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ShareScore release 0.9.0
Dataset results
9 results for “weighted regression”
Synthesized Dataset of Length-Weight Regression Coefficients for Delta Fish
This dataset is a compilation of length-weight regression coefficients for fish species commonly found in the freshwater tidal habitats of the San Francisco Estuary. This effort was born out of the Delta Smelt Resiliency Strategy Aquatic Weed Control Action study, which, in order to calculate fish biomass, needed to calculate individual fish weights from their measured lengths. The Aquatic Weed Control study was supported by Interagency Ecological Program through the Endangered Species Act and is included in the Interagency Ecological Program 2017-2019 workplan. Weight is estimated from length using the exponential function W=a\ L^b. These can be calculated using the linear regression of the log-transformed equation (log(W)=log(a)+b log(L)). This dataset provides the species-specific a and b parameters. Associated publication(s) and relevant metadata information are included. Data was obtained either via database (fishbase.us) or peer-reviewed scientific papers.
Data from: Improving performance of hurdle models using rare-event weighted logistic regression: An application to maternal mortality data
<p>In this paper, the performance of hurdle models in rare events data is improved by modifying their binary component. The rare-event weighted logistic regression model is adopted in place of logistic regression to deal with class imbalance due to rare events. Poisson Hurdle Rare Event Weighted Logistic Regression (REWLR) and Negative Binomial Hurdle (NBH) REWLR are developed as two-part models which use the REWLR model to estimate the probability of a positive count and a Poisson or NB zero-truncated count model to estimate non-zero counts. The obtained results are numerically validated and then discussed from both the mathematical and the maternal mortality perspective. Numerical simulations are also presented to give a more complete representation of the model dynamics. Results obtained suggest that NB Hurdle REWLR is the best-performing model for zero-inflated count data due to rare events.</p>
Data from: Improving performance of hurdle models using rare-event weighted logistic regression: An application to maternal mortality data
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Dataset Used For Research On: Exploring Geographically Weighted Regression In Water Demand Forecasting For A Rapidly Developing City
<div> <p><span><span>Dataset Used For Research On: Exploring Geographically Weighted Regression In Water Demand Forecasting For A Rapidly Developing City</span></span></p> </div> <p> </p>
Intravoxel incoherent motion model of diffusion weighted imaging and diffusion kurtosis imaging in differentiating of local colorectal cancer recurrence from scar/fibrosis tissue by multivariate logistic regression analysis
<p>We uploaded mean of diffusion coefficient (MD) and mean of diffusional Kurtosis values of 56 patients related to the manuscript: Fusco, Roberta, Vincenza Granata, Mario Sansone, Robert Grimm, Paolo Delrio, Daniela Rega, Fabiana Tatangelo, Antonio Avallone, Nicola Raiano, Giuseppe Totaro, Vincenzo Cerciello, Biagio Pecori, and Antonella Petrillo. 2020. "Intravoxel Incoherent Motion Model of Diffusion Weighted Imaging and Diffusion Kurtosis Imaging in Differentiating of Local Colorectal Cancer Recurrence from Scar/Fibrosis Tissue by Multivariate Logistic Regression Analysis" Applied Sciences 10, no. 23: 8609. https://doi.org/10.3390/app10238609</p>
Spatiotemporal Weighted Regression (Video)
<p>This is the third session video recording of the Goldschmidt 2020 Virtual Workshop: Earth Science meets Data Science - Services & Systems, Policies & Procedures, Tools & Techniques for Geochemistry. Moderated by Shaunna Morrison (Carnegie Earth and Planets Laboratory).</p> <p>Github repository: <a href="https://github.com/quexiang/STWR">https://github.com/quexiang/STWR</a></p>
A Gaussian Process Regression weights file for the Zundel cation
<p>Trained weight files for a kernel model of the potential energy of the Zundel cation, to be used in an example of the <a href="http://ipi-code.org">i-PI</a> interface of the <a href="https://github.com/lab-cosmo/librascal">librascal</a> ML library</p>
Geographically Weighted Regression Modeling of a Nighttime Urban Heat Island in Dar es Salaam Metropolitan Areas
<p>Urban Heat Islands (UHI) is the urban microclimate with higher air temperature than surrounding areas. It is caused by both man-made and natural factors which vary geographically based on weather periods. To have a sustainable future in the environment, there is a need to regulate influence levels of various causative factors that generate UHI. Geographically Weighted Regression (GWR) model is among the spatial regression models that define the non-stationarity of variables. It generates a new equation on each sampled data unlikely global models like Ordinary Least Square (OLS). The study used GWR model to determine the influencing levels of three independent factors named Indexed-based Built-up Index (IBI), albedo and wind speed. Datasets were retrieved from MODIS satellite during the dry period of July from 2000 to 2019. IBI and Albedo were observed to have a strong negative influence with the maximum value of -0.045 and -0.053 respectively although, we expected to observe a positive influence on IBI since buildings emit absorbed energy during the night. Wind speed has a positive influence with the maximum value of 0.028 leading to the shift of heatwaves hence being termed as the secondary driving factor while IBI and Albedo as the primary driving factors. Wind speed is the highest driving factor that shifts emitted energies to other areas. We encourage an innovation in technology that produce higher albedo construction materials. We should improve environmental policies by introducing green cities through horizontal and vertical forests which might decrease the emitted energy into the atmosphere.</p>
Geographically Weighted Regression Modeling of a Daytime Urban Heat Island in Dar es Salaam Metropolitan Areas.
<p>Urban heat island is the phenomenon of having higher temperatures in urban areas compared to surrounding areas. It is caused by the replacement of natural vegetation with construction materials. Geographically Weighted Regression Model (GWR) determines non-stationarity among variables by generating a new equation for each sample size. Moderate Resolution Imaging Spectroradiometer (MODIS) products such as MOD11A1, MCD43A1, MOD09A1 and MOD13A1 are used to acquire Land Surface Temperature (LST), Albedo, Indexed-Based Built-Up Index (IBI) and Enhanced Vegetation Index (EVI) respectively. The highest and lowest coverage of Urban Heat Island of 69% and 43% were observed in 2000 and 2005 respectively. IBI is the leading causative factor by having an influence of 0.023 followed by Albedo, wind speed and EVI with an average of 0.019, 0.016 and -0.015 respectively. We recommend innovation in producing higher albedo construction materials and introducing green city policies.</p>
ScienceDex guides
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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.