Find research datasets worth reusing

opencc-by-4.0Jul 2022View details →

MPAS-A v7.0 variable resolution simulations over China

Dynamical downscaling is conducted by MPAS-A v7.0 with a global variable resolution (MPAS-VR) and truncated regional (MPAS-RCM) configurations. Two sets of MPAS experiments are carried out to evaluate the simulation performance of summer precipitation in China. One is a global variable resolution run (VR) using 4 times grid refinement over the domain center (32oN, 100oE), and the grid spacing smoothly varies from ~25km to ~92km outside the refinement. The other is a regional configuration experiment (RCM) with the grid truncated according to an ellipse in the same center.

The MET-Pottery–Variables_System

The variable system was created to compile the 'MET-Pottery–Dataset'  within the SNSF project No 100011_156205 ‘Mobilities, Entanglements and Transformations in Neolithic Societies of the Swiss Plateau (3900-3500 BC)’, short ‘MET-project’, conducted at the Institute of Archaeological Sciences, University of Bern between 2014 and 2018 (<a href="https://data.snf.ch/grants/grant/156205">https://data.snf.ch/grants/grant/156205</a>; <a href="https://boris.unibe.ch/77649/">https://boris.unibe.ch/77649/</a>). It represents the largest and temporally most highly resolved collection of morphological pottery data of the Central European Neolithic. It offers data of 1046 ceramic vessels of different styles that originate out of 44 archaeological features of wetland and dryland sites of the northern Alpine Space and adjacent regions. Most of the archaeological contexts – anthropogenic layers of settlements, pits, and ditches – are typology-independent dated using dendrochronology or C14-dates. The data set includes a spreadsheet of nominal and numeric morphological variables, the collection of the vessels’ semi-profile silhouettes and the typological drawings from which all data was collected. In the scope of the MET-project ‘the data was used to elaborate a new mixed method research (MMR) methodology to investigate social relations beyond problematic concepts of homogeneous ‘archaeological cultures’. It is highly relevant for further methodological morphology-based research on pottery.

Synthetic dataset used for validating MDSPACE method for analyzing continuous conformational variability of biomolecules in cryo-EM single particle images

Synthetic dataset used for validating MDSPACE method for analyzing continuous conformational variability of biomolecules in cryo-EM single particle images. A README file with the contents of the dataset is included.

Optimising multispectral active fluorescence to distinguish the photosynthetic variability of cyanobacteria and algae

Dataset underlying the following paper: Courtecuisse, E.; Marchetti, E.; Oxborough, K.; Hunter, P.D.; Spyrakos, E.; Tilstone, G.H.; Simis, S.G.H. Optimising Multispectral   Active Fluorescence to Distinguish the Photosynthetic Variability of Cyanobacteria and Algae. Sensors 2023, 23 This study assesses the ability of a new active fluorometer, the LabSTAF, to diagnostically assess the physiology of freshwater cyanobacteria in a reservoir exhibiting annual blooms. Specifically, we analyse the correlation of relative cyanobacteria abundance with photosynthetic parameters derived from fluorescence light curves (FLCs) obtained using several combinations of excitation wavebands, photosystem II (PSII) excitation spectra and the emission ratio of 730 over 685 nm (Fo(730/685)) using obtained with excitation protocols with varying degrees of sensitivity to cyanobacteria and algae. FLCs captured obtained with blue excitation (B) and green–orange–red (GOR) excitation wavebands capture physiology parameters of algae and cyanobacteria, respectively. The green–orange (GO) protocol, expected to have the best diagnostic properties for cyanobacteria, did not guarantee PSII saturation. PSII excitation spectra showed distinct response from cyanobacteria and algae, depending on spectral optimisation of the light dose. Fo(730/685), obtained using a combination of GOR excitation wavebands, Fo(GOR, 730/685), showed a significant correlation with the relative abundance of cyanobacteria (linear regression, p-value < 0.01, adjusted R2 = 0.42). We recommend using, in parallel, Fo(GOR, 730/685), PSII excitation spectra (appropriately optimised for cyanobacteria versus algae), and physiological parameters derived from the FLCs obtained with GOR and B protocols to assess the physiology of cyanobacteria and to ultimately predict their growth. Higher intensity LEDs (G and O) should be considered to reach PSII saturation to further increase diagnostic sensitivity to the cyanobacteria component of the community.

Reproduction package for the paper "Measuring the variability of directly imaged exoplanets using vector Apodizing Phase Plates combined with ground-based differential spectrophotometry"

This is a basic reproduction package for the paper <a href="https://doi.org/10.1093/mnras/stad249">"Measuring the variability of directly imaged exoplanets using vector Apodizing Phase Plates combined with ground-based differential spectrophotometry" by Sutlieff et al. (2023)</a>. It aims to provide the most important data products to check and reproduce the main results of the paper.

fish larvae abundance as a function of oceanographic variables in GoM deep waters

We describe the larval occurrence and abundance of six fish species with contrasting life histories and examine their relationship with oceanographic variables during two seasons in the deep-water region (>1000 m) of the southern Gulf of Mexico based on 12 cruises (2011-2018). Given that Caranx crysos adults are neritic, larval presence close to the continental shelf indicates offshore cross-shelf transport to oceanic waters, which likely leads to mortality. Generalized additive models indicated C. crysos abundance  was not related with oceanographic variables, while that of Auxis spp. (with neritic and oceanic adults) was related to wind speed, sea surface temperature and height and surface chlorophyll a. The mesopelagic Benthosema suborbitale, Notolychnus valdiviae and Bregmaceros atlanticus were more abundant and broadly distributed, and higher abundance was found in conditions indicative of higher nutrient availability and productivity, suggesting greater feeding success and survival. The distribution of the epi- and mesopelagic Cubiceps pauciradiatus extended through the southern Gulf of Mexico, and was related to wind speed, SST, stratification and chlorophyll a. Our results suggest that the abundance of the neritic species in oceanic waters could be mediated by regional cross-shelf transport, while that of oceanic species is linked with productivity.

opencc-by-4.0Dec 2021View details →

Data for Figures in "Unexpected Long-Term Variability in Jupiter's Tropospheric Temperatures

This repository provides the data submitted by the primary author that were used to create Figures 1, 2, 3, 4 and 5 in the text for the article: "Unexpected Long-Term Variability in Jupiter's Tropospheric Temperatures", by Glenn S. Orton, Arrate Antuñano, Leigh N. Fletcher, James Sinclair, Thomas Momary, Takuya Fujoyoshi, Padma Yanamandra-Fisher, Padraig T. Donnelly, Jennifer Greco, Anna Payne, Kimberly Boydstun, Laura Wakefield. The repository consists of five files in text (ASCII) format: (1) User Guide to Data (16 kb), (2) figure1_data.txt (1.5 MB), (3) figure2_data.txt (142 kb), figure4_data.txt (61 kb), and figure5_data.txt (59 kb).  Data for Figure 3 are provided in the User Guide.

IRIS preprocessed data used in paper "Multi variables time series information bottleneck"

Prprocessed data used in paper "Multi variables time series information bottleneck" with the <a href="https://github.com/DenisUllmann/IB-MTS">GitHub</a> code This dataset is created from a public available dataset of observations performed by IRIS, a NASA small explorer mission developed and operated by LMSAL with mission operations executed at NASA Ames Research Center and major contributions to downlink communications funded by ESA and the Norwegian Space Centre. Multiple Time Series of IRIS level 2 data are available <a href="https://iris.lmsal.com/search/">here</a> The selected data was labeled using these definitions: QS: Quiet Sun AR: Active Regions of the Sun FL: Flare A time series is labeled QS when every single time step refer to a quiet sun activity. When a given time series is partially composed of flaring events, the global time series is labeled as FL. The npz file is a numpy (np) compressed data and can be loaded using np.load with allow_pickle=True Loaded data is then a python dict described bellow. Each sample 'data' is a np.ndarray with 2 dimensions: time (various length) and wavelength (length=240 representing a range between 2793.8401Å and 2806.02Å). Each sample is given a 'position' which is a list of length 4: position[1] is a string that gives the name of the event position[4] is a boolean vector that gives the time positionsof the corresponding sample in the original sequence of public IRIS level2 data Data file info : Type: .npz Size: 11.89GB *** Key: 'data_TR_QS' ndarray data of length 2467 containing np.ndarray of shapes ['various', 240]   *** Key: 'data_TR_AR' ndarray data of length 1042 containing np.ndarray of shapes ['various', 240]   *** Key: 'data_TR_FL' ndarray data of length 1055 containing np.ndarray of shapes ['various', 240]   *** Key: 'data_VAL_QS' ndarray data of length 325 containing np.ndarray of shapes ['various', 240]   *** Key: 'data_VAL_AR' ndarray data of length 1042 containing np.ndarray of shapes ['various', 240]   *** Key: 'data_VAL_FL' ndarray data of length 714 containing np.ndarray of shapes ['various', 240]   *** Key: 'data_TE_QS' ndarray data of length 1428 containing np.ndarray of shapes ['various', 240]   *** Key: 'data_TE_AR' ndarray data of length 792 containing np.ndarray of shapes ['various', 240]   *** Key: 'data_TE_FL' ndarray data of length 356 containing np.ndarray of shapes ['various', 240]   *** Key: 'data_TR' ndarray data of length 4564 containing np.ndarray of shapes ['various', 240]   *** Key: 'data_VAL' ndarray data of length 2081 containing np.ndarray of shapes ['various', 240] *** Key: 'data_TE' ndarray data of length 2576 containing np.ndarray of shapes ['various', 240]   *** Key: 'position_TR_QS' ndarray data of length 2467 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}   *** Key: 'position_TR_AR' ndarray data of length 1042 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}   *** Key: 'position_TR_FL' ndarray data of length 1055 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}   *** Key: 'position_VAL_QS' ndarray data of length 325 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}   *** Key: 'position_VAL_AR' ndarray data of length 1042 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}   *** Key: 'position_VAL_FL' ndarray data of length 714 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}   *** Key: 'position_TE_QS' ndarray data of length 1428 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}   *** Key: 'position_TE_AR' ndarray data of length 792 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}   *** Key: 'position_TE_FL' ndarray data of length 356 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}   *** Key: 'position_TR' ndarray data of length 4564 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}   *** Key: 'position_VAL' ndarray data of length 2081 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}   *** Key: 'position_TE' ndarray data of length 2576 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}

opencc-by-4.0Jul 2022View details →

Meteorological variables for Agriculture: a Dataset for the Italian Area (MADIA)

The dataset is the supplementary material for the following journal paper: Parisse B.*, Alilla R., Pepe A.G., De Natale F., MADIA - Meteorological variables for Agriculture: a Dataset for the Italian Area, Data in Brief, 46 (2023), 108843, <a href="http://doi.org/10.1016/j.dib.2022.108843">10.1016/j.dib.2022.108843</a>, (<a href="https://www.sciencedirect.com/science/article/pii/S2352340922010460">https://www.sciencedirect.com/science/article/pii/S2352340922010460</a>) <ol> </ol>   Abstract The MADIA gridded dataset provides the series of the main agro-meteorological variables derived from ERA5 hourly surface data, across the Italian domain for the period 1981-2022, and their respective 1981-2010 and 1991-2020 climate normals, as well as the following statistics on the 30-year dekadal values of each variable: absolute minimum and maximum, 5th, 10th, 50th, 90th, 95th percentiles. Temporal and spatial resolutions are 10-daily and 0.25 degrees respectively. The dataset contains time series of minimum, average and maximum air temperature, minimum and maximum air relative humidity, wind speed, solar radiation, precipitation and reference evapotranspiration according to the FAO Penman-Monteith method. The dataset is provided in both NetCDF and csv format. In addition, discovery and description metadata are provided. In order to facilitate the data reuse for computing statistics at Italian NUTS 2 and 3 levels, a complementary vector file is provided which reports the cell weight in terms of fraction covered of each administrative unit considered. Another vector file is included with the ERA5 cell polygons covering the Italian country for visualizing and mapping csv data.  A daily version of the MADIA dataset (only in csv format) is also available on Zenodo at <a href="http://doi.org/10.5281/zenodo.7621453">https://doi.org/10.5281/zenodo.7621453</a>. Both MADIA datasets will be periodically updated. Attached content A ZIP archive composed by the following folders <ol> <li>nc_data: annual time series from 1981 to 2022 and climate normals (1981-2010 and 1991-2020) in NetCDF format</li> <li>csv_data: annual time series from 1981 to 2022 and climate normals (1981-2010 and 1991-2020) in csv format</li> <li>metadata: discovery and description metadata </li> <li>shp_data: two complementary vector layers with the NUTS2-3 cover fractions and the ERA5 cell polygons for Italy</li> </ol> Acknowledgments This work was supported by the Italian Ministry of Agricultural, Food and Forestry Policies (AgriDigit-Agromodelli, DM n. 36502 of 20/12/2018)

opencc-by-4.0Jan 2020View details →

Cordilleran ice sheet glacial cycle simulations continuous variables

These data contain a subset of time-dependent glacier model output variables: Reference: <ul> <li>J. Seguinot, I. Rogozhina, A. P. Stroeven, M. Margold, and J. Kleman. Numerical simulations of the Cordilleran ice sheet through the last glacial cycle, The Cryosphere, 10(2):639–664, <a href="https://doi.org/10.5194/tc-10-639-2016">10.5194/tc-10-639-2016</a>, 2016.</li> </ul> File names: <pre><code>ciscyc4.{10km|5km}.{forcing}.{ex.100a|ex.1ka|ts.10a}.nc</code></pre> <ul> <li>Horizontal resolution: <ul> <li>10km: 10 km horizontal resolution</li> <li>5km: 5 km horizontal resolution</li> </ul> </li> <li>Temperature forcing: <ul> <li>epica: EPICA ice core temperature forcing</li> <li>grip: GRIP ice core temperature forcing</li> <li>ngrip: NGRIP ice core temperature forcing</li> <li>odp1012: ODP 1012 ocean core temperature forcing</li> <li>odp1020: ODP 1020 ocean core temperature forcing</li> <li>vostok: Vostok ice core temperature forcing</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. Changelog: <ul> <li>Version 2: <ul> <li>Add spatial diagnostics every hundred years (*.ex.100a.nc)</li> </ul> </li> <li>Version 1: <ul> <li>Initial version.</li> </ul> </li> </ul>

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-4.0Apr 2023View details →

Rereferenced Chemical Shift files used to select dihedrals for the 5 secondary structure conformations used to calculate Conformational Variability (ConVa).

These Chemical Shifts in this repository were processed with ShiftCrypt (1) and then processed as described in the manuscript.    1- Gabriele Orlando, Daniele Raimondi, Luciano Porto Kagami, Wim F Vranken, ShiftCrypt: a web server to understand and biophysically align proteins through their NMR chemical shift values, Nucleic Acids Research, Volume 48, Issue W1, 02 July 2020, Pages W36–W40, <a href="https://doi.org/10.1093/nar/gkaa391">https://doi.org/10.1093/nar/gkaa391</a>

Dataset for: "Last century changes in annual precipitation in a Mediterranean area and their spatial variability. Insights from northern Tuscany (Italy)"

The version 1.0 contains the supporting data for the work (still under submission) "Last century changes in annual precipitation in a Mediterranean area and their spatial variability. Insights from northern Tuscany (Italy)". The following files are here available (all file are georeferenced in EPSG: 3003): - AVG_Rainfall_1990-2019.tif -> Raster map of the mean annual precipitation for the northern Tuscany, Italy. It encompasses the portion of the Tuscany region northern of the cities of Livorno - Florence. The interpolation was validated via a leave one out cross-validation procedure. - D3-1_Area2_ApuanAlps.tif -> Raster map of the differences in mean annual precipitation between the two 3-decades periods 1921 to 1950 and 1990 to 2019 for the Apuan Alps mountain ridge (Tuscany, Italy). - D3-2_Area2_ApuanAlps.tif -> Raster map of the differences in mean annual precipitation between the two 3-decades periods 1951 to 1980 and 1990 to 2019 for the Apuan Alps mountain ridge (Tuscany, Italy). - DeltaSHP_Points_AVG_Annual_Rainfall.zip -> Shape file of the raingauges locations with the mean annual precipitation values of the period 1990 to 2019. - RaingaugesSHP_Points_AVG_Annual_Rainfall_1990-2019.zip -> Shape file of the raingauges locations with the following information: differences in the mean annual precipitation values between the two 3-decades periods 1951 to 1980 and 1990 to 2019 (named D3-2); p values of the t-test for significance of the differences between the mean annual precipitation ofthe two 3-decades periods 1951 to 1980 and 1990 to 2019; difference in the mean annual precipitation values between the two 3-decades periods 1921 to 1950 and 1990 to 2019 (named D3-1); p values of the t-test for significance of the differences between the mean annual precipitation ofthe two 3-decades periods 1921 to 1950 and 1990 to 2019.

opencc-by-4.0Mar 2023View details →

Data set: Monthly averaged RACMO2.3p2 variables (1979-2022); Antarctica

This is a data set of monthly averaged variables from January 1979 to December 2022 simulated by the hydrostatic regional atmospheric climate model RACMO2.3p2 over Antarctica. At the lateral and ocean boundaries the model is forced by ERA5 reanalysis data every 3 hours from 1979-2022. The model is run at a horizontal resolution of 27 km and 40 vertical levels for the entire Antarctic ice sheet, which constitutes an update of the simulation forced from 1979-2018 by ERA-Interim reported in van Wessem et al., 2018. Upper air relaxation of wind, humidity and temperature is also active (Van de Berg et al., 2016). This version of the model is specifically applied to the polar regions by interactive coupling to a multilayer snow model that calculates melt, refreezing, percolation and runoff of meltwater (Ettema et al., 2010). In addition, snow albedo is calculated through a prognostic scheme for snow grain size (Kuipers Munneke et al., 2011) while a drifting snow scheme simulates the interaction of the near-surface air with drifting snow (Lenaerts et al., 2010).  This dataset is provided on a rotated polar coordinate grid. In such a rotated pole projection the grid is defined over the equator and then rotated to the area of interest. One of the advantages is that the grid distance can be defined in fraction of degrees, which results in near equidistant grid cells as long as the domain is small enough, and provides the most accurate model calculations. However, re-projecting these data on other grids is often troublesome, as after rotation the grid is non-equidistant and most software packages cannot directly handle this. Stef Lhermitte provided a nice solution for reprojecting the RACMO data on his gitlab-page: https://gitlab.tudelft.nl/slhermitte/manuals/blob/master/RACMO_reproject.md. The dataset includes the following surface- and atmospheric variables. Additional variables and higher temporal resolutuon up to 3 hourly are available on request: Surface mass balance (SMB) variables (in kg m-2 mo-1 or mm water equivalent mo-1) smb : (Specific) Surface mass balance defined as SMB = Total precipitation + sublimation - runoff snowmelt : Surface snowmelt production refreeze : Refreezing of meltwater snowfall : Solid precipitation precip : Total precipitation (snowfall + rainfall); to calculate rainfall use rainfall = precip - snowfall runoff : Surface meltwater runoff subl : Snow sublimation (including sublimation of drifting snow). Negative values are sublimation, positive values are snow deposition. erds : erosion of drifting snow Atmospheric variables t2m : 2-m Temperature q2m : 2-m Specific humidity rh2m : 2-m Relative humidity (RH) tskin : Surface/skin temperature. Calculated from closing the surface energy budget. psurf : Surface pressure u10m : Zonal wind speed at 10 m v10m : Meridional wind speed at 10 m ff10m : Wind speed at 10 m u0500 : Zonal wind speed at 500 hPa v0500 : Meridional wind speed at 500 hPa z0500 : Geopotential height at 500 hPa Surface Energy Budget (SEB) variables (in J m-2); SEB = LWnet+SWnet+SHF+LHF+GHF Values are monthly cumulative: to convert to W m-2 divide by amount of seconds in a month: 'nrdaysmonth'*24*3600. lwsn : Net longwave radiation (LWnet=LWdown-LWup) swsn : Net shortwave radiation (SWnet=SWdown-SWup) lwsd : Downwelling longwave radiation at the surface swsd : Downwelling shortwave radiation at the surface swsu : Upwelling shortwave radiation at the surface senf : Upward Sensible Heat Flux (SHF) at the surface latf : Upward Latent Heat Flux (LHF) at the surface (our simulated LHF doesn't explicitly close the SEB, as it also includes in-air sublimation, but the effect should be rougly neglible) gbot : Soil/Ground Heat Flux (GHF) Snow variables totpore : Vertically integrated pore space (m) totwat : Total liquid water content of the snowpack (kg m-2) zsnow : Total snowpack thickness (m) Grid, elevation, coordinates and masks in Height_latlon_ANT27.nc (240 by 262 grid boxes) mask2d : Full ice mask fraction (grounded ice + floating ice shelves) [0..1] maskgrounded2d : Grounded ice sheet mask fraction [0..1] height : Surface elevation (m) slope : Surface slope (m m-1) aspect : Direction of surface slope (degrees) lat : Latitude (polar) lon : Longitude (polar) Ice shelf and ice sheet drainage basins mask in TotIS_RACMO_ANT27_IMBIE2.nc This file contains masks on the RACMO grid for the drainage basins as defined in <a href="http://imbie.org/imbie-3/drainage-basins/">http://imbie.org/imbie-3/drainage-basins/</a> (Rignot et al., 2013, IMBIE2, IMBIE3), including masks seperately for the ice shelves they drain into, numbered counterclockwise from 0 to 18. mask2dF : Full ice mask including ice shelves IceShelves : Ice shelf masks GroundedIce : Grounded ice sheet drainage basins

opencc-by-4.0May 2023View details →

CAELUS: Classification of sky conditions from 1-min time series of global solar irradiance using variability indices and dynamic thresholds

CAELUS, a novel classification algorithm that relies on various thresholds to separate all possible sky conditions into six classes, is presented in Ruiz-Arias and Gueymard (2023, doi: <a href="https://doi.org/10.1016/j.solener.2023.111895">10.1016/j.solener.2023.111895</a>). This dataset was used to develop, validate and benchmark CAELUS. It is made up by 1-min quality-assured observations of global horizontal irradiance (GHI) and diffuse horizontal irradiance at 54 stations of the Baseline Surface Radiation Network (BSRN) archive, which is publicly available (see download instructions in https://bsrn.awi.de/data). The dataset includes 5 years of data per station, except in two of them (Petrolina, Brazil, and Solar Village, Saudi Arabia), combined with other variables that are required to run CAELUS, namely: solar zenith angle (sza), extraterrestrial horizontal solar irradiance (eth), clear-sky GHI (ghics) and GHI in a clean and dry atmosphere (ghicda). In addition, the dataset also provides the sky classification obtained with CAELUS. Further details about CAELUS and the dataset compilation is available in Ruiz-Arias and Gueymard (2023, doi: <a href="https://doi.org/10.1016/j.solener.2023.111895">10.1016/j.solener.2023.111895</a>). A Python implementation of CAELUS is available in https://github.com/jararias/caelus.

Dataset for "On the variability of the leaf relative uptake rate of carbonyl sulfide compared to carbon dioxide: insights from a paired field study with two soybean varieties"

Data of measurements and model output of the publication "On the variability of the leaf relative uptake rate of carbonyl sulfide compared to carbon dioxide: insights from a paired field study with two soybean varieties". NO DOI YET The data consists of micrometeorological data, COS,CO2 and H2O flux measurements and resistances of two soybean cultivars at an agricultural field in Italy. For additional information, please contact: <a href="mailto:felix.spielmann@uibk.ac.at">Felix.Spielmann@uibk.ac.at</a> or <a href="mailto:Georg.Wohlfahrt@uibk.ac.at">Georg.Wohlfahrt@uibk.ac.at</a>.

opencc-by-4.0Jun 2023View details →

Simulated Geomorphically Relevant Palaeoclimate Variables for the Late Cenozoic (from Mutz and Ehlers, 2019)

Contents Description: (see more details in file header) The files contains long term means of several variables derived from an ECHAM5 palaeoclimate simulation. Details about the type of means and simulations are given in the file name (see explanation below). For information about the simulation setup and boundary conditions, we refer the user to the Mutz et al. (2018) publication. For details about the construction of contained variables, we refer the reader to the Mutz and Ehlers (2019) publication. Data package alterm contains long term annual means, whereas package mlterm contains long term monthly means. The variables included in the data packages are: - csfd: consecutive freezing days (days) - fthd: freeze thaw days (days) - cswd: consecutive wet days (days) - csdd: consecutive dry days (days) - t2am: 2m air temperature amplitude (°C) - tsam: surface temperature amplitude (°C) - pamp: precipitation amplitude (mm/day) - pmax: maximum daily precipitation (mm/day) Publications: Derived Variables (contained in these files): Mutz S.G. and Ehlers T. A. (2019). Detection and Explanation of Spatiotemporal Patterns in Late Cenozoic Palaeoclimate Change Relevant to Earth Surface Processes. Earth Surface Dynamics. doi.org/10.5194/esurf-7-663-2019 Original Simulations: Mutz S.G., Ehlers T. A., Werner M., Lohmann G., Stepanek C., Li J., (2018). Estimates of Late Cenozoic climate change relevant to Earth surface processes in tectonically active orogens. Earth Surface Dynamics. doi.org/10.5194/esurf-6-271-2018 Authors: Mutz S.G., Ehlers T. A. License: This work is distributed under the Creative Commons Attribution 4.0 International License Format: netCDF (.nc) File Names (nc): [publication]_[experiment ID]_[time period]_[horizontal resolution][vertical resolution]_[means].nc   <table> <tbody> <tr> <td>experiment ID</td> <td>usually a letter followed by 3-5 digits, e007_2 (pre-industrial), e008 (Mid-Holocene), e009 (Last Glacial Maximum), e010 (Pliocene)</td> </tr> <tr> <td>time period</td> <td>PI (pre-industrial), MH (Mid-Holocene), LGM (Last Glacial Maximum), PLIO (Pliocene)</td> </tr> <tr> <td>horizontal resolution</td> <td>spectral resolution, t followed by a number, e.g. t159</td> </tr> <tr> <td>vertical resolution</td> <td>number of vertical levels, l followed by a number, e.g. l31</td> </tr> <tr> <td>means</td> <td>alterm (annual long term means) or mlterm (monthly long term means)</td> </tr> </tbody> </table> Correspondence: Sebastian G. Mutz (sebastian@sebastianmutz.com)

opencc-by-4.0Jun 2023View details →

CMIP6 variable counts per model

The number of variables (y-axis) published for the historical simulation by each model (as represented in the DKRZ Earth System Grid Federation (ESGF) index node March 2022) is shown in blue columns against the model rank, where models are ranked in order of decreasing variable count. Also shown, in orange, is the number of variables which are included by all models up to the given rank. Data provided by Martin Juckes, image created by Beth Dingley

opencc-by-4.0Nov 2022View details →

The role of the intraspecific variability of hydraulic traits for modelling the plant water use in different European forest ecosystems: scripts, model output, and parameter files

This repository contains the model outputs and R scripts used to process the data to analyze the impact of the plant hydraulic parameterization of the manuscript: "The role of the intraspecific variability of hydraulic traits for modelling the plant water use in different European forest ecosystems". The following is a detailed description of the content of this repository: model_output.zip: This compressed file contains the results of all the individual numerical experiments per experimental site as produced by the Comunity Land Model version 5. The files are stored in NETCDF format per year. The folder is arranged with subfolders containing the individual results from each experimental site as follows: <ul> <li>rc: model output with the results of the resistant configuration of experiment 1 (RC)</li> <li>vc: model output with the results of the vulnerable configuration of experiment 1 (VC)</li> <li>k_dc: model output with the results of the default configuration used for experiments 1 and 2 (DC or DCkmax)</li> <li>k_rc: model output with the results of the low plant hydraulic conductance (Lkmax) for experiment 2</li> <li>k_irc: model output with the results of the intermediate low plant hydraulic conductance (ILkmax) for experiment 2</li> <li>k_vc: model output with the results of the high plant hydraulic conductance (Hkmax) for experiment 2</li> <li>k_ivc: model output with the results of the intermediate high plant hydraulic conductance (IHkmax) for experiment 2</li> <li>k_iirc: model output with the results of the additional intermediate low plant hydraulic conductance (IILkmax) for experiment 2</li> <li>ko_dc: model output with the results of the best kmax and the default configuration of the PVC used in experiment 3</li> <li>ko_rc: model output with the results of the best kmax and the resistant configuration of the PVC used in experiment 3</li> <li>ko_vc: model output with the results of the best kmax and the vulnerable configuration of the PVC used in experiment 3</li> </ul> The scripts were written for use in RStudio, and each contains a detailed description of the data requirements and outputs. Each script was developed to read directly the netcdf files of the model output and the csv files containing the transpiration estimates calculated from the SAPFLUXNET per experimental site (script 1).