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26 results for “computational physics”
Data of the article Analysis of the self-archiving policies of journals in the highest rank category of the Finnish journal classification system within computer science, physics and electronic engineering
<p>The publication forum level three journals representing the three fields of science of computer science, computer science and electrical engineering were identified by utilizing the MinEdu field search filter while searching for the top-ranked journals from the publication channel search (https://www.tsv.fi/julkaisufoorumi/haku.php?lang=en), which is based on Field of Science, Statistics Finland classification (https://www.stat.fi/meta/luokitukset/tieteenala/001-2010/index_en.html). The data were extracted during august 2017 consists of total of 127 individual journals. It is worth noting that circa 30 journals were classified into more than one fields of sciences under scrutiny. First, the journals were divided into representing gold and hybrid model journals. Second, green open access policies of the identified hybrid journals were analyzed using Laakso’s (2014) publisher policy coding framework. Also publishers of the individual journals were identified and subsequently added to the data.</p> <p>NOTE! The data includes the shortest embargo to either institutional or subject repositories. For example, Elsevier had no embargo to opening accepted manuscripts from arXiv subject repository and thus no embargoes to Elsevier's journals are included within this datasheet.</p> <p>Data is in CSV. format</p> <p> </p> <p> </p>
Intrinsische Motivation von Schülerinnen und Schülern beim Physical Computing im Informatikunterricht
<p>Der KIM-Fragebogen wurde genutzt, um die intrinsische Motivation von Schülerinnen und Schülern bezüglich des Physical-Computing-Unterrichts zu erheben. Anschließend wurde der Fragebogen verwendet um zu untersuchen, welche Physical-Computing-Tätigkeiten besonders positiv auf die intrinsische Motivation wirken können. Zusätzlich wurde in offenen Fragen erhoben, welche Tätigkeiten die Schülerinnen und Schüler im Unterricht besonders mochten. </p>
Data associated to the manuscript "Computational Screening of the Physical Properties of Water-in-Salt Electrolytes"
<p>Contains input files and data used to generate the figures of the article:</p> <p>Computational Screening of the Physical Properties of Water-in-Salt Electrolytes</p> <p>Trinidad Mendez-Morales, Zhujie Li and Mathieu Salanne,<br> *ChemRxiv*, 13012646v2, 2020</p> <p>[https://doi.org/10.26434/chemrxiv.13012646.v2](https://doi.org/10.26434/chemrxiv.13012646.v2)</p> <p>*WiS-inputs.zip* contains typical [LAMMPS](https://lammps.sandia.gov/) input files for all the systems.</p> <p>The folder *transport_coefficients* contains the computed viscosities, conductivities and diffusion coefficients for all the systems.</p> <p>The folder *radial_distribution_functions* contains all the partial radial distribution functions for all the systems. The nomenclature of the atoms of the anion is provided in the file *figureS1.pdf* and the atoms from the water molecules are labelled Ow and Hw.</p>
Dataset for the publication titlted "A computational mechanics model for producing molecular assembly using molecularly woven pantographs" in the journal Cell Reports Physical Science, authored by Byeonghwa Goh and Joonmyung Choi.
<p>Dataset for the publication titlted "A computational mechanics model for producing molecular assembly using molecularly woven pantographs" in the journal Cell Reports Physical Science, authored by Byeonghwa Goh and Joonmyung Choi.</p>
Features computed from physical exercises measurements
<p><span> </span><span>The data represents time series features from an accelerometer and gyroscope extracted from</span> <span><a href="../records/10984138">Physical Exercise Measurements with Accelerometer and Gyroscope (zenodo.org)</a></span><span>. The data consists of 5 feature sets.</span></p> <p><span>Description of feature sets:</span></p> <p><strong><span>1. </span></strong><strong><span>RQA features set</span></strong></p> <p><span>• "RR" - Recurrence rate</span><span><br></span><span>• "DET" - Determinism, count recurrence points in diagonal lines of length >= lmin</span><span><br></span><span>• "RATIO" - DET/RR</span><span><br></span><span>• "AVG" - average length of diagonal lines of length >= lmin</span><span><br></span><span>• "MAX" - maximal length of diagonal lines of length >= lmin</span><span><br></span><span>• "DIV" - Divergence, 1/MAX</span><span><br></span><span>• "LAM" - Laminarity, VLRP/TR</span><span><br></span><span>• "TT" - Trapping time, average length of vertical lines of length >= lmin</span><span><br></span><span>• "MAX_V" - maximal length of vertical lines of length >= lmin</span><span><br></span><span>• "TR" - Total number of recurrence points</span><span><br></span><span>• "DLRP" - Recurrence points on the diagonal lines of length of length >= lmin</span><span><br></span><span>• "DLC" - Count of diagonal lines of length of length >= lmin</span><span><br></span><span>• "VLRP" - Recurrence points on the vertical lines of length of length >= lmin</span><span><br></span><span>• "VLC" - Count of vertical lines of length of length >= lmin</span></p> <p><span>Was calculated by Chaos01 R package.</span></p> <p><span><a href="https://cran.r-project.org/package=Chaos01">https://CRAN.R-project.org/package=Chaos01</a></span></p> <p><span>The parameters were chosen so that the embedding will create a vector of one value of each axis of the accelerometer/gyroscope measurements. Therefore the used parameters were:</span></p> <table> <tbody> <tr> <td>Function argument</td> <td>Value</td> </tr> <tr> <td>embedding dimension (dim)</td> <td>3</td> </tr> <tr> <td>embedding lag (lag)</td> <td>time series length</td> </tr> <tr> <td>Minimal length of recurrence line (lmin)</td> <td>20</td> </tr> </tbody> </table> <p><strong><span>For Chaos01 we change eps argument and calculated it by following formula:</span></strong></p> <p><code><span># Calculate eps for acc and gyro Chaos 01----</span></code></p> <p><code><span>get_eps <- function(input_data, scale = 1) {</span></code></p> <p><code><span> # Calculate eps for acc and gyro</span></code></p> <p><code><span> eps_a <-</span></code></p> <p><code><span> purrr::map_dbl(input_data$data,</span></code></p> <p><code><span> ~ .x |></span></code></p> <p><code><span> select(Ax, Ay, Az) |></span></code></p> <p> <code><span> as.matrix() |></span></code></p> <p> <code><span> as.vector() |></span></code></p> <p> <code><span> sd()) |></span></code></p> <p> <code><span> mean() * scale</span></code></p> <p><code><span> eps_g <-</span></code></p> <p><code><span> purrr::map_dbl(input_data$data,</span></code></p> <p><code><span> ~ .x |></span></code></p> <p> <code><span> select(Gx, Gy, Gz) |> </span></code></p> <p> <code><span> as.matrix() </span></code></p> <p> <code><span> as.vector() |></span></code></p> <p> <code><span> sd()) |></span></code></p> <p> <code><span> mean() * scale</span></code></p> <p><code><span>return(list(a = eps_a, g = eps_g))</span></code></p> <p><code><span>}</span></code></p> <p><span>"TREND" - Trend of the number of recurrent points depending on the distance to the main diagonal.</span></p> <p><span>Was calculated by nonlinearTseries R package.</span></p> <p><span> </span><span><a href="https://cran.r-project.org/package=nonlinearTseries">https://CRAN.R-project.org/package=nonlinearTseries</a></span></p> <p><strong><span> </span></strong></p> <p><strong><span>All following feature sets was calculated by Python package</span></strong></p> <p><span><span><strong>https://tsfresh.readthedocs.io/en/latest/index.html</strong></span></span></p> <p><strong><span> </span></strong></p> <p><span>The used dictionary is included in file named tsfresh_autocorr_spectral_features.py.</span></p> <p><strong><span> </span></strong></p> <p><strong><span>2. </span></strong><strong><span>Autocorrelation features set</span></strong></p> <p><span>#https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.autocorrelation</span></p> <p><span>#https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.agg_autocorrelation</span></p> <p><span> </span></p> <p><strong><span>3. </span></strong><strong><span>Spectral features set</span></strong></p> <p><span>#https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.fft_aggregated</span></p> <p><span>#https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.approximate_entropy</span></p> <p><span>#https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.fft_coefficient</span></p> <p><span>#https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.fourier_entropy</span></p> <p><span>#https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.spkt_welch_density</span></p> <p><span>#https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.ar_coefficient</span></p> <p><span> </span></p> <p><strong><span>4. </span></strong><strong><span>Mix RQA/Spectral/Autocorrelation features set</span></strong></p> <p><strong><span> </span></strong></p> <p><strong><span>5. </span></strong><strong><span>Tsfresh all features set</span></strong></p> <p><span>#</span><span><span>https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html</span></span></p> <p><span> </span></p> <p><strong><span>Versions of the software:</span></strong></p> <p><span>Python (version 3.8.10) </span></p> <p><span>tsfresh = 0.20.2</span></p> <p><span>R (version 4.3.2) </span></p> <p><span>Chaos01 = Version 1.2.1 </span></p> <p><span>nonlinearTseries = 0.3.0 </span></p>
Supplementary materials for the paper "Computing with liquid crystal fingers: Models of geometric and logical computation." Physical Review E 84.6 (2011): 061702.
<p>When a voltage is applied across a thin layer of cholesteric liquid crystal, fingers of cholesteric alignment can form and propagate in the layer. In computer simulation, based on experimental laboratory results, we demonstrate that these cholesteric fingers can solve selected problems of computational geometry, logic, and arithmetics. We show that branching fingers approximate a planar Voronoi diagram, and nonbranching fingers produce a convex subdivision of concave polygons. We also provide a detailed blueprint and simulation of a one-bit half-adder functioning on the principles of collision-based computing, where the implementation is via collision of liquid crystal fingers with obstacles and other fingers.</p>
In materia implementation strategies of physical reservoir computing with memristive nanonetworks - Dataset
<p>This is the dataset of "In materia implementation strategies of physical reservoir computing with memristive nanonetworks"</p>
International E- Conference on "Recent Trends in Chemical Science, Physical Science, Life Science and Computer Technology (ICRTCPLCT–2022)
<p><strong>International E- Conference</strong> on “Recent Trends in Chemical Science, Physical Science, Life Science and Computer Technology (ICRTCPLCT–2022)” By Anjuman Islam Janjira Degree College of Science, Murud on <strong>29<sup>th</sup> March 2022.</strong></p>
Physical Unclonable In-Memory Computing for Simultaneous Protecting Private Data and Deep Learning Models
Open the record for dataset details and reuse information.
Dataset for the plots in paper: 'A Kronecker product accelerated efficient sparse Gaussian Process (E-SGP) for flow emulation' in 'Journal of Computational Physics'
<p>The .xlsx file contains the data used for the plots Fig. 3, 4, 7, 8 and 9 in the paper 'Kronecker product accelerated efficient sparse Gaussian Process (E-SGP) for flow emulation'.</p>
Computer-Based Physical Activity Advice for Ethnic Minority Aging Adults
ClinicalTrials.gov study NCT01144767. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Computer Application (ICOGNI) Based Training Along With Routine Physical Therapy on Executive Functions and Motor Skills in Cerebral Palsy
ClinicalTrials.gov study NCT05399810. IPD Sharing: NO. Countries: 1. Publications: 2.
Computer Assisted Learning (CAL) Module Use for Re-training of Blood Pressure Measurements by Physical Therapy Students
ClinicalTrials.gov study NCT05915039. IPD Sharing: NO. Countries: 1. Publications: 13.
Effectiveness of a Web-based, Computer-tailored, Pedometer-based Physical Activity Intervention for Adults: a Cluster-randomized Controlled Trial
ClinicalTrials.gov study NCT02080585. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Physical and Social Benefits of Multi-Player Interactive Computer Play Games in Youth With Cerebral Palsy
ClinicalTrials.gov study NCT01901211. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A pore-scale 3D dynamic morphological modeling and physical characterization of hydrate-bearing sediment based on computed tomography images
<p><strong>Introduction</strong></p> <p>This supporting information includes one figure S1, which is the representative elemental volume (REV) used in this study. And the figure S1 is consisted of 300 slices with a voxel size of 4.4 μm. There are two phases in the REV: the sand and the pore.</p> <p> </p> <p>Figure S1 is uploaded with file name Figure S1-REV300.am, which is the representative elemental volume (REV) used in this study</p>
Pore-scale 3D dynamic morphological modeling and physical characterization of hydrate-bearing sediment based on computed tomography images
<p>This supporting information includes one Figure-S1-REV300, which is the representative elemental volume (REV) used in this study. And the Figure-S1-REV300 is 300*300*300 with a voxel size of 4.4 μm. There are two phases in the REV: the sand and the pore.</p> <p>Figure S1 is uploaded with file name Figure S1-REV300.tif, which is the representative elemental volume (REV) used in this study</p>
Data from: Computed tomography shows high fracture prevalence among physically active forager-horticulturalists with high fertility
<p>Modern humans have more fragile skeletons than other hominins, which may result from physical inactivity. Here we test whether reproductive effort also compromises bone strength, by measuring using computed tomography thoracic vertebral bone mineral density (BMD) and fracture prevalence among physically active Tsimane forager-horticulturalists. Earlier onset of reproduction and shorter interbirth intervals are associated with reduced BMD for women. Tsimane BMD is lower versus Americans, but only for women, contrary to simple predictions relying on inactivity to explain skeletal fragility. Minimal BMD differences exist between Tsimane and American men, suggesting that systemic factors other than fertility (e.g. diet) do not easily explain Tsimane women's lower BMD. Tsimane fracture prevalence is also higher versus Americans. Lower BMD increases Tsimane fracture risk, but only for women, suggesting a role of weak bone in women's fracture etiology. Our results highlight the role of sex-specific mechanisms underlying skeletal fragility that operate long before menopause.</p>
Build-up Computed Assisted History Taking, Physical Examination and Diagnosis System of Emergency Patient Through Machine Learning (II)
ClinicalTrials.gov study NCT05596929. IPD Sharing: NO. Countries: 1. Publications: 0.
Computer-Based Behavior Change Guide in Increasing Physical Activity in Patients With Prostate Cancer Who Have Received Androgen Deprivation Therapy
ClinicalTrials.gov study NCT02869412. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
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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)
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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.