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

envidat

en: EnviDat: the Environmental Data Portal

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


$oid: OBSOLETE

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 intercomparison specific-surface-area icecube snow

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.

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

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

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

de: EnviDat: das Umwelt-Datenportal

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

type: dataset

key: guid

key: uri

value: Envidat Harvester

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

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.

format: XLS

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.

format: ZIP

media_type: application/zip

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: 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: 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.

format: PDF

media_type: application/pdf

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: 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.

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.

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

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

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

format: CSV

media_type: text/csv

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

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

envidat

en: EnviDat: the Environmental Data Portal

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:
Aim: While species distribution models (SDMs) are standard tools to predict species distributions, they can suffer from observation and sampling biases, particularly presence-only SDMs that often rely on species observations from non-standardized sampling efforts. To address this issue, sampling background points with a target-group strategy is commonly used, although more robust strategies and refinements could be implemented. Here, we exploited a dataset of plant species from the European Alps to propose and demonstrate efficient ways to correct for observer and sampling bias in presence-only models.
Innovation: Recent methods correct for observer bias by using covariates related to accessibility in model calibrations (classic bias covariate correction, Classic-BCC). However, depending on how species are sampled, accessibility covariates may not sufficiently capture observer bias. Here, we introduced BCCs more directly related to sampling effort, as well as a novel corrective method based on stratified resampling of the observational dataset before model calibration (environmental bias correction, EBC). We compared, individually and jointly, the effect of EBC and different BCC strategies, when modelling the distributions of 1’900 plant species. We evaluated model performance with spatial block split-sampling and independent test data, and assessed the accuracy of plant diversity predictions across the European Alps.
Main conclusions: Implementing EBC with BCC showed best results for every evaluation method. Particularly, adding the observation density of a target group as bias covariate (Target-BCC) displayed most realistic modelled species distributions, with a clear positive correlation (r≃0.5) found between predicted and expert-based species richness. Although EBC must be carefully implemented in a species-specific manner, such limitations may be addressed via automated diagnostics included in a provided R function. Implementing EBC and bias covariate correction together may allow future studies to address efficiently observer bias in presence-only models, and overcome the standard need of an independent test dataset for model evaluation.

en: Novel methods to correct for observer and sampling bias in presence-only species distribution models

en: plant-species independent-dataset covariate-correction cluster target-group environmental-stratification survey-effort point-process-model background-data random-stratified-sampling

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.

envi

en: Environment

background-data

display_name: background-data

cluster

display_name: cluster

covariate-correction

display_name: covariate-correction

environmental-stratification

display_name: environmental-stratification

independent-dataset

display_name: independent-dataset

plant-species

display_name: plant-species

point-process-model

display_name: point-process-model

random-stratified-sampling

display_name: random-stratified-sampling

survey-effort

display_name: survey-effort

target-group

display_name: target-group


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: Niche partitioning between wild bees and honeybees

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

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: 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: 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.

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: 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

beekeeping

display_name: beekeeping

bettergardens

display_name: bettergardens

gardens

display_name: gardens

honeybees

display_name: honeybees

pollinator-experiment

display_name: pollinator-experiment

resource-availability

display_name: resource-availability

urban-biodiversity

display_name: urban-biodiversity

urban-ecosystems

display_name: urban-ecosystems

urbanization

display_name: urbanization

wild-bees

display_name: wild-bees

zurich

display_name: zurich


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

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

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

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

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: 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.

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

envidat

en: EnviDat: the Environmental Data Portal

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

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

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.

envi

en: Environment

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:

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: 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:

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

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

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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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

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: 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: 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: 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: 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: 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: 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.

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 dataset contains all data, on which the following publication below is based.
**Paper Citation**:
Neff, F., Resch, M. C., Marty, A., Rolley, J. D., Schütz, M., Risch, A. C, Gossner, M. M. 2020. Long-term restoration success of insect herbivore communities in semi-natural grasslands: a functional approach. Ecological Applications, 30, e02133. [10.1002/eap.2133](https://doi.org/10.1002/eap.2133)
Please cite this paper together with the citation for the datafile.
 Methods
 Study site
The study area is situated within and nearby to Eigental nature reserve (47°27’36” to 47°29’06” N, 8°37’12” to 8°37’44” E, 461 to 507 m a.s.l.) in the vicinity of Zurich airport (Canton Zurich, Switzerland). Mean annual precipitation and temperature is 903 ± 136 mm and 9.14°C ± 0.50°C (mean ± SD for 2007-2017 (*MeteoSchweiz 2018*)). In 1967, the Eigental nature reserve was established to protect small and isolated remnants of species-rich, semi-natural grasslands (roughly 12 ha), which were embedded in an otherwise intensively managed landscape. It is characterized by oligo- to mesotrophic Molinion (semi-wet, matrix species *Molinia caerulea*) and Mesobromion (semi-dry, matrix species *Bromus erectus*) meadows (*Delarze et al. 2015*), reflecting small-scale habitat heterogeneity, mainly due to site-specific groundwater levels and slope inclination. As in most Central European grasslands, management is necessary to prevent shrub and tree invasions as well as to secure low levels of available soil nutrients and thus to maintain these species-rich habitats ([*Poschlod and WallisDeVries 2002*](https://doi.org/10.1016/S0006-3207(01)00201-4)).
In 1990, the government of the Canton Zurich decided to enlarge the Eigental nature reserve as a counter measure against degradation and biodiversity loss in semi-natural grasslands due to overutilization and the excessive input of nutrients (mostly nitrogen). Eleven patches of adjacent intensively managed grassland (in total roughly 20 ha) were targeted to be transformed into semi-natural grasslands. As a first restoration measure, fertilization was ceased, and biomass harvested three times to remove excessive soil nutrients from the original system and thus benefit plant species with low competitive ability on the long run. In 1995, the restoration efforts were increased and a large-scale experiment comprising three restoration measures with increasing intervention intensities was implemented:
- **Harvest only**: Initial restoration measures were continued with mowing and removing of the aboveground biomass two times a year (early summer and autumn).
- **Topsoil**: Removal of topsoil, depending on the thickness of the A horizon the upper 10 to 20 cm, in four randomly selected areas within the eleven patches in late autumn 1995. The size of the restoration area depended on individual patch size (2700 to 7000 m2).
- **Topsoil + Propagules**: Plant propagules were added on half of the area where topsoil was removed via application of fresh, seed-containing hay and hand-collected propagules of target species originating from semi-dry and semi-wet species-rich grasslands with local and regional provenance (within radius of 7 to 30 km) (1995, 1996, 1997).
Management of *Topsoil* and *Topsoil + Propagules* started five years after treatment implementation and included yearly mowing and removing of aboveground biomass (late summer or early autumn). The experiment was complemented with intensively managed grassland sites that share the same agricultural history as the restored sites (**Initial**; swards dominated by *Lolium perenne*, *L. multiflorum* and *Trifolium repens*): mowing and subsequent fertilizing (manure) up to five times a year, as well as different tillage regimes. Finally, sites were selected in target semi-dry and semi-wet grasslands (**Target**) located within the Eigental nature reserve and another nature reserve nearby (Altläufe der Glatt; 47°28’29” to 47°27’41” N, 8°31’56” to 8°32’26” E, 418 to 420 m a.s.l.). The selected target sites are mown and aboveground biomass removed once a year in late summer or early autumn. For each of the five treatments, we selected eleven plots (5 m × 5 m) spread across the sites. Altogether, the experiment included 55 plots.
 Arthropod sampling
Aboveground arthropods were sampled using suction sampling on four consecutive days in early July 2017 before the grasslands were mown. Arthropods were sampled in two locations on each 5 m × 5 m plot, once in the south-western and once in the north-eastern corner to account for possible spatial heterogeneity within the plots. Arthropods were sorted to order or lower taxonomic levels and individuals were identified to species level. We focused on three groups (Hemiptera: Auchenorrhyncha, Hemiptera: Heteroptera, Orthoptera),
 Functional traits
We used two sets of functional traits in this study.
**Morphometric traits**:
Body volume, body shape, hind femur shape, hind/front leg ratio, wing length, leg length, antenna length and eye width. We used trait measurements from [*Simons et al. (2016)*](http://dx.doi.org/10.1890/15-0616.1) and [*Neff et al. (2019)*](https://doi.org/10.1007/s10980-019-00872-1) and complemented them with measurements on study specimens. These measurements were conducted using a high-resolution measuring stereo microscope (Leica DVM6, Leica Microsystems) including automated high-resolution photo stacking with the software Leica Application Suite X (LAS X, © 2018 Leica Microsystems CMS GmbH) and Leica Map Premium (Leica Microsystems, © 1996-2017 Digital Surf) at WSL Birmensdorf. The eight morphometric traits were calculated from direct measurements of body parts on specimens of all sampled species. From each species, we measured at least one female and one male specimen. Additionally, for species that show wing dimorphism, we included the different wing morphs and weighted them by their prevalence reported in literature. For few species, of which not all wing morphs were available for measurements (10 cases), we estimated relative wing length from congeneric species or from the literature.
**Life-history traits**
Based on an existing data set collected by [*Gossner et al. (2015)*](http://dx.doi.org/10.1890/14-2159.1). We included traits describing different life-history characteristics of herbivore insect species, namely: feeding specialization, feeding tissue, hibernation stage and number of generations per year, which are related to insect species’ vulnerability to changes in plant community composition, microhabitat use and disturbance tolerance. To represent potential changes in habitat moisture with abandonment of intensive land use (e.g., change in ground-water level), we also included two traits related to preferred habitat moisture of the study species: moisture preference, describing species’ optimum habitat moisture, and moisture range, which describes the species’ range of preferable moisture conditions.
 References
Delarze, R., Y. Gonseth, S. Eggenberg, and M. Vust. 2015. Lebensräume der Schweiz: Ökologie - Gefährdung - Kennarten. 3rd ed. Ott, Bern.
Gossner, M. M., N. K. Simons, R. Achtziger, T. Blick, W. H. O. Dorow, F. Dziock, F. Köhler, W. Rabitsch, and W. W. Weisser. 2015. A summary of eight traits of Coleoptera, Hemiptera, Orthoptera and Araneae, occurring in grasslands in Germany. Scientific Data 2:150013.
MeteoSchweiz. 2018. Klimabulletin Jahr 2017. MeteoSchweiz, Zürich.
Neff, F., N. Blüthgen, M. N. Chisté, N. K. Simons, J. Steckel, W. W. Weisser, C. Westphal, L. Pellissier, and M. M. Gossner. 2019. Cross-scale effects of land use on the functional composition of herbivorous insect communities. Landscape Ecology 34:2001–2015.
Poschlod, P., and M. F. WallisDeVries. 2002. The historical and socioeconomic perspective of calcareous grasslands—lessons from the distant and recent past. Biological Conservation 104:361–376.
Simons, N. K., W. W. Weisser, and M. M. Gossner. 2016. Multi-taxa approach shows consistent shifts in arthropod functional traits along grassland land-use intensity gradient. Ecology 97:754–764.

en: Grassland restoration: insects and insect traits

en: functional-diversity grassland diversity functional-traits long-term-experiment insects topsoil-removal restoration

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: Insect abundance
    Size: 33818 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e4752eb3-24f1-48b5-a6c4-e0a4134af08d/resource/2d60fcd6-a2f1-45fd-a450-3d560eb43822/download/insect_abundance.csv
    Description: Abundance of adult insects recorded in the samplings on all experimental sites

- **Plot**: ID of Plot
- **Treatment**: Experimental treatment
- **Species**:  Insect species name (following [Fauna Europaea](https://fauna-eu.org))
- **Author**: Author and year of species' description (following [Fauna Europaea](https://fauna-eu.org))
- **Group**: Insect group as used in the analyses
- **Number_Adults**: Number of adult individuals recorded
- **Herbivore**: Logical indicating whether the species is feeding as a herbivore (*TRUE*) or not (*FALSE*)

en:
Resource Title: Insect traits
    Size: 11167 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e4752eb3-24f1-48b5-a6c4-e0a4134af08d/resource/bddffb02-61f5-44b1-b96a-92bb043415b4/download/insect_traits.csv
    Description: Functional traits of all herbivorous insect species sampled in the study.

- **Species**: Insect species name (following [Fauna Europaea](https://fauna-eu.org))
- **Author**: Author and year of species' description (following [Fauna Europaea](https://fauna-eu.org))
- **Group**: Insect group as used in the analyses
- **Body volume**: Body volume determined by body length, body width and body height (in mm$^3$)
- **Body_shape**: Ratio of body length to body width
- **Hind_femur_shape**: Ratio of hind-femur length to hind-femur width
- **Hind_front_leg_ratio**: Ratio of front-leg length to hind-leg length (leg length = femur length + tibia length)
- **Wing_length_rel**: Ratio of wing length to body length
- **Leg_length_rel**: Ratio of front-leg length to body length (leg length = femur length + tibia length)
- **Antenna_length_rel**: Ratio antenna length to body length
- **Eye_width_rel**: Ratio of eye width to head width (Auchenorrhyncha, Heteroptera) / to head height (Orthoptera)
- **Feeding_specialization**: Insect specialization on food source (*m*: monophagous, *o*: oligophagous, *p*: polyphagous)
- **Feeding_tissue**: Feeding tissue used for feeding (*c*: chewers, *x*: xylem, *p*: phloem, *m*: mesophyll, *m-p/r*: mesophyll as well as phloem and/or reproductive organs, *r*: reproductive organs, *se*: ripe seeds)
- **Hibernation**: Hibernation stage (*e*: egg, *e/n*: egg and nymph, *n*: nymph, *a*: adult)
- **Generations**: Number of generations per year (*1.5* stands for *1-2* generations per year)
- **Moisture_pref_mean**: mean of preferred habitat moistures (*1*: xerophilous, *2*: moderately xerophilous, *3*: moderately mesohygrophilous, *4*: mesohygrophilous, *5*: moderately hygrophilous, *6*: hygrophilous, *7*: moderately hydrophilous, *8*: hydrophilous)
- **Moisture_pref_range**: range of preferred habitat moistures (indicating degree of specialization)

en:
Resource Title: Plot information
    Size: 4938 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e4752eb3-24f1-48b5-a6c4-e0a4134af08d/resource/04c37490-b0c2-4681-aded-068fa4ee8290/download/plot_info.csv
    Description: Information on the experimental plots.

- **Plot**: ID of Plot
- **Treatment**: Experimental treatment
- **Landuse_history**: recent agricultural use for Initial; agricultural use before restoration for all restored sites
- **Last_tilled**: Last time the site was tilled
- **Intensity**: Frequency and intensity of mowing and manuring (*intensity1*: mowing once and no manure, i*ntensity2*: 2-3 times mowing and no manure, *intensity3*: 2-3 times mowing and manuring, *intensity4*: 4-5 times mowing and manuring)
- **Slope**: slope gradient according to classes defined by FAO (1990)
- **X**: X coordinate of the plot (CH1903)
- **Y**: Y coordinate of the plot (CH1903)
- **Cluster**: Spatial cluster of the plot

diversity

display_name: diversity

functional-diversity

display_name: functional-diversity

functional-traits

display_name: functional-traits

grassland

display_name: grassland

insects

display_name: insects

long-term-experiment

display_name: long-term-experiment

restoration

display_name: restoration

topsoil-removal

display_name: topsoil-removal


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

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

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: 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: 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


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: Observed and simulated snow profile data

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

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: 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: 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: 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.

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.

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


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: intratrait

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

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

title_for_slug: intratrait

envi

en: Environment

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 high-resolution complex-terrain numerical-simulation cosmo-wrf

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: 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: 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: 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).

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: 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)

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


rorcc

en:
The dataset "RoRCC" consists of simulation-based results on climate change impacts on Alpine RoR power production; it is based on 21 Swiss RoR power plants, with a total production of 5.9 TWh a-1. The dataset contains the following information:
1) metadata on the RoR power plants under consideration, 2) annual and seasonal production potential scenarios under into three emission scenarios (RCP2.6, RCP4.5, RCP8.5) and three future periods (T1: 2020–2049, T2: 2045–2074, T3: 2070–2099), 3) annual and seasonal streamflow scenarios, 4) annual and seasonal production loss due to environmental flow requirements, 5) annual and seasonal the technical increase potential (via design discharge optimisation) and 6) annual and seasonal changes in the hydrological cycle.

en: RoRCC

en: environmental-flow hydropower run-of-river-power-plants climate-change design-discharge

title_for_slug: rorcc

design-discharge

display_name: design-discharge

environmental-flow

display_name: environmental-flow

hydropower

display_name: hydropower

run-of-river-power-plants

display_name: run-of-river-power-plants


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: Preprocessing Antarctic Weather Station (AWS) data in python

en: consistent aws python antarctica cryowrf

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.

antarctica

display_name: antarctica

aws

display_name: aws

consistent

display_name: consistent

cryowrf

display_name: cryowrf

python

display_name: python


en:
Based on the detailed tree stump inventory implemented in the Swiss NFI5 (https://www.lfi.ch/lfi/lfi.php), a study was conducted to obtain an accurate assessment of the stumps pool in the Swiss NFI over the last 30 years and to identify its significance for the total dead wood (DW) pool. The NFI5 includes a detailed stump inventory to improve accuracy and completeness of the above-ground DW- pool. Based on available data, stump volume estimates were derived at different accuracies to evaluate the contribution to the total DW-pool over time.
The study found that in Swiss Forests the contribution of stumps to total DW-pool is approximately 25%, and that applying simplifying assumptions to estimate stump volume can result in a significant underestimation of the true size of this pool. This study demonstrates that stumps can be a significant proportion of DW in forests, which should be accounted for in order to improve accuracy and completeness of NFI estimates and derived data such as C stocks for greenhouse gas reporting.
The study is published in Annals of Forest Science (2022) 79:34, https://doi.org/10.1186/s13595-022-01155-7 (open access).
The data can be obtained from the authors upon reasonable request.

en: Stumps as a dead wood resource in forests - data based on the Swiss National Forest Inventory

en: biomass national-forest-inventory biodiversity carbon volume protection-forest

envidat

en: EnviDat: the Environmental Data Portal

envi

en: Environment

en:
Resource Title: NFI3
    Size: 482387 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/9d7ccc56-42e8-4005-9cd1-648e0205a82b/resource/f8dc0cfb-50f1-4f74-b428-27b8a690e406/download/nfi3.zip
    Description: Data on dead standing and downed trees, lying dead wood, and stumps for the NFI3

en:
Resource Title: NFI1
    Size: 42999 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/9d7ccc56-42e8-4005-9cd1-648e0205a82b/resource/9f931dea-8d9b-4d16-b8bd-d933a98e8d9e/download/nfi1.zip
    Description: Data on dead standing and downed trees for the NFI1

en:
Resource Title: NFI4
    Size: 526483 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/9d7ccc56-42e8-4005-9cd1-648e0205a82b/resource/94d92a0e-4b3a-43e0-9c1b-1293acda5938/download/nfi4.zip
    Description: Data on dead standing and downed trees, lying dead wood, and stumps for the NFI4

en:
Resource Title: NFI5
    Size: 488946 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/9d7ccc56-42e8-4005-9cd1-648e0205a82b/resource/b27ae215-7eff-4546-9d08-b0c15d82a6e0/download/nfi5.zip
    Description: Data on dead standing and downed trees, lying dead wood, and stumps for the NFI5

en:
Resource Title: Site Description
    Size: 510752 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9d7ccc56-42e8-4005-9cd1-648e0205a82b/resource/723bfcc7-f2b5-4030-94d5-86bac0f52250/download/sites_2022-03-09.csv
    Description: information on NFI sites throughout the five NFIs considered in this study

en:
Resource Title: ReadMe_Files
    Size: 1278 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9d7ccc56-42e8-4005-9cd1-648e0205a82b/resource/8de64d6a-4d7a-4233-a5ab-c4485b1f40a6/download/readme_files.csv
    Description: Files and contents

en:
Resource Title: Artikel - Annals of Forest Science
    Size: 1673213 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/9d7ccc56-42e8-4005-9cd1-648e0205a82b/resource/dd13b5a9-758e-4416-ac74-446370307374/download/didionabegg2022.pdf
    Description:

en:
Resource Title: Lanz 2014
    Size: 105307 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/9d7ccc56-42e8-4005-9cd1-648e0205a82b/resource/72f417d6-3d19-4790-831c-9511f2169b8d/download/international_de.pdf
    Description: A short overview over stump assessment (field protocol) in some European NFIs

en:
Resource Title: ReadMe_Variables
    Size: 3109 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9d7ccc56-42e8-4005-9cd1-648e0205a82b/resource/82cad928-4741-415e-a3c7-f19ca4a0d72e/download/readme_variables.csv
    Description: Variable definitions for the datasets NFI1, NFI2, NFI3, NFI4 and NFI5

en:
Resource Title: NFI2
    Size: 78643 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/9d7ccc56-42e8-4005-9cd1-648e0205a82b/resource/69071e62-9dc2-4b61-91c1-322b0c340a60/download/nfi2.zip
    Description: Data on dead standing and downed trees for the NFI2

biodiversity

display_name: biodiversity

biomass

display_name: biomass

carbon

display_name: carbon

national-forest-inventory

display_name: national-forest-inventory

protection-forest

display_name: protection-forest

volume

display_name: volume


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

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

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

en:
Resource Title: Manual snow pits and other snow density measurements
    Size: 30761 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/02867726-da8e-4e54-ada7-058649af366b/resource/b234793d-1a5f-47f8-a7e2-c9e6099dbb7a/download/snowpits.txt
    Description: Manual snow pits and other snow density measurements

format: TXT

media_type: text/plain

en:
Resource Title: Python code exported from the Jupyter notebook for processing the SnowMicroPen files
    Size: 5439 bytes
    Format: text/x-python
    URL: https://www.envidat.ch/dataset/02867726-da8e-4e54-ada7-058649af366b/resource/80f9bd9a-ba5f-461d-87ad-b0d65b0bb7ed/download/smp.py
    Description: Python code exported from the Jupyter notebook for processing the SnowMicroPen files

en:
Resource Title: SnowMicroPen pts files and processed txt files
    Size: 579091098 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/02867726-da8e-4e54-ada7-058649af366b/resource/3072110f-5284-4b7c-84c7-81ed98d3e6ee/download/smp_files.zip
    Description: Zip archive containing the acquired *.pts files (binary files recorded by the SnowMicroPen), and the converted *.txt files using the SMP.ipynb jupyter notebook (see the python code in the other resources in this dataset)

en:
Resource Title: List of SnowMicroPen measurements
    Size: 182060 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/02867726-da8e-4e54-ada7-058649af366b/resource/f993bafd-41b1-4978-884b-ee7e721a7f95/download/smp_list.txt
    Description: List of all SnowMicroPen measurements, including date and location, and processing status. This list has been generated by the python code (see other resources in this dataset).

en:
Resource Title: Documentation SnowMicroPen measurements
    Size: 94028379 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/02867726-da8e-4e54-ada7-058649af366b/resource/4c1ab2e9-cfad-463b-bf94-b9d55794371a/download/documentation.zip
    Description: This *.zip archive contains all available documentation of the SnowMicroPen measurements, including the mapping of the SMP measurements in the terrestrial laser scan field at PEA (see documentation for 20161220) and at the Hammarryggen Ice Rise (see documentation for 20190104 and 20190111).

en:
Resource Title: Jupyter notebook for processing the SnowMicroPen files
    Size: 7240 bytes
    Format: application/x-ipynb+json
    URL: https://www.envidat.ch/dataset/02867726-da8e-4e54-ada7-058649af366b/resource/b18e5359-0134-47d6-aaf9-9f5a04a71543/download/smp.ipynb
    Description: Jupyter notebook for processing the SnowMicroPen files.

microstructure

display_name: microstructure

snow-density

display_name: snow-density

snow-density-profile

display_name: snow-density-profile

snowmicropen

display_name: snowmicropen


en: This questionnaire survey was conducted as an online survey and aimed at investigating the relationship between physical forest characteristics, visual attractiveness of forest and the perception of ecological and cultural ecosystem services in urban forests. Each participant was shown 6 photos out of a pool of 50 photos taken from the Swiss National Forest Inventory (NFI) database. Physical forest characteristics were derived from the photos. The study was conducted as part of the "WaMos meets LFI" (WML) project.

en: Relationship between physical forest characteristics, visual attractiveness and perception of ecosystem services in urban forests

en: online-questionnaire-survey nfi-photos visual-attractiveness perception-of-ecosystem-services adults-and-teenagers

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: Codebook to SPSS-file
    Size: 325674 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4b2ca666-a329-4445-98c1-8d7ce3bca5a5/resource/8a7f9645-ca1b-4fb3-8d9c-94240c0e235e/download/codebookwmlbilderstudie.pdf
    Description:

en:
Resource Title: Questionnaire Italian
    Size: 10955256 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4b2ca666-a329-4445-98c1-8d7ce3bca5a5/resource/f4fe792f-05dc-4896-8b1e-541611d9f21c/download/umfrageerwachseneitalienisch.pdf
    Description:

en:
Resource Title: SPSS-Dataset
    Size: 9307772 bytes
    Format: application/x-spss-sav
    URL: https://www.envidat.ch/dataset/4b2ca666-a329-4445-98c1-8d7ce3bca5a5/resource/795a1d55-ec18-4153-9c93-e93109a67bd5/download/umfrage_mitlfi2_2.sav
    Description:

en:
Resource Title: Questionnaire French
    Size: 16449968 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4b2ca666-a329-4445-98c1-8d7ce3bca5a5/resource/ee508ecd-8d0f-4be5-b985-69acfdc3fb24/download/umfrageerwachsenefranzosisch.pdf
    Description:

en:
Resource Title: Questionnaire German
    Size: 15478868 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4b2ca666-a329-4445-98c1-8d7ce3bca5a5/resource/31a62f4f-08ae-4baa-98af-f742ea96bc1d/download/umfrageerwachsenedeutsch.pdf
    Description:

en:
Resource Title: Dataset in csv-format
    Size: 5628069 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/4b2ca666-a329-4445-98c1-8d7ce3bca5a5/resource/ced93b4f-1424-4a54-ad92-753d96133fab/download/umfrage_mitlfi2_2.csv
    Description:

adults-and-teenagers

display_name: adults-and-teenagers

nfi-photos

display_name: nfi-photos

online-questionnaire-survey

display_name: online-questionnaire-survey

perception-of-ecosystem-services

display_name: perception-of-ecosystem-services

visual-attractiveness

display_name: visual-attractiveness


en:
The wind-driven saltation of sand and snow shapes dunes and ripples, generates dust emission, and erodes the surface of the Antarctic ice sheet. Here, we use a model based on the discrete element method to simulate grain-flow interactions and study the effect of particle cohesion on saltation dynamics.
The data contains the model output of granular splash simulations and saltation simulations. Granular splash, the main particle entrainment process in saltation, occurs upon impact of saltating particles with the granular bed. We performed Monte Carlo simulations of granular splash for loose sand grains and for cohesive ice grains. The analysis indicate that different values of cohesion have significant effects not on the number of splashed grains, on the ejection velocity, and the rebound velocity.
In our saltation simulations, we trigger particle movement with a single splash event at the inlet section section and let the system evolve until steady state. Our results show that saltation over cohesive surfaces is difficult to initiate but easy to sustain at low wind speed. The occurrence of transport thus depends on the history of the wind speed, a phenomenon known as hysteresis. We also show that saltation over cohesive surfaces presents higher mass fluxes but requires longer distances to saturate, which increases the size of the smallest stable surface ripples. Our model results have implications for large-scale aeolian processes on Earth and Titan, where sand grains are thought to be very cohesive.

en: Saltation of cohesive granular materials

en: sand sediment saltation snow cohesion wind

cohesion

display_name: cohesion

saltation

display_name: saltation

sand

display_name: sand

sediment

display_name: sediment

snow

display_name: snow

wind

display_name: wind


disdrometer-data-laret

en:
A laser optical disdrometer (Parsivel&178; , OTT Hydromet) was used to measure hydrometeors by extinction when passing a laser beam. The instrument can classify eight different kinds of precipitation, including rain, hail, snow, drizzle, and hybrid forms.
The dataset contains information on precipitation amount and type for the period of February 7 to March 29 2019 in Laret.

en: Disdrometer Data Laret

en: disdrometer precipitation hydrometeors visibility raclets

title_for_slug: disdrometer-data-laret

disdrometer

display_name: disdrometer

hydrometeors

display_name: hydrometeors

precipitation

display_name: precipitation

raclets

display_name: raclets

visibility

display_name: visibility


disdrometer-data-gotschnagrat

en: A laser optical disdrometer (Parsivel² , OTT Hydromet) was deployed at Gotschnagrat (LON: 9.849, LAT: 46.859) to measure hydrometeors by extinction when passing a laser beam. The instrument can classify eight different kinds of precipitation, including rain, hail, snow, drizzle, and hybrid forms. The dataset contains information on precipitation amount and type for the period of February 11 to March 27 2019 at Gotschnagrat.

en: Disdrometer Data Gotschnagrat

en: hydrometeors raclets disdrometer precipitation visibility

title_for_slug: disdrometer-data-gotschnagrat


en:
This repository contains the source code of the analysis presented in the related paper. The code can be found in the following github repository: https://github.com/Chelmy88/temporal_downscaling
This code can be used to perform temporal downscaling of meteorological time series from daily to hourly time steps and to perform the quality assessment described in the paper.
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

en: Source code for: Climate change scenarios at hourly time-step over Switzerland from an enhanced temporal downscaling approach

en: alpine-regions climate-change-scenarios delta-change quantile-mapping switzerland meteoswiss python temporal-downscaling r-script imis

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:
Resource Title: 2_assessment
    Size: 12817 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/73aa91a9-61b6-473c-9962-8d4c28016191/resource/8990361d-87b0-4320-9d79-7788832ffa1d/download/2_assessment.zip
    Description: Python and R scripts to perform the data quality analysis

en:
Resource Title: LICENSE
    Size: 35149 bytes
    Format: No Info
    URL: https://www.envidat.ch/dataset/73aa91a9-61b6-473c-9962-8d4c28016191/resource/f8e60185-27c1-4abf-ba3e-e272d86c52c8/download/license
    Description:

en:
Resource Title: README
    Size: 2098 bytes
    Format: text/markdown
    URL: https://www.envidat.ch/dataset/73aa91a9-61b6-473c-9962-8d4c28016191/resource/0ebfca71-d997-4586-93fe-7b6fcce8bc0f/download/readme.md
    Description:

format: markdown

en:
Resource Title: 1_downscaling
    Size: 13945 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/73aa91a9-61b6-473c-9962-8d4c28016191/resource/3aa56f6a-4e60-4ba7-866a-e27b06315bea/download/1_downscaling.zip
    Description: Python scripts to perform the temporal downscaling

alpine-regions

display_name: alpine-regions

climate-change-scenarios

display_name: climate-change-scenarios

delta-change

display_name: delta-change

imis

display_name: imis

meteoswiss

display_name: meteoswiss

python

display_name: python

quantile-mapping

display_name: quantile-mapping

r-script

display_name: r-script

switzerland

display_name: switzerland

temporal-downscaling

display_name: temporal-downscaling


en:
Dataset description-br /-
This dataset is a recalculation of the Copernicus 2015 high resolution layer (HRL) of imperviousness density data (IMD) at different spatial/territorial scales for the case studies of Barcelona and Milan. The selected spatial/territorial scales are the following:
 * a)	Barcelona city boundaries
 * b)	Barcelona metropolitan area, Àrea Metropolitana de Barcelona (AMB)
 * c)	Barcelona greater city (Urban Atlas)
 * d)	Barcelona functional urban area (Urban Atlas)
 * e)	Milan city boundaries
 * f)	Milan metropolitan area, Piano Intercomunale Milanese (PIM)
 * g)	Milan greater city (Urban Atlas)
 * h)	Milan functional urban area (Urban Atlas)-br /-
In each of the spatial/territorial scales listed above, the number of 20x20mt cells corresponding to each of the 101 values of imperviousness (0-100% soil sealing: 0% means fully non-sealed area; 100% means fully sealed area) is provided, as well as the converted measure into squared kilometres (km2). -br /- -br /-
-br /- Dataset composition-br /-
The dataset is provided in .csv format and is composed of: -br /-
_IMD15_BCN_MI_Sources.csv_: Information on data sources -br /-
 _IMD15_BCN.csv_: This file refers to the 2015 high resolution layer of imperviousness density (IMD) for the selected territorial/spatial scales in Barcelona:
 * a)	Barcelona city boundaries (label: bcn_city)
 * b)	Barcelona metropolitan area, Àrea metropolitana de Barcelona (AMB) (label: bcn_amb)
 * c)	Barcelona greater city (Urban Atlas) (label: bcn_grc)
 * d)	Barcelona functional urban area (Urban Atlas) (label: bcn_fua)-br /-
 _IMD15_MI.csv_: This file refers to the 2015 high resolution layer of imperviousness density (IMD) for the selected territorial/spatial scales in Milan:
 * e)	Milan city boundaries (label: mi_city)
 * f)	Milan metropolitan area, Piano intercomunale milanese (PIM) (label: mi_pim)
 * g)	Milan greater city (Urban Atlas) (label: mi_grc)
 * h)	Milan functional urban area (Urban Atlas) (label: mi_fua)-br /-
_IMD15_BCN_MI.mpk_: the shareable project in Esri ArcGIS format including the HRL IMD data in raster format for each of the territorial boundaries as specified in letter a)-h). -br /-
Regarding the territorial scale as per letter f), the list of municipalities included in the Milan metropolitan area in 2016 was provided to me in 2016 from a person working at the PIM. -br /-
In the IMD15_BCN.csv and IMD15_MI.csv, the following columns are included:
 * Level: the territorial level as defined above (a)-d) for Barcelona and e)-h) for Milan);
 * Value: the 101 values of imperviousness density expressed as a percentage of soil sealing (0-100%: 0% means fully non-sealed area; 100% means fully sealed area);
 * Count: the number of 20x20mt cells corresponding to a certain percentage of soil sealing or imperviousness;
 * Km2: the conversion of the 20x20mt cells into squared kilometres (km2) to facilitate the use of the dataset.-br /- -br /-
-br /- Further information on the Dataset-br /-
This dataset is the result of a combination between different databases of different types and that have been downloaded from different sources. Below, I describe the main steps in data management that resulted in the production of the dataset in an Esri ArcGIS (ArcMap, Version 10.7) project.-br /-
 1. The high resolution layer (HRL) of the imperviousness density data (IMD) for 2015 has been downloaded from the official website of Copernicus. At the time of producing the dataset (April/May 2021), the 2018 version of the IMD HRL database was not yet validated, so the 2015 version was chosen instead. The type of this dataset is raster.
 2. For both Barcelona and Milan, shapefiles of their administrative boundaries have been downloaded from official sources, i.e. the ISTAT (Italian National Statistical Institute) and the ICGC (Catalan Institute for Cartography and Geology). These files have been reprojected to match the IMD HRL projection, i.e. ETRS 1989 LAEA.
 3. Urban Atlas (UA) boundaries for the Greater Cities (GRC) and Functional Urban Areas (FUA) of Barcelona and Milan have been checked and reconstructed in Esri ArcGIS from the administrative boundaries files by using a Eurostat correspondence table. This is because at the time of the dataset creation (April/May 2021), the 2018 Urban Atlas shapefiles for these two cities were not fully updated or validated on the Copernicus Urban Atlas website. Therefore, I had to re-create the GRC and FUA boundaries by using the Eurostat correspondence table as an alternative (but still official) data source. The use of the Eurostat correspondence table with the codes and names of municipalities was also useful to detect discrepancies, basically stemming from changes in municipality names and codes and that created inconsistent spatial features. When detected, these discrepancies have been checked with the ISTAT and ICGC offices in charge of producing Urban Atlas data before the final GRC and FUA boundaries were defined.-br /-
Steps 2) and 3) were the most time consuming, because they required other tools to be used in Esri ArcGIS, like spatial joins and geoprocessing tools for shapefiles (in particular dissolve and area re-calculator in editing sessions) for each of the spatial/territorial scales as indicated in letters a)-h). -br /-
Once the databases for both Barcelona and Milan as described in points 2) and 3) were ready (uploaded in Esri ArcGIS, reprojected and their correctness checked), they have been ‘crossed’ (i.e. clipped) with the IMD HRL as described in point 1) and a specific raster for each territorial level has been calculated. The procedure in Esri ArcGIS was the following:
 * Clipping: Arctoolbox - Data management tools - Raster - Raster Processing - Clip. The ‘input’ file is the HRL IMD raster file as described in point 1) and the ‘output’ file is each of the spatial/territorial files. The option "Use Input Features for Clipping Geometry (optional)” was selected for each of the clipping.
 * Delete and create raster attribute table: Once the clipping has been done, the raster has to be recalculated first through Arctoolbox - Data management tools - Raster - Raster properties - Delete Raster Attribute Table and then through Arctoolbox - Data management tools - Raster - Raster properties - Build Raster Attribute Table; the "overwrite" option has been selected. -br /-
-br /- Other tools used for the raster files in Esri ArcGIS have been the spatial analyst tools (in particular, Zonal - Zonal Statistics). As an additional check, the colour scheme of each of the newly created raster for each of the spatial/territorial attributes as per letters a)-h) above has been changed to check the consistency of its overlay with the original HRL IMD file. However, a perfect match between the shapefiles as per letters a)-h) and the raster files could not be achieved since the raster files are composed of 20x20mt cells.-br /-
The newly created attribute tables of each of the raster files have been exported and saved as .txt files. These .txt files have then been copied in the excel corresponding to the final published dataset.

en: Soil sealing Barcelona and Milan different territorial levels

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

envi

en: Environment

en:
Resource Title: IMD15_BCN_MI_Sources
    Size: 2730 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/34fc9c0c-1c90-4f87-b3a9-76cc0e888eda/resource/d023c81b-4318-492a-abbf-44e429e9855d/download/imd15_bcn_mi_sources.csv
    Description: Information on data sources

en:
Resource Title: IMD15_MI
    Size: 9229 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/34fc9c0c-1c90-4f87-b3a9-76cc0e888eda/resource/e4750d6b-e92c-4266-8d15-a04a673ba248/download/imd15_mi.csv
    Description: 2015 high resolution layer of imperviousness density (IMD) for the selected territorial/spatial scales in Milan

en:
Resource Title: IMD15_BCN
    Size: 9402 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/34fc9c0c-1c90-4f87-b3a9-76cc0e888eda/resource/b49ab377-1058-44b0-9db3-eae5af4bb518/download/imd15_bcn.csv
    Description: 2015 high resolution layer of imperviousness density (IMD) for the selected territorial/spatial scales in Barcelona

en:
Resource Title: IMD15_BCN_MI
    Size: 9649908 bytes
    Format: .mpk
    URL: https://www.envidat.ch/dataset/34fc9c0c-1c90-4f87-b3a9-76cc0e888eda/resource/feb17f6a-9d3d-49d7-b072-654e3d210975/download/imd15_bcn_mi.mpk
    Description: Shareable project in Esri ArcGIS format including the HRL IMD data in raster format for each of the territorial boundaries as specified in letter a)-h), see Dataset description

barcelona

display_name: barcelona

copernicus

display_name: copernicus

imperviousness

display_name: imperviousness

milan

display_name: milan

soil-sealing

display_name: soil-sealing

urban-atlas

display_name: urban-atlas


en:
The R script IRFnnhs.R, which efficiently estimates impulse response functions for environmental systems that are nonlinear, nonstationary, or heterogeneous, based on their input and output time series.
Scripts and results for a series of benchmark tests are also provided, to accompany Kirchner, J.W., Impulse response functions for heterogeneous, nonstationary, and nonlinear systems, estimated by deconvolution and demixing of noisy time series, _Sensors_, 22(9), 3291, https://doi.org/10.3390/s22093291, 2022.

en: Impulse response functions for nonlinear nonstationary and heterogeneous systems

en: system-identification transfer-function arma-noise deconvolution time-series-analysis irfnnhsr de-mixing greens-function irf impulse-response-function nonlinear-deconvolution

en:
Resource Title: README file for IRFnnhs.R and benchmark demonstration scripts
    Size: 1315 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/c093641a-874e-4f8e-a04d-62a07d3855e1/resource/de609e80-41d7-41d8-8c73-a58d2c2fdc83/download/irf-code-folder-readme.txt
    Description:

en:
Resource Title: Benchmark demonstration scripts and results
    Size: 27341717 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/c093641a-874e-4f8e-a04d-62a07d3855e1/resource/b2a57bb7-8466-4b71-9837-d500e8fa0226/download/benchmark-demonstration-scripts-and-results.zip
    Description:

en:
Resource Title: Impulse Response Function script
    Size: 123160 bytes
    Format: text/x-r
    URL: https://www.envidat.ch/dataset/c093641a-874e-4f8e-a04d-62a07d3855e1/resource/7fb322da-6bd9-4e68-a8e7-33383b1db36c/download/irfnnhs.r
    Description:

arma-noise

display_name: arma-noise

de-mixing

display_name: de-mixing

deconvolution

display_name: deconvolution

greens-function

display_name: greens-function

impulse-response-function

display_name: impulse-response-function

irf

display_name: irf

irfnnhsr

display_name: irfnnhsr

nonlinear-deconvolution

display_name: nonlinear-deconvolution

system-identification

display_name: system-identification

time-series-analysis

display_name: time-series-analysis

transfer-function

display_name: transfer-function