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3,688 results for “Computer”
Data from: Redescription of Phymolepis cuifengshanensis (Antiarcha: Yunnanolepididae) using high-resolution computed tomography and new insights into anatomical details of the endocranium in antiarchs
Background. Yunnanolepidoids constitute either the basal-most consecutive segments or the most primitive clade of antiarchs, a highly diversified jawed vertebrate group from the Silurian and Early Devonian periods. Although the general morphology of yunnanolepidoids is well established, their endocranial features remain largely unclear, thus hindering our further understanding of antiarch evolution, and early gnathostome evolution. Phymolepis cuifengshanensis, a yunnanolepidoid from the Early Devonian of southwestern China, is re-described in detail to reveal the information on endocranial anatomy and additional morphological data of head and trunk shields. Methods. We scanned the material of P. cuifengshanensis using high-resolution computed tomography and generated virtual restorations to show the internal morphology of its dermal shield. The dorsal aspect of endocranium in P. cuifengshanensis was therefore inferred. The phylogenetic analysis of antiarchs was conducted based on a revised and expanded dataset that incorporates ten new cranial characters. Results. The lateroventral fossa of trunk shield and Chang's apparatus are three-dimensionally restored in P. cuifengshanensis. The canal that is positioned just anterior to the internal cavity of Chang's apparatus, probably corresponds to the rostrocaudal canal of euantiarchs. The endocranial morphology of P. cuifengshanensis corroborates a general pattern for yunnanolepidoids with additional characters distinguishing them from sinolepids and euantiarchs, such as a developed cranio-spinal process, an elongated endolymphatic duct, and a long occipital portion. Discussion. In light of new data from Phymolepis and Yunnanolepis, we summarized the morphology on the visceral surface of head shield in antiarchs, and formulated additional ten characters for the phylogenetic analysis. These cranial characters exhibit a high degree of morphological disparity between major subgroups of antiarchs, and highlight the endocranial character evolution in antiarchs.
Data from: Computational research on mobile pastoralism using agent-based modeling and satellite imagery
Dryland pastoralism has long attracted considerable attention from researchers in diverse fields. However, rigorous formal study is made difficult by the high level of mobility of pastoralists as well as by the sizable spatio-temporal variability of their environment. This article presents a new computational approach for studying mobile pastoralism that overcomes these issues. Combining multi-temporal satellite images and agent-based modeling allows a comprehensive examination of pastoral resource access over a realistic dryland landscape with unpredictable ecological dynamics. The article demonstrates the analytical potential of this approach through its application to mobile pastoralism in northeast Nigeria. Employing more than 100 satellite images of the area, extensive simulations are conducted under a wide array of circumstances, including different land-use constraints. The simulation results reveal complex dependencies of pastoral resource access on these circumstances along with persistent patterns of seasonal land use observed at the macro level.
In Vivo Rodent Cervicothoracic Vasculature Imaging Using Photoacoustic Computed Tomography
<p>These video clips are the supplementary video for the manuscript "<em>In Vivo</em> Rodent Cervicothoracic Vasculature Imaging Using Photoacoustic Computed Tomography" submitted to <em>Photonics</em>.</p>
FIGURES 1–4 in The first extinct species of Monolepta Chevrolat (Coleoptera: Chrysomelidae Galerucinae) from Bitterfeld amber, described using X-ray micro-computed tomography
FIGURES 1–4. Photomicrographs of Monolepta rappsilberi sp. nov., holotype, No T-I-K-30 [CIR], female: 1—habitus, dorsal view; 2—habitus, ventral view; 3—details of abdomen; 4—tarsi. Abbreviations: ca—carina, mst—mesotibia, mtt—metatibia, prt—protibia, v1–v5—abdominal ventrites 1–5. Not reproduced to the same scale.
FIGURES 12–13. X in The first extinct species of Monolepta Chevrolat (Coleoptera: Chrysomelidae Galerucinae) from Bitterfeld amber, described using X-ray micro-computed tomography
FIGURES 12–13. X-ray micro-CT renderings of Monolepta rappsilberi sp. nov., holotype, No T-I-K-30 [CIR]: 12—head (right antenna removed), frontal view; 13—details of forebody without legs, ventral view. Abbreviations: as—antennal sock- et, c—clypeus, fc—frontal carina, ft—frontal tubercle, l—labrum, mp3–mp4—maxillary palpomeres 3–4, os—orbital sulcus, scs—supracallar sulcus, sos—supraorbital sulcus. Scale bar represents 0.5 mm.
FIGURES 5–8. X in The first extinct species of Monolepta Chevrolat (Coleoptera: Chrysomelidae Galerucinae) from Bitterfeld amber, described using X-ray micro-computed tomography
FIGURES 5–8. X-ray micro-CT renderings of Monolepta rappsilberi sp. nov., holotype, No T-I-K-30 [CIR], habitus: 5—dorsal view; 6—ventral view; 7—right lateral vie; 8—left lateral view. Scale bar represents 1.0 mm.
FIGURES 9–11. X in The first extinct species of Monolepta Chevrolat (Coleoptera: Chrysomelidae Galerucinae) from Bitterfeld amber, described using X-ray micro-computed tomography
FIGURES 9–11. X-ray micro-CT renderings of Monolepta rappsilberi sp. nov., holotype, No T-I-K-30 [CIR]: 9—habitus, caudal view; 10—details of forebody, dorsal view; 11—habitus without legs, ventral view. Not reproduced to the same scale.
Data from: Computed tomography scanning as a tool for linking the skeletal and otolith-based fossil records of teleost fishes
Micro-computed tomography scanning (µCT scanning) now represents a standard tool for non-destructive study of internal or concealed structure in fossils. Here we report on otoliths found in situ during routine µCT scanning of three-dimensionally preserved skulls of Palaeogene and Cretaceous fishes. Comparisons are made with isolated otolith-based taxa in order to attempt correlations between the body fossil and otolith fossil records. In situ otoliths previously extracted mechanically from specimens of Apogon macrolepis and Dentex laekeniensis match our µCT models. In some cases, we find a high degree of congruence between previously independent taxonomic placements for otolith and skeletal remains (Rhinocephalus, Osmeroides, Hoplopteryx). Unexpectedly, in situ otoliths of the aulopiform Apateodus match isolated otoliths of Late Cretaceous age previously interpreted as belonging to gempylids, a group of percomorph fishes that do not appear in the body fossil record until the Palaeogene. This striking example of convergence suggests constraints on otolith geometry in pelagic predators. The otoliths of Apateodus show a primitive geometry for aulopiforms and lack the derived features of Alepisauroidea, the lizardfish clade to which the genus is often attributed. In situ otoliths of Early Cretaceous fishes (Apsopelix) are not well preserved, and we are unable to identify clear correlations with isolated otolith morphologies. We conclude that the preservation of otoliths suitable for µCT scanning appears intimately connected with the taphonomic history, lithological characteristics of surrounding matrix, and syn- and postdepositional diagenetic effects.
PyGEDM pre-computed maps of Galactic electron density models
<p>Pre-computed HDF5 data cubes for <a href="https://github.com/FRBs/pygedm">PyGEDM</a> web app.</p> <p>Datasets in HDF5 file:</p> <p>Dimension scales distance (pc), DM (pc/cm3), galactic latitude and galactic longitude (deg).</p> <pre>[('dist', <HDF5 dataset "dist": shape (10,), type "<i8">), ('dm', <HDF5 dataset "dm": shape (12,), type "<i8">), ('gb', <HDF5 dataset "gb": shape (361,), type "<f8">), ('gl', <HDF5 dataset "gl": shape (721,), type "<f8">), gl = np.linspace(-180, 180, 360*2+1) gb = np.linspace(-90, 90, 180*2+1) dist = np.array((0.1, 0.2, 0.5, 1, 2, 5, 8.5, 10, 20, 50)) dm = np.array((1, 2, 5, 10, 20, 50, 100, 200, 500, 1000, 2000, 5000)) Galactocentric coordinates XYZ (pc): ('x', <HDF5 dataset "x": shape (100,), type "<i8">), ('y', <HDF5 dataset "y": shape (100,), type "<i8">), ('z', <HDF5 dataset "z": shape (100,), type "<i8">)]</pre> <p>NE2001 model precomputed datasets:</p> <pre> ('ne2001', <HDF5 group "/ne2001" (5 members)>),</pre> <pre>[('ddm', <HDF5 dataset "ddm": shape (12, 361, 721), type "<f8">), ('ddm_tau', <HDF5 dataset "ddm_tau": shape (12, 361, 721), type "<f8">), ('dmd', <HDF5 dataset "dmd": shape (10, 361, 721), type "<f8">), ('dmd_tau', <HDF5 dataset "dmd_tau": shape (10, 361, 721), type "<f8">), ('xyz', <HDF5 dataset "xyz": shape (100, 100, 100), type "<f8">)] YMW16 precomputed datasets: ('ymw16', <HDF5 group "/ymw16" (5 members)>),</pre> <pre>[('ddm', <HDF5 dataset "ddm": shape (12, 361, 721), type "<f8">), ('ddm_tau', <HDF5 dataset "ddm_tau": shape (12, 361, 721), type "<f8">), ('dmd', <HDF5 dataset "dmd": shape (10, 361, 721), type "<f8">), ('dmd_tau', <HDF5 dataset "dmd_tau": shape (10, 361, 721), type "<f8">), ('xyz', <HDF5 dataset "xyz": shape (100, 100, 100), type "<f8">)]</pre>
Supporting data for 'Computing with spin qubits at the surface code error threshold'
<p>IMPORTANT:</p> <p>The data must not be reused in other studies or publications without approval of the authors.</p>
The efficacy of X-ray micro-computed tomography for acorn worm taxonomy: Balanoglossus occidentalis (manuscript species) from Puget Sound, Washington
<p>Micro-CT scan data of Balanoglossus occidentalis generated with a Zeiss Xradia Versa 520. A first scan was made of the whole specimen with AMC drift correction, 0 beam hardening, voltage of 60 kV, currant of 82 µA, exposure time of 1.7 sec, LE2 source filter, LE6 secondary filter, 1601 Projection numbers, single FOV, vertical stitching and a resolution of 9,0373 µm. Then, a second scan was made focusing on the collar and the base of the proboscis because these regions are particularly informative to taxonomic descriptions of acorn worms. This second scan had a Projection number of 2401 and a resolution of 4.5075 µm. Each scan took 3 hours.</p>
DFT-based Phonon-computations for "Considering the Role of Ion Transport in Diffuson-Dominated Thermal Conductivity"
<p>These are the harmonic phonon and Grüneisen parameter computations for the publication "Considering the Role of Ion Transport in DiffusonDominated Thermal Conductivity". VASP and Phonopy outputs are included in this data set.</p>
Efficient parallelization of tensor network contractions for simulating quantum computation
<p> In this paper, we demonstrate a classical simulation framework for quantum computation by contracting tensor networks of sizes previously deemed out of reach. The main contribution of this work is a parallelization scheme called <em>index slicing</em> that breaks down an infeasibly large tensor network contraction task into smaller subtasks that can be executed fully in parallel, without interdependencies or intermediate communications. As a benchmarking example, we show that our algorithm can reduce the simulation of the Sycamore random circuit sampling task to less than 20 days, achieving an acceleration of over five orders of magnitude compared to the original proposal. We then showcase the capabilities of the simulation framework via investigations of near-term quantum algorithms and quantum error correction. Given the ubiquity of tensor networks in quantum information science, we believe that our simulation framework will be a valuable tool in the era of quantum information technology.</p>
Coronavirus Disease 2019 (COVID-19) in Italy: features on Chest Computed Tomography using a structured report system
<p>We uploaded a dataset including the presence of GGO and its distribution for each patient presented in the manuscript: Grassi R, Fusco R, Belfiore MP, Montanelli A, Patelli G, Urraro F, Petrillo A, Granata V, Sacco P, Mazzei MA, Feragalli B, Reginelli A, Cappabianca S. Coronavirus disease 2019 (COVID-19) in Italy: features on chest computed tomography using a structured report system. Sci Rep. 2020 Oct 14;10(1):17236. doi: 10.1038/s41598-020-73788-5. Erratum in: Sci Rep. 2021 Feb 15;11(1):4231. PMID: 33057039; PMCID: PMC7566610.</p>
Preliminary Report on Computed Tomography Radiomics Features as biomarkers to Immunotherapy selection in Lung Adenocarcinoma Patients
<p>We uploaded all radiomics metrics extracted for 88 patients enrolled in the study: Preliminary Report on Computed Tomography Radiomics Features as biomarkers to Immunotherapy selection in Lung Adenocarcinoma Patients.</p> <p>Each radiomic feature is described in the Appendix of the manuscript.</p> <p>Moreover Overall survival (OS) and Progression free survival (PFS) for each patient is provided.</p>
Measuring the Capabilities of Quantum Computers
<p>This is supplemental data and code for: T. Proctor et al., <em><a href="https://www.nature.com/articles/s41567-021-01409-7">Measuring the Capabilities of Quantum Computers</a>,</em> Nature Physics <strong>18</strong>, 75-79 (2022).</p> <p>This folder contains all the data and the analysis code to generate the results presented in that paper. The core data analysis routines use PyGSTi, which can be found at <a href="https://github.com/pyGSTio/pyGSTi">https://github.com/pyGSTio/pyGSTi</a>.</p> <p>Please direct any questions to Timothy Proctor (tjproct@sandia.gov).</p>
Scalable Randomized Benchmarking of Quantum Computers using Mirror Circuits
<p>This is supplemental data and code for: T. Proctor et al., <em><a href="http://https://doi.org/10.48550/arXiv.2112.09853">Scalable randomized benchmarking of quantum computers using mirror circuits</a>, </em>arXiv 2112.09853 (2021).</p> <p>This folder contains all the data and the analysis code to generate the results presented in that paper. The core data analysis routines use PyGSTi, which can be found at <a href="https://github.com/pyGSTio/pyGSTi">https://github.com/pyGSTio/pyGSTi</a>.</p> <p>Please direct any questions to Timothy Proctor (tjproct@sandia.gov).</p>
Event-Based Computing Packet Storm Dataset - 512 Square Heated Plate
<p>Instrumentation data for a 512x512 heated plate run on the POETS event-based compute platform.</p>
Figure 2. Bayesian phylogram computed for cytochrome oxidase I in Bythinella Moquin-Tandon, 1856 (Gastropoda: Rissooidea: Bythinellidae) in Romania: species richness in a glacial refugium
Figure 2. Bayesian phylogram computed for cytochrome oxidase I (COI) sequences with MRBAYES, Bayesian probabilities for branches are given.
The influence coefficients used in Wind Energy Science paper "A computationally efficient engineering aerodynamic model for swept wind turbine blades"
<p>The influence coefficients for the convective correction with full double-precision floating-point accuracy. This is the supplement for the research article: "A computationally efficient engineering aerodynamic model for swept wind turbine blades", submitted to Wind Energy Science journal.</p> <p>Code language: Fortran</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.