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3,688 results for “Computer”
Computing Optimal Hypertree Decompositions with SAT - Results
<p>The data from our experiments.</p>
Dataset for Fig. 1d, Replicate 1 of DNA Nanopore Computing
<p>FAST5 files containing raw nanopore current data for the manuscript "A nanopore interface for higher bandwidth DNA computing".</p> <p>This set includes the data used for Replicate 1 of Fig. 1d.</p> <table> <thead> <tr> <th scope="col">File Name</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>DESKTOP_CHF4GRO_20190328_FAK62104_MN21390_sequencing_run_03_28_19_run03_d.fast5</td> <td>Replicate 1, 0.02 uM</td> </tr> <tr> <td>DESKTOP_CHF4GRO_20190328_FAK62104_MN21390_sequencing_run_03_28_19_run03_f.fast5</td> <td>Replicate 1, 0.1 uM</td> </tr> <tr> <td>DESKTOP_CHF4GRO_20190328_FAK62104_MN21390_sequencing_run_03_28_19_run03_h.fast5</td> <td> <p>Replicate 1, 0.2 uM</p> </td> </tr> <tr> <td>DESKTOP_CHF4GRO_20190328_FAK62104_MN21390_sequencing_run_03_28_19_run03_j.fast5</td> <td>Replicate 1, 0.5 uM</td> </tr> <tr> <td>DESKTOP_CHF4GRO_20190328_FAK62104_MN21390_sequencing_run_03_28_19_run03_l.fast5</td> <td>Replicate 1, 1.0 uM</td> </tr> </tbody> </table> <p> </p>
Data for "Computational Design of Alloy Nanostructures for Optical Sensing of Hydrogen"
<p>This record contains data pertaining to the publication "Computational Design of Alloy Nanostructures for Optical Sensing of Hydrogen".</p>
Dataset for Fig. 4d, Replicate 1 of DNA Nanopore Computing
<p>FAST5 files containing raw nanopore current data for the manuscript "A nanopore interface for higher bandwidth DNA computing".</p> <p>This set includes the data used for Replicate 1 of Fig. 4d.</p> <table> <thead> <tr> <th scope="col">File Name</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>DESKTOP_CHF4GRO_20210221_FAP42740_MN21390_sequencing_run_02_21_21_run01_b.fast5</td> <td>No circuits activated</td> </tr> <tr> <td>DESKTOP_CHF4GRO_20210221_FAP42740_MN21390_sequencing_run_02_21_21_run01_d.fast5</td> <td>Circuits 5 and 9 activated </td> </tr> <tr> <td>DESKTOP_CHF4GRO_20210221_FAP42740_MN21390_sequencing_run_02_21_21_run01_f.fast5</td> <td>Circuits 1, 7, and 8 activated</td> </tr> </tbody> </table> <p> </p>
Erkomaishvili Dataset: A Curated Corpus of Traditional Georgian Vocal Music for Computational Musicology
<p><strong>Abstract</strong></p> <p>The analysis of recorded audio material using computational methods has received increased attention in ethnomusicological research. We present a curated dataset of traditional Georgian vocal music for computational musicology. The corpus is based on historic tape recordings of three-voice Georgian songs performed by the the former master chanter Artem Erkomaishvili. In this article, we give a detailed overview on the audio material, transcriptions, and annotations contained in the dataset. Beyond its importance for ethnomusicological research, this carefully organized and annotated corpus constitutes a challenging scenario for music information retrieval tasks such as fundamental frequency estimation, onset detection, and score-to-audio alignment. The corpus is publicly available and accessible through score-following web-players.</p> <p><strong>License</strong></p> <p>This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.</p> <p><strong>Copyright of Audio (wav)</strong></p> <p>Ministry of Culture, Sports and Youth of Georgia<br> Legal Entity of Public Law<br> Vano Sarajishvili Tbilisi State Conservatoire (TSC)<br> 8-10, GRIBOEDOV St, TBILISI 0108, GEORGIA Tel. / fax :(+995 32) 2 999 144,<br> www.tsc.edu.ge E-mail: info@tsc.edu.ge; inter@tsc.edu.ge</p> <p>We thank the rector of TSC, Nana Sharikadze, for the permission to publish the recordings along with our annotations on Zenodo.</p> <p><strong>Copyright of Annotations (csv)</strong></p> <p>Sebastian Rosenzweig^1, Frank Scherbaum^2, David Shugliashvili^3, Vlora Arifi-Müller^1, and Meinard Müller^1<br> ^1: International Audio Laboratories Erlangen, Germany<br> ^2: University of Potsdam, Germany<br> ^3: Tbilisi State Conservatoire, Georgia</p> <p>The provided digital sheet music in MusicXML-format is based on the transcriptions by David Shugliashvili as published in the book:</p> <p>David Shugliashvili<br> Georgian Church Hymns, Shemokmedi School<br> Georgian Chanting Foundation, 2014.</p> <p><strong>References</strong></p> <p>If you use the Erkomaishvili dataset in your research, please cite:</p> <p>Sebastian Rosenzweig, Frank Scherbaum, David Shugliashvili, Vlora Arifi-Müller, and Meinard Müller<br> Erkomaishvili Dataset: A Curated Corpus of Traditional Georgian Vocal Music for Computational Musicology<br> Transactions of the International Society for Music Information Retrieval (TISMIR), 3(1): 31–41, 2020.</p>
A computational study of accelerating, steady and fading negative streamers in ambient air
<p>This dataset contains the data used to generate the results presented in the article "A computational study of accelerating, steady and fading negative streamers in ambient air". This dataset includes (1) the source code; (2) two transport data files, which contain a list of included reactions, with their reaction rate coefficients, and transport coefficients; (3) configuration files for running the simulations and (4) output log files, which contain information about the physics and numerical properties of the simulations.</p>
Supplementary Materials: Next Generation Computational Tools for the Modeling and Design of Particle Accelerators at Exascale
<p>Supplementary materials (aka data artifact or data archive) for our NAPAC22 publication: "Next Generation Computational Tools for the Modeling and Design of Particle Accelerators at Exascale" (Paper ID: TUYE2).</p> <p>Work supported by the Exascale Computing Project (17-SC-20-SC), a joint project of the U.S. Department of Energy's Office of Science and National Nuclear Security Administration, responsible for delivering a capable exascale ecosystem, including software, applications, and hardware technology, to support the nation's exascale computing imperative. This work was supported by the Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory under U.S. Department of Energy Contract No. DE-AC02-05CH11231.<br> This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under Contract No. DE-AC02-05CH11231.</p>
Improvements in airflow characteristics and effect on the NOSE score after septoturbinoplasty: A computational fluid dynamics analysis
<p><span>Septoturbinoplasty is a surgical procedure that can improve nasal congestion symptoms in patients with nasal septal deviation and inferior turbinate hypertrophy. However, it is unclear which physical domains of nasal airflow after septoturbinoplasty are related to symptomatic improvement. This work employs computational fluid dynamics modeling to identify the physical variables and domains associated with symptomatic improvement. Sixteen numerical models were generated using eight patients' pre- and postoperative computed tomography scans. Changes in unilateral nasal resistance, surface heat flux, relative humidity, and air temperature and their correlations with improvement in the </span><span>Nasal Obstruction Symptom Evaluation (NOSE) score were analyzed. The NOSE score significantly improved after septoturbinoplasty, from 14.4 ± 3.6 to 4.0 ± 4.2 (p < 0.001). The surgery not only increased the airflow partition </span><span>on the more obstructed side (MOS) from 31.6 ± 9.6 to 41.9 ± 4.7% (p = 0.043), but also reduced the unilateral nasal resistance in the MOS from 0.200 ± 0.095 to 0.066 ± 0.055 Pa/(mL</span><span>×</span><span>s) (p = 0.004). Improvement in the NOSE score correlated significantly with the reduction in unilateral nasal resistance in the preoperative MOS (<em>r</em>=0.81). Also, improvement in the NOSE score correlated better with the increase in surface heat flux in the preoperative MOS region from the nasal valve to the choanae (<em>r</em>=0.87) than in the vestibule area (<em>r</em>=0.63). Therefore, </span><span>unilateral nasal resistance and mucous cooling in the preoperative MOS can explain the perceived improvement in symptoms after septoturbinoplasty. Moreover, the physical domain between the nasal valve and the choanae might be more relevant to patient-reported patency than the vestibule area. </span></p>
Datasets for computational counterselection manuscript
<p>Training and raw datasets for manuscript titled: "Computational counterselection identifies nonspecific therapeutic biologic candidates". Training data is in "data.zip". Raw data with Excel file for label identities in "raw_data.zip".</p> <p>If you use this data in your work, please cite: </p> <p>Saksena SD, Liu G, Banholzer C, Horny G, Ewert S, Gifford DK. 2022. Computational counterselection identifies nonspecific therapeutic biologic candidates. <em>Cell Rep Methods</em> <strong>2</strong>: 100254. http://dx.doi.org/10.1016/j.crmeth.2022.100254.</p>
Thermochronology data in Ebro basin and model input parameters for computing cooling histories
<p>Two files (word and excel) containing Table DR1 that refer to the model input parameters and Table DR2 with details of the (U-Th-Sm)/He analyses. </p>
Dataset of Maass forms of squarefree level computed via the Trace Formula
<p>This is data of 33214 Maass forms of squarefree level <span class="math-tex">\(N\)</span> between <span class="math-tex">\(2 \leq N \leq 105\)</span>. This data is complementary to the paper <a href="https://arxiv.org/abs/2201.08760">arxiv:2201.08760</a></p> <p>Each Maass form has its own .txt with the following format:</p> <ul> <li>Laplace eigenvalue, Error bound of Laplace eigenvalue</li> <li>Level</li> <li>0 or 1 (0 for even form, 1 for odd form)</li> <li>Fricke sign</li> <li>Fourier coefficient a_1(should be = 1), error bound</li> <li>Fourier coefficient a_2, error bound</li> <li>.</li> <li>.</li> <li>.</li> </ul> <p>If the Fricke sign is 0 then it means the code didn't have enough precision to compute it. In this case, the a_n for <span class="math-tex">\(gcd(n,N) > 1\)</span> have been assigned zero as a placeholder. The Fourier coefficients a_n with <span class="math-tex">\(gcd(n,N) = 1\)</span> will be correct though.</p>
Local Gyrification Index computed with FreeSurfer 6 for all ABIDE I subjects
<p># ABIDE I FreeSurfer 6 'local gyrification index' (lGI) data</p> <p>DOI of this dataset: 10.5281/zenodo.7132610</p> <p>This directory 'abide_freesurfer6_lgi' contains the ABIDE I FreeSurfer 6 'local gyrification index' (lGI) data and meshes.</p> <p><br> ## Credits</p> <p>This data is derived from the MRI scans of the ABIDE I dataset:</p> <p>* ABIDE I dataset: https://fcon_1000.projects.nitrc.org/indi/abide/</p> <p>Quoting from that website:</p> <p> "The Autism Brain Imaging Data Exchange I (ABIDE I) represents the first<br> ABIDE initiative. Started as a grass roots effort, ABIDE I involved 17<br> international sites, sharing previously collected resting state functional<br> magnetic resonance imaging (R-fMRI), anatomical and phenotypic datasets<br> made available for data sharing with the broader scientific community.<br> This effort yielded 1112 dataset, including 539 from individuals with<br> ASD and 573 from typical controls (ages 7-64 years, median 14.7 years<br> across groups). This aggregate was released in August 2012. Its<br> establishment demonstrated the feasibility of aggregating resting<br> state fMRI and structural MRI data across sites; the rate of these<br> data use and resulting publications (see Manuscripts) have shown its<br> utility for capturing whole brain and regional properties of the brain<br> connectome in Autism Spectrum Disorder (ASD). In accordance with<br> HIPAA guidelines and 1000 Functional Connectomes Project / INDI<br> protocols, all datasets have been anonymized, with no protected<br> health information included."</p> <p>Citation: Di Martino, A., Yan, C. G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., ... & Milham, M. P. (2014).<br> The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular psychiatry, 19(6), 659-667.</p> <p>## How this data was produced</p> <p>The following steps were used to create the data:</p> <p>* We downloaded all available MRI scans for the ABIDE I subjects (1035 subjects).<br> * We pre-processed all subjects in FreeSurfer version 6 (https://freesurfer.net) by running the full recon-all pipeline for each subject.<br> - We did not run any quality metrics on the scans or exclude any subjects.<br> * We computed pial-lgi (Schaer et al. 2008, https://doi.org/10.1109/TMI.2007.903576) as implemented in FreeSurfer 6 for all subjects.<br> - For some of the subjects, MRI data was not available or lgi could not be computed due to very bad quality of the (or a completely failed) surface reconstruction. These subjects are listed in the file 'subjects_lgi_computation_failed.txt' (14 of 1035 subjects).<br> - All subjects for which lgi computation succeeded for both hemispheres are listed in the file 'subjects.txt' (1021 of 1035 subjects).</p> <p><br> ## Contained files</p> <p>* In order to reduce the size of this dataset, for each subject, we only included the following files:<br> - <subject>/surf/lh.pial : the pial surface mesh for the left hemisphere, in FreeSurfer surf format.<br> - <subject>/surf/rh.pial : the pial surface mesh for the right hemisphere, in FreeSurfer surf format.<br> - <subject>/surf/lh.pial_lgi : the per-vertex lgi values for the left hemisphere, in FreeSurfer curv format.<br> - <subject>/surf/rh.pial_lgi : the per-vertex lgi values for the right hemisphere, in FreeSurfer curv format.</p> <p>See the section 'How this data was produced' for information on the files 'subjects.txt' and 'subjects_lgi_computation_failed.txt'.</p> <p>## What is NOT contained</p> <p>* The ABIDE demographics information (metadata on the subjects, like age, ...) is not included, you can get it from the ABIDE website.<br> * The lgi values are only contained in native space. Standard space data is available as a separate download on Zenodo.</p> <p>## Author and License</p> <p>Note: For the authors of the original ABIDE I dataset, see the Credits section above.</p> <p>This lgi data was created by:</p> <p> Dr. Tim Schäfer<br> Postdoc Computational Neuroimaging<br> Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy<br> University Hospital Frankfurt, Goethe University Frankfurt am Main, Germany<br> http://rcmd.org/ts</p> <p>The data is published under the following license:</p> <p>Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)</p> <p>See https://creativecommons.org/licenses/by-nc-sa/3.0/legalcode.txt or the file LICENSE for the full legal code.</p> <p>See https://creativecommons.org/licenses/by-nc-sa/3.0/ for an easy explanation of what this license means for you.</p> <p> </p>
Geometry and Integral Files for the manuscript "Quantum Computation for Periodic Solids in Second Quantization"
<p>Geometry and Integral Files for the manuscript "Quantum Computation for Periodic Solids in Second Quantization" <a href="https://doi.org/10.48550/arXiv.2210.02403">https://doi.org/10.48550/arXiv.2210.02403</a></p>
Met4DX: A mass spectrum-oriented computational framework for ion mobility-resolved untargeted metabolomics
<p><strong>Raw LC-IM-MS data</strong> to run Met4DX and <strong>RT recalibration table</strong> for multi-dimensional match</p> <p>Each dataset was archieved into a zip, containing raw LC-IM-MS data (.d format) and corresponding RT recalibration table (.csv format).</p>
Pre-computed MGCs from human microbiome reference genomes
<p>This dataset contains non-redundant metabolic gene clusters (MGCs) collected by running gutSMASH and BiG-MAP on a collection of unique high-quality reference genomes. This collection consist of MGCs predicted by gutSMASH using 1,520 genomes from the Culturable Genome Reference (CGR), 2,308 genomes from the Human Microbiome Project (HMP) and 414 Clostridia genomes as input and then filtered for redundancy using the family module of BiG-MAP. For more information: <a href="http://doi.org/10.1101/2021.02.25.432841">https://doi.org/10.1101/2021.02.25.432841</a></p> <p><strong>BiG-MAP_mg.pickle</strong> -> suitable for <strong>metagenome</strong> analyses</p> <p><strong>BiG-MAP_mt.pickle </strong>-> suitable for <strong>metatranscriptome </strong>analyses</p> <p>The files can be used as direct input in the third module of BiG-MAP (BiG-MAP.map.py: <a href="https://github.com/medema-group/BiG-MAP">https://github.com/medema-group/BiG-MAP</a>).</p>
Supplementary material 3 from: Okanishi M, Fujita T, Maekawa Y, Sasaki T (2017) Non-destructive morphological observations of the fleshy brittle star, Asteronyx loveni using micro-computed tomography (Echinodermata, Ophiuroidea, Euryalida). ZooKeys 663: 1-19. https://doi.org/10.3897/zookeys.663.11413
Figure S3 : Explanation note: The interactive 3D model of µ CT surface rendering images of the isolated vertebral ossicles of Asteronyx loveni (NSMT E-5638).
Supplementary material 2 from: Okanishi M, Fujita T, Maekawa Y, Sasaki T (2017) Non-destructive morphological observations of the fleshy brittle star, Asteronyx loveni using micro-computed tomography (Echinodermata, Ophiuroidea, Euryalida). ZooKeys 663: 1-19. https://doi.org/10.3897/zookeys.663.11413
Figure S2 : Explanation note: The interactive 3D model of µ CT surface rendering images of the basal part of an arm of Asteronyx loveni (NSMT E-5638).
Supplementary material 1 from: Okanishi M, Fujita T, Maekawa Y, Sasaki T (2017) Non-destructive morphological observations of the fleshy brittle star, Asteronyx loveni using micro-computed tomography (Echinodermata, Ophiuroidea, Euryalida). ZooKeys 663: 1-19. https://doi.org/10.3897/zookeys.663.11413
Figure S1 : Explanation note: The interactive 3D model of µ CT surface rendering images of the entire body of Asteronyx loveni (NSMT E-6986). This image can be activated by clicking on the image in Adobe Acrobat Reader (version 8 or higher) and can be rotated, moved and magnified.
Computational Phylogenetics and the Internal Structure of Pama-Nyungan: Dataset
<p>Dataset of cognate judgements on which Bowern and Atkinson (2012) was based. Note that this dataset has been largely superseded by subsequent work which adds more languages and forms.</p>
Positive-time FTLE in the Gulf Stream computed from SSALTO/DUACS
<p>SCII data set of positive-time finite-time Lyapunov exponent (FTLE) in the Gulf Stream calculated with velocity fields from Segment-Sol multi-missions d’ALTimétrie, Orbitographie et localisation précise/Data Unification and Altimeter Combination System (SSALTO/DUACS) absolute dynamic topography and absolute geostrophic velocity products.</p> <p>There are 7961 data sets: one per day from 7 January 1993 until 17 October 2014. Calculation of the FTLE is described in “Gulf Stream transport and mixing processes via coherent structure dynamics” by Yi Liu et al., submitted to JGR Oceans, 2017. The data can be uncompressed with standard tar commands.</p> <p>Data dimension in x and y are given in the first two lines of each set (1200x600), but are the same for each set. The grid coordinates are not included in the data set, but can be easily recreated knowing that the longitudes (x-direction) range from -89.875 (degree) to -30 (degree), and the latitudes (y-direction) range from 25.125 (degree) to 55 (degree). The data were written in a nested loop, with the y-dimension inside the x-dimension loop.</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.