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106 results for “Frequency analysis”
Regional Flood Frequency Analysis of the Sava River in South-Eastern Europe
<p>Journal: Sustainability</p> <p>Abstract: Regional flood frequency analysis (RFFA) is a powerful method for interrogating hydrological series since it combines observational time series from several sites within a region to estimate risk-relevant statistical parameters with higher accuracy than from single-site series. Since RFFA extreme value estimates depend on the shape of the selected distribution of the data-generating stochastic process, there is need for a suitable goodness-of-distributional-fit measure in order to optimally utilize given data. Here we present a novel, least-squares-based measure to select the optimal fit from a set of five distributions, namely Generalized Extreme Value (GEV), Generalized Logistic, Gumbel, Log-Normal Type III and Log-Pearson Type III. The fit metric is applied to annual maximum discharge series from six hydrological stations along the Sava River in South-eastern Europe, spanning the years 1961 to 2020. Results reveal that (1) the Sava River basin can be assessed as hydrologically homogeneous and (2) the GEV distribution provides typically the best fit. We offer hydrological‒meteorological insights into the differences among the six stations. For the period studied, almost all stations exhibit statistically insignificant trends, which renders the conclusions about flood risk as relevant for hydrological sciences and the design of regional flood protection infrastructure.</p> <p>URL: https://www.mdpi.com/2071-1050/14/15/9282</p> <p>The uploaded datasets are the Annual Maximum Series of Sava River runoff for the six analysed hydrological stations: Radovljica, Čatež, Zagreb, Jasenovac, Županja and S. Mitrovica.</p>
Solar and interplanetary magnetic field data analyzed in "Optimal frequency-domain analysis for spacecraft time series: Introducing the missing-data multitaper power spectrum estimator"
<p>This dataset contains simultaneous measurements of the interplanetary magnetic field magnitude <B> and the sun's radio flux at 10.7 cm <F10.7>. <B> measurements come from a series of spacecraft located at the L1 point, while <F10.7> was measured by the ongoing monitoring program by Canada's Dominion Radio Astrophysical Observatory. Bartels rotation-averaged data were downloaded from NASA's OMNIWeb, https://omniweb.gsfc.nasa.gov/html/ow_data.html. The file contains other solar wind plasma parameters that were not used in the analysis.</p>
Analysis of internet usage and bioblitz frequency in the Global South
<p>This dataset was used to analyse factors contributing to the number of bioblitzes conducting in countries in the Global South.</p> <p>This is part of a review into the effectiveness of bioblitz as a method for collecting data on biodiversity.</p> <p>We modeled population and internet usage with the number of iNaturalist Bioblitzes in a country from our sample (Groom 2021). We only just looked at those global regions where citizen science has tended to have lower prevalence in the past (Africa, Asia, and Latin America, and the Caribbean) compared to other regions. We identified a total of 254 Bioblitz projects from iNaturalist in 37 countries, in Africa (30 projects in 13 countries); Asia (71 projects in 11 countries); and Latin America and the Caribbean (153 projects in 13 countries).</p> <p>We took the total population of each country from the mean of 2015-18 values in millions from <a href="https://population.un.org/wpp/Download/Standard/Population/">https://population.un.org/wpp/Download/Standard/Population/</a> (United Nations, Department of Economic and Social Affairs, Population Division (2019). <em>World Population Prospects 2019, Online Edition. Rev. 1.</em>). Internet usage was taken as the percentage of individuals using the internet in 2017 (<a href="http://data.un.org/">http://data.un.org/</a>).</p> <p>The natural log of the number of iNaturalist projects was modelled against the log of the population in millions and the internet usage using the lm package of R.</p> <pre><code>SUMMARY_DATA <- read.delim2("summary_data.tsv", row.names=1) model <- lm(log(projects) ~log(population) + internet, data=SUMMARY_DATA) summary(model) </code></pre> <table> <tbody> <tr> <td> <p>Variable</p> </td> <td> <p>Coefficient</p> </td> <td> <p>Std. Error</p> </td> <td> <p>t-Statistic</p> </td> <td> <p>Prob.</p> </td> </tr> <tr> <td> <p>log(population) in millions</p> </td> <td> <p>0.345</p> </td> <td> <p>0.0865</p> </td> <td> <p>3.99</p> </td> <td> <p>0.0003 ***</p> </td> </tr> <tr> <td> <p>internet usage as a percentage of individuals per country</p> </td> <td> <p>0.016</p> </td> <td> <p>0.0060</p> </td> <td> <p>2.62</p> </td> <td> <p>0.0130 *</p> </td> </tr> </tbody> </table> <p>Residual standard error: 0.9167 on 34 degrees of freedom</p> <p>Multiple R-squared: 0.3911, Adjusted R-squared: 0.3553 </p> <p>F-statistic: 10.92 on 2 and 34 DF, p-value: 0.0002176</p> <p>To view properties of the model to ensure it conformed to the assumptions of the model and was a good fit. Plots are included in the attached files.</p> <pre><code>par(mfrow = c(2,2)) plot(model)</code></pre> <p>To view other correlations in the data the following code can be used. The output is included in the attached files.</p> <pre><code>correlations <- cor(SUMMARY_DATA[,c(2,3,4,6,11,13,14)],method = c("spearman")) install.packages("corrplot") library("corrplot") corrplot(correlations, method="square") </code></pre> <p> </p>
A scalable, accurate, and universal analysis framework using individual-level allele frequency for large-scale genetic association studies in an admixed population
<p>Inclusion of individuals with diverse or admixed genetic ancestries is crucial to discover novel findings that may be missed by genomics analyses rooted solely in Caucasian population. Here, we present an analysis framework, SPAmix, which is scalable to a large-scale biobank data analysis including hundreds of thousands of admixed individuals and is universally applicable to various types of complex traits including binary trait, quantitative trait, time-to-event trait, longitudinal traits, etc. For each genetic variant, SPAmix uses genotype data and genetic principal components (PCs) to estimate individual-level allele frequency, which is subsequently used to calibrate p values via a retrospective analysis. A hybrid strategy including saddlepoint approximation (SPA) can greatly increase the accuracy to analyze rare genetic variants, especially if the phenotypic distribution is unbalanced or extremely unbalanced. Compared to Tractor, SPAmix does not require local ancestry information and can be straightforwardly applicable to a multi-way admixed population. Meanwhile, SPAmix can also be extended to SPAmix<sub>local</sub> in which the local ancestry can be incorporated if available. In addition, we propose SPAmix<sub>CCT</sub> to combine the p values of SPAmix and SPAmix<sub>local</sub> via Cauchy combination (CCT). SPAmix<sub>local</sub> performs close to Tractor when analyzing quantitative traits and is more accurate when analyzing binary traits with an unbalanced case-control ratio. And SPAmix<sub>CCT </sub>is an optimal unified approach for various cross-ancestry genetic architectures. Extensive simulation studies and real data analyses of 369,314 UK Biobank individuals from multiple ancestries demonstrated that SPAmix is scalable and can discover novel hits while controlling type I error rates well.</p>
Radio-frequency C-V measurements with subattofarad sensitivity: data and analysis script
<p>The attached files include:</p> <ul> <li>QCoDeS database containing all of the raw data underlying the results presented in the publication "Radio-frequency CV measurements with subattofarad sensitivity" by F.K. Malinowski at al. published in Physical Review Applied in 2022</li> <li>Jupyter Notebook file with Python scripts, that processes the raw data and outputs the figures embedded in the publication (except for the schematics of the devices and the rf circuitry).</li> </ul>
Plant‐eating carnivores: Multispecies analysis on factors influencing the frequency of plant occurrence in obligate carnivores
<p>Plant-eating behavior is one of the greatest mysteries in obligate carnivores. Despite unsuitable morphological and physiological traits for plant consumption, the presence of plants in scat or stomach contents has been reported in various carnivorous species. However, researchers' interpretations of this subject are varied, and knowledge about it is scarce, without any multispecies studies. This study assessed the extent of variation in the frequency of plant occurrence in scat and stomach contents, as well as its relationship with various factors in 24 felid species using data from 213 published articles. Since the frequency of plant occurrence has not always been reported, we created two-part models and estimated parameters in a Bayesian framework. We found a significant negative relationship between the frequency of plant occurrence and body mass. This may be because plant-eating behavior reduces the energy loss caused by parasites and increases the efficiency of energy intake, which has a greater importance in smaller animals that have relatively high metabolic rates. This exploratory study highlights the importance of considering plant consumption in dietary studies on carnivorous species to understand the adaptive significance of this behavior and the relationship between obligate carnivores and plants.</p>
GLOBMAP SWF: a global annual surface water cover frequency dataset since 2000 for change analysis of inland water bodies
<p>The extent of surface water has been changing significantly due to climatic change and human activities. However, it is challenging to capture the interannual changes and trends of inland water bodies due to their high seasonal variation and abrupt change. We generated a global annual surface water cover frequency dataset (GLOBMAP SWF) from the MODIS land surface reflectance products to describe the seasonal and interannual dynamics of surface water. Surface water cover frequency (SWF) was proposed as the percentage of the time period when a pixel is covered by water in a year. Instead of determination of the water observations directly, the SWF was estimated indirectly by identifying land observations among annual clear-sky observations to reduce the influence of clouds and variability of water body and surface background characteristics, which helps to improve the applicability of the algorithm for different regions across the globe. Regional analysis demonstrates that our estimation results show reasonable performances on frozen water, saline lake, bright surface and cloud-frequent regions. This dataset can be used to analyze the interannual variation and change trend of highly dynamic inland water body extent with consideration of its seasonal variation.</p> <p>The GLOBMAP SWF dataset is provided in Version 1.0 (https://zenodo.org/record/6462883#.YxC16HZBw2w). Here we provide the number of MOD09A1 (MODIS 8-day composite land surface reflectance) clear-sky snow/ice-free observations (<em>N<sub>Clear</sub></em>) data as a quality dataset of GLOBMAP SWF product. The clear-sky observation refers to the valid MOD09A1 observation that not covered with clouds and snow/ice. The more available clear-sky observations, the more reliable the estimated SWF.</p> <p>The <em>N<sub>Clear </sub></em>dataset is provided by 296 1200 km × 1200 km tiles at annual temporal and 500 m spatial resolutions in the sinusoidal projection with Geotiff format for each year during 2000-2020. The file is named as "GLOBMAPClearCount. AYYYY001.hHHvVV.V01.tif", where “YYYY” refers to the year of the file, and “HH” and “VV” explains the number of tiles that are the same with MODIS standard tile. The valid range is 0-46, scale factor is 1.0. The <em>N<sub>Clear </sub></em>of permanent water (land obervation count of 46), permanent snow/ice and terrain shadows are set to 50.</p>
Figure 4 in Frequency of pyrethroid resistance in human head louse treatment: systematic review and meta-analysis
Figure 4. Forest plots of the proportion of heterozygote resistance and 95% confidence interval based on a random effect model in metaanalysis.
Figure 3 in Frequency of pyrethroid resistance in human head louse treatment: systematic review and meta-analysis
Figure 3. Forest plots of the proportion of homozygote resistant and 95% confidence interval based on a random effect model in metaanalysis.
Figure 2 in Frequency of pyrethroid resistance in human head louse treatment: systematic review and meta-analysis
Figure 2. Forest plots of the proportion of resistance in lice and 95% confidence interval based on a random effect model in meta-analysis.
BRAIN Journal-Novel Detection Features for SSVEP Based BCI: Coefficient of Variation and Variation Speed-Figure 1: Time vs frequency analysis of 10 Hz SSVEP response
<p>The stability of the SSVEP signal was examined by using wavelet analysis (Wu and Yao 2008). Since there is a trade-off between time and frequency resolution in wavelet analysis, examining the stability of SSVEP with wavelet analysis is getting harder in systems where the visual stimulus frequencies are close to each other, as shown in Figure 1. </p>
Alert Type Frequency Assessment of Open-Source Static Analysis Tools and Codebases
<p>This includes all data needed to replicate and validate our frequency analysis of static analysis (SA) alerts produced using open-source SA tools on several OSS codebases. It includes instructions how to get and run the SA tools, a Dockerfile to conveniently get and use the SA tools, raw SA tool output, some python scripts to parse that output, parsed SA data and aggregate analyses, and SA data augmented with CERT coding rule and CWE data. </p> <p>The SA tools used:</p> <ul> <li>clang-tidy version 15.07 </li> <li>cppcheck version 2.9 </li> <li>CERT Rosecheckers </li> </ul> <p>The codebases analyzed:</p> <ul> <li>zeek version 5.1.1</li> <li>git version 2.39.0</li> <li>dos2unix version 7.4.3</li> </ul>
Plant‐eating carnivores: Multispecies analysis on factors influencing the frequency of plant occurrence in obligate carnivores
Open the record for dataset details and reuse information.
Snapshots, frequency contact maps analysis, Poisson Boltzmann calculations, and data scripts for characterization of structural and energetic differences between conformations of the SARS-CoV-2 spike protein
<p><strong>Molecular dynamics simulation</strong> trajectories, which have been performed using the Amber ff14SB force field running with the Amber18 package at the NSF-funded (OAC-1826915, OAC-1828163) ELSA high performance computing cluster at The College of New Jersey. Simulation methodology and further details are described in [1] and [2]. For further details on the trajectories, please contact Joseph Baker (bakerj@tcnj.edu).</p> <p>The <strong>Poisson Boltzmann </strong>energy calculations have been achieved by using the input_files.tar.xz found here and solving the Poisson Boltzmann equation with pygbe. A more detailed example and tutorial can be found at [4]. For further details contact Horacio V Guzman.</p> <p><strong>The dataset contains </strong></p> <ul> <li><strong>A total of 30 snapshots of the three trajectories (10 snapshots each system = two per replica x 5 replicas/system):</strong></li> </ul> <ol> <li>SARS-CoV-2002 spike protein with three RBD in the down positions: "COV2-DDD/PDB/" .</li> <li>SARS-CoV-2002 spike protein with one RBD in the up and two RBD in the down positions: "COV2-UDD/PDB/".</li> <li>SARS-CoV-2002 spike protein with two RBD in the up and one RBD in the down positions: "COV2-DUU/PDB/".</li> </ol> <ul> <li><strong>Input files for Poisson-Boltzmann analysis</strong>:</li> </ul> <ol> <li>PoissonBoltzmann/input_files.tar.xz</li> </ol> <ul> <li><strong>Data for the frequency contact map and processing scripts</strong>:</li> </ul> <ol> <li>cov2-ddd.pdb, cov2-udd.pdb, cov2-duu.pdb reference PDB files.</li> <li>Contact maps [3] at "COV2-DDD/CONTACT_MAP/", "COV2-UDD/CONTACT_MAP/", "COV2-DUU/CONTACT_MAP/".</li> <li>frequency.lua: get frequency of contacts from a set of contacts map files.</li> <li>diff_frequency.lua: get differential frequency of contacts from a set of frequency files.</li> <li>Frequency of contacts listed in frequency.data files at "COV2-DDD/", "COV2-UDD/" and "COV2-DUU/" directories.</li> </ol> <p>Read the "INFO" files for further informations.</p> <p>This dataset and the code is part of a collaboration between:</p> <ul> <li>The Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland (supported by the National Science Centre, Poland, under grant No. 2017/26/D/NZ1/0046)</li> <li>Department of Chemistry, The College of New Jersey, New Jersey, United States (supported by National Science Foundation under grant numbers OAC-1826915 and OAC-1828163).</li> <li>Jozef Stefan Institute, Ljubljana, Slovenia (supported by the Slovenian Research Agency (Funding No. P1-0055)).</li> <li>School of engineering in bioinformatics, University of Talca, Talca, Chile.</li> </ul> <p>[1] Rodrigo A. Moreira, Mateusz Chwastyk, Joseph L. Baker, Horacio V Guzman, & Adolfo B. Poma. (2020). All-atom simulations snapshots and contact maps analysis scripts for SARS-CoV-2002 and SARS-CoV-2 spike proteins with and without ACE2 enzyme (Version 0.1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3817447</p> <p>[2] Chad W. Hopkins, Scott Le Grand, Ross C. Walker, and Adrian E. Roitberg. Long-Time-Step Molecular Dynamics through Hydrogen Mass Repartitioning. Journal of Chemical Theory and Computation 2015 11 (4), 1864-1874. http://doi.org/10.1021/ct5010406</p> <p>[3] Rodrigo A. Moreira, Mateusz Chwastyk, Joseph L. Baker, Horacio V Guzman, & Adolfo B. Poma. Quantitative determination of mechanical stability in the novel coronavirus spike protein. Nanoscale, 2020,12, 16409-16413. <a href="https://doi.org/10.1039/D0NR03969A">https://doi.org/10.1039/D0NR03969A</a></p> <p>[4] https://github.com/pyF4all</p>
Ringlaser and seismic data at Fürstenfeldbruck and Wettzell for time-frequency analysis of microseisms
<p>Ringlaser rotation data and seismic data at Fürstenfeldbruck and Wettzell for the time-frequency analysis of seismic noise. Programs are also attached.</p>
Dataset for article: Perakakis, P., Taylor, M., Martinez-Nieto, E., Revithi, I., Vila, J. (2009). Breathing Frequency Bias in Fractal Analysis of Heart Rate Variability. Biological Psychology, 82(1), pp. 82-88
<p>Dataset for article:</p> <p>Perakakis, P., Taylor, M., Martinez-Nieto, E., Revithi, I., Vila, J. (2009). Breathing Frequency Bias in Fractal Analysis of Heart Rate Variability. Biological Psychology, 82(1), pp. 82-88</p>
Breathing frequency bias in fractal analysis of heart rate variability (datasets)
<p>data form the article:</p> <p>Perakakis, P., Taylor, M., Martinez-Nieto, E., Revithi, I., Vila, J. (2009). Breathing Frequency Bias in Fractal Analysis of Heart Rate Variability. Bi- ological Psychology, 82(1), pp. 82-88 </p>
(variable) Frequency-based Layer Identification Procedure (FLIP and vFLIP) for spectrolaminar analysis
<p class="MsoNormal">The mammalian cerebral cortex is anatomically organized into a six-layer motif. In this dataset and associated paper (Mendoza-Halliday et al., 2023) we show that a corresponding laminar motif of neuronal activity patterns exists across the cortex in the power of local field potentials (LFP). Using laminar probes, we recorded LFPs in five macaque monkeys in 14 cortical areas across the cortical hierarchy. The laminar locations of recordings were histologically identified via electrolytic lesions. Across all areas, we found a ubiquitous spectrolaminar pattern characterized by an increasing deep-to-superficial layer gradient of high-frequency power peaking in layers 2/3 and an increasing superficial-to-deep gradient of alpha-beta power peaking in layers 5/6. Our results suggest the existence of a canonical layer- and frequency-based mechanism for cortical computation.</p> <p class="MsoNormal">To facilitate the detection of these spectrolaminar patterns, we are releasing data associated with "Study 1" and "Study 2" as reported in Mendoza-Halliday et al., 2023. This comprises relative power of LFPs recorded in 4 macaque monkeys from areas V4, 7A, MT, MST, LIP, and LPFC. The FLIP and vFLIP algorithms were created to facilitate electrophysiological characterization of cortical layers based on power analysis of the local field potential recordings (LFP) that are provided in these datasets. The algorithms can also be used to perform the same analysis on any other multi-channel LFP dataset. We have verified that the spectrolaminar pattern is highly preserved in macaque monkeys, marmosets, and humans, but is more dissimilar in mouse. </p>
Data and code for the publication "Multi-method analysis of microplastic distribution by flood frequency and local topography in Rhine floodplains"
<p><strong>Background</strong></p> <p>The dataset contains data on soil properties and microplastic abundance in soil samples taken in the floodplains Langel-Merkenich, Poller Wiesen and Westhovener Aue (Cologne, Germany). They were analysed in the paper by M. Rolf, H. Laermanns, J. Horn, L. Kienzler, C. Pohl, G. Dierkes, S. Kernchen, C. Laforsch, M.G.J. Löder and C. Bogner, “Multi-method analysis of microplastic distribution by flood frequency and local topography in Rhine floodplains” <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.scitotenv.2024.171927" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.scitotenv.2024.171927</a>) published in Science of the Total Environment.</p>
Scripts used for the analysis in the paper "How certain are ENSO frequency changes in CMIP6 models?"
<p>These are scripts used for the analysis of CMIP6 piCOntrol and 1pctCO2 simulations.</p> <p>They produce results and plots used in the paper "How certain are ENSO frequency changes in CMIP6 models, submitted to "International Journal of Climatology".</p> <p> </p> <p>Paper_ONIcontrol.sh, Prepare_CMIP.sh and PrepareCMIP_long.sh were used to process the original data.</p> <p>The notebooks and python scripts are used for analysis and plotting.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.