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256 results for “Computational models”
Processed FDG-PET data from: A computational model of neurodegeneration in Alzheimer’s disease
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Data from: Computational model of the full-length TSH receptor
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Computationally modelled structure of type IV pilus PilA of Aggregatibacter actinomycetemcomitans
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Results of high-performance computing parameter sweeps associated with the Zimbabwe agro-pastoral management model
<p>This dataset includes all the results of the model runs used to explore the parameters in the Zimbabwe Agro-Pastoral Management Model, archived at ComSES.net (<a href="https://doi.org/10.25937/ta23-sn46">https://doi.org/10.25937/ta23-sn46</a>). This model has been created with and for the researcher-farmers of the Muonde Trust (<a href="http://www.muonde.org/">http://www.muonde.org/</a>), a registered Zimbabwean non-governmental organization dedicated to fostering Indigenous innovation. The results in this dataset were generated using the BehaviorSpace functionality in NetLogo, running headless on a high-performance computing cluster (499,200 runs). The dataset includes model output variables summarized for each model run as well as input values for management variables, underlying parameters, and rainfall models for those runs. A detailed description of the model and its variables (including units of measurement where applicable) is available in Eitzel et al. (2020) at DOI: 10.1371/journal.pone.0237638 and in the Overview, Design, and Details documentation on CoMSES.net.</p>
Pitfalls of Computed Tomography 3D Reconstruction Models in Cranial Nonmetric Analysis
<p>Many studies in the literature have highlighted the utility of virtual 3D databanks as a substitute for real skeletal collections and the important application of radiological records in personal identification. However, none have investigated the accuracy of virtual material compared to skeletal remains in nonmetric variant analysis using 3D models. The present study investigates the accuracy of 20 computed tomography (CT) 3D reconstruction models compared to the real crania, focusing on the quality of the reproduction of the real crania and the possibility to detect 29 dental/cranial morphological variations in 3D images. An interobserver analysis was performed to evaluate trait identification, number, position, and shape. Results demonstrate a false bone loss in 3D models in some cranial regions, specifically the maxillary and occipital bones in 85% and 20% of the samples. Additional analyses revealed several difficulties in the detection of cranial nonmetric traits in 3D models, resulting in incorrect identification in circa 70% of the traits. In particular, pitfalls included the detection of erroneous position, error in presence/absence rates, in number, and in shape. The lowest percentages of correct evaluations were found in traits localized in the lateral side of the cranium and for the infraorbital suture, mastoid foramen, and crenulation. The present study highlights important pitfalls in CT scan when compared with the real crania for nonmetric analysis. This may have crucial consequences in cases where 3D databanks are used as a source of reference population data for nonmetric traits and pathologies and during bone-CT comparisons for identification purposes.</p>
Frequency-dependent cortical plasticity: evidence from psychophysics, functional imaging and computational modelling.
<p>fMRI data relating to the paper entitled 'Frequency-dependent cortical plasticity: evidence from psychophysics, functional imaging and computational modelling'. </p>
Effects of individualized Electrical Impedance Tomography and image reconstruction settings upon the assessment of regional ventilation distribution: Comparison to 4-dimensional Computed Tomography in a porcine model
<p>Reconstruction Models used for identification of optimal settings for comparison to CT images. Forward models are available in the supplement of the article but were removed form the inverse models due to redundance storage within each model.</p> <p>Prior reconstruction in EIDORS, forward models have to be added again to<em> imdl.fwd_model</em> and <em>imdl.jacobian_background.fwd_model</em>.</p>
Physical Unclonable In-Memory Computing for Simultaneous Protecting Private Data and Deep Learning Models
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Dataset for paper "Motoneuron-driven computational muscle modelling with motor unit resolution and subject-specific musculoskeletal anatomy"
<p>This dataset collects all the material that was used to create the personalised musculoskeletal model employed in the publication by Caillet et al. "Motoneuron-driven computational muscle modelling with motor unit resolution and subject-specific musculoskeletal anatomy" published in PLOS Computational Biology in 2023. The aim of sharing this material is to allow reproducibility of the results and increase adoption of the semi-automatic techniques for musculoskeletal modelling that were used in the publication.</p>
A computationally efficient method for parameter sensitivity analysis of microbially-explicit biogeochemical models accounting for long-term behavior
<p>The dataset is for the manuscript entitled "A computationally efficient method for parameter sensitivity analysis of microbially-explicit biogeochemical models accounting for long-term behavior".</p>
Data and models for "An image-computable model of speeded decision-making"
<p>Lost in Migration gameplay data and trained models for:</p> <div>Jaffe, P. I., Gustavo, X. S. R., Schafer, R. J., Bissett, P. G., Poldrack, R. A. An image-computable model of speeded decision-making. <em>eLife</em> <strong>13</strong>, RP98351 (2024).</div> <div> </div> <p>This dataset can be used to reproduce all of the results of the manuscript, following the instructions in the code repository for the paper: <a href="https://github.com/pauljaffe/vam">https://github.com/pauljaffe/vam</a>.</p> <p>The dataset includes the following components:</p> <p><strong>gameplay_data.zip:</strong> Trial-level gameplay metadata for Lost in Migration. Lost in Migration is a variant of the flanker task offered as a part of the Lumosity cognitive training platform (Lumos Labs, Inc.). The .zip file includes a separate .csv file for each of the 75 Lumosity users (participants) that we trained models on. Each .csv file has one row per trial with the following fields/columns: "anon_id", numerical identifier for the Lumosity user; "nth_play", the nth gameplay of Lost in Migration for this user; "trial", the nth trial for the current gameplay; "xpos", the signed horizontal distance from the center of the target bird to the left edge of the game window (pixels, non-negative); "ypos", the signed vertical distance from the center of the target bird to the bottom edge of the game window (pixels, non-negative); "flanker_direction", (L/R/U/D); "response_direction", (L/R/U/D); "target_direction", (L/R/U/D); "response_time", (ms); "stimulus_layout", numerical code for the layout of the bird flock for the current trial (0: horizontal line, 1: vertical line, 2: cross, 3: <, 4: >, 5: v, 6: ^)<strong>.</strong></p> <p><strong>vam_models.zip:</strong> Parameters for the 75 visual accumulator models (VAMs) analyzed in the manuscript.</p> <p><strong>task_opt_models.zip:</strong> Parameters for the 75 task-optimized models analyzed in the manuscript.</p> <p><strong>metadata.csv:</strong> Metadata for each Lumosity user that a VAM/task-optimized model was trained on. The .csv file has one row per user with the following fields/columns: "user_id", numerical identifier for the Lumosity user (same as "anon_id" in gameplay_data.zip); "gender", self-reported gender ('m', 'f', or null, indicating no response was given); "binned_age", age bucketed into decade-long bins (20-29, 30-39... 80-89).</p> <p><strong>derivatives.zip:</strong> The RTs/choices generated by the trained models, organized into separate folders by model type (vam/task_opt/binned_rt) and user ID. Also includes a "summary_stats" folder with analysis products of the model activations and outputs.</p> <p><strong>graphics.zip:</strong> Image files used to create the visual stimuli from the gameplay metadata.</p> <p><strong>example_model_inputs.zip: </strong>The processed visual stimuli and gameplay data used as inputs to train one model (user ID 182). Note we provide instructions to recreate the stimuli and other model inputs for all models in the code repository.</p>
Computational dataset, scripts and models for 'Lipid shape as a membrane activity modulator of a model antimicrobial peptide'
<p>Analysis scripts and computational models used in the manuscript 'Lipid shape as a membrane activity modulator of a model antimicrobial peptide', by Marcin Makowski, Octávio L. Franco, Nuno C. Santos and Manuel N. Melo.</p>
Machine learning models predict calculation outcomes with the transferability necessary for computational catalysis
<p>data files, including ML models of dynamic classifiers, trajectories of electronic structure and geometric features, optimized geometries, and final csv files.</p>
Data and Codes of Characterizing Uncertainties of Earth System Modeling with Heterogeneous Many-core Architecture Computing
<p>These are the supporting information to verify the results in the paper, including input data, model outputs, the postprocessing scripts and the source codes.</p>
Osteolitic vs Osteoblastic metastatic lesion: Computational modeling of fracture risk in the human vertebra after screws fixation procedure
<p>Metastatic lesions compromise the mechanical integrity of vertebrae, increasing the fracture risk. Screwfixation is usually performed to guarantee spinal stability and prevent dramatic fracture events. Accordingly, predicting the overall mechanical response in such conditions is critical to planning and optimizing the surgical treatment. This work proposes an image-basedfinite element computational approach describing the mechanical behavior of a patient-specific instrumented metastatic vertebra by assessing the effect of lesion size, location, type and shape on the fracture load and fracture patterns under physiological loading conditions. A specific constitutive model for the metastasis is integrated to account for the effect of the diseased tissue on the bone material properties. Computational results demonstrate that size, location, and type of metastasis significantly affect the overall vertebral mechanical response, and suggest better account these parameters in estimating the fracture risk. Combining multiple osteolytic lesions to account for irregular shape of the overall metastatic tissue has a not significant effect on fracture load of vertebra macroscopically. In addition, the combination of loading mode and metastasis type is shown for the first time as a critical modeling parameter in determining the fracture risk. The proposed computational approach moves towards defining a clinically integrated tool to improve the management of metastatic vertebrae and quantitatively evaluate fracture risk.</p>
Data from: Computational Modeling of Gluteus Medius Muscle Moment Arm in Caviomorph Rodents Reveals Ecomorphological Specializations
<p>The data stored in this repository allow the reproduction of the study described in the following. Vertebrate musculoskeletal locomotion is realized through lever-arm systems. The instantaneous muscle moment arm (IMMA), which is expected to be under selective pressure and thus of interest for ecomorphological studies, is a key aspect of these systems. The IMMA changes with joint motion and its length change is technically difficult to acquire—usually, proxies such as osteological in-levers are used instead—and has not been compared in a larger phylogenetic ecomorphology framework, yet. We used 18 species of the ecologically diverse clade of caviomorph rodents to test whether its diversity is reflected in the IMMA of the hip extensor M. gluteus medius. A large IMMA is beneficial for torque generation; a small IMMA facilitates fast joint excursion. We expected large IMMAs in scansorial species, small IMMAs in fossorial species, and somewhat intermediate IMMAs in cursorial species, depending on the relative importance of acceleration and joint angular velocity. We modelled the IMMA over the entire range of possible hip extensions and applied macroevolutionary model comparison to selected joint poses. We also obtained the osteological in-lever of the M. gluteus medius to compare it to the IMMA. At small hip extension, the IMMA was largest on average in scansorial species, while the other two lifestyles were similar. We interpret this as an emphasized need for increased hip joint torque when climbing on inclines, especially in a crouched posture. Cursorial species might benefit from a fast joint excursion, but their similarity with the fossorial species is difficult to interpret and could hint at ecological similarities. At larger extension angles, cursorial species displayed the second-largest IMMAs after scansorial species. The larger IMMA optimum results in powerful hip extension which coincides with forward acceleration at late stance beneficial for climbing, jumping, and escaping predators. This might be less relevant for a fossorial lifestyle. The results of the in-lever only matched the IMMA results of larger hip extension angles, suggesting that the modelling of the IMMA provides more nuanced insights into adaptations of musculoskeletal lever arm systems than this osteological proxy.</p>
Supplementary Materials: Next Generation Computational Tools for the Modeling and Design of Particle Accelerators at Exascale
<p>Supplementary materials (aka data artifact or data archive) for our NAPAC22 publication: "Next Generation Computational Tools for the Modeling and Design of Particle Accelerators at Exascale" (Paper ID: TUYE2).</p> <p>Work supported by the Exascale Computing Project (17-SC-20-SC), a joint project of the U.S. Department of Energy's Office of Science and National Nuclear Security Administration, responsible for delivering a capable exascale ecosystem, including software, applications, and hardware technology, to support the nation's exascale computing imperative. This work was supported by the Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory under U.S. Department of Energy Contract No. DE-AC02-05CH11231.<br> This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under Contract No. DE-AC02-05CH11231.</p>
Thermochronology data in Ebro basin and model input parameters for computing cooling histories
<p>Two files (word and excel) containing Table DR1 that refer to the model input parameters and Table DR2 with details of the (U-Th-Sm)/He analyses. </p>
Rotating single star models for the Galaxy computed with MESA
<p>The model dataset described/employed in the paper "Boron depletion in Galactic early B-type stars reveals two different main sequence star populations" by Harim Jin , Norbert Langer, Daniel J. Lennon , and Charles R. Proffitt (2024).</p> <p> </p> <p><strong>single_star_models</strong></p> <p>A grid of rotating single star models for Z=0.0154 computed with MESA</p> <p>The models are named by their initial masses in log (in solar mass) and initial rotational velocities (in km/s).</p> <p>E.g., 1.080_200 is a 12 Msun with an initial velocity of 200 km/s.</p> <p>Initial masses: 5-40 Msun</p> <p>Initial rotational velocities: 0-600 km/s (or up to break-up)</p> <p> </p> <p><strong>MESA_input_data</strong></p> <p>MESA input data files to reproduce the single star models</p> <p>Works with MESA version of 10398 and MESA SDK version of x86_64-linux-20180822.</p>
Structural models associated with computational study of PolB2 and Pol V mutasomes
<p>This data set contains several collections of 3D models of protein complexes predicted using AlphaFold-Multimer or AlphaFold3. Detailed information regarding the composition of protein complexes and the confidence scores of models is available in the Model_data.xls file.</p> <div> </div>
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.