Find research datasets worth reusing

opencc-by-4.0Sep 2021View details →

Microfossildataset "Alpine glacier reveals ecosystem impacts of Europe's prosperity and peril over the last millennium"

Full palynological dataset from Colle Gnifetti glacier in the Monte Rosa Massif (Swiss Alps; 45°55'45.7'' N, 7°52'30.5'' E; 4450m a.s.l.) The dataset contains raw counts for pollen, ferns, NPP (Non-pollen palynomorphs), microscopic charcoal concentrations. Ice core depths are given as m weq = meters waterequivalent. Chronology is based on Jenk et al. (2009) and Sigl et al. (2018). "yr. BP" indicates "years before present" with "present" defined as 1950 CE. Further information on the dataset is given in the paper "Alpine glacier reveals ecosystem impacts of Europe’s prosperity and peril over the last millennium" by Brugger et al. published in Geophysical Research Letters.

opencc-by-4.0Sep 2021View details →

WP3-CT2 Alpine Glaciers Disappearance Tipping Point

Glaciers Length changes starting from small, medium, and large glaciers.

opencc-by-4.0Nov 2022View details →

Data from: Shifts in ground-dwelling predator communities in response to changes in management intensity in Alpine meadows

Here, we provide raw abundance data from a small-scale case study on the effects of management intensity on ground-dwelling macro-invertebrate communities in extensively and intensively managed hay meadows in South Tyrol, Italy. The fauna was sampled with the pitfall trap methods in two seasons (autumn 2018 and spring 2019). The predatory groups Araneae, Opiliones, Carabidae, Staphylinidae, and Formicidae were identified to species level, the rest – where possible – to family level. The data can be found as absolute numbers (i.e., individuals per pitfall trap) and as standardised numbers (i.e., individuals per sampling day). Additionally, we provide ecological species traits on rarity (for the area of South Tyrol), moisture requirements and ecological tolerance, as well as the Red List statuses.

opencc-by-4.0Feb 2022View details →

Bathymetry and Sediment thickness distribution of Lago dei Seracchi alpine lake, Rutor basin, Aosta Valley, Italy

Maps of water depth and sediment accumulation in an Italian proglacial lake, done by Ground Penetrating Radar (GPR) in July 2021. Supporting Time domain reflectometry surveys and geotechnical analyses on the sediments are also provided. For details, see the readme file in the dataset folder.

opencc-by-4.0Sep 2018View details →

Alpine ice sheet glacial cycle simulations continuous variables

These data contain a subset of time-dependent glacier model output variables. Reference: <ul> <li>Seguinot, J., Ivy-Ochs, S., Jouvet, G., Huss, M., Funk, M., and Preusser, F.: Modelling last glacial cycle ice dynamics in the Alps, The Cryosphere, 12, 3265-3285, doi:<a href="https://doi.org/10.5194/tc-12-3265-2018">10.5194/tc-12-3265-2018</a>, 2018.</li> </ul> File names: <pre><code>alpcyc.{1km|2km}.{epic|grip|md01}.{cp|pp}.{ex.100a|ex.1ka|ts.10a}.nc</code></pre> <ul> <li>Horizontal resolution: <ul> <li>1km: 1 km horizontal resolution</li> <li>2km: 2 km horizontal resolution</li> </ul> </li> <li>Temperature forcing: <ul> <li>epic: EPICA ice core temperature forcing</li> <li>grip: GRIP ice core temperature forcing</li> <li>md01: MD01-2444 core temperature forcing</li> </ul> </li> <li>Precipitation forcing: <ul> <li>cp: constant precipitation</li> <li>pp: palaeo-precipitation reduction</li> </ul> </li> <li>Variable types: <ul> <li>ex.100a: spatial diagnostics every hundred years</li> <li>ex.1ka: spatial diagnostics every thousand years</li> <li>ts.10a: scalar time-series every ten years</li> </ul> </li> </ul> Data format: The data use compressed netCDF format. For quick inspection I recommend ncview. Spatial diagnostics (*.ex.*.nc) can be converted to GeoTIFF (and other GIS formats) e.g. using GDAL: <pre><code>gdal_translate NETCDF:filename.nc:variable -b band filename.variable.band.tif</code></pre> The list of variables (subdatasets) can be obtained from ncdump or gdalinfo. The band number equals 120 minus the age in ka. Band information can be displayed with: <pre><code>gdalinfo NETCDF:filename.nc:variable</code></pre> Variable long names, units, PISM configuration parametres and additional information are contained within the netCDF metadata. Also see <a href="https://doi.org/10.5281/zenodo.1423159">aggregated</a> variables. Changelog: <ul> <li>Version 3: <ul> <li>Add spatial diagnostics every hundred years (*.ex.100a.nc)</li> </ul> </li> <li>Version 2: <ul> <li>Add age coordinate in kiloyears (ka) before present.</li> <li>Replace NCO by Xarray workflow (no effect on the results).</li> </ul> </li> <li>Version 1: <ul> <li>Initial version.</li> </ul> </li> </ul>

opencc-by-4.0Jun 2023View details →

DCA and GNMDS output for 4640 subplots and 95 vascular plant species in four alpine grasslands

Ordination output from detrended correspondence analysis (DCA) and global non-metric multidimensional scaling (GNMDS). Analyses were performed in R with the vegan package (Oksanen 2022) for the entire data set of 4630 subplots and 95 species' occurrences ('global', indicated by global or missing site name in file names), and for each of four sites: Skjellingahaugen (skj), Gudmedalen (gud), Låvisdalen (lav), and Ulvehaugen (ulv). Access .Rds files with readRDS in R/RStudio. For GNMDS files, k indicates the chosen number of dimensions. See GitHub repository for scripts to produce and perform further analysis with the files in this archive. Analyses performed by EL with scripts based on originals by RH.

opencc-by-4.0Jun 2023View details →

Model output for five Hmsc models of alpine grassland communities

Model output from five joint species distribution models made with Hmsc in R. One 'global' model with all data, and one model for each of four sites Skjellingahaugen, Gudmedalen, Låvisdalen, and Ulvehaugen. Omegas are species co-occurrence estimates. Models defined by EL, OO, data formatted by EL, model fit by OO. Scripts for model fitting and presenting output are not published here but follow the generic Hmsc pipeline as published in Ovaskainen & Abrego (2020). Joint Species Distribution Modelling With Applications in R. Cambridge university press. DOI: <a href="https://doi.org/10.1017/9781108591720">https://doi.org/10.1017/9781108591720</a>

opencc-by-4.0Sep 2023View details →

Asynchronous life cycles contribute to reproductive isolation between two Alpine butterflies

Data from: Asynchronous life cycles contribute to reproductive isolation between two Alpine butterflies Abstract Geographic isolation often leads to the emergence of distinct genetic lineages that are at least partially reproductively isolated. Zones of secondary contact between such lineages are natural experiments that allow investigating how reproductive isolation evolves and co-existence is maintained. While temporal isolation through allochrony has been suggested to promote reproductive isolation in sympatry, its potential for isolation upon secondary contact is far less understood. Sampling two contact zones of a pair of mainly allopatric Alpine butterflies over several years and taking advantage of museum samples, we show that the contact zones have remained geographically stable over several decades. Furthermore, they seem to be maintained by the asynchronous life cycles of the two butterflies, with one reaching adulthood primarily in even and the other primarily in odd years. Genomic inferences document that allochrony is leaky and that gene flow from allopatric sites scales with the degree of geographic isolation. Overall, we show that allochrony has the potential to contribute to the maintenance of secondary contact zones of lineages that diverged in allopatry.               Morphology contains the following files: wing_morpho.R R scripts for data transformation of wing shape genital_morpho.R R scripts for data transformation of genital morphology Models_used.R: R scripts used to produce the statistical analyses. genital_morpho_master_with_pca.txt Phenotypic data for genital morphology wing_contemporary_morpho_master_with_pca.txt Phenotypic data for contemporary wing patterns wing_historic_morpho_master_with_pca.txt Phenotypic data for wing patterns from museum samples The text files contains the following information: ID    = Individual ID genotyped_allopatric = was the individual genotyped latitude longitude DATE    = Date of collection DAY    = Day of collection MONTH = Month of collection YEAR = Year of collection SPOT = Collection site boxplotID = ID to reproduce boxplot order as used in the paper colory = color code to plot cycle = year cycle (2018/19 or 2020/21) yeartype = even or odd year genital_x_LM1 = linear measure of genital landmark 1 along the x axis genital_y_LM1 = linear measure of genital landmark 1 along the y axis genital_x_LM2 = linear measure of genital landmark 2 along the x axis     genital_y_LM2 = linear measure of genital landmark 2 along the y axis     genital_x_LM3 = linear measure of genital landmark 3 along the x axis     genital_y_LM3 = linear measure of genital landmark 3 along the y axis     genital_x_LM4 = linear measure of genital landmark 4 along the x axis     genital_y_LM4 = linear measure of genital landmark 4 along the y axis     genital_x_LM5 = linear measure of genital landmark 5 along the x axis     genital_y_LM5 = linear measure of genital landmark 5 along the y axis     v_t1 = length relationship between v and t1 v_t2 = length relationship between v and t2     v_t3 = length relationship between v and t3     t3_t1 = length relationship between t3_t1     t3_t2 = length relationship between t3_t2     t2_t1 = length relationship between t2_t1     v_tg = length relationship between v and tg     PC1.x    = PC1 axis for unprojected morphospace PC2.x    = PC2 axis for unprojected morphospace     PC3.x    = PC3 axis for unprojected morphospace     PC4.x    = PC4 axis for unprojected morphospace     PC5.x    = PC5 axis for unprojected morphospace     PC6.x    = PC6 axis for unprojected morphospace     PC7.x    = PC7 axis for unprojected morphospace     PC1.y    = PC1 axis for projected morphospace     PC2.y    = PC2 axis for projected morphospace     PC3.y    = PC3 axis for projected morphospace     PC4.y    = PC4 axis for projected morphospace     PC5.y    = PC5 axis for projected morphospace     PC6.y    = PC6 axis for projected morphospace     PC7.y    = PC7 axis for projected morphospace   wing_ProcCoord1 = Procrustes coordinate 1 wing_ProcCoord2 = Procrustes coordinate 2 wing_ProcCoord3 = Procrustes coordinate 3 wing_ProcCoord4 = Procrustes coordinate 4 wing_ProcCoord5 = Procrustes coordinate 5 wing_ProcCoord6 = Procrustes coordinate 6 wing_ProcCoord7 = Procrustes coordinate 7 wing_ProcCoord8 = Procrustes coordinate 8 wing_ProcCoord9 = Procrustes coordinate 9 wing_ProcCoord10 = Procrustes coordinate 10 wing_ProcCoord11 = Procrustes coordinate 11 wing_ProcCoord12 = Procrustes coordinate 12 wing_ProcCoord13 = Procrustes coordinate 13 wing_ProcCoord14 = Procrustes coordinate 14 wing_ProcCoord15 = Procrustes coordinate 15 wing_ProcCoord16 = Procrustes coordinate 16 wing_ProcCoord17 = Procrustes coordinate 17 wing_ProcCoord18 = Procrustes coordinate 18 wing_ProcCoord19 = Procrustes coordinate 19 wing_ProcCoord20 = Procrustes coordinate 20 wing_ProcCoord21 = Procrustes coordinate 21 wing_ProcCoord22 = Procrustes coordinate 22 wing_ProcCoord23 = Procrustes coordinate 23 wing_ProcCoord24 = Procrustes coordinate 24 wing_ProcCoord25 = Procrustes coordinate 25 wing_ProcCoord26 = Procrustes coordinate 26 wing_ProcCoord27 = Procrustes coordinate 27 wing_ProcCoord28 = Procrustes coordinate 28 wing_ProcCoord29 = Procrustes coordinate 29 wing_ProcCoord30 = Procrustes coordinate 30 wing_ProcCoord31 = Procrustes coordinate 31 wing_ProcCoord32 = Procrustes coordinate 32 wing_ProcCoord33 = Procrustes coordinate 33 wing_ProcCoord34 = Procrustes coordinate 34 wing_ProcCoord35 = Procrustes coordinate 35 wing_ProcCoord36 = Procrustes coordinate 36 wing_ProcCoord37 = Procrustes coordinate 37 wing_ProcCoord38 = Procrustes coordinate 38 wing_ProcCoord39 = Procrustes coordinate 39 wing_ProcCoord40 = Procrustes coordinate 40 wing_ProcCoord41 = Procrustes coordinate 41 wing_ProcCoord42 = Procrustes coordinate 42 wing_ProcCoord43 = Procrustes coordinate 43 wing_ProcCoord44 = Procrustes coordinate 44 wing_ProcCoord45 = Procrustes coordinate 45 wing_ProcCoord46 = Procrustes coordinate 46 wing_ProcCoord47 = Procrustes coordinate 47 wing_ProcCoord48 = Procrustes coordinate 48 wing_ProcCoord49 = Procrustes coordinate 49 wing_ProcCoord50 = Procrustes coordinate 50 wing_ProcCoord51 = Procrustes coordinate 51 wing_ProcCoord52 = Procrustes coordinate 52 wing_ProcCoord53 = Procrustes coordinate 53 wing_ProcCoord54 = Procrustes coordinate 54 PC1.x = PC1 unprojected PC2.x = PC2 unprojected PC3.x = PC3 unprojected PC4.x = PC4 unprojected PC5.x = PC5 unprojected PC6.x = PC6 unprojected PC7.x = PC7 unprojected PC8.x = PC8 unprojected PC9.x = PC9 unprojected PC10.x = PC10 unprojected PC11.x = PC11 unprojected PC12.x = PC12 unprojected PC13.x = PC13 unprojected PC14.x = PC14 unprojected PC15.x = PC15 unprojected PC16.x = PC16 unprojected PC17.x = PC17 unprojected PC18.x = PC18 unprojected PC19.x = PC19 unprojected PC20.x = PC20 unprojected PC21.x = PC21 unprojected PC22.x = PC22 unprojected PC23.x = PC23 unprojected PC24.x = PC24 unprojected PC25.x = PC25 unprojected PC26.x = PC26 unprojected PC27.x = PC27 unprojected PC28.x = PC28 unprojected PC29.x = PC29 unprojected PC30.x = PC30 unprojected PC31.x = PC31 unprojected PC32.x = PC32 unprojected PC33.x = PC33 unprojected PC34.x = PC34 unprojected PC35.x = PC35 unprojected PC36 = PC36 unprojected PC37 = PC37 unprojected PC38 = PC38 unprojected PC39 = PC39 unprojected PC40 = PC40 unprojected PC41 = PC41 unprojected PC42 = PC42 unprojected PC43 = PC43 unprojected PC44 = PC44 unprojected PC45 = PC45 unprojected PC46 = PC46 unprojected PC47 = PC47 unprojected PC48 = PC48 unprojected PC49 = PC49 unprojected PC50 = PC50 unprojected PC51 = PC51 unprojected PC52 = PC52 unprojected PC53 = PC53 unprojected PC54 = PC54 unprojected PC1.y = PC1 projected PC2.y = PC2 projected PC3.y = PC3 projected PC4.y = PC4 projected PC5.y = PC5 projected PC6.y = PC6 projected PC7.y = PC7 projected PC8.y = PC8 projected PC9.y = PC9 projected PC10.y = PC10 projected PC11.y = PC11 projected PC12.y = PC12 projected PC13.y = PC13 projected PC14.y = PC14 projected PC15.y = PC15 projected PC16.y = PC16 projected PC17.y = PC17 projected PC18.y = PC18 projected PC19.y = PC19 projected PC20.y = PC20 projected PC21.y = PC21 projected PC22.y = PC22 projected PC23.y = PC23 projected PC24.y = PC24 projected PC25.y = PC25 projected PC26.y = PC26 projected PC27.y = PC27 projected PC28.y = PC28 projected PC29.y = PC29 projected PC30.y = PC30 projected PC31.y = PC31 projected PC32.y = PC32 projected PC33.y = PC33 projected PC34.y = PC34 projected PC35.y = PC35 projected       Genomics contains the following files (Genomic data is available from NCBI BioProject: PRJNA1019795): all_euryale_calls.vcf.gz The unfiltered VCF file euryale_V2.sh Shell script for the genomic data analysis introgress.R R script for running Introgress introgress_all_east2.txt Output of Introgress for the Eastern contact zone introgress_all_west2.txt Output of Introgress for the Western contact zone Admixture_output.txt Output of Admixture assuming either 2 or 3 genomic clusters (K) with the respective population and ID Outliers2BombyxMori.txt BLAST summary of outlier regions against Bombyx Mori Outliers2ManjolaJurtina.txt BLAST summary of outlier regions against Manjola jurtina Outliers2ParargeAegeria.txt BLAST summary of outlier regions against Pararge aegeria

openCC (other)May 2021View details →

Mammalian herbivores restrict the altitudinal range limits of three alpine grass species (transplant and herbivore exclusion experiment and demographic data from natural populations), West Elk Mountains, Colorado, USA 2015-2018

Though rarely experimentally tested, biotic interactions have long been hypothesized to limit low-elevation range boundaries of species. We tested the effects of herbivory on three alpine-restricted plant species by transplanting plants below (novel), at the edge (limit), or in the center (core) of their current elevational range and factorially fencing-out above- and belowground mammals in the West Elk Mountains, Colorado, USA from 2015-2018. Herbivore damage was greater in range limit and novel habitats than in range cores. Exclosures increased plant biomass and reproduction more in novel habitats than in range cores, suggesting demographic costs of novel interactions with herbivores. We then used demographic models to project population growth rates, which increased 5-20% more under herbivore exclosure at range limit and novel sites than in core habitats. Our results identify mammalian herbivores as key drivers of the low-elevation range limits of alpine plants and indicate that upward encroachment of herbivores could trigger local extinctions by depressing plant population growth.

openCC (other)Jan 2020View details →

Soil inorganic and organic property data for subalpine forest, treeline, and alpine zone, 1999.

This study was initiated to examine the nitrogen content of three montane soils: subalpine, treeline and alpine; and to determine if the differences in soil nitrogen content were attributed to plant community and elevation. Soil organic matter, soil carbon, bulk density, pH and soil moisture were also measured for each site. Soil samples were collected from 64 total plots [22 subalpine,15 treeline and 27 alpine sites]. The subalpine site plots included aspen, fir, lodgepole, spruce and meadow vegetation cover. The treeline site plots included fir, spruce and meadow vegetation cover. The alpine site plots included dry meadow and mesic meadow fertilization (control, N, P, NP) plots. Soil cores were removed with 3.5-cm interior diameter PVC pipe that was driven into the soil by use of a rubber mallet. The minimum depth of individual cores was 10 cm. Cores were taken at each site three times over the period between 29 June 1999 and 29 July 1999.

openCC (other)Jan 2020View details →

Alpine tundra and krummholz soil temperature data for Saddle and North of Tvan, 1994 - 1999.

Growing season soil temperatures (typically at 2 depths in a given location) were measured (1) to quantify the soil temperature environment across the landscape mosaic of alpine tundra, and (2) to compare temperatures between tundra and adjacent krummholz vegetation. Measurements were made at sites differing in aspect (south-facing and north-facing) and moisture conditions (dry, mesic, and wet). In addition, soil temperature was measured at a site characterized by persistent snow cover, as well as at sites within and adjacent to a krummholz patch. Data are presently collected using an Omnidata DP212 datapod.

openCC (other)Oct 2019View details →

Snow depth sensor measurement data for Alpine site, 2010 - 2015

Effects of infrared heaters on snow accumulation, snowmelt, and snow–atmosphere energy exchange were examined at Niwot Ridge, Colorado (CO). These .zip data files contains hourly snow depth measurements collected using Judd snow depth sensors for water year 2010-2015 (1 October 2009 – 30 September 2015) at the Alpine site, located just southwest of the Tundra Lab in the Niwot Ridge Long-Term Ecological Research (NWTLTER) project area. The file contains both level 0 and level 1 (see details in “Process Description” below) hourly snow depth data measured in centimeters, and an accompanying metadata file.

openCC (other)Oct 2019View details →

Snow depth sensor measurement data for Lower Sub Alpine site, 2010 - 2015

Effects of infrared heaters on snow accumulation, snowmelt, and snow–atmosphere energy exchange were examined at Niwot Ridge, Colorado (CO). These .zip data files contains hourly snow depth measurements collected using Judd snow depth sensors for water year 2010-2015 (1 October 2009 – 30 September 2015) at the Lower Sub Alpine site, located southeast of the Tundra Lab, below treeline in the Niwot Ridge Long-Term Ecological Research (NWTLTER) project area. The file contains both level 0 and level 1 (see details in “Process Description” below) hourly snow depth data measured in centimeters, and an accompanying metadata file.

openCC (other)Oct 2022View details →

Alpine plant seed microbiomes, germination, and plant-soil feedbacks, Niwot Ridge and Green Lakes Valley, 2018.

Seed and soil microbiomes strongly affect plant performance, and these effects can scale-up to influence plant community structure. However, seed and soil microbial community composition are variable across landscapes, and different microbial communities can differentially influence multiple plant metrics (biomass, germination rate), and community stabilizing mechanisms. We measured how microbiomes inside seeds and in soils varied among alpine plant species and communities that differed in plant species richness and density. Across 10 common alpine plant species, we found a total of 318 bacterial and 128 fungal operational taxonomic units (OTUs) associated with seeds, with fungal richness affected by plant species identity more than sampling location. However, seed microbes had only marginally significant effects on plant germination success and timing. In contrast, soil microbes associated with two different plant species had significant effects on plant biomass, and their effect depended both on the plant species and the location the soils were sampled from.

openCC (other)Dec 2025View details →

ReESH_US-NR3: Niwot Ridge Alpine (T-Van West)

This data package contains metadata for, and links to the Regional Ecosystem Soil Hydraulic (ReESH) sampling at US-NR3 (Tvan West). The Regional Ecosystem Soil Hydraulic (ReESH) project expands the representation of soil water potential data for both the AmeriFlux and wider flux community. This dataset contains the summary of the soil hydraulic characteristics that were measured including soil texture and water retention curves, which enable the conversion of soil water content to soil water potential. The published data may be found on zenodo at: https://doi.org/10.5281/zenodo.17555020 and are not re-published here.

Figures 7-9 in The first lowland species of the Holarctic alpine ground spider genus Parasyrisca (Araneae, Gnaphosidae) from Hungary

Figures 7-9. Parasyrisca arrabonica Szinetár & Eichardt, sp. n. 7 male pedipalp, ventral view 8 same, semi-retrolateral view, showing the pointed tip of the conductor (c – arrowed) 9 same, retrolateral view. Scale bar = 0.3.

Figures 12-14 in The first lowland species of the Holarctic alpine ground spider genus Parasyrisca (Araneae, Gnaphosidae) from Hungary

Figures 12-14. Parasyrisca arrabonica Szinetár & Eichardt, sp. n., male habitus: 12 paratype from Orgovány, prosoma, dorsolateral view, showing the strong setae on the paturon 13 same specimen, dorsal view 14 same specimen, ventral view. Scale bar = 2.0.

Figures 1-6 in The first lowland species of the Holarctic alpine ground spider genus Parasyrisca (Araneae, Gnaphosidae) from Hungary

Figures 1-6. Parasyrisca arrabonica Szinetár & Eichardt, sp. n.: Male holotype: 1 pedipalp, prolateral view 2 same, ventral view 3 same, retrolateral view. Female: 4 epigyne, ventral view 5 vulva, dorsal view 6 posterior ridge (PRE) of the female epigyne, rear view. Scale bars: 1-3 0.3 4-6 0.2.

Figures 18-19 in The first lowland species of the Holarctic alpine ground spider genus Parasyrisca (Araneae, Gnaphosidae) from Hungary

Figures 18-19. Distribution maps of the genus Parasyrisca: 18 Eurasian distribution of the species groups of Parasyrisca (red, pink – potanini group, pink – P. arrabonica, P. turkenica, P. songi, yellow – vinosus group, light blue – guzeripli group, dark blue – breviceps group) 19 European distribution of Parasyrisca (red – potanini group, yellow – vinosus group). Yellow question marks represent doubtful records.