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305 results for “characterization model”
Raw data for "Development and characterization of a non-human primate model of disseminated synucleinopathy"
<p><span>In this study, the performance and biodistribution of the retrogradely-spreading AAV9-SynA53T vector was evaluated in the NHP brain. Conducted intraparenchymal deliveries of viral suspensions in the left putamen gave rise to a disseminated synucleinopathy in a circuit-specific basis.</span></p>
Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer
<p>Dataset of the paper "Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer" published in Remote Sensing [1].</p> <p>[1] Brugger P, Fuertes FC, Vahidzadeh M, Markfort CD, Porté-Agel F. Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer. <em>Remote Sensing</em>. 2019; 11(19):2247. https://doi.org/10.3390/rs11192247.</p>
Model Outputs for Characterizing the Atmospheric Mn Cycle and Its Impact on Terrestrial Biogeochemistry
<p>Includes the model output files used in calculations regarding the research article "Characterizing the Atmospheric Mn Cycle and Its Impact on Terrestrial Biogeochemistry". Output files contains: 1) surface Mn concentrations, annual; 2) Mn deposition, monthly; 3) soil Mn map; 4) soil Mn "pseudo" turnover time.</p>
Statistical characterization of Andalusian wave climate for several combinations of Global Climate Models and Regional Climate Models and periods 2026 - 2045 and 2081 - 2100.
<p>The following text is an extract of the extended abstract entitled "<strong>Parametric Characterization of Wave Climate along the Andalusian Coast for Non-Stationary Stochastic Simulation</strong>" whose authors are Manuel Cobos, Pedro Magaña, Pedro Otiñar and Asunción Baquerizo, and that was included in proceedings of <em>39th IAHR World Congress</em> where this dataset is included.</p> <p><em>Processed data comes from PIMA Adapta Costas project (Ramírez et al., 2019), in particular, from projections of maritime climate for 2026-2045 and 2081-2100. Sea climate contains, among other information, time series of the significant wave height (H<sub>s</sub>) obtained for several combinations of GCM-RCM projections of EUR-11 for the RCP 8.5. GCM-RCM combinations ACCE, CMCC, CNRM, GFDL, HADG, IPSL, MIRO with a 0.1 degrees grid were used for the Atlantic facade while CNRM, HADG, IPSL, MIRO, MEDC, MPIE, ESM2, EART models with 1/11 degrees were used for the Mediterranean one. A total of 210 locations were analyzed, 54 at the Atlantic facade and 156 at the Mediterranean one (Figure 1). The data was bias adjusted using the Empirical Quantile Mapping (Déqué et al., 2007; Michelangeli et al., 2009). Information of the significant wave height and the dependence between the values at a given time with previous values with a VAR(q) model is already available. </em></p> <p><em>At each location, the methodology of Lira-Loarca et al. (2021) was applied, using the software described in Cobos et al. (2022a). More precisely, for every GCM-RCM (hereinafter, model n for n = 1, .., N where N = 7 for Atlantic data and N = 8 for the Mediterranean data), a non-stationary marginal distribution of H<sub>s</sub>, , assuming that the year was the largest periodicity of the climate, was fitted to data using a lognormal model for the central part and two generalized Pareto distribution for the lower and upper tails, as in Solari and Losada (2011). The non- stationarity is considered by assuming a decomposition of the parameters of the distribution and of the percentiles of the common end points of the interval into a trigonometric truncated expansion.</em></p> <p><em>In addition, the coefficients of the matrix, C<sub>n</sub>, of a VAR(q) model with q up to 92 hours were estimated. The ensemble multi-model characteristics of the data were obtained from the compound distributions and the weighted averaged matrix coefficients. </em></p> <p><em>Soon, the results of the peak period (T<sub>p</sub>) and mean incoming wave direction (ϑ<sub>m</sub>) and the coefficients of the multivariate VAR model will also be included.</em></p> <p> </p> <p> </p>
ConFiRMa dataset_01: simulation of CRM characterization tests with the OOFEM code (detailed level modelling)
<p>The Dataset collects the input files developed for the simulation of characterization tests performed on Composite Reinforced Mortar samples with the free open-source code OOFEM (detailed level modelling). The description of the numerical models and the analysis and comparison of the results can be found in paper "Characterization of Textile Reinforced Mortar: state of the art and detailed modelling with a free open source finite element code" (<a href="https://doi.org/10.1061/(ASCE)CC.1943-5614.0001240">https://doi.org/10.1061/(ASCE)CC.1943-5614.0001240</a>).</p> <p>OOFEM Version 2.5 (https://doi.org/10.5281/zenodo.4339630) was used for running the analyzes.</p> <p>ReadMe file provide a description of the different input files.</p> <p> </p>
Dataset for a physical model characterizing visualization of the cervix during pelvic exams
<p>This dataset accompanies our manuscript draft: <em>A physical model for improving visualization of the cervix during pelvic exams: A steppingstone towards reducing disparities in women's health</em>.</p> <p><strong>Manuscript Draft Abstract</strong></p> <p>Pelvic exams are frequently complicated by collapse of the lateral vaginal walls, obstructing the physician’s view of the cervix. A commonly utilized method in the clinical setting, passed down from mentors to trainees, is repurposing either a condom or a glove as a sheath placed over the speculum blades to retract the lateral vaginal walls during the exam. Despite their regular use in clinical practice, little research has been done comparing the relative efficacy of these methods. Better visualization of the cervix can benefit patients by decreasing examination-related discomfort, aiding in cancer screening, and preventing the need to move the examination to the operating room under general anesthesia.</p> <p>This study presents a physical model that simulates vaginal pressure being exerted around a speculum. Using it, we then compare the efficacy of different condom types, glove materials, glove sizes, and methods of application onto the speculum.</p> <p>The results showed that condoms provided minimal lateral wall retraction, while vinyl-material gloves with the speculum placed into the third finger had the best lateral wall retraction. However, the nitrile-material gloves are overall preferred over the vinyl gloves as they provided adequate lateral wall retraction without applying a significant vertical compressive effect on the speculum, and thus had overall better cervical visualization. Glove size had minimal impact.</p> <p>This study serves as a guide for clinicians as they use tools commonly found in a clinical setting to perform difficult pelvic exams. We recommend that clinicians consider the use of a nitrile glove as a sheath around a speculum. Additionally, this study demonstrates proof-of-concept of a physical model that can quantitatively describe different materials on their ability to improve cervical visualization. This model can be used in future research with more speculum and material combinations, including with materials custom-designed materials for this purpose.</p>
Multiscale analysis of triglycerides with X-ray scattering: Implementing a shape-dependent model for CNP characterization - Supporting Dataset
<p>This dataset contains the files used to substantiate the outcomes of the publication "<em>Multiscale analysis of triglycerides with X-ray scattering: Implementing a shape-dependent model for CNP characterization</em> <em>" </em></p> <p>The dataset includes:</p> <ul> <li>X-ray scattering profiles - in absolute units</li> <li>Images used to measure CNP distributions</li> </ul> <p>Relevant abbreviations: </p> <ul> <li>SSS - Tristearin</li> <li>OOO - Triolein</li> <li>FHRO - Fully Hydrogenated Rapeseed Oil</li> <li>HOSO - High Oleic Sunflower Oil</li> </ul>
Stellar Evolution Models from "Finding the Fuse: Prospects for the Detection and Characterization of Hydrogen-Rich Core-Collapse 5 Supernova Precursor Emission with the LSST"
<p>These data consist of all runs from the Modules for Experiments in Stellar Astrophysics (MESA; Paxton et al. 2011, 2013, 2015, 2018, 2019) code, used to construct radius priors for modeling supernova precursor emission in<em> <a href="https://arxiv.org/abs/2408.13314">Finding the Fuse: Prospects for the Detection and Characterization of Hydrogen-Rich Core-Collapse 5 Supernova Precursor Emission with the LSST</a></em> (Gagliano+2024, submitted). </p> <p>The contents of the data files are detailed in the file <strong>ReadmeMESA.txt</strong>. Additional detail concerning the simulations can be found in Section 2.2 of the linked publication. </p>
Supplementary datasets for the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" - Part 2
<p>Supplementary files containing datasets needed to reproduce the results of the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" by S. Choudhury et al.</p> <p>The code to use with these data and reproduce the manuscript results is available at https://github.com/EPFL-LCSB/renaissance and https://gitlab.com/EPFL-LCSB/renaissance. The execution of parts of this code is dependent on the SkimPy toolbox (https://github.com/EPFL-LCSB/skimpy). Refer to the readme files on the RENAISSANCE code repositories for more details.</p> <p>The dataset contains the following files:</p> <p>1. param_fixing.zip - self-explanatory (Figure 4 & 5); contains an explanatory note for this part (experiment_details.txt), and the file containing Km values fetched from the BRENDA database (Km_database.csv).</p> <p>2. scripts.zip - scripts to generate figure 2-5 on toy data</p>
Supplementary datasets for the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" - Part 1
<p><strong>Supplementary files containing datasets needed to reproduce the results of the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" by S. Choudhury et al (https://doi.org/10.1101/2023.02.21.529387).</strong></p> <p>The code to use with these data and reproduce the manuscript results is available at https://github.com/EPFL-LCSB/renaissance and https://gitlab.com/EPFL-LCSB/renaissance. The execution of parts of this code is dependent on the SkimPy toolbox (https://github.com/EPFL-LCSB/skimpy). Refer to the readme files on the RENAISSANCE code repositories for more details.</p> <p>The dataset contains the following files:</p> <p>1. models.zip - contains thermodynamically curated steady-state and nonlinear kinetic models of <em>E. coli </em>metabolism used in this study. Also contains the samples of steady-state metabolite concentrations and metabolic fluxes used in the study presented in Figure 3 (steady-state samples used for preparing Figures 2 and 4).</p> <p>2. renaissance_incidence_results.zip - self-explanatory (Figure 2a and 2b)</p> <p>3. ODE_solutions.zip - self-explanatory (Figure 2c)</p> <p>4. bioreactor_simulations1-3.zip - self-explanatory (Figure 2d)</p> <p>5. steady_state_analysis.zip - RENAISSANCE results obtained for each of the steady states (Figure 3a)</p> <p>6. subspace_analysis.zip - RENAISSANCE results presented in Figure 3b-g</p> <p><strong>The remaining datasets are published in the following links</strong></p> <p><em> - https://doi.org/10.5281/zenodo.7930084</em></p> <p><em> - https://doi.org/10.5281/zenodo.10391802</em></p>
Input and output data (images + boulder labels, model setup, model weights and more) for the manuscript "Automatic characterization of boulders on planetary surfaces from high-resolution satellite images"
<p><strong>File 1:</strong> raw_data_BOULDERING.zip</p> <p><strong>Size:</strong> 8.8 GB</p> <p><strong>Summary: </strong>It contains all of the rasters (planetary images) and labeled boulders (raw data):</p> <ul> <li> <p>a boulder-mapping file, which is the manually digitized outline of boulders.</p> </li> <li> <p>a ROM file (stands for Region of Mapping), which depicts the image patches on which the boulder mapping has been conducted.</p> </li> <li> <p>a global-tiles file, which shows all of the image patches within a raster.</p> </li> </ul> <p>There are multiple locations/images per planetary body.</p> <p><strong>Structure:</strong></p> <pre>. └── raw_data/ ├── earth/ │ └── image_name/ │ ├── shp/ │ │ ├── <image_name>-ROM.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp │ └── raster/ │ └── <image_name>.tif ├── mars/ │ └── image_name/ │ ├── shp/ │ │ ├── <image_name>-ROM.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp │ └── raster/ │ └── <image_name>.tif └── moon/ └── image_name/ ├── shp/ │ ├── <image_name>-ROM.shp │ ├── <image_name>-boulder-mapping.shp │ └── <image_name>-global-tiles.shp └── raster/ └── <image_name>.tif</pre> <p> </p> <p><strong>File 2:</strong> best_model.zip</p> <p><strong>Size:</strong> 624.7 MB</p> <p><strong>Summary:</strong></p> <p>This zip file contains all of the inputs and outputs required/obtained from the training of the BoulderNet Mask R-CNN model (model setup, augmentation pipeline, model weights, log during training, logged metrics):</p> <ul> <li> <p>augmentation_pipeline.json (required as inputs for the training of the algorithm to apply augmentations). See <a href="https://github.com/astroNils">https://github.com/astroNils</a> and the MLtools repository for more information.</p> </li> </ul> <ul> <li> <p>Base-RCNN-FPN.yaml (base model setup file).</p> </li> <li> <p>config.yaml (complete model setup file, merge of the base and Mars-Moon-Earth setup file).</p> </li> <li> <p>Mars-MoonEarth-v050...yaml (model setup file).</p> </li> <li> <p>log.txt (log during training of the algorithm).</p> </li> <li> <p>model_0055999.pth (model weights at second last saving step)</p> </li> <li> <p>model_0063999.pth (model weights at last saving step)</p> </li> </ul> <p>We advice the use of model weights model_0055999.pth (to avoid slight overfitting).</p> <p><strong>File 3:</strong> Apr2023-Mars-Moon-Earth-mask-5px.zip (pre-processed input images)</p> <p><strong>Size:</strong> 252.8 MB</p> <p><strong>Summary:</strong></p> <p>This zip files contains the input data (images and boulder outlines) for the train, validation and test datasets. See <a href="https://github.com/astroNils">https://github.com/astroNils</a> and the MLtools repository for more information in how-to-use the different files.</p> <ul> <li> <p>The json folder contains json files that can be given as input (as a custom dataset) to the Detectron2 platform. The only differences between the two files is how the bounding boxes around masks have been generated. We advised to use "Apr2023-Mars-Moon-Earth-mask-5px.json".</p> </li> <li> <p>The pkl folder and pickle file includes some informations about the 950 image patches in our boulder dataset.</p> </li> <li> <p>The pre-processing folder contains all of the training, validation and test image patches and corresponding shapefiles.</p> </li> <li> <p>The shapefile folder is actually empty (it should not be there!).</p> </li> </ul> <p><strong>Structure:</strong></p> <pre>. └── preprocessed_inputs/ ├── json ├── pkl ├── preprocessing/ │ ├── train/ │ │ ├── images │ │ └── labels │ ├── validation/ │ │ ├── images │ │ └── labels │ └── test/ │ ├── images │ └── labels └── shp</pre> <p> </p>
Data and code related to the article "Characterization of the Log-normal Model for Received Signal Strength Measurements in Real Wireless Sensor Networks"
<p>This upload contains the data and code related to the article "Characterization of the Log-normal Model for Received Signal Strength Measurements in Real Wireless Sensor Networks", (D.O.I: <a href="https://doi.org/10.3390/jsan9010012">10.3390/jsan9010012</a>) published in the the special issue on "Localization in Wireless Sensor Networks" of the <a href="https://www.mdpi.com/journal/jsan"><em>Journal of Sensor and Actuator Networks</em></a> (ISSN 2224-2708).</p> <p>The data and code included allows to replicate the results of the article.</p>
Biochemical Characterization of Mouse Retina of an Alzheimer's Disease Model by Raman Spectroscopy
<p>Raman raw data for the paper "Biochemical Characterization of Mouse Retina of an Alzheimer’s Disease Model by Raman Spectroscopy"</p> <ul> <li>two datasets of Raman images from cross-sectional and en face mouse retinas without processing</li> </ul>
I-Seed_DS1 – CHARACTERIZATION OF BIOMECHANICS AND MATERIALS OF NATURAL SEEDS MODEL
<p>Dataset I-Seed_DS1 focus on bioengineering investigations of plant seed models, to define useful specifications for the design of the artificial systems in terms of multi-functional materials and morphological computation.</p> <p>Task 3.1. Erodium cicutarium seeds: from natural features to robotic specifics.</p> <p>Task 3.2. Samara seeds: from natural features to robotic specifics</p>
Рис. 6. МоΔеΛирование экоΛогических ниш коΛораΔского жука ΔΛя ΔаΛьневосточного, европейского и североамериканского ареаΛов метоΔом метрического Δвухмерного шкаΛирования с применением коэффициента Жаккара Fig. 6. Models of ecological niches of the Colorado potato beetle for the Far Eastern, European, and North-American habitats (metric multidimensional scaling, Jaccard index) in Comparative characterization of the ecology of native (Henosepilachna vigintioctomaculata) and invasive (Leptinoatrsa decemlineata) species under the conditions of the monsoon climate in the southern part of the Russian Far East
Рис. 6. МоΔеΛирование экоΛогических ниш коΛораΔского жука ΔΛя ΔаΛьневосточного, европейского и североамериканского ареаΛов метоΔом метрического Δвухмерного шкаΛирования с применением коэффициента Жаккара Fig. 6. Models of ecological niches of the Colorado potato beetle for the Far Eastern, European, and North-American habitats (metric multidimensional scaling, Jaccard index)
Deep learning model for characterizing protein-RNA interactions from sequence at single-base resolution
<p> </p> <p><a href="https://zenodo.org/api/records/14021440/draft/files/encode_eclip.h5/content" target="_blank" rel="noopener noreferrer">encode_eclip.h5</a> - This file contains the training, validation, and test data for the Reformer model.</p> <p><a href="https://zenodo.org/api/records/14021440/draft/files/encode_eclip_bc.h5/content" target="_blank" rel="noopener noreferrer">encode_eclip_bc.h5</a> - This file contains the training, validation, and test data for the Reformer-BC model.</p> <p><a href="https://zenodo.org/api/records/14027315/draft/files/Reformer-code.zip/content" target="_blank" rel="noopener">Reformer-code.zip</a> - This file contains the training code of Reformer.</p>
Recombination and In-silico protein modelling and functional characterization of copLAB genes from Trinidadian Xanthomonas campestris and melonis isolates
<p>RDP, GARD, RaptorX and InterProScan outputs relating to the publication tentatively titled "Heavy metal resistance islands associated with a putative Tn in Trinidadian copper resistant <em>Xanthomonas campestris </em>and <em>melonis </em>strains are strongly linked to homologs from the <em>Stenotrophomonas </em>genus"</p>
MESA histories for "Characterizing Observed Extra Mixing Trends in Red Giants using the Reduced Density Ratio from Thermohaline Models"
<p>This repository provides MESA history files for each of the stellar models in the publication "Characterizing Observed Extra Mixing Trends in Red Giants using the Reduced Density Ratio from Thermohaline Models". The MESA version used was stable release version 21.12.21. Runs are organized into tarballs according to the thermohaline mixing prescription used:</p> <ul> <li>BGS13 = Brown, Garaud, Stellmach 2013</li> <li>Kipp1e-1 = Kippenhahn with alpha_th = 0.1</li> <li>Kipp2 = Kippenhahn with alpha_th = 2</li> <li>Kipp7e2 = Kippenhahn with alpha_th = 700</li> </ul> <p>and are additionally grouped according to the stellar mass (M = 0.9, 1.1, 1.3, 1.5, 1.7 in units of Msol). Within each tarball is a number of run directories which contain a LOGS/history.data file from the MESA run. The subdirectories in the tarball contain runs at various metallicities; conversion between Z (MESA input) and [Fe/H] (paper reported value) are found in Table 2 of the manuscript.<br> <br> Inlists and information for recreating these MESA simulations can be found online at the paper's github repository: <a href="https://github.com/afraser3/Empirical-Magnetic-Thermohaline">https://github.com/afraser3/Empirical-Magnetic-Thermohaline</a> (a copy of the code from this Github repository is located in this Zenodo repository in: Empirical-Magnetic-Thermohaline-main.zip)</p>
Site characterization, water balance modeling, and regeneration attributes of managed and unmanaged ponderosa pine sites in the southwestern United States
<p>This dataset contains biotic and abiotic site characterization data and SOILWAT2 water balance model simulation outputs (two daily outputs: 1915-2011, 1980-2020) for 77 ponderosa pine forest sites in the southwestern United States. Data were collected in summer 2019 and summer 2021. Overviews of the sampling and modeling methodologies are detailed in the following publications:</p> <p>Pirtel NL, Bradford JB, Hubbard RM, Abella SR, Kolb TE, Litvak ME, Porter SL and Petrie MD. 2021. The aboveground and belowground growth characteristics of juvenile conifers in the southwestern United States, Ecosphere 12: e03839, doi:10.1002/ecs2.3839.</p> <p>Petrie MD, Hubbard RM, Bradford JB, Kolb TE, Moser WK, Noel A, Schlaepfer DR, Bowen MA, Fuller LR and Moser WK. 2023. Widespread regeneration failure in ponderosa pine forests of the southwestern United States, Forest Ecology and Management: in press.</p> <p> </p>
Characterizing the Neural Substrates of Irritability in Women: an Experimental Neuroendocrine Model
ClinicalTrials.gov study NCT04051320. IPD Sharing: YES. Countries: 1. Publications: 98.
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.