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3,688 results for “Computer”
Data for the analysis of aquifer-system deformation in the Doñana Natural Space (Spain) using unsupervised cloud-computed InSAR data and wavelet analysis
<p>This are the data necessary to correlate InSAR and hydrogeological information through wavelet analysis, by WaSAR Python script (Jiménez-González & Guardiola-Albert, 2022, http://doi.org/10.5281/zenodo.6334996). The structure and information about the data is the following:</p> <p>PSBAS: Processed Interferometric Synthetic Aperture Radar (InSAR) data from the European Space Agency (ESA) Sentinel-1 satellites to estimate line-of-sight (LOS) ground motion in the period 2014-2020 in the Doñana area (SW Spain). These images have been processed using the P-SBAS approach (Parallel Small BAseline Subset), which is the parallel computing solution for the SBAS processing chain at the ESA Geohazards Exploitation Platform (GEP) by CNR-IREA.</p> <p>Aggregates deformation: Former InSAR information aggregated in polygons</p> <p>Climate: rainfall and ET information in the Doñana area for the 2014-2020 period. Daily records of evapotranspiration and precipitation have been obtained from the agroclimatic stations belonging to the Junta de Andalucía (https://www.juntadeandalucia.es/agriculturaypesca/ifapa/riaweb/web/).</p> <p>Piezometry: piezometry information in Doñana area for the 2014-2020 period. Groundwater level information was provided by the piezometric networks of the Guadalquivir Hydrographic Confederation and the Geological and Mining Institute of Spain.</p> <p>Pump rates: estimated pumping rate time series in the Matalascañas touristic resort</p>
Computationally profiling peptide:MHC recognition by T-cell receptors and T-cell receptor-mimetic antibodies
<p>Supporting datasets for preprint version of "Computationally profiling peptide:MHC recognition by T-cell receptors and T-cell receptor-mimetic antibodies".</p>
Lid-Driven Cavity Re=400 flow solution computed using LUMA and Code_Saturne coupled to each other
<p>This dataset is the result of running the Code_Saturne and LUMA codes coupled to each other to simulate a standard Re=400 Lid-Driven Cavity problemon ARCHER2. This is a test case for the coupling of the two codes.</p> <p>The domain is a unit cube. LUMA evolved the portion $x \le 0.6$, and Code\_Saturne evolved the portion $x \ge 0.4$. The boundary $x=0$ was driven with a velocity $u_y = 1$. Boundary data at the coupling boundaries is obtained from the other code using the PLE library.</p> <p>See https://github.com/cfdemons/cs-luma-archer/blob/main/tutorial.md for details to reproduce this dataset.</p> <p> </p>
Cophylogeny reconstruction allowing for multiple associations through approximate Bayesian computation
<p>Phylogenetic tree reconciliation is extensively employed for the examination of coevolution between host and symbiont species. An important concern is the requirement for dependable cost values when selecting event-based parsimonious reconciliation. Although certain approaches deduce event probabilities unique to each pair of host and symbiont trees, which can subsequently be converted into cost values, a significant limitation lies in their inability to model the <em>invasion</em> of diverse host species by the same symbiont species (termed as a spread event), which is believed to occur in symbiotic relationships. Invasions lead to the observation of multiple associations between symbionts and their hosts (indicating that a symbiont is no longer exclusive to a single host), which are incompatible with the existing methods of coevolution. </p> <p>Here, we present a method called AmoCoala (an enhanced version of the tool Coala) that provides a more realistic estimation of cophylogeny event probabilities for a given pair of host and symbiont trees, even in the presence of spread events. We expand the classical 4-event coevolutionary model to include 2 additional spread events (vertical and horizontal spreads) that lead to multiple associations. In the initial step, we estimate the probabilities of spread events using heuristic frequencies. Subsequently, in the second step, we employ an approximate Bayesian computation (ABC) approach to infer the probabilities of the remaining 4 classical events (cospeciation, duplication, host switch, and loss) based on these values.</p> <p>By incorporating spread events, our reconciliation model enables a more accurate consideration of multiple associations. This improvement enhances the precision of estimated cost sets, paving the way to a more reliable reconciliation of host and symbiont trees. To validate our method, we conducted experiments on synthetic datasets and demonstrated its efficacy using real-world examples. Our results showcase that AmoCoala produces biologically plausible reconciliation scenarios, further emphasizing its effectiveness.The software is accessible at <a href="https://github.com/sinaimeri/AmoCoala" rel="noopener">https://github.com/sinaimeri/AmoCoala</a>.</p>
Scaling in the Immune System and Computational Immunology: Lecture Series
<p>How different is the immune system in a human from that of a mouse? Do pathogens replicate at the same rate in different species? Answers to these questions have impact on human health since multi-host pathogens that jump from animals to humans affect millions worldwide.</p> <p>It is not known how rates of immune response and viral dynamics vary from species to species and how they depend on species body size. Metabolic scaling theory predicts that intracellular processes will be slower in larger animals since cellular metabolic rates are slower. We test how rates of pathogenesis and immune system response rates depend on species body size.</p> <p>We hypothesize that immune response rates are invariant with body size. Our work suggests how the physical architecture of the immune system and chemical signals within it may lead to nearly scale-invariant immune search and response.</p> <p>We fit mathematical models to experimental West Nile Virus (WNV, a multi-host pathogen) infection data and investigate how model parameters characterizing the pathogen and the immune response change with respect to animal mass.</p> <p>Phylogeny also affects pathogenesis and immune response. We use a hierarchical Bayesian model, that incorporates phylogeny, to test hypotheses about the role of mass and phylogeny on pathogen replication and immune response. We observe that:</p> <p><br> 1. Hierarchical models (informed by phylogeny) make more accurate predictions of experimental data and more realistic estimates of biologically relevant parameters characterizing WNV infection.</p> <p>2. Rates of WNV production decline with species body mass, modified by a phylogenetic influence.</p> <p> </p> <p>Our work is the first to systematically explore the role of host body mass in pathogenesis using mathematical models and empirical data. We investigate the complex interplay between the physical structure of the immune system and host body mass in determining immune response. The modeling strategies and tools outlined here are likely to be applicable to modeling of other multi-host pathogens. This work could also be extended to understand how drug and vaccine efficacy in humans may systematically differ from that in model organisms like mice, in which most initial experimental studies are conducted.</p> <p> </p>
Serum albumin domain structures in human blood serum by mass spectrometry and computational biology
<p>Contact prediction data generated by EPC-map used in the paper "Serum Albumin Domain Structures in Human Blood Serum by Mass Spectrometry and Computational Biology" by Rappsilber et al.</p>
Figure 6: Simulation computation, at successive steps: iterations 0, 152, 250, 370, 601, 1601. 29
<p>On the figure 6, we present a complete simulation with several centers<br> and several queens [13]. On each center, a queen is emitting several colored<br> pheromons and are able to attract some multi-colored material according to<br> their initial location. Simulation outputs at different iteration times are presented,<br> from RePast implementation and OpenMap GIS visualization.</p>
Figure 3: AntCo2 algorithm for graph clustering: on the left the output of the computation on a communication network; on the right the output on a regular grid
<p>Social and human developments are typical complex systems. Urban development<br> and dynamics are the perfect illustration of systems where spatial<br> emergence, self-organization and structural interaction between the system<br> and its components occur [3, 4, 5, 6]. In figure 4, we concentrate on the emergence<br> of organizational systems from geographical systems.</p>
FIGURE 7 in Differentiating convergent pathologies in turtle shells using computed tomographic scanning of modern and fossil bone
FIGURE 7. Location and geologic position of the Woodbine Group. A. General stratigraphic sequence and timescale for the Cretaceous of central and north central Texas showing the position of the Woodbine Group. Position of the AAS within the Woodbine is marked with an arrowhead. Terrestrial deposits represented by stippled intervals. Time scale based on Denne et al. (2016). Modified from Adams et al. (2011). B. Generalized map of geological units present as surface exposures in the Fort Worth basin with location of AAS shown. Modified after Strganac (2015) and Barnes et al. (1972).
FIGURE 4 in Differentiating convergent pathologies in turtle shells using computed tomographic scanning of modern and fossil bone
FIGURE 4. Modern Trachemys scripta plastron elements (UTK 2317) with shell disease. Photograph (A) and orthographic model based on µCT data (B) shown in ventral view. Frames on the photograph and model highlight specific areas of shell disease, shown on the right as both direct µCT data (C, E, G) and heatmapped slices illustrating bone density changes (D, F, H). In the heatmapped cross sections, colors range from purple (lowest density), to orange (medium density), to white (highest density). Patches of shell disease are indicated with purple arrows. Scale bars in A and B equal 5 cm. Scale bars in C, E, and G equal 5 mm.
FIGURE 2 in Differentiating convergent pathologies in turtle shells using computed tomographic scanning of modern and fossil bone
FIGURE 2. Fossil turtle shell fragments (DMNH 2013-07-1319) with putative bite marks. Photographs (A, G) and orthographic models based on µCT data (B, H) shown in external view. Frames on the photograph and model highlight specific areas with bite marks as both direct µCT data (C, E, I) and heatmapped slices illustrating bone density changes (D, F, J). In the heatmapped cross sections, colors range from purple (lowest density), to orange (medium density), to white (highest density). Specific bite marks are indicated with purple arrows. Scale bars in A, B, G, and H equal 2 cm. Scale bars in C, E, and I equal 5 mm.
FIGURE 1 in Differentiating convergent pathologies in turtle shells using computed tomographic scanning of modern and fossil bone
FIGURE 1. Modern Trachemys scripta shell (SAAF) with bite marks attributed to Mecistops cataphractus. Orthographic models of the shell, based on µCT data shown in dorsal (A) and ventral (B) views. Frames on the models highlight specific bite marks, shown on the right as both direct µCT data (C, E, G) and heatmapped slices illustrating bone density changes (D, F, H). In the heatmapped cross sections, colors range from purple (lowest density), to orange (medium density), to white (highest density). Specific bite marks are indicated with purple arrows. Scale bars in A and B equal 5 cm. Scale bars in C, E, and G equal 5 mm.
FIGURE 6 in Differentiating convergent pathologies in turtle shells using computed tomographic scanning of modern and fossil bone
FIGURE 6. Fossil turtle shell fragment (DMNH 2013-07-0563) with putative shell disease.. Photograph (A) shown in external view. Frames on the photograph and highlight specific areas with shell disease as both direct µCT data (B, D) and heatmapped slices illustrating bone density changes (C, E). In the heatmapped cross sections, colors range from purple (lowest density), to orange (medium density), to white (highest density). Specific patches of shell disease are indicated with purple arrows. Scale bar in A equals 2 cm. Scale bars in B and D equal 5 mm.
FIGURE 8 in Differentiating convergent pathologies in turtle shells using computed tomographic scanning of modern and fossil bone
FIGURE 8. Characteristic examples of shell disease and bite marks in modern and fossil turtle shells. Modern shell disease on the plastron of Trachemys scripta, specimen UTK 2317 (A). Modern bite marks (bisected punctures) on the plastron of Trachemys scripta, specimen SAAF unnumbered (B). Fossil shell disease on a fragment of turtle shell, specimen DMNH 2013-07-0563 (C). Fossil bite marks (four scores and one pit) on a fragment of turtle shell, specimen DMNH 2013-07-1319 (D). Scale bars equal 10 mm.
FIGURE 3 in Differentiating convergent pathologies in turtle shells using computed tomographic scanning of modern and fossil bone
FIGURE 3. Fossil turtle shell fragment (DMNH 2013-07-0567) with putative bite marks. Photograph (A) and orthographic model based on µCT data (B) shown in external view. Frames on the photograph and model highlight specific areas with bite marks as both direct µCT data (C, E) and heatmapped slices illustrating bone density changes (D, F). In the heatmapped cross sections, colors range from purple (lowest density), to orange (medium density), to white (highest density). Specific bite marks are indicated with purple arrows. Scale bars in A and B equal 2 cm. Scale bars in C and E equal 5 mm.
FIGURE 5 in Differentiating convergent pathologies in turtle shells using computed tomographic scanning of modern and fossil bone
FIGURE 5. Modern Trachemys scripta plastron and partial carapace elements (UTK 1844) with shell disease. Photograph (A) and orthographic model based on µCT data (B) shown in ventral view. Frames on the photograph and model highlight specific areas of shell disease, shown on the right as both direct µCT data (C, E, G) and heatmapped slices illustrating bone density changes (D, F, H). In the heatmapped cross sections, colors range from purple (lowest density), to orange (medium density), to white (highest density). Patches of shell disease are indicated with purple arrows. Scale bars in A and B equal 5 cm. Scale bars in C, E, and G equal 5 mm.
Green Function Database in ak135 for synthetic cross-correlation computation in WMSAN.
<p>## Description<br>This file is an HDF5 file containing synthetic seismic waveforms computed with AxiSEM in an axisymmetric Earth in model ak135f.<br>It contains waveforms at various distances for a vertical point force source of 1E20 N.</p> <p>## Parameters</p> <p>Distance range from 0° to 180° with a 0.1° step.<br>Source location latitude = 90°, longitude = 0°.<br>Sampling frequency 1Hz. <br>Duration 3600s.<br>Dominant period 1s.<br><br>## Architecture<br>Network "L" </p> <p>Station "SYNTH0000" : station at distance = 0° from the source location.</p> <pre>|-- <a href="../records/11126562" target="_blank" rel="noopener">NOISE_vertforce_dirac_0-ak135f_1.s_3600s.h5</a>/ │ └── L/ │ └── SYNTH0000/<br>│ └── ...<br>│ └── SYNTH1800/<br>│ └── _metadata/</pre> <p> </p>
Optimizing the design of a bioabsorbable metal stent using computer simulation methods: Supporting Data
<p>Data including UMATs and Abaqus input files related to the paper 'Optimizing the design of a bioabsorbable metal stent using computer simulation methods' <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.biomaterials.2013.07.010" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.biomaterials.2013.07.010</span></a></p> <p> </p>
Computational micromechanics of bioabsorbable magnesium stents: Supporting Data
<p>Data including UMATs, Abaqus input files and experimental measurements related to the paper 'Computational micromechanics of bioabsorbable magnesium stents' <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jmbbm.2014.01.007" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.jmbbm.2014.01.007</span></a></p>
CFRP Micro-computed tomography - twill weave
<p>A dataset containing three micro-computed tomography scans of a carbon fibre-reinforced polymer. The composites consist of twill weave reinforcement and epoxy matrix. Two samples (P1 and P3) have random stacking sequences, sample P5 has controlled stacking sequence. The uploaded dataset contains metadata files.</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.