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209 results for “probabilistic”
Probabilistic-deterministic storm surge return level dataset for the Bengal delta
<p>Bengal delta shoreline, spanning Bangladesh and India, gets hit every 3 years on average by a major tropical cyclone. Although their occurrence is relatively moderate compared to other tropical regions (accounting for only 5% of global cyclones), the impact of these events is major, accounting for 50% of the victims recorded worldwide. This is due to the very low topography of the delta above sea level (less than 5 meters), high storm surge induced water level and flooding, combined with the high density of the vulnerable population. </p> <p>On one hand, the unavailability of long-term reliable water level data on a sparse tide-gauge network along the coastline has hindered the assessment of storm surge hazards. The application of hydrodynamic modelling to fill the data gap also suffers from the unavailability of a reliable long-term storm dataset over the region. The complex topography of the Bengal delta, with defence structures, and a dense network of rivers presents another modelling challenge. Finally, the interaction of tide, surge and wave further complicate the numerical complexity, needing a coupled modelling framework. </p> <p>Thanks to advancements made to acquire high-quality regional nearshore bathymetry and topography (Krien et al. 2016, Khan et al. 2019), as well as coupled storm surge modelling (Krien et al. 2017, Khan et al. 2021), the tidal and storm surge dynamics over the Bengal delta is now well captured by recent high-resolution coupled SCHISM-WWM Bay of Bengal model (Khan et al. 2021). To estimate the risk of storm surge and associated flooding across the Bengal delta, we have integrated the wave-coupled hydrodynamic model of Khan et al. (2021) for a large ensemble (~3600 cyclones, ~5000 years of storm activity) of synthetic cyclones generated through the statistical-deterministic method of Emanuel (2006). Our storm and surge ensemble covers the whole range of natural variability of storm frequency, size, intensity and track location, with a dense spatial distribution. The interactions among the tide, surge, and waves are modelled explicitly at high spatial resolution. The storm surge-induced water level at various return periods, up to 500 years, is then determined at high spatial resolution (250m at the coast) using a ranking-based technique.</p> <p>The dataset distributed here represents the storm surge water level estimate (e.g. total water level from the tide, surge, and wave computed dynamically through the model) at 25 to 500 year return period (25-year step). The corresponding variable in the self-describing netCDF data file is 'maxelev'. The estimated storm surge water level values are interpolated in a 30" (~1km at the equator) structured grid over the Bengal delta from the original unstructured-grid model outputs (250m resolution at the coast). </p> <p>This dataset is a part of a manuscript, currently being submitted to Natural Hazards and Earth System Sciences (https://nhess.copernicus.org/). Please cite the original paper, along with the dataset if used in your work as - Khan, M. J. U., Durand, F., Emanuel, K., Krien, Y., Testut, L., and Islam, A. K. M. S.: Storm surge hazard over Bengal delta: A probabilistic-deterministic modelling approach, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2021-329, in review, 2021.</p>
Malawi probabilistic seismic hazard analysis (PSHA) using the Malawi Seismogenic Source Model (MSSM). Supplementary Files v1.1
<p>Updated (October 2022) version of supplementary files for running probabilistic seismic hazard analysis (PSHA) MATLAB codes for Malawi. The PSHA codes themselves (v1.0) are available at: https://doi.org/10.5281/zenodo.7265781and the most recent version will be available on GitHub at: https://github.com/jack-williams1/Malawi_PSHA. Note the variables stored here are not stored on GitHub due to the file size.</p> <p>Includes both input files for performing PSHA and output ground motions for plotting PSHA results.</p> <p>Files are:</p> <ul> <li>malawi_Vs30_active.txt: Input USGS slope-based Vs30 values for Malawi (Wald and Allen 2007)</li> <li>EQCAT_comb.mat: MSSM Direct catalog for all possible rupture weightings (stored as MATLAB variable)</li> <li>GM_MSSM_em_20221027: Ground motions for plotting PSHA maps (stored as MATLAB variable)</li> <li>GM_MSSM_20221021.mat: Ground motions needed for plotting PSHA-site analysis figures (stored as MATLAB variable)</li> <li>mssm_comb.mat: Matlab file for combined MSSM Direct and Adapted MSSM catalogs (stored as MATLAB variable)</li> <li>MSSM_Catalog_Adapted_em.mat: Adapated MSSM event catalog (stored as MATLAB variable)</li> <li>syncat_bg.mat: Areal source stochastic event catalog (stored as MATLAB variable)</li> </ul> <p>Further descriptions of these files and how to use them are provided on Github. An open-access manuscript describing the PSHA is available at: </p> <p>Williams J. N., Werner M. J., Goda K., Wedmore L. N. J., De Risi R., Biggs J., Mdala H., Dulanya Z., Fagereng Å, Mphepo F., Chindandali P. (2023). Fault-based probabilistic seismic hazard analysis in regions with low strain rates and a thick seismogenic layer: a case study from Malawi, Geophysical Journal International, Volume 233, Issue 3, June 2023, Pages 2172–2206, <a href="https://doi.org/10.1093/gji/ggad060">https://doi.org/10.1093/gji/ggad060</a></p> <p>Please reference this publication along with this repository when using these data.</p> <p>USGS vs30 value compilation described in:</p> <p>Allen, T. I., and Wald, D. J., 2009, On the use of high-resolution topographic data as a proxy for seismic site conditions (Vs30), Bulletin of the Seismological Society of America, 99, no. 2A, 935-943.</p> <p> </p>
EEG: Probabilistic Learning with Affective Feedback: Exp #2
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EEG: Probabilistic Learning with Affective Feedback: Exp #1
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A whole-cortex probabilistic diffusion tractography connectome
<p>This is a collection of the results data for the eNeuro article of the same name, <a href="http://doi.org/10.1523/ENEURO.0416-20.2020">https://doi.org/10.1523/ENEURO.0416-20.2020</a>. Please cite this publication when using these data. Files with the .mat extension are matlab v7.3 files. The raw data from which these data were derived are available from <a href="https://db.humanconnectome.org">https://db.humanconnectome.org</a> and <a href="https://f-tract.eu">https://f-tract.eu</a>.</p>
Dataset In-vivo probabilistic atlas of human thalamic nuclei based on diffusion weighted magnetic resonance imaging
<p>This is the dataset related to the paper "In-vivo probabilistic atlas of human thalamic nuclei based on diffusion weighted magnetic resonance imaging", E. Najdenovska*, Y. Aléman-Gómez*, G. Battistella, M. Descoteaux, P. Hagmann, S. Jacquemont, P. Maeder, J.-P. Thiran, E. Fornari and M. Bach Cuadra, Sci. Data. 5:180270 doi: 10.1038/sdata.2018.270 (2018). *Equally contributed authors.</p> <p>We provide NifTI-1 files representing a digital atlas of seven thalamic subparts per hemisphere. More precisely, the files include the spatial probabilistic atlas maps for each thalamic subpart (Thalamus_Nuclei-HCP-4DSPAMs.nii.gz) and the maximum likelihood atlas (Thalamus_Nuclei-HCP-MaxProb.nii.gz) in MNI space. The region corresponding to each labeled thalamic part respectively is given in the look-up table Thalamic_Nuclei-ColorLUT.txt. The NIFTI files can be visualised with the main available tools such as tkmedit, freeview or 3D-Slicer.</p> <p>We also provide a step by step pseudo code for creating the atlas.</p>
DPMFA_EU_ENM_2000-2020: Dynamic Probabilistic Material Flows of Engineered Nanomaterials from 2000 to 2020 - Raw results
<p>This dataset is related to the following publication:</p> <p>Title: Dynamic probabilistic material flow analysis of engineered nanomaterials in European waste treatment systems</p> <p>Authors: Sana Rajkovic, Nikolaus A. Bornhöf<span>t</span>, Renata van der Weijden, Bernd Nowack, Véronique Adam</p> <p>Submitted to the journal Waste Management in September 2019.</p> <p>The files contain key values of probability distributions associated with the emissions of selected engineered nanomaterials to the environment.</p>
EEG: Probabilistic Selection and Depression
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Tooling, Data and Results for "Components in Probabilistic Systems: Suitable by Construction"
<p>The tooling, data and results for the racetrack case study in the paper <em> Components in Probabilistic Systems: Suitable by Construction, ISoLA 2020, <a href="https://doi.org/10.1007/978-3-030-61362-4_13">DOI</a></em></p>
Dataset and plot generation script for article "Probabilistic short-range forecasts of high precipitation events : optimal decision thresholds and predictability limits" by Francois Bouttier and Hugo Marchal, submitted in Dec 2023.
<p>Dataset and plot generation script for article "Probabilistic short-range forecasts of high precipitation events : optimal decision thresholds and predictability limits" by Francois Bouttier and Hugo Marchal, submitted in NHESS journal in Dec 2023.</p> <p>For further technical details read the file READMEdata in the zipfile. The script MAKEFIG remakes all the figures from the data.</p> <p>For scientific details read the associated article preprint on the NHESS egusphere website.</p>
Probabilistic projections of granular energy technology diffusion at subnational level - solar photovoltaics, heat pumps, and battery electric vehicles in Switzerland
<p>The probabilistic projections are part of the work: <br><em>Nik Zielonka, Xin Wen, Evelina Trutnevyte, Probabilistic projections of granular energy technology diffusion at subnational level, PNAS Nexus, Volume 2, Issue 10, October 2023, pgad321, </em><a href="https://doi.org/10.1093/pnasnexus/pgad321"><em>https://doi.org/10.1093/pnasnexus/pgad321</em></a></p> <p>Please cite the article together with the Zenodo link when you use the data.</p> <p>The provided data files contain the estimated probabilistic projections for all Swiss municipalities on the actual diffusion of solar photovoltaics (PV), heat pumps, and battery electric vehicles (BEVs) in Switzerland for the indicated years:</p> <p>Version 2022-2050: Projections for the years 2022-2050 as presented by Zielonka et. al (2023), PNAS Nexus.<br>Version 2023-2050: Projections for the years 2023-2050, using the latest data of 2022.<br>Version 2024-2050: Projections for the years 2024-2050, using the latest data of 2023.</p> <p>The computations were performed at University of Geneva using Baobab HPC service.</p> <p>This research was carried out with the support of the Swiss Federal Office of Energy SFOE as part of the SWEET project SURE (N.Z., E.T.) and the Swiss National Science Foundation Eccellenza Grant as part of the project "Accuracy of long-range national energy projections" (Grant no. 186834, X.W., E.T.). The authors bear sole responsibility for the conclusions and the results.</p>
Prospective dynamic and probabilistic material flow analysis of graphene-based materials in Europe from 2004 to 2030
<p>This dataset is related to the following publication:</p> <p>Title: Prospective dynamic and probabilistic material flow analysis of graphene-based materials in Europe from 2004 to 2030</p> <p>Authors: Hyunjoo Hong, Florian Part, Bernd Nowack </p> <p>Submitted to the journal Environmental Science & Technology in June 2022.</p> <p>The files contain an input file and final codes and raw results of the DMFA model for graphene-based materials.</p>
Supplement to "Probabilistic load forecasting for the low voltage network: forecast fusion and daily peaks"
<p>This deposit contains the scripts and data used in the research article "Probabilistic load forecasting for the low voltage network: forecast fusion and daily peaks", which proposed a novel method for electricity demand forecasting in low voltage networks.</p> <p>The scripts are written in the form of R markdown and include additional commentary on the methodology. Both input data and the resulting forecast data and evaluation results are provided, though the latter two may be regenerated by running the scripts.</p>
Data and Models for "Probabilistic Imaging of Tsunamigenic Seafloor Deformation During the 2011 Tohoku-oki Earthquake"
<p><strong>Directory "waveform_data"</strong> includes 13 tsunami time series data from different instruments: TM1, TM2, KPG1, KPG2, GB801, GB802, GB803, GB804, GB806, GB807, D21401, D21413, and D21418. Each data file (*.dat) has two columns for (1) the time since earthquake initiation (min) and (2) ocean surface or seafloor displacement amplitude (m).</p> <p><strong>Directory “kin_models”</strong> includes the following:</p> <p>1. Seafloor Mesh Geometry</p> <ul> <li>The entire seafloor mesh consists of two separate parts (422 and 136 nodes each; 558 in total) due to the need to resolve potential discontinuity at the trench. The files “*Pt{1,2}-SM2.PointCoord.txt” consists of six columns for the ID, longitude (deg), latitude (deg), East (km), North (km) and depth (km) of the nodes in each triangular mesh. The E/N coordinates are calculated in UTM projection system, relative to an arbitrary reference point.</li> <li>The files “*.ClipPath.txt” includes the ID/lon/lat of mesh boundary nodes, which can be used for plotting.</li> <li>Visualization of the mesh parts are provided in PDF files.</li> <li>The file “*Total-SM2.PointCoord.txt” excludes boundary nodes and contains seafloor locations (504 nodes) that are directly used in tsunami arrival time calculations.</li> </ul> <p>2. Posterior Mean Models</p> <ul> <li>Ensemble-averaged models of seafloor displacements and uncertainty estimates, with no spatial averaging (“0R” in the file name) or with one-ring spatial averaging (“1R”). These models are shown in Figures 5 and 6 of <em>Jiang and Simons</em> (2016). The data files “posterior_mean_{0,1}R.txt” have three columns for (1) vertical seafloor displacement (m), (2) one-sigma standard deviation of displacement (m), and (3) corresponding resolution length (km). The model values (558 rows) correspond to nodes in files “*Pt{1,2}-SM2.PointCoord.txt” concatenated in sequential order.</li> <li>Tsunami arrival times (in sec) are calculated from the posterior mean values of propagation speeds, with zero sec at the earthquake epicenter. The coordinates (508 nodes) are included in geometry file “*Total-SM2.PointCoord.txt.”</li> </ul> <p><strong>Directory “kin_ensemble”</strong> includes the entire posterior model ensemble (98304 samples) in HDF5 format. Using a Linux command <em>h5dump</em> will show the following information about the contained datasets, with their names and dimensions. The main datasets are: (1) Covariance (1008×1008); (2) Data Log-likelihood (98304×1); (3) Posterior Log-likelihood (98304×1); and (4) Sample Set (98304×1008). Each model has 1008 parameters (504 for displacement and 504 for propagation speeds). The source coordinates (504 nodes) are included in geometry file "*Total-SM2-Parameter.PointCoord.txt."</p> <p><strong>Note:</strong> three different geometries files above are used for (1) posterior mean displacements (558 nodes), (2) arrival time calculation (508 nodes), and (3) source inversion models (504 nodes). </p>
An Analysis of the Probabilistic Track of the IPC 2018
<p>This archive contains data and results used for the paper 'An Analysis of the Probabilistic Track of the IPC 2018'. See the attached README for more details.<br> </p>
Supplementary Material for "Probabilistic Forecasting of Regional Net-load with Conditional Extremes and Gridded NWP"
<p>Supplementary material to accompany pre-print of "Probabilistic Forecasting of Regional Net-load with Conditional Extremes and Gridded NWP" by Jethro Browell and Matteo Fasiolo available on on arXiv. This is version 3. The only changes from version 1 & 2 to forecast evaluation (significance testing and additional plots). Future releases are subject to change following revisions of this article.</p>
Data for "Mapping the ghost: Estimating probabilistic snow leopard distribution across Mongolia"
<p>Data and code used for a country-wide occupancy survey of snow leopards in Mongolia, accompanying the paper "Mapping the ghost: Estimating probabilistic snow leopard distribution across Mongolia".</p> <p>This data contains the results of a survey of 1017 20x20km sampling units, out of a total of 1200 sampling units identified as potential snow leopard habitat (183 could not be sampled for various reasons), a near complete survey of potential snow leopard habitat in Mongolia, nearly 500,000 square kilometers, and an enormous effort by many researchers. If you make use of the data, please cite the following sources:</p> <ul> <li><em>Data for "Mapping the ghost: Estimating probabilistic snow leopard distribution across Mongolia".</em> (2021). Gantulga Bayandonoi, Koustubh Sharma, Justine Shanti Alexander, Purevjav Lkhagvajav, Ian Durbach, Darryl MacKenzie, Chimeddorj Buyanaa, Bariushaa Munkhtsog, Munkhtogtokh Ochirjav, Sergelen Erdenebaatar, Bilguun Batkhuyag, Nyamzav Battulga, Choidogjamts Byambasuren, Bayartsaikhan Uudus, Shar Setev, Lkhagvasuren Davaa, Khurel-Erdene Agchbayar, Naranbaatar Galsandorj, David Borchers. doi: https://doi.org/10.5281/zenodo.5257572</li> <li><em>Mapping the ghost: Estimating probabilistic snow leopard distribution across Mongolia. </em>(2021). Gantulga Bayandonoi, Koustubh Sharma, Justine Shanti Alexander, Purevjav Lkhagvajav, Ian Durbach, Darryl MacKenzie, Chimeddorj Buyanaa, Bariushaa Munkhtsog, Munkhtogtokh Ochirjav, Sergelen Erdenebaatar, Bilguun Batkhuyag, Nyamzav Battulga, Choidogjamts Byambasuren, Bayartsaikhan Uudus, Shar Setev, Lkhagvasuren Davaa, Khurel-Erdene Agchbayar, Naranbaatar Galsandorj, David Borchers. To appear in <em>Diversity and Distributions</em></li> </ul> <p><strong>Contents of zip file</strong></p> <p><em>Data</em></p> <p>The main dataset is contained in `data\Mongolia_occupancy_inputs.Rdata` . Please see the paper for more detail on data collection. The following objects are contained in the file:</p> <p>- Pres: presence/absence occupancy survey results, used for model fitting<br> - Site_Cov: unit-specific covariates, used for model fitting<br> - SurvCov: survey-specific covariates, used for model fitting<br> - Mongolia_studyarea: covariates for whole survey area, used for prediction<br> - Mongolia_fullrange: covariates across whole expected snow leopard range, used for prediction</p> <p><em>Code</em></p> <p>Code is cloned from the GitHub repository <a href="https://github.com/iandurbach/mongolia-occupancy">https://github.com/iandurbach/mongolia-occupancy</a>, which may contain updates. The version here reproduces the analyses in the paper above. The run these analyses:</p> <p>- run *occupancy-analysis.R* to fit the main occupancy models (these are also saved in the `\output` folder), do model selection, and plot covariate effects<br> - run *occupancy-goodness-of-fit.R* to calculate the c-hat statistic giving an indication of model fit for the best model<br> - run *comparing-maps.R* to compare the occupancy results with similar metrics generated using a presence-only analysis (using MaxEnt) or an expert map generated through qualitative discussion (reproduces Figure 3 in the paper).</p> <p>Code in *occupancy-data-preproc.R* is not needed but included for completeness. It converts the csv files in `data\csv`, which contain various input datasets used by the occupancy model, into a single .Rdata file (`data\Mongolia_occupancy_inputs.Rdata`), which is then used by the scripts above. Some minimal pre-processing (excluding ununsed variables, renaming for consistency, etc) is performed. </p>
Identification of high-wind features within extratropical cyclones using a probabilistic random forest - Part 2: Climatology - Dataset
<p>This dataset provides output of RAMEFI for the wind feature climatology presented in Eisenstein et al. (2023; 10.5194/wcd-2023-10) for the winter months October to March 2000-2019 using COSMO-REA6 (https://reanalysis.meteo.uni-bonn.de/?COSMO-REA6).</p> <p><strong>rf_crea_<yyyymm>.nc</strong> include the unfiltered probabilities for 'no feature' (p0), warm jet (p1), cold-frontal convection (p2), cold jet (p3) and cold-sector winds (p5) for each month.</p> <p>To filter for cyclone tracks, use <strong>cyclone_tracks.csv</strong>. The<strong> </strong>file includes interpolated ERA5 cyclone tracks for the investigated area and time period (see Section 2.4 of the paper).</p> <p><strong>mask.nc</strong> includes a land sea mask, height of surface level and a mask to exclude certain grid points as discussed in the manuscript (e.g., grid points with an altitude over 800m and the Balkans) for further filtering.</p>
Dataset: "Probabilistic Framework for Integration of Mass Spectrum and Retention Time Information in Small Molecule Identification"
<p>The SQLite database contains the pre-computed tandem mass spectra (MS2) and retention order scores used for the experiments in the publication: "<a href="https://doi.org/10.1093/bioinformatics/btaa998">Probabilistic Framework for Integration of Mass Spectrum and Retention Time Information in Small Molecule Identification</a>" by Bach et al. (2020).</p> <p>A detailed description of the database structure is given in the 'README.md' and can also be found in the <a href="https://github.com/aalto-ics-kepaco/msms_rt_score_integration/tree/master/data">code-repository associated with the publication</a>. The database layout is illustrated in the 'db_layout.png' file.</p> <p>The SQLite file 'ms_and_rt_score_DB_bach_etal_2020.db.gz' is compressed using <a href="https://en.wikipedia.org/wiki/Gzip">gzip</a>.</p>
KaKiOS-16: a probabilistic, non-linear, absolute location catalog of the 1981-2011 Southern California seismicity
<p>This is the KaKiOS-16 earthquake catalog for southern California. We locate the southern California seismicity using the state-of-the-art probabilistic and nonlinear method NonLinLoc. We use only the P wavepicks to avoid introducing the velocity-model and picking-time errors of the S phase, which is harder to detect and thus less constrained. Using a subset of the best locatable earthquakes, we conduct a joint inversion using the VELEST software to obtain a minimum 1D velocity model and station corrections. We use the NonLinLoc method with this 1D velocity model and the inferred model uncertainties to obtain realistic location distributions for each event.<br> </p>
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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.