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209 results for “probabilistic”
Probabilistic Machine Learning Estimation of Ocean Mixed Layer Depth from Dense Satellite and Sparse In-Situ Observations: Preprocessed Satellite and In-situ observation datasets
<p>This record includes all of the prepared data used in the manuscript, "Probabilistic Machine Learning Estimation of Ocean Mixed Layer Depth from Dense Satellite and Sparse In-Situ Observations" (citation information forthcoming). As a part of this manuscript, we analyzed the ability for machine learning models to extract sea surface information (from salinity, temperature, sea height anomaly) to predict mixed layer depth. In this manuscript there are two experimental datasets: (1) info derived from CESM POP2 ocean model dataset (1989-1998), and (2) info derived from a combination of satellite sources and MLD from Argo profiles. More details below. </p> <p>All of these data files are preprocessed and organized to be used with the ml-ocean-bl github code found at https://github.com/NCAR/ml-ocean-bl/mloceanbl/.</p> <ul> <li><strong>CESM POP2 Ocean model dataset</strong></li> </ul> <p>Preprocessed sea surface salinity (SSS), temperature (SST), sea surface height anomalies (SSH), and ocean mixed layer depth (MLD, or HMXL) derived from the CESM POP2 Ocean model. Specifically, CESM POP2 model in a hindcast forced by JRA55do atmospheric reanalysis from 1958 to present and initialized with an oceanic climatology as in e.g. <a href="https://journals.ametsoc.org/view/journals/phoc/aop/JPO-D-20-0217.1/JPO-D-20-0217.1.xml">Deppenmeier et al. (2021)</a>. The model outputs include the ocean mixed layer depth (MLD), sea surface salinity (SSS), sea surface temperature (SST), and sea height anomaly (SSH) at a temporal frequency of 5-days and an approximate latitude and longitude resolution of 0.1 degrees.</p> <p>Relevant files:</p> <ol> <li>full_EPO.nc, full_SIO.nc <ul> <li>NetCDF4 containing SSS, SST, SSH, MLD for the equatorial Pacific Ocean (EPO) and southern Indian Ocean (SIO) (see manuscript for details). Data is regridded onto a 1/2 degree lat/lon 5 day grid to correspond with data used for Argo datasets (see below).</li> </ul> </li> <li>clim_EPO.nc, clim_SIO.nc, clim_std_EPO.nc, std_clim_EPO.nc, std_clim_SIO.nc <ul> <li>NetCDF4 containing mean and standard deviation climatologies of SSS, SST, SSH, and MLD for EPO and SIO.</li> </ul> </li> <li>std_anomalies_EPO.nc, std_anomalies_SIO.nc <ul> <li>NetCDF4 containing SSS, SST, SSH, and MLD standardized anomalies for EPO and SIO. This is the dataset directly used for training in aforementioned manuscript. Use with ml-ocean-bl/ml-ocean-test/data. </li> </ul> </li> </ol> <ul> <li><strong>Satellite and Argo datasets</strong></li> </ul> <p>Preprocessed satellite sea surface salinity (SSS), temperature (SST), and sea surface height anomalies (SSH) and Argo-based mixed layer depth (MLD) profiles. Original data can be found at:</p> <p>(SST): Remote Sensing Systems. 2017. MW optimum interpolated SST data set. Ver. 5.0. PO.DAAC, CA, USA. Further information available at at <a href="https://doi.org/10.5067/GHMWO-4FR05">https://doi.org/10.5067/GHMWO-4FR05</a>. Data can be accessed at https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L4/GLOB/REMSS/mw_OI/v5.0/.</p> <p>(SSS): Oleg Melnichenko. 2018. Aquarius L4 Optimally Interpolated Sea Surface Salinity. Ver. 5.0. PO.DAAC, CA, USA. Further information at <a href="https://doi.org/10.5067/AQR50-4U7CS">https://doi.org/10.5067/AQR50-4U7CS</a>. Data can be accessed at https://podaac-tools.jpl.nasa.gov/drive/files/SalinityDensity/aquarius/L4/IPRC/v5/7day. </p> <p>(SSH): Zlotnicki, Victor; Qu, Zheng; Willis, Joshua. 2019. SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL1609. Ver. 1812. PO.DAAC, CA, USA. Information available at <a href="https://doi.org/10.5067/SLREF-CDRV2">https://doi.org/10.5067/SLREF-CDRV2</a>. Data can be accessed at https://podaac-tools.jpl.nasa.gov/drive/files/SeaSurfaceTopography/merged_alt/L4/cdr_grid</p> <p>(MLD) Argo-based ocean surface mixed layer depths using the buoyancy gradient definition of Whitt Nicholson and Carranza (2019) processed dataset available at https://doi.org/10.5281/zenodo.4291175.</p> <p>Relevant files:</p> <ol> <li>https://github.com/NCAR/ml-ocean-bl/mloceanbl/preprocess_mld.py and .../preprocess_sss_sst_ssh.py. <ul> <li>Preprocessing code</li> </ul> </li> <li>sss_sst_ssh_anomalies.nc. <ul> <li>Regridded and resampled SSS, SST, SSH onto a 1/2 degree lat/lon 7day grid. Contains preprocessed seasonal data along with anomalies.</li> </ul> </li> <li> mldb_climatology_climatologystd_binned.nc <ul> <li>Smoothed argo-based mixed layer depths are used to calculate climatologies and standardized climatologies. 4 degree lat/lon gridded climatologies.</li> </ul> </li> <li>mldb_full_anomalies_stdanomalies_climatology_stdclimatology.nc <ul> <li>Contains the Argo profile-derived MLD, anomalies, standard anomalies, climatologies, and standardized climatologies with corresponding argo locations, times, and corresponding weeks. </li> </ul> </li> <li>equatorial_pacific_model_oi_re.nc, southern_indian_model_oi_re.nc <ul> <li>Model outputs for the Equatorial Pacific Ocean and Southern Indian Ocean. These gridded files contain the model outputs (vlcnn, vlcnn variance, OI, OI variance, reanalysis, and reanalysis variance - see manuscript for nomenclature details) at each of the 200 weeks available. It should be noted that, in the equatorial Pacific Ocean, the lat/lon location of (-138.75, -9.75) is masked during the training and filled with a NaN in the .nc files. </li> </ul> </li> </ol> <p> </p> <p> </p> <p>Contact D. Foster with any questions.</p> <p> </p>
Dataset: A multi-scale probabilistic atlas of the human connectome
<p>This repository complements the paper submitted to <strong>Scientific Data</strong> named <em>A multi-scale probabilistic atlas of the human connectome</em>.</p> <p><strong>Introduction</strong></p> <p>The assessment of the networks underlying brain processes is key to understand brain-related disorders. However, groundbreaking connectomics research is highly demanding in terms of equipment and expertise. The aim of this work is to create a multiscale probabilistic atlas of the human white matter (WM) to carry out network analyses in the context of clinical research, particularly when diffusion data is not directly available.</p> <p><strong>Methods</strong></p> <p>Sixty six subjects from the Human Connectome Project (HCP) database (29 males, age: 22-36 years old) were used to build the WM probabilistic atlas. MRI acquisition protocols are described in (Van Essen et al, 2012). Besides T1-, T2- and diffusion-weighted (DW) images, the HCP database provides the FreeSurfer outputs (Glasser et al, 2016) namely cortical surfaces (pial and white), subcortical segmentation and a cortical surface parcellation containing 34 structures for each hemisphere (Desikan et al, 2006).</p> <p>For each subject, the DWIs were employed to segment each thalamus in seven nuclei (Battistella et al, 2016) and to estimate the WM streamlines distribution. The constrained spherical deconvolution (Tournier et al, 2007) algorithm was used to compute the intravoxel fiber distribution functions for the anatomically-constrained particle-filter tractography approach (Descoteaux et al, 2009) to compute the WM streamlines.</p> <p>Subcortical, thalamic and multiscale cortical (Cammoun et al, 2012) parcellations were gathered to obtain four individual gray matter (GM) parcellations. Finally, for each scale, individual fiber bundles, were created by selecting the streamlines connecting each pair of GM regions (Figure 1a).</p> <p><em>Atlas construction</em></p> <p>The T1 and T2 images were non-linearly warped to their corresponding MNI templates (Evans et al, 2012, mni_icbm152_tal_nlin_asym_09c version) using ANTs (Avants et al, 2010). The resulting spatial transformations were applied to warp the individual fiber bundles to stereotactic space and the normalized tract density images (TDIs) were created. In these images, each voxel contains the number of streamlines passing through it. Finally, the spatial probability map for each bundle was obtained by binarizing and averaging the bundle TDIs across the subjects (Figure. 1b).</p> <p>Different views of the developed probabilistic multi-scale connectome atlas are shown in Figure 2.</p>
Supplemental Material to Journal Article "Probabilistic Approach for the Fatigue Strength Prediction of Polymers"
<p>This set supplements the figure and table data to the article "Probabilistic Approach for the Fatigue Strength Prediction of Polymers", DOI: <a href="https://doi.org/10.2514/1.J060444">https://doi.org/10.2514/1.J060444</a></p> <p>The Python code to reproduce the results shown in the article is released as v1.0 of the MAMO toolbox for material models DOI:</p> <p>The latest code development can be cloned at <a href="https://github.com/mrosemeier/mamo">https://github.com/mrosemeier/mamo</a>.</p> <p> </p>
Data for "Development of a joint probabilistic rainfall-runoff model for high-to-extreme flow simulation and projection in a changing climate"
<p>Data for "<strong>Development of a joint probabilistic rainfall-runoff model for high-to-extreme flow simulation and projection in a changing climate"</strong></p>
datset of "Networks and genes modulated by posterior hypothalamic stimulation in patients with aggressive behaviours: Analysis of probabilistic mapping, normative connectomics, and atlas-derived transcriptomics of the largest international multi-centre dataset"
<p>This dataset accompanies the manuscript:<br> "Networks and genes modulated by posterior hypothalamic stimulation in patients with aggressive behaviours: Analysis of probabilistic mapping, normative connectomics, and atlas-derived transcriptomics of the largest international multi-centre dataset."<br> DOI: (https://doi.org/10.1101/2022.10.29.22281666)</p> <p>by</p> <p>Flavia Venetucci Gouveia1,2,3*†,Jürgen Germann4,5†, Gavin JB Elias4,5, Alexandre Boutet4,6, Aaron Loh4,5, Adriana Lucia Lopez Rios7,8, Cristina V Torres Diaz9, William Omar Contreras Lopez10,11, Raquel CR Martinez3,12, Erich T Fonoff13, Juan C Benedetti-Isaac14, Peter Giacobbe 2,15,16, Pablo M Arango Pava17, Han Yan5,18, George M Ibrahim5, 18,19,20, Nir Lipsman2,5,15, Andres M Lozano4,5, Clement Hamani2,5,15*</p> <p>1. Neuroscience and Mental Health, Hospital for Sick Children Research Institute; Toronto, Canada <br> 2. Sunnybrook Research Institute; Toronto, Canada<br> 3. Division of Neuroscience, Sírio-Libanês Hospital; São Paulo, Brazil<br> 4. Division of Neurosurgery, Department of Surgery, University Health Network, Toronto, Canada<br> 5. Division of Neurosurgery, Department of Surgery, University of Toronto; Toronto, Canada<br> 6. Joint Department of Medical Imaging, University of Toronto; Toronto, Canada<br> 7. Department of Functional and Stereotactic Neurosurgery, University Hospital San Vicente Fundación,<br> Medellín, Colombia<br> 8. Department of Functional and Stereotactic Neurosurgery, San Vicente Fundación, Rionegro, Colombia<br> 9. Department of Neurosurgery, University Hospital La Princesa; Madrid, Spain<br> 10. Nemod Research Group, Universidad Autónoma de Bucaramanga; Bucaramanga, Colombia<br> 11. Division of Functional Neurosurgery, Department of Neurosurgery, FOSCAL Clinic; Bucaramanga,<br> Colombia<br> 12. LIM 23, Institute of Psychiatry, School of Medicine, University of São Paulo; São Paulo, Brazil<br> 13. Department of Neurology, Integrated Clinic of Neuroscience, School of Medicine, University of São Paulo;<br> São Paulo, Brazil.<br> 14. Stereotactic and Functional Neurosurgery Division of the International Misericordia Clinic; Barranquilla,<br> Colombia<br> 15. Harquail Centre for Neuromodulation, Sunnybrook Health Sciences Centre; Toronto, Canada<br> 16. Department of Psychiatry, University of Toronto; Toronto, Canada<br> 17. Servicio de Neuocirugia Funcional y Esterotaxia, Clinica Comuneros Bucaramanga, Clinica Desa y Clinica<br> Dime Neurocardiovascular de Cali; Clinica Nueva del Lago, Bogota, Colombia.<br> 18. Division of Neurosurgery, The Hospital for Sick Children; Toronto, Canada<br> 19. Institute of Biomedical Engineering, University of Toronto; Toronto, Canada<br> 20. Institute of Medical Science, University of Toronto; Toronto, Canada<br> † Flavia Venetucci Gouveia and Jürgen Germann contributed equally to this work and share first authorship.</p> <p>* Corresponding Author: Dr. Flavia Venetucci Gouveia. Neuroscience and Mental Health, Hospital for Sick Children Research Institute. 686, Bay Street, Toronto, ON, M5G 0A4, Canada. flavia.venetuccigouveia@sickkids.ca<br> * Corresponding Author: Dr. Clement Hamani. Sunnybrook Research Institute. 2075 Bayview Ave, S126. Toronto, ON, M4N3M5, Canada. clement.hamani@sunnybrook.ca</p> <p>It contains a zip folder ("estimated_binary_Volume_of_Tissue_Activated.zip") with one file (in nii.gz format) per patient estimating the Volume of Activated Tissue for that patient (the estimated 'reach' of the active DBS stimulation) and a demographics file.<br> The case numbers are identical to Table 1 in the manuscript.</p>
Data for: A new paradigm for medium-range severe weather forecasts: Probabilistic random forest-based predictions
<p>Historical observations of severe weather and simulated severe weather environments (i.e., features) from the Global Ensemble Forecast System v12 (GEFSv12) Reforecast Dataset (GEFS/R) are used in conjunction to train and test random forest (RF) machine learning (ML) models to probabilistically forecast severe weather out to days 4–8. RFs are trained with ~9 years of the GEFS/R and severe weather reports to establish statistical relationships. Feature engineering is briefly explored to examine alternative methods for gathering features around observed events, including simplifying features using spatial averaging and increasing the GEFS/R ensemble size with time-lagging. Validated RF models are tested with ~1.5 years of real-time forecast output from the operational GEFSv12 ensemble and are evaluated alongside expert human-generated outlooks from the Storm Prediction Center (SPC). Both RF-based forecasts and SPC outlooks are skillful with respect to climatology at days 4 and 5 with diminishing skill thereafter. The RF-based forecasts exhibit tendencies to slightly underforecast severe weather events, but they tend to be well-calibrated at lower probability thresholds. Spatially averaging predictors during RF training allows for prior-day thermodynamic and kinematic environments to generate skillful forecasts, while time-lagging acts to expand the forecast areas, increasing resolution but decreasing overall skill. The results highlight the utility of ML-generated products to aid SPC forecast operations into the medium range.</p>
European migration scenarios with probabilistic uncertainty assessment – Data Description
<p>This open data deposit contains the data and code accompanying used in the report: Bijak (2023): European migration scenarios with probabilistic uncertainty assessment, QuantMig Project Deliverable D9.4. The cover note should be read in conjunction with the report, available via www.quantmig.eu, and with the individual readme files in the data folders that can be found within this Zenodo repository (DOI: 10.5281/zenodo.7954150).</p>
On the importance of additional behavioral observation in behavioral psychopharmacology research: a case study on agomelatine's effects on feedback sensitivity in probabilistic reversal learning test in rats (data&code)
<p>Dataset to the manuscript entitled: On the importance of additional behavioral observation in behavioral psychopharmacology research: a case study on agomelatine's effects on feedback sensitivity in probabilistic reversal learning test in rats. Code in R used for analysis data from probabilistic reversal learning (PRL) test performed in operant conditioning boxes. Script analyzes data files from MED-PC IV software.</p>
Test cases for the Geospatial Probabilistic Estimation Package (GPEP)
<p>The dataset contains test cases for the Geospatial Probabilistic Estimation Package (GPEP, https://github.com/NCAR/GPEP) and relevant publications. The two current test cases are based on prior implementations for GMET version 2.0 (https://github.com/NCAR/GMET and https://zenodo.org/records/5498408) for applications, and include:</p><ul><li>a GMET version 2.0 test case of daily meteorological forcing generation at 1/16th degree resolution for February 2017 for a region of the US Southwest encompassing California, US (Bunn et al, 2022).</li><li>a test case for daily meteorological forcing generation at 0.01 degree resolution from 1970-2022 for a smaller region of the US Southwest encompassing the upper Colorado River basin and Colorado Front Range.</li></ul><p>The associated meteorological station forcings and gridded parameter inputs were developed for water resources research projects at NCAR (PI A. Wood) sponsored by the United States Army Corps of Engineers Climate Preparedness and Resilience Program and the United States Bureau of Reclamation Science and Technology Program. </p><p>References:</p><p>Bunn, PTW, AW Wood, AJ Newman, H Chang, CL Castro, MP Clark and JR Arnold, 2022, Improving station-based ensemble surface meteorological analyses using numerical weather prediction: A case study of the Oroville Dam crisis precipitation event. <i>J. Hydromet.</i> 23(7), 1155-1169. https://doi-org.cuucar.idm.oclc.org/10.1175/JHM-D-21-0193.1<br> </p>
Single unit electrophysiology of optogenetically identified dopamine neurons in classical conditioning with probabilistic outcome of reward and punishment
<p>We collected single-unit data (.mat file) from optogenetically identified dopamine neurons in the lateral VTA in mice. These dopamine neurons were recorded while animals performed 4 different variation of classical conditioning tasks with different ratio of probabilistic outcomes of water reward and aversive air puff. In one task, one cue was associated with both water and puff in a probabilistic manner.</p>
Data from: Estimating genome-wide phylogenies using probabilistic topic modeling
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Surrogate flash flooding: Probabilistic excessive rainfall predictions from the High Resolution Ensemble Forecast (HREF) system
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Single unit electrophysiology of optogenetically identified dopamine neurons in classical conditioning with probabilistic outcome of reward and punishment
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Data for: A new paradigm for medium-range severe weather forecasts: Probabilistic random forest-based predictions
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Cosmos: A data-driven probabilistic time series simulator for chemical plumes across spatial scales
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Data for: Cholinergic activity during probabilistic Pavlovian conditioning in mice
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Probabilistic inference of the genetic architecture of functional enrichment of complex traits
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Extended-variable probabilistic computing with probabilistic d-dimensional bits
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The Probabilistic Model Checker Storm: Evaluation Results and Replication Package
<p>This package contains logdata and replication scripts for the evaluation of the model checker Storm (www.stormchecker.org) as part of the paper:<br> "The Probabilistic Model Checker Storm" by Christian Hensel, Sebastian Junges, Joost-Pieter Katoen, Tim Quatmann, and Matthias Volk</p>
Data files for Corron et al.'s article, "How low can we go? A skeletal maturity threshold for probabilistic visual sex estimation from immature human os coxae".
<p>Data files for Corron et al.'s article, "How low can we go? A skeletal maturity threshold for probabilistic visual sex estimation from immature human <em>os coxae</em>", published in <em>Forensic Science International</em>. doi: <a href="https://doi.org/10.1016/j.forsciint.2021.110854">10.1016/j.forsciint.2021.110854</a>.</p>
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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.