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3,688 results for “computer”
Multiscale continuum figures from Tratnyek et al. (2017) "In silico environmental chemical science: Properties and processes from statistical and computational modelling"
<p>Accessible versions of selected figures from Tratnyek et al. (2017) "In silico environmental chemical science: Properties and processes from statistical and computational modelling" Environ. Sci. Processes Impacts 19(3): 188-202. DOI: 10.1039/C7EM00053G.</p> <p>The Abstract Art figure shows a classification of variables for predictive/diagnostic models used in silico environmental chemical science, in terms of system scales and variable types. Figure 3 shows a continuum of system scales encompassing the whole scope of predictive/diagnostic modelling for in silico environmental chemical sciences, juxtaposing earth and biological scales.</p> <p>The published version of Figure 3 is tall, for two-column page-layouts, but a wide version of Figure 3 is provided for landscape oriented formats. The 300 dpi versions of each figure should be adequate resolution for most purposes, and therefore are recommended. The large versions of the figures may take significant time to download, but may be useful for high resolution applications.</p> <p>This work is from the perspectives/review paper at the beginning of a themed issue on "Quantitative Structure-Activity Relationships (QSARs) and Computational Chemistry Methods in the Environmental Chemical Sciences", published in the March 2017 issue of the Royal Society of Chemistry journal Environmental Sciences: Process and Impacts. The whole collection of papers can be accessed at rsc.li/qsars.</p>
Dataset of imaged commercial and custom-made printing filament materials for Computed Tomography imaging of organ body phantoms
<p>The dataset includes a total of 29 filament materials 7 custom-made materials and the selection of 22 commercially available materials.</p> <p>All the materials were printed with a Longer LK4 Pro printer into cubes with dimensions 20 mm x 20 mm x 10 mm.</p> <p>A part of each filament was grinded into pellets, placed into metallic cylinder container and then were heated up to their melting points to receive a homogeneous cylindrical sample of this material.</p> <p>The cubes and the cylindrical samples were scanned at a clinical CT scanner at three anode voltages (kV) and a slice thickness of 0.6 mm.</p>
Release dates for role playing computer games (RPGs)
<p>Release date information from Steam using web data scraping through Python coding. <br>Code can be located on GitHub: <a href="https://github.com/kyoraven/genderGames">kyoraven/genderGames (github.com)</a></p>
Gender in role playing computer games (RPGs)
<p>Modified data from:</p> <p>Rennick, S., Clinton, M., Ioannidou, E., Oh, L., Clooney, C., T, E., Healy, E., & Roberts, S. G. (2023). <em>The Video Game Dialogue Corpus</em> [Data set]. GitHub, Incorporated. <a href="https://github.com/seannyD/VideoGameDialogueCorpusPublic">https://github.com/seannyD/VideoGameDialogueCorpusPublic</a></p> <p>Stephanie Rennick, Melanie Clinton, Elena Ioannidou, Liana Oh, Charlotte Clooney, E. T., Edward Healy, Seán G. Roberts (2023) Gender bias in video game dialogue. <em>Royal Society Open Science</em> 10(5). <a href="https://royalsocietypublishing.org/doi/10.1098/rsos.221095" rel="nofollow">https://royalsocietypublishing.org/doi/10.1098/rsos.221095</a></p> <p>Modified data was filtered and analysed using Excel and Python. Python coding can be found: <a href="https://github.com/kyoraven/genderGames/tree/code">kyoraven/genderGames at code (github.com)</a> </p>
Indie role playing computer games (RPGs)
<p>Indie role playing computer games (RPGs) from Steam, with 'choices matter' tag from publisher Choice of Games. Name, publishing date, price, and description fields only. 116 records. Please note that Choice of Games has a scripting language that enables authors/developers to use, publish and sell their work. An analysis of those works may be conducted at a later date.</p> <p>v0.1.1 now includes gender options, orientation options</p>
Computational Design of Multimodal Combinatorial Mechanical Metamaterials
<p>This dataset contains the data used to design multimodal mechanical metamaterials as described in the paper 'Prospecting for Pluripotency in Metamaterial Design', as published in Phys. Rev. Research 7(2), 023299.</p> <p>In this paper, the data is used to design 5×5 unit cells with desired deformation (zero) modes. The dataset contains the data used to train neural networks (CNN_data.zip), the designs generated by genetic algorithm (step_i.zip) and their mode structures (step_ii.zip), and the designs obtained through our design approach as described in the paper (step_ii.zip). Additionally, there is data comparing the efficiency of using a genetic algorithm or a hill climbing method to generate designs with a large number of intensive modes (step_i.zip).</p>
Participant survey for the article: More than Formulas - Integrity, Communication, Computing and Reproducibility in Statistics Education
<p>The artcile More than Formulas - Integrity, Communication, Computing and Reproducibility in Statistics Education concerns the introduction of a new course format in the Master Program in Biostatistics at the University of Zurich. This data set contains the results fo a survey among the participants in this new course.</p> <p>Sepcifically it contains the answers of 22 participants to the following questions:</p> <p>1) Did you use the following concepts or tools since you took STA472? <br>Good practice for...</p> <p>... spreadsheets<br>... file and folder organization<br>... version control<br>... dynamic reporting<br>... LaTeX<br>... presentation slide design <br>... oral presentations<br>... designing graphs<br>... designing tables<br>... structure for manuscript<br>... logic of a paragraph<br>... writing style<br>... writing R functions<br>... using unit tests<br>... setting up simulations<br>... code styling<br>... writing vectorized code<br>... writing parallelized code<br>... containerizing code</p> <p>Answers are in the scale: never since, rarely, sometimes, often, frequently, I do not know</p> <p>2) If you used the above concepts at least rarely, did the training of STA472 help you?</p> <p>Good paractice for...</p> <p>... spreadsheets<br>... file and folder organization<br>... version control<br>... dynamic reporting<br>... LaTeX<br>... presentation slide design <br>... oral presentations<br>... designing graphs<br>... designing tables<br>... structure for manuscript<br>... logic of a paragraph<br>... writing style<br>... writing R functions<br>... using unit tests<br>... setting up simulations<br>... code styling<br>... writing vectorized code<br>... writing parallelized code<br>... containerizing code</p> <p>Answers are in the scale: Not really Somewhat Definitively I do not know I do not use this concept</p>
Computational models for kaolinite nano-particles (Generations 1-3) and their comprehensive FTIR spectra
<p>The dataset contains a large number of computational models and detailed spectral comparison, fitting, and deconvolution of a large set of FTIR data for crystalline and exfoliation kaolinite, nano-kaolinite and halloysite, nano-halloysite samples.<br> The <strong>G1.xyz</strong>, <strong>G2.xyz</strong>, and <strong>G3.xyz</strong> files contain the initial structures for the first three generations of nano-kaolinite molecules.<br> The compressed folder <strong>SVP-def2TZVP.zip</strong> contains the structural information relevant for comparing and contrasting the performance a double-zeta (SVP) and triple-zeta (TZVP) basis sets.<br> The <strong>edge_protonation.zip</strong> folder guides the reader through the stepwise evaluation of various edge protonation models and shows the final converged results.<br> The <strong>full_optimization.zip</strong> folder summarizes the stationary structure calculations at various levels of theory carried out for the G2 model.<br> </p>
Mobile Cloud Computing Bibliographic Results from Google Scholar
<p>This dataset contains all the results for the term "Mobile Cloud Computing" on Google Scholar until June 2018. The data was acquired using Publish or Perish. The data has been cleaned such that the wrong and invalid results have been removed, duplicates have been removed. Titles are accurate and fine but authors and publishers info. etc. is still unclean. For textual analysis based on paper titles, this dataset is fine. For any other factor, such as institutional or journal or authorship analysis, this isn't a good choice. </p>
Mobile Edge Computing Bibliographic Results from Google Scholar
<p>This dataset contains all the results for the term "Mobile Edge Computing" on Google Scholar until June 2018. The data was acquired using Publish or Perish. The data has been cleaned such that the wrong and invalid results have been removed, duplicates have been removed. Titles are accurate and fine but authors and publishers info. etc. is still unclean. For textual analysis based on paper titles, this dataset is fine. For any other factor, such as institutional or journal or authorship analysis, this isn't a good choice. </p>
Edge Computing Bibliographic Results from Google Scholar
<p>This dataset contains all the results for the term "Edge Computing" on Google Scholar until June 2018. The data was acquired using Publish or Perish. The data has been cleaned such that the wrong and invalid results have been removed, duplicates have been removed. Titles are accurate and fine but authors and publishers info. etc. is still unclean. For textual analysis based on paper titles, this dataset is fine. For any other factor, such as institutional or journal or authorship analysis, this isn't a good choice. </p>
Fog Computing Bibliographic Results from Google Scholar
<p>This dataset contains all the results for the term "Fog Computing" on Google Scholar until June 2018. The data was acquired using Publish or Perish. The data has been cleaned such that the wrong and invalid results have been removed, duplicates have been removed. Titles are accurate and fine but authors and publishers info. etc. is still unclean. For textual analysis based on paper titles, this dataset is fine. For any other factor, such as institutional or journal or authorship analysis, this isn't a good choice. </p>
Computational data for selection of PBE+50HFX functional for studying Ru-Cl/H-PR3 complexes
<p>The dataset provides all relevant computational data for selecting the hybrid GGA functional (PBE) with 50% HF exchange and saturated (def2TZVP) basis set as the most reasonable level of theory that reproduces the current golden standard of CCSD(T) results for Ru-Cl/H-PR3 complexes.<br> The file <strong>inventory</strong> gives you an overview of the data entries provided. <br> The PDF file <strong>labelling.pdf</strong> defines the symbols used for labelling various complexes.<br> In brief, the archive content is as follows:</p> <p><strong>basis sets</strong> - definition of BS1 to BS5 <br> <strong>Z-matrix definitions</strong> - definition of internal coordinates for isomers considered<br> <strong>operational procedures</strong> - operation procedures for how to derive atomic orbital compositions</p> <p><strong>density functionals</strong> - optimized structures calculated using various density functionals<br> <strong>wave functions</strong> - optimized structures calculated at various levels of wave function theory<br> <strong>population analyses</strong> - summary of electronic structure analysis<br> <strong>difference density</strong> - manipulated cube files used for electron density contours</p> <p><strong>CCDB</strong> - optimized structures of XRD characterized Ru-Cl/H-PR3 complexes<br> <strong>chemical speciation</strong> - structural optimization and energetics of reaction pathways</p>
Computed Basic Statistics of Hydraulics and Discharge Measures at USGS River Monitoring Stations
<p>The shared table contains basic statistics (average, standard deviation, minimum, maximum, and coefficient of variation [%]) river channel hydraulics and discharge records of the 4472 USGS river monitoring stations. The required raw data are free to access by the USGS-<em>National Water Information System</em> (<a href="https://waterdata.usgs.gov/nwis">https://waterdata.usgs.gov/nwis</a>). Hydraulics and discharge records that measured at each USGS monitoring site, given a long time period, were assembled, assessed, and finally used for computing the basic statistics. </p>
Code and Data from: Segmenting Root Systems in X-Ray Computed Tomography Images Using Level Sets
<p>This record contains code and data for segmentation using a three-dimensional level-set method, written by Amy Tabb in C++. The record also contains two datasets of root systems in media imaged with X-Ray CT, and the results of running the code on those datasets. The code will also perform a pre-processing task in three-dimensional image sets, and a dataset for that purpose is included as well. This work is a companion to the paper : "Segmenting root systems in X-ray computed tomography images using level sets" (WACV 2018) by the authors or this record, and and open-access version of the paper is here -- https://arxiv.org/abs/1809.06398 . The code is also available from Github: https://github.com/amy-tabb/tabb-level-set-segmentation , using a DOI and stable releases https://doi.org/10.5281/zenodo.3344906.</p> <p>Format of the data:</p> <p>Three input datasets are provided; two for the segmentation functionality of the code, and one to test the pre-processing functionality. The two segmentation sets are the same as were used in the paper, and are CassavaDataset, and SoybeanDataset. The pre-processing set is CassavaSlices. The output set for Soybean is SoybeanResultsJul11. The Cassava result set is large, so I broke it into three compressed folders, CassavaResultsJul12_A, _B, _C. _B is the largest, and only contains the results overwritten on the original X-Ray images. Unless your connection to Zenodo is extremely fast, it will be faster to compute the result than to download it.</p> <p> </p> <p> </p><p> </p><p> </p> <p></p> <p></p>
A cross-linguistic computational approach on chance resemblances
<p>Material for the "A cross-linguistic computational approach on chance resemblances" presentation, with raw wordlists, lexeme analyses, and distribution statistics.</p>
DeepPredSpeech: computational models of predictive speech coding based on deep learning
<p>This dataset contains all data, source code, pre-trained computational predictive models and experimental results related to: </p> <p>Hueber T., Tatulli E., Girin L., Schwatz, J-L "How predictive can be predictions in the neurocognitive processing of auditory and audiovisual speech? A deep learning study." (<a href="https://doi.org/10.1101/471581">biorXiv preprint https://doi.org/10.1101/471581</a>). </p> <ul> <li>Raw data are extracted from the publicly available database NTCD-TIMIT (10.5281/zenodo.260228). <ul> <li>Audio recordings are available in the audio_clean/ directory</li> <li>Post-processed lip image sequences are available in the lips_roi/ directory (67x67 pixels, 8bits, obtained by lossless inverse DCT-2D transform from the DCT feature available in the original repository of NTCD-TIMIT)</li> <li>Phonetic segmentation (extracted from NTCD-TIMIT original zenodo repository) is available in the HTK MLF file volunteer_labelfiles.mlf</li> </ul> </li> <li>Audio features (MFCC-spectrogram and log-spectrogram) are available in the mfcc_16k/ and fft_16k/ directories. </li> <li>Models (audio-only, video-only and audiovisual, based on deep feed-forward neural networks and/or convolutional neural network, in .h5 format, trained with Keras 2.0 toolkit) and data normalization parameters (in .dat scikit-learn format) are available in models_mfcc/ and models_logspectro/ directories</li> <li>Predicted and target (ground truth) MFCC-spectro (resp. log-spectro) for the test databases (1909 sentences), and for the different values of <span class="math-tex">\(\tau_p\)</span> or <span class="math-tex">\(\tau_f\)</span> are available in pred_testdb_mfccspectro/ (resp. pred_testdb_logspectro/) directory</li> </ul> <p>Source code for extracting audio features, training and evaluating the models is available on GitHub https://github.com/thueber/DeepPredSpeech/</p> <p>All directories have been zipped before upload.</p> <p>Feel free to contact me for more details.</p> <p>Thomas Hueber, Ph. D., CNRS research fellow, GIPSA-lab, Grenoble, France, thomas.hueber@gipsa-lab.fr </p>
Computer Applications in Archaeology conference proceedings citation analysis
<p>Comma-separated values (csv) files to accompany the paper: </p> <p>Huggett, J. (2024). 'Changing Theory and Practice? CAA and Archaeology's Digital Turn'. <em>Journal of Computer Applications in Archaeology</em> 7(1), pp. 316–331. DOI: <a href="https://doi.org/10.5334/jcaa.144" target="_blank" rel="noopener">10.5334/jcaa.144</a></p> <p>The data are based on the Scopus citation database (see <a href="https://www.elsevier.com/products/scopus" target="_blank" rel="noopener">https://www.elsevier.com/products/scopus</a>). The copyright for the database itself is held by Elsevier and requires a subscription to access. These files are derived data only, consisting of counts and associated statistics.</p>
EOL computer vision pipelines: Classification for Image Tagging: Flower Fruit
<p>Angiosperms: Stats from Colab:</p> <ul> <li>Number of positive identified reproductive structures: 490</li> <li>Number of possible identified reproductive structures: 4611</li> <li>Number of negative identified reproductive structures: 14833</li> </ul> <p> </p>
EOL computer vision pipelines: Classification for Image Tagging: Image Type: Anura
<p>Produced by the EOL Image Type Classifier. Classifies images as map, phylogeny, illustration, herbarium sheet, or none. Dataset generated for EOL Anura images. See model on the CV for <a href="https://www.kaggle.com/models/eolorg/image-quality-rating-bad-vs-good" target="_blank" rel="noopener">EOL Images Model Zoo on Kaggle</a>.</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.