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1,782 results for “algorithms”
Files and plotting scripts for "Parallel tridiagonal matrix inversion with a hybrid multigrid--Thomas algorithm method"
<p>This archive contains the files required to reproduce the results and figures presented in <em>Parallel tridiagonal matrix inversion with a hybrid multigrid--Thomas algorithm method</em>, J. T. Parker, P. A. Hill, D. Dickinson and B. D. Dudson.</p> <p>Also available at the repository: https://gitlab.com/JosephThomasParker/files-and-plotting-scripts-for-parallel-tridiagonal-matrix-inversion-with-a-hybrid-multigrid-thomas-algorithm-method/</p> <p>Questions to joseph.parker@ukaea.uk.</p> <p>This version is before submission to journal.</p>
F.A.I.R. open dataset of brushed DC motor faults for testing of AI algorithms
<p>Practical research in AI often lacks of available and reliable datasets so the practitioners can try different algorithms. The field of predictive maintenance is particularly challenging in this aspect as many researchers don't have access to full-size industrial equipment or there is not available datasets representing a rich information content in different evolutions of faults.</p> <p>This dataset presents the evolution of typical faults (commutator, winding and brush wear) in inexpensive DC motors under extensive monitoring (vibration, temperature, voltage, current and noise). These motors exhibit a particularly short useful life when operating out of nominal conditions (from 30 minutes to 6 hours) which make them very interesting to test different signal processing algorithms and introduce students and researchers into signal processing, fault detection and predictive maintenance.</p> <p>The data-set comprises two main elements:</p> <ul> <li>A spreadsheet with the processing of each raw data file</li> <li>4 folders with raw data files in HDF5 format</li> </ul> <p>The spread sheet contains the following columns</p> <ul> <li>filename of the raw data file</li> <li>timestamp of the raw data file</li> <li>speed of the motor in rpm</li> <li>speed of the motor in Hz</li> <li>Current of the moter (A)</li> <li>Voltage supply (V)</li> <li>surface motor temperature (ºC)</li> <li>ambient temperature (ºC)</li> <li>For each measured signal (Vibration, current, voltage) the vibration of the main harmonic (at the speed of the motor) and it's first 10 multiple.</li> <li>For each measured signal (Vibration, current, voltage) the vibration in 4 bands: 0-4kHz, 4kHz-8kHz, 8kHz-16kHz, 16kHz-26kHz</li> </ul> <p>The raw data files in HDF5 format contains the instantaneous measured vibration (g), current (A) and voltage (volts) of the DC motor at 51.200 Hz.</p>
Data set of the article: Language Bias in the Google Scholar Ranking Algorithm
<p>Data of investigation published in the article Cristòfol Rovira; Lluís Codina; Carlos Lopezosa Language Bias in the Google Scholar Ranking Algorithm. Future Internet, 2021, 13.</p> <p><strong>Abstract: </strong>The visibility of academic articles or conference papers depends on their being easily found in academic search engines, above all in Google Scholar. To enhance this visibility, search engine optimization (SEO) has been applied in recent years to academic search engines in order to optimize documents and, thereby, ensure they are better ranked in search pages (i.e., academic search engine optimization or ASEO). To achieve this degree of optimization, we first need to further our understanding of Google Scholar’s relevance ranking algorithm, so that, based on this knowledge, we can highlight or improve those characteristics that academic documents already present and which are taken into account by the algorithm. This study seeks to advance our knowledge in this line of research by determining whether the language in which a document is published is a positioning factor in the Google Scholar relevance ranking algorithm. Here, we employ a reverse engineering research methodology based on a statistical analysis that uses Spearman’s correlation coefficient. The results obtained point to a bias in multilingual searches conducted in Google Scholar with documents published in languages other than in English being systematically relegated to positions that make them virtually invisible. This finding has important repercussions, both for conducting searches and for optimizing positioning in Google Scholar, being especially critical for articles on subjects that are expressed in the same way in English and other languages, the case, for example, of trademarks, chemical compounds, industrial products, acronyms, drugs, diseases, etc.</p>
The Choice is Yours? How Algorithm Bias Impacts Fairness and Accessibility of Knowledge
<p><strong>Episode Summary</strong></p> <p>In this episode we talked about 'almighty' algorithms with Carlos Castillo, Lorenzo Porcaro, Marzieh Karimihaghighi, David Solans, and Francesco Fabbri from the Web Science & Social Computing Research Group, and the department of Engineering in Information & Communication Technologies, in Universitat Pompeu Fabra in Barcelona. We discussed how bias can enter into algorithm systems, how bias is measured, and what systems are impacted by it. </p> <p><strong>Episode Links</strong></p> <p><a href="https://www.upf.edu/web/wssc/">Web Science and Social Computing Research Group</a></p> <ul> <li><a href="https://www.upf.edu/web/etic/entry/-/-/24095/adscripcion/carlos-alberto-alejandro-castillo">Carlos Castillo</a></li> <li><a href="https://www.linkedin.com/in/marzieh-karimihaghighi-706b5554/?originalSubdomain=ir">Marzieh Karimihaghighi</a></li> <li><a href="https://www.linkedin.com/in/david-solans-noguero-48269b85/?originalSubdomain=es">David Solans</a></li> <li><a href="https://www.linkedin.com/in/francesco-fabbri/?originalSubdomain=it">Francesco Fabbri</a></li> <li><a href="https://www.linkedin.com/in/lorenzo-porcaro-7a8792b1/?originalSubdomain=es">Lorenzo Porcaro</a></li> </ul>
Dataset for Millimeter-wave Mobile Sensing and Environment Mapping: Models, Algorithms and Validation
<p>Dataset of paper "Millimeter-wave Mobile Sensing and Environment Mapping: Models, Algorithms and Validation".</p> <p>The measurement data contains indoor mapping results using millimeter-wave 5G NR signals at 28 GHz. The measurement campaign was conducted in an indoor office environment in Hervanta Campus of Tampere University. Six different sets of measurements contain the range profiles after the proposed radar processing. The shared data contains the IQ data of both transmit and receive signals used during the measurement campaign.</p> <p>The file "main.m" shows how to process and plot the shared data.</p>
Data for Solar Field Output Temperature Optimization Using a MILP Algorithm and a 0D Model in the Case of a Hybrid Concentrated Solar Thermal Power Plant for SHIP Applications
<p>These data were generated for the Open-Acces Article :</p> <p>Kamerling, S.; Vuillerme, V.; Rodat, S. Solar Field Output Temperature Optimization Using a MILP Algorithm and a 0D Model in the Case of a Hybrid Concentrated Solar Thermal Power Plant for SHIP Applications. <em>Energies</em> <strong>2021</strong>, <em>14</em>, 3731. https://doi.org/10.3390/en14133731</p> <p>In these dataset, the data for the Case Study and the Sensitivity Analysis are available. Jupyter Notebooks for further process of these data are also available. The NoteBooks AnalyseHourlyValues, AnalyseDailyValues and AnalyseMonthlyValues allow for easy change of variable, whereas CaseStudyAnalysis is for one specific set of data. The AnalyseSets were created in order to analyse the influence of the optimization on the solar fraction of the different datasets.</p>
Reconstruction Algorithms in Undersampled AFM Imaging - results
<p>This data set contains numerical simulation results from experiments for the paper "Review of compressed sensing reconstruction algorithms in AFM cell imaging", submitted to IEEE Journal of Selected Topics in Signal Processing.</p> <p>The data set consists of an HDF5 file containing the simulation results as well as MD5 and SHA checksums of the HDF5 database for validating the integrity of the data after download.</p> <p>The data set is licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/).</p> <p>Python scripts used for producing these results as well as Python scripts for extracting images and data used in the accompanying paper from the database can be found in the accompanying deposition http://doi.org/10.5281/zenodo.18745.</p> <p>The data set contains images, and reconstructed versions of these, originally published in the data set available at http://dx.doi.org/10.5281/zenodo.17573.</p>
Reconstruction Algorithms in Undersampled AFM Imaging - final results
<p>This data set contains numerical simulation results from experiments for the paper "Reconstruction Algorithms in Undersampled AFM<br /> Imaging", published in IEEE Journal of Selected Topics in Signal Processing.</p> <p>The data set consists of a set of HDF5 files containing the simulation results as well as MD5 and SHA checksums of the HDF5 databases for validating the integrity of the data after download.</p> <p>The data set is licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/).</p> <p>Python scripts used for producing these results as well as Python scripts for extracting images and data used in the accompanying paper from the database can be found in the accompanying deposition http://dx.doi.org/10.5281/zenodo.32959.</p> <p>The data set contains images, and reconstructed versions of these, originally published in the data set available at http://dx.doi.org/10.5281/zenodo.17573.</p>
Spatial Evolve Algorithm Results for Erdos Renyi Topology Median Normalized Rank - MSc Dissertation
<p>A data set containing the results of the spatial evolve lookup algorithm. The topology used for the spatial tournaments has been the Erdős Rényi random network. The objective function taken into account has been the median normalized rank. Three files are contained here based on the list of strategies,deterministic and non, and on the sample size. </p>
Spatial Evolve Algorithm Results for Random Topology Median Normalized Rank - MSc Dissertation
<p>A data set containing the results of the spatial evolve lookup algorithm. The topologies used for the spatial tournaments has been random between, small world, random and complete. The objective function taken into account has been the median normalized rank. Three files are contained here based on the list of strategies,deterministic and non, and on the sample size. </p>
Spatial Evolve Algorithm Results for Watts Strogatz Topology Median Normalized Rank - MSc Dissertation
<p>A data set containing the results of the spatial evolve lookup algorithm. The topology used for the spatial tournaments has been the Watts Strogatz small world network. The objective function taken into account has been the median normalized rank. Three files are contained here based on the list of strategies,deterministic and non, and on the sample size. </p>
Spatial Evolve Algorithm Results for Erdős Rényi Topology Median Normalized Rank - MSc Dissertation
<p>A data set containing the results of the spatial evolve lookup algorithm. The topology used for the spatial tournaments has been the Erdős Rényi random network. The objective function taken into account has been the median normalized rank. Three files are contained here based on the strategies list, deterministic and non and on the sample size. </p>
Spatial Evolve Algorithm Results for Complete Topology Median Normalized Rank - MSc Dissertation
<p>A data set containing the results of the spatial evolve lookup algorithm. The topology used for the spatial tournaments has been a complete network. The objective function taken into account has been the median normalized rank. Three files are contained here based on the list of strategies,deterministic and non, and on the sample size. </p>
Spatial Evolve Algorithm Results for Random Topology Minimum Normalized Rank - MSc Dissertation
<p>A data set containing the results of the spatial evolve lookup algorithm. The topologies used for the spatial tournaments has been random between, small world, random and complete. The objective function taken into account has been the minimum normalized rank. Two files are contained here based on the sample size. All 132 strategies of the Axelrod have been used. </p>
Algorithms for Reconstruction of Undersampled Atomic Force Microscopy Images Dataset
<p>This deposition contains the results from a simulation of reconstructions of undersampled atomic force microscopy (AFM) images. The reconstructions were obtained using a variety of interpolation and reconstruction methods.</p> <p>The deposition consists of:</p> <ol> <li>An HDF5 database containing the results from simulations of reconstructions of undersampled atomic force microscopy images (reconstruction_goblet_ID_0_of_1.hdf5).</li> <li>The Python script which was used to create the database (reconstruction_goblet.py).</li> <li>Auxillary Python scripts needed to run the simulations (optim_reconstructions.py, it_reconstruction.py, interp_reconstructions.py, gamp_reconstructions.py, and utils.py).</li> <li>MD5 and SHA256 checksums of the database and Python script files (reconstruction_goblet.MD5SUMS, reconstruction_goblet.SHA256SUMS).</li> </ol> <p>The HDF5 database is licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/) . Since the CC BY 4.0 license is not well suited for source code, the Python script is licensed under the BSD 2-Clause license (http://opensource.org/licenses/BSD-2-Clause) .</p> <p><strong>The files are provided as-is with no warranty as detailed in the above mentioned licenses.</strong></p> <p>The simulation results in the database are based on "Atomic Force Microscopy Images of Cell Specimens" and "Atomic Force Microscopy Images of Various Specimens" by Christian Rankl licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). The original images are available at http://dx.doi.org/10.5281/zenodo.17573 and http://dx.doi.org/10.5281/zenodo.60434. The original images are provided as-is without warranty of any kind. Both the original images as well as adapted images are part of the dataset. </p>
A geometry preserving, conservative, mesh-to-mesh isogeometric interpolation algorithm for spatial adaptivity of the multigroup, second-order even-parity form of the neutron transport equation
<p>In this paper a method is presented for the application of energy-dependent spatial meshes applied to the multigroup, second-order, even-parity form of the neutron transport equation using Isogeometric Analysis (IGA). The computation of the inter-group regenerative source terms is based on conservative interpolation by Galerkin projection. The use of Non-Uniform Rational B-splines (NURBS) from the original computer-aided design (CAD) model allows for efficient implementation and calculation of the spatial projection operations while avoiding the complications of matching different geometric approximations faced by traditional finite element methods (FEM). The rate-of-convergence was verified using the method of manufactured solutions (MMS) and found to preserve the theoretical rates when interpolating between spatial meshes of different refinements. The scheme’s numerical efficiency was then studied using a series of two-energy group pincell test cases where a significant saving in the number of degrees-of-freedom can be found if the energy group with a complex variation in the solution is refined more than an energy group with a simpler solution function. Finally, the method was applied to a heterogeneous, seven-group reactor pincell where the spatial meshes for each energy group were adaptively selected for refinement. It was observed that by refining selected energy groups a reduction in the total number of degrees-of-freedom for the same total L2 error can be obtained.</p>
Data and code for the paper "Precision Groundwater Modeling: when cokriging meets evolutionary and iterative algorithms"
<ul> <li>exemplary dataset for 2019 yearly water table measurements in Northeaster Italy</li> <li>MATLAB code for the pre-processing GA-driven and the post-processing iterative validation part</li> </ul>
Supplementary tables for publication "A reference-free algorithm discovers regulation in the plant transcriptome"
<p>Supplementary tables for publication "A reference-free algorithm discovers regulation in the plant transcriptome" (doi: https://doi.org/10.1101/2024.05.23.595613)</p> <p>Table A: complete list of significant anchors and associated genes from analysis of sorghum dataset</p> <p>Table B: complete list of significant anchors and associated genes from analysis of maize dataset</p> <p>Table C: complete list of significant anchors and associated genes from analysis of Arabidopsis P/Fe dataset</p> <p>Table D: complete list of significant anchors and associated genes from analysis of Arabidopsis FLOE1 dataset</p> <p>arabidopsis_floe1_ALL_anchors_satc_truncated.txt: data from the Arabidopsis FLOE1 dataset used to generate figures in the paper. Columns are sample ID, anchor, target, and counts of that anchor/target combination in that particular sample. </p> <p>arabidopsis_pfe_ALL_anchors_satc_truncated.txt: data from the Arabidopsis P/Fe dataset used to generate figures in the paper. Columns are sample ID, anchor, target, and counts of that anchor/target combination in that particular sample. </p> <p>maize_pollen_ALL_anchors_satc_truncated.txt: data from the maize dataset used to generate figures in the paper. Columns are sample ID, anchor, target, and counts of that anchor/target combination in that particular sample. </p> <p>sorghum_drought_ALL_anchors_satc_truncated.txt: data from the sorghum dataset used to generate figures in the paper. Columns are sample ID, anchor, target, and counts of that anchor/target combination in that particular sample.</p> <p>cryptic_splicing_anchors.tsv: list of anchors described in Supplementary Information section of the article that are examples of cryptic splicing. Columns are dataset name, gene name/ID, anchor sequence, target 1 sequence, and target 2 sequence. </p>
Survey Results - User Accuracy Effects on Algorithmic Accuracy
<p>The survey was hosted on Qualtrics and participants recruited via Cloud Research. Participants are US-only. The data includes those who did not finish. No PII data was collected. </p><p>The survey included a deception scenario for a mortgage application followed by a battery of questions to assess ratings of the algorithm, assess participant honesty, and assess algorithmic awareness.</p>
Estimating the valence, arousal and balance of dyadic conversations using regression algorithms with autonomic nervous system responses
<p>This repository contains extracted data features and all questionnaires from our study "Estimating the valence, arousal and balance of dyadic conversations using regression algorithms with autonomic nervous system responses". </p><p> </p><p>Data_FinalFeatureSet.xlsx contains data for the 42 dyads who completed the study protocol. Rows represent individual participants, with the two participants in the same dyad always on consecutive rows. Columns consist of:</p><ul><li>Participant gender and age.</li><li>Group that dyads were assigned to. PosInit/NeutInit/NegInit represent positive, neutral or negative initial prompts. Devil1st/NoEmot1st represent which of the two secret prompts was presented first ("devil's advocate" or "no emotion").</li><li>A column stating which of the two participants was given the secret prompts (participant on left or right).</li><li>A column stating whether the participants had already known each other before the session (Y/N).</li><li>Extracted physiological features for 12 intervals: the first baseline (interval 1), 10 conversation intervals (intervals 2-11), and the second baseline (interval 12). Individual features are present for all individual participants while synchrony features exist for dyads (not individuals) and are thus present for only one row of a dyad.</li><li>Raw data from three personality questionnaires: the Brief Fear of Negative Evaluation Scale (BFNES), the Questionnaire of Cognitive and Affective Empathy (QCAE) and the Center for Epidemiologic Studies Depression Scale (CESD).</li><li>Self-reported results of the Self-Assessment Manikin (SAM) for the 10 conversation intervals, with the three columns in each interval corresponding to valence, arousal and balance.</li></ul><p>Note that one dyad's physiological data were corrupted and that dyad was not used for further analysis. Their demographics and questionnaire data are included, but no physiological features were calculated.</p><p> </p><p>Questionnaire files include the BFNES, QCAE and CESD as well as three versions of our modified SAM: one with no secret prompts, one with secret prompts for participants who saw the "devil's advocate" prompt first, and one with secret prompts for participants who saw the "no emotion" prompt first.</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.