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").
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 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: climate net-radiation air-temperature greenland aws automatic-weather-station

en: Greenland Climate Network (GC-Net) Data

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

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Resource Title: GC Net Chart Parameters
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Resource Title: Archived GC-Net Data (deprecated)
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    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.

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    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

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


$oid: OBSOLETE

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

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

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

type: dataset

key: guid

key: uri

value: Envidat Harvester

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

en:
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    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

format: ZIP

media_type: application/zip

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


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 stillberg forest larix-decidua picea-abies treeline climate-change seedling-recruitment swiss-alps

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

format: TXT

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

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

en:
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en:
Resource Title: How to balance forestry and biodiversity conservation? A view across Europe.
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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: stem-radius-changes treenet dendrometer drought-stress time-series machine-learning software tree-water-deficit tree-growth

en: TreeNetAI

title_for_slug: treenetai

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

envidat

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

en: EnviDat: the Environmental Data Portal

en: Swiss Canopy Crane II research site

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

en:
Resource Title: Research site metadata
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format: XLS

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

swiss-forest-lab

display_name: swiss-forest-lab


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

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

envi

en: Environment

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

en: Data analysis toolkits

title_for_slug: data-analysis-toolkits

en:
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    Description:

en:
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    Description: An overview of the math, logic, and pitfalls of ordinary least-squares linear regression

en:
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    Description: Gaussian error propagation, monte carlo methods, and first-order, second-moment error propagation

en:
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    Description: The ladder of powers and a few other cool transformations

en:
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    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
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en:
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    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 11: serial correlation
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    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 2: quantifying data distributions using descriptive statistics
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    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 8: Hodges-Lehmann estimators
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    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:
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    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 12: weighted averages and their uncertainties
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    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 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

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: madagascar geology endemism landscape-evolution flora

envidat

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

en: EnviDat: the Environmental Data Portal

en:
Resource Title: 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:
*Currently, this dataset is intended solely for article review purposes and may not be used for any other purposes.*
This dataset consists of a series of full snow profiles collected at the field site of Weissfluhjoch, Davos, Switzerland over the two snow seasons of 2022-2023 and 2023-2024. This series of snow profiles was collected with a high vertical and temporal resolution, with a specific focus on the melting seasons, during which up to 3 snow profiles per week were sampled. This dataset is part of the measurement campaign carried out within the SnowTinel project, whose aim is to explore the Sentinel-1 SAR backscattering response to melting Alpine snowpacks. Therefore, this dataset contains manual measurements of the main properties of snow which are responsible for scattering, i.e. temperature, density, specific surface area (SSA), liquid water content (LWC), surface roughness and snow water equivalent (SWE).
The dataset is composed of a total of 85 snow profiles, 38 carried out in 2022-2023 and 47 in 2023-2024.
  - Profiles of snow **temperature** were sampled at a vertical resolution of 10 cm (2022-2023) and 5 cm (2023-2024) using HI98501 Checktemp from Hanna Instruments;
   - Profiles of snow **density** were sampled at a vertical resolution of 3 cm using a box density cutter and a digital scale;
   - Profiles of snow **specific surface area** were sampled at a vertical resolution of 4 cm using the InfraSnow sensor from FPGA;
   - Profiles of snow **liquid water content** were sampled at a vertical resolution of 2 cm using the Denothmeter (2022-2023) and the New Capacitive Sensor from FPGA (2023-2024). In conditions of ripe snow, dielectric measurements were backed up by melting calorimetry measurements following a recently revised field protocol;
   - Snow **surface roughness** is expressed by the Root Mean Square of the Heights (RMSH) and the correlation length (CL), both computed from a digitized snow transect obtained by a digital photograph of a panel vertically inserted into the snow.
- **Snow water equivalent** was sampled in sections from the surface to the bottom of the snowpack with a cylinder cutter.

Additionally, **runoff** was automatically measured in the close proximity to the measurement field at a sub-hourly resolution by a lysimeter. The instrument was unfortunately discovered to be clogged at the start of the runoff in 2023 and only repaired in late May 2023. Therefore, the time series is not explanatory for the runoff start in 2023. The instrument was inspected timely and assessed as fully functional for the following snow season. The manually measured values of **SWE** are complemented by an automatically recorded time series at sub-hourly time intervals with a SSG1000 snow scale manufactured by Sommer Messtechnik, Austria.

 ---

- References:
  - HI98501 Checktemp: [https://www.hannainstruments.co.uk/modules/teapotknowledgehub/uploads/ist98501_06_18-60d496ca2cd31.pdf](https://www.hannainstruments.co.uk/modules/teapotknowledgehub/uploads/ist98501_06_18-60d496ca2cd31.pdf)
  - InfraSnow sensor: Wolfsperger, Fabian & Ziegler, Silvio & Schneebeli, Martin & Löwe, Henning. (2022). Evaluation of the InfraSnow: a handheld device to measure snow specific surface (SSA). 10.13140/RG.2.2.31566.95047; [https://snow-sen.com/infrasnow-ssa-sensor-2/](https://snow-sen.com/infrasnow-ssa-sensor-2/)
  - Denothmeter: Denoth, A. “An Electronic Device for Long-Term Snow Wetness Recording.” Annals of Glaciology 19 (1994): 104–6. [https://doi.org/10.3189/S0260305500011058](https://doi.org/10.3189/S0260305500011058)
  - New Capacitive Sensor: Wolfsperger, Fabian & Geisser, Michel & Ziegler, Silvio & Löwe, Henning. (2023). A NEW HANDHELD CAPACITIVE SENSOR TO MEASURE SNOW DENSITY AND LIQUID WATER CONTENT; [https://snow-sen.com/slf-snowpro-40/](https://snow-sen.com/slf-snowpro-40/)
  - Melting calorimetry: Barella, R., Bavay, M., Carletti, F., Ciapponi, N., Premier, V., and Marin, C.: Unlocking the potential of melting calorimetry: a field protocol for liquid water content measurement in snow, The Cryosphere, 18, 5323–5345, [https://doi.org/10.5194/tc-18-5323-2024](https://doi.org/10.5194/tc-18-5323-2024), 2024.
  - Surface roughness: B. Riccardo, C. Marin, M. Callegari, M. Gianinetto, T. Moranduzzo and C. Notarnicola, "A Low-Cost Portable Automatic System for Snow Surface Roughness Measurements Based on Digital Photography," 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 2021, pp. 5562-5565, doi: [https://doi.org/10.1109/IGARSS47720.2021.9553989](https://doi.org/10.1109/IGARSS47720.2021.9553989).
  - SSG: [https://www.sommer.at/de/produkte/schnee-eis/schneewaage-ssg-2](https://www.sommer.at/de/produkte/schnee-eis/schneewaage-ssg-2)

en: snow-roughness wet-snow snow-temperature sentinel-1 snow-melt surface-roughness snow-lwc radar snow-ssa snow-density satellite snow-profiles

en: SnowTinel high-temporal-resolution ground truth dataset for SAR remote sensing of snow

envi

en: Environment

en:
Resource Title: ssg1000_data_2022_2024.smet
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    Description: Snow water equivalent time series automatically recorded from the snow scale (2022-2024).

format: OCTET

en:
Resource Title: snowtinel_profiles_2022_2023.csv
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    Description: Snow profiles collected on the snow season 2022-2023.

format: CSV

media_type: text/csv

en:
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    Description: Snow profiles collected on the snow season 2023-2024.

en:
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    URL: https://www.envidat.ch/dataset/dd565b06-d9bd-4774-b140-5bb0dcdb70f5/resource/40dcf569-8e0b-4496-a556-79259095bf34/download/snowtinel_swe_2023_2024.csv
    Description: Manually measured time series of snow water equivalent (2023-2024).

en:
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en:
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en:
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en:
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en:
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    Format: text/csv
    URL: https://www.envidat.ch/dataset/dd565b06-d9bd-4774-b140-5bb0dcdb70f5/resource/41520f26-a39e-40b9-b966-cc2ffec6f95d/download/snowtinel_swe_2022_2023.csv
    Description: Manually measured time series of snow water equivalent (2022-2023).

radar

display_name: radar

satellite

display_name: satellite

snow-density

display_name: snow-density

snow-lwc

display_name: snow-lwc

snow-melt

display_name: snow-melt

snow-profiles

display_name: snow-profiles

snow-roughness

display_name: snow-roughness

snow-ssa

display_name: snow-ssa

snow-temperature

display_name: snow-temperature

surface-roughness

display_name: surface-roughness

wet-snow

display_name: wet-snow


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

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

en:
Resource Title: HS and SWE observations
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    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:
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    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

en:
Resource Title: Station forcing data
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    Format: application/zip
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    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: READ_ME
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    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

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

envidat

en: EnviDat: the Environmental Data Portal

envi

en: Environment

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


en:
Model code, technical documentation and auxiliary files for the dynamic ecohydrological model FORHYCS (FORests and HYdrology under Climate change in Switzerland). FORHYCS combines two pre-existing models, the hydrological model PREVAH and the forest landscape model TreeMig.
License: GPL v3

en: modeling hydrology forest-dynamics climate-change-impact forest

en: FORHYCS v. 1.0.0 model code

en:
Resource Title: Documentation
    Size: 293179 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/41bdb335-fa7d-4682-b51a-1c28867f9fed/resource/438b76c4-eebe-4722-9f66-dd012eac183f/download/forhycs_v110_manual.pdf
    Description: Technical documentation for the FORHYCS model. Includes an overview of the model code; changes from the parent models PREVAH and TreeMig; a description of the file format for grid inputs/outputs and a description of the file structure on a HPC cluster.

en:
Resource Title: FORHYCS v 1.0.0 model code (28-10-2019)
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    Format: application/zip
    URL: https://www.envidat.ch/dataset/41bdb335-fa7d-4682-b51a-1c28867f9fed/resource/057ee6c3-428d-47f5-a9ed-1b5b27a1e177/download/src.zip
    Description: Model code for FORHYCS as described in the Speich et al. discussion paper in GMDD. Includes code for the parent models PREVAH and TreeMig, as well as the new interface routines and output modules.

en:
Resource Title: Scripts and sample datasets v. 1.0.0
    Size: 2596848 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/41bdb335-fa7d-4682-b51a-1c28867f9fed/resource/0416f804-6b86-4d1b-afd2-423b6467d52f/download/scripts_and_aux.zip
    Description: Scripts and sample files for the documentation of FORHYCS (version 1.0.0)

en:
Resource Title: Link to FORHYCS in Github
    Size: 0
    Format: No Info
    URL: https://github.com/mspeich/FORHYCS/
    Description:

climate-change-impact

display_name: climate-change-impact

forest

display_name: forest

forest-dynamics

display_name: forest-dynamics

hydrology

display_name: hydrology

modeling

display_name: modeling


en:
Individual genotypes assessed at six nuclear microsatellite loci (two alleles per locus) for individuals of the butterfly Melanargia galathea (Marbled white) that were collected throughout Switzerland, along the regular grid of the Biodiversity Monitoring (BDM) of Switzerland. For each individual, the sampling site (number) and the genotype are given. Note that due to privacy restrictions, the original geographic coordinates remain disclosed. For original coordinates, data holders should be contacted (Swiss Federal Office for the Environment; Biodiversity Monitoring Switzerland).
The data refer to the following publication: Terzer et al., Distinct spatial patterns of genetic structure and diversity in the butterfly Marbled White (Melanargia galathea) inhabiting fragmented grasslands. Conservation Genetics, https://doi.org/0.1007/s10592-023-01593-4.

en: conservation-genetics monitoring genetic-diversity melanargia-galathea genetic-structure

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: Nuclear microsatellite genotypes of the butterfly Melanargia galathea

conservation-genetics

display_name: conservation-genetics

genetic-diversity

display_name: genetic-diversity

genetic-structure

display_name: genetic-structure

melanargia-galathea

display_name: melanargia-galathea

monitoring

display_name: monitoring


en: The dataset contains individual genotypes at 11 nuclear microsatellite markers of samples of Swiss stone pine (*Pinus cembra*). Samples were collected in 12 natural and presumably planted stands in the canton of Fribourg (Switzerland). The dataset is complemented by individual genotypes (same markers) from samples of a subset of 40 populations across the European Alps (taken from Gugerli et al., Journal of Biogeography 2023; https://doi.org/10.1111/jbi.14586; dataset archived at Dryad, https://doi.org/10.5061/dryad.866t1g1v6).

en: pinus-cembra genetic-structure reproductive-matieral molecular-markers afforestation

en: Nuclear microsatellite genotypes of Pinus cembra

en:
Resource Title: PopInfoFR.csv
    Size: 887 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/71fd7622-d0a6-421d-b6ad-436c2cafebd6/resource/5fd90c6f-ee7d-4b34-bfb3-01a457760aa2/download/popinfofr.csv
    Description:

en:
Resource Title: GenIndFR.csv
    Size: 29239 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/71fd7622-d0a6-421d-b6ad-436c2cafebd6/resource/8a9e5104-43b4-4e09-a3b8-ceae90131f0d/download/genindfr.csv
    Description:

en:
Resource Title: PopInfoAlps.csv
    Size: 2271 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/71fd7622-d0a6-421d-b6ad-436c2cafebd6/resource/80c99523-79be-4f9d-b9e6-f42932bfe941/download/popinfoalps.csv
    Description:

en:
Resource Title: PopInfoAlps.csv
    Size: 2271 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/71fd7622-d0a6-421d-b6ad-436c2cafebd6/resource/557bdce7-f96a-474f-8302-d1ff00cae2b7/download/popinfoalps.csv
    Description:

en:
Resource Title: Sonnenwyl_etal_ReadMe.txt
    Size: 1978 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/71fd7622-d0a6-421d-b6ad-436c2cafebd6/resource/1d053730-6b8c-42a6-9339-a4fafbaebd5e/download/sonnenwyl_etal_readme.txt
    Description: metadata and description of dataset

afforestation

display_name: afforestation

molecular-markers

display_name: molecular-markers

pinus-cembra

display_name: pinus-cembra

reproductive-matieral

display_name: reproductive-matieral


en:
The Jorat is one of the largest continously forested areas in the Swiss Plateau. On a forested area of 778 ha, the Parc Naturel du Jorat (PNJ), a periurban parc, has been established in 2022. To document the initial state of the forest within the perimeter of the PNJ, a sample plot inventory (SPI) was carried out on 132 sample plots (SP) in winter 2021/22.
This dataset contains results from this sample plot inventory. It consists of the following files:
- results_trees.csv: Results for or living and dead trees.
- results_regeneration.csv: Results for trees with DBH - 7.0 cm, assessed in three height classes.
- results_lying_deadwood.csv: Results for lying deadwood, assessed no three line transects
- results_trems.csv: Results for occurence of tree related microhabitats (TreMs)
- results_habitat_trees.csv: Results for occurence / densities of trees carrying at least one TreM or with a DBH -= 80 for living trees or -= 36 cm for dead trees respectively.
- lookup.csv: Contains lookup tables which describe the respective results in-depth.
- data_description.pdf: Briefly describes the datasets mentioned above.

en: forest-in temperate-forests swiss-plateau forest-reserves beech

envidat

en: EnviDat: the Environmental Data Portal

en: Sample plot inventory data form the Parc Naturel du Jorat

envi

en: Environment

en:
Resource Title: Results habitat trees
    Size: 1098 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/b91d79a0-616c-4f60-9a51-b74c9384e0d6/resource/5f308438-a867-4aa0-8e2d-1e8d1f65c423/download/results_habitat_trees.csv
    Description: The file results_habitat_trees.csv contains results for the number of habitat trees.

en:
Resource Title: Results TreMs
    Size: 9840 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/b91d79a0-616c-4f60-9a51-b74c9384e0d6/resource/93a5dba0-ae68-482d-bb87-5f5c62538cf3/download/results_trems.csv
    Description: The file results_trems.csv contains results for the density of TreMs per ha.

en:
Resource Title: Results lying deadwood
    Size: 2436 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/b91d79a0-616c-4f60-9a51-b74c9384e0d6/resource/c8f653da-6b07-4949-a7c7-19f9ae1b51e0/download/results_lying_deadwood.csv
    Description: The file results_lying_deadwood.csv contains results of the lying deadwood survey.

en:
Resource Title: Lookup
    Size: 9034 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/b91d79a0-616c-4f60-9a51-b74c9384e0d6/resource/6e728238-c52f-4b72-ae3f-a5109aefbd72/download/lookup.csv
    Description: The file lookup.csv contains description for the values shown in the 5 result tables.

en:
Resource Title: Results regeneration
    Size: 6112 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/b91d79a0-616c-4f60-9a51-b74c9384e0d6/resource/db868a5f-c640-4346-ac88-10e1b7052dba/download/results_regeneration.csv
    Description: The file results_regeneration.csv contains results of the regeneration survey.

en:
Resource Title: Data description
    Size: 102082 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/b91d79a0-616c-4f60-9a51-b74c9384e0d6/resource/f3201ed0-992c-4d3d-a460-1fa1ce4478a0/download/description_envidat.pdf
    Description: This document briefly describes the datasets.

en:
Resource Title: Results trees
    Size: 494778 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/b91d79a0-616c-4f60-9a51-b74c9384e0d6/resource/008f44a9-3aa7-4cb6-a69c-f11bbd63c8e7/download/results_trees.csv
    Description: The file results_trees.csv contains results of living, standing and lying and dead standing
trees as well as for stumps (trees found on the plot but broken / cut below 1.3 m).

beech

display_name: beech

forest-in

display_name: forest-in

forest-reserves

display_name: forest-reserves

swiss-plateau

display_name: swiss-plateau

temperate-forests

display_name: temperate-forests


en:
In the associated study [1], two time-lapse temperature gradient metamorphism series of three-dimensional micro-computed tomography images of snow (obtained by [2]) have been used to model the decrease of specific surface area (SSA) over time based on the pore-scale physics. We conducted finite element simulations of one-way coupled heat and mass diffusion in order to estimation the spatial pattern of water vapor deposition and sublimation, which controls the evolution of the SSA over time. We notably studied the influence of the condensation coefficient, a key but poorly constrained physical parameter.
This dataset provides the parameters used for the mesh generation and the finite element simulations. It also includes the ice fraction, specific surface area per unit volume and surface average of mean curvature and vapor field obtained as outputs from the mesh process and the simulations.
[1]: Braun, A., Fourteau, K., and Löwe, H.: A rigorous approach to the specific surface area evolution in snow during temperature gradient metamorphism, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-1947, 2023.
[2]: Pinzer, B. R., Schneebeli, M., and Kaempfer, T. U.: Vapor flux and recrystallization during dry snow metamorphism under a steady temperature gradient as observed by time-lapse micro-tomography, The Cryosphere, 6, 1141–1155, https://doi.org/10.5194/tc-6-1141-2012, 2012.

en: time-series snow metamorphism microstructure fem x-ray-tomography

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: Simulation parameters and outputs for a rigorous approach to the specific surface area evolution in snow during temperature gradient metamorphism

format: ODS

fem

display_name: fem

metamorphism

display_name: metamorphism

microstructure

display_name: microstructure

snow

display_name: snow

time-series

display_name: time-series

x-ray-tomography

display_name: x-ray-tomography


en:
The list gives the clearly identifiable plant species from Schinz (1775). Die Reise auf den Uetliberg. Verlag des Waysenhauses, Zürich). Several species given in Schinz (1775) are not identifiable to the species level and are not included in the list. Similarly, *Vicia pisiformis*, Erbsenartige Wicke, given by Schinz (1775) is probably a misidentification and also not included in the list.
Latin plant species name Schinz 1775: Latin plant name (if) given by Schinz (1775); na: not available
German plant species name Schinz 1775: German plant name given by Schinz (1775)
Latin plant species name Info Flora 2024: Latin plant name according to www.infoflora.ch in 2024
German plant species name Info Flora 2024: German plant name according to www.infoflora.ch in 2024
Plant species still occurring in 2020: Plant species given in Schinz (1775) still occurring at the Uetliberg in 2020 (Zürcherische Botanische Gesellschaft. 2020. Flora des Kanton Zürich. Haupt, Bern); occurrence of *Thesium alpinum*, Alpen-Bergflachs, according to floristic knowledge of Rolf Holderegger and Michèle Büttner; 1: still occurring; 0: no longer occurring / extinct.

en: zurich flora-change uetliberg occurrence plant-species

en: Plant species list from 1775 and 2020 for Uetliberg Zürich

flora-change

display_name: flora-change

occurrence

display_name: occurrence

plant-species

display_name: plant-species

uetliberg

display_name: uetliberg

zurich

display_name: zurich


en:
The available lichen data consists of detection/nondetection data (1/0) of 373 tree-inhabiting (*corticolous*) lichen species from 416 plots surveyed 1-2 times. The lichen data were originally collected for the purpose of the Red List of epiphytic lichen species in Switzerland ([Scheidegger *et al.* 2002](https://www.bafu.admin.ch/bafu/de/home/themen/biodiversitaet/publikationen-studien/publikationen/rote-liste-gefaehrdete-arten-baum-erdbewohnende-flechten.html)), but updated to recent nomenclature for the purpose of this study.
This repository contains all the supporting data and R code for the paper:
von Hirschheydt, G., Kéry, M., Ekman, S., Stofer, S., Dietrich, M., Keller, C., Scheidegger, C. (2024) **Occupancy model reveals limited detectability of lichens in a standardised large-scale monitoring**. *Journal of Vegetation Science*.
Results and figures presented in the manuscript should be reproducible (with small differences in the latter digits due to stochasticity of the MCMC sampler) with the provided data and code.
The downloadable `.zip` folder has the following structure:
* `0_data/`
* `1_code/`
* `2_output/`
* `lichen_detectability.Rproj`
* `README.txt`
* `workflow.html`
- `workflow.Rmd`

The main folder and the three subordinate folders each have their own `README*.txt` file. These describe each available file in detail and should be consulted prior to using the data or running any code. The file `lichen_detectability.Rproj` stores the information about the R project. The user can open the project by clicking/double-clicking on this file which will automatically define the repository as working directory for the R session. If the user does not use RStudio/Posit, they may have to set the working directory manually to the stored location in the R files. The files `workflow.*` guide the user through the analysis (`1_code/*.R`) in the correct order so that they can:
- bundle the cleaned data into a data list readable for JAGS
- fit the multi-species occupancy model to the data and store the output
- assess the goodness-of-fit of the model to the data
- conduct a prior sensitivity analysis with 2 additional sets of priors
- extract the summary statistics reported in the manuscript and supplementary materials
- generate the figures shown in the manuscript and supplementary materials

en: bayesian-inference biodiversity cryptogams detectability detection-probability occupancy-model inter-observer-difference lichens monitoring sessile-species swisslichens observer-error

envidat

en: EnviDat: the Environmental Data Portal

en: Repeated detection-nondetection data of corticolous lichens from a standardised monitoring across Switzerland

bayesian-inference

display_name: bayesian-inference

biodiversity

display_name: biodiversity

cryptogams

display_name: cryptogams

detectability

display_name: detectability

detection-probability

display_name: detection-probability

inter-observer-difference

display_name: inter-observer-difference

lichens

display_name: lichens

monitoring

display_name: monitoring

observer-error

display_name: observer-error

occupancy-model

display_name: occupancy-model

sessile-species

display_name: sessile-species

swisslichens

display_name: swisslichens


en: This dataset collection contains all datasets collected during the ring wind tunnel experiments in January and May 2023 at the SLF cold laboratory facilities in Davos. A full description of the experiment set-up can be found in Wahl et al. (2024). The collection contains data from 19 experiments. The dataset collection entails measurements of the stable water isotopic composition of snow samples and the water vapour inside the wind tunnel, measurements of the meteorological variables inside the wind tunnel and snow sample properties as measured with microCT measurements (sphere size distribution and specific surface area (SSA)).

en: snow-saltation cold-lab drifting-snow water-isotopes metamorphism experiments blowing-snow wind-tunnel

en: Ring wind tunnel experiments - airborne snow metamorphism and stable water isotopes

envi

en: Environment

en:
Resource Title: Snow samples - microCT measurements
    Size: 3800 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/f2ebc8e9-e757-47a8-8713-8892cb1cb25f/resource/93606a69-9f2e-4f8d-bca5-3f28001ee056/download/windtunnel2023_ct_measurements.txt
    Description: The data of the snow samples that were analysed in a Scanco® CT-40 scanner using a sample holder of 15 mm diameter which allowed for a high-resolution analysis with voxel size of 8 μm. The timestamp (local time, Davos, Switzerland) of snow sampling is given as well as the type of snow sample (see Snow samples - stable water isotopic composition dataset), the corresponding wind tunnel experiment number ("ExpNo", Wahl et al. (2024), Table 1) and the time since the snow introduction to the wind tunnel. The measured SSA is given in units 1/mm. The column "hist_filename" lists the name of the corresponding .txt file which contains the sphere size statistics of the snow samples. The sphere size statistics files are compressed in a .zip file.

en:
Resource Title: Wind tunnel water vapour isotopic composition
    Size: 46068957 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/f2ebc8e9-e757-47a8-8713-8892cb1cb25f/resource/e1ffe37c-e289-4eb5-a65f-af474e56fae7/download/windtunnel2023_vapor_isotopes.txt
    Description: The water vapour and its calibrated isotopic composition sampled inside the wind tunnel at 46cm height during the experiments. The vapour was pumped through a heated copper tube at a rate of 0.2 l/min to the CRDS instrument located in a temperate room outside the cold laboratory. The data has 1 second resolution with the timestamp in UTC (CRDS reference time). The measured variables are the calibrated d18O (‰), dD (‰) and d-excess (‰) signals and the uncalibrated ("H2Oraw", ppmv) and calibrated ("H2Ocali",ppmv) humidity readings of the CRDS and converted to specific humidity ("spechum", g/kg). The isotope data was corrected for its humidity-isotope dependency and referenced against the VSMOW-SLAP scale. Calibration runs were performed daily and details can be found in Wahl et al. (2024) including the Supplementary Materials.

en:
Resource Title: Snow samples - sphere size statistics files
    Size: 62459 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f2ebc8e9-e757-47a8-8713-8892cb1cb25f/resource/d91180d7-3057-462d-b893-20a01974b0a1/download/snowsamples_spheresizedistributions.zip
    Description: The individual measurement reports of the microCT scan for each snow sample as combined zip file. The file names and the sample number indicate which snow sample it is, see "Snow samples - microCT measurements". Each file contains the statistics and the count of fitted sphere size diameters and the respective share (in-%) in the frequency distribution. Immediately after snow sampling, a small portion of snow was transferred to the cylindrical μCT sample holder with a diameter of 15 mm and analysed at -20°C using a Scanco® CT-40 scanner. The analysis followed Hildebrand and Rüegsegger (1997).

en:
Resource Title: Meteorological variables measured inside the wind tunnel during the May 2023 experiments
    Size: 290145 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/f2ebc8e9-e757-47a8-8713-8892cb1cb25f/resource/1fcb9bb3-769d-40ee-948f-1b6899c6d488/download/windtunnel2023_metvariables_1min.txt
    Description: The dataset contains the 1-minute averages of the calibrated meteorological variables: temperature (T, °C), relative humidity (RH, %), relative humidity referenced against saturation over ice (RHice, %), specific humidity (q, g/kg) and wind speed (WS, m/s) as measured in the observation section of the wind tunnel at different heights and the room temperature of the laboratory measured outside the wind tunnel (Lab_Ta, °C). The measurement heights correspond to: "Top"=42.5cm, "Mid"=30cm, "Bot"=15cm, "PT100_Ta"=3cm. "WS" was measured at 36.5cm height. The time was synchronised to the concurrent CRDS measurements (which are UTC) and adjusted for the time lag resulting from pumping vapour from inside the wind tunnel to the CRDS. The timestamps indicate the beginning of the averaging period.

en:
Resource Title: Snow samples - stable water isotopic composition
    Size: 20365 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/f2ebc8e9-e757-47a8-8713-8892cb1cb25f/resource/68873e4c-7b30-4f82-b97c-446c80546216/download/windtunnel2023_snow_isotopes.txt
    Description: The dataset contains the stable water isotopic composition (d18O, dD. d-excess) of the snow samples taken during the ring wind tunnel experiments in January and May 2023 at the SLF cold laboratory facilities in Davos. The snow samples were collected in air-tight WhirlPaks and kept frozen at -20°C until they were melted in the closed sampling bags, filtered and 1.8ml were transferred into a 2ml glass vial and shipped to the Paul Scherrer Institut in Villigen, Switzerland where they were analysed following established laboratory procedures. The combined uncertainty on the individual isotopic composition is <0.5‰ for dD and <0.1‰ for d18O which yields an uncertainty of 0.9‰ on d-excess.
Timestamps are given for local Davos time (LT) and synchronised to the CRDS instrument which is in UTC. The "ExpNo" column refers to the experiment numbers given in Wahl et al. (2024), Table 1. The "type" of the snow samples is: "ini" = initial snow before introduction to the wind tunnel, "airborne" = snow sampled out of the air, "acc_in" = inner part of accumulated snow, "acc_out" = outer surface layer of accumulated snow.

en:
Resource Title: Meteorological variables measured inside the wind tunnel during the January 2023 experiments
    Size: 41430 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/f2ebc8e9-e757-47a8-8713-8892cb1cb25f/resource/a9edeb00-0547-47a3-821d-0a70b86af7ab/download/windtunnel2023_janmetvariables_1min.txt
    Description: The dataset contains the 1-minute averages of the calibrated meteorological variables: temperature (T, °C), relative humidity (RH, %), relative humidity referenced against saturation over ice (RHice, %), specific humidity (q, g/kg) and wind speed (WS, m/s) as measured in the observation section of the wind tunnel at different heights. The measurement heights correspond to: "Top"=42.5cm, "Mid"=30cm, "Bot"=15cm, "PT100_Ta"=3cm. "WS" was measured at 36.5cm height. The timestamp is in local time (UTC+1) and indicates the start of the averaging period.

blowing-snow

display_name: blowing-snow

cold-lab

display_name: cold-lab

drifting-snow

display_name: drifting-snow

experiments

display_name: experiments

metamorphism

display_name: metamorphism

snow-saltation

display_name: snow-saltation

water-isotopes

display_name: water-isotopes

wind-tunnel

display_name: wind-tunnel


en:
This dataset includes the necessary files to reproduce the results presented in Melo et al. (2024):
- code version used in the simulations (also available at the institutional [GitLab repository](https://gitlabext.wsl.ch/atmospheric-models/les-lsm) - tag 'r2024')
- input files to run the simulations
- post-processing scripts (MATLAB and FORTRAN)
- selected raw output files
- post-processed results (MATLAB data files).
 	**Note:** Direct model outputs are not provided because of their size, but they can be shared upon request. They can be reproduced by running the code. The post-processed results allow you to reproduce all figures in Melo et al. (2024).

en: aerodynamic-entrainment splash drifting-snow large-eddy-simulation snow-saltation saltation-parameterizations

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: Understanding snow saltation parameterizations - Reproducibility

aerodynamic-entrainment

display_name: aerodynamic-entrainment

large-eddy-simulation

display_name: large-eddy-simulation

saltation-parameterizations

display_name: saltation-parameterizations

splash

display_name: splash


en: With this study, our aim was to estimate the nutrient fluxes relevant for assessing nutrient sustainability as accurately as possible and to calculate nutrient balances for alternative forest management scenarios. Furthermore, we tested whether mapping units from existing geologic maps can serve as a basis for forest practitioners to estimate nutrient sustainability or whether more detailed data is needed. Positive fluxes include deposition and weathering, while negative fluxes include losses due to leaching and nutrient removal through timber harvesting in the balance. Weathering and leachate losses were modeled with a geochemical model. The SwissStandSim model was used to simulate the biomass growth under different harvesting and silvicultural strategies, allowing sustainability to be assessed for each nutrient at a given intensity of use. This assessment was made per rotation period of 100 years based on two criteria: i) nutrient supply; and ii) total stocking volume.

en: beech soil mass-balance sustainability nutrient

en: Nutrient sustainability in beech forests

en:
Resource Title: Nutrient_tree_compartments.xlsx
    Size: 39892 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/b938dd05-54ab-4dc0-b7d8-a106567322f4/resource/59464da8-a4aa-4adf-86d1-df763223e9c4/download/nutrient_tree_compartments.xlsx
    Description:

en:
Resource Title: Soil_Properties_TabA2.xlsx
    Size: 50339 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/b938dd05-54ab-4dc0-b7d8-a106567322f4/resource/ba211033-8761-4d53-a1fd-6e741926f3d6/download/soil_properties_taba2.xlsx
    Description:

en:
Resource Title: Nutrient_export_harvesting.xlsx
    Size: 15618 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/b938dd05-54ab-4dc0-b7d8-a106567322f4/resource/3f5aa362-7738-4a1e-8cce-9c0b60f0ef71/download/nutrient_export_harvesting.xlsx
    Description:

en:
Resource Title: Nutrient_mass_balances.xlsx
    Size: 27714 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/b938dd05-54ab-4dc0-b7d8-a106567322f4/resource/23bb8222-159c-42b8-8e06-dd14b8469600/download/nutrient_mass_balances.xlsx
    Description:

beech

display_name: beech

mass-balance

display_name: mass-balance

nutrient

display_name: nutrient

soil

display_name: soil

sustainability

display_name: sustainability


en:
On this repository you can find the monitoring data of the 3 PV power-plants analysed in the article _"Confirmation of the power gain for solar photovoltaic systems in alpine areas"_ (doi: 10.3389/fenrg.2024.1372680) . Each folder contains the following two elements:
1.  **README.txt**  : describes the content of the folder. Especially it describes the plant's monitoring system's architecture and relates it to the various files included in the data sub-folder.  It also provides a detailed description of all processing steps leading to the data sets as available in the data sub-folder and used in the pre-cited article.
3. **data/** : sub-folder containing the file(s) used in the referenced article.

en: bifaciality winter-electricity alps snow photovoltaic

envidat

en: EnviDat: the Environmental Data Portal

en: Monitoring data sets of alpine photovoltaic power-plants

envi

en: Environment

en:
Resource Title: A1.zip
    Size: 5999205 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/40024d58-c912-499e-8f4f-8a9b9189955a/resource/1a7597fd-203a-427f-851a-009673c29374/download/a1.zip
    Description: Title: Monitoring data set at A1 (test-site of Totalp), years 2018-2022
Provider: ZHAW - EKZ

Edition: Y.Frischholz, Laboratory of Cryospheric Sciences, EPFL
Last edition: 17.01.2024

File name structure:
The test site contains 6 segments named from A to F. The six files provided in the data/ subfolder follow the same naming convention (e.g. for segment A, the file name is: “A1_A.csv”). Refer to the publication for further details on the setups of the segments.

File format:
All files are provided in the CSV format.

Column headings:
	•	timestamp (datetime64): time stamp in format “YYYY-MM-DD HH:mm:ss”, time-zone: UTC
	•	poa_front (double): front plane-of-array irradiation (W/m**2)
	•	poa_back (double): rear plane-of-array irradiation (W/m**2)
	•	mod_temp (double): module’s temperature (°C)
	•	P_AC (double): output AC power (W)

Preprocessing:
	Outliers:
	⁃	Quantiles Q1 and Q99 are computed for each variable
	⁃	Values below Q1 and above Q99 are considered outliers
	⁃	Outliers are replaced by NaNs.

	Missing data:
	⁃	Data holes of less than 3 hours are interpolated linearly for all variables
	⁃	Data holes of more than 3 hours are replaced using the monthly nan-mean of the considered hour if more than 50% of the qualifying data points are available. For instance, if the data point 2022-12-12 12:12:00, is missing for a given variable and is part of a data hole larger than 3 hours, then it is replaced by the mean of all data points with hour = 12 of the month of December 2022, if 50% of all the qualifying data points are not NaN. In case more than 50% of the qualifying points are missing, the data point remains as missing (NaN).

	Wiring losses :
	⁃	As described in [1] , the test-site setup caused abnormally large wiring losses,  mainly due to the distance to optimisers and to the low installed power per DC-wire.
	⁃	The wiring losses comprised between 10% and 15%, are averaged hourly and added to the measured DC power.

	Misc.:
	⁃	P_AC is computed for each segment based on the measured DC power and the average inverter’s efficiency (96.1%).
	⁃	As described in [1],  the peak power of the installed module, used to compute the final yield is slightly different from the values announced by the manufacturer.

Mean uptime:
	Raw:
	⁃	95.2%
	Processed:
	⁃	100%

References:
[1] 	D. Anderegg, S. Strebel, and J. Rohrer, “Alpine Photovoltaik Versuchsanlage Davos Totalp : Erkenntnisse aus 5 Jahren Betrieb,” de, Sep. 	2023, Accepted: 2023-09-29T09:13:16Z Publisher: ZHAW Zürcher Hochschule für Angewandte Wissenschaften.DOI: 10.21256/	zhaw-2524. [Online]. Available: https://digitalcollection.zhaw.ch/handle/11475/28797 (visited on 12/15/2023).

en:
Resource Title: A2.zip
    Size: 27177550 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/40024d58-c912-499e-8f4f-8a9b9189955a/resource/e68bb1d0-915a-49a5-a108-c607356b1889/download/a2.zip
    Description: Title: Monitoring data set at A2 (utility-scale power-plant Muttsee), months 10.2022-09.2023
Provider: AXPO - IWB Industrielle Werke Basel
Last edition: 17.01.2024
Editor: Y.Frischholz, Laboratory of Cryospheric Sciences, EPFL

File name structure:
The power-plant is made of 23 string boxes (GAK) and inverters (WR) named after their given number. One file per device is provided in the data/ subfolder, following the same naming convention  (e.g. for the string box number 1, the file name is: “A2_GAK01.csv” and for the corresponding inverter, the file name is “A2_WR01.csv”). Refer to the publication for further details on the setup of the power plant.

File format:
All files are provided in the CSV format.

Column headings:
	String boxes and inverters (GAK & WR):
	⁃	device_name (string): name of the device (same as file name)
	⁃	timestamp (datetime64): time stamp in format “YYYY-MM-DD HH:mm:ss”, time-zone: UTC

	String boxes (GAK):
	⁃	I_# (double): input current from the substring number # (W)
	⁃	I_SUM (double): output current of string box (A)
	⁃	U_DC (double): output voltage of string box (V)
	⁃	P_DC_# (double):  input DC power from the substring number # (W)
	⁃	P_DC (double): output DC power of string box (W)
	⁃	$S$_T#_$T$ (double): temperature from the sensor connected to the string box (empty if not existing), placed on the “front” or “back” side (S)  of table T. Two types of temperature measurements (#) are available: 1=standard cell temperature (°C) , 2=module temperature on the rear side (°C) .
	⁃	$S$_SRAD_T (double): poa irradiation (W/m**2) from the sensor connected to the string box (empty if not existing), placed on the “front” or “back” side (S)  of table T.

		Inverters (WR):
	⁃	I_DC (double): input DC current(A)
	⁃	U_DC(double): input DC voltage(V)
	⁃	P_DC (double): input DC power (W)
	⁃	P_AC (double): output AC power (W)

Preprocessing:
	1. Outliers:
	⁃	Quantiles Q1 and Q99 are computed for each variable
	⁃	Values below Q1 and above Q99 are considered outliers
	⁃	Outliers are replaced by NaNs.

	2. Missing data:
	⁃	Data holes of less or equal to 3 hours are interpolated linearly for all variables
	⁃	Data holes of more than 3 hours are replaced using the monthly nan-mean of the considered hour if more than 50% of the qualifying data points are available. For instance, if the data point 2022-12-12 12:12:00, is missing for a given variable and is part of a data hole larger than 3 hours, then it is replaced by the mean of all data points with hour = 12 of the month of December 2022, if 50% of all the qualifying data points are not NaN. In case more than 50% of the qualifying points are missing, the data point remains as missing (NaN).

	3. Wiring losses:
	⁃	To be consistent with A1, wiring losses are corrected at A2. The wiring loss between the string boxes and the inverters is removed by multiplying the value of input P_DC at the inverter level (P_DC,inv) by the mean ratio P_DC,inv / P_DC,arr.
	⁃	The computed mean wiring loss is 2.3\%. It is computed from 2023-02-01 to 2023-09-30, after the long data hole in the data sets of the string boxes.

Mean uptime:
	Raw:
	⁃	WR: 98.2%
	⁃	GAK*: 38.5%
	Processed:
	⁃	WR: 99.6%
	⁃	GAK*: 56.4%

	*The uptime at the string box level (GAK) is low because PMUs at this level shut down during steady and low signal leading to unavailable data at night. In addition, the 23 days downtime of 	January 2023 could not be interpolated nor replaced.

alps

display_name: alps

bifaciality

display_name: bifaciality

photovoltaic

display_name: photovoltaic

snow

display_name: snow

winter-electricity

display_name: winter-electricity


en:
Environmental DNA complements scientific trawling in surveys of marine fish biodiversity (Dataset 2019)
In October 2019 we chose 15 sites from the 2019 EVHOE survey for eDNA sampling. The French international EVHOE bottom trawl survey is carried out annually during autumn in the BoB to monitor demersal fish resources. At each site, we sampled seawater using Niskin bottles deployed with a circular rosette. There were nine bottles on the rosette, each of them able to hold ∼5 l of water. At each site, we first cleaned the circular rosette and bottles with freshwater, then lowered the rosette (with bottles open) to 5 m above the sea bottom, and finally closed the bottles remotely from the boat. The 45 l of sampled water was transferred to four disposable and sterilized plastic bags of 11.25 l each to perform the filtration on-board in a laboratory dedicated to the processing of eDNA samples. To speed up the filtration process, we used two identical filtration devices, each composed of an Athena® peristaltic pump (Proactive Environmental Products LLC, Bradenton, Florida, USA; nominal flow of 1.0 l min–1 ), a VigiDNA 0.20 μm filtration capsule (SPYGEN, le Bourget du Lac, France), and disposable sterile tubing. Each filtration device filtered the water contained in two plastic bags (22.5 l), which represent two replicates per sampling site. We followed a rigorous protocol to avoid contamination during fieldwork, using disposable gloves and single-use filtration equipment and plastic bags to process each water sample. At the end of each filtration, we emptied the water inside the capsule that we replaced by 80 ml of CL1 conservation buffer and stored the samples at room temperature following the specifications of the manufacturer (SPYGEN, Le Bourget du Lac, France). We processed the eDNA capsules at SPYGEN, following the protocol proposed by Polanco-Fernández et al., (2020). We performed library preparation and sequencing at Fasteris (Geneva, Switzerland). Specifically, we prepared four libraries using the MetaFast protocol (a ligation-based method) and sequenced them separately. We carried out paired-end sequencing using a MiSeq sequencer (2 × 125 bp, Illumina, San Diego, CA, USA) on two MiSeq Flow Cell Kits (v3; Illumina), following the manufacturer’s instructions. We analysed the sequence reads using the OBITools package (http://metabarcoding.org/obitools; Boyer et al., 2016), following the protocol described by Valentini et al. (2016).
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: atlantic-region fish-composition bayofbiscay metabarcoding actinopterygii edna teleo chondrichthyes

en: Environmental DNA Marine France Evhoe 2019

actinopterygii

display_name: actinopterygii

atlantic-region

display_name: atlantic-region

bayofbiscay

display_name: bayofbiscay

chondrichthyes

display_name: chondrichthyes

edna

display_name: edna

fish-composition

display_name: fish-composition

metabarcoding

display_name: metabarcoding

teleo

display_name: teleo


en: Data (Quantitative Wood Anatomy, Maximum Latewood Density, Volumetric Density, holo-cellulose-to-wood-ratio) used in the manuscript "New Prospects for Temperature Reconstructions from Relict Wood Using Tree-Ring Anatomy". Wood samples stem from Larix sibirica collected in the Yamal region, northeastern Siberia, Russia. We investigated wood properties of living trees and subfossil wood buried in riverbank sediments, dated to approximately -2760 to -2600 BCE. Our goal was to evaluate the integrity of climate proxies in sub-fossil wood and develop practical methods to detect wood degradation when reconstructing Late Holocene and Common Era climate conditions using tree-ring data.

en: tree-ring paleoclimate temperature-reconstruction wood-anatomy mxd

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: Quantitative Wood Anatomy and Maximum Latewood Density from Yamal peninsula

en:
Resource Title: Figure3_dataset.txt
    Size: 8581 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/743a897f-26e7-4871-a014-b0f95238564d/resource/2435516e-8dfc-4347-a35c-893eadcdf569/download/figure3_dataset.txt
    Description: This dataset contains the mean chronologies of MXD and QWA of the relict and living wood material presented in Figure 3. It also contains the JJA temperature record of the Salekhard Meteo station.

en:
Resource Title: Figure2_dataset.csv
    Size: 1668 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/743a897f-26e7-4871-a014-b0f95238564d/resource/b0e00f50-78a2-4006-ab8d-7d4d796efc05/download/bulkdensity.csv
    Description: This dataset contains data presented in Figure 2 in the paper "New Prospects for Temperature Reconstructions from Relict Wood Using Tree-Ring Anatomy".

Sample ID = Sample ID
group = living or relict wood material
Bulk wood density = volumetric measured density (kg / m^3)
Mean Tree Ring Density	= Density derived via X-ray densitometry (kg / m^3)
Cellulose_to_wood ratio = Holo-cellulose-to-wood ratio
Anatomical density = Area of cell wall / (area of cell wall + lumen area)

mxd

display_name: mxd

paleoclimate

display_name: paleoclimate

temperature-reconstruction

display_name: temperature-reconstruction

tree-ring

display_name: tree-ring

wood-anatomy

display_name: wood-anatomy


en: This dataset contains the raw ring-width and climate data (1952-2020), as well as the R code to run the model and the model output used in the research article "No future growth enhancement expected at the northern edge for European beech due to continued water limitation".

en: tree-ring dendroecology network-analysis beech dendrochronology fagus-sylvatica

en: European Beech tree-ring dataset 1952-2020

en:
Resource Title: Model output
    Size: 679105614 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/6b052bdb-9e18-4f00-92b6-e102347245ec/resource/c0117599-93c9-4ea0-8f12-753c711f003c/download/ebtrnplus_model_newcwbmean2
    Description: Output of the Mixed-effects model

en:
Resource Title: final_cwb_model.R
    Size: 13781 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/6b052bdb-9e18-4f00-92b6-e102347245ec/resource/5d54048a-25a6-4861-89a3-6586b970177d/download/final_cwb_model.r
    Description: R code to run the model behind the results of the research article "No future growth enhancement expected at the northern edge for European beech due to continued water limitation".
Requires beech_rawtrw.txt.

en:
Resource Title: beech_rawtrw.txt
    Size: 332605512 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/6b052bdb-9e18-4f00-92b6-e102347245ec/resource/f3bc670c-3474-4987-966e-c9a2e94bebd3/download/beech_rawtrw.txt
    Description: This dataset contains the raw ring-width and climate data (1952-2020) used in the research article "No future growth enhancement expected at the northern edge for European beech due to continued water limitation".

RW= ring width
Year= Calendar year
CN = core number
ID3= plot identifier
longitude = longitude (°)
latitude = latitude (°)
elevation = elevation (m)
age= estimated age in calendar year
radius= estimated radius in calendar year (mm)
MAT= mean annual temperature (°C)
MAP= mean annual precipitation (mm)
MCWB= mean climatic water balance (P-PET; mm)
amplitude= simple continentality measure (July - January mean maximum temperatures)
MATspline1 and 2, MCWBspline1 and 2, radiusspline4 and 5= natural spline transformed values of MAT, MCWB and radius #ns(x,2)
CWD_pMay_Jun= Climatic Water Balance of previous year May to June #!!!#please note CWD here is NOT Climatic Water Deficit, this was a labeling error in the creation of the dataset headers
CWD_May_Jun= Climatic Water Balance of current year May to June
Tmax_pMay_Jun= mean monthly maximum temperature of previous year May to June
Tmax_May_Jun= mean monthly maximum temperature of current year May to June
weight = site replication-based weighting input for model
SOM= membership of regional cluster 1-8 based on Supervised Organized Mapping

beech

display_name: beech

dendrochronology

display_name: dendrochronology

dendroecology

display_name: dendroecology

fagus-sylvatica

display_name: fagus-sylvatica

network-analysis

display_name: network-analysis