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3,688 results for “Computer”
Dataset from Computational modeling of anthocyanin pathway evolution: Biases, hotspots, and trade-offs
<p>This directory contains the scripts used to run simulations, Jupyter notebook with complete analysis, and serialized (pickled) raw simulated dataset from our paper "<em>Computational modeling of anthocyanin pathway evolution: Biases, hotspots, and trade-offs</em>". These materials are referenced in the main text and supplemental text of the publication. The purpose of this repository is to facilitate replication of our analysis by any interested parties. </p>
Fig. 20. A in Micro-computed tomography for natural history specimens: a handbook of best practice protocols
Fig. 20. A. Part of a pinned Omorgus gigas (Harold, 1872) beetle a few hundred slices away from the pinned area. B. The normal morphology of the beetle is no longer visible due to the metal artefact appearing in the pinned area. Image by the Royal Belgian Institute of Natural Sciences (RBINS) / DIGIT-3 Belspo, CC-BY-NC-ND Jonathan Brecko.
Fig. 15 in Micro-computed tomography for natural history specimens: a handbook of best practice protocols
Fig. 15. Polychaete specimen (Lumbrineris latreillii Audouin & Milne Edwards, 1834) in a composite rendering showing the location of organs of interest within the animal. Soft tissues are volume-rendered, jaws were segmented individually and surface-rendered in different colours. The coloured arrows at the upper left corner indicate the orientation of the scanned specimen in three views (x, y and z axes). Image by HCMR micro-CT lab, CC-BY Sarah Faulwetter.
Fig. 1 in Micro-computed tomography for natural history specimens: a handbook of best practice protocols
Fig. 1. Schematic overview of the image acquisition process. Image by the Hellenic Centre for Marine Research (HCMR) micro-CT lab.
Fig. 19 in Micro-computed tomography for natural history specimens: a handbook of best practice protocols
Fig. 19. Scan of a marine worm (polychaete) with motion artefacts. The structures are not clearly defined due to specimen movement during the scanning procedure. Image by HCMR micro-CT lab.
Fig. 4 in Micro-computed tomography for natural history specimens: a handbook of best practice protocols
Fig. 4. Example of the projection images resulting from the scanning process. Image by HCMR micro- CT lab.
Fig. 22. A. Monkey vertebra without metal support. B. A in Micro-computed tomography for natural history specimens: a handbook of best practice protocols
Fig. 22. A. Monkey vertebra without metal support. B. A metal artefact (yellow arrow) is created due to the metal rod used to support a series of vertebrae on a mounted skeleton. Photo courtesy of the Royal Belgian Institute of Natural Sciences (RBINS) / DIGIT-3 Belspo, CC-BY-NC-ND Jonathan Brecko.
Fig. 3 in Micro-computed tomography for natural history specimens: a handbook of best practice protocols
Fig. 3. The spectrum generated by an X-ray generator at 100kV with and without filtering. Image generated by the simulation environment https://www.oem-xray-components.siemens.com/x-ray-spectra-simulation.
Fig. 21. A. 3D in Micro-computed tomography for natural history specimens: a handbook of best practice protocols
Fig. 21. A. 3D model of the Omorgus gigas (Harold, 1872) beetle after a quick segmentation, including the metal artefact. B. 3D model of the same specimen after manual removal of the pin in the Dragonfly software (http://www.theobjects.com/dragonfly/). Clicking on the image opens the 3D model. Photo courtesy of the Royal Belgian Institute of Natural Sciences (RBINS) / DIGIT-3 Belspo, CC-BY-NC-ND Jonathan Brecko.
Fig. 10. Measuring the maximum width W in Micro-computed tomography for natural history specimens: a handbook of best practice protocols
Fig. 10. Measuring the maximum width W (in pixels) of the projected specimen (as the distance from the rotation axis - dotted line - to the farthest end of the sample) to calculate the number of radiographs needed. This measurement is done for the angular position of the rotating platform where the projected specimen is the widest. For a complete rotation, the projected specimen would stay within the limits of the rectangle. Photo by MNHN.
Fig. 17 in Micro-computed tomography for natural history specimens: a handbook of best practice protocols
Fig. 17. Scan of a marine worm (polychaete) without (A) and with (B) ring artefacts correction during the reconstruction procedure. The red square indicates the presence of ring artefacts which are reduced in (B) following the ring artefacts correction. Images by HCMR micro-CT lab.
Fig. 9 in Micro-computed tomography for natural history specimens: a handbook of best practice protocols
Fig. 9. External morphology of the polychaete Lumbrineris latreilli Audouin & Milne Edwards, 1834. Specimen had been preserved in ethanol and was subsequently blotted dry on a tissue and scanned in air. The remaining ethanol (shown by arrows) can be observed clinging to the anterior end of the polychaete. Image by HCMR micro-CT lab, CC-BY Sarah Faulwetter.
Fig. 12 in Micro-computed tomography for natural history specimens: a handbook of best practice protocols
Fig. 12. Cross-section image without (A) and with (B) a selection of a region of interest (red square) for the reconstruction of polychaete jaws. Image by HCMR micro-CT lab.
Polynomial chaos to efficiently compute the annual energy production in wind farm layout optimization
<p>Data for the Wind Energy Science paper "Polynomial chaos to efficiently compute the annual energy production in wind farm layout optimization".</p> <p>The data includes a file describing the wind direction distribution. The i<sup>th</sup> probability value corresponds to the probability of the wind coming between direction i and i+1.</p> <p>The other data files, corresponding to the wind farm layouts, provide the x,y coordinates of the wind turbines. </p>
Carbon-latitude analysis of the ocean carbon cycle computed from NEMO-Medusa
<p>NetCDF files containing carbon-latitude stream functions computed from the coupled physical-biogeochemical model NEMO-Medusa. The stream functions have been computed for the last years of a historical run and the first years of the RCP 8.5 scenario run (2000-2009). The dataset includes a climatology of the most important tracers, the computed meridional carbon transports in the ocean, and volume distribution in the carbon-latitude space.</p>
Traces in seconds for cloud computing research
<p>Traces in seconds for research in allocation in cloud computing. The code to generate this traces can be obtained from <a href="https://github.com/asi-uniovi/traces-seconds">https://github.com/asi-uniovi/traces-seconds</a>. Additional explanation of these traces is in the paper <em>Influence of the trace resolution and length in the cost optimization process in cloud computing</em>, published in the 2019 International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS 2019). Please, cite this paper if you use this traces in your research.</p>
Brain Invaders Solo versus Collaboration: Multi-User P300-based Brain-Computer Interface Dataset (bi2014b)
<p><strong>Summary:</strong></p> <p>This dataset contains electroencephalographic (EEG) recordings of 38 subjects playing in pair to the multi-user version of a visual P300-based Brain-Computer Interface (BCI) named <em>Brain </em><em>Invaders </em>(Congedo et al., 2011). The interface uses the oddball paradigm on a grid of 36 symbols (1 Target, 35 Non-Target) that are flashed pseudo-randomly to elicit a P300 response, an evoked-potential appearing about 300ms after stimulation onset. EEG data were recorded using 32 active wet electrodes per subjects (total: 64 electrodes) during three randomized conditions (Solo1, Solo2, Collaboration). The experiment took place at GIPSA-lab, Grenoble, France, in 2014. A full description of the experiment is available at <a href="https://hal.archives-ouvertes.fr/hal-02173958">https://hal.archives-ouvertes.fr/hal-02173958</a>. Python code for manipulating the data is available at <a href="https://github.com/plcrodrigues/py.BI.EEG.2014b-GIPSA">https://github.com/plcrodrigues/py.BI.EEG.2014b-GIPSA</a>. The ID of this dataset is <em>bi2014b.</em></p> <p> </p> <p><strong>Full description of the experiment and dataset: </strong><a href="https://hal.archives-ouvertes.fr/hal-02173958">https://hal.archives-ouvertes.fr/hal-02173958</a></p> <p> </p> <p><strong><em>Investigators</em>:</strong> Eng. Louis Korczowski, B. Sc. Ekaterina Ostaschenko</p> <p> </p> <p><strong><em>Technical</em></strong> <strong><em>Support</em></strong>: Eng. Anton Andreev, Eng. Grégoire Cattan, Eng. Pedro. L. C. Rodrigues, M. Sc. Violette Gautheret</p> <p> </p> <p><strong><em>Scientific Supervisor:</em></strong> Ph.D. Marco Congedo</p> <p> </p> <p><strong>ID of the dataset: </strong><em>bi2014b</em></p>
Brain Invaders Cooperative versus Competitive: Multi-User P300-based Brain-Computer Interface Dataset (bi2015b)
<p><strong>Summary:</strong></p> <p>This dataset contains electroencephalographic (EEG) recordings of 44 subjects playing in pair to the multi-user version of a visual P300 Brain-Computer Interface (BCI) named <em>Brain </em><em>Invaders</em>. The interface uses the oddball paradigm on a grid of 36 symbols (1 or 2 Target, 35 or 34 Non-Target) that are flashed pseudo-randomly to elicit the P300 response. EEG data were recorded using 32 active wet electrodes per subjects (total: 64 electrodes) during four randomised conditions (Cooperation 1-Target, Cooperation 2-Targets, Competition 1-Target, Competition 2-Targets). The experiment took place at GIPSA-lab, Grenoble, France, in 2015. A full description of the experiment is available at <a href="https://hal.archives-ouvertes.fr/hal-02173913">https://hal.archives-ouvertes.fr/hal-02173913</a>. Python code for manipulating the data is available at <a href="https://github.com/plcrodrigues/py.BI.EEG.2015b-GIPSA">https://github.com/plcrodrigues/py.BI.EEG.2015b-GIPSA</a>. The ID of this dataset is <em>bi2015b.</em></p> <p> </p> <p><strong>Full description of the experiment and dataset: </strong><a href="https://hal.archives-ouvertes.fr/hal-02173913">https://hal.archives-ouvertes.fr/hal-02173913</a></p> <p> </p> <p><strong><em>Investigators</em>:</strong> Eng. Louis Korczowski, B. Sc. Martine Cederhout</p> <p> </p> <p><strong><em>Technical</em></strong> <strong><em>Support</em></strong>: Eng. Anton Andreev, Eng. Grégoire Cattan, Eng. Pedro. L. C. Rodrigues, M. Sc. Violette Gautheret</p> <p> </p> <p><strong><em>Scientific Supervisor:</em></strong> Ph.D. Marco Congedo</p> <p> </p> <p><strong>ID of the dataset: </strong><em>bi2015b</em></p>
Design and in vitro realization of carbon-conserving photorespiration - Computational Analysis
<p>Our aim is to develop a framework for modeling C3 photosynthesis in mesophyll cells that allows us to compare native photorespiration with engineered photosynthetic shunts. In particular, we want to model conditions that are most relevant to agricultural crops, i.e. a range of light intensities and both ambient and low CO2 intercellular airspace concentrations. Here we presented our computational analysis based on pathSeekR, the stoichiometric-kinetic model, kinetic models of photorespiration shunts and pathSeekR pathway architectures. </p>
Cloud-Repro: Reproducible Workflow on a Public Cloud for Computational Fluid Dynamics
<p>In a new effort to make our research transparent and reproducible by others, we developed a workflow to run computational studies on a public cloud. It uses Docker containers to create an image of the application software stack. We also adopt several tools that facilitate creating and managing virtual machines on compute nodes and submitting jobs to these nodes. The configuration files for these tools are part of an expanded "reproducibility package" that includes workflow definitions for cloud computing, in addition to input files and instructions. This facilitates re-creating the cloud environment to re-run the computations under the same conditions.</p> <p>The present Zenodo dataset contains all secondary data required to reproduce the figures of the manuscript ("Reproducible Workflow on a Public Cloud for Computational Fluid Dynamics") without running the CFD simulations again.</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.