en: Raw data and R Code to do all the analysis performed for the paper "Advancing sustainable LED solutions to mitigate light-pollution impacts on arthropods". Including data for both flight-active insects and ground-dwelling arthropods combined (PT_TRF_all.csv), flight-active insects alone (ALANeX_PT_all_clean_control.csv) and ground-dwelling insects alone (ALANeX_TRF_all_clean_control.csv). For the NMDS analysis, there is a data set for the flight-active insects (ALANeX_PT_NMDS.csv) and the ground-dwelling arthropods (ALANeX_TRF_NMDS.csv). For the Morans I use the 'ALANeX_MoransI.csv' dataset.

en: outdoor-lighting sustainability artificial-lighting alan ecosystem-services

en:
Resource Title: ALANeX_PT_all_clean_control.csv
    Size: 82055 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/f85c7171-2635-43ba-81f5-6b01b56500d2/resource/aa8b6e52-c154-482c-a4db-2a9ff2ceaff2/download/alanex_pt_all_clean_control.csv
    Description: Raw data for flying insects analysis

en:
Resource Title: ALANeX_TRF_all_clean_control.csv
    Size: 62191 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/f85c7171-2635-43ba-81f5-6b01b56500d2/resource/388d1352-f4ab-4c80-9439-b47ff1b2d84a/download/alanex_trf_all_clean_control.csv
    Description: Raw data for ground-dwelling arthropod analysis

en:
Resource Title: ALANeX_PT_NMDS.csv
    Size: 34799 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/f85c7171-2635-43ba-81f5-6b01b56500d2/resource/92df1cc5-e8f7-4ed8-98b6-8f512edd3504/download/alanex_pt_nmds.csv
    Description: Raw data for flying insects NMDS analysis

en:
Resource Title: PT_NMDS.R
    Size: 5615 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/f85c7171-2635-43ba-81f5-6b01b56500d2/resource/1b4bf7ce-faae-43da-a051-dce85fb4a35b/download/pt_nmds.r
    Description: R code for NMDS of flight-active insects

en:
Resource Title: TRF_NMDS.R
    Size: 5646 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/f85c7171-2635-43ba-81f5-6b01b56500d2/resource/78ab68b0-cb5a-4228-b165-0a9249833467/download/trf_nmds.r
    Description: R code for NMDS of ground-dwelling arthropods

en:
Resource Title: ALANeX_TRF_NMDS.csv
    Size: 50258 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/f85c7171-2635-43ba-81f5-6b01b56500d2/resource/7aa3a91d-3ed9-477c-aa15-68c42f50392b/download/alanex_trf_nmds.csv
    Description: Raw data for ground-dwelling arthropod NMDS analysis

en:
Resource Title: PERMANOVA_PT_NMDS.R
    Size: 866 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/f85c7171-2635-43ba-81f5-6b01b56500d2/resource/80cd6f62-ec98-4675-a3fd-2dff6894fc7f/download/permanova_pt_nmds.r
    Description: R code for PERMANOVA Analysis of flight-active insects.

en:
Resource Title: Regression_TRF_Taxa.R
    Size: 12379 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/f85c7171-2635-43ba-81f5-6b01b56500d2/resource/33788f24-dd2b-4137-b2f6-878ac0f98e88/download/regression_trf_taxa.r
    Description: R code of ground-dwelling arthropod analysis.

en:
Resource Title: PT_TRF_all.csv
    Size: 40556 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/f85c7171-2635-43ba-81f5-6b01b56500d2/resource/3aecf663-0494-4d8d-9a9b-70ee70584c33/download/pt_trf_all.csv
    Description: Raw data for flying insects and ground-dwelling arthropod combined analysis

en:
Resource Title: PERMANOVA_TRF_NMDS.R
    Size: 880 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/f85c7171-2635-43ba-81f5-6b01b56500d2/resource/6733f71f-1d72-4730-8bfe-e1158349c383/download/permanova_trf_nmds.r
    Description: R code for PERMANOVA Analysis of ground-dwelling arthropods.

en:
Resource Title: Regression_PT_Taxa.R
    Size: 12359 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/f85c7171-2635-43ba-81f5-6b01b56500d2/resource/a24b15bc-039a-4611-9efc-3bacd43be0b9/download/regression_pt_taxa.r
    Description: R code of flight-active insects analysis.

en:
Resource Title: ALANeX_MoransI.csv
    Size: 70008 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/f85c7171-2635-43ba-81f5-6b01b56500d2/resource/352f7a23-41a2-4160-b5da-000ca56511bb/download/alanex_moransi.csv
    Description: Raw data Moran's I test

en:
Resource Title: Regression_PT&TRF_AllGroups.R
    Size: 6609 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/f85c7171-2635-43ba-81f5-6b01b56500d2/resource/6cede3a0-0f23-4660-b8b9-d7b4b2a6dd51/download/regression_pttrf_allgroups.r
    Description: R code of flight-active and ground-dwelling arthropod combined analysis.

alan

display_name: alan

artificial-lighting

display_name: artificial-lighting

ecosystem-services

display_name: ecosystem-services

outdoor-lighting

display_name: outdoor-lighting

sustainability

display_name: sustainability


$oid: OBSOLETE

en:
Data associated to the publication "Rapid field-based detection of a threatened and elusive species with environmental DNA and CRISPR-Dx"
-br-
 Authors:
-br-
Flurin Leugger, Martina Lüthi, Michel Schmidlin, Zacharias Kontarakis and Loïc Pellissier
-br-
 Contact:
-br-
flurinleugger@gmail.com
loic.pellissier@usys.ethz.ch
-br-
 Project description:
-br-
We collected eDNA samples in the Conkouati-Douli National Park in the Republic of Congo in 2023 in collaboration with Beauval Nature and HELP Congo. We analyzed the samples on site in the national park using CRISPR-Dx assays to detect the African manatee (*Trichechus senegalensis*). Additionally, we transported the remaining samples (buffer) to Switzerland and used a high-sensitivity protocol with CRISPR-Dx assays and a qPCR assay to verify the field-based detections. The qPCR of Hunter et al. (2018) detects the genus manatee (Trichechus). See publication for more details (doi will be added after publication).
-br-
 Folders:
-br-
 lateral_flow_tests_images\:
-br-
Images of the lateral flow tests used to analyze the eDNA extracts in the national park. File names correspond to the site names. At each site, we took two filter replicates. We ran 8 PCRs per filter/extract and pooled two PCRs together, resulting in 4 CRISPR-Dx assays per extract. The EVE number on top of the tests indicates the environmental extract number we use to track the extracts in our data base. It corresponds to the extract_number_field_based in the metadata and data folder. The negative extraction control with the strongest "band" was placed right to the eDNA extract on each image (labelled hand written with NC x.y).
-br-
 metadata\:
-br-
File containing overview of site names and extract number (field-based or lab-based extraction). The extract_number_field_based corresponds to the identically called column in CRISPR_field_based_classifications.csv file, as the extract_number_lab_based to the column in the file CRISPR_lab_based_classifications.csv file and the qPCR_Cp_2nd_derivative.csv file. The file includes the GPS coordinates of the sampling location (transect start point).
-br-
 data\:
-br-
Folder containing the files with Cp values (qPCR) and the CRISPR-Dx classifications. CRISPR_field_based_classifications.csv file contains the classifications per pool per extract (0: no detection, 1: detection) for the African manatee (Trichechus senegalensis).
CRISPR_lab_based_classifications.csv file contains the classifications per pool per extract (0: no detection, 1: detection).
qPCR_Cp_2nd_derivative.csv file contains the Cp value of the 8 replicates of each extract.

en: Field-based eDNA analysis to detect the threatened and elusive African manatee

en: edna biodiversity-monitoring field-based-protocol qpcr crispr-dx environmental-dna threatened-species african-manatee crispr-cas

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

en: EnviDat: the Environmental Data Portal

fr: EnviDat: le portail de données environnementales

it: EnviDat: il portale dei dati ambientali

political_level: other

state: active

type: organization

wsl

capacity: public

de: EnviDat

type: dataset

key: guid

key: uri

value: Envidat Harvester

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

en:
Resource Title: data.zip
    Size: 2126 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/4a99830f-a254-4179-a490-d7ca76c5ed56/resource/0b379b83-6480-4aca-b189-ff2435deb297/download/data.zip
    Description: Folder containing the data files with Cp values (qPCR) and the CRISPR-Dx classifications. CRISPR_field_based_classifications.csv file contains the classifications per pool per extract (0: no detection, 1: detection) for the African manatee (Trichechus senegalensis).
CRISPR_lab_based_classifications.csv file contains the classifications per pool per extract (0: no detection, 1: detection).
qPCR_Cp_2nd_derivative.csv file contains the Cp value of the 8 replicates of each extract.

format: ZIP

media_type: application/zip

en:
Resource Title: Readme.txt
    Size: 2387 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/4a99830f-a254-4179-a490-d7ca76c5ed56/resource/ad00caf8-bff2-4b98-be8b-122329cee39e/download/readme.txt
    Description: Description of project and folders and files

format: TXT

media_type: text/plain

en:
Resource Title: lateral_flow_tests_images.zip
    Size: 7430379 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/4a99830f-a254-4179-a490-d7ca76c5ed56/resource/196f5458-7ced-49e3-a693-357ee2816f5a/download/lateral_flow_tests_images.zip
    Description: Images of the lateral flow tests used to analyze the eDNA extracts in the national park. File names correspond to the site names. At each site, we took two filter replicates. We ran 8 PCRs per filter/extract and pooled two PCRs together, resulting in 4 CRISPR-Dx assays per extract. The EVE number on top of the tests indicates the environmental extract number we use to track the extracts in our data base. It corresponds to the extract_number_field_based in the metadata and data folder. The negative extraction control with the strongest "band" was placed right to the eDNA extract on each image (labelled hand written with NC x.y).

en:
Resource Title: metadata.zip
    Size: 10066 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/4a99830f-a254-4179-a490-d7ca76c5ed56/resource/8501e9b9-e55f-49de-86ac-0754f15579dd/download/metadata.zip
    Description: File containing overview of site names and extract number (field-based or lab-based extraction). The extract_number_field_based corresponds to the identically called column in CRISPR_field_based_classifications.csv file in the data folder, as the extract_number_lab_based to the column in the file CRISPR_lab_based_classifications.csv file and the qPCR_Cp_2nd_derivative.csv file (also in the data folder). The file includes the GPS coordinates of the sampling location (transect start point).

african-manatee

display_name: african-manatee

biodiversity-monitoring

display_name: biodiversity-monitoring

crispr-cas

display_name: crispr-cas

crispr-dx

display_name: crispr-dx

edna

display_name: edna

environmental-dna

display_name: environmental-dna

field-based-protocol

display_name: field-based-protocol

qpcr

display_name: qpcr

threatened-species

display_name: threatened-species


en: The dataset contains for three variables (snow water equivalent, surface water input and liquid precipitation) 50 realizations of current and future climate periods for two time horizons (mid end end of century), two emission senarions (RCP 4.5 and 8.5) and 10 climate model chains (all EUR11 chains within CH2018). To quantify natural climate variability for projections of snow conditions and resulting rain-on-snow (ROS) flood events, a weather generator was applied to simulate inherently consistent climate variables for multiple realizations of current and future climates at 100 m spatial and hourly temporal resolution over a 12 x 12 km high-altitude study area in the Swiss Alps. The output of the weather generator was used as input for subsequent simulations with an energy balance snow model. The data was extracted in 2021 from original model output.

en: Multiple realizations of daily snow water equivalent, surface water input and liquid precipitation projections for mid- and late-century

en: rain-on-snow uncertainty snow-water-equivalent cryosphere climate-change-impact natural-climate-variability

climate-change-impact

display_name: climate-change-impact

cryosphere

display_name: cryosphere

natural-climate-variability

display_name: natural-climate-variability

rain-on-snow

display_name: rain-on-snow

snow-water-equivalent

display_name: snow-water-equivalent

uncertainty

display_name: uncertainty


en:
This dataset consist of simulated hourly power production from an Enercon E82 Turbine at 100 m hub-height. It describes the hourly power output a 1MW turbine would produce in each 0.01° grid cell for the years 2016 and 2017.
100 m wind speed data was taken from the COSMO-1 model (Consortium for Small-scale Modeling 2017), which has a 0.01° horizontal resolution. The domain covered is the whole of Switzerland, with the exclusion of lakes. As such, the number of 0.01◦ pixels within Switzerland amounts to 48657.
Conversion to power output was done based on the power curve of the Enercon E82 Turbine. As power output is lower at altitude due to lower air density, we corrected for this effect as described in (Kruyt et al. 2017).
Please cite the following paper in connection with the dataset:
Paper Citation:
 - _Bert Kruyt, Jérôme Dujardin, and Michael Lehning: Improvement of wind power assessment in complex terrain: The case of COSMO-1 in the Swiss Alps, Front. Energy Res., [doi:10.3389/fenrg.2018.00102] (https://doi.org/10.3389/fenrg.2018.00102)_

en: Dataset on Cosmo-1 based Energy Potential in Swiss Alps

en:
Resource Title: Data Source
    Size: 3144305336 bytes
    Format: application/x-r-data
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.51/dataset-on-wind-fields-and-energy-potential-in-swiss-alps/unit_pow_16_17.rdata
    Description: Hourly Gridded Wind Power Time Series

en:
Resource Title: Description
    Size: 42299 bytes
    Format: application/pdf
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.51/dataset-on-wind-fields-and-energy-potential-in-swiss-alps/envidat_cosmo-desc.pdf
    Description: Full Data Description

format: PDF

media_type: application/pdf

en:
Resource Title: Coordinates of Grid Points
    Size: 255151 bytes
    Format: application/x-rds
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.51/dataset-on-wind-fields-and-energy-potential-in-swiss-alps/gridch.rds
    Description: Coordinates of Grid Points

mountain-wind-potential

display_name: mountain-wind-potential

topography

display_name: topography

wind-energy

display_name: wind-energy


en:
Environmental pressures on species can cascade within food webs and even extend beyond individual ecosystems to interconnected systems at large spatial scales. To facilitate the exploration of these dynamics, we constructed trophiCH: a data-based national trophic meta-food web (henceforth the metaweb), that includes vertebrates, invertebrates, and vascular plants within Switzerland's national boundaries, and drawing from literature published between 1862 and 2024. Our comprehensive dataset catalogues 1,112,073 trophic interactions involving 23,151 species and 125 feeding guilds (e.g., detritivores, fungivores, etc). Interactions were primarily documented at the species level from the literature. Additional species-level interactions were inferred by resolving coarser taxonomic records (e.g., using “species A feeds on genus B” to infer interactions between species A and species within genus B) using taxonomic and habitat co-occurrence information.
We provide seven datasets: 1) the metaweb, 2) the taxa checklist, 3) the data source meta-dataset, 4) the list of generalist basal and predator families and polylectic species with citations and 5) a dataset with citations for the inferences of missing predators, 6) a dataset with citations for the parallel inference of diets from similar species and 7) a list of other existing metawebs. The empirical dataset is 00_empirical_metaweb.csv. To reproduce the final metaweb (01_final_metaweb.csv), start the R Project file: trophich.Rproj. We provide five scripts, accompanying functions, and the raw data required to run these scripts to reproduce the taxonomic expansion and validation of the datasets. In the first script (01_inferring_interactions.R), we infer interactions using genus and family level interactions and for basal feeding groups (see Methods: Taxonomic expansion). In the second script (02_inferring_interactions_special_cases.R), we infer further interactions for a few special cases with detailed explanations. In the third script (03_metaweb_comparisons.Rmd), we provide the statistical comparisons between our metaweb and other empirical metawebs as an R Markdown document. We additionally provide a a Python Jupyter Notebook document, outlining the error validation of the data extraction process (04_error_validation.ipynb and an accompanying .html file). Finally, we provide a script to reproduce figure 1 from the associated data paper (05_metaweb_summary_figure_1.R).
This is a first step towards a comprehensive food web for Switzerland, but data gaps remain (see Reji Chacko et al 2024, Fig.1C). We encourage addition of new datasets to this metaweb and welcome any collaborations to contruibute to future versions of this dataset. Please contact: merin.rejichacko@gmail.com.

en: trophiCH v1 - a food web for Switzerland

en: invertebarates metaweb food-webs switzerland trophic-networks vertebrates vascular-plants

en:
Resource Title: trophich 0.1
    Size: 34142639 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/4f9547a1-3018-46f1-a399-09d65a3223f3/resource/fe802436-f927-4243-b199-0829a3e0d21b/download/trophich.zip
    Description: Deprecated version from before the first round of review at Scientific Data

en:
Resource Title: trophiCH 1.0
    Size: 33123041 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/4f9547a1-3018-46f1-a399-09d65a3223f3/resource/6d7f57cc-a98f-4513-9c65-c0230a15bba7/download/trophich.zip
    Description: Most current version of the dataset, after revision round 1 at Scientific Data.

food-webs

display_name: food-webs

invertebarates

display_name: invertebarates

metaweb

display_name: metaweb

switzerland

display_name: switzerland

trophic-networks

display_name: trophic-networks

vascular-plants

display_name: vascular-plants

vertebrates

display_name: vertebrates


en: This dataset contains the results of a five-day field excursion which the extent to which eDNA sampling can capture the diversity of a region with highly heterogeneous habitat patches across a wide elevation gradient through multiple hydrological catchments of the Swiss Alps. Using peristaltic pumps, we filtered 60 L of water at five sites per catchment for a total volume of 1 800 L. Using an eDNA metabarcoding approach focusing on vertebrates and plants, we detected 86 vertebrate taxa spanning 41 families and 263 plant taxa spanning 79 families across ten catchments. This dataset includes two sets of data. The first (Genomic data) includes all the necessary data for the bioinformatic pipeline, whereas the second (Analysis Figures) contains tidied data and scripts for the reproduction of all figures/analyses in the article describing this study.

en: Vertebrate and plant taxa recovered from 10 catchments in Vaud using an eDNA-metabarcoding approach

en: dna-metabarcoding biodiversity-assessment spatial-ecology environmental-dna biomonitoring

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: Analysis and Figures
    Size: 238547 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f1df9abc-3fb8-4253-83a8-733f712ef768/resource/a255cdf5-9d88-4ba7-83af-df37d3c6e665/download/02_analysis_figures.zip
    Description: This zipped file includes R scripts and the datasets required to run the analyses. A README describes the columns in each dataset.

en:
Resource Title: Field Metadata
    Size: 6661 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/f1df9abc-3fb8-4253-83a8-733f712ef768/resource/4d6f2c3d-0ae9-473e-8023-adf6e99d4aa7/download/field_metadata.csv
    Description: Information concerning the locations and times of sampling, as well as the identity of the samplers and relevant comments.

format: CSV

media_type: text/csv

en:
Resource Title: Genomic data
    Size: 5153960755 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.409/eDNA_paper_bioinformatic_pipeline.zip
    Description: Fastq files, primer descriptions and the final output dataset.

biodiversity-assessment

display_name: biodiversity-assessment

biomonitoring

display_name: biomonitoring

dna-metabarcoding

display_name: dna-metabarcoding

environmental-dna

display_name: environmental-dna

spatial-ecology

display_name: spatial-ecology


en:
The WFJ_DailySMP dataset contains daily SnowMicroPen (SMP) measurements conducted throughout the winter seasons 2015-2025 (ongoing) at the Weissfluhjoch research site, Davos, Switzerland. The measurement program DailySMP started in winter 2015/2016 during which the measurement protocol was established as part of the RHOSSA campaign, described in the related publication.
Since then, measurements have been repeated every winter as part of the standard snow monitoring program by the PhD students at SLF. All published SMP files have undergone manual quality control by an expert, including a supervised detection of the snow surface, which is included in the dataset.
In addition to the raw data, we provide a rudimentary post-processing workflow in which the derived SSA and density profiles are adjusted by the total snow height obtained from the adjacent IMIS snow station (https://www.slf.ch/en/avalanche-bulletin-and-snow-situation/measured-values/description-of-automated-stations/) and their evolution is plotted over time. Please refer to the README for detailed information on how to get started.

en: WFJ_DailySMP - High-Resolution Snow Stratigraphy at Weissfluhjoch -- Davos 2015-ongoing

en: snow-ssa weisfluhjoch snow-measurements wfj-research-site snow-density weissfluhjoch snow microstructure snow-stratigraphy snowmicropen

microstructure

display_name: microstructure

snow

display_name: snow

snow-density

display_name: snow-density

snow-measurements

display_name: snow-measurements

snow-ssa

display_name: snow-ssa

snow-stratigraphy

display_name: snow-stratigraphy

snowmicropen

display_name: snowmicropen

weisfluhjoch

display_name: weisfluhjoch

weissfluhjoch

display_name: weissfluhjoch

wfj-research-site

display_name: wfj-research-site


en:
This repository contains all observational data sets during CALISHTO campaign used for the paper: Gao, K., Vogel, F., Foskinis, R., Vratolis, S., Gini, M. I., Granakis, K., Zografou, O., Fetfatzis, P., Berne, A., Papagiannis, A., Möhler, O., Eleftheridadis, K., and Nenes, A.: On the drivers of ice nucleating particle diurnal variability in Eastern Mediterranean clouds, npj Climate and Atmospheric Science,  Preprint link: https://www.researchsquare.com/article/rs-4378562/v1.
CALISHTO campaign was conducted between 12 October and 27 November 2021 to observe ice nucleating particles and aerosol properties at the Helmos Hellenic Atmospheric Aerosol and Climate Change station in Eastern Mediterranean, to understand cloud-aerosol interactions. To evaluate the spatial-temporal variabilities and characteristics of ice nucleating particles, a high-time resolution ice nucleation spectrometer was employed and different online aerosol  property measurements were conducted in-situ, including particle number concentration, particle size distribution, particle fluorescent properties, and particle chemical composition. In addition, planetary boundary layer conditions at the observation site were determined by remote sensing techniques employed at a lower site.

en: CALISHTO campaign dataset for the publication On the Drivers of Ice Nucleating Particle Diurnal Variability in Eastern Mediterranean Clouds

en: biological-particles aerosol-cloud-interactions ice-nucleation diurnal-variability dust-particles fluorescent-particles

en:
Resource Title: Figure 6.xlsx
    Size: 43273 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/cf74de68-a026-4c43-abc9-f5cfb4beadb9/resource/b2d4280e-e366-40e2-9bb1-2480dbb19f6c/download/figure-6.xlsx
    Description: This file contains data presented in Figure 6 in the main text.

format: XLS

en:
Resource Title: Figure 1.xlsx
    Size: 19296 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/cf74de68-a026-4c43-abc9-f5cfb4beadb9/resource/0459364f-4c2f-4bd2-9825-1a3b527f0550/download/figure-1.xlsx
    Description: This file contains data presented in Figure 1 in the main text.

en:
Resource Title: Figure 4.xlsx
    Size: 40769 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/cf74de68-a026-4c43-abc9-f5cfb4beadb9/resource/8756a238-ca3d-43e1-8f4b-60877d046731/download/figure-4.xlsx
    Description: This file contains data presented in Figure 4 in the main text.

en:
Resource Title: Figure 5.xlsx
    Size: 39112 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/cf74de68-a026-4c43-abc9-f5cfb4beadb9/resource/ed2dec5e-6d02-4835-8c7c-d9054b8bd9da/download/figure-5.xlsx
    Description: This file contains data presented in Figure 5 in the main text.

en:
Resource Title: Figure 3.xlsx
    Size: 41657 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/cf74de68-a026-4c43-abc9-f5cfb4beadb9/resource/7d25f431-719b-4e81-9e87-71d6b73cac97/download/figure-3.xlsx
    Description: This file contains data presented in Figure 3 in the main text.

en:
Resource Title: Figure 2.xlsx
    Size: 41737 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/cf74de68-a026-4c43-abc9-f5cfb4beadb9/resource/c95e8863-99bb-4070-8967-9c0c5a767f26/download/figure-2.xlsx
    Description: This file contains data presented in Figure 2 in the main text.

en:
Resource Title: Data for figures in the Supplementary.xlsx
    Size: 2644777 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/cf74de68-a026-4c43-abc9-f5cfb4beadb9/resource/e3581bf2-16cd-4e3e-9c16-80be733e57fd/download/data-for-figures-in-the-supplementary.xlsx
    Description: This file contains data presented in Figure S2 to S36 in the supplementary.

en:
Resource Title: Figure 7.xlsx
    Size: 91997 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/cf74de68-a026-4c43-abc9-f5cfb4beadb9/resource/63f8b6ff-76a2-4328-a51d-5aaf64705381/download/figure-7.xlsx
    Description: This file contains data presented in Figure 7 in the main text.

aerosol-cloud-interactions

display_name: aerosol-cloud-interactions

biological-particles

display_name: biological-particles

diurnal-variability

display_name: diurnal-variability

dust-particles

display_name: dust-particles

fluorescent-particles

display_name: fluorescent-particles

ice-nucleation

display_name: ice-nucleation


photogrammetric-drone-data-dorfberg

en: The data was collected with a Wingtra Gen II drone and a Sony RX1R II sensor. In total, 10 flights were conducted at different dates, both in summer and winter. A DSM, an orthophoto, a snow depth raster and the original drone images from every flight are available at a high resolution (10cm and 3cm, respectively).

en: Photogrammetric Drone Data Dorfberg

en: drone-data uav snow-depth-mapping dsm photogrammetry snow-distribution snow orthophoto

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: photogrammetric-drone-data-dorfberg

envi

en: Environment

drone-data

display_name: drone-data

dsm

display_name: dsm

orthophoto

display_name: orthophoto

photogrammetry

display_name: photogrammetry

snow-depth-mapping

display_name: snow-depth-mapping

snow-distribution

display_name: snow-distribution

uav

display_name: uav


en:
This dataset contains all processed data, simulation outputs, and R/Python scripts used to assess the robustness of regional multi-habitat food webs in Switzerland under various non-random species extinction scenarios. It accompanies the manuscript submitted to Communications Biology, and available as a preprint here:
Reji Chacko, M,  Albouy, C., Altermatt, F., et al. Decreases in the robustness of regional food webs to sequential species extinctions following habitat loss, 04 December 2024, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-5225132/v1]
Please note that the most up-to-date version of the simulations code is available here: https://github.com/mrejichacko/casCHades

en: Simulation data and analyses of regional food web robustness under habitat loss

en: food-webs robustness habitat-fragmentation network-analysis trophic-networks trophich metaweb

food-webs

display_name: food-webs

habitat-fragmentation

display_name: habitat-fragmentation

metaweb

display_name: metaweb

network-analysis

display_name: network-analysis

robustness

display_name: robustness

trophic-networks

display_name: trophic-networks

trophich

display_name: trophich


en:
Background
Urban ecosystems are associated with socio-ecological conditions that can filter and promote taxa. However, the strength of the effect of ecological filtering on biodiversity could vary among biotic and abiotic factors. Here, we provide the data used to investigate the effects of habitat amount, temperature, and host-enemy biotic interactions in shaping communities of cavity-nesting bees and wasps (CNBW) and their natural enemies. To do so, we installed trap-nests in 80 sites distributed along urban intensity gradients in 5 European cities (Antwerp, Paris, Poznan, Tartu and Zurich). We quantified the species richness and abundance of CNBW hosts and their natural enemies, as well as two performance traits (survival and parasitism) and two life-history traits (sex ratio and number of offspring per nest for the hosts).
The dataset contains:
* The taxonomic metrics on CNBW
* The taxonomic metrics on the natural enemies from CNBW
* The life-history traits and performance traits

en: Community structure, life-history traits and performance traits of urban cavity-nesting bees annd wasps

en: urbanization urban-ecology natural-enemies hymenoptera cavity-nesting hymennoptera life-history-traits wasps cities bees performance-traits richness

en:
Resource Title: Information on the study sites
    Size: 14688 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/1994598f-ea53-4394-b6de-9c5cd06ecc64/resource/527f3701-eabd-4f19-a49c-890fd892c418/download/16072024_study_sties.csv
    Description: For each site, the IDs, city, coordinates, structure where the trapnest was attached and height is provided

en:
Resource Title: Taxonomic metrics on the natural enemies
    Size: 3882 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/1994598f-ea53-4394-b6de-9c5cd06ecc64/resource/1d628995-7010-42e5-b2b4-61e74ad78207/download/25042022_enemy_info.csv
    Description: It contains the species richness and the abundance of natural enemies per site.

en:
Resource Title: Taxonomic metrics on CNBW
    Size: 14605 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/1994598f-ea53-4394-b6de-9c5cd06ecc64/resource/971340ee-519a-4616-a8fb-58a854a3374b/download/25042022_community_structure_life_history_site.csv
    Description: The data contains at the site level the species richness of cavity-nesting bees and wasps. It also contains site level estimates of the life-history traits and the performance traits.

We calculated species richness, abundance, and number of nests of the hatched hosts separately for bees and wasps. We calculated the abundance of hatched hosts as the number of brood cells where an individual emerged, i.e. the total number of brood cells minus the number of parasitized cells and the number of cells with no emergence for reasons other than parasitism.

City codes: An =Antwerp, Pa = Paris; Po = Poznan; Ta =Tartu; Zu= Zurich

en:
Resource Title: Performance traits and life-history traits
    Size: 411653 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/1994598f-ea53-4394-b6de-9c5cd06ecc64/resource/34eacb06-974a-43c5-9c9b-e347cd84705a/download/25042022_life_history_nest.csv
    Description: It contains the data of two performance traits (survival and parasitism) and two life-history traits (sex ratio and number of offspring per nest for the hosts) at the nest level.

We assessed performance traits, i.e. survival and parasitism, at the individual cell level. Specifically, for each cell within a nest, we noted whether the cell was alive or dead (survival), and whether it had a parasite or not (parasitism). Further, we calculated the parasitism rate as the number of cells with a parasite divided by the total number of cells. Concerning life-history traits, we calculated the total number of brood cells per nest as the total number of brood cells within a single nest, regardless of whether the larvae were dead, alive or parasitized. Finally, we used the probability that a femalehost emerged from a given cell as a proxy for the sex ratio. To do so, we first identified the sex of the individual hosts. Then, we noted it in a binary way to indicate whether the host was a female or not.  We could identify the sex of all hatched hosts. Further, because CNBW first lay females and then males, on some occasions we could also identify the host sex in dead and parasitized cells, that is, when we had already identified the sex of the preceding and the following cells.

en:
Resource Title: Predictors of the study
    Size: 32135 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/1994598f-ea53-4394-b6de-9c5cd06ecc64/resource/f510735e-de09-44fd-a355-997320ef1db1/download/predictors.non.scaled.csv
    Description:

bees

display_name: bees

cavity-nesting

display_name: cavity-nesting

cities

display_name: cities

hymennoptera

display_name: hymennoptera

hymenoptera

display_name: hymenoptera

life-history-traits

display_name: life-history-traits

natural-enemies

display_name: natural-enemies

performance-traits

display_name: performance-traits

richness

display_name: richness

urban-ecology

display_name: urban-ecology

urbanization

display_name: urbanization

wasps

display_name: wasps


en:
Description: Fish environmental DNA data set collected in 2023 in the Calanques National Park
The eDNA samples were collected in 2023 in two locations (Moyades, M−FPA and “Impérial du large”, I-LPA), during the winter (January- February) season, with the sampling dates depending on the weather conditions. For the M−FPA, samples were collected between Moyades island and Riou island, while for the I-LPA, they were collected on the south side of the “Impérial du large” island. To account for the existing bathymetry, in the M−FPA the samples were taken at two sampling sites with depths of 20 and 40 m, while in the I-LPA they were taken at two sites with depths of 20 and 80 m.
At each site, in-situ filtration of seawater was performed using a double-head submersible pump (Subspace, Geneva, Switzerland; nominal flow of ca. 1 L/min) strapped to an underwater scooter with 2 VigiDNA 0.20 µm filtration capsules (SPYGEN, le Bourget du Lac, France), along with disposable sterile tubing. The samples were collected along two horizontal transects (up to 400 m in length) during each closed-circuit rebreather dive, enabling the filtration of a water volume of 15 L/filter per depth, as close as possible to the substrate. Two filter replicates were collected by two divers at each sampling site, except in two cases where bad weather conditions or logistical issues meant that only one replicate was sampled.
After the filtration, the remaining seawater was emptied from the capsule back on the boat and replaced by a 80 mL CL1 conservation buffer (SPYGEN, le Bourget du Lac, France). To prevent any contamination, a strict protocol was followed during the entire process, requiring disposable gloves and single-use filtration equipment. Finally, the samples were stored at room temperature. We followed a strict contamination control protocol in both field and laboratory stages. Each water sample processing included the use of disposable gloves and single-use filtration equipment to avoid any risk of contamination. Libraries were prepared with ligation using the MetaFast protocol (Fasteris).
Data content:
	* rawdata/: contains the raw reads for each individual sample. One archive contains the paired-end reads specified by the _R1 or _R2 suffix as well as individually tagged PCR replicates (if available) together with an archive containing all extraction and PCR blank samples of the library. Reads have been demultiplexed using cutadapt but not trimmed, individual demultiplexing tags and primers remain present in the sequences.
	* taxadata/: contains the table with all detected taxonomy for each sample after bioinformatic processing (see Polanco et al. 2020 for details; https://doi.org/10.1002/edn3.140) and associated field metadata.
	* metadata/: contains two metadata files, one related to the data collected in the field for each filter, and the second related to the sequencing process in the lab (including the tag sequence, library name, and marker information for each sample)

en: Environmental DNA Marine France Calanques 2023

en: edna fish-composition marseille teleo mediterraneaen marine

edna

display_name: edna

fish-composition

display_name: fish-composition

marine

display_name: marine

marseille

display_name: marseille

mediterraneaen

display_name: mediterraneaen

teleo

display_name: teleo


en:
Long-term phenological data in alpine regions are often limited to a few locations and thus, little is known about climate-change induced plant phenological shifts above the treeline. Because plant growth initiation in seasonally snow-covered regions is largely driven by snowmelt timing and local temperature, it is essential to simultaneously track phenological shifts, snowmelt, and near-ground temperatures. In this study, we make use of ultrasonic snow height sensors installed at climate stations in the Swiss Alps to reveal phenological advance of grassland ecosystems and relate them to climatic changes over 25 years (1998 – 2023). When snow is absent, these snow height sensors additionally provide information on plant growth at a uniquely fine temporal scale. Daily temperatures and snow- / plant height values, annual phenological- and melt-out dates were derived from IMIS climate station data (doi.org/10.16904/envidat.406). We observed an advance of green-up by -2.4 days/decade coinciding with strong warming of up to +0.8°C/decade. Although the timing of melt-out has not changed significantly over the study period in this focal region, phenological responses to early melt-out years varied due to differing influences of photoperiodic and thermal constraints, which were not equally important across elevations and communities. Phenological shifts of alpine grasslands are thus likely to become even more pronounced if melt-out timing advances in the future as predicted.
This repository relates to the publication Zehnder et al. (2025). Snow height sensors reveal phenological advance in alpine grasslands. Global Change Biology. accepted. The model data and code that support our findings are deposited in this repository.

en: IMIS Phenology Data Since 1998

en: long-term grassland phenology snow monitoring snowmelt imis alpine ultrasonic climate

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:
Resource Title: station_info.csv
    Size: 14695 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/53a457eb-0915-43d1-98ec-a11b4963b6ff/resource/1e867868-1e64-4ec6-9788-aaa7ab9fd561/download/station_info.csv
    Description: Contains IMIS station location data

en:
Resource Title: grass.png
    Size: 30676 bytes
    Format: image/png
    URL: https://www.envidat.ch/dataset/53a457eb-0915-43d1-98ec-a11b4963b6ff/resource/c190e750-7951-4434-b3e1-325bca7a6ddc/download/grass.png
    Description: Snow3.png and grass.png are needed for plotting the methods plot in 5_methods_plots.Rmd

format: PNG

media_type: image/png

en:
Resource Title: snow3.png
    Size: 12808 bytes
    Format: image/png
    URL: https://www.envidat.ch/dataset/53a457eb-0915-43d1-98ec-a11b4963b6ff/resource/afa71796-2a2e-4b7b-9885-d2a0fa47ed31/download/snow3.png
    Description: Snow3.png and grass.png are needed for plotting the methods plot in 5_methods_plots.Rmd

en:
Resource Title: 4_functions.R
    Size: 8508 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/53a457eb-0915-43d1-98ec-a11b4963b6ff/resource/57a701f9-40be-4068-9ae6-b2a101e8d802/download/4_functions.r
    Description: Contains custom functions required for the scripts; this file must be sourced before running other scripts.

format: OCTET

en:
Resource Title: 0_readme.Rmd
    Size: 7952 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/53a457eb-0915-43d1-98ec-a11b4963b6ff/resource/89105a24-c1b0-457f-8ad4-deb0a91e2aee/download/0_readme.rmd
    Description: Is the readme that documents all data and code in this repository

en:
Resource Title: 1_phenofit.R
    Size: 14137 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/53a457eb-0915-43d1-98ec-a11b4963b6ff/resource/08e70804-eed7-4a7c-8ad8-1a28c85deb2d/download/1_phenofit.r
    Description: implements the phenofit method. Note: This script is computationally intensive and may take a while to run. We recommend using the temporary data created from it (i.e. starting from 2_temperature.R). For a better description of the phenofit R package and the different methods to determine the SOG, see Kong et al. (2022): https://doi.org/10.1111/2041-210X.13870

en:
Resource Title: IMIS_daily.csv
    Size: 198550707 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/53a457eb-0915-43d1-98ec-a11b4963b6ff/resource/4d2df843-22cf-46a4-84bb-a5eb09e6d1e0/download/imis_daily.csv
    Description: Contains daily climate station data including daily median temperatures, snow depth, plant height, and annual melt-out timings.

en:
Resource Title: 3_models.Rmd
    Size: 70950 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/53a457eb-0915-43d1-98ec-a11b4963b6ff/resource/3799ac14-e0c6-4651-a18f-bc507ebb87d2/download/3_models.rmd
    Description:  Documents and reproduces the statistical models used in the analysis.

en:
Resource Title: all_stations.csv
    Size: 1351408 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/53a457eb-0915-43d1-98ec-a11b4963b6ff/resource/2f390316-91a2-4600-a188-b6c1045065c1/download/all_stations.csv
    Description: Contains unfiltered annual phenological, temperature and snow-climatic data for all IMIS stations.

en:
Resource Title: 5_method_plots.Rmd
    Size: 14013 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/53a457eb-0915-43d1-98ec-a11b4963b6ff/resource/91df036c-3904-4c7b-84de-355012cbbad9/download/5_method_plots.rmd
    Description:  Generates the methods-related figures used in the publication.

en:
Resource Title: 2_temperature.R
    Size: 23036 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/53a457eb-0915-43d1-98ec-a11b4963b6ff/resource/19951a82-e3bf-4650-bc36-dbe502dff703/download/2_temperature.r
    Description: Contains temperature-related calculations.

en:
Resource Title: phenofit_outputlist.rds
    Size: 132907 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/53a457eb-0915-43d1-98ec-a11b4963b6ff/resource/95fca3e1-8f8f-4b1b-bc53-9c9778462b67/download/phenofit_outputlist.rds
    Description: Temporary data. Contains annual unfiltered output of phenofit (i.e. the variable names are in line with the phenofit documentation), including all curve parametrization variables for plotting and additional variables, which are not necessarily used in the analysis.

en:
Resource Title: pheno_params.csv
    Size: 417970 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/53a457eb-0915-43d1-98ec-a11b4963b6ff/resource/1a1b4c9a-2fe8-471f-8c36-616d5fd17738/download/pheno_params.csv
    Description: Contains annual phenological variables and phenofit outputs, sorted, ordered and renamed according with the readme in this repository

en:
Resource Title: phenostations.csv
    Size: 463262 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/53a457eb-0915-43d1-98ec-a11b4963b6ff/resource/210f372a-6bf4-42ce-aadc-6751e00c2a74/download/phenostations.csv
    Description: Contains filtered annual phenological data from 40 IMIS stations suitable and used for phenological analysis

alpine

display_name: alpine

climate

display_name: climate

grassland

display_name: grassland

imis

display_name: imis

long-term

display_name: long-term

monitoring

display_name: monitoring

phenology

display_name: phenology

snow

display_name: snow

snowmelt

display_name: snowmelt

ultrasonic

display_name: ultrasonic


en: The data described in this article were collected daily over the period 4 June 2018 to 23 August 2018 and contains information of several data sources. The database includes information on national recipes and their ingredients for 171 countries, measures for food taste similarities between all 171 countries as well as bilateral migration and agro-food trade data for 5 years. The database can be used for analyzing e.g., the relation between food preferences and international trade or food preferences and health outcomes (e.g., obesity) across countries.

en: Data of national dishes, their similarity and trade flows

en: latent-semantic-analysis food-preferences agro-food-trade food-taste-similarity national-dishes

envidat

en: EnviDat: the Environmental Data Portal

envi

en: Environment

en:
Resource Title: dyadic food similarity measures, pairwise similarity and dissimilarity (chord diagram)
    Size: 1794299 bytes
    Format: application/x-7z-compressed
    URL: https://www.envidat.ch/dataset/a7717d8a-4086-453b-868d-02295bcecf8a/resource/6b96fac9-bab5-4afb-8fb5-b016897861f6/download/diagrams.7z
    Description: The figures show the distribution of two dyadic food tastes similarity measures on the base of
the national dishes: (i) the Manhattan distance and the (ii) latent semantic analysis. The chord diagram
shows the results for country pairs with most and less similarity on basis of the LSA-approach.

en:
Resource Title: National dishes, migration data and data for bilateral food trade flows
    Size: 22226815 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/a7717d8a-4086-453b-868d-02295bcecf8a/resource/47eb0962-d57c-4252-9743-8c9e965e786f/download/gravity_food_sim_final.csv
    Description: The data include information on national dishes, migration data and data for bilateral food
trade flows for 5 years as well as importer and exporter GDP, population, distance between
most populated cities, bilateral information on Preferential Trade Agreements, common
official primary langauge, common colonizer post-1945, colonial relationship for overall
171 countries. The final data set contains 145,350 observations (29,070 country pairs for
5 years)

en:
Resource Title: information on ingredients of national dishes
    Size: 95593 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/a7717d8a-4086-453b-868d-02295bcecf8a/resource/2bb64e3b-48cc-404d-9fb9-2b7d86e61e76/download/foodculture_dishes_rev2.csv
    Description:  shows information of ingredients of all national dishes of 171 countries and their names

agro-food-trade

display_name: agro-food-trade

food-preferences

display_name: food-preferences

food-taste-similarity

display_name: food-taste-similarity

latent-semantic-analysis

display_name: latent-semantic-analysis

national-dishes

display_name: national-dishes


en:
This dataset accompanies the publication "How Flat is Flat? Investigating the spatial variability of snow surface temperature and roughness on landfast sea ice using UAVs in McMurdo Sound, Antarctica".
The dataset consists of 1) processed orthomosaic of RGB values, snow topography (digital elevation model), and surface temperature, 2) topography-dependent irradiance map, and 3) point data of measured radiation and atmospheric parameters in the McMurdo Sound, October-December 2022.  The data was collected as part of the New Zealand Marsden Fund Research Grant 21-VUW-103 "Can Snow Change the Fate of Antarctic Sea Ice?". Unprocessed data associated with this field campaign are published in 10.16904/envidat.633.
For the airborne measurements, we used a DJI Matrice 30T, a multi-rotor (quadcopter) UAV with self-heating batteries, suitable for operations in polar conditions (temperature range from -20 to +50 °C). The UAV has a wide-angle camera (12 megapixels) for RGB images and a thermal camera (Uncooled Vox Microbolometer, long-wave infrared spectrum 8-14 µm, 1.31 megapixels) for TIR images with an accuracy of ±2 °C or ± 2 %.
GNSS data includes high-precision location information (latitude, longitude, ellipsoidal height, receiver clock offset, and timestamps formatted in decimal hours and day-of-year notation).
WE calculated the distribution of received solar irradiance over the sea-ice surface using a terrain-corrected radiative transfer model and measured shortwave radiation components, accounting for surface roughness, aspect, slope, and solar position during the flight, while applying terrain shading and measured diffuse radiation.
The broadband radiation station data consists of shortwave (Sw) and longwave (Lw) radiation measurements (Kipp & Zonen CMP22 pyranometers and CGR4 pyrgeometers).
Surface temperature readings from cold, sediment, and hot targets were recorded using Apogee SI-121-SS sensors.
This dataset also contains automated weather station (AWS) data (air and snow surface temperatures, relative humidity, wind direction, and wind speed).
For detailed information, please use the README file.

en: Snow On Antarctic Sea Ice - McMurdoSound 2022 - UAV Retrievals of Snow Topography and Snow Surface Temperature

en: snow-temperature snow uav snow-roughness irra sea-ice drone antarctica

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

drone

display_name: drone

irra

display_name: irra

sea-ice

display_name: sea-ice

snow-roughness

display_name: snow-roughness

snow-temperature

display_name: snow-temperature

uav

display_name: uav


greenland-climate-network-gc-net-data

en:
In Memory of Dr. Konrad (Koni) Steffen
-br /-
-br /-
Update October 2022: The GC-Net is kindly continued by the Geological Survey of Denmark and Greenland (GEUS).
Starting October 3, 2022, the access to the latest versions of the "ready to use" L1 data has been migrated to GEUS. Future data versions will be available at:  [https://doi.org/10.22008/FK2/VVXGUT](https://doi.org/10.22008/FK2/VVXGUT)
 Background
Starting with a single station in 1991, the Greenland Climate Network (commonly known as GC-Net) is a set of Automatic Weather Stations (AWS) set up and managed by the late Prof. Dr. Konrad (Koni) Steffen, and spanning the Greenland Ice Sheet (GrIS). This first station was "Swiss Camp" or the "ETH-CU" camp (GC-Net station 01) which was used as a field science and education site by Koni for years. The GC-Net was expanded with multiple NASA, NOAA, and NSF grants throughout the years, and then supported by WSL in the later years. These data (see "C-file" below) were previously hosted by the Cooperative Institute for Research in Environmental Sciences (CIRES) in Boulder, Colorado.
 Overview
Provided in this dataset are the 16 longest running stations in the network, which are spread over a significant area of the GrIS and the majority of the unique climatic zones. From the South Dome high point in the South, to the Western Jakobshavn ablation region in the west, to the Petermann glacier in the North across east of the Northeast Greenland Ice Stream to the east, GC-Net is the longest running climatological record of Greenland.
 The standard GC-Net station consists of:
* Air temperature measurements at 2 heights above the surface
* Temperature and humidity measurements at 2 heights above the surface
* Wind speed and direction measured at 2 heights above the surface
* Sonic distance sounder measurements for 2 snow height and distance of instruments to surface
* Incoming shortwave radiation measurement
* Reflected shortwave radiation measurement
* Net broadband radiation (long- and short-wave) measurement
* Air pressure measurement
Data have often been repatriated in near-real time using one of either the GOES geostationary satellite or the ARGOS polar orbiting satellite transmission system. The stations were visited typically every 1-2 years for maintenance and service, and to download full uncorrupted data directly from the dataloggers. GC-Net stations were visited by Twin Otter equipped with snow skids to land directly on the open-ice at the AWS locations, or by helicopter near the west coast. The AWSs operate on solar and battery power and occasionally lost power during the dark and cold winter months, particularly when the batteries were aging.
 Dataset
This dataset consists of 2 main data levels; Level 0 and Level 1.
Level 0 is the raw data from the dataloggers, historical processing codes, satellite transmissions, and Koni’s personal data archive. Level 0 data (.zip) directories contain subdirectories:
* “C file” - contains the historical processed datafile for each station.
* “Campbell logger files” - contains the raw csv datafiles from the stations’ Campbell Scientific dataloggers since the CR1000 era (~2007-2008 for most stations).
* “Photos” - contains photographs of the station when available marked by year.
Level 1 is the appended, calibrated, cleaned, and quality flagged data. The full processing scheme is open-source and publicly available on the following GitHub repository (please also check GitHub for the latest L1 data):
[GC-Net L1 data on GitHub](https://github.com/GEUS-Glaciology-and-Climate/GC-Net-level-1-data-processing "GC-Net-level-1-data-processing")
Level 1 data is provided in the newly described csv-compatible [NEAD format](https://www.envidat.ch//metadata/nead "NEAD format").
-br /-
 Additional Details
Dataset description publication will be forthcoming.
The Geological Survey of Denmark and Greenland (GEUS) has been imperative in the reprocessing and continuity mission of GC-Net. Multiple GC-Net stations have been replaced with updated and upgraded AWS hardware at the same coordinates by GEUS. This effort will ensure continuity of the GC-Net dataset into the future.

en: Greenland Climate Network (GC-Net) Data

en: automatic-weather-station air-temperature aws climate net-radiation greenland

title_for_slug: greenland-climate-network-gc-net-data

en:
Resource Title: 23-NEEM
    Size: 65661066 bytes
    Format: No Info
    URL: https://dataverse.geus.dk/api/access/datafile/:persistentId?persistentId=doi:10.22008/FK2/VVXGUT/XQGUP7
    Description:

en:
Resource Title: 01-SwissCamp
    Size: 80269102 bytes
    Format: No Info
    URL: https://dataverse.geus.dk/api/access/datafile/:persistentId?persistentId=doi:10.22008/FK2/VVXGUT/DZSKEM
    Description:

en:
Resource Title: 15-NASA-SE
    Size: 121694130 bytes
    Format: No Info
    URL: https://dataverse.geus.dk/api/access/datafile/:persistentId?persistentId=doi:10.22008/FK2/VVXGUT/DD4FR3
    Description:

en:
Resource Title: 04-GITS
    Size: 63894886 bytes
    Format: No Info
    URL: https://dataverse.geus.dk/api/access/datafile/:persistentId?persistentId=doi:10.22008/FK2/VVXGUT/SJRCOC
    Description:

en:
Resource Title: 06-Summit
    Size: 88354682 bytes
    Format: No Info
    URL: https://dataverse.geus.dk/api/access/datafile/:persistentId?persistentId=doi:10.22008/FK2/VVXGUT/UFCSAS
    Description:

en:
Resource Title: 24-E-GRIP
    Size: 26425867 bytes
    Format: No Info
    URL: https://dataverse.geus.dk/api/access/datafile/:persistentId?persistentId=doi:10.22008/FK2/VVXGUT/B6HOFS
    Description:

en:
Resource Title: 11-SouthDome
    Size: 105216949 bytes
    Format: No Info
    URL: https://dataverse.geus.dk/api/access/datafile/:persistentId?persistentId=doi:10.22008/FK2/VVXGUT/4ONGVQ
    Description:

en:
Resource Title: GC Net Config for Stations
    Size: 101376 bytes
    Format: application/json
    URL: https://frontend-static.s3-zh.os.switch.ch/gcnet/stationsconfig.json
    Description: Stations Config from the static bucket

format: JSON

media_type: application/json

en:
Resource Title: 03-NASA-U
    Size: 86554893 bytes
    Format: No Info
    URL: https://dataverse.geus.dk/api/access/datafile/:persistentId?persistentId=doi:10.22008/FK2/VVXGUT/XOMPZ0
    Description:

en:
Resource Title: 00-SwissCamp10m
    Size: 42123942 bytes
    Format: No Info
    URL: https://dataverse.geus.dk/api/access/datafile/:persistentId?persistentId=doi:10.22008/FK2/VVXGUT/7ELDZR
    Description:

en:
Resource Title: Access the latest L1 data
    Size: 1181116006 bytes
    Format: application/xhtml+xml
    URL: https://dataverse.geus.dk/dataset.xhtml?persistentId=doi:10.22008/FK2/VVXGUT
    Description: The GC-Net is kindly continued by the Geological Survey of Denmark and Greenland (GEUS).

Starting October 3, 2022, the access to the latest versions of the "ready to use" L1 data has been migrated to GEUS, (doi:10.22008/FK2/VVXGUT). Future versions will be available from GEUS, while the initial version of L1 data has been archived.

format: xhtml+xml

en:
Resource Title: 08-DYE2
    Size: 92384540 bytes
    Format: No Info
    URL: https://dataverse.geus.dk/api/access/datafile/:persistentId?persistentId=doi:10.22008/FK2/VVXGUT/WAYCGA
    Description:

en:
Resource Title: 05-Humboldt
    Size: 63987342 bytes
    Format: No Info
    URL: https://dataverse.geus.dk/api/access/datafile/:persistentId?persistentId=doi:10.22008/FK2/VVXGUT/BUOC7Y
    Description:

en:
Resource Title: 07-Tunu-N
    Size: 57976839 bytes
    Format: No Info
    URL: https://dataverse.geus.dk/api/access/datafile/:persistentId?persistentId=doi:10.22008/FK2/VVXGUT/H2MOL7
    Description:

en:
Resource Title: GC Net Chart Parameters
    Size: 8192 bytes
    Format: application/json
    URL: https://frontend-static.s3-zh.os.switch.ch/gcnet/stationparameters.json
    Description: Stations parameters from static bucket

en:
Resource Title: 22-Petermann ELA
    Size: 55732780 bytes
    Format: No Info
    URL: https://dataverse.geus.dk/api/access/datafile/:persistentId?persistentId=doi:10.22008/FK2/VVXGUT/ZIOAIX
    Description:

en:
Resource Title: 02-CrawfordPoint1
    Size: 61973379 bytes
    Format: No Info
    URL: https://dataverse.geus.dk/api/access/datafile/:persistentId?persistentId=doi:10.22008/FK2/VVXGUT/IAHZYS
    Description:

en:
Resource Title: 12-NASA-E
    Size: 73642934 bytes
    Format: No Info
    URL: https://dataverse.geus.dk/api/access/datafile/:persistentId?persistentId=doi:10.22008/FK2/VVXGUT/FFJIRB
    Description:

en:
Resource Title: Archived GC-Net Data (deprecated)
    Size: 0
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fenvicloud%2F&prefix=doi%2Fenvidat.1%2Fgcnet%2Fdata%2Fwsl-geus-cooperation%2F
    Description: The GC-Net is kindly continued by the Geological Survey of Denmark and Greenland (GEUS).

Starting October 3, 2022, the access to the latest versions of the "ready to use" L1 data has been migrated to GEUS, (doi:10.22008/FK2/VVXGUT). Future versions will be available from GEUS, while the initial versions of L0 and L1 data have been archived.

en:
Resource Title: 09-JAR1
    Size: 113751799 bytes
    Format: No Info
    URL: https://dataverse.geus.dk/api/access/datafile/:persistentId?persistentId=doi:10.22008/FK2/VVXGUT/9RJ9SC
    Description:

en:
Resource Title: 10-Saddle
    Size: 85106917 bytes
    Format: No Info
    URL: https://dataverse.geus.dk/api/access/datafile/:persistentId?persistentId=doi:10.22008/FK2/VVXGUT/MPAAGY
    Description:

air-temperature

display_name: air-temperature

automatic-weather-station

display_name: automatic-weather-station

aws

display_name: aws

climate

display_name: climate

greenland

display_name: greenland

net-radiation

display_name: net-radiation


en:
Background information
Climate change-induced range expansion of treeline populations depends on their successful recruitment, which requires dispersal of viable seeds followed by successful establishment of individual propagules. The Global Treeline Range Expansion Experiment (G-TREE) is a global initiative involving researchers from Europe, North America, Australia and New Zealand (Brown et al., 2013). At 15 alpine and Arctic treeline sites worldwide the mechanisms determining the elevational and latitudinal distribution of tree populations are studied using a standardized experimental approach. In summer 2013, a multifactorial seedling recruitment experiment has been established at the Stillberg ecological treeline research site. The aim of this experiment, is to quantify the effect of multiple abiotic and biotic drivers on emergence, survival, and growth of *Larix decidua* and *Picea abies* seedlings in replicated plots along an elevation gradient with three sites below (1930 m a.s.l.), at (2090 m a.s.l.), and above treeline (2410 m a.s.l.; Frei et al., 2018). All plots have been surveyed annually to count seedlings and to measure their total height. Additional environmental factors, such as soil temperature, have been recorded.
 Experimental design
The Stillberg research area is located in the Eastern Swiss Alps near Davos, Switzerland. The site has been used for several long-term monitoring as well as experimental studies for the last four decades. Our G-TREE experiment consists of a lowest site located in a subalpine Larch-Spruce forest (*Larici-Picetum*) dominated by *Larix decidua* and *Picea abies* (1930 m a.s.l.), a transition zone site dominated by alpine shrubs (2100 m a.s.l.), and an uppermost site in an alpine meadow with some dwarf shrubs (2390 m a.s.l.). The three experimental sites were set up following the standard protocol of the global G-TREE initiative (Brown et al., 2013). In a split-plot design, 20 plots (224 cm × 45 cm) were established at each site, which were randomly assigned to the 2 × 2 treatment combinations of the main factors seeding and scarification (i.e. seeding and scarification, seeding only, scarification only, and full control), resulting in five replications per main treatment combination. Each plot was divided into 16 split-plots (22.5 cm × 28 cm), to which treatment combinations of four additional two-level factors species (larch and spruce), provenance (low- and high-elevation), herbivore exclosure (with and without exclosure), and seeding year (2013, 2014) were randomly assigned, which resulted in a total of 960 split-plots (Details see Frei et al. 2018).
 Data description
All plots have been surveyed annually to count seedlings and to measure their total height. Seedling height was assessed with a hand ruler as the total length from the original emerging point to the apical meristem (Details see Frei et al. 2018). Additionally, soil temperature at each site, has been continuously recorded since 2013. Here, we present data from eight years (2013–2021).

en: G-TREE: Global Treeline Range Expansion Experiment Davos, Switzerland

en: picea-abies g-tree larix-decidua treeline seedling-recruitment climate-change swiss-alps stillberg forest

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

climate-change

display_name: climate-change

forest

display_name: forest

g-tree

display_name: g-tree

larix-decidua

display_name: larix-decidua

picea-abies

display_name: picea-abies

seedling-recruitment

display_name: seedling-recruitment

stillberg

display_name: stillberg

swiss-alps

display_name: swiss-alps

treeline

display_name: treeline


en:
This dataset groups numerical simulation outputs and validation measurement data produced in the context of the following [publication]: _not published yet_
The snow transport model [snowBedFoam] was used to analyse snow deposition around a specific type of Alpine PV structures named HELIOPLANT®.
The results of a sensitivity analysis of multiple key parameters that govern the spatial organisation of these structures are provided here. Measurements of snow distribution taken from the test-site of the [Gondosolar] project, are provided too.

To reproduce the results of the aforementioned publication, follow the instructions on this [repository].
[publication]: https://
[snowBedFoam]: https://www.doi.org/10.16904/envidat.223
[Gondosolar]: https://www.gondosolar.ch
[repository]: https://github.com/frischwood/snowbed-helio.git

en: Drifting and blowing snow distribution around structures for Alpine PV applications

en: alpine photovoltaic renewable-energy preferential-deposition snow-transport

en:
Resource Title: Browse simulation files
    Size: 1024 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fenvicloud%2F&prefix=doi%2Fenvidat.589%2Fsimulations%2F
    Description:

en:
Resource Title: simulations.zip
    Size: 11811160064 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.589/simulations.zip
    Description: This file contains the snowBedFoam simulations carried out to produce the results presented in: _article not published yet_.

## Content

### 1. /simulations
Groups all simulations folders organised by parameter (azimuth, groupsize, height, interspace) and by type of experiment (single_ref, group_ref).

### 2. /simulations/[parameter]/
For each parameter multiple simulations were carried out. Each simulation folder follows the same naming systematic:

`helioplant_n#_h#_ARL#_BL#_az#_windDir#`
- n: number of Helioplant units
- h: height-above-surface
- sp: regular distance between the units
- ARL: additional refinement level
- BL: boundary layer first cell height
- windDir: wind direction
- stag: staggered arangement (suffix)

Here is an example of a folder content for a specific parameter
(simulations/azimuth/):
- `helioplant_n9_h07_sp10_ARL45_BL0015_az30_windDir5`: following the naming systematic this folder corresponds to a simulation with 9 aligned Helioplant units with 0.7m of height-above-surface, an inter-unit distance of 10m an azimuth of 30° and a wind direction of 5°.

### 3. /simulations/[parameter]/[helioplant_n#_h#_ARL#_BL#_az#_windDir#]
Each simulation folder contains multiple sub-folders (0/, 100/, 300/, constant/ etc.) and files that define the experiment. For each simulation, the files used in the processing [scripts] are the following:
- 0/Cx: contains the x coordinates of sampling grid cells and of the Helioplants structures.
- 0/Cy: contains the y coordinates of sampling grid cells and of the Helioplants structures.
- 300/kinematicCloud-massCheckPatterns: contains the snow mass distribution values [kg/m2] for each grid cell.

[scripts]: https://github.com/frischwood/snowbed-helio.git

alpine

display_name: alpine

photovoltaic

display_name: photovoltaic

preferential-deposition

display_name: preferential-deposition

renewable-energy

display_name: renewable-energy

snow-transport

display_name: snow-transport


swiss-fluxnet-site-lageren

en:
The Swiss FluxNet Site Lägeren is a managed mixed deciduous mountain forest located on the steep Lägeren mountain (NW of Zurich, Swiss Plateau). The forest is highly diverse, dominated by beech, but also including ash, maple, spruce and fir trees. Eddy covariance flux measurements were started in April 2004. The site was part of the international CarboEurope IP network and the National Air Pollution Monitoring Network (NABEL). In addition to Swiss FluxNet, the site is part of the Long-term Forest Ecosystem Research (LWF) of WSL and the biological drought and growth indicator network (TreeNet) of WSL.
Measurements
- Ecosystem flux measurements of CO2, H2O vapour are performed with the eddy-covariance method. This method is based on measurements of trace gas mixing ratios, using infrared gas analyzers (for CO2, H2O vapor), combined with wind speed and wind direction measurements, using 3D sonic anemometers. To resolve the short-term turbulent fluctuations in the atmosphere, very fast measurements are needed: we measure at 10-20 Hz, i.e., 10-20 times per second. To assess the energy budget of each ecosystem, also radiation sensors and soil climate profiles are installed at the site.
- Sub-canopy eddy fluxes (CO2, H2O), soil respiration campaigns
- Continuous CO2 profile measurements.
- Auxiliary micrometeorology and soil climate measurements.
Data availability
All data are available from the European Fluxes Database Cluster, but are also part of Fluxnet2015 dataset.
Data policy
ICOS data license:
[https://www.icos-cp.eu/data-services/about-data-portal/data-license](https://www.icos-cp.eu/data-services/about-data-portal/data-license)
Detailed site info:
[https://www.swissfluxnet.ethz.ch/index.php/sites/ch-lae-laegeren/site-info-ch-lae//](https://www.swissfluxnet.ethz.ch/index.php/sites/ch-lae-laegeren/site-info-ch-lae/)

en: Swiss FluxNet Site Lägeren

en: greenhouse-gas swiss-fluxnet swiss-forest-lab eddy-covariance forest meteorological-measurements flux

title_for_slug: swiss-fluxnet-site-lageren

en:
Resource Title: Data access
    Size: 1024 bytes
    Format: No Info
    URL: http://www.europe-fluxdata.eu/home/site-details?id=CH-Lae
    Description: The flux and meteo data of the Swiss FluxNet site Lägeren are openly available from the European Fluxes Database Cluster.

en:
Resource Title: Site description
    Size: 512219 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4666c33f-b2af-495c-ac49-4fdec3e26bd1/resource/3e9fc3ba-afc4-41f9-b8ca-488ca6810669/download/swissfluxnet_lae_sitedescription.pdf
    Description:

eddy-covariance

display_name: eddy-covariance

flux

display_name: flux

greenhouse-gas

display_name: greenhouse-gas

meteorological-measurements

display_name: meteorological-measurements

swiss-fluxnet

display_name: swiss-fluxnet

swiss-forest-lab

display_name: swiss-forest-lab


en:
Forests cover about a third of the European land area and provide a multitude of ecosystem services. They are subject to constant change and increasing demands with respect to the ecosystem services they provide. As well as provision of timber, non-timber forest goods (e.g. berries and mushrooms), clean water, carbon sequestration, and protection against natural disasters, there is increasing emphasis being placed on conservation and promotion of biodiversity within forests. However, the state of biodiversity in European forests varies widely. Because of the different biogeographic, sociocultural, political, and climatic conditions at small scales, there is a wide variety of situations, approaches, and legacies across the continent.
This book strives to show the challenges that forest managers are faced with to fulfil the societal demands with regard to forests, and especially to integrate the promotion of biodiversity. In a first section (12 chapters), 44 authors from science, policy, and practice describe  the driving factors of forest management; these factors include national laws and legacies, ownership structures, forest history, and socio-structural conditions. In a second section, 113 authors present 32 case examples from 19 European countries that demonstrate the different approaches to integrate the locally requested ecosystem services. These experiences are synthesised in a final section, and a toolbox of integrative measures is presented that is intended to support forest owners and managers to choose appropriate measures for targeted integrated management in their forests.
Copyright reserved WSL and EFI.

en: How to balance forestry and biodiversity conservation? - A view across Europe

en: europe multifunctional-forestry timber-production integrative-forest-management biodiversity

en:
Resource Title: Link to DORA WSL
    Size: 1 bytes
    Format: No Info
    URL: https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:25581
    Description:

en:
Resource Title: How to balance forestry and biodiversity conservation? A view across Europe.
    Size: 213891396 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/9ed59d04-de36-484f-ac2c-71dbc91adaa3/resource/97860f83-945b-43b7-ad41-fe9fa5ce2929/download/schuck-2020-how_to_balance_forestry_and-published_version.pdf
    Description:

biodiversity

display_name: biodiversity

europe

display_name: europe

integrative-forest-management

display_name: integrative-forest-management

multifunctional-forestry

display_name: multifunctional-forestry

timber-production

display_name: timber-production


treenetai

en:
A Toolbox designed around neural networks for time series analysis of TreeNet data. The software containing the machine learning models is stored as a repository on GitHub (https://github.com/treenet/treenetai) and licensed under GNU GENERAL PUBLIC LICENSE version 3.
TreeNet (treenet.info) is an international monitoring and research network in which automated tree stem radius fluctuations measured with point dendrometers are analyzed in terms of forest ecosystem responses to climate change. A continuous stream of microclimate and tree physiology data provides realtime information on tree water relations and tree growth.
TreeNet aims to link research results from carbon flux sites with dendrometer data to entire landscapes. Further it provides online-tools to its partners to automatically interpret stem radius fluctuations in terms of tree water deficit, wood growth and related indicators for forest ecosystem carbon sink and drought stress.
The project was initiated by Roman Zweifel, WSL and Werner Eugster, ETHZ in 2009.

en: TreeNetAI

en: tree-growth machine-learning drought-stress dendrometer time-series stem-radius-changes tree-water-deficit treenet software

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: treenetai

en:
Resource Title: treenetai-main.zip
    Size: 1103745 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/cc075630-946c-4b19-9f5e-d081a683494b/resource/c7a7ff59-403e-4faa-a848-8f65ad2cffb9/download/treenetai-main.zip
    Description: main branch from 06.03.2026

en:
Resource Title: TreeNetAI
    Size: 1024 bytes
    Format: No Info
    URL: https://github.com/treenet/treenetai
    Description:

dendrometer

display_name: dendrometer

drought-stress

display_name: drought-stress

machine-learning

display_name: machine-learning

software

display_name: software

stem-radius-changes

display_name: stem-radius-changes

time-series

display_name: time-series

tree-growth

display_name: tree-growth

tree-water-deficit

display_name: tree-water-deficit

treenet

display_name: treenet


swiss-canopy-crane-ii-research-site

en:
![alt text](https://www.envidat.ch/dataset/fc69e369-eee9-42ab-8486-d2c38cff317d/resource/68fa7065-de32-4343-aa26-71094c5254ae/download/sccii.jpg "Swiss Canopy Crane II")
This research site is located near Hölstein in Canton Basel-Landschaft in a mature temperate forest that harbours more than 400 trees from 14 different species. The 1.6 ha site is equipped with the latest infrastructure, including 60 automated point dendrometers, automated soil respiration chambers, 72 ceramic suction cups at various locations and depths across the site, and a range of automated environmental sensors in the soil, the forest floor and in the canopy. A key piece of infrastructure is the new Swiss Canopy Crane II (SCC II), a 50 m tall crane with a 50 m jib that provides canopy access to 250 trees from 12 different species.

en: Swiss Canopy Crane II research site

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

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

envi

en: Environment

en:
Resource Title: Research site metadata
    Size: 14985 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/fc69e369-eee9-42ab-8486-d2c38cff317d/resource/f2c3b300-e834-438a-84cb-a93b7a273fdd/download/meta-data-sccii-sfl_env.xlsx
    Description:

en:
Resource Title: Swiss Canopy Crane II
    Size: 552040 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/fc69e369-eee9-42ab-8486-d2c38cff317d/resource/68fa7065-de32-4343-aa26-71094c5254ae/download/sccii.jpg
    Description:

format: jpeg

climate-change

display_name: climate-change

damage-of-tree

display_name: damage-of-tree

drought

display_name: drought

ecosystem-functions

display_name: ecosystem-functions

forest

display_name: forest

infrastructure

display_name: infrastructure


en:
This dataset contains all data, on which the following publication below is based.
Paper Citation:
_Schlierenzauer, C., Risch, A.C., Schütz, M., Firn, J. 2021. Non-native Eragrostis curvula reduces plant species diversity in pastures of South-eastern Australia even when native Themeda triandra remains co-dominant. Plants 10, 596._
Please cite this paper together with the citation for the datafile.
Study area
The study was conducted in the lowland grassy woodlands of the Bega Valley Region, which is located in the south-east corner of New South Wales, Australia. Embedded between the Pacific Ocean and the Australian Alps, the lowland grassy woodlands are mostly located on granitic substrates and reach elevations of roughly 500 m above sea level. Typically, these grassy woodlands receive less precipitation (mean annual precipitation between 700-1100 mm) compared to the more elevated areas that surround them (NSW Government - Office of Environment and Heritage 2017).
The vegetation is dominated by an open tree canopy layer consisting of Eucalyptus tereticornis Sm, Angophora floribunda Sm. (Sweet) and a range of other eucalypt species. Sometimes shrub or small trees are also present, whereas grasses and forbs form the ground-cover. In areas without intensive agricultural history, this layer is dominated by perennial, tussock grasses such as Themeda triandra Forssk, Microlaena stipoides R.Br (Weeping Grass), Eragrostis leptostachya Steud. (Paddock Lovegrass) and Echinopogon ovatus P.Beauv (Forest Hedgehog Grass). The remaining inter-tussock spaces are occupied by a diversity of growth-restricted grasses and herbaceous forbs (NSW - Department of Planing, Industries and Environment 2019; NSW Government - Office of Environment and Heritage 2017).
Clearing, pasture sowing, fertilizer application and livestock grazing resulted in a dramatic decrease in the extent of these natural woodlands, with less than five percent within conservation reserves and overall, with only about 20% of their original extent in New South Wales still existing (Tozer et al. 2010). The remaining areas outside of reserves are threatened by altered fire frequencies, habitat clearing, livestock grazing and especially by non-native plant invasion, particularly Eragrostis curvula (Schrad.) Nees. For this reason, the grassy woodlands are listed as an endangered ecological community in the NSW state legislation. Additionally, they are considered as critically endangered by the Commonwealth of Australia (Threatened Species Scientific Committee (TSSC) 2013).
Experimental design and sampling
The study was conducted on six farms and in each of them two sites were chosen, representing a paired design. One of the sites at each farm is dominated by native Themeda triandra, the other one co-dominated by non-native Eragrostis curvula and Themeda triandra. All farms are within a radius of approximately 10 km from the town Candelo. Three of the farms are located North (36°40’ to 36°42’ S and 149°38’ to 149°42’ E) and three of them are located South (36°51’ to 36°49’ S and 149°38’ to 149°42’ E) of Candelo.
Non-native herbivores (mainly cattle, sheep and rabbits) and native herbivorous marsupials (mainly kangaroos, wallabies and wombats) are present in the area of these sites. On each site, data was collected within four plots (each 1 x 1 m) in May and November 2020. All plant species found within a plot were recorded and their relative abundance was estimated.
References
NSW - Department of Planing, Industries and Environment. 2019. “Lowland Grassy Woodland in the South East Corner Bioregion - Endangered Ecological Community Listing.” https://www.environment.nsw.gov.au/topics/animals-and-plants/threatened-species/nsw-threatened-species-scientific-committee/determinations/final-determinations/2004-2007/lowland-grassy-woodland-south-east-corner-bioregion-endangered-ecological-community-l (February 18, 2021).
NSW Government - Office of Environment and Heritage. 2017. “Lowland Grassy Woodland in the South East Corner Bioregion - Profile.” https://www.environment.nsw.gov.au/threatenedSpeciesApp/profile.aspx?id=20070 (January 31, 2021).
Threatened Species Scientific Committee (TSSC). 2013. Environment Protection and Biodiversity Conservation Act 1999 (EPBC Act) Conservation Advice for Lowland Grassy Woodland in the South East Corner Bioregion. http://www.environment.gov.au/biodiversity/threatened/communities/pubs/82-conservation-advice.pdf.
Tozer, Mark et al. 2010. “Native Vegetation of Southeast NSW: A Revised Classification and Map for the Coast and Eastern Tablelands.” Cunninghamia : a journal of plant ecology for eastern Australia 11(3): 359–406.

en: Native and no-native plant interactions in Australian grasslands

en: herbivory grassland native-plant weed-management non-native-plant

grassland

display_name: grassland

herbivory

display_name: herbivory

native-plant

display_name: native-plant

non-native-plant

display_name: non-native-plant

weed-management

display_name: weed-management


data-analysis-toolkits

en:
These are condensed notes covering selected key points in data analysis and statistics.  They were developed by James Kirchner for the course "Analysis of Environmental Data" at Berkeley in the 1990's and 2000's.  They are not intended to be comprehensive, and thus are not a substitute for a good textbook or a good education!
License: These notes are released by James Kirchner under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license.

en: Data analysis toolkits

en: bayesian-inference hypothesis-testing error-propagation uncertainty data-analysis statistics

title_for_slug: data-analysis-toolkits

en:
Resource Title: Toolkit 3: tools for transforming data
    Size: 223799 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/bb3d4263-4c88-4886-bc74-ba48f0398144/download/toolkit-03-transforming-distributions.pdf
    Description: The ladder of powers and a few other cool transformations

en:
Resource Title: Toolkit 8: Hodges-Lehmann estimators
    Size: 87630 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/b796d0b3-535a-481c-8110-4947b8408186/download/toolkit-08-hodges-lehmann-estimators.pdf
    Description: A quick introduction to Hodges-Lehmann estimators (nonparametric measures of effect size)

en:
Resource Title: Toolkit 1: graphically displaying data distributions
    Size: 198277 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/40c94808-e96f-4826-812d-844de07dfe42/download/toolkit-01-graphing-distributions.pdf
    Description:

en:
Resource Title: Toolkit 7: hypothesis testing, significance, and power
    Size: 180657 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/8c6c7021-0811-480f-ab52-00fbe3886591/download/toolkit-07-hypothesis-testing.pdf
    Description: An overview of basic concepts -- and common pitfalls -- in statistical hypothesis testing, statistical significance, and power analysis

en:
Resource Title: Toolkit 10: linear regression
    Size: 695233 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/12fa9470-b900-4858-9ba9-ddf0978c2470/download/toolkit-10-linear-regression.pdf
    Description: An overview of the math, logic, and pitfalls of ordinary least-squares linear regression

en:
Resource Title: toolkit 6: averaging in nonlinear functions
    Size: 112578 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/35650811-6329-4025-9240-bad37169e31f/download/toolkit-06-nonlinear-averaging.pdf
    Description: The average of a nonlinear function will not equal the function evaluated at its average inputs (the mean of the function will differ from from the function of the mean).  This toolkit shows how to estimate that averaging bias.

en:
Resource Title: Toolkit 2: quantifying data distributions using descriptive statistics
    Size: 91590 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/90b055a6-f835-4029-b926-52d5b96c9022/download/toolkit-02-quantifying-distributions.pdf
    Description:

en:
Resource Title: Toolkit 11: serial correlation
    Size: 284194 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/8ed5ef3a-173f-4089-b6f0-f5095c9d1b2d/download/toolkit-11-serial-correlation.pdf
    Description: Tools for detecting and correcting for serial correlation in analyses of time series

en:
Resource Title: Toolkit 12: weighted averages and their uncertainties
    Size: 115056 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/78995d64-cf7c-4564-96f2-72763bc2288b/download/toolkit-12-weighted-averages-and-uncertainties.pdf
    Description: There are two different ways to calculate the uncertainty of a weighted average.  Software packages often don't tell you which one they are reporting, and the resulting errors can be huge.

en:
Resource Title: Toolkit 9: Experimentwise error rates and statistical fishing expeditions
    Size: 106670 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/557b276f-7c37-4687-bb6b-665bb6e040eb/download/toolkit_09.pdf
    Description: Statistical fishing expeditions, and how to avoid being misled by them

en:
Resource Title: Toolkit 5: uncertainty analysis and error propagation
    Size: 308876 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/5074901f-3168-4b40-b8f0-20dc5acc6248/download/toolkit-05-error-propagation.pdf
    Description: Gaussian error propagation, monte carlo methods, and first-order, second-moment error propagation

en:
Resource Title: Toolkit 4: confidence intervals
    Size: 155626 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/a06f6e74-1065-4dd3-af6b-9af0acf44f61/download/toolkit-04-confidence-intervals.pdf
    Description:

bayesian-inference

display_name: bayesian-inference

data-analysis

display_name: data-analysis

error-propagation

display_name: error-propagation

hypothesis-testing

display_name: hypothesis-testing

statistics

display_name: statistics

uncertainty

display_name: uncertainty


en: Although much of the endemic biodiversity of Madagascar can be attributed to its isolation as an island in the Indian Ocean, the high rates of speciation throughout its geologic history suggest an influence of local-scale landscape dynamics. The topographic evolution of Madagascar is dominated by the formation of high-relief continental rift escarpment and we argue that the erosion and landward retreat of this topography creates habitat heterogeneity that has served as a speciation pump for the island. The highest plant richness is found along the escarpment and is characterized by steady diversification rates over the last 45 Ma. Modeled landscape evolution by escarpment retreat demonstrates opportunities for allopatric speciation by transient habitat fragmentation through multiple mechanisms, including catchment expansion, isolation of highland remnants and formation of topographic and river barriers The segregation of floral phylogenetic turnover parallel to the escarpment is consistent with these mechanisms and indicates the importance of erosion-driven landscape dynamics on speciation.

en: landscape-evolution flora endemism geology madagascar

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: Data v1.0
    Size: 777360730 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/5fe6b561-d506-47f4-944b-a5fdc8fc3fe2/resource/b2aad61d-11f6-48ca-94d3-056c95916c7c/download/data.zip
    Description:

en:
Resource Title: Escarpment evolution drives the diversification of the Madagascar flora
    Size: 2087857142 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/5fe6b561-d506-47f4-944b-a5fdc8fc3fe2/resource/896d9a92-5cde-4259-a50b-3762698a1d61/download/data.zip
    Description: This upload contains the data (species checklist, species range maps, time-calibrated phylogenies, topographic change, etc.) generated, analyzed, and presented in the paper .

endemism

display_name: endemism

flora

display_name: flora

geology

display_name: geology

landscape-evolution

display_name: landscape-evolution

madagascar

display_name: madagascar


en: These data were used to drive and evaluate Jules Investigation Model (JIM) snow simulations. The data provided are the forcing data used for the "deterministic" runs as described in Winstral et al., 2019. The bias-detecting ensemble (Winstral et al., 2019) used observed snow depths (HS) to detect biases in these deterministic simulations related to precipitation and energy inputs to JIM. Simulations that included the BDE evaluations substantially improved JIM simulations.

en: Meteorological data used to develop and validate the bias-detecting ensemble (BDE)

en: snow-depth snow-simulation jules-investigation-model snow jim

en:
Resource Title: READ_ME
    Size: 1134 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/04470a99-93e5-499d-b869-6c30dffdc2f5/resource/30d1a503-fd18-43a5-9af9-e0e0f1855272/download/read_me.txt
    Description: File/data descriptor

en:
Resource Title: Station forcing data
    Size: 152284372 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/04470a99-93e5-499d-b869-6c30dffdc2f5/resource/282b7bc4-a607-4927-a2fd-858610a973c8/download/forcings.zip
    Description: All forcing data need to run JIM at HS and SWE stations. These are data downscaled directly from COSMO NWP data (WY14, WY15) and further enhanced by additional station observations of snow depth (HS) and air temperature (Ta) in WY17. Further information can be found in README file. Note that in WY14 and WY15, shortwave radiation was not split into direct and diffuse (not necessary for JIM) and that the default method in JIM for splitting precipitation phase was used. In WY 17, direct and diffuse were combined as input to JIM and JIM was forced with pre-processed liquid and solid precipitation phases.

en:
Resource Title: HS and SWE observations
    Size: 1214806 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/04470a99-93e5-499d-b869-6c30dffdc2f5/resource/d1a89df0-4991-48a0-8fbc-35f72afb953e/download/validation.zip
    Description: Automated (HS) and manual (SWE) data used to evaluate model simulations

en:
Resource Title: DEM
    Size: 568273 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/04470a99-93e5-499d-b869-6c30dffdc2f5/resource/705365e0-bdd9-4dca-966d-e8f9b9b7ede5/download/dem.txt
    Description: 1-km resolution DEM used for downscaling gridded NWP data to stations and for distributing BDE evaluations at HS-stations to SWE-stations

jim

display_name: jim

jules-investigation-model

display_name: jules-investigation-model

snow

display_name: snow

snow-depth

display_name: snow-depth

snow-simulation

display_name: snow-simulation


en:
Ontologies used for the case study in the publication.
Creative Commons (CC) license: CC BY-NC-SA -https://creativecommons.org/licenses/by-nc-sa/3.0/-

en: disease-control conflict-resolution infectious-disease meaning-negotiation ontology-authoring

conflict-resolution

display_name: conflict-resolution

disease-control

display_name: disease-control

infectious-disease

display_name: infectious-disease

meaning-negotiation

display_name: meaning-negotiation

ontology-authoring

display_name: ontology-authoring