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ShareScore release 0.9.0
Dataset results
52 results for “probabilistic modeling”
A hybrid model for fast and probabilistic urban pluvial flood prediction - dataset
<p>A hybrid model for fast and probabilistic urban pluvial flood prediction - dataset </p> <p>This dataset presents the data used in the paper "A hybrid model for fast and probabilistic urban pluvial flood prediction". Data used to build, calibrate and evaluate the model and data that support the findings of the study are given for two cases, namely Gent and Antwerp. </p> <p>Under the folder of each case, one could find the following content:</p> <ul> <li>Model building: Data used to build the model topology. It includes geo-data of the 1D sewer network, boundary conditions of the catchment and spatial rainfall configurations </li> <li>Calibration: Rainfall data and flood records data used for calibration of the model.</li> <li>Results and evaluation: Rainfall events used for evaluation, flood probability predicted by the hybrid models and flood records used for comparing with the hybrid models. </li> </ul>
Probabilistic Model-Based Analysis to Improve Software Energy Efficiency - Presentation
<p>Presentation</p> <p>Paper: Probabilistic Model-Based Analysis to Improve Software Energy Efficiency</p> <p>SBES 2020 - Research Track</p>
Mechanistic Exploration and Kinetic Modeling through In-Silico Data Generation and Probabilistic Machine Learning Analysis
<p>This zip file includes the dataset 'two_reactions_022624.csv,' which is used for training and testing ML/DL models in the paper 'Mechanistic Exploration and Kinetic Modeling through In-Silico Data Generation and Probabilistic Machine Learning Analysis,' as well as trained models and some files used for training the model. When running the model downloaded from GitHub, copy and paste the files downloaded from here into the subfolder with the same name and path as the one downloaded from GitHub.</p>
Out of Control: Reducing Probabilistic Models by Control-Flow Reduction - Experiments
<p>Scripts and benchmark files for reproducing the experiments regarding the reduction of probabilistic models reported in our submission.</p>
A Study to Evaluate a Multiple Model Probabilistic Predictive Controller (MMPPC) for Closed Loop Insulin Delivery
ClinicalTrials.gov study NCT01492062. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A probabilistic generative model for quantification of DNA modifications enables analysis of demethylation pathways [T-cells]
GEO Series GSE68575. Mus musculus. 18 samples. Type: Methylation profiling by high throughput sequencing.
Continuous-trait probabilistic model for comparing multi-species functional genomic data
GEO Series GSE111733. Nomascus leucogenys; Pan troglodytes; Pongo pygmaeus; Chlorocebus aethiops; Homo sapiens. 5 samples. Type: Other.
Probabilistic modeling of personalized drug combinations from integrated chemical screen and molecular data in sarcoma
GEO Series GSE128766. Homo sapiens; Mus musculus. 3 samples. Type: Expression profiling by high throughput sequencing.
Probabilistic Risk Modeling for Disorder Post Traumatic Stress (PTSD) After the Attacks in Paris In November 2015
ClinicalTrials.gov study NCT02853513. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Probabilistic Model-Based Diagnosis for Electrical Power Systems
We present in this article a case study of the probabilistic approach to model-based diagnosis. Here, the diagnosed system is a real-world electrical power system, namely the Advanced Diagnostic and Prognostic Testbed (ADAPT) located at the NASA Ames Research Center. Our probabilistic approach is formally well-founded, and based on Bayesian networks and arithmetic circuits. We pay special attention to meeting two of the main challenges model development and real-time reasoning often associated with real-world application of model-based diagnosis technologies. To address the challenge of model development, we develop a systematic approach to representing electrical power systems as Bayesian networks, supported by an easy-touse specication language. To address the real-time reasoning challenge, we compile Bayesian networks into arithmetic circuits. Arithmetic circuit evaluation supports real-time diagnosis by being predictable and fast. In experiments with the ADAPT Bayesian network, which contains 503 discrete nodes and 579 edges and produces accurate results, the time taken to compute the most probable explanation using arithmetic circuits has a mean of 0.2625 milliseconds and a standard deviation of 0.2028 milliseconds. In comparative experiments, we found that while the variable elimination and join tree propagation algorithms also perform very well in the ADAPT setting, arithmetic circuit evaluation was an order of magnitude or more faster. **Reference:** O. J. Mengshoel, M. Chavira, K. Cascio, S. Poll, A. Darwiche, and S. Uckun. "Probabilistic Model-Based Diagnosis: An Electrical Power System Case Study”. Accepted to IEEE Transactions on Systems, Man, and Cybernetics, Part A, 2009.
Probabilistic modeling of personalized drug combinations from integrated chemical screen and molecular data in sarcoma
DNA and RNA were isolated from snap frozen primary tumor tissue (human and mouse), and DNA was isolated from matched normal tissue (region matched non-tumor tissue for human, tail section for mouse). A mouse leg was also injected with cardiotoxin to induce tissue damage was taken following sacrifice and was snap frozen, then processed for RNA isolation to serve as a mouse tissue-normal for RNA sequencing. All sequencing was performed at the OHSU MPSSR core.
Dataset of e-Poster titled "A Simplistic Unsaturated Zone Leaching Model-based Probabilistic Human Health Risk Assessment of Groundwater around the Ariyamangalam Dumping Site"
<p>This database contains an "excel sheet" and ".docx" files having input parameters and health risk metrics generated from a Simplistic Unsaturated Zone Leaching Model-based Probabilistic Human Health Risk Assessment Framework for Ariyamangalam dumping site.</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.