSMO-WRF simulation (see Section 6 in COSMOWRFdocumentation.pdf)

en:
Resource Title: MeteorologicalData
    Size: 8317 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/30c985c3-4a14-4cd5-936e-9eb848175812/resource/54f99acd-e86a-4513-b1ea-d59d678f13ed/download/meteorologicaldata.zip
    Description: Scripts to process meteorological data (see Section 2 in COSMOWRFdocumentation.pdf).

complex-terrain

display_name: complex-terrain

cosmo-wrf

display_name: cosmo-wrf

high-resolution

display_name: high-resolution

meteoswiss

display_name: meteoswiss

numerical-simulation

display_name: numerical-simulation


en:
There are many sources providing atmospheric weather station data for the Antarctic continent. However, variable naming, timestamps and data types are highly variable between the different sources. The published python code intends to make processing of different AWS sources from Antarctica easier. For all datasets that are taken into account variables are renamed in a consistent way. Data from different sources can then be handled in one consistent python dictionary.
The following data sources are taken into account:
* AAD: Australian Antarctic Division (https://data.aad.gov.au/aws)
* ACECRC: Antarctic Climate and Ecosystems Cooperative Research Centre by the Australian Antarctic Division
* AMRC: Antarctic Meteorological Research Center (ftp://amrc.ssec.wisc.edu/pub/aws/q1h/)
* BAS: British Antarctic Survey (ftp://ftp.bas.ac.uk/src/ANTARCTIC_METEOROLOGICAL_DATA/AWS/; https://legacy.bas.ac.uk/met/READER/ANTARCTIC_METEOROLOGICAL_DATA/)
* CLIMANTARTIDE: Antarctic Meteo-Climatological Observatory by the italian National Programme of Antarctic Research (https://www.climantartide.it/dataaccess/index.php?lang=en)
* IMAU: Institute for Marine and Atmospheric research Utrecht (Lazzara et al., 2012), https://www.projects.science.uu.nl/iceclimate/aws/antarctica.ph
* JMA: Japan Meteorological Agency (https://www.data.jma.go.jp/antarctic/datareport/index-e.html)
* NOAA: National Oceanic and Atmospheric Administration (https://gml.noaa.gov/aftp/data/meteorology/in-situ/spo/)
* Other/AWS_PE: Princess Elisabeth (PE), KU Leuven, Prof. N. van Lipzig, personal communication
* Other/DDU_transect: Stations D-17 and D-47 (in transect between Dumont d’Urville and Dome C, Amory, 2020)
* PANGAEA: World Data Center (e.g. König-Langlo, 2012)
Important notes
* Information about data sources is available. Some downloading scripts are included in the provided code. However, users should make sure to comply with the data providers terms and conditions.
* Given changing download options of the differnent institutions the above links may not permanently work and data has to be retrieved by the user of this dataset.
* No quality control is applied in the provided preprocessing software - quality control is up to the user of the datasets. Some dataset are quality controlled by the owner.
Acknowledgements
--------------------------
We thank all the data providers for making the data publicly available or providing them upon request. Full acknowledgements can be found in Gerber et al., submitted.
References
---------------
Amory, C. (2020). “Drifting-snow statistics from multiple-year autonomous measurements in Adélie Land, East
Antarctica”. The Cryosphere, 1713–1725. doi: 10.5194/tc-14-1713-2020
Gerber, F., Sharma, V. and Lehning, M.: CRYOWRF - a validation and the effect of blowing snow on the Antarctic SMB, JGR - Atmospheres, submitted.
König-Langlo, G. (2012). “Continuous meteorological observations at Neumayer station (2011-01)”. Alfred Wegener
Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, PANGAEA, doi: 10.1594/PANGAEA.
775173

en: consistent aws python cryowrf antarctica

en: Preprocessing Antarctic Weather Station (AWS) data in python

antarctica

display_name: antarctica

aws

display_name: aws

consistent

display_name: consistent

cryowrf

display_name: cryowrf

python

display_name: python