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144 results for “statistical model”
Statistical and Dynamic Model of Surface Morphology Evolution during Polishing in Additive Manufacturing
<p>This repository maintains data and code associated with our accepted paper in IISE Transactions titled "Statistical and Dynamical model of Surface Morphology Evolution during Polishing in Additive Manufacturing". To briefly summarize,</p> <p><strong>1. Polishing_stagewise_data.zip</strong> - Contains height values measured at 32 different locations on the 3D printed sample using an optical profilometer prior to polishing (Stage 0) and post every stage of polishing (Stages 1 to 6). Please refer to the following paper for experimentation details and process parameters: "<em>Jin, S., A. Iquebal, S. Bukkapatnam, A. Gaynor, and Y. Ding (2019, 10). A gaussian process model-guided surface polishing process in additive manufacturing. Journal of Manufacturing Science and Engineering 142, 1–17.</em>"</p> <p><strong>2. Initial_surface_generation.m</strong> - Script containing the Initial surface generation algorithm using the random circle packing algorithm. This file generates the surface asperity distribution and their graph connectivity of a 3D printed sample prior to polishing (Figure 4(b) in paper). One such realization is stored and compared with experimental data (Refer #3).</p> <p><strong>3. Stage0_fitted_data.mat</strong> - .mat file containing data pertaining to height measures of the 3D printed sample prior to polishing and generated initial surface (simulation) which is statistically similar to the actual data.</p> <p><strong>4. Parameter_fitting_Polishing.m</strong> - Script containing the model capturing polishing dynamics with network formation, evaluated at each stage of polishing. This file generates the Bearing Area Curves of the initial surface simulated after each stage of polishing and compares with experimental data (Figures 3, 5, 6, 7 and 8 in paper). (The script makes use of other functions defined in #5).</p> <p><strong>5. surface_roughness.m, graph_evolution.m, solve_for_d.m, KLDiv.m</strong> and <strong>Gen_hurst.m</strong> - Matlab scripts containing functions that are called within the main script (Parameter_fitting_Polishing.m)</p> <p><strong>6. Simulated_Annealing.zip</strong> - Zip file containing files related to Simulated Annealing Algorithm. Please read the <strong>README_Simulated_Annealing.txt</strong> for instructions to reproduce the optimized parameter solutions.</p> <p><strong>7. pub_fig.m</strong> - Script containing the formatting options for plots and figures.</p>
Data for: Nitrogen deposition in forests: Statistical modeling of total deposition from throughfall loads
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The western United States large forest-fire stochastic simulator (WULFFSS) 1.0: A monthly gridded forest-fire model using interpretable statistics
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Data from: Subgenome-informed statistical modeling of transcriptomes in 25 common wheat accessions reveals cis- and trans- regulation architectures
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Discretized U.S. drought data to support statistical modeling
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Statistical analysis code for output from a model used to simulate foot-and-mouth disease dynamics in the United Kingdom
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Chronogram or phylogram for ancestral state estimation? Model-fit statistics indicate the branch lengths underlying a binary character’s evolution: R scripts and simulated trees
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MOSTWAS models, TWAS summary statistics, and simulation results for Bhattacharya and Love, 2020
<p>This compressed folder contains three sub-folders that all pertain to models and results generated with Multi-Omic Strategies for Transcriptome-Wide Association Studies (MOSTWAS):</p> <ol> <li><em>MOSTWAS_Models</em> contains compressed folders for MOSTWAS models trains on TCGA breast cancer and ROS/MAP pre-frontal cortex multi-omic data.</li> <li><em>Simulations </em>contains Simulation_Results_MOSTWAS.xlsx that provides full simulation results outlined in Bhattacharya and Love, 2020 (paper accompanying MOSTWAS).</li> <li><em>TWAS_associations</em> contains four Excel files that provide MOSTWAS and local-only TWAS associations for breast cancer-specific survival (using iCOGs GWAS summary statistics), late-onset Alzheimer's disease risk (using IGAP GWAS summary statistics), and major depressive disorder risk (using PGC GWAS and UK Biobank GWAX summary statistics).</li> </ol>
SUPPLIMENTARY Dataset - A STUDY ON STATISTICAL MODELLING ON RECOVERY INFORMATICS OF COVID19
<p>Collection of data – a set of data of number of days with seven days interval vs number of recovered cases with effect from 7th September – 25th October ,2020 .Active Covid cases with respect to Sept,7 to Oct ,25 -2020 in India . Retrieved from -Sharpest weekly fall as cases down 16%, toll 19% | Man hits docs over Covid death, -Oct 26, 2020, ethealthworld.com. ( data report ) .The above dataset is converted in terms of number of Recovered Covid-cases with respect .Data Informatics Tool : Standard Statistical Software Curve Expert v.1.4 .</p>
Biological and environmental data for a study on transferability of statistical and machine learning models using North Sea Macrozoobenthos
<p>General</p> <p>Data documented here are not the product of our research but was scraped from various sources and processed - so no genuine reupload. This collection is a contribution to reproduceable reseach. All datasets are given in "RData" binary format</p> <p> </p> <p>Data description</p> <p>majornorthseabenthos </p> <p>This is macrozoobenthos data as data frame scraped from the GBIF repository (gbif.org). Species are Corbula gibba, Tellina fabula, Turritella communis, Euspira pulchella, Corystes cassive- launus, Upogebia deltaura, Lanice conchilega, Nephtys hombergii, Echinocardium cordatum, and Amphiura filiformis. data was postprocessed to have only single occurrence fon the approxinatel 1x1 km grid used for this study. Also, occurrences closer than 5 km close to shore were removed - including occurrences on land.</p> <p> </p> <p>Predictors</p> <p>A SpatialPixelsDataFrame in EPSG 4326 with five layers: Median grain size in micrometers, mud content in percent (both MUDAB database), water depth in meters above MSL (Weatherall et al, 2015), modelled average bottom shear stress from waves in N/sqrm (The Wamdi Group, 1988) and climatologival average winter bottom water temperature in deg. C (Stips et al, 2004).</p> <p> </p> <p> </p> <p>References</p> <p>Stips A, Bolding K, Pohlmann T, Burchard H (2004) Simulating the temporal and spatial dy- namics of the North Sea using the new model GETM (general estuarine transport model). Ocean Dynamics 54(2):266–283</p> <p>The Wamdi Group (1988) The WAM model-a third generation ocean wave prediction model. Journal of Physical Oceanography 18(12):1775–1810</p> <p>Weatherall P, Marks K, Jakobsson M, Schmitt T, Tani S, Arndt JE, Rovere M, Chayes D, Ferrini V, Wigley R (2015) A new digital bathymetric model of the world’s oceans. Earth and Space Science 2(8):331–345</p> <p> </p>
Data from: Statistical stream temperature modelling with SSN and INLA: an introduction for conservation practitioners
<p>Statistical stream temperature models can predict the fine-scale spatial distribution of water temperatures and guide species recovery and habitat restoration efforts. However, stream temperature modelling is complicated by spatial autocorrelation arising from non-independence data collected within dendritic networks. We used data from miniature sensors deployed in Canadian Rocky Mountain streams to develop and validate two statistical stream temperature modelling techniques that account for spatial autocorrelation. The first was based on spatial steam network models (SSNs) specifically developed to account for spatial autocorrelation in dendritic stream networks. The second used integrated nested Laplace approximation (INLA) that accounts for spatial autocorrelation but was not designed to address anisotropic stream network data. We evaluated the best-fitted SSN and INLA models using leave-one-out cross validation from the data collected along the stream network. Both modelling techniques had similar RMSE and MAE (near 1<sup>o</sup>C) and r<sup>2</sup> (> 0.6) values, and proved flexible with respect to implementation; however, the SSN models required more preprocessing steps before incorporating spatially correlated random errors. We provide practical advice and open-access data and r-script to help non-experts develop statistical stream temperature models of their own.</p>
Raw data and R code for statistical analyses from: Sensory trap leads to reliable communication without a shift in nonsexual responses to the model cue
<p>The sensory trap model of signal evolution suggests that males manipulate females into mating using traits that mimic cues used in a nonsexual context. Despite much empirical support for sensory traps, little is known about how females evolve in response to these deceptive signals. Female sea lamprey (<em>Petromyzon marinus</em>) evolved to discriminate a male sex pheromone from the larval odor it mimics and orient only towards males during mate search. Larvae and males release the attractant 3-keto petromyzonol sulfate (3kPZS), but spawning females avoid larval odor using the pheromone antagonist, petromyzonol sulfate (PZS), which larvae but not males, release at higher rates than 3kPZS. We tested the hypothesis that migratory females also discriminate between larval odor and the male pheromone and orient only to larval odor during anadromous migration, when they navigate within spawning streams using larval odor before they begin mate search. In-stream behavioral assays revealed that, unlike spawning females, migratory females do not discriminate between mixtures of 3kPZS and PZS applied at ratios typical of larval versus male odorants. Our results indicate females discriminate between the sexual and nonsexual sources of 3kPZS during but not outside of mating and show sensory traps can lead to reliable sexual communication without females shifting their responses in the original context.</p>
Data and code for: Fixed effects or random effects in statistical models? fewer than five levels of a grouping factor
<p>This is code (and simulated data from that code) to assess how sample size and the numbers of levels of random effects influence parameter estimates of fixed effects in linear mixed-effects models. </p>
Artifact of Infrastructure and Tools in the Context of Deep Statistical Model Checking
<p>This artifact contains all infrastructure, tools, and additional material used in the context of Deep Statistical Model Checking (DSMC). This includes the DSMC implementation in modes of the Modest Toolset, the infrastructure used to perform case studies on DSMC, the environment and scripts in which the scalability study on DSMC has been performed, the integration of DSMC in MoGym, and also the TraceVis tool. The content has partially been covered in artifacts accompanying individual papers on DSMC, but is combined here based on a single infrastructure. Each folder contains its own readme describing how to use the scripts, tools, and infrastructure, often also with concrete instructions on how to execute exemplary experiments.</p>
Artifact for the Scalability Study of the STTT Paper "Analyzing Neural Network Behavior through Deep Statistical Model Checking"
<p>Scripts and infrastructure for the scalability study on DSMC published in the STTT paper "Analyzing Neural Network Behavior through Deep Statistical Model Checking".</p>
Figure 5 in Statistical analysis on the cnidome of genus Hydra using Generalized Linear Models
Figure 5. Relative abundances of each type of cnidocyst for each species. (A) stenotele, (B) desmoneme, (C) atrichous isorhiza and (D) holotrichous isorhiza.
Figure 1 in Statistical analysis on the cnidome of genus Hydra using Generalized Linear Models
Figure 1. Cnidome of Hydra viridissima. (A) stenotele, (B) desmoneme, (C) atrichous isorhiza and (D) holotrichous isorhiza. Scale bar: 3 μm.
Figure 4 in Statistical analysis on the cnidome of genus Hydra using Generalized Linear Models
Figure 4. Different morphotypes of holotrichous isorhiza. (A) Hydra viridissima, (B) Hydra vulgaris pedunculata, C and (D) Hydra vulgaris. Scale bar: 2.45 μm.
Figure 8 in Statistical analysis on the cnidome of genus Hydra using Generalized Linear Models
Figure 8. GLM adjustment graphs used for comparison between species. (A) scatter plot, (B) Q-Q Plots.
Figure 3 in Statistical analysis on the cnidome of genus Hydra using Generalized Linear Models
Figure 3. Cnidome of Hydra vulgaris pedunculata. (A) stenotele, (B) desmoneme, (C) atrichous isorhiza and (D) holotrichous isorhiza. Scale bar: 2.7 μm.
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.