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3,688 results for “Computer”
Dataset: Quantum Computing Inc. (QUBT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
The state-of-the-art machine learning model for Plasma Protein Binding Prediction: computational modeling with OCHEM and experimental validation
<p><span>Institute of Materia Medica, Chinese Academy of Medical Sciences purchased 10,000 ChemDiv databases.</span></p>
Datasets on approved and ongoing standards for Cloud, Edge and IoT computing in the continuum and analysis and assessment of relevance
<p>Two datasets:</p> <ol> <li><span>the database of standards relevant to the Cloud-Edge-IoT continuum and to the ACES-EDGE Research and Innovation Action funded under the grant agreement No. 101093126 call HORIZON-CL4-2022-DATA-01-02.</span></li> <li><span>the Excel workbook with the analysis of the assessment of the standards in relation to the needs of the ACES-EDGE implementation.</span></li> </ol> <p><span>Both datasets will be used for a more deep assessment of the standardisation requirements of the ongoing technolgical developments.</span></p>
FIGURE 7 in Synchrotron-radiation computed tomography uncovers ecosystem functions of fly larvae in an Eocene forest
FIGURE 7. Diversity of fly larvae in Baltic amber. A, SMF-BE-10652, ventral view; B, same, trunk end, ventral view; C, Heleomyzidae, puparium, Dip-00890, dorsal; D, same, anterior spiracles; E, same, ventral view; F, same, posterior spiracles, dorsal view.
FIGURE 4 in Synchrotron-radiation computed tomography uncovers ecosystem functions of fly larvae in an Eocene forest
FIGURE 4. Representatives of Cyclorrhapha, morphotype 1, AKBS-0030. A, reconstruction of the Baltic amber forest: feces with larvae of Cyclorrhapha, morphotype 1 larvae at the front; the adult fly Gedanoleria eocenica Woźnica, 2019 (Heleomyzidae) at the feces; the early horse Eurohippus messelensis feeding at the background, representing hypothetical herbivores, which may have left feces, preserved as organic mass in the amber piece (Artist: Natalia Jagielska); B, SR-µCT scan render of the full amber piece, organic mass in light-grey and larvae in red; C, surface rendering on the SR-µCT scan, organic mass in violet and larvae in orange; D, surface renders of the individual larvae.
FIGURE 12 in Computational fluid dynamics modeling of fossil ammonoid shells
FIGURE 12. Simulated Nautilus data plotted alongside live Nautilus behavior data from Niel and Askew (2018).
FIGURE 8 in Computational fluid dynamics modeling of fossil ammonoid shells
FIGURE 8. Plot of the coefficient of drag versus Reynolds number for each of the 10 morphotypes in this study. Drag coefficient and Reynolds number were calculated following the equations of Jacobs (1992). Only shells that had a uniform diameter of approx. 5 cm from aperture to venter are shown.
FIGURE 7 in Computational fluid dynamics modeling of fossil ammonoid shells
FIGURE 7. Plot of drag force versus velocity for each of the 10 different morphotypes used in this study. Only shells that had a uniform diameter of approx. 5 cm from aperture to venter are shown.
FIGURE 5 in Computational fluid dynamics modeling of fossil ammonoid shells
FIGURE 5. Coefficient of drag results from Scheme 1 (green) and Scheme 3 (blue) plotted against Re compared against the data from Jacobs (1992; black). Comparisons shown are for Sphenodiscus (left) and Oppelia (right).
FIGURE 3 in Computational fluid dynamics modeling of fossil ammonoid shells
FIGURE 3. An illustration of the computational domain of the simulation. The model target (an ammonoid in this case) is shown as a circle. Each arrow indicates a distance from the shell to a target face of the computational domain. These arrows represent the straight-line distance between the nearest edge of the shell (not the shell's midpoint) and the corresponding wall as per the methods of Shiino, Kuwazuru, and Yoshikawa (2009). Dimensions in the figured example correspond to those of Scheme 3 (see Table 1)
FIGURE 4 in Computational fluid dynamics modeling of fossil ammonoid shells
FIGURE 4. Hemisphere simulation data plotted as velocity versus % difference from the literature baseline (Blevins 1984). Velocities shown are within a range in which the drag coefficient of a hemisphere is relatively stable around a value of 1.17 (Blevins, 1984). The drag values used to derive this plot are given in Appendix 3.
FIGURE 1 in Computational fluid dynamics modeling of fossil ammonoid shells
FIGURE 1. An outline of the workflow from model creation to completed simulation. Boxes are colored based on the general process they are included in: Case generation (blue), Mesh generation (purple), and numerical set-up (green). Two tracks are shown for case generation: one in which a model is created in blender from measurement data (below the dotted line) and the other where the model is created using a Structure from Motion technique such as laser scanning or photogrammetry (above the dotted line). Software used in each process is noted in "()" outside its respective step.
FIGURE 11 in Computational fluid dynamics modeling of fossil ammonoid shells
FIGURE 11. Plots of pressure overlain with water velocity vectors for the Serpenticone and Oxycone shells at both 15 cm/s (A) and 5 cm/s (B) inlet velocities. At 15 cm/s the flow around the Serpenticone shell is more chaotic and there is a buildup of pressure at around the trailing coils compared to the Oxycone shell. This difference mostly disappears at 5 cm/s.
FIGURE 9 in Computational fluid dynamics modeling of fossil ammonoid shells
FIGURE 9. Water velocity around the Sphenodiscus shell at inlet velocities of 15 cm/s (A) and 5 cm/s (B). Areas of slow water velocity caused by viscous interactions are larger to the sides and immediately behind the shell at the lower velocity because water is less readily shed.
Data and scripts for: Bayesian Phylogenetic Analysis on multi-core Compute Architectures: Implementation and evaluation of BEAGLE in RevBayes with MPI
<p>Phylogenies are central to many research areas in biology and commonly estimated using likelihood-based methods. Unfortunately, any likelihood-based method, including Bayesian inference, can be restrictively slow for large datasets–with many taxa and/or many sites in the sequence alignment–or complex substitution models. The primary limiting factor when using large datasets and/or complex models in probabilistic phylogenetic analyses is the likelihood calculation, which dominates the total computation time. To address this bottleneck, we incorporated the high-performance phylogenetic library BEAGLE into RevBayes, which enables multi-threading on multi-core CPUs and GPUs, as well as hardware-specific vectorized instructions for faster likelihood calculations. Our new implementation of RevBayes+BEAGLE retains the flexibility and dynamic nature that users expect from vanilla RevBayes. Additionally, we implemented a native parallelization within RevBayes without an external library using the message passing interface (MPI); RevBayes+MPI. We evaluated our new implementation of RevBayes+BEAGLE using multi-threading on CPUs and a powerful NVidia Titan V GPU against our native implementation of RevBayes+MPI. We found good improvements in speedup when multiple cores were used with up to 20-fold speedup when using multiple CPUs and over 90-fold speedup when using multiple GPU cores. The improvement depended on the data type used, DNA or amino acids, and the size of the alignment, but less on the size of the tree. We additionally investigated the cost of rescaling partial likelihoods to avoid numerical underflow and showed that unnecessarily frequent rescaling can increase runtimes 2.5 to 3-fold. Finally, we presented and compared a new approach to store partial likelihoods on branches instead of nodes which can speed up computations but comes at twice the memory requirements.</p> <p>Availability: The software described in the paper is available at https://github.com/revbayes/revbayes with documentation and tutorials found at https://revbayes.github.io.</p>
Figure 1 in The first extinct species of Acritus LeConte, 1853 (Histeridae: Abraeinae) from Eocene Baltic amber: a microscopic beetle inclusion studied with X-ray micro-computed tomography
Figure 1. Photomicrographs of Acritus sutirca sp. nov., holotype, no. 5541 (MAIG), habitus: (a) ventral view; (b) dorsal view; (c) left lateral view; (d) frontal view. Scale bar represents 0.2 mm.
Figure 2. X in The first extinct species of Acritus LeConte, 1853 (Histeridae: Abraeinae) from Eocene Baltic amber: a microscopic beetle inclusion studied with X-ray micro-computed tomography
Figure 2. X-ray micro-CT renderings of Acritus sutirca sp. nov., holotype, no. 5541 (MAIG), habitus: (a) dorsal view; (b) left lateral view; (c) ventral view; (d) right lateral view. Scale bar represents 0.2 mm.
Figure 4. X in The first extinct species of Acritus LeConte, 1853 (Histeridae: Abraeinae) from Eocene Baltic amber: a microscopic beetle inclusion studied with X-ray micro-computed tomography
Figure 4. X-ray micro-CT renderings of Acritus sutirca sp. nov., holotype, no. 5541 (MAIG): (a–d) aedeagus in dorsal, ventral view and lateral views; (e) antennae. Scale bar represents 0.1 mm.
Figure 3. X in The first extinct species of Acritus LeConte, 1853 (Histeridae: Abraeinae) from Eocene Baltic amber: a microscopic beetle inclusion studied with X-ray micro-computed tomography
Figure 3. X-ray micro-CT renderings of Acritus sutirca sp. nov., holotype, no. 5541 (MAIG), habitus: (a) frontal view; (b) caudal view. Scale bar represents 0.2 mm. Abbreviations: a1 – antennomere 1 (scape); ey – compound eye; py – pygidium; pp – propygidium.
Sulfaguanidine Solid-State forms: An Experimental and Computational Study
<p><span>Computational data:</span></p> <ul> <li><span>Crystal structure prediction anhydrates</span></li> <li><span>Crystal structure prediction monohydrates</span></li> </ul> <p><span>Experimental data:</span></p> <ul> <li><span>IR spectra (dat files)</span></li> <li><span>Moisture sorption/desorption data</span></li> <li><span>C16 Solubility data</span></li> </ul> <p> </p> <p> </p>
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.