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3,688 results for “Computer”
Data from "Transforming the Energy Landscape of a Coiled-coil Peptide via Point Mutations" in J. Chem. Theor. Comput., 2017
<p>Data set for the work presented in "Transforming the Energy Landscape of a Coiled-coil Peptide via Point Mutations"</p> <p>A README file gives instructions on each file. </p> <p>Due to the size of the databases and trajectories, only a representative sample is given here. </p> <p>For more information please contact the authors. (Prof. David Wales at dw34@cam.ac.uk)</p> <p>This work was funded by a PhD scholarship of the Engineering and Physical Sciences Research Council UK.</p> <p>When using this data, please reference it accordingly.</p>
FIGURES 8–21 in A new species of anapid spider (Araneae: Araneoidea, Anapidae) in Eocene Baltic amber, imaged using phase contrast X-ray computed micro-tomography
FIGURES 8–21. CT reconstructions of Balticoroma wheateri new species (male holotype, GPIH). (8) frontal view showing chelicerae and labral spur; (9) view of right pedipalp showing embolus; (10–14) various views of right metatarsus 1, showing y-shaped clasping structure; (15) anterior view of specimen showing the section taken through the chelicerae to produce the raw data slice in Figure 16; (16) raw data slice demonstrating that the chelicerae and clypeal extentions are clearly separated; (20–21) various views of the right pedipalp. C, chelicera; ce, clypeal extension; co, dorsal cymbial outgrowth; cy, cymbium; e, embolus; eb, embolic base; ec, embolic coil;?fc, functional conductor sensu Wunderlich (2004); ls, labral spur; t, tegulum.
FIGURE 1 in A new species of anapid spider (Araneae: Araneoidea, Anapidae) in Eocene Baltic amber, imaged using phase contrast X-ray computed micro-tomography
FIGURE 1. Microphotograph of Balticoroma wheateri new species (male holotype, GPIH). Body length = 1.8 mm.
FIGURES 2–7 in A new species of anapid spider (Araneae: Araneoidea, Anapidae) in Eocene Baltic amber, imaged using phase contrast X-ray computed micro-tomography
FIGURES 2–7. CT reconstructions of Balticoroma wheateri new species (male holotype, GPIH). (2) right lateral view; (3) left lateral view; (4) dorsal view; (5) ventral view; (6) anterior view; (7) posterior view. Body length = 1.8 mm. Mt1, metatarsus 1; ta1, tarsus 1; ti1, tibia 1.
FIGURE 4 A–I in High-resolution X-ray computed tomography of an extant new Donuea (Araneae: Liocranidae) species in Madagascan copal
FIGURE 4 A–I. Donuea collustrata sp. n., male palp. A–C, rl; D–F, ve; G–I, pl; A, D, G, X-ray CT images of right palp of copal specimen, inverted; B, E, H, stereomicroscope photographs of left palp of holotype; C, F, I, drawings of left palp of holotype. Scale bar: 0.25.
FIGURE 2 A–H in High-resolution X-ray computed tomography of an extant new Donuea (Araneae: Liocranidae) species in Madagascan copal
FIGURE 2 A–H. Donuea collustrata sp. n. A, copal piece holding specimen, after trimming; B, copal preserved specimen, do; C, copal specimen, ve; D, frontal view of copal specimen, showing opaque layer of air bubbles; E, leg spination scheme of holotype, legend of unfolded article below; F, copal specimen, reconstruction of habitus, do; G, right male palp of copal specimen, rl; H, left male palp of copal specimen, pl. Scale bars: B, C: 1.0; F: 1.5; H: 0.25.
FIGURE 3 A–M in High-resolution X-ray computed tomography of an extant new Donuea (Araneae: Liocranidae) species in Madagascan copal
FIGURE 3 A–M. Donuea collustrata sp. n., copal specimen, X-ray CT images. A, habitus, ve; B, transversal section of right male palp, showing conductor (green) and embolus (pink) originating from behind MA (blue); C, frontal view of right male palp, colour coding as in B; D, left male palp, pl; E, left male palp, pl-ve; F, left male palp, ve; G, left male palp, rl-ve; H, left male palp, rl; I, right male palp, rl-do view, showing embolus, PTA and RTA; J, right male palp, rl-ve, showing bifid RTA; K, right male palp, rl; L, caudal view of right male palp, showing RTA (left) and PTA (right); M, frontal view of eye region, chilum artificially darkened.
FIGURE 1 A–E in High-resolution X-ray computed tomography of an extant new Donuea (Araneae: Liocranidae) species in Madagascan copal
FIGURE 1 A–E. Donuea collustrata sp. n., holotype. F. Donuea decorsei, holotype, MNHN. A, habitus, do; B, habitus, lat.; C, habitus, ve; D, body, do; E, prosoma, ve; F, left male palp, ve. Scale bars: D, E: 1.0; F: 0.5.
regNet: Data sets and computations
<p>Data sets compatible with the R package regNet available from GitHub under https://github.com/seifemi/regNet.</p> <p>The Zip-File contains all data sets that are required to run the regNet code usage examples.</p>
Computer Graphics Animations of Mondrian Spaces
<p><strong>CG visualisations of Mondrian’s interior design</strong></p> <p>(A) ‘Salon’, without rectangle outlines as in Mondrian’s original drawings</p> <p>(B) Model of a Mondrian-like cylindrical ‘Salon</p> <p>(C) Video of illusory rotating of ‘Reverspective’ Mondrian space</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
Open the record for dataset details and reuse information.
Pore-Opening and Ion-Conduction Mechanism in Channelrhodopsins C1C2, ChR2, and iChloC by Computational Electrophysiology and Constant-pH Simulations
<p>The simulation run input files, comprising the starting configuration and all necessary parameters for performing the<br>MD simulations.</p>
Code and data for "An integrated microwave neural network for broadband computation and communication"
<div> <div> </div> </div> <div> <div> <div> <div> <div> <div> <p>This repository contains code and data used in the presentation of results in the article "An integrated microwave neural network for broadband computation and communication". The contents of the zipped files are:</p> <ul> <li><strong>Spectrum Analyzer Outputs (Datasets and ML scripts for digital emulation, radar and signal encoding classification.zip)</strong>: Reduced-bandwidth outputs used to train the backend for results presented in Figs. 3 and 4 and Supplementary Fig. 3.</li> <li><strong>Simulation Code (Coupled mode simulation of integrated MNN.zip) </strong>: For modeling the coupled MNN system shown in Fig. 2 and Extended Figs. 4 and 5.</li> <li><strong>Radar Signal Simulation (Training data and code for simulating dynamic targets in simulated airspace.zip)</strong>: Code to simulate received baseband signals from radar targets.</li> </ul> <p>Each folder contains readme files on how to run the code and analyze data.</p> <p>Please install a recent Python release (https://www.python.org/downloads/) and a recent release of MATLAB (https://www.mathworks.com/help/install/) to run the code. No non-standard hardware is required. </p> </div> </div> </div> </div> </div> </div>
Data from: Inferring state-dependent diversification rates using approximate Bayesian computation (ABC)
<p><span>State-dependent speciation and extinction (SSE) models provide a framework for quantifying whether species traits have an impact on evolutionary rates and how this shapes the variation in species richness among clades in a phylogeny. However, SSE models are becoming increasingly complex, limiting the application of likelihood-based inference methods. Approximate Bayesian computation (ABC), a likelihood-free approach, is a potentially powerful alternative for estimating parameters. One of the key challenges in using ABC is the selection of efficient summary statistics, which can greatly affect the accuracy and precision of the parameter estimates. In state-dependent diversification models, summary statistics need to capture the complex relationships between rates of diversification and species traits. Here, we develop an ABC framework to estimate state-dependent speciation, extinction and transition rates in the BiSSE (binary state dependent speciation and extinction) model. Using different sets of candidate summary statistics, we then compare the inference ability of ABC with that of using likelihood-based maximum likelihood (ML) and Markov chain Monte Carlo (MCMC) methods. Our results show the ABC algorithm can accurately estimate state-dependent diversification rates for most of the model parameter sets we explored. The inference error of the parameters associated with the species-poor state is larger with ABC than in the likelihood estimations only when the speciation rate is highly asymmetric between the two states (</span><em><span>λ</span></em><sub><span>1</span></sub><span> / <em>λ</em><sub>0 </sub></span><span>= 5). Furthermore, we find that the combination of normalized lineage-through-time (nLTT) statistics and phylogenetic signal in binary traits (Fitz and Purvis’s <em>D</em>) constitute efficient summary statistics for the ABC method. By providing insights into the selection of suitable summary statistics, our work aims to contribute to the use of the ABC approach in the development of complex state-dependent diversification models, for which a likelihood is not available.</span></p>
Data from: Inferring state-dependent diversification rates using approximate Bayesian computation (ABC)
<p>State-dependent speciation and extinction (SSE) models provide a framework for quantifying whether species traits have an impact on evolutionary rates and how this shapes the variation in species richness among clades in a phylogeny. However, SSE models are becoming increasingly complex, limiting the application of likelihood-based inference methods. Approximate Bayesian computation (ABC), a likelihood-free approach, is a potentially powerful alternative for estimating parameters. One of the key challenges in using ABC is the selection of efficient summary statistics, which can greatly affect the accuracy and precision of the parameter estimates. In state-dependent diversification models, summary statistics need to capture the complex relationships between rates of diversification and species traits. Here, we develop an ABC framework to estimate state-dependent speciation, extinction and transition rates in the BiSSE (binary state dependent speciation and extinction) model. Using different sets of candidate summary statistics, we then compare the inference ability of ABC with that of using likelihood-based maximum likelihood (ML) and Markov chain Monte Carlo (MCMC) methods. Our results show the ABC algorithm can accurately estimate state-dependent diversification rates for most of the model parameter sets we explored. The inference error of the parameters associated with the species-poor state is larger with ABC than in the likelihood estimations only when the speciation rate is highly asymmetric between the two states (<em>λ</em><sub>1</sub> / <em>λ</em><sub>0 </sub>= 5). Furthermore, we find that the combination of normalized lineage-through-time (nLTT) statistics and phylogenetic signal in binary traits (Fitz and Purvis’s <em>D</em>) constitute efficient summary statistics for the ABC method. By providing insights into the selection of suitable summary statistics, our work aims to contribute to the use of the ABC approach in the development of complex state-dependent diversification models, for which a likelihood is not available.</p>
DistSNE: Distributed computing and online visualization of DNA methylation-based central nervous system tumor classification
<p><strong>The current state-of-the-art analysis of central nervous system (CNS) tumors through DNA methylation profiling relies on the tumor classifier developed by Capper and colleagues, which centrally harnesses DNA methylation data provided by users. Here, we present a distributed-computing-based approach for CNS tumor classification that achieves a comparable performance to centralized systems while safeguarding privacy. We utilize the t-distributed neighborhood embedding (t-SNE) model for dimensionality reduction and visualization of tumor classification results in two-dimensional graphs in a distributed approach across multiple sites (DistSNE). DistSNE provides an intuitive web interface (https://gin-tsne.med.uni-giessen.de) for user-friendly local data management and federated methylome-based tumor classification calculations for multiple collaborators in a DataSHIELD environment. The freely accessible web interface supports convenient data upload, result review, and summary report generation. Importantly, increasing sample size as achieved through distributed access to additional datasets allows DistSNE to improve cluster analysis and enhance predictive power. Collectively, DistSNE enables a simple and fast classification of CNS tumors using large-scale methylation data from distributed sources, while maintaining the privacy and allowing easy and flexible network expansion to other institutes. This approach holds great potentialfor advancing human brain tumor classification and fostering collaborative precision medicine in neuro-oncology. </strong></p>
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>
Figure 14 in Computational investigation of cicada aerodynamics in forward flight
Figure 14. Vortex structure at the end of downstroke (coloured by unified spanwise vorticity, colour max/min ¼ ±4). (Online version in colour.)
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.