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10,119 results for “IT Support”
Dataset supporting the paper: "Identifying the Parametric Occurrence of Multiple Steady States for some Biological Networks"
<p>Dataset supporting the paper:</p> <p>Russell Bradford, James H. Davenport, Matthew England, Hassan Errami, Vladimir Gerdt, Dima Grigoriev, Charles Hoyt, Marek Košta, Ovidiu Radulescu, Thomas Sturm, and Andreas Weber.<br> Identifying the Parametric Occurrence of Multiple Steady States for some Biological Networks.<br> To appear in the Journal of Symbolic Computation.</p> <p>We provide all the accompanying material for the main computations, split up into sections as presented in the paper. For Sections 3 and 9 the computations are introduced by a plain text ReadMe. For Sections 4 and 6 they are introduced by a Maple (also available ad pdf printout).</p>
Dispersoid Composition in Zirconium Containing Al-Zn-Mg-Cu (AA7010) Aluminium Alloy - Supporting Data
<p>Data related to measured dispersoid compositions and calculated dispersoid volume fractions:</p> <p>Dispersoid Composition in Zirconium Containing Al-Zn-Mg-Cu (AA7010) Aluminium Alloy<br> A.M. Cassell, J. D. Robson, C. P. Race, A. Eggeman, T. Hashimoto, M. Besel.</p> <p>Submitted to Acta Materialia.</p> <p>Datafile of compositions to reproduce Fig.8 in paper (.mat Matlab format)</p> <p>Datafile of predicted dispersoid volume fractions on which calculations were performed to produce Fig. 10 in paper (comment in datafile provides further details)</p>
Data support for: "CCPi-Regularisation Toolkit for computed tomographic image reconstruction with proximal splitting algorithms"
<p>Provided tomographic projection data supports the publication in SoftwareX journal "<strong>CCPi-Regularisation Toolkit for computed tomographic image reconstruction with proximal splitting algorithms</strong>" published in 2019.</p> <ul> <li><em>TomoSim_data1550671417.h5</em> - is a simulated 3D tomographic projection data with noise and artifacts. The simulation is implemented using <a href="https://github.com/dkazanc/TomoPhantom">TomoPhantom</a> software.</li> <li><em>DendrData_3D.h5 - </em>is a real dataset obtained at I13 branchline of Diamond Light Source. It features a selected time frame out of dynamically collected tomographic data. Data shows a <a href="https://www.sciencedirect.com/science/article/pii/S1359645418302994?via%3Dihub">dendritic grain growth in Mg alloys</a>.</li> </ul> <p>The scripts to replicate the results shown in the paper are available at the Github page of the project: <a href="https://github.com/vais-ral/CCPi-Regularisation-Toolkit">CCPi-Regularisation-Toolkit</a></p> <p> </p> <p> </p>
Supporting data for: A method of sexing the human os coxae based on logistic regressions and Bruzek's nonmetric traits
<p>The three following datasets are related to the article <em>A method of sexing the human os coxae based on logistic regressions and Bruzek's nonmetric traits</em> (Santos, Guyomarc'h, Rmoutilova, & Bruzek, 2019):</p> <ul> <li><strong>data_refPELVIS_Santos2019AJPA.csv</strong>: is described as the "reference sample" of 592 ossa coxae in the article. This is the learning dataset available in the <a href="https://gitlab.com/f.santos/pelvis">PELVIS R package</a></li> <li><strong>data_518RightBones_Santos2019AJPA.csv</strong>: the dataset of 518 right ossa coxae used to discuss asymmetry and the impact of lateralization on the sex estimates produced by PELVIS</li> <li><strong>data_3D_Santos2019AJPA.csv</strong>: the virtual coxal data acquired through 99 CT-scan images</li> </ul>
Supporting data for: "Diaphysator: an online application for the exhaustive cartography and user-friendly statistical analysis of long bone diaphyses"
<p>Example of dataset to be used with the R-shiny application “Diaphysator”, composed of right tibiae and femora.</p> <p>These data file have been published in: Lacoste Jeanson, A., Santos, F., Villa, C., Banner, J., & Bruzek, J. (2018). Architecture of the femoral and tibial diaphyses in relation to body mass and composition: Research from whole-body CT. <em>American Journal of Physical Anthropology</em>, 167, 813– 826. doi: <a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/ajpa.23713">10.1002/ajpa.23713</a></p> <p>This zip file contains:</p> <ul> <li>an “Information file” in CSV format</li> <li>various data files for human femora and tibiae in CSV format</li> </ul> <p>For all CSV files, the field separator is the comma “,” and the character used for decimal points is the dot “.”</p>
Supporting Data: Voting on the threat of exclusion in a public goods experiment
<p>Additional material for the paper:</p> <p>Dannenberg, A., Haita-Falah, C. & Zitzelsberger, S. Voting on the threat of exclusion in a public goods experiment. Exp Econ 21, 266; 10.1007/s10683-019-09609-y (2019).</p> <p> </p>
Fig. 6. Skeletal arrangement. A in Molecular study supports the position of the New Zealand endemic genus Lamellomorpha in the family Vulcanellidae (Porifera, Demospongiae, Tetractinellida), with the description of three new species
Fig. 6. Skeletal arrangement. A. Lamellomorpha australis Kelly & Cárdenas sp. nov. showing dense, crustose ectosome packed with microrhabds and other microscleres, a rough megasclere bundle in the subectosome, emerging perpendicular to the surface, and the choanosome densely packed with microscleres (NIWA 89736 leg., holotype). B. L. australis sp. nov. showing the subectosome densely packed with microscleres, and megascleres radiating in the plane of the sponge body (NIWA 89717 leg.). C. L. strongylata Bergquist, 1968 showing a thin, fleshy ectosome packed with microrhabds and other microscleres, through which projects an 'extra-axial' tract of megascleres, traversing the subectosome and projecting through the surface (NIWA 93474 leg.). D. L. strongylata showing the 'axial' arrangement of contort strongyloxeas in the deep choanosome (NIWA 93474 leg.). E. Poecillastra ducitriaenea Kelly & Cárdenas sp. nov. showing a densely packed, crustose ectosome through which subectosomal megascleres project, a relatively cavernous subectosomal region packed with microxeas, and in the deep choanosome, huge swathes of oxeas (NIWA 61944 leg., holotype). Scale bars = 500 µm.
Fig. 5 in Molecular study supports the position of the New Zealand endemic genus Lamellomorpha in the family Vulcanellidae (Porifera, Demospongiae, Tetractinellida), with the description of three new species
Fig. 5. Morphology, megascleres and microscleres of Poecillastra macquariensis Kelly & Cárdenas sp. nov. (NIWA 52640 leg., holotype). A. Stalk, showing smooth attachment base with specks of remaining substrate, and broken edge of the upper part of the sponge. B. Microxeas. C. Detail of microxea showing roughened surface. D. Contort oxea of choanosome. E. Range of triaenes with medium to short shafts (calthrops). F. Plesiasters. G. Spiraster to metaster-like streptasters.
Fig. 3 in Molecular study supports the position of the New Zealand endemic genus Lamellomorpha in the family Vulcanellidae (Porifera, Demospongiae, Tetractinellida), with the description of three new species
Fig. 3. Morphology, megascleres, and microscleres of Lamellomorpha australis Kelly & Cárdenas sp. nov. A. Bilamellate form (NIWA 89736 leg., holotype). B. Club-shaped morphology showing both sides (NIWA 93483 leg., paratype). C. Sinuous oxeas (NIWA 44388 leg.). D. Curved, roughened microstrongyles (rounded ends) and microxeas (blunt-pointed ends) (NIWA 44388 leg.). E. Metasterlike streptasters with heavily spined, relatively long rays (NIWA 89736 leg. and NIWA 89717 leg.). F. Spirasters with dense, short, microspined rays (NIWA 44388 leg. and NIWA 89736 leg.).
Fig. 2 in Molecular study supports the position of the New Zealand endemic genus Lamellomorpha in the family Vulcanellidae (Porifera, Demospongiae, Tetractinellida), with the description of three new species
Fig. 2. Morphology, megascleres and microscleres of Lamellomorpha strongylata Bergquist, 1968. A. Deck image showing midnight blue colouration and palmate morphology (NIWA 93474 leg., image Lori J. Bell, CRRF). B. Loss of colouration and radiating skeleton (NIWA 73253 leg.). C. Range of roughened microstrongyles showing centrotylote forms and more typical curved forms (penultimate: NIWA 356 leg., rest: NIWA 93474 leg.). D. Sinuous oxeas (NIWA 93474 leg.). E. Metaster- to amphiaster-like streptasters (left: NIWA 93474 leg., center and right: NIWA 356 leg.); F. Metaster- to amphiaster-like streptasters (left and center: NIWA 356 leg., right: NIWA 93474 leg.).
Fig. 1 in Molecular study supports the position of the New Zealand endemic genus Lamellomorpha in the family Vulcanellidae (Porifera, Demospongiae, Tetractinellida), with the description of three new species
Fig. 1. Study area showing the collection localities for: Lamellomorpha strongylata Bergquist, 1968, around Middlesex Bank, Three Kings Islands, and Spirits Bay (triangles); L. australis Kelly & Cárdenas sp. nov., Bounty Platform, Campbell Plateau, Solander Trough, and Macquarie Ridge (Australian EEZ) (circles); Poecillastra ducitriaena Kelly & Cárdenas sp. nov., on the eastern edge of the Snares Platform (square); and Poecillastra macquariensis Kelly & Cárdenas sp. nov., on Seamount 5, Macquarie Ridge (star).
Supporting data for "Benchmarking Gate Fidelities in a Si/SiGe Two-Qubit Device" (arXiv:1811.04002)
<p>Supporting data and analysis scripts for FIG. 2 - FIG. 6 of "Benchmarking Gate Fidelities in a Si/SiGe Two-Qubit Device" (arXiv:1811.04002, Physical Review X in press)</p> <p>Data for FIG. 2 and FIG. 3 and analysis script are in the file 'single_two_qubit_RB.zip'.</p> <p>Data for FIG. 4 and analysis script are in the file 'CRB.zip'.</p> <p>Data for FIG. 5 and FIG. 6 and analysis script are in the file 'Append.zip'.</p> <p>To run the scripts, QCoDeS needs to be installed. Please refer to:</p> <p>http://qcodes.github.io/Qcodes/</p> <p>for instructions about installation and Dataset. If there's a problem, try to use an earlier version 0.1.7.</p> <p>For more information, contact the author: Xiao Xue, x.xue-1@tudelft.nl </p>
Optical spectroscopy supporting Chandra observations of Herbig AeBe stars
<p>This is optical data taken by AAVSO observers to support Chandra observations of Herbig AeBe stars. The purpose if this data is to check the accretion rate close in time to the Chandra observations.</p>
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.
Datasets and Supporting Materials for the IPIN 2018 Competition Track 4 (Foot-Mounted IMU based Positioning, off-site)
<p>This package contains the datasets and supplementary materials used in the IPIN 2018 Competition (Nantes, France).</p> <p><strong>Contents:</strong></p> <ol> <li>IPIN2018_CallForCompetition_v2.1: Call for competition including the technical annex describing the competition </li> <li>01-Logfiles: This folder contains 2 zip files.<br> - HKB08.zip : for sensors bias estimation.<br> - HKB82.zip : for trajectory estimation.<br> Each archive contains 4 files :<br> - HKBxx_mag.csv : magnetometer data<br> - HKBxx_sti.csv : inertial data<br> - HKBxx_ublox.ubx : GNSS data<br> - HKBxx_INFO.txt : info file<br> see page 16 of IPIN2018_CallForCompetition_v2.1.pdf for more details.</li> <li>02-Supplementary_Materials: This folder contains the datasheet files of the different sensors.</li> <li>03-Evaluation: This folder contains the scripts used to calculate the competition metric, the 75th percentile on all evaluation points. The ground truth is provided csv file.</li> </ol> <p><strong>Please, cite the following works when using the datasets included in this package:</strong></p> <ul> <li>Ortiz, M.; Perul, J.; Torres-Sospedra, J. Renaudin, V. Datasets and Supporting Materials for the IPIN 2018 Competition Track 4 (Foot-Mounted IMU based Positioning, off-site), Zenodo 2018 <a href="http://dx.doi.org/10.5281/zenodo.3228012">http://dx.doi.org/10.5281/zenodo.3228012</a></li> </ul> <p><strong>Additional information can be found at:</strong></p> <ul> <li><a href="http://evaal.aaloa.org/2018/call-for-competitions">http://evaal.aaloa.org/2018/call-for-competitions</a></li> <li><a href="http://ipin-conference.org/2018/ipincompetition/">http://ipin-conference.org/2018/ipincompetition/</a></li> </ul> <p><strong>For any further questions about the database and this competition track, please contact to: </strong></p> <ul> <li> <p>Miguel Ortiz (<a href="mailto:miguel.ortiz@ifsttar.fr">miguel.ortiz@ifsttar.fr</a>) at the French Institute of Science and Technology for Transport, Development and Networks (IFSTTAR) France.</p> <p> </p> </li> </ul>
Datasets and Supporting Materials for the IPIN 2016 Competition Track 3 (Smartphone-based, off-site)
<p>This package contains the datasets and supplementary materials used in the IPIN 2016 Competition (Alcalá, Spain).</p> <p><strong>Contents:</strong></p> <ol> <li>Track3_LogfileDescription_and_SupplementaryMaterial.pdf: Description of the logfiles and supplemental materials.</li> <li>Track3_TechnicalAnnex.pdf: Technical annex describing the competition </li> <li>01-Logfiles: This folder contains a subfolder with the 17 training logfiles and a subfolder with the 9 blind evaluation logfiles as provided to competitors.</li> <li>02-Supplementary_Materials: This folder contains the Matlab/octave parser, the raster maps and the visualization of the training routes.</li> <li>03-Evaluation: This folder contains the scripts used to calculate the competition metric, the 75th percentile on the 578 evaluation points. The ground truth is also provided in MatLab format and as a CSV file. Since the results must be provided with a 2Hz freq. starting from apptimestamp 0, the GT includes the closest timestamp matching the timing provided by competitors.</li> </ol> <p><strong>Please, cite the following works when using the datasets included in this package:</strong></p> <ul> <li>Torres-Sospedra, J.; Jiménez, A.; Knauth, A.; Moreira, A.; Beer, Y.; Fetzer, T.; Ta, V.-C.; Montoliu, R.; Seco, F.; Mendoza, G.; Belmonte, O.; Koukofikis, A.; Nicolau, M.J.; Costa, A.; Meneses, F.; Ebner, F.; Deinzer, F.; Vaufreydaz, D.; Dao, T.-K.; and Castelli, E. The Smartphone-based Off-Line Indoor Location Competition at IPIN 2016: Analysis and Future work Sensors Vol. 17(3), 2017. <a href="http://dx.doi.org/10.3390/s17030557">http://dx.doi.org/10.3390/s17030557</a></li> <li>Jimenez, A.R.; Mendoza-Silva, G.M.; Montoliu, R.; Seco, F.; Torres-Sospedra, J. Datasets and Supporting Materials for the IPIN 2016 Competition Track 3 (Smartphone-based, off-site). <a href="http://dx.doi.org/10.5281/zenodo.2791530">http://dx.doi.org/10.5281/zenodo.2791530</a></li> </ul> <p><strong>Additional information can be found at:</strong></p> <ul> <li><a href="http://evaal.aaloa.org/2016/competition-home">http://evaal.aaloa.org/2016/competition-home</a></li> <li><a href="http://indoorloc.uji.es/ipin2016track3/">http://indoorloc.uji.es/ipin2016track3/</a> </li> </ul> <p><strong>For any further questions about the database and this competition track, please contact: </strong></p> <ul> <li>Joaquín Torres (<a href="mailto:jtorres@uji.es?subject=IPIN%202016%20Competition%20Dataset%20(Zenodo)">jtorres@uji.es</a>) Institute of New Imaging Technologies, Universitat Jaume I, Spain. </li> <li>Antonio R. Jiménez (<a href="mailto:antonio.jimenez@csic.es?subject=IPIN%202016%20Competition%20Dataset%20(Zenodo)">antonio.jimenez@csic.es</a>) Center of Automation and Robotics (CAR)-CSIC/UPM, Spain. </li> </ul> <p> </p>
Supporting Data for "Refractive index matched, nearly hard polymer colloids" (Proc. R. Soc. A, doi:10.1098/rspa.2018.0763)
<p>SAXS data [Q / Å^{-1}, I(Q) / Arb. unit, error I(Q) / Arb. unit] as *.dat files</p> <p>Data for Figures 1, 2, and 5 [description and units in column headers] as *.csv files</p>
Supporting Information for "Geochemistry constrains global hydrology on Early Mars"
<p>Copy of the Supporting Information for "Geochemistry constrains global hydrology on Early Mars", by Edwin S. Kite and Mohit Melwani Daswani.</p>
3D-structured Supports create complete Data Sets for Electron Crystallography
<p>Each tar file contains the raw files in HDF5 format, together with the XDS.INP file used for data integration.</p> <p>NB: The meta-data in the HDF5 files have no meaning, please refer to the respective XDS.INP file for respective information.</p>
Supporting data for "Arctic sea ice response to flooding of the snow layer in future warming scenarios"
<p>Supporting data for "Arctic sea ice response to flooding of the snow layer in future warming scenarios" submitted to Earth's Future in April 2021</p> <p>Contains model output from both the Icepack and CCSM4 experiments from the paper. File descriptions for the Icepack and CCSM4 data are contained in the files README_icepack and README_CCSM respectively.</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.