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256 results for “Computational models”
Test Model for GTT Computational Fluid Dynamics Simulation by TransAT
<p>This archive contains the model and associated code need to run the GTT benchmark test for TransAT.</p>
Dataset: Methods for computing the maximum performance of computational models of fMRI responses.
<p>Accompanying data for the revised version of the manuscript: Methods for computing the maximum performance of computational models of fMRI responses. written by Agustin Lage-Castellanos, Giancarlo Valente, Elia Formisano, Federico De Martino, submitted for publication in Plos Computational Biology, November 2018.</p> <p>The dataset (01.rar) contains the fMRI time series for one subject in Nifti format acquired on an actively shielded MAGNETOM 7T whole body system driven by a Siemens console at Scannexus (<a href="http://www.scannexus.nl)">www.scannexus.nl)</a>. Every folder (24 runs, one folder per run) contains 150 Nifti files, one Nifti file for each fMRI volume. Preprocessing consisted of slice scan-time correction (with sinc interpolation), 3-dimensional motion correction, and temporal high pass filtering (removing drifts of 4 cycles or less per run).</p> <p>The matlab file dmS01_24runs.mat contains a 24-length cell array of fMRI design matrices, one for every run. Every fMRI design matrix is size 150 volumes x 51 covariates. The first 42 columns correspond to the stimuli presented (42 sounds per run). Columns 43, and 44, correspond to the run mean and the linear trend covariates. The rest of the columns correspond to the covariates obtained with GLMdenoise. The matlab variable <em>stimulus</em> of size 24 x 42 contains the index of the sounds presented at every run. A total of 168 sounds were presented, each sound was presented 6 times across the 24 fMRI runs.</p> <p>The file SPMgls0.rar contains the Beta images in Nifti format for every column of the fMRI design matrix, including noise covariates, for every fMRI run. This model was estimated assuming i.i.d fMRI noise (OLS). The codes for computing the noise ceiling are available in the file nccodes.rar, together with a two of examples of their use. SPM is required.</p>
Figures for "New method for computing post-seismic deformations in a realistic gravitational viscoelastic Earth model"
<p>Here are all the figures used in the paper "New method for computing post-seismic deformations in a realistic gravitational viscoelastic Earth model". </p>
Computational Modeling of the Anti-Inflammatory Complexes of IL37
<p>This repository contains IL37 complex structures that are implicated in anti-inflammation.</p>
Computational modeling and analytical validation of singular geometric effects in fault data using a combinatorial approach - Input and processed data
<p>The archive contains the input and processed data for the companion manuscript.</p> <p>The input data contains XYZ coordinates of points documenting the investigated interfaces. The output datasets contain directional data from applying the combinatorial algorithm to point data sets.</p> <p>We have also included .VTU and .PVSM files for visualization of the geological settings in ParaView.</p>
Implementing amplified MRI in a computational fluid dynamics model of the cerebrospinal fluid
<div> <p>This folder respository contains files to implement measured amplified MRI motion into a CFD model. The files should be imported in FLUENT (Ansys Inc.) in the following order:</p> <ol> <li><strong>setup_case.jou</strong>: journal file containing the commands to setup the CFD model in FLUENT (Ansys Inc.). This file also compiles and loads the UDF <strong>mesh_motion_all_v2023.c</strong>. The UDF is directly based on the code provided in the Github respiratory (<a href="https://pyfsi.github.io/coconut/">CoCoNuT</a>). A define-on-demand function should then be executed to export the unique node ids from FLUENT. </li> <li>Run the Python file <strong>link_node_id_spline_interpolation.ipynb</strong> which returns .dat files for the different timesteps. </li> <li><strong>run_motion.jou</strong>: journal file that reads the .dat files generated in step 2 and applies the mesh motion in the CFD model.</li> </ol> </div>
Data from: Computational research on mobile pastoralism using agent-based modeling and satellite imagery
Dryland pastoralism has long attracted considerable attention from researchers in diverse fields. However, rigorous formal study is made difficult by the high level of mobility of pastoralists as well as by the sizable spatio-temporal variability of their environment. This article presents a new computational approach for studying mobile pastoralism that overcomes these issues. Combining multi-temporal satellite images and agent-based modeling allows a comprehensive examination of pastoral resource access over a realistic dryland landscape with unpredictable ecological dynamics. The article demonstrates the analytical potential of this approach through its application to mobile pastoralism in northeast Nigeria. Employing more than 100 satellite images of the area, extensive simulations are conducted under a wide array of circumstances, including different land-use constraints. The simulation results reveal complex dependencies of pastoral resource access on these circumstances along with persistent patterns of seasonal land use observed at the macro level.
PyGEDM pre-computed maps of Galactic electron density models
<p>Pre-computed HDF5 data cubes for <a href="https://github.com/FRBs/pygedm">PyGEDM</a> web app.</p> <p>Datasets in HDF5 file:</p> <p>Dimension scales distance (pc), DM (pc/cm3), galactic latitude and galactic longitude (deg).</p> <pre>[('dist', <HDF5 dataset "dist": shape (10,), type "<i8">), ('dm', <HDF5 dataset "dm": shape (12,), type "<i8">), ('gb', <HDF5 dataset "gb": shape (361,), type "<f8">), ('gl', <HDF5 dataset "gl": shape (721,), type "<f8">), gl = np.linspace(-180, 180, 360*2+1) gb = np.linspace(-90, 90, 180*2+1) dist = np.array((0.1, 0.2, 0.5, 1, 2, 5, 8.5, 10, 20, 50)) dm = np.array((1, 2, 5, 10, 20, 50, 100, 200, 500, 1000, 2000, 5000)) Galactocentric coordinates XYZ (pc): ('x', <HDF5 dataset "x": shape (100,), type "<i8">), ('y', <HDF5 dataset "y": shape (100,), type "<i8">), ('z', <HDF5 dataset "z": shape (100,), type "<i8">)]</pre> <p>NE2001 model precomputed datasets:</p> <pre> ('ne2001', <HDF5 group "/ne2001" (5 members)>),</pre> <pre>[('ddm', <HDF5 dataset "ddm": shape (12, 361, 721), type "<f8">), ('ddm_tau', <HDF5 dataset "ddm_tau": shape (12, 361, 721), type "<f8">), ('dmd', <HDF5 dataset "dmd": shape (10, 361, 721), type "<f8">), ('dmd_tau', <HDF5 dataset "dmd_tau": shape (10, 361, 721), type "<f8">), ('xyz', <HDF5 dataset "xyz": shape (100, 100, 100), type "<f8">)] YMW16 precomputed datasets: ('ymw16', <HDF5 group "/ymw16" (5 members)>),</pre> <pre>[('ddm', <HDF5 dataset "ddm": shape (12, 361, 721), type "<f8">), ('ddm_tau', <HDF5 dataset "ddm_tau": shape (12, 361, 721), type "<f8">), ('dmd', <HDF5 dataset "dmd": shape (10, 361, 721), type "<f8">), ('dmd_tau', <HDF5 dataset "dmd_tau": shape (10, 361, 721), type "<f8">), ('xyz', <HDF5 dataset "xyz": shape (100, 100, 100), type "<f8">)]</pre>
The influence coefficients used in Wind Energy Science paper "A computationally efficient engineering aerodynamic model for swept wind turbine blades"
<p>The influence coefficients for the convective correction with full double-precision floating-point accuracy. This is the supplement for the research article: "A computationally efficient engineering aerodynamic model for swept wind turbine blades", submitted to Wind Energy Science journal.</p> <p>Code language: Fortran</p>
T-REX: computational model data
<p>Datasets generated by the computational neural mass network model of the brain during the study of " A multiscale brain network model links Alzheimer's disease-mediated neuronal hyperactivity to large-scale oscillatory slowing".</p> <p>Data includes raw files of simulated neurophysiology timeseries, generated by iterating the Brainnet coupled neural mass model in Brainwave, available from <a href="http://home.kpn.nl/stam7883/brainwave.html">http://home.kpn.nl/stam7883/brainwave.html</a>. </p> <p>Each file contains data of a single scenario and can be viewed using Brainwave.</p>
Figure 3 in Identification of morphologically cryptic species with computer vision models: wall lizards (Squamata: Lacertidae: Podarcis) as a case study
Figure 3. Example of Grad-CAM heatmaps obtained for Podarcis lusitanicus. The upper images show two common patterns observed in male dorsal images (also found, albeit with some differences, in females). The bottom images exhibit the patterns most frequently found in male and female head lateral images (here illustrated in two females).
Figure 2 in Identification of morphologically cryptic species with computer vision models: wall lizards (Squamata: Lacertidae: Podarcis) as a case study
Figure 2. Confusion matrix for male (upper) and female (lower) image classification for the two-class case based on a combination of predictions from six models. Abbreviations used: Pboc, P. bocagei; Plus, P. lusitanicus.
Highly variable (no clear pattern). All portions of the dorsal views were equally used. In head images the area around the eye, the top of the head, the snout and the throat were all used in similar proportions. P. carbonelli Variable for both views. Snout and middle of the dorsum used in dorsal view. Top of the head most frequently (but not strictly) used in lateral view. P. guadarramae Whole body used for dorsal view (but variable); either throat (most common) or ear region used in head lateral views. P. hispanicus Variable. Anterior portion of snout used more frequently than in other species for both dorsal and head lateral views. P. liolepis Highly variable. Whole body used in most dorsal images, area around the eye and throat used in head lateral views, but other patterns common. P. lusitanicus Highly variable. All parts of the dorsum used (but frequently the most posterior part); area around the ear frequently used in head lateral images. P. tunesiacus Highly variable. Dorsal area near the insertion of the posterior limbs used more frequently than in other species; different regions of the head used, often simultaneously. P. Ʋaucheri Highly variable. Different regions of dorsum (from head to the posterior region) used in dorsal images, all portions of the head, but most frequently the throat, used in lateral images. P. Ʋirescens Highly variable. All parts of both images used. Head and anterior part of the dorsum more used than in other species. in Identification of morphologically cryptic species with computer vision models: wall lizards (Squamata: Lacertidae: Podarcis) as a case study
Highly variable (no clear pattern). All portions of the dorsal views were equally used. In head images the area around the eye, the top of the head, the snout and the throat were all used in similar proportions. P. carbonelli Variable for both views. Snout and middle of the dorsum used in dorsal view. Top of the head most frequently (but not strictly) used in lateral view. P. guadarramae Whole body used for dorsal view (but variable); either throat (most common) or ear region used in head lateral views. P. hispanicus Variable. Anterior portion of snout used more frequently than in other species for both dorsal and head lateral views. P. liolepis Highly variable. Whole body used in most dorsal images, area around the eye and throat used in head lateral views, but other patterns common. P. lusitanicus Highly variable. All parts of the dorsum used (but frequently the most posterior part); area around the ear frequently used in head lateral images. P. tunesiacus Highly variable. Dorsal area near the insertion of the posterior limbs used more frequently than in other species; different regions of the head used, often simultaneously. P. Ʋaucheri Highly variable. Different regions of dorsum (from head to the posterior region) used in dorsal images, all portions of the head, but most frequently the throat, used in lateral images. P. Ʋirescens Highly variable. All parts of both images used. Head and anterior part of the dorsum more used than in other species.
Figure 1 in Identification of morphologically cryptic species with computer vision models: wall lizards (Squamata: Lacertidae: Podarcis) as a case study
Figure 1. The two image types analysed in this study (before pre-processing): above, a dorsal view; below, a head lateral image. Both images correspond to the same Podarcis Ʋaucheri s.l. male.
Highly variable. Mid-portion of the dorsum used frequently (although other areas as well). Tip of the snout used often, but area around the ear and throat are also relevant. P. carbonelli Variable. In the dorsal view, the tip of the snout is frequently used. In the head lateral view, the tip of the snout is also com- monly used, as well as the most posterior region of the head. P. guadarramae Variable. Mid portion of the dorsum and tip of the snout are the regions used more frequently in dorsal and head lateral views, respectively. P. hispanicus Variable. The head and most anterior part of the dorsum are frequently used in the dorsal view. Snout and/or top of posterior region of head used. P. liolepis Variable. Different parts of the dorsum are used, whereas the tip of the snout is used in most head lateral images. P. lusitanicus Anterior dorsum, in the dorsal view, and both snout and posterior side of the head (in head lateral views) frequently used. P. tunesiacus Variable. Tip of the snout and posterior part of the trunk more used than in other species; snout and top head region behind the eye used with some frequency. P. Ʋaucheri Highly variable. All parts of the dorsum used in dorsal images, various parts of the head (but frequently snout and throat combined) used in head lateral images. P. Ʋirescens Highly variable. All portions of the dorsum used in dorsal images, region around and behind the ear more used than in other species for head lateral images. in Identification of morphologically cryptic species with computer vision models: wall lizards (Squamata: Lacertidae: Podarcis) as a case study
Highly variable. Mid-portion of the dorsum used frequently (although other areas as well). Tip of the snout used often, but area around the ear and throat are also relevant. P. carbonelli Variable. In the dorsal view, the tip of the snout is frequently used. In the head lateral view, the tip of the snout is also com- monly used, as well as the most posterior region of the head. P. guadarramae Variable. Mid portion of the dorsum and tip of the snout are the regions used more frequently in dorsal and head lateral views, respectively. P. hispanicus Variable. The head and most anterior part of the dorsum are frequently used in the dorsal view. Snout and/or top of posterior region of head used. P. liolepis Variable. Different parts of the dorsum are used, whereas the tip of the snout is used in most head lateral images. P. lusitanicus Anterior dorsum, in the dorsal view, and both snout and posterior side of the head (in head lateral views) frequently used. P. tunesiacus Variable. Tip of the snout and posterior part of the trunk more used than in other species; snout and top head region behind the eye used with some frequency. P. Ʋaucheri Highly variable. All parts of the dorsum used in dorsal images, various parts of the head (but frequently snout and throat combined) used in head lateral images. P. Ʋirescens Highly variable. All portions of the dorsum used in dorsal images, region around and behind the ear more used than in other species for head lateral images.
Figure 4 in Identification of morphologically cryptic species with computer vision models: wall lizards (Squamata: Lacertidae: Podarcis) as a case study
Figure 4. Confusion matrix for male (upper) and female (lower) image classification for the nine-class experiment based on a combination of predictions from six models. Abbreviations used: Pboc, P. bocagei; Pcar, P. carbonelli; Phis, P. hispanicus; Plio, P. liolepis; Plus, P. lusitanicus; Pvsl, P. Ʋaucheri s.l.; Pvss, P. Ʋaucheri s.s.; Pvir, P. Ʋirescens.
Dataset comparing 2D and 3D culture for the calibration/validation of computational models
<p>Dataset comparing invasion adhesion and response to cisplatin and paclitaxel measured in PEO4 cells maintained either in 2D or 3D.</p> <p>Paper submitted to PLOS Computational Biology</p>
Prediction Model Based on Computed Tomography Liver Volume for the Short-term Mortality in Hepatitis B Related Acute-on-Chronic
ClinicalTrials.gov study NCT03977857. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Predicting Rehabilitation Outcomes in Bilingual Aphasia Using Computational Modeling
ClinicalTrials.gov study NCT02916524. IPD Sharing: NO. Countries: 1. Publications: 2.
Computational Model of SNS (Sacral Nerve Stimulation) Induced Electrical Current Flow Using Tractography Imaging
ClinicalTrials.gov study NCT05049486. IPD Sharing: NO. Countries: 1. Publications: 6.
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.