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3,688 results for “Computer”
Micro-urban environment experimental dataset to validate performance of different Computational Fluid Dynamics methodologies.
<p><span><span>This dataset enclosed wind 3D geolocated wind flow and air concentrations </span><span>5-minutal </span><span>data </span><span>collected in </span><span>El Prat del Llobregat (Spain) </span><span>between January and August 2022 in the context of the experiment 1012-ibam of the FF4EuroHPC European project. The intention of this dataset is to provide a </span><span>resource to do performance benchmark of micro-urban chemical – dispersion models to assess their performance</span><span>. To do so, we enclose experimental data collected by </span><span>Bettair</span><span> Mk2 Series Air quality monitors, 2 Air Quality Monitoring stations equipped with reference instruments f</span><span>rom “La </span><span>Xarxa</span><span> de </span><span>Vigilància</span> <span>i</span> <span>Previsió</span><span> de la </span><span>Contaminació</span> <span>Atmosfèrica</span><span> (XVPCA)”</span><span>, and different data from the repository of the ECMWF Era-5 land and CAMS. We also provide the </span><span>3D watertight geometry model of the </span><span>el</span><span> Prat de Llobregat (Spain) in step file format</span><span> (layout from 2020)</span><span>.<br></span></span></p>
Characterization Data for the Manuscript: "Unraveling Metal Effects on CO2 Uptake in Pyrene-based Metal-Organic Frameworks through Integrated Lab and Computer Experiments"
<p>This entry contains characterization data for the manuscript "Unraveling Metal Effects on CO2 Uptake in Pyrene-based Metal-Organic Frameworks through Integrated Lab and Computer Experiments".</p>
Data for "Computational design of developable therapeutic antibodies: efficient traversal of binder landscapes and rescue of escape mutations"
Open the record for dataset details and reuse information.
Monolayer nanocrystalline graphene synthesized from pyrolyzing Langmuir monolayer of polyaromatic hydrocarbon: Computational Data
<p><strong>Computational Data for Publication:</strong></p> <p> </p> <p><strong>NOTE for nomenclature</strong>: TPY in the computational data = <span>HTPHPB in the main text</span></p> <p> </p> <p><strong>Optimized TPY Geometry</strong>: tpy_optimized_coord.xyz</p> <p> </p> <p><strong>Data for Figure 1d</strong>: 1tpy_hbnum.kde.2.dat, 5tpy_hbnum.kde.2.dat</p> <p><strong>Data for Figure 1e</strong>: 1tpy_alldihedral.kde.1.dat, 5tpy_alldihedral.kde.1.dat</p> <p> </p> <p><strong>TPY_DATA.zip</strong>: Results of the minimization and MD equilibrations for 1 TPY and 5 TPY cases.</p> <p>1tpy:</p> <p>tpy_wat_inter_minimenergy</p> <p>tpy_wat_inter_70knvt_eq1</p> <p>tpy_wat_inter_300knvt_eq1</p> <p>tpy_wat_inter_300knvt_eq2</p> <p>5tpy:</p> <p>tpy_wat_inter_minimenergy</p> <p>tpy_wat_inter_70knvt_eq1</p> <p>tpy_wat_inter_300knvt_eq1</p> <p>tpy_wat_inter_300knvt_eq2</p> <p><strong>Data for Supplementary Table 1: 1tpy_dftb_d3bj_sp.zip</strong> and <strong>5tpycluster_dftb_d3bj_sp.zip</strong>: DFTB-D3BJ single point calculation results for 1 TPY and 5 TPY cluster geometries extracted from each last MD equilibration. </p> <p> </p>
Dataset for publication: Statistically Equivalent Virtual Microstructures for Modeling of Complex Polycrystalline Alloys Using a Generative Adversarial Network (GAN)-Enabled Computational Platform
<p>This dataset provides the necessary data to get the images and results shown in the paper "Statistically Equivalent Virtual Microstructures for Modeling of Complex Polycrystalline Alloys Using a Generative Adversarial Network (GAN)-Enabled Computational Platform". </p> <p>Source Data Raw.zip has the entire data set used to generate the images.</p> <p>Source Data.zip contains the processed data from "Source Data Raw.zip". </p> <p>Files with extension .dream3d are accompained by a file with extension .xdmf. This files can be opened with Paraview. And their data can be accesible using python or matlab.</p> <p>For more information contact Proffesor Somnath Ghosh at Johns Hopkins University, Civil and Systems Engineering Department.</p>
Conceptual Change Texts in Computer Science to Expand Students' Conceptions on the Topic of Artificial Intelligence
<p><strong><span>General information on the survey and cohort</span></strong></p> <p><span>The present data were collected in 2023 at a secondary school in Hamburg. A questionnaire was used as a pre- and post-test in computer science courses in years 10 and 11 to investigate the agreement with various items and how this changed. The questionnaires were transferred to SPSS for analysis.</span></p> <p><span>76 students aged 15-17 took part in the survey and the teaching intervention. This data set only contains the data that is available in full. It therefore includes the responses of 69 students, of which a total of 22 felt assigned to the female gender and 47 to the male gender.</span></p> <p> </p> <h3><span>Data set</span></h3> <p><span>The data is purely quantitative, as this is only the evaluation of the pre- and post-test and not the evaluation of the conceptual change texts themselves. The item list is made up of 20 items relating to artificial intelligence, which were drawn up according to the Big Ideas of the AI4K12 initiative [1]. The students' perceptions of the items come from various past studies that have surveyed students' conceptions of AI.</span></p> <p><span>The data has a pseudonymized code that can be linked to the conceptual change text of the experimental group and the data can be assigned accordingly. The data is also divided into the experimental group (abbreviation 2) and the control group (abbreviation 1) so that a comparison between the groups is quickly possible. The abbreviation W in front of the items stands for a “true” statement and SV for “student conception”. A Likert scale from 0 - does not apply at all to 3 - applies completely was used.</span></p> <p><span>The data set is cleansed data. All data sets that were not complete or that were given different codes in the pre-test and post-test and therefore could no longer be clearly assigned were removed.</span></p> <p><strong><span>Literature</span></strong></p> <p><span>[1]</span><span> </span>AI<span> </span>for<span> </span>K12<span> </span>(Hrsg.)<span> </span><span>(2020):<span> </span><em>AI4K12.org.<span> </span></em>Available online at:<span> </span>https://ai4k12.org/<span> </span>[last checked 12.03.2023]</span></p>
Dataset for the research paper "Computational and experimental investigation of thermally auxetic multi-metal lattice structures produced by Laser Powder Bed Fusion"
<p>The aim of this study is to investigate the potential of tailoring the structural thermal expansion properties of a multi-metal re-entrant lattice structure made of 316L stainless steel and CuCr1Zr copper alloy. Several geometric configurations with different layout of parent materials were designed and tested for their ability to thermally expand at elevated temperature. The study showed that one of the geometric configurations with the chosen material layouts allows to exceed the expansion range that can be achieved by both parent materials. The prediction of the finite element analysis was thus confirmed by experimental measurements. In addition, the influence of manufacturing imperfections in the form of geometric deviations and non-optimal material deposition was also investigated, and the results showed that this has a significant influence on the overall expansion. In conclusion, it was found that it is possible to tailor multi-metal lattice structures to a specific expansion, but the disadvantages associated with manufacturing must first be eliminated.</p>
Programmable nonlinear optical neuromorphic computing with bare 2D material MoS2
<p>This data set contains all resources for the research project "<span>Programmable nonlinear optical neuromorphic computing with bare 2D material MoS2" (published in Nature Communications (2024)).</span></p>
Computational Approach to Discovering Plastic Degradation Enzymes
<p><strong>With at least 150 million tons of plastic already in oceans and over 10 million tons of plastic entering oceans annually, plastic waste has become a major global problem. Current methods to address this problem such as incineration and landfills are unsustainable and environmentally harmful. More trending approaches such as the degradation of plastic using microbial enzymes are rarely efficient enough to be applied industrially. To fill this gap in our knowledge, we developed a computational method called IPDE (Identification of Plastic Degradation Enzymes) to systematically identify promising enzymes, enzyme combinations, and microbial species for effective plastic waste degradation. Using IPDE, we discovered 32 enzymes in ocean microbiomes, with at least 16 (50.0%) having a role in plastic degradation. Additionally, we identified 37 significant enzyme combinations, 8 (21.6%) of which contain enzymes that co-occur in the same metabolic pathways. Furthermore, we found 60 microbial species, 16 (26.6%) of which have been implied to be linked to plastic degradation in literature. The results from IPDE provide promising candidates for experimental validation, protein engineering, and industrial application to tackle this plastic waste problem. The IPDE tool is freely available at https://github.com/SophieL8/Plastic-degrading-enzymes.</strong></p>
Data sets - The attitude of computer science teachers to inclusive education, Motivation to teach, Perception of the possible impact of computer science on students with mental disabilities
<p>Data sets </p> <p>The attitude of computer science teachers to inclusive education, Motivation to teach, Perception of the possible impact of computer science on students with mental disabilities. <br>In the period from February to October 2024, a survey of 112 computer science teachers in Kazakhstan (Pavlodar region) was conducted to determine attitudes to inclusive education, motivation to teach, and perception of the possible impact of computer science on students with mental disabilities.</p> <p>Questionnaire <br>https://docs.google.com/document/d/1LzukKSqW_mHMZXbMtN0ecmmU4cKJiwgf0laTWBHQSng/edit?usp=sharing</p> <p><strong>This research has been funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. AP14872400).</strong></p>
The ECOLOPES Voxel Model: Multi-domain data integration for ontology-aided generative computational design of ecological building envelopes
<p>The research portrayed in this article is part of the research project ‘ECOlogical building enveLOPES: a game-changing design approach for regenerative ecosystems’ funded by Horizon 2020 Future and Emerging Technologies. The overall research project focuses on developing a multi-domain data-driven computational design framework for the design of ecological building enclosures that addresses humans, plants, animals and microbiota. This article focuses on the development of a key component of the computational workflow in which initial designs are computationally initiated generated and analyzed, namely the ECOLOPES Voxel Model that contains and correlates multi-domain spatialised data for the design process, and its interactions with other components of the ontology-aided generative computational design process for ecological building envelopes.</p> <p>This repository contains all relevant data produced in this paper. Extended technical description is available in the Appendix A to the published paper, containing listing and description of individual voxel data layers. Data were exported from the RDB server (PostgreSQL) in text-based, future-proof format (csv).</p>
Regression models generated by APRANK (computational prioritization of antigenic proteins and peptides from complete pathogen proteomes)
<p>Availability of highly parallelized immunoassays has renewed interest in the discovery of serology-based biomarkers for infectious diseases. Protein and peptide microarrays now provide a high-throughput platform for immunological screening of potential antigens and B-cell epitopes. However, there is still a need to prioritize relevant probes when designing these arrays. In this work we describe a computational method called APRANK (Antigenic Protein and Peptide Ranker) which integrates multiple molecular features to prioritize antigenic targets starting from a given pathogen proteome. These features include subcellular localization, presence of repetitive motifs, natively disordered regions, secondary structure, transmembrane spans and predicted interaction with the immune system. We applied this method to the prioritization of potential diagnostic antigens and peptides in a number of pathogen proteomes and human diseases: Borrelia burgdorferi (Lyme disease), Brucella melitensis (Brucellosis), Coxiella burnetii (Q fever), Escherichia coli (Gastroenteritis), Francisella tularensis (Tularemia), Leishmania braziliensis (Leishmaniasis), Leptospira interrogans (Leptospirosis), Mycobacterium leprae (Leprae), Mycobacterium tuberculosis (Tuberculosis), Plasmodium falciparum (Malaria), Porphyromonas gingivalis (Periodontal disease), Staphylococcus aureus (Bacteremia), Streptococcus pyogenes (Group A Streptococcal infections), Toxoplasma gondii (Toxoplasmosis) and Trypanosoma cruzi (Chagas Disease). After training a linear regression model the method achieves good to excellent performance on most species, measured by the enrichment of validated antigens at the top of the ranking. An unbiased validation using independent data sets shows APRANK is successful in predicting antigenicity for all pathogen species tested. We make APRANK available to facilitate the identification of novel diagnostic antigens in infectious diseases.</p>
Supporting data for: "Hybrid Computational-Experimental Data-Driven Design of Self-Assembling π-Conjugated Peptides"
<p>This repository contains supporting data and code for the paper titled "Hybrid Computational-Experimental Data-Driven Design of Self-Assembling π-Conjugated Peptides" by Kirill Shmilovich, Sayak Subhra Panda, Anna Stouffer, John D. Tovar, and Andrew L. Ferguson.</p>
Supporting dataset for "Influence of urban forms on -long-duration urban flooding: laboratory experiments and computational analysis"
<p>In this dataset, we provide two parts of data:</p> <p>(1) Figures in format .fig that are included in the main text and supplementary material</p> <p>(2) Experimental datasets for the five configurations, including flow depth, discharge partition, and the flow surface velocity,</p> <p>- the data is written in a .h5 file that can be read by different languages (ex. Python),</p> <p>- a document PDF and a text file are available to visualize the data structure</p> <p>- a code of Python for reading the data in the .h5 file </p> <p> </p>
Fig. 3 in SNAIL - an interactive computer program for the determination of Central European freshwater gastropods
Fig. 3: The commputer programm SNAIL (version 1.0) with its eentrance menu (aa) as well as thee main menu (b).
Fig. 5 in SNAIL - an interactive computer program for the determination of Central European freshwater gastropods
Fig. 5: Key ooffered by the computer progrram SNAIL forr determining tthe species Lymmnaea stagnalis.
Figure 5 in The skull of the rare Malaysian snake Anomochilus leonardi Smith, based on high-resolution X-ray computed tomography
Figure 5. Three-dimensional cutaway views along the frontal axis of Anomochilus leonardi (FRIM 0026) based on HRXCT data. A, approximately 0.97 mm depth; and B, approximately 1.34 mm depth. Scale bar = 1 mm. See key for abbreviations.
Figure 4 in The skull of the rare Malaysian snake Anomochilus leonardi Smith, based on high-resolution X-ray computed tomography
Figure 4. Three-dimensional cutaway views along the sagittal axis of Anomochilus leonardi (FRIM 0026) based on HRXCT data. A, approximately 0.69 mm depth; and B, approximately 1.45 mm depth. Scale bar = 1 mm. See key for abbreviations.
Figure 3 in The skull of the rare Malaysian snake Anomochilus leonardi Smith, based on high-resolution X-ray computed tomography
Figure 3. Three-dimensional cutaway views along the transverse axis of Anomochilus leonardi (FRIM 0026) based on HRXCT data. A, approximately 0.36 mm depth; B, approximately 1.29 mm depth; C, approximately 1.58 mm depth; D, approximately 1.97 mm depth; E, approximately 5.19 mm depth; F, approximately 5.81 mm depth; and G, approximately 5.94 mm depth. Scale bar = 1 mm. See key for abbreviations.
Figure 2 in The skull of the rare Malaysian snake Anomochilus leonardi Smith, based on high-resolution X-ray computed tomography
Figure 2. Three-dimensional reconstruction of the lower jaw of Anomochilus leonardi (FRIM 0026) based on HRXCT data. A, lateral view; B, medial view; C, dorsal view; and D, ventral view. Scale bar = 1 mm. See key for abbreviations.
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.