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3,688 results for “Computer”
FIGURE 107 in Osteology of Tyrannosaurus rex: insights from a nearly complete skeleton and high-resolution computed tomographic analysis of the skull
FIGURE 107. FMNH PR2081, Tyrannosaurus rex. Phalanges of right pes in lateral view. Scale = 10 cm. Photographs by J. Weinstein.
FIGURE 89 in Osteology of Tyrannosaurus rex: insights from a nearly complete skeleton and high-resolution computed tomographic analysis of the skull
FIGURE 89. FMNH PR2081, Tyrannosaurus rex. Articulated right manus, dorsal view. Scale = 5 cm. Photograph by J. Weinstein.
Computational insights into voltage dependence of polyamine block in a strong inwardly rectifying K+ channel
<p>Supplemental Material to the study "<em>Computational insights into voltage dependence of polyamine block in a strong inwardly rectifying K<sup>+</sup> channel</em>", to be published in <em>Frontiers in Pharmacology</em>.</p> <p>The upload contains (1) a representative trajectory (run4), and (2) two frames of the SPM inward-pulling simulations:</p> <ul> <li>(1) Free MD run4 with outward driving force, containing the compressed .xtc, the .tpr, and the final step as .gro file</li> <li>(2) steered MD, containing the starting frame (SPM in the central cavity below the Selectivity Filter) and a representative frame of SPM interacting with the CTD-binding site. </li> </ul>
Hydrate spatial distribution influence on the mechanical behavior hydrate-bearing sediment using computed tomography
<p>This supporting information includes two Movie S1-2, illustrating the deformation evolution of the specimen # O-1 and specimen # O-2.</p> <p>Movie S1 is uploaded with file name Movie S1.gif. Detailed information includes the deformation evolution of the specimen # O-1.</p> <p>Movie S2 is uploaded with file name Movie S2. gif. Detailed information includes the deformation evolution of the specimen # O-2.</p>
Noise study data for: Mechanisms of root-reinforcement in soils: an experimental methodology using four-dimensional X-ray computed tomography and digital volume correlation
<p>This dataset contains noise study data used in the paper: Mechanisms of root-reinforcement in soils: an experimental methodology using four-dimensional X-ray computed tomography and digital volume correlation. These include raw CT scans and processed digital volume correlation data.</p> <p>This dataset is part of another dataset which covers other aspects of the paper DOI: <a href="http://www.doi.org/10.5281/zenodo.3352268">10.5281/zenodo.3352268</a></p> <p>The structure of the dataset is as follows:</p> <ul> <li>Noise study CT raw volumes are contained in a zip file. There are four raw files corresponding to the four noise study steps. These files are 8-bit unsigned, dimensions are 1800 x 1800 x 1400 pixels. A txt file giving more details to the data is included. <ul> <li><strong>CT_Raw_data_Noise_Scans.zip</strong></li> </ul> </li> <li>Metadata files generated for each scan given details of scan parameters are found in the zip file: <ul> <li><strong>CT_Scan_Metadata.zip</strong></li> </ul> </li> <li>Tabulated digital volume data for the noise study scans are contained in the zip file. Tabulated data for each subset size is included in subfolders. A .txt file explains the structure of the tab separated .dat files, i.e. what each column of data represents, and what CT scan each of the four .dat files relate to. <ul> <li><strong>DVC_Noise_Study_Data.zip</strong></li> </ul> </li> </ul> <p> </p> <p> </p>
Large Scale Computational Analysis of Coding and Non-coding Element Expression in Mycobacterium tuberculosis Unannotated CDS
<p>Large Scale Computational Analysis of Coding and Non-coding Element Expression in Mycobacterium tuberculosis Unannotated CDS data for the Applied Medical Science MSci Research Project. </p>
Individual versus computer-supported collaborative self-explanations: how do their writing analytics differ? Coh-Metrix results and students' excerpts.
<p>Coh-Metrix results and students' excerpts of the publication: Albó, L., Beardsley, M., Amarasinghe, I. & Hernández-Leo, D. (2020). Individual versus computer-supported collaborative self-explanations: how do their writing analytics differ? In International conference on Advanced Learning Technologies and Technology-enhanced Learning (ICALT). </p>
Computational Study on Effectiveness of Knowledge Transfer in Dynamic Multi-objective Optimization
<p>This file is the output data obtained when running the experiments from the paper below:</p> <p>Ruan, G., Minku, L., Menzel, S., Sendhoff, B., Yao., “Computational Study on Effectiveness of Knowledge Transfer in Dynamic Multi-objective Optimization” <em>2020 IEEE Congress on Evolutionary Computation</em></p> <p>Transfer learning has been used for solving multiple optimization and dynamic multi-objective optimization problems, since transfer learning is believed to be able to transfer useful information from one problem instance to help solving another related problem instance. This paper aims to study how effective transfer learning is in dynamic multi-objective optimization (DMO). Through computation time analysis of transfer learning, we show that the ‘inner’ optimization problem introduced by transfer learning is very time-consuming. In order to enhance the efficiency, two alternatives are computationally investigated on a number of dynamic bi- and tri-objective test problems. Experimental results have shown that the greatly enhanced efficiency does not result in much degeneration on the performance of transfer learning. Considering the high computational cost of transfer learning, it is likely that the original purpose of using transfer learning in DMO might be negated. In other words, the computation time saved in optimization is eaten up by computationally expensive transfer learning. As a result, there is less gain than expected in the overall computational efficiency. To verify this, experiments have been conducted, regarding using computational cost of transfer learning to optimize randomly generated solutions. The results have demonstrated that the convergence and diversity of final solutions generated from the random solutions are significantly better than those generated from transferred solutions under the same total computational budget.</p>
Data from: Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning
<p><b>Objectives: </b>To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation.</p> <p><b>Background: </b>Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images.</p> <p><b>Methods: </b>Images from a multicenter registry of patients that underwent clinically-indicated CCTA were used. The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall (RVW) and left atrial wall (LAW) were annotated as ground truth. The U-net-derived deep learning model was trained, validated and tested in a 70:20:10 split.</p> <p><b>Results: </b>The dataset comprised 206 patients, with 5.130 billion pixels. Mean age was 59.9 ± 9.4 yrs., and was 42.7% female. An overall median Dice score of 0.820 (0.782, 0.843) was achieved. Median Dice scores for PAA, DA, SVC, IVC, PA, CS, RVW and LAW were 0.969 (0.979, 0.988), 0.953 (0.955, 0.983), 0.937 (0.934, 0.965), 0.903 (0.897, 0.948), 0.775 (0.724, 0.925), 0.720 (0.642, 0.809), 0.685 (0.631, 0.761) and 0.625 (0.596, 0.749) respectively. Apart from the CS, there were no significant differences in performance between sexes or age groups.</p> <p><b>Conclusions: </b>An automated deep learning model demonstrated segmentation of multiple cardiovascular structures from CCTA images with reasonable overall accuracy when evaluated on a pixel level.</p>
Figure 3 in A Computational Analysis of Limb and Body Dimensions in Tyrannosaurus rex with Implications for Locomotion, Ontogeny, and Growth
Figure 3. Models: right lateral view. See Figure 2ı but skeleton scans/models are ordered from top to bottom. doi:10.1371/journal.pone.0026037.g003
Figure 5. Muscle mass reconstruction method for M in A Computational Analysis of Limb and Body Dimensions in Tyrannosaurus rex with Implications for Locomotion, Ontogeny, and Growth
Figure 5. Muscle mass reconstruction method for M. caudofemoralis longus (see Methods); Carnegie specimen depicted. Dorsal and right lateral views are shown on topı and in the bottom row are caudal views of the right femur and then caudal vertebrae (8th and 17th). Red shaded volumes are the M. caudofemoralis longus reconstruction. Note a small space for M. caudofemoralis brevis (not reconstructed) is left around the ilium/sacrum and lateral to the CFL insertion. doi:10.1371/journal.pone.0026037.g005
Figure 2 in A Computational Analysis of Limb and Body Dimensions in Tyrannosaurus rex with Implications for Locomotion, Ontogeny, and Growth
Figure 2. Models: cranial view. From left to right for each specimen: 3D scan of skeleton (not shown for Jane due to copyright issues)ı minimal modelı and maximal model. Not to scale. doi:10.1371/journal.pone.0026037.g002
Figure 1 in A Computational Analysis of Limb and Body Dimensions in Tyrannosaurus rex with Implications for Locomotion, Ontogeny, and Growth
Figure 1. Modelling procedureı showing the Carnegie specimen. From left to rightı top to bottom these show the scannedı reconstructedı and straightened skeleton; the skeleton with elliptical hoops that define fleshy boundaries; the air spaces representing pharynxı sinusesı lungs and other airways including air sacs; and the final meshed reconstruction used for mass and COM estimates. doi:10.1371/journal.pone.0026037.g001
Petermann Fjord Sediment Core Computed Tomography (CT) Scans (Cruise OD1507)
<p>Computed tomography (CT) scans of sediment cores collected from Petermann Fjord during the PETERMANN15 expedition of the Swedish Icebreaker Oden, OD1507. Included cores, 03TC, 03PC, 04GC, 06PC, 08GC, 10PC, 10TC, 40PC, 40TC, and 41GC. Data include 2 mm thick coronal slices in DICOM format and SedCT products, including images and CT numbers.</p>
Grades of Computer Science Students in a Nigerian University
<p><strong>Brief Description of Dataset</strong></p> <p>The dataset contains information about students in a 5-year Bachelor of Technology Degree in Computer Science from a North Eastern Nigerian University of Technology. The year of enrolment of the students ranges from 2005 to 2015. In the dataset, “NA” means that the student did not attempt the course.</p> <p><strong>Data Cleaning</strong></p> <p>First steps: the student marks that are less than 40 are excluded, as the course has to be retaken to be passed with a minimum of 50 marks. In addition, courses that are taken outside of graduation audit by students are eliminated. </p> <p>There were 430 students screened for enrolment in the study with 95 being excluded because they did not take the course within the period of degree program for their early exemption. The exact ages of the participants are unknown other than all students enrolled were aged above 18 years of age.</p> <p> </p> <p> </p>
Quantum Synth: a Quantum-Computing-based synthesizer - Supplementary videos
<p>These are supplementary videos for the AudioMostly 2020 conference paper titled: "Quantum Synth: a Quantum-Computing-based synthesizer".</p> <p>Video 1: 00_video_subtractive_backends_take2.mov</p> <p>Video 2: 01_video_subtractive_grover_cut.mov</p>
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>
Predictors of abnormal computed tomography findings for paediatric head injury: a retrospective cohort study
<p class="MDPI17abstract"><b>Objectives:</b> Head injuries in children are common causes for visits to the emergency department (ED). Computed tomography<b> (</b>CT) scans are useful for confirming head injury diagnoses. However, radiation exposure from CT scans might cause lethal malignancies. We aimed to examine predictors for the indication of performing CT scans necessary for diagnosis.</p> <p class="MDPI17abstract"><b>Design:</b> Retrospective cohort study.</p> <p class="MDPI17abstract"><b>Setting:</b> Three EDs in Japan</p> <p class="MDPI17abstract"><b>Participants</b>: Patients aged <16 years with head trauma who underwent CT.</p> <p class="MDPI17abstract"><b>Primary and Secondary Outcome Measures</b>: The primary outcome measure was abnormal CT findings that were evaluated using the area under the receiver-operating characteristic curve (AUC). We derived predictors from three existing CDRs: Canadian Assessment of Tomography for Childhood Head Injury (CATCH), Children's Head Injury Algorithm for the Prediction of Important Clinical Events (CHALICE), and Paediatric Emergency Care Applied Research Network (PECARN).</p> <p class="MDPI17abstract"><b>Results:</b> Of 1,103 eligible patients, 410 were included in this study. There were 283 (68%) boys, and the median age was 2 years. In total, 35 (9%) patients showed an abnormality, 73 (18%) were admitted, and 3 (0.7%) underwent neurosurgery. We developed a CDR consisting of 6 predictors for identifying children with abnormal CT findings: (1) severe or worsening headache; (2) GCS <15; (3) signs of skull fracture; (4) hematoma; (5) loss of consciousness; and (6) altered mental status. Our CDR had a sensitivity of 74.3%, a specificity of 75.2%, a negative predictive value of 96.9%, and a positive predictive value of 21.8%. The AUC for our rule was not inferior to those for CATCH, CHALICE, and PECARN {0.75 (95% confidence interval [CI], 0.67-0.81) versus 0.64 (95% CI, 0.56-0.73; p<0.05), 0.68 (95% CI, 0.60–0.76; p=0.28), and 0.67 (95% CI, 0.60-0.74); p=0.10}.</p> <p class="MDPI17abstract"><b>Conclusions:</b> Our findings suggest that a CDR, which lowers the frequency of CT in children with head injuries, must be developed and validated.</p>
Datasets for the computational workflow of multidimensional photoemission spectroscopy
<p>Recorded single-electron event data of bulk 2H-WSe<sub>2</sub> photoemission from a commercial momentum microscope (SPECS METIS 1000).These data are used for demonstration of the computational workflow explained in the following publication.<br> <br> <strong>R. P. Xian, Y. Acremann, S. Y. Agustsson, M. Dendzik, K. Bühlmann, D. Curcio, D. Kutnyakhov, F. Pressacco, M. Heber, S. Dong, T. Pincelli, J. Demsar, Wilfried Wurth, Ph. Hofmann, M. Wolf, M. Scheidgen, L. Rettig, R. Ernstorfer, An open-source, end-to-end workflow for multidimensional photoemission spectroscopy, Scientific Data 7, 442 (2020). DOI: 10.1038/s41597-020-00769-8</strong><br> <br> The zip files are not directly usable for running the computational workflow, but requires first to unzip into HDF5 format (.h5).</p>
HTTP Traffic Datasets for Research in Service-Oriented Computing
<p>We present three HTTP datasets for experimenting on various aspects of service-oriented computing.</p> <p>The datasets were generated by creating random traffic targeting the services offered by <a href="https://developers.google.com/tasks">Google Tasks</a>, <a href="https://api.slack.com/methods">Slack</a>, and <a href="https://developer.twitter.com/en/docs/tweets/post-and-engage/overview">Twitter</a>. In order to form transactions, various operations to create, read, update and delete (CRUD) service-specific resources were created and the respective responses were recorded, simulating service interactions by users through applications. The resources the operations interacted with are lists (Google Tasks), messages (Slack) and tweets (Twitter). </p> <p>The input generation used fuzzing techniques. In particular, <a href="https://jmeter.apache.org/">Apache JMeter</a> was used as it has the functionality to fuzz RESTful services (randomly generate various types of API calls by providing different inputs) and recording interactions in a suitable textual format for further processing. The fuzzing was guided by a light-weight semantic service model provided as <a href="https://github.com/OAI/OpenAPI-Specification">Swagger</a> (recently renamed as OpenAPI) spec. </p> <p>The datasets reflect the richness of modern Web APIs for experiments and are in the XML format.</p>
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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.