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608 results for “ensemble”
The extrAIM dataset: A merged satellite-based daily precipitation dataset for the Mediterranean region (including an ensemble of 20 synthetic realisations)
<p><strong>extrAIM </strong>dataset is a <strong>new merged daily precipitation product</strong> (extraim_merged_data.nc) for the Mediterranean region with the following characteristics:</p> <ul> <li><strong>Dataset format:</strong> NetCDF</li> <li><strong>Spatial resolution:</strong> 25 x 25 km</li> <li><strong>Temporal resolution:</strong> 1 day</li> <li><strong>Spatial coverage:</strong> Longitude: from -6.25 to 38.25, Latitude: 27.75 to 49</li> <li><strong>Temporal coverage: </strong>01-01-2007 to 30-09-2021</li> <li><strong>Merging approach: </strong>Two-step merging (classification and regression) <ul> <li><strong>Algorithm: </strong>Random Forest for both classification and regression</li> <li><strong>Training strategy:</strong> Full training strategy</li> </ul> </li> <li><strong>Merged precipitation products: </strong>SM2Rain-ASCAT and GPM Late Run</li> <li><strong>Reference precipitation product:</strong> EMO5</li> <li><strong>Static covariates: </strong>Longitude, Latitude and Elevation, in both classification and regression step <ul> <li><strong>Classification step:</strong> probability dry and probability dry of the 5 neighboring points around the target locations</li> <li><strong>Regression step:</strong> mean, standard deviation and skewness of daily precipitation, of the entire series and non-zero amounts, as well as mean precipitation of the 5 neighboring points around the target locations</li> </ul> </li> </ul> <p>In addition, an <strong>ensemble of 20 synthetic realizations</strong> (equiprobable and bias-adjusted) of the merged dataset is provided (files named: “extraim_realisation_XX.nc”). The synthetic realisations were produced using the extrAIM’s uncertainty-quantification approach and the associated conditional sampling method.</p>
spermatogenesis across mammals - Ensembl Release 112 mapping
<p>This repo contains remapped single-cell Anndata (h5ad) objects from the original publication "The molecular evolution of spermatogenesis across mammals (Florent Murat, Noe Mbengue et al 2023; https://doi.org/10.1038/s41586-022-05547-7)". Data was (pseudo-)remapped against Ensembl Release 112 genomes and annotations with kb-python (v0.28.2). For Macaca mulatta the NCBI GCF_003339765.1_Mmul_10_genomic.fna and GCF_003339765.1_Mmul_10_genomic.gtf was used due to low protein number in Ensembl Release 112 file Macaca_mulatta.Mmul_10.pep.all.fa.gz. Only filtered counts are provided. </p> <p>If you use this data, please cite Murat and Mbengue et al. 2023.</p>
A large ensemble of CMIP6-based transient climate scenarios for impact assessment in Great Britain.
<p>Climate change impact assessments often require a large ensemble of local-scale transient climate scenarios. Each ensemble member represents plausible long weather series at a local scale. The climate projections from Global Climate Models (GCMs) are difficult to use at local scale due to their coarse spatial and temporal resolution. Moreover, very few projections are usually available for each GCM due to a high computational cost. An alternative approach involves employing a stochastic weather generator to produce a large number of transient scenarios based on the climate projections from GCMs. In a current dataset, transient climate scenarios were generated using the LARS-WG weather generator, based on climate projections from GCMs from the CMIP6 ensemble across 26 representative sites throughout the UK. Each transient scenario spans the period from 2020 to 2090. At each site, 100 transient scenarios were generated for two emission scenarios (SSP2-4.5 and SSP5-8.5) and five selected GCMs from CMIP6 (ACCESS-ESM1-5, CNRM-CM6-1, HadGEM3-GC31-LL, MPI-ESM1-2-LR, and MRI-ESM2-0). The choice of GCMs were based on their performance over northern Europe and their climate sensitivity. The use of a subset of GCMs substantially reduces computational time required for impact assessment, while allowing to quantify uncertainties in impacts related to uncertain future climate. The dataset can be used with impact models in various fields, including, land and water resources, agriculture and food production, ecology and epidemiology, and human health and welfare, when undertaking impact assessment of climate change and decision support for mitigation and adaptation.</p>
Ensemble calculations of "98perc_sfcWindmax" from EURO-CORDEX data for Europe
<p><strong>Climate Index: </strong>98perc_sfcWindmax</p> <p><strong>Definition:</strong> Average of the annual 98<sup>th</sup> percentile of the daily maximum wind speed over a 30-year time-period.</p> <p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily maximum near-surface wind speed (sfcWindmax).</p> <p>Results (ensemble mean and ensemble standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) time periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p> <p>The EURO-CORDEX climate model simulations used are:</p> <ul> <li>SMHI-RCA4/ ICHEC-EC-EARTH, SMHI-RCA4/ MOHC-HadGEM2-ES</li> <li>CLMcom-CCLM4-8-17/ ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/ MOHC-HadGEM2-ES</li> <li>DMI-HIRHAM5/ ICHEC-EC-EARTH</li> <li>KNMI-RACMO22E/ ICHEC-EC-EARTH, KNMI-RACMO22E/ MOHC-HadGEM2-ES</li> </ul>
Ensemble calculations of "Fmax" from EURO-CORDEX data for Europe
<p><strong>Climate Index: </strong>Fmax</p> <p><strong>Definition:</strong> Average of the annual maximum of the daily maximum wind speed over a 30-year time-period.</p> <p><strong>Additional information:</strong> The dataset is based on an ensemble of EURO-CORDEX model simulations of daily maximum near-surface wind speed (sfcWindmax).</p> <p>Results (ensemble mean and ensemble standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) time periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.</p> <p>The EURO-CORDEX climate model simulations used are:</p> <ul> <li>SMHI-RCA4/ ICHEC-EC-EARTH, SMHI-RCA4/ MOHC-HadGEM2-ES</li> <li>CLMcom-CCLM4-8-17/ ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/ MOHC-HadGEM2-ES</li> <li>DMI-HIRHAM5/ ICHEC-EC-EARTH</li> <li>KNMI-RACMO22E/ ICHEC-EC-EARTH, KNMI-RACMO22E/ MOHC-HadGEM2-ES</li> </ul>
Dataset for "Remapping of Greenland ice sheet surface mass balance anomalies for large ensemble sea-level change projections"
<p>This dataset is used to reproduce the results presented in the following publication:</p> <p>Goelzer, H., Noel, B. P. Y., Edwards, T. L., Fettweis, X., Gregory, J. M., Lipscomb, W. H., van de Wal, R. S. W., and van den Broeke, M. R.: Remapping of Greenland ice sheet surface mass balance anomalies for large ensemble sea-level change projections, The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-188, in review, 2019.</p> <p> </p>
LOBSTER (Ligand Overlays from Binding SiTe Ensemble Representatives)
<p>LOBSTER ("Ligand Overlays from Binding SiTe Ensemble Representatives") is a dataset of ligand overlays designed to evaluate small molecule superposition tools.</p> <p><br>Based on all structures from the RCSB PDB, the dataset generation and filtering protocols are fully automated to avoid subjectivity in the selection of protein-ligand complexes and to gain the largest possible set of refined compounds. Affinity and activity data have been processed to select ligands with a high ligand efficiency.<br>Ligands were superimposed in their crystal pose by aligning the corresponding binding pockets to so-called ensembles. For poses generated in benchmark experiments, this offers an objective comparison to the superimposed ligand crystal poses. A clustering of ensembles created with the same protein-ligand complexes ensures the diversity of the LOBSTER set.<br>The 671 ligand ensembles comprise a total of 3212 unique ligands from 3521 different protein-ligand complexes. A total of 72 734 ligand pairs have been derived from the ensembles. Ten subsets were generated from the pairs according to the shape overlap of the pairs, quantified by the Shape Tversky Index.</p>
BioVars - bioclimatic datasets for Europe based on a large regional climate ensemble for periods between 1971 to 2098
<p>We present 26 bio-climatic variables that are calculated based on a large ensemble consisting of 70 bias-adjusted GCM-RCM (Global Climate Model – Regional Climate Model) simulations for 1971 to 2098. Both, the historic and the projection periods were calculated using the same models to ensure consistency between the periods. The variables are validated against E-OBS observations from which we calculated the same bio-climatic variables. For projection periods we chose 20 year ranges between 2021 to 2098. Here, we offer two versions of them 1) variables separated into RCP 2.6, 4.5 and 8.5 including the 5th, 50th and 95th percentiles among the realisations and within the RCPS. And 2) variables per realisation separately. We then extracted the temporal 5th, 50th and 95th percentile per period as representing values. Each zipped file contains these 26 bio-climatic variables according to their aggregation. The variables and the units are explained within the data descriptor publication. </p> <p> </p> <p><strong>File descriptions</strong></p> <ul> <li>bioVars_1971-2000_met.tar.gz >> Projections per realisations for period 1971-2000</li> <li>bioVars_2021-2040_met.tar.gz >> Projections per realisations for period 2021-2040</li> <li>bioVars_2041-2060_met.tar.gz >> Projections per realisations for period 2041-2060</li> <li>bioVars_2061-2080_met.tar.gz >> Projections per realisations for period 2061-2080</li> <li>bioVars_2079-2098_met.tar.gz >> Projections per realisations for period 2079-2098</li> <li>bioVars_2021-2040_rcp.tar.gz >> Projections per RCP for period 2021-2040</li> <li>bioVars_2041-2060_rcp.tar.gz >> Projections per RCP for period 2041-2060</li> <li>bioVars_2061-2080_rcp.tar.gz >> Projections per RCP for period 2061-2080</li> <li>bioVars_2079-2098_rcp.tar.gz >> Projections per RCP for period 2079-2098</li> <li>validation.tar.gz >> Validation using E-OBS (v20.0) and Worldclim (version 2.1)</li> </ul> <p> </p> <p><strong>References</strong> <br>Reichmuth, A., Rakovec, O., Boeing, F. <em>et al.</em> BioVars - A bioclimatic dataset for Europe based on a large regional climate ensemble for periods in 1971–2098. <em>Sci Data</em> <strong>12</strong>, 217 (2025). https://doi.org/10.1038/s41597-025-04507-w</p>
Graph Data: Hydrological impact of widespread afforestation in Great Britain using a large ensemble of modelled scenarios
<p>Data used for creating the figures in the paper: Hydrological impact of widespread afforestation in Great Britain using a large ensemble of modelled scenarios.</p> <p>It contains the flow exceedances (as mm day<sup>-1</sup>), flow duration slope, median elasticity and runoff ratio for the different afforestation scenarios. Also included is the information on the changes of broadleaf afforestation. </p> <p>If you have any questions, please email marcus.buechel@ouce.ox.ac.uk.</p>
Data for paper "Convolutional neural network-based statistical post-processing of ensemble precipitation forecasts"
<p>The forecasts and observation datasets are used in the paper "Convolutional neural network-based statistical post-processing of ensemble precipitation forecasts". https://doi.org/10.1016/j.jhydrol.2021.127301</p> <p>The forecast data is a subset of the "ensemble for machine learning dataset (ENS4ML)" from ECMWF. </p> <p>The Python codes are stored in Github: https://github.com/wentao-bnu/LeNet_CSG_Precip</p>
Local explanation SHAP approach applied to MIROC5,RCP8.5-forced multi-model ensemble study of GrIS future sea-level contributions
<p>The repository contains materials for analysing the results of the Local explanation named SHAP-CTREE (Redelmeier et al., 2020) approach applied to the MIROC5,RCP8.5-forced multi-model ensemble study of GrIS future sea-level contributions from Goelzer et al. (2020).</p> <p>The available files are:<br> - run_SupplMat.R: the main R script to perform the diagnostics and the different analyses (levels 1 - 3)<br> - utilsPLOT.R: functions for plotting<br> - Diagnostics.zip: the zip file with the png figures, named 'GrIS_CaseXXX_yYYY.png', that depict the diagnostic for case XXX for prediction time YYY<br> - SupplementaryMaterials.zip<br> - RData files for each prediction time YYY "Shapley_yYYY" with:<br> S: matrix N=55 cases x d+1: SHAP values for the d inputs (+ average sea level value at time YYY)<br> YHAT: ML-based predictions of the sea level for the 55 cases<br> YTRUE: true values for the 55 cases<br> mae: mean absolute error<br> - RData file containing the design of experiments "DOE_GrIS_MIROC5-RCP85.RData"<br> doe: matrix with values of the d=9 inputs</p> <p>These constitute the supplementary materials of Rohmer et al. (2022, The Cryosphere). All technical details are provided in this reference.</p>
Performance Criteria and Example Parameter Sets Comparing Different Variants of the Ensemble Kalman Filter as Applied to Volcanology
<p>This dataset contains the results of various Ensemble Kalman Filter (EnKF) inversions in which synthetic GNSS and InSAR observations from an inflating magma system are assimilated into numerical models of rock deformation around a pressurized ellipsoidal magma reservoir. Each inversion uses a different variant of the EnKF, with changes to workflow meta-parameters such as the number of ensemble members or the particular update algorithm used. In particular, each filter variant is evaluated by comparing the final output model to the original synthetic model. The specific performance criteria used include (1) the root mean square error (RMSE) between the model predictions and the assimilated observations, as well as normalized misfit terms measuring the filter's ability to resolve (2) reservoir wall tensile stress, (3) easily-observable unique parameters such as reservoir position and aspect ratio, and (4) difficult-to-derive non-unique parameters such as the specific size and internal pressure of the reservoir. The assimilated data include two different scenarios, one in which inflation is caused by pressurization and another in which it is driven by a lateral reservoir expansion. Both datasets are tested with each EnKF variant. Finally, we include example matrices from within an EnKF update step to demonstrate inter-parameter correlations that develop during the assimilation and how they can be mitigated through randomization.</p>
Ensemble of NEMO present-day (1989-2009) and future (2080-2100 under RCP8.5) ocean properties and ice shelf melt rates in the Amundsen Sea
<p>Model outputs used in <a href="https://www.essoar.org/doi/10.1002/essoar.10511482.3">Jourdain et al. (GRL, 2022)</a></p> <p>The output files consist of monthly climatologies over either 1989-2009 or 2080-2100. The file names have the form:</p> <p><strong>climato_monthly_AMUXL12-GNJ002_<simu>_<group>_1989_2009.nc</strong>, where :</p> <ul> <li><simu> is either : <ul> <li>"BM02MAR" (ensemble member A, present-day),</li> <li>"BM03MAR" (ensemble member B, present-day),</li> <li>"BM04MAR" (ensemble member C, present-day),</li> <li>"BM02MARrcp85" (ensemble member A, future for both surface and lateral boundaries),</li> <li>"BM03MARrcp85" (ensemble member B, future for surface BUT NOT for lateral boundaries),</li> <li>"BM03MARrcBDY" (ensemble member B, future for both surface and lateral boundaries),</li> <li>"BM04MARrcp85" (ensemble member C, future for both surface and lateral boundaries),</li> </ul> </li> <li><group> is either : <ul> <li>"SBC" (surface boundary conditions),</li> <li>"icemod" (sea ice variables),</li> <li>"gridT" (temperature, salinity),</li> <li>"gridU" (zonal velocities),</li> <li>"gridV" (meridional velocities).</li> </ul> </li> </ul> <p>Grid information in:</p> <ul> <li>mesh_mask_AMUXL12_BedMachineAntarctica-2019-05-24.nc (ensemble member A),</li> <li>mesh_mask_AMUXL12_BedMachineAntarctica-2020-07-15_v02_ICB380.nc (ensemble members B & C).</li> </ul> <p>where:</p> <ul> <li>glamt : longitude</li> <li>gphit: latitude</li> <li>e1t, e2t, e3t_0 : mesh size (in meters) along x, y, z</li> <li>tmask = 1 for ocean mesh, = 0 otherwise (land, continental ice).</li> </ul> <p> </p> <p><strong>Acknowledgments:</strong> This work was granted access to the HPC resources of CINES (occigen) under the allocation A0100106035 attributed by GENCI.</p>
Results of "Ensemble Kalman Filter for the Thermosphere Ionosphere", CHAMP neutral density assimilation into CTIPe for March 20, 2007
<p>These data are the result of assimilating neutral density measurements from the CHAMP satellite on March 20, 2007 into the CTIPe model and and comparison of results with observations made by the GRACE satellite. Data assimilation is performed in three configurations: Configuration (i) is ds, state correction. Configuration (ii) is dfds, both input estimatation and state correction. Configuration (iii) is df, estimation of model inputs only.</p> <p>This data is associated with the following publication:</p> <blockquote> <p>Codrescu S., M.V. Codrescu, and M. Fedrizzi (2018), An Ensemble Kalman Filter for the Thermosphere-Ionosphere, Space Weather, 16, doi:<a href="http://dx.doi.org/10.1002/2017SW001752" title="Link to external resource: 10.1002/2017SW001752">10.1002/2017SW001752</a>.</p> </blockquote> <p> </p>
Months-long tracking of neuronal ensembles spanning multiple brain areas with Ultra-Flexible Tentacle Electrodes
<p>This dataset contains some of the raw and preprocessed data presented in the manuscript "Months-long tracking of neuronal ensembles spanning multiple brain areas with Ultra-Flexible Tentacle Electrodes" submitted to Nature Communications. Detailed information on each individual file is as follows: </p> <ul> <li><strong>256ch_device2_impedance_spectroscopy.csv:</strong> Impedance magnitudes presented in Fig. 2b.</li> <li><strong>rat1_impedances.csv:</strong> Impedance magnitudes belonging to Rat #1 (presented in Fig. 4c).</li> <li><strong>rat2_impedances.csv:</strong> Impedance magnitudes belonging to Rat #2 (presented in Fig. 4c).</li> <li><strong>MAX_TBY37_s1_n1_1776_1_12_hires_neuron.tif:</strong> Max. intensity projection image of Nissl staining shown in Fig. 4g. </li> <li><strong>MAX_TBY37_s1_n1_1776_1_12_hires_IBA.tif:</strong> Max. intensity projection image of IBA staining shown in Fig. 4g. </li> <li><strong>MAX_TBY37_s1_n1_1776_1_12_hires_GFAP.tif:</strong> Max. intensity projection image of GFAP staining shown in Fig. 4g. </li> <li><strong>AVG_TBY37_s1_n1_1776_1_12_neuron_4x4bins.tif: </strong>z-stack-averaged and binned image of Nissl staining used in histology analysis shown in Fig. 4g.</li> <li><strong>AVG_TBY37_s1_n1_1776_1_12_IBA_4x4bins.tif:</strong> z-stack-averaged and binned image of IBA staining used in histology analysis shown in Fig. 4g.</li> <li><strong>AVG_TBY37_s1_n1_1776_1_12_GFAP_4x4bins.tif:</strong> z-stack-averaged and binned image of GFAP staining used in histology analysis shown in Fig. 4g.</li> <li><strong>neuron_fluo_ds.npy: </strong>The downsampled sample points used in the histology analysis for Nissl staining (Fig. 4g). </li> <li><strong>gfap_fluo_ds.npy: </strong>The downsampled sample points used in the histology analysis for GFAP staining (Fig. 4g). </li> <li><strong>iba_fluo_ds.npy: </strong>The downsampled sample points used in the histology analysis for IBA staining (Fig. 4g). </li> <li><strong>256ch_device2_phase_spectroscopy.csv: </strong>Impedance phases presented in Supplementary Fig. 7a.</li> <li><strong>rat1_impedance_phases.csv: </strong>Impedance phases belonging to Rat #1 (presented in Supplementary Fig. 7b).</li> <li><strong>rat2_impedance_phases.csv:</strong> Impedance phases belonging to Rat #2 (presented in Supplementary Fig. 7b).</li> <li><strong>mouseLL2_impedances_magnitudes.csv:</strong> Impedance magnitudes presented in Supplementary Fig. 9a.</li> <li><strong>mouseLL2_impedances_phases.csv: </strong>Impedance phases presented in Supplementary Fig. 9b. </li> <li><strong>mouseLL2_single_unit_SNRs.csv: </strong>Single unit SNRs presented in Supplementary Fig. 9c.</li> <li><strong>mouseLL2_single_unit_lifetimes.csv: </strong>Single unit lifetimes presented in Supplementary Fig. 9d. </li> <li>Figure_5_data.mat: Data used in Figure 5 (can be imported into the corresponding Matlab script in the GitHub repository).</li> </ul> <p>The rest of the data supporting the figures is provided in the Source File and Supplementary Data files, which are available through the online version of the article. Any additional requests for information can be directed to, and will be fulfilled by, the corresponding author. </p>
Supplementary files for Vertical Displacements and Sea-Level Changes in Eastern North America Driven by Glacial Isostatic Adjustment: an Ensemble Modeling Approach
<p>Model input and output files associated with the manuscript entitled "Vertical Displacements and Sea-Level Changes in Eastern North America Driven by Glacial Isostatic Adjustment: an Ensemble Modeling Approach" that will be submitted to Journal of Geophysical Research.</p>
Dataset: Ensemble results comparing L-dependent radial diffusion
<p>Simulation data used in the creation of plots in "Two methods to analyse radial diffusion ensembles: the peril of space- and time- dependent diffusion".</p>
Great Lakes WRF-FVCOM model ensemble outputs: Summer 2018 daily LST and T2m
<p>Postprocessed model data for the paper: "Coupled Lake-Atmosphere-Land Physics Uncertainties in a Great Lakes Regional Climate Model"</p> <p>Perturbed Physics Ensemble outputs from a coupled lake-atmosphere-land Great Lakes regional model: <br>- Time period: May, June, July of 2018 <br>- Computational domain: Great Lakes region as contained within <a href="../api/records/10806629/draft/files/wrf_grid.nc/content" target="_blank" rel="noopener noreferrer">wrf_grid.nc</a> (atmosphere-land) and <a href="../api/records/10806629/draft/files/fvcom_grid.nc/content" target="_blank" rel="noopener noreferrer">fvcom_grid.nc</a> (lake).<br>- Quantities of interest: lake surface temperature and 2-m near-surface air temperature<br>- Training set: "<a href="../api/records/10806629/draft/files/wfv_global_daily_temperature_training_set.pkl/content" target="_blank" rel="noopener noreferrer">wfv_global_daily_temperature_training_set.pkl</a>" [18 members]. Associated with "<a href="../api/records/10806629/draft/files/perturbation_matrix_9variables_korobov18.nc/content" target="_blank" rel="noopener noreferrer">perturbation_matrix_9variables_korobov18.nc</a>" input model configuration matrix.<br>- Test set: "<a href="https://zenodo.org/api/records/13863491/draft/files/wfv_global_daily_temperature_test_set.pkl/content" target="_blank" rel="noopener noreferrer">wfv_global_daily_temperature_test_set.pkl</a>" [9 members]. Associated with "<a href="https://zenodo.org/api/records/13863491/draft/files/perturbation_matrix_9variables_latin_hypercube9.nc/content" target="_blank" rel="noopener noreferrer">perturbation_matrix_9variables_latin_hypercube9.nc</a>" input model configuration matrix.</p>
Ensemble of ice shelf basal melt rates and ocean properties for tipped-over continental shelves
<p><strong>Summary</strong><strong>:</strong></p> <p>This dataset contains the reference and tipped states from several model configurations developed at the <a href="https://www.awi.de/en/">Alfred Wegener Institute (AWI)</a> and the <a href="https://www.ige-grenoble.fr/?lang=en">Institut des Géosciences de l’Environnement (IGE)</a>. They were gathered here in the context of the <a href="https://www.tipaccs.eu">TiPACCs European project</a> and constitute a useful ensemble of reference and tipped ocean–ice-shelf simulations that <strong>can be used to feed ice-sheet simulations or to train melt parameterizations</strong>.</p> <p>The simulations produced by AWI are based on the <a href="https://fesom.de">FESOM</a> global ocean–sea-ice model using either Z- or Sigma- coordinates and all show a cold-to-warm tipping point for Filchner-Ronne Ice Shelf. The two sets of simulations produced by IGE are based on the <a href="https://www.nemo-ocean.eu">NEMO</a> ocean–sea-ice model. They include a global configuration showing a cold-to-warm tipping point for Ross Ice Shelf, and regional Amundsen Sea configuration showing a warm-to-warmer transition (likely not a proper tipping point). </p> <p>The files include 3-dimensional and sea-floor ocean temperatures and salinities, ice-shelf melt rates, as well as topographic and grid data. All variables are interpolated onto the common 8km stereographic grid that was used to provide ocean forcing in ISMIP6 (<a href="https://doi.org/10.5194/tc-14-2331-2020">Nowicki et al. 2020</a>).</p> <p>We provide the reference state and the anomaly, so that the tipped state is:</p> <ul> <li><em>Tipped = Reference + Anomaly</em></li> </ul> <p>To have an overview of the reference and tipped states, have a look at these figures:</p> <ul> <li><em>figure_ref_and_anomalies_1.pdf</em></li> <li> <p><em>figure_ref_and_anomalies_2.pdf</em></p> </li> <li> <p><em>figure_seafloor_temp_zooms.pdf</em></p> </li> </ul> <p> </p> <p>_______________________________________________</p> <p><strong>Detailed Data Description</strong><strong>:</strong></p> <p> </p> <ul> <li><strong>reference_high_FESOM_sigma_AWI_TiPACCs.nc</strong> <ul> <li>contact: Ralph Timmermann <a href="mailto:ralph.timmermann@awi.de">ralph.timmermann@awi.de</a>, Verena Haid <a href="mailto:verena.haid@awi.de">verena.haid@awi.de</a></li> <li>model: FESOM1.4, sigma-coordinates (global with refined grid around Antarctica)</li> <li>atmospheric forcing: HadCM3 20C</li> <li>provided average: 1990-1999 (10-year mean)</li> <li>more: <a href="https://doi.org/10.1007/s10236-013-0642-0">Timmermann and Hellmer (2013)</a></li> </ul> </li> </ul> <p> </p> <ul> <li><strong>reference_low_FESOM_sigma_AWI_TiPACCs.nc</strong> <ul> <li>contact: Ralph Timmermann <a href="mailto:ralph.timmermann@awi.de">ralph.timmermann@awi.de</a>, Verena Haid <a href="mailto:verena.haid@awi.de">verena.haid@awi.de</a></li> <li>model: FESOM1.4, sigma-coordinates (global with refined grid around Antarctica)</li> <li>atmospheric forcing: HadCM3 20C</li> <li>provided average: 1990-1999 (10-year mean)</li> <li>more: <a href="https://doi.org/10.5194/os-13-765-2017">Timmermann and Goeller (2017)</a></li> </ul> </li> </ul> <p> </p> <ul> <li><strong>reference_FESOM_z_AWI_TiPACCs.nc</strong> <ul> <li>contact: Verena Haid <a href="mailto:verena.haid@awi.de">verena.haid@awi.de</a></li> <li>model: FESOM1.4, Z-coordinates (global with refined grid around Antarctica)</li> <li>atmospheric forcing: ERA Interim</li> <li>provided average: 2008-2017 (10-year mean), i.e. model year 30-39</li> <li>more: same mesh as <a href="https://doi.org/10.5194/tc-13-2317-2019">Gürses et al. (2019)</a></li> </ul> </li> </ul> <p> </p> <ul> <li><strong>reference_NEMO4_eORCA025.L121_IGE_TiPACCs.nc</strong> <ul> <li>contact: Pierre Mathiot <a href="mailto:pierre.mathiot@univ-grenoble-alpes.fr">pierre.mathiot@univ-grenoble-alpes.fr</a></li> <li>model: NEMO-4.0, eORCA025.L121 (Global, 1/4°, 121 vertical levels)</li> <li>atmospheric forcing: JRA55do</li> <li>provided average: 2<sup>nd</sup> cycle of 1989-1998 (10-year mean); we first run 1979-2018, and we redo 1979-1998 starting from the 2018 state.</li> <li>more: <a href="https://pmathiot.github.io/NEMOCFG/docs/build/html/simu_eORCA025_OPM021.html">https://pmathiot.github.io/NEMOCFG/docs/build/html/simu_eORCA025_OPM021.html</a></li> </ul> </li> </ul> <p> </p> <ul> <li><strong>reference_NEMO3_AMUXL12.L75_IGE_TiPACCs.nc</strong> <ul> <li>contact: Nicolas Jourdain <a href="mailto:nicolas.jourdain@univ-grenoble-alpes.fr">nicolas.jourdain@univ-grenoble-alpes.fr</a></li> <li>model: NEMO-3.6, AMUXL12.L75 (Amundsen, 1/12°, 75 vertical levels)</li> <li>atmospheric forcing: MAR (<a href="https://doi.org/10.5194/tc-14-229-2020">Donat-Magnin et al. 2020</a>)</li> <li>provided average: 1989-2009 (21-year mean)</li> <li>more: similar model set-up as <a href="https://doi.org/10.1016/j.ocemod.2018.11.001">Jourdain et al. (2019)</a>.</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>anomaly_high_FESOM_sigma_AWI_TiPACCs.nc</strong> <ul> <li>continuation of reference_high_FESOM_sigma_AWI_TiPACCs.nc</li> <li>forced with HadCM3 A1B</li> <li>provided average: 2190-2199 (10-year mean)</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>anomaly_low_FESOM_sigma_AWI_TiPACCs.nc</strong> <ul> <li>continuation of reference_low_FESOM_sigma_AWI_TiPACCs.nc</li> <li>forced with HadCM3 A1B</li> <li>provided average: 2190-2199 (10-year mean)</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>anomaly_high_FESOM_z_AWI_TiPACCs.nc</strong> <ul> <li>same model set-up as reference_FESOM_z_AWI_TiPACCs.nc</li> <li>atmospheric forcing south of 60°S HadCM3 A1B starting 2050, otherwise ERA Interim starting 1979</li> <li>provided average: model year 69-78 (10-year mean), i.e. 2008-2017 of 2<sup>nd</sup> 39yr-cycle</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>anomaly_medium_FESOM_z_AWI_TiPACCs.nc</strong> <ul> <li>same model set-up as reference_FESOM_z_AWI_TiPACCs.nc</li> <li>atmospheric forcing: ERA Interim modified with a strong imprint of the seasonal cycle of HadCM3 A1B 2070-2089</li> <li>provided average: model year 69-78 (10-year mean), i.e. 2008-2017 of 2<sup>nd</sup> 39yr-cycle</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>anomaly_low_FESOM_z_AWI_TiPACCs.nc</strong> <ul> <li>same model set-up as reference_FESOM_z_AWI_TiPACCs.nc</li> <li>atmospheric forcing: manipulated ERA Interim with prolongued summer and shorter, milder winter south of 50°S, additional modification of winds in Weddell Sea region</li> <li>provided average: model year 108-117 (10-year mean), i.e. 2008-2017 of 3<sup>rd</sup> 39yr-cycle</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>anomaly_NEMO4_eORCA025.L121_IGE_TiPACCs.nc</strong> <ul> <li>similar to reference_NEMO4_eORCA025.L121_IGE_TiPACCs.nc</li> <li>perturbation of the model parameters: Different iceberg distribution and different sea-ice–ocean drag and snow conductivity on sea-ice, leading to less sea-ice production in the eastern Ross Sea.</li> <li>More: <a href="https://pmathiot.github.io/NEMOCFG/docs/build/html/simu_eORCA025_OPM020.html">https://pmathiot.github.io/NEMOCFG/docs/build/html/simu_eORCA025_OPM020.html</a></li> </ul> </li> </ul> <p> </p> <ul> <li><strong>anomaly_NEMO3_AMUXL12.L75_IGE_TiPACCs.nc</strong> <ul> <li>similar to reference_NEMO3_AMUXL12.L75_IGE_TiPACCs.nc</li> <li>perturbation of atmospheric forcing: MAR forced by the CMIP5 multi-model anomaly under the RCP8.5 scenario (<a href="https://doi.org/10.5194/tc-15-571-2021">Donat-Magnin et al. 2021</a>).</li> <li>provided average: 2080-2100 (21-year average)</li> </ul> </li> </ul> <p> </p>
Ensemble Digital Terrain Model (EDTM) of the world
<p>Layers include: Ensemble Digital Terrain Model (EDTM) in 250-m resolution. Unit is in metre(m) and precision is in decimetre (dm). Maps are downscaled from 30-m resolution to 250-m in order to fit the size limit. We provide 30-m EDTM and its standard deviation as links:</p> <ul> <li><strong>30-m EDTM</strong></li> </ul> <p><a href="https://s3.eu-central-1.wasabisys.com/openlandmap/dtm/dtm.bareearth_ensemble_p10_30m_s_2018_go_epsg4326_v20230221.tif">https://s3.eu-central-1.wasabisys.com/openlandmap/dtm/dtm.bareearth_ensemble_p10_30m_s_2018_go_epsg4326_v20230221.tif</a></p> <ul> <li><strong>Standard deviation</strong></li> </ul> <p><a href="https://s3.eu-central-1.wasabisys.com/openlandmap/dtm/dtm.bareearth_ensemble_std_30m_s_2018_go_epsg4326_v20230221.tif"><strong>https://s3.eu-central-1.wasabisys.com/openlandmap/dtm/dtm.bareearth_ensemble_std_30m_s_2018_go_epsg4326_v20230221.tif </strong></a></p> <p>Derived using <a href="https://www.eorc.jaxa.jp/ALOS/en/dataset/aw3d30/aw3d30_e.htm">ALOS AW3D</a>, <a href="https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model">GLO-30</a>, <a href="http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/">MERITDEM</a>, and national DTMs. We derived a lower 10% quantile from all maps. In order to create bare earth data, we used <a href="https://glad.umd.edu/dataset/gedi/">canopy height</a> (canopy height > 2m) and standard deviation (sd > 6m) to mask building and forest in AW3D and GLO-30. Practical processing is written <a href="https://gitlab.opengeohub.org/yu-feng.ho/faen-artifact/-/blob/main/ensemble_dtm.ipynb">here</a> in Python.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">https://gitlab.com/openlandmap/global-layers/-/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL in Cloud Optimised GeoTiff (COG). File naming convention:</p> <ul> <li>dtm.bareearth = variable: digital terrain model (m), bare earth</li> <li>ensemble = determination method: ensemble of mutli-source dsm and dtm</li> <li>p10/std = aggregation/statistics method: 10th percentile / standard deviation</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>s = vertical reference: at surface,</li> <li>go = bounding box: global land without Antarctica</li> <li>epsg.4326 = ESPG code: epsg.4326</li> <li>v20230221 = version code: creation date 20230221</li> </ul>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.