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795 results for “Fluctuations”
Barn owl diet and prey fluctuations in the Jura mountains, eastern France
<p>Based on pellet collection, the diet of the Barn Owl (<em>Tyto alba</em>) was studied over a 8-year period in the Jura mountains, eastern France, during two population surges of its main prey (common vole, <em>Microtus arvalis </em>and montane water vole, <em>Arvicola amphibius</em>); Small mammals were sampled by trapping and index methods. Results have been published in the Canadian Journal of Zoology (<a href="http://doi.org/10.1139/z10-011">Bernard et al. 2010</a>). Dominique Michelat collected Barn Owl pellets and identified prey items, Pierre Delattre, Jean-Pierre Quéré Jean-Pierre Damange and Patrick Giraudoux sampled small mammals. Patrick Giraudoux managed the data.</p> <p><a href="https://zenodo.org/record/6945677/files/Small_mammals_trapping.txt?download=1">Small_mammals_trapping.txt </a>is the file of the raw trapping results for small mammals (instant abundance index i<sub>t</sub> in the article)</p> <p><a href="https://zenodo.org/record/6945677/files/diet_smm.txt?download=1">diet_smm.txt</a> is a file with:</p> <ul> <li>the small mammal density computed by season (d<sub>t</sub> in the article, rough estimate of densities in number of individuals per ha for <em>Apodemus spp.</em>, <em>Myodes glareolus</em>, <em>Microtus arvalis</em>, or weighted interpolated i<sub>t</sub> for the other species)</li> <li>the ratios of each category of prey items on the total number of items collected in the church tower of three sites, <a href="https://www.openstreetmap.org/#map=16/46.9536/6.1189">Levier</a>, <a href="https://www.openstreetmap.org/search?whereami=1&query=46.9321%2C6.1666#map=16/46.9321/6.1666">Chapelle d'Huin</a> and <a href="https://www.openstreetmap.org/search?whereami=1&query=46.9382%2C6.1976#map=16/46.9382/6.1976">Le Souillot</a>.</li> </ul> <p>For details see the material and methods of the article.</p> <p><strong>FILE DESCRIPTION</strong></p> <p><a href="https://zenodo.org/record/6945677/files/diet_smm.txt?download=1">diet_smm.txt</a></p> <ul> <li>date, year and season: year on two digits, then P, E, A, H respectively for<em> Printemps</em> (Spring), <em>Été</em> (Summer), <em>Automne</em> (Autumn), <em>Hiver</em> (Winter)</li> <li>at_t, abundance index of <em>Arvicola amphibius</em> (ex A.<em> terrestris</em>)</li> <li>ap_t, rough density estimate of <em>Apodemus sp.</em></li> <li>cg_t, rough density estimate of <em>Myodes glareolus</em></li> <li>ma_t, rough density estimate of <em>Microtus arvalis</em></li> <li>sa_t, relative abundance of <em>Sorex spp.</em></li> <li>n_l, number of prey items at Levier</li> <li>ma_p_l, ratio of <em>M. arvalis prey</em> items at Levier</li> <li>at_p_l, ratio of <em>A. amphibius</em> prey items at Levier</li> <li>apcg_p_l, ratio of <em>Apodemus spp.</em> or <em>Myodes glareolus</em> prey items at Levier</li> <li>sa_p_l, ratio of <em>Sorex spp.</em> prey items at Levier</li> <li>au_p_l, ratio of other prey items at Levier</li> <li>n_ch, number of prey items at Chapelle d'Huin</li> <li>map_ch, ratio of <em>M. arvalis prey</em> items at Chapelle d'Huin</li> <li>at_p_ch, ratio of <em>A. amphibius</em> prey items at Chapelle d'Huin</li> <li>apcg_p_ch, ratio of <em>Apodemus spp.</em> or <em>Myodes glareolus</em> prey items at Chapelle d'Huin</li> <li>sa_p_ch, ratio of <em>Sorex spp. </em>prey items at Chapelle d'Huin</li> <li>au_p_ch, ratio of other prey items at Chapelle d'Huin</li> <li>n_ls, number of prey items at Le Souillot</li> <li>ma_p_ls, ratio of <em>M. arvalis prey</em> items at Le Souillot</li> <li>at_p_ls, ratio of <em>A. amphibius</em> prey items at Le souillot</li> <li>apcg_p_ls, ratio of <em>Apodemus spp.</em> or <em>Myodes glareolus</em> prey items at Le Souillot</li> <li>sa_p_ls, ratio of <em>Sorex spp.</em> prey items at Le Souillot</li> <li>au_p_ls, ratio of other prey items at Le Souillot</li> </ul> <p><a href="https://zenodo.org/record/6945677/files/Small_mammals_trapping.txt?download=1">Small_mammals_trapping.txt </a></p> <ul> <li>date, trapping period: digit 1-2, year; digit 3-4, month. Example: 8704 = April 1987.</li> <li>n_traplines_f, number of traplines in forest</li> <li>ap_f, average number of <em>Apodemus spp</em>. captured in forest</li> <li>cg_f, average number of <em>Myodes glareolus</em> captured in forest</li> <li>sa_f, average number of <em>Sorex spp</em>. captured in forest</li> <li>n_traplines_hfb, number of traplines in hedges and forest borders</li> <li>ap_hfb, average number of <em>Apodemus spp</em>. captured in hedges and forest borders</li> <li>cg_hfb, average number of <em>Myodes glareolus</em> captured in hedges and forest borders</li> <li>sa_hfb, average number of <em>Sorex spp.</em> captured in hedges and forest borders</li> <li>n_traplines_g, number of traplines in grassland</li> <li>ma_g, average number of Microtus arvalis captured in grassland</li> <li>sa_g, average number of <em>Sorex spp.</em> captured in grassland</li> </ul> <p><a href="https://zenodo.org/record/6945677/files/SmallMammalSamplingArea.kml?download=1">SmallMammalSamplingArea.kml</a> kml file locating the small mammal sampling area<br> </p>
Dataset of "Atomic-level description of thermal fluctuations in inorganic lead halide perovskites" publication
<p>The "Zenodo_22-02-2022.zip" file contains a "files" folder and a jupyter notebook to plot the figures reported in the publication. The "files" folder contains several subfolders with the txt files required to plot the figures.</p> <p>The "XAS_simulations.zip" file contains a README.txt and few subfolders with input and output files needed to reproduce the XAS simulations. The README.txt explains the structure of the archive and the content of each subfolders.</p>
Dataset supplementing Schütz, I. & Einhäuser, W. (2018) Visual awareness in binocular rivalry modulates induced pupil fluctuations.
<p>This dataset supplements the publication:</p> <p>Schütz, I. & Einhäuser, W. (2018). Visual awareness in binocular rivalry modulates induced pupil fluctuations.</p> <p><br> Raw data are available in two formats: the actual raw EDF data files as returned by the eyetracking device (*.edf) for reference, and as MATLAB files into which all relevant information has been extracted (*.mat) for analysis.</p> <p>Variables in the pft_*.mat files include<br> - raw eye position in the variable scan (t,x,y,p), where t is the timestamp of the eyetracker, x/y the position on the screen and p the pupil diameter in arbitrary units<br> - fixation, saccade and blink events in variables fix, sac and blink<br> - raw button presses in cell arrays buttonDn and buttonUp (6 - left button, 7 - right button) including timestamps<br> - stimulus presentation cycle timestamps in the variable stimperiod<br> - timestamps for auditory attentional instruction in the variable attends</p> <p>For analysis, data from all participants and sessions is imported into pftdata.mat, where it is stored in cell arrays of the format "samples{subject_no, condition}".</p> <p><br> Data Files<br> ==========</p> <p>- rawdata.zip<br> - rawdata/*.edf: raw EyeLink 2000 EDF data files<br> - rawdata/*.mat: EyeLink data converted to MATLAB data file</p> <p>- analysis.zip<br> - pftdata.mat: preprocessed eye tracking and response data for analysis<br> - face.png, house.png: stimulus images used for the experiment<br> - resp_anova.csv: response data for ANOVA (generated by preprocessing.m)<br> - fig4_anova.csv: complex plane data for R T-Test (generated by figure4_complex_plane.m)<br> - analysis code files, see below</p> <p><br> Analysis Functions<br> ==================</p> <p>Run the following functions in the listed order to reproduce figures and data in results/.</p> <p>- preprocessing.m:<br> - convert EDF data files to ASCII using SR-Research edf2asc, import into MATLAB<br> - remove EyeLink detected blinks and interpolate (cubic spline)<br> - z-score pupil data within each experimental block<br> - add button press / reported percept to sample data<br> - save response data for RM-ANOVA in R</p> <p>- figure1_methods.m:<br> - recreates Figure 1 (stimulus figure from images)</p> <p>- figure2_example_plot.m:<br> - recreate Figure 2 (example data from one participant)</p> <p>- figure3_averaged_response.m<br> - recreates Figure 2 (averaged F1 FFT component by condition)</p> <p>- figure4_complex_plane.m:<br> - recreates Figure 4 (complex plane analysis of pupil response)</p> <p>- stats_auc_decoding.m:<br> - moment-by-moment decoding analysis using AUC<br> - recreates stats_AUC.txt</p> <p>- pft_statistics.R:<br> - R statistics, recreates stats_responses.txt and stats_Zvalues.txt</p> <p><br> Output Files<br> ============</p> <p>results/<br> - Paper Figures (not layouted): figure1.tif, figure2.png, figure3.png, figure4.png<br> - stats_responses.txt: behavioral analyses results,<br> - stats_Zvalues.txt: complex plane analysis results<br> - stats_AUC.txt: moment-by-moment AUC decoding results</p> <p> </p>
Quantitative comparison of camera technologies for cost-effective Super-resolution Optical Fluctuation Imaging (SOFI) [raw datasets]
<p>Raw datasets accompanying the analysis in "Quantitative comparison of camera technologies for cost-effective Super-resolution Optical Fluctuation Imaging (SOFI)"</p> <p>The datasets contain raw fluorescence microscopy images aimed to be processed in a SOFI analysis. They are acquired with different camera technologies, allowing for direct comparison of an industry-grade CMOS detector with both a scientific-grade sCMOS and emCCD detector.</p>
Dataset used in the publication entitled "Decomposition by Approximation with Pulse Waves Allowing Further Research on Sources of Voltage Fluctuations"
<p>Dataset obtained from experimental research carried out in the prepared laboratory setup. Based on the dataset, the proposed new decomposition method by approximation with pulse waves has been validated in the publication: Kuwałek P., Decomposition by Approximation with Pulse Waves Allowing Further Research on Sources of Voltage Fluctuations. The description of the prepared laboratory setup is presented in this publication. The research results are part of the work under the project entitled "Voltage fluctuation diagnostic focused on identification and localization disturbing loads in power grids" funded by the National Science Centre, Poland - 2021/41/N/ST7/00397.</p>
Thermal site energy fluctuations in photosystem I: New insights from MD/QM/MM calculations
<p>We calculated the site energies of all 96 chlorophylls in a fully atomistic, trimeric and membrane-embedded model of cyanobacterial photosystem I (<a href="https://www.rcsb.org/structure/1JB0">PDB Code: 1JB0</a>). The resulting data set accounts for </p> <ul> <li> <p>thermal fluctuations within the entire photosystem by sampling 40 conformational snapshots from molecular dynamics trajectories.</p> </li> <li> <p>the electrostatic effects of the natural environment on the chlorophyll excitation by means of QM/MM calculations.</p> </li> <li> <p>the multireference nature of chlorophyll excitations by using the high-level DFT/MRCI method in the QM region.</p> </li> </ul> <p>Analysis of the data set reveals thermal fluctuations which prevent the formation of a single prominent energy funnel in the antenna complex. We conclude that energy transfer pathways may form only transiently at physiological temperatures, as thermal fluctuations overcome energy barriers.</p> <p><strong>v1.1.0</strong></p> <p>Added more snapshots for reaction center chlorophylls, results from statistical analysis and excitonic couplings in the point dipole approximation.</p> <p><strong>v1.0.0</strong></p> <p>Site energies, site energy shifts and absorption spectra of each chlorophyll in each snapshot, computed with and without environmental point charges. Structures and newly derived or modified force field parameters are also provided.</p> <p>For a detailed overview of each file, please refer to the README.</p>
Measuring Magnetic 1/f Noise in Superconducting Microstructures and the Fluctuation-Dissipation Theorem - Data
<p>Figures and corresponding data associated with the manuscript 'Measuring Magnetic 1/f Noise in Superconducting Microstructures and the Fluctuation-Dissipation Theorem' by Herbst et al.</p>
Data to support "Stochastic density effects on adult fish survival and implications for population fluctuations"
Data on stage-specific abundance of black surfperch (Embiotoca jacksoni), the amount of foraging habitat and the availability of surfperch prey (crustaceans) were collected at fixed sites on the north shore of Santa Cruz Island, California annually (autumn) from 1993-2009. Data are grouped into four regions. Counts of fish distinguished among young-of-year, juveniles (1 year old) and adults (>= 2 years old). These data have been presented in Okamoto, D. K., R. J. Schmitt and S. J. Holbrook. 2016. Sochastic density effects on adult fish survival and implications for population fluctuations. Ecology Letters, 19:153-162. doi: 10.1111/ele.12547.
Raw data of "Seasonal fluctuations of ichthyoplankton assemblage in the northeastern South China Sea influenced by the Kuroshio intrusion"
<p>The uploaded data here is the raw data of the manuscript "Seasonal fluctuations of ichthyoplankton assemblage in the northeastern South China Sea influenced by the Kuroshio intrusion" submitted to the Journal of Geophysical Research-Oceans. The CTD file (.cnv) is the data recorded by a Sea-Bird conductivity, temperature and depth (CTD) in the sampling stations. This data is used to analyze the water masses during the study period. It can be analyzed with free software of the Ocean Data View 4 (http://odv.awi.de/) or the MATLAB R2017 (http://www.mathworks.com/products/matlab/). The sequence data (.fasta) is used to evaluate the species composition. The data can be analyzed with free software of the BOLD Identification tool (http://www.boldsystems.org/), the basic local-alignment search tool (BLAST) (https://www.ncbi.nlm.nih.gov/), the Clustal X 2.1 (http://www.clustal.org/) and the MEGA 7 (http://www.megasoftware.net/). In additon, the .nc files are the data of surface temperature during the sampling periods. The data can be analyzed with the MATLAB R2017 (http://www.mathworks.com/products/matlab/).</p>
Simulated X-ray Photon Fluctuation Spectroscopy Dataset
<p>Dataset for simulated X-ray Photon Fluctuation Spectroscopy (XPFS) detector images and photon maps. XPFS is a X-ray speckle imaging technique used at SLAC National Accelerator Laboratory to study ultrafast materials dynamics. </p>
Sea ice proxy data from "Sea ice fluctuations in the Baffin Bay and the Labrador Sea during glacial abrupt climate changes"
<p>Dataset s1: Sub-decadal sodium, bromine, and bromine enrichment data from NEEM ice core between 34-42 ka b2k.</p> <p>Dataset s2: Magnetic susceptibility, total organic carbon (TOC) and biomarkers data (IP25, brassicasterol, HBI-III) from the Eirik Drift core GS16-204-23CC, covering 31-42 ka b2k.</p>
Code and data from "Mother cells control daughter cell proliferation in intestinal organoids to minimize proliferation fluctuations"
<p>Includes the microscopy images, cell tracking data and scripts used in the publication Huelsz-Prince, Guizela, et al. "Mother cells control daughter cell proliferation in intestinal organoids to minimize proliferation fluctuations." <em>eLife </em> 11:e80682 (2022). <a href="https://doi.org/10.7554/eLife.80682"> https://doi.org/10.7554/eLife.80682</a> .</p>
Data supporting 3D Super-resolution Optical Fluctuation Imaging with Temporal Focusing with two-photon excitation
<p>Data to support the publication combining temporal focusing two photon excitation with super-resolution optical fluctuation imaging.</p> <div>This research was funded by National Centre of Science, grant number: 2022/47/B/ST7/03465. For the purpose of Open Access, the author has applied a</div> <div>CC-BY public copyright licence to any author Accepted Manuscript (AAM) version arising from this submission</div>
Dataset for "Effects of weather and climate on fluctuations of grain prices in southwestern Bohemia, 1725–1824 CE"
<p><span>This deposit contains three .xlsx files.</span></p> <p><span>The file „01_prices_Sušice_1725-1824“ contains two sheets with mean annual prices (in Lower Austrian <em>měřice</em>) of four cereals (wheat, rye, barley and oats) in Sušice for the period 1725–1824. On the first sheet are compiled series, on the second detrended series (using high-pass filter).</span></p> <p><span>The file „02_monthly_prices_1725-1738“ contains four sheets with mean monthly prices (in Lower Austrian <em>měřice</em>) of four cereals in Sušice from July 1725 to July 1738 (July 1737 is missing). Sheets represent individual cereals – wheat, rye, barley and oats.</span></p> <p><span>The file „03_climate_char_1725-1824“ contains two sheets with mean seasonal (DJF, MAM and JJA) temperature, precipitation and scPDSI expressed in deviations relative to the 1961–1990 reference period. On the first sheet are original series, on the second detrended series (using high-pass filter). The original series are reconstructed temperatures for central Europe (Dobrovolný et al., 2010), reconstructed precipitation for the Czech Lands (Dobrovolný et al., 2015) and both of them were used for creation of scPDSI series (Brázdil et al., 2016).</span></p>
Raw and post-processed data for the microscopic investigation of the effect of random envelope fluctuations on phoneme-in-noise perception
<p>The current dataset consists of three main folders:</p> <ul> <li><strong>01-Stimuli/</strong>: Contains the three sets of noises (white noise, bump noise, MPS noise) for the 12 study participants (S01 to S12).</li> <li><strong>02-Raw-data/fastACI/</strong>: Contains the raw data as obtained for each participant, which are also available within the GitHub repository of the fastACI toolbox, using the same directory tree. The results for each (anonymised) participant (under: <strong>publ_osses2022b/data_SXX/1-experimental_results/</strong>) include their audiometric thresholds (folder: <strong>audiometry</strong>), the results for the Intellitest speech test (folder: <strong>intellitest</strong>), and for the phoneme-in-noise test /aba/-/ada/ for the three noises (savegame files in MAT format).</li> <li><strong>02-Raw-data/ACI_sim/</strong>: Contains the raw data as obtained for the artificial listener, i.e., the model osses2022a.m (available within the fastACI toolbox). Twelve sets of simulations (using the waveforms of participants S01 to S12) were run for the three types of test noises. The results of the simulations of the phoneme-in-noise test are stored in the savegame MAT files. The template derived from 100 repetitions of /aba/ and /aba/ at an SNR=-6 dB in white noise is also included (template-osses2022a-speechACI_Logatome-abda-S43M-trial-1-v1-white-2022-7-15-N-0100.mat). The same template was used in all simulations.</li> <li><strong>03-Post-proc-data/ACI_exp/</strong>: Auditory classification images (ACIs) derived from the participants' data (folder: <strong>ACI_exp</strong>) and from the simulations (folder: <strong>ACI_sim</strong>). For each participant (or artificial listener) there are three ACIs (MAT files) for each of the corresponding noises. Cross predictions are also included with performance predictions across 'participants' (Crosspred.mat, 12 cross predictions for each noise) or across 'noises' (Crosspred-noise.mat, 3 cross predictions for each participant). The cross predictions all have the same names but are stored in dedicated directories.</li> </ul> <p><strong>Use these data:</strong></p> <ol> <li>Download all these data, place them in a local directory of your computer. If you have MATLAB and you downloaded a local copy of the fastACI toolbox (open access at: <a href="http://github.com/aosses-tue/fastACI">GitHub</a>) you can recreate the figures of our paper.</li> <li>After initialising the toolbox (type 'startup_fastACI;', without quotation marks in MATLAB) and then type either of the following commands, to recreate the figure you want. To recreate the figures in the main text:</li> </ol> <pre><code class="language-javascript">publ_osses2022b_JASA_figs('fig1','zenodo'); publ_osses2022b_JASA_figs('fig2a','zenodo'); publ_osses2022b_JASA_figs('fig2b','zenodo'); publ_osses2022b_JASA_figs('fig3','zenodo'); publ_osses2022b_JASA_figs('fig4','zenodo'); publ_osses2022b_JASA_figs('fig5','zenodo'); publ_osses2022b_JASA_figs('fig6','zenodo'); publ_osses2022b_JASA_figs('fig7','zenodo'); publ_osses2022b_JASA_figs('fig8','zenodo'); publ_osses2022b_JASA_figs('fig8b','zenodo'); publ_osses2022b_JASA_figs('fig9','zenodo'); publ_osses2022b_JASA_figs('fig9b','zenodo'); publ_osses2022b_JASA_figs('fig10','zenodo');</code></pre> <p>To generate the figures of the supplementary materials (Appendix in the BioRxiv preprint):</p> <pre><code class="language-javascript">publ_osses2022b_JASA_figs('fig1_suppl','zenodo'); publ_osses2022b_JASA_figs('fig2_suppl','zenodo'); publ_osses2022b_JASA_figs('fig3_suppl','zenodo'); publ_osses2022b_JASA_figs('fig3b_suppl','zenodo'); publ_osses2022b_JASA_figs('fig4_suppl','zenodo'); publ_osses2022b_JASA_figs('fig4b_suppl','zenodo'); publ_osses2022b_JASA_figs('fig5_suppl','zenodo'); publ_osses2022b_JASA_figs('fig5b_suppl','zenodo');</code></pre> <p><strong>References:</strong></p> <ul> <li><strong>Preprint</strong>: Alejandro Osses, Léo Varnet. "A microscopic investigation of the effect of random envelope fluctuations on phoneme-in-noise perception." BioRxiv.</li> <li><strong>fastACI toolbox</strong>: Alejandro Osses, Léo Varnet. fastACI toolbox: the MATLAB toolbox for investigating auditory perception using reverse correlation (v1.2). Zenodo. doi:<a href="https://doi.org/10.5281/zenodo.7314014">10.5281/zenodo.7314014</a>. Supplement to: <a href="http://github.com/aosses-tue/fastACI/tree/v1.2">https://github.com/aosses-tue/fastACI/tree/v1.2</a></li> </ul>
Dataset for Efficient, robust, and versatile fluctuation data analysis using MLE MUtation Rate calculator (mlemur)
<p>This file contains the R and C++ code used for simulating experiments, simulated fluctuation data, and the results of estimations used in the paper "Efficient, robust, and versatile fluctuation data analysis using MLE MUtation Rate calculator (mlemur)".</p>
Dataset used in the publication entitled "Decomposition Problem in Process of Selective Identification and Localization of Voltage Fluctuation Sources in Power Grids" presented at 2022 20th International Conference on Harmonics and Quality of Power (ICHQP)
<p>Dataset obtained from experimental research carried out in a real power grid. Based on the dataset, the problem of decomposition in identification of sources of voltage fluctuations has been presented in the publication: Kuwałek P., Decomposition Problem in Process of Selective Identification and Localization of Voltage Fluctuation Sources in Power Grids, <em>Proceedings of the 20th International Conference on Harmonics and Quality of Power</em>, IEEE , art. no. 43, 2022, Italy, Naples. The description of the power grid model is presented in this publication. The research results are part of the work under the project entitled "Voltage fluctuation diagnostic focused on identification and localization disturbing loads in power grids" funded by the National Science Centre, Poland - 2021/41/N/ST7/00397.</p>
Data related to the article "Frequency-dependent impedance of nanocapacitors from electrode charge fluctuations as a probe of electrolyte dynamics"
<p>Contains input files and data used to generate the figures of the article:</p> <p>Frequency-dependent impedance of nanocapacitors from electrode charge fluctuations as a probe of electrolyte dynamics<br> (Giovanni Pireddu and Benjamin Rotenberg)</p> <p>arXiv: https://arxiv.org/abs/2206.13322</p> <p>The folder EXAMPLE_INPUT_FILES contains typical <a href="https://doi.org/10.21105/joss.02373">MetalWalls</a> (<a href="https://gitlab.com/ampere2/metalwalls">repository</a>) input files used to perform the simulations.</p> <p>The folder DATA_FIGURES contains the processed data used to plot all the figures of the paper (see below).<br> </p> <p>The 'd*' labels are used in the directory or file names to refer to the following interelectrode distances considered:<br> - 'd1' corresponds to 2.51 nm;<br> - 'd2' corresponds to 4.94 nm;<br> - 'd3' corresponds to 9.76 nm;<br> - 'd4' corresponds to 19.42 nm;</p> <p><br> Figure 1:<br> - 'Fig1_Continuum.dat': <br> - Capacitance calculated considering a continuum approximation<br> - 'Fig1_DDS.dat':<br> - Capacitance calculated considering the DDS model (three capacitors in series)<br> - 'Fig1_MD.dat':<br> - Capacitance calculated from MD simulations. Includes the capacitance of the empty capacitor.</p> <p>Figure 2:<br> Panel A<br> - 'Fig2_QACFd*.dat':<br> - Time autocorrelation function of the electrode charge fluctuations<br> Panel B<br> - 'Fig2_Qrampd*.dat':<br> - Charge profile upon a step in voltage (0 to 1 V)<br> - 'Fig2_Vramp.dat':<br> - Voltage ramp related to the charging profiles<br> Panel C<br> - 'Fig2_QACFNormd*.dat':<br> - Normalized charge autocorrelation function<br> -'Fig2_QrampNormd*.dat'<br> - Normalized charge profile</p> <p>Figure 3:<br> - 'Fig3_MDd*.dat':<br> - Real and imaginary parts of impedance as estimated from MD simulations<br> - 'Fig3_ECd*.dat'<br> - Real and imaginary parts of impedance as calculated from the equivalent circuit models</p> <p>Figure 4:<br> - 'Fig4_MDd*.dat':<br> - Magnitude of admittance as estimated from MD simulations<br> - 'Fig4_Debyed*.dat':<br> - Magnitude of admittance as calculated from the Debye relaxation model<br> Inset<br> - 'Fig4_MDTau.dat':<br> - Relaxation time estimated from MD simulations<br> - 'Fig4_TauFit.dat':<br> - Fit of the relaxation time</p>
Bandgap fluctuations and robustness in two-dimensional hyperuniform dielectric materials
<p>These are the data associated with the paper "Bandgap fluctuations and robustness in two-dimensional hyperuniform dielectric materials" published in <a href="http://doi.org/10.1364/OE.484232"><em>Optics Express</em> <strong>31</strong>, 18509 (2023)</a></p> <ul> <li>gapsFrequenciesAndWidths.h5: each line of the table lists the largest and second largest gap widths and frequencies in GHz for all 5000 samples studied in the paper <table align="center"> <thead> <tr> <th scope="col">Column</th> <th scope="col">Quantity</th> </tr> </thead> <tbody> <tr> <td>0</td> <td><span class="math-tex">\(\chi\)</span></td> </tr> <tr> <td>1</td> <td>largest gap frequency (GHz)</td> </tr> <tr> <td>2</td> <td>largest gap width (GHz)</td> </tr> <tr> <td>3</td> <td>second largest gap frequency (GHz)</td> </tr> <tr> <td>4</td> <td>second largest gap width (GHz)</td> </tr> </tbody> </table> </li> <li>DOS.h5: normalized density of states as a function of the frequency (GHz) averaged over all samples for all <span class="math-tex">\(\chi\)</span> values studied in the paper <table align="center"> <thead> <tr> <th scope="col">Column</th> <th scope="col">Quantity</th> </tr> </thead> <tbody> <tr> <td>0</td> <td><span class="math-tex">\(\chi\)</span></td> </tr> <tr> <td>1</td> <td>Frequency (GHz)</td> </tr> <tr> <td>2</td> <td>nDOS</td> </tr> </tbody> </table> <p> </p> </li> </ul>
Dataset from University of Idaho 2004, master's thesis [Littoral ecology of epilithic algae in the Rocky Reach Pool, Mid-Columbia River (Washington State) - The effects of reservoir fluctuations.]
(Abstract from thesis) Epilithic algae, water column physical/chemical properties, and sediments were examined in the impounded Mid-Columbia River including the Rocky Reach Reservoir. Primary objectives included determination of the effects reservoir drawdown has on epilithic algae and potential nutrient enrichment via sediment. Epilithic algae were analyzed by pigment concentration, gravimetrically, and species composition. Reservoir elevation fluctuated at higher rates at tailrace sites (0.41-0.25 m/hr) compared to the forebay site (0.06-0.08 m/hr). Littoral exposure times were also greater at tailrace sites (mean of 8 hrs compared to 0 hr at the forebay site). Mean epilithic algae monochromatic chlorophyll a over all sampling periods at mainstem sites was 76.7 ± 4.8 mg/m2 (95 % C.I.). Epilithic algae monochromatic chlorophyll a in the zone of water fluctuation (0-1 m) was less at Wells tailrace (38.8 mg/m2) compared to Rocky Reach forebay (141.3 mg/m2) during summer, 2000 and 2001. Mean epilithic biofilm ash-free oven-dry weight over all sampling periods at mainstem sites was 25.6 ± 1.5 g/m2 (95 % C.I.). Mean autotrophic index across all mainstem locations was 439 indicating a large heterotrophic component within the epilithic biofilms. Epilithic algae communities were dominated by diatoms (50.2 %) and cyanobacteria (35.9 %), with some green algae (13.8 %). Canonical correlation analysis indicated that temperature, depth, site, and the water elevation change rate were important controllers of epilithic algae chlorophyll pigments. Mean textural characteristics of dredged sediment were 51.1 % sand, 43.2 % silt, and 5.7 % clay. Mean organic matter content in this sediment was 4.1 %. The mean seston sedimentation rate across mainstem locations was 11.3 g m-2 d-1 and organic matter comprised 14.7 % of the material collected from the water column.
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