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795 results for “Fluctuations”
Soil microbial community composition (16S) data from a laboratory redox fluctuation experiment conducted with an Oxisol and Mollisol
To test the response of microbial communities to periodic oxygen limitation, we conducted a laboratory experiment where two contrasting soils (a rainforest Oxisol from Puerto Rico, and an Iowa cropland Mollisol) were incubated under headspace treatments where oxygen availability varied cyclically over time. Treatments consisted of 0, 2, 4, 8, or 12 d of anoxic conditions (dinitrogen headspace) followed by 4 d of oxic conditions (i.e., ambient oxygen concentrations), and these treatments were repeated for a total of 384 d. At 0, 48, and 384 days, DNA was extracted from replicates from each treatment for sequencing of 16S rRNA amplicons. Companion biogeochemical measurements from this experiment were published previously by Huang et al. (2021a,b). These data support the Hall et al. (2022) manuscript published in Frontiers in Microbiology.
Dataset for: Diurnal patterns in solute concentrations measured with in situ UV-Vis sensors: natural fluctuations or artefacts?
<p>This dataset contains high-resolution (10-minute interval) data for nitrate, dissolved organic carbon, precipitation and discharge at four measurement stations (NF, SHA, TTP, OUT) in the South West Mau, Kenya. This data was used for the analysis of diurnal patterns in nitrate and dissolved organic carbon concentrations. The zipped folder contains the following files and data:</p> <ul> <li>Calibration.csv: <ul> <li>site = name of measuring site</li> <li>date = date and time of grab sample (yyyy-mm-dd hh:mm:ss)</li> <li>DOC = dissolved organic carbon concentration in grab sample (mg C/L)</li> <li>nitrate = nitrate concenctration in grab sample (mg N/L)</li> </ul> </li> <li>Files with suffix ".ts.csv" (time series data from 1-11-2014 to 31-10-2019; prefix indicates measuring site): <ul> <li>date = date and time of measurement (yyyy-mm-dd hh:mm:ss)</li> <li>nit.raw = nitrate concentration measured by sensor (mg N/L)</li> <li>nit.flag = indication of validity of nitrate measurement (if NA, measurement is valid; for explanation of flags, see supplement of <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR021592">Jacobs et al. 2018</a>)</li> <li>nit.proc = processed nitrate concentration (mg N/L)</li> <li>nit.bg = background concentration of nitrate (48-h moving median; mg N/L)</li> <li>nit.patt = deviation from background concentration of nitrate (nit.proc minus nit.bg; mg N/L)</li> <li>doc.raw = dissolved organic carbon concentration measured by sensor (mg C/L)</li> <li>doc.flag = indication of validity of dissolved organic carbon measurement (if NA, measurement is valid)</li> <li>doc.proc = processed dissolved organic carbon concentration (mg C/L)</li> <li>doc.bg = background concentration of dissolved organic carbon (48-h moving median; mg C/L)</li> <li>doc.patt = deviation from background concentration of dissolved organic carbon (doc.proc minus doc.bg; mg C/L)</li> <li>p = precipitation (mm/10 mins)</li> <li>q = discharge (m³/s)</li> <li>sensor = serial number of sensor</li> </ul> </li> <li>Files with suffix ".exp.csv" (data for sensor comparison experiment from 5-9-2017 to 1-12-2017; prefix indicates measuring site): <ul> <li>date = date and time of measurement (yyyy-mm-dd hh:mm:ss)</li> <li>prec = precipitation (mm/10 mins)</li> <li>nit.orig = processed nitrate concentration measured by fixed sensor (mg N/L)</li> <li>nit.bg.orig = background concentration of nitrate measured by fixed sensor (48-h moving median; mg N/L)</li> <li>nit.patt.orig = deviation from background concentration of nitrate measured by fixed sensor (nit.orig minus nit.bg.orig; mg N/L)</li> <li>nit.dup = processed nitrate concentration measured by mobile sensor (mg N/L)</li> <li>nit.bg.dup = background concentration of nitrate measured by mobile sensor (48-h moving median; mg N/L)</li> <li>nit.patt.dup = deviation from background concentration of nitrate measured by mobile sensor (nit.dup minus nit.bg.dup; mg N/L)</li> <li>doc.orig = processed dissolved organic carbon concentration measured by fixed sensor (mg C/L)</li> <li>doc.bg.orig = background concentration of dissolved organic carbonmeasured by fixed sensor (48-h moving median; mg C/L)</li> <li>doc.patt.orig = deviation from background concentration of dissolved organic carbonmeasured by fixed sensor (doc.orig minus doc.bg.orig; mg C/L)</li> <li>doc.dup = processed dissolved organic carbonconcentration measured by mobile sensor (mg C/L)</li> <li>doc.bg.dup = background concentration of dissolved organic carbonmeasured by mobile sensor (48-h moving median; mg C/L)</li> <li>doc.patt.dup = deviation from background concentration of dissolved organic carbon measured by mobile sensor (doc.dup minus doc.bg.dup; mg C/L)</li> <li>set = experimental treatment</li> </ul> </li> </ul>
Tilted fluctuation electron microscopy data from simulated and deposited amorphous Ta
<p>These datasets were used to compare fluctuation electron microscopy analysis methods on simulated and sputter deposited amorphous tantalum. The Ta is 8 nm thick in both the simulated and deposited samples. The deposited Ta is sandwiched between two layers of amorphous 10 nm-thick SiN<sub>x</sub>. Data are also provided for SiN<sub>x</sub> deposited on SiN<sub>x</sub>.</p> <p>Deposited Ta FEM patterns were collected on a TitanX at 200 kV with a convergence angle of 0.51 mrad and a camera length of 300 mm. Simulated Ta FEM patterns were generated using the Prismatic STEM simulation software (see references).</p> <p>The samples were tilted between 0<sup>o</sup> and 45<sup>o</sup> in 15<sup>o</sup> increments. </p> <p><strong>Deposited Ta: </strong></p> <p>.dm4 (Gatan DigitalMicrograph) files are provided with the raw scanning nanodiffraction data for each tilt angle</p> <p>.png images of the mean CBED pattern for each tilt angle are provided</p> <p><strong>Simulated Ta:</strong></p> <p>.h5, .xyz (atomic coordinates), and .txt (Prismatic input parameters defined) files are provided with the raw scanning nanodiffraction data for each tilt angle</p> <p>.png images of the mean CBED pattern for each tilt angle are provided</p> <p> </p> <p>The atomic coordinates for the simulated Ta were provided by Jun Ding. Simulated FEM patterns were produced by Luis Rangel DaCosta using Prismatic STEM simulation software. Neal Reynolds grew the experimental Ta and SiN<sub>x</sub> thin films.</p>
Some Similarities and Differences between the Observed Alfvénic Fluctuations in the Fast Solar Wind and Navier-Stokes Turbulence
<p>Three text data sets are uploaded: wind tunnel data (modane1.txt), solar-wind magnetic-field data (Flat1maginterp.txt) and solar-wind velocity data (Flat15interp3DP.txt)</p>
Magnitude and predictability of pH fluctuations shape plastic responses to ocean acidification
<p>Phenotypic plasticity is expected to facilitate the persistence of natural populations as global change progresses. The attributes of fluctuating environments that favor the evolution of plasticity have received extensive theoretical investigation, yet empirical validation of these findings is still in its infancy. Here, we combine high-resolution environmental data with a laboratory-based experiment to explore the influence of habitat pH fluctuation dynamics on the plasticity of gene expression in two populations of the Mediterranean mussel, <i>Mytilus galloprovincialis</i>. We linked differences in the magnitude and predictability of pH fluctuations in two habitats to population-specific gene expression profiles in ambient and stressful pH treatments. The results presented demonstrate population-based differentiation in gene expression plasticity, whereby mussels native to a habitat exhibiting a large magnitude of pH fluctuations with low predictability display reduced phenotypic plasticity between experimentally imposed pH treatments. This work validates recent theoretical findings on evolution in fluctuating environments using an ecologically important marine bivalve, and suggests that populations inhabiting regions exposed to unpredictably fluctuating selection pressures may exhibit reduced plasticity as global change progresses.</p>
Dataset for Dual Fraunhofer interference and charge fluctuations in long quantum phase slip wires, Phys. Rev. B 102, 144509 (2020)
<p>Dataset for manuscript 'Dual Fraunhofer interference and charge fluctuations in long quantum phase slip wires', Phys. Rev. B 102, 144509 (2020). Dataset contains data from numerical simulations presented in the manuscript and generating scripts. The data describes the quantum phase slip rate and its suppression due to charge and width disorder in superconducting nanowires in the phase slip regime. This disorder leads to temporal fluctuations which affects the current-voltage characteristics of nanowires. </p>
Turbulent Fluctuations During Pellet Injection into a Dipole Confined Plasma Torus
<p>Description for Zenodo Data Set DOI:10.5281/zenodo.45507</p> <p>This dataset accompanies the article, submitted to Physics of Plasmas, titled "Turbulent Fluctuations During Pellet Injection into a Dipole Confined Plasma Torus," by Garnier, Mauel, Roberts, Kesner, and Woskov. </p> <p>---------------------------------------------</p> <p>Data is presented as HDF5 datafiles <br /> (see https://www.hdfgroup.org/HDF5/) <br /> as HDF4 datafiles<br /> (see https://www.hdfgroup.org/release4/doc/index.html)<br /> and as CSV datafiles <br /> (see http://www.digitalpreservation.gov/formats/fdd/fdd000323.shtml).</p> <p>Data files are associated with FIGURES 2, 3, 4, 5, 6</p> <p>---------------------------------------------<br /> start of figure list<br /> ---------------------------------------------<br /> FIGURE 1: No data set</p> <p>---------------------------------------------<br /> FIGURE 2: (HDF4 Files)</p> <p>Four-channel Microwave (60 GHz) Interferometer <br /> S140529016_DensityData.hdf <br /> time range: 5.00 sec - 7.00 sec<br /> time sample: 8 micro-sec<br /> samples: 250,000 x 4 channels + 250,000 (total)<br /> Unit: radian "Interferometer"<br /> Unit: 1.0E18 particles "Total-Particles"</p> <p>16-channel Photodiode Array 1 <br /> S140529016_PDAData.hdf <br /> time range: 5.00 sec - 7.00 sec<br /> time sample: 20 micro-sec<br /> samples: 100,000 x 16 channels<br /> Unit: A.U. "PDA-1"</p> <p>TOTAL ECRH Injected Heating Power<br /> S140529016_ECRHData.hdf <br /> time range: 5.00 sec - 7.00 sec<br /> time sample: 20 micro-sec<br /> samples: 100,000<br /> Unit: kW "Microwave-Power"</p> <p>S140529016_LoopVoltage.hdf <br /> time range: 5.00 sec - 7.00 sec<br /> time sample: 80 micro-sec<br /> samples: 25,000 <br /> Unit: milli-Volt x sec "Loop-Voltage"</p> <p>---------------------------------------------<br /> FIGURE 3(a): (HDF4 Files)</p> <p>Four-channel Microwave (60 GHz) Interferometer <br /> S140529016_DensityData.hdf <br /> time range: 5.00 sec - 7.00 sec<br /> time sample: 8 micro-sec<br /> samples: 250,000 x 4 channels + 250,000 (total)<br /> Unit: radian "Interferometer"<br /> Unit: 1.0E18 particles "Total-Particles"</p> <p>16-channel Photodiode Array 1 <br /> S140529016_PDAData.hdf <br /> time range: 5.00 sec - 7.00 sec<br /> time sample: 20 micro-sec<br /> samples: 100,000 x 16 channels<br /> Unit: A.U. "PDA-1"</p> <p>---------------------------------------------<br /> FIGURE 4 (a), (b), (c): (CSV Files)</p> <p>time period: 5.0 - 6.0 sec<br /> Ensemble Window: 8 msec<br /> sample period: 8 micro-sec<br /> Nyquist Freq: 62.475 kHz</p> <p>Line-Density-Coherence.csv<br /> Frequency (Hz) Hz<br /> d(nl-1)^2 dimensionless<br /> d(nl-2)^2 dimensionless <br /> d(nl-3)^2 dimensionless<br /> d(nl-4)^2 dimensionless<br /> Kappa 1-2 dimensionless<br /> Kappa 1-3 dimensionless<br /> Kappa 1-4 dimensionless</p> <p>Isat-Coherence.csv <br /> Frequency (Hz) Hz<br /> d(I)^2 dimensionless<br /> Kappa 8deg dimensionless<br /> Kappa 16deg dimensionless<br /> Kappa 24deg dimensionless</p> <p>Float-Potential-Coherence.csv<br /> Frequency (Hz) Hz<br /> d(Pot)^2/Te^ dimensionless<br /> Kappa 8deg dimensionless<br /> Kappa 16deg dimensionless<br /> Kappa 24deg dimensionless</p> <p>---------------------------------------------<br /> FIGURE 5 (a), (b): (CSV Files)</p> <p>Figure 5(a)<br /> time period: 5.0 - 6.0 sec<br /> Ensemble Window: 8 msec<br /> sample period: 8 micro-sec<br /> Nyquist Freq: 62.475 kHz</p> <p>Float-Isat-CrossPhase.csv <br /> Frequency (Hz) Hz<br /> alpha-Float degree<br /> alpha-Isat degree</p> <p>Figure 5(b)<br /> time period: 6.02 - 6.05 sec<br /> Ensemble Window: 1.6 msec<br /> sample period: 8 micro-sec<br /> Nyquist Freq: 62.475 kHz</p> <p>Float-Isat-DuringCrossPhase.csv <br /> Frequency (Hz) Hz<br /> alpha-Float degree<br /> alpha-Isat degree<br /> kappa-Float dimensionless<br /> kappa-Isat dimensionless<br /> ---------------------------------------------<br /> FIGURE 6: No data set</p> <p>---------------------------------------------<br /> FIGURE 7: No data set</p> <p>---------------------------------------------<br /> FIGURE 8: No data set</p> <p>---------------------------------------------<br /> FIGURE 9: No data set</p> <p>---------------------------------------------<br /> FIGURE 10: No data set</p> <p>---------------------------------------------<br /> end of figure list<br /> ---------------------------------------------<br /> ---------------------------------------------<br /> start of file list<br /> ---------------------------------------------<br /> Filename Size <br /> ----------------------------------------------<br /> Potential-Time-Angle-Data.h5 254.68 KB <br /> All-Probe-Data.h5 9.81 MB <br /> Average-Probe-Data.h5 1.06 MB <br /> Average-Isat-Data.h5 422.22 KB <br /> S140529016_PDAData.hdf 6.80 MB <br /> S140529016_DensityData.hdf 6.00 MB <br /> S140529016_ECRHData.hdf 803.39 KB <br /> S140529016_LoopVoltage.hdf 203.39 KB <br /> Line-Density-Coherence.csv 420.59 KB <br /> Isat-Coherence.csv 258.51 KB <br /> Float-Potential-Coherence.csv 257.90 KB<br /> Float-Isat-DuringCrossPhase.csv 48.87 KB <br /> Float-Isat-CrossPhase.csv 138.47 KB <br /> LDX-Pellet-Supplementary.pdf 1.84 MB <br /> S140529016_frPlots.mp4 3.24 MB <br /> ---------------------------------------------<br /> end of file list<br /> ---------------------------------------------</p>
Turbulent Fluctuations During Pellet Injection into a Dipole Confined Plasma Torus
<p><strong>Description for Zenodo Data Set DOI:10.5281/zenodo.220992</strong></p> <p>This dataset accompanies the article, to appear in Physics of Plasmas, titled "Turbulent Fluctuations During Pellet Injection into a Dipole Confined Plasma Torus," by Garnier, Mauel, Roberts, Kesner, and Woskov. </p> <p>---------------------------------------------</p> <p>Data is presented as HDF5 datafiles <br> (see https://www.hdfgroup.org/HDF5/) <br> as HDF4 datafiles<br> (see https://www.hdfgroup.org/release4/doc/index.html)<br> and as CSV datafiles <br> (see http://www.digitalpreservation.gov/formats/fdd/fdd000323.shtml).</p> <p>Data files are associated with FIGURES 2, 3, 4, 5</p> <p>Data plotted in other figures are derived from the dataset as described<br> in the paper.</p> <p>---------------------------------------------<br> start of figure list<br> ---------------------------------------------<br> <strong>FIGURE 1: </strong> No data set</p> <p>---------------------------------------------<br> <strong>FIGURE 2:</strong> (HDF4 Files)</p> <p>Four-channel Microwave (60 GHz) Interferometer <br> S140529016_DensityData.hdf <br> time range: 5.00 sec - 7.00 sec<br> time sample: 8 micro-sec<br> samples: 250,000 x 4 channels + 250,000 (total)<br> Unit: radian "Interferometer"<br> Unit: 1.0E18 particles "Total-Particles"</p> <p>16-channel Photodiode Array 1 <br> S140529016_PDAData.hdf <br> time range: 5.00 sec - 7.00 sec<br> time sample: 20 micro-sec<br> samples: 100,000 x 16 channels<br> Unit: A.U. "PDA-1"</p> <p>TOTAL ECRH Injected Heating Power<br> S140529016_ECRHData.hdf <br> time range: 5.00 sec - 7.00 sec<br> time sample: 20 micro-sec<br> samples: 100,000<br> Unit: kW "Microwave-Power"</p> <p>S140529016_LoopVoltage.hdf <br> time range: 5.00 sec - 7.00 sec<br> time sample: 80 micro-sec<br> samples: 25,000 <br> Unit: milli-Volt x sec "Loop-Voltage"</p> <p>---------------------------------------------<br> <strong>FIGURE 3(a)</strong>: (HDF4 Files)</p> <p>Four-channel Microwave (60 GHz) Interferometer <br> S140529016_DensityData.hdf <br> time range: 5.00 sec - 7.00 sec<br> time sample: 8 micro-sec<br> samples: 250,000 x 4 channels + 250,000 (total)<br> Unit: radian "Interferometer"<br> Unit: 1.0E18 particles "Total-Particles"</p> <p>16-channel Photodiode Array 1 <br> S140529016_PDAData.hdf <br> time range: 5.00 sec - 7.00 sec<br> time sample: 20 micro-sec<br> samples: 100,000 x 16 channels<br> Unit: A.U. "PDA-1"</p> <p>---------------------------------------------<br> <strong>FIGURE 4 (a), (b), (c), (d), (e), (f): </strong> (CSV Files)</p> <p>time period: 5.0 - 6.0 sec<br> Ensemble Window: 8 msec<br> sample period: 8 micro-sec<br> Nyquist Freq: 62.475 kHz</p> <p>(a) Fig4a-Line-Density-Coherence.csv<br> Frequency (Hz) Hz<br> d(nl-1)^2 dimensionless<br> d(nl-2)^2 dimensionless <br> d(nl-3)^2 dimensionless<br> d(nl-4)^2 dimensionless<br> Lambda 1-2 dimensionless<br> Lambda 1-3 dimensionless<br> Lambda 1-4 dimensionless</p> <p>(b) Fig4b-Isat-Coherence.csv <br> Frequency (Hz) Hz<br> d(I)^2 dimensionless<br> Lambda 8deg dimensionless<br> Lambda 16deg dimensionless<br> Lambda 24deg dimensionless</p> <p>(c) Fig4c-Float-Potential-Coherence.csv<br> Frequency (Hz) Hz<br> d(Pot)^2/Te^ dimensionless<br> Lambda 8deg dimensionless<br> Lambda 16deg dimensionless<br> Lambda 24deg dimensionless</p> <p>(d) Fig4d-Line-Density-Coherence.csv<br> Frequency (Hz) Hz<br> d(nl-1)^2 dimensionless<br> d(nl-2)^2 dimensionless <br> d(nl-3)^2 dimensionless<br> d(nl-4)^2 dimensionless<br> Lambda 1-2 dimensionless<br> Lambda 1-3 dimensionless<br> Lambda 1-4 dimensionless</p> <p>(e) Fig4e-Isat-Coherence.csv <br> Frequency (Hz) Hz<br> d(I)^2 dimensionless<br> Lambda 8deg dimensionless<br> Lambda 16deg dimensionless<br> Lambda 24deg dimensionless</p> <p>(f) Fig4f-Float-Potential-Coherence.csv<br> Frequency (Hz) Hz<br> d(Pot)^2/Te^ dimensionless<br> Lambda 8deg dimensionless<br> Lambda 16deg dimensionless<br> Lambda 24deg dimensionless</p> <p>---------------------------------------------<br> <strong>FIGURE 5 (a), (b):</strong> (CSV Files)</p> <p>Figure 5(a)<br> time period: 5.0 - 6.0 sec<br> Ensemble Window: 8 msec<br> sample period: 8 micro-sec<br> Nyquist Freq: 62.475 kHz</p> <p>(a) Fig5a-Float-Isat-CrossPhase.csv <br> Frequency (Hz) Hz<br> alpha-Float degree<br> alpha-Isat degree</p> <p>Figure 5(b)<br> time period: 6.02 - 6.05 sec<br> Ensemble Window: 1.6 msec<br> sample period: 8 micro-sec<br> Nyquist Freq: 62.475 kHz</p> <p>(b) Fig5b-Float-Isat-DuringCrossPhase.csv <br> Frequency (Hz) Hz<br> alpha-Float degree<br> alpha-Isat degree<br> kappa-Float dimensionless<br> kappa-Isat dimensionless<br> ---------------------------------------------<br> <strong>FIGURE 6: </strong> No data set</p> <p>---------------------------------------------<br> <strong>FIGURE 7: </strong> No data set</p> <p>---------------------------------------------<br> <strong>FIGURE 8:</strong> No data set</p> <p>---------------------------------------------<br> <strong>FIGURE 9: </strong> No data set</p> <p>---------------------------------------------<br> <strong>FIGURE 10:</strong> No data set</p> <p>---------------------------------------------<br> end of figure list<br> ---------------------------------------------<br> ---------------------------------------------<br> <strong>start of file list</strong><br> ---------------------------------------------<br> Filename Size <br> ----------------------------------------------<br> All-Probe-Data.h5 9.8 MB<br> Average-Isat-Data.h5 422 KB<br> Average-Probe-Data.h5 1.1 MB<br> Fig4a-Line-Density-Coherence.csv 413 KB<br> Fig4b-Isat-Coherence.csv 251 KB<br> Fig4c-Float-Potential-Coherence.csv 250 KB<br> Fig4d-Line-Density-Coherence.csv 103 KB<br> Fig4e-Isat-Coherence.csv 62 KB<br> Fig4f-Float-Potential-Coherence.csv 62 KB<br> Fig5a-Float-Isat-CrossPhase.csv 138 KB<br> Fig5b-Float-Isat-DuringCrossPhase.csv 36 KB<br> Potential-Time-Angle-Data.h5 255 KB<br> S140529016_DensityData.hdf 6 MB<br> S140529016_ECRHData.hdf 803 KB<br> S140529016_LoopVoltage.hdf 203 KB<br> S140529016_PDAData.hdf 6.8 MB</p> <p><br> LDX-Pellet-Supplementary.pdf 1.8 MB<br> S140529016_frPlots.mp4 3.2 MB<br> ---------------------------------------------<br> <strong>end of file list</strong><br> ---------------------------------------------</p> <p> </p> <p> </p>
PsPM-BAER: Skin conductance fluctuations in a public speaking paradigm with a repeated-measures design
<p>This dataset contains four skin conductance response (SCR) measurements of 60 s duration from each of 40 healthy male university students (18-35 years) who participated in a public speaking anticipatory anxiety paradigm with a repeated-measures factorial design.</p>
PsPM-NK: Skin conductance fluctuations in a public speaking paradigm with a between-subjects design
<p>This dataset includes four skin conductance response (SCR) measurements of 60 s duration for each of 32 healthy female university students (19-29 years) who participated in a public speaking anticipatory anxiety paradigm with a between-subjects design. Half of the participants were to deliver a public speech, and the other half a speech without an audience.</p>
Quantum Fluctuations in Quasi-One-Dimensional Dipolar Bose-Einstein Condensates
<p>Raw data for the depicted graphs in the given paper/letter</p>
Continuously fluctuating selection reveals extreme granularity and parallelism of adaptive tracking
<p>Temporally fluctuating environmental conditions are a ubiquitous feature of natural habitats. Yet, how finely natural populations adaptively track fluctuating selection pressures via shifts in standing genetic variation is unknown. We generated high-frequency, genome-wide allele frequency data from a genetically diverse population of Drosophila melanogaster in extensively replicated field mesocosms from late June to mid-December, a period of ~12 generations. Adaptation throughout the fundamental ecological phases of population expansion, peak density, and collapse was underpinned by extremely rapid, parallel changes in genomic variation across replicates. Yet, the dominant direction of selection fluctuated repeatedly, even within each of these ecological phases. Comparing patterns of allele frequency change to an independent dataset procured from the same experimental system demonstrated that the targets of selection are predictable across years. In concert, our results reveal fitness-relevance of standing variation that is likely to be masked by inference approaches based on static population sampling, or insufficiently resolved time-series data. We propose such fine-scaled temporally fluctuating selection may be an important force maintaining functional genetic variation in natural populations and an important stochastic force affecting levels of standing genetic variation genome-wide.</p>
Figure 1 in Population fluctuation of some economically important mites on two mango cultivars in Qalyubia governorate, Egypt
Figure 1. Population fluctuation of plant-feeding and predacious mites on "Naomi" mango cultivar at Qalyubia governorate during 2020–2022 seasons.
Figure 2 in Population fluctuation of some economically important mites on two mango cultivars in Qalyubia governorate, Egypt
Figure 2. Population fluctuation of plant-feeding and predacious mites on "Heidi" mango cultivar at Qalyubia governorate during 2020–2022 seasons.
Russian Short-Term Mortality Fluctuations database
<p><strong>1. Database contents</strong></p><p>The Russian Short-Term Mortality Fluctuations database (RusSTMF) contains a series of standardized and crude death rates for men, women and both sexes for Russia as a whole and its regions for the period from 2000 to 2021.</p><p>All the output indicators presented in the database are calculated based on data of deaths registered by the Vital Registry Office. The weekly death counts are calculated based on depersonalized individual data provided by the Russian Federal State Statistics Service (Rosstat) at the request of the HSE. Time coverage: 03.01.2000 (Week 1) – 31.12.2021 (Week 1148)</p><p><strong>2. A brief description of the input data on deaths</strong></p><p><i>Date of death:</i> date of occurrence</p><p><i>Unit of time</i>: week</p><p><i>First and last days of the week:</i> Monday – Sunday</p><p><i>First and last week of the year:</i> The weeks are organized according to ISO 8601:2004 guidelines. Each week of the year, including the first and last, contains 7 days. In order to get 7-day weeks, the days of previous years are included in this first week (if January 1 fell on Tuesday, Wednesday or Thursday) or in the last calendar week (if December 31 fell on Thursday, Friday or Saturday).</p><p><i>Age groups:</i> the entire population</p><p>Sex: men, women, both sexes (men and women combined)</p><p><i>Restrictions and data changes</i>: data on deaths in the Pskov region were excluded for weeks 9-13 of 2012</p><p><i>Note: </i>Deaths with an unknown date of occurrence (unknown year, month, or day) account for about 0.3% of all deaths and are excluded from the calculation of week-age-specific and standardized death rates.</p><p><strong>3. Description of the week-specific mortality rates data file</strong></p><p>Week-specific standardized death rates for Russia as a whole and its regions are contained in a single data file presented in .csv format. The format of data allows its uploading into any system for statistical analysis. Each record (row) in the data file contains data for one calendar year, one week, one territory, one sex. </p><p>The decimal point is dot (.) </p><p>The first element of the row is the territory code ("PopCode" column), the second element is the year ("Year" column), the third element ("Week" column) is the week of the year, the fourth element ("Sex" column) is sex (F – female, M – male, B – both sexes combined). This is followed by a column "CDR" with the value of the crude death rate and "SDR" with the value of the standardized death rate. If the indicator cannot be calculated for some combination of year, sex, and territory, then the corresponding meaningful data elements in the data file are replaced with ".".</p>
Data from: Fluctuation of ecological niches and geographic range shifts along chile pepper's domestication gradient
<p>Domestication is an ongoing well-described process. However, while many have stud- ied the changes domestication causes in plant genetics, few have explored its impact on the portion of the geographic landscape in which the plants exist. Therefore, the goal of this study was to understand how the process of domestication changed the geographic space suitable for chile pepper (<em>Capsicum annuum</em>) in its center of origin (domestication). <em>C. annuum</em> is a major crop species globally whose center of domes- tication, Mexico, has been well-studied. It provides a unique opportunity to explore the degree to which ranges of different domestication classes diverged and how these ranges might be altered by climate change. To this end, we created ecological niche models for four domestication classes (wild, semiwild, landrace, modern cultivar) based on present climate and future climate scenarios for 2050, 2070, and 2090. Considering present environment, we found substantial overlap in the geographic niches of all the domestication classes. Yet, environmental and geographic aspects of the current ranges did vary among classes. Wild and commercial varieties could grow in desert conditions, while landraces could not. With projections into the future, habitat was lost asymmetrically, with wild, semiwild, and landraces at greater risk of territorial declines than modern cultivars. Further, we identified areas where future suitability overlap between landraces and wilds is expected to be lost. While range expansion is widely associated with domestication, we found little support of a con- stant niche expansion (either in environmental or geographical space) throughout the domestication gradient in chile peppers in Mexico. Instead, particular domestication transitions resulted in loss, followed by capturing or recapturing environmental or geographic space. The differences in environmental characterization among domes- tication gradient classes and their future potential range shifts increase the need for conservation efforts to preserve landraces and semiwild genotypes</p>
Scaling regimes and fluctuations of observables in computer glasses approaching the unjamming transition
<p>This dataset can be used to reproduce the figures in the corresponding manuscript, in preparation for publication in the Journal of Chemical Physics. As described in the manuscript, the (mostly statistical) quantities presented here were computed from ensembles of simulated disordered solids (harmonic sphere packings, diluted spring networks, and packing derived networks). The individual simulations involve the minimization of the system's energy which is computed from positional degrees of freedom (of particles in a packing or nodes in a network). Macroscopic quantities (such as shear modulus or contact number/excess connectivity) are then computed for each sample. The displacement response feilds are computed by solving a linear matrix equation involving the Hessian of 2D packings of harmonic spheres. See the manuscript for more details.</p>
Best of both worlds: Acclimation to fluctuating environments confers advantages and minimizes costs of constant environments
<ol> <li><span>Thermal acclimation is often considered critical in organismal responses to novel thermal conditions. Our understanding of the physiological implications of acclimation is largely derived from lab studies with simplified thermal regimes that fail to account for any variation that animals would experience naturally (i.e. diel variation). As such, constant temperature acclimation experiments may produce a flawed understanding of acclimation in the wild. </span></li> <li><span>To fill this gap, we acclimated lizards (<em>Amphibolurus</em> <em>muricatus</em>) under three thermal regimes (Hot Constant, Cold Constant and Alternating) and compared their physiological responses (Metabolic Rate, Sprint Speed, Thermal Preferences and Thermal Limits). </span></li> <li> <span>We found that animals maintained constantly at hot temperatures (preferred temperature, 35</span>°<span>C) gained sprint performance increases, not seen in those maintained constantly at cold temperatures (20</span>°<span>C), yet suffered costs to growth (in younger animals) and maintenance (mass loss in older animals). Animals maintained at alternating temperatures (12 hr 20</span>°<span>C; 12 hr 35</span>°<span>C) had performance benefits matching animals in the hot treatment, without experiencing reductions in juvenile growth and adult mass. </span> </li> <li><span>Animals acclimated under hot temperatures showed a significant lower preferred and voluntary maximum temperatures compared to animals acclimated under a cold temperature regime. </span></li> <li><span>We found no impact of acclimation treatment on behavioural thermal limits or Standard Metabolic Rate. </span></li> </ol> <p><span>Overall, we show that alternating between access to preferred temperatures and having periods of energetic rest confer the greatest benefits for our animals. These results highlight the importance of natural body temperature variation for enhancing overall ectotherm performance and physiology, and the costs of novel thermal environments that fail to provide this variation.</span><span><br></span></p>
Fig. 4 in Fluctuating Asymmetry And Length-Weight Relationship Of The Three Populations Of Sardinella Lemuru (Clupeiformes, Dorosomatidae) From Surigao Del Norte, Philippines
Fig. 4. Principal components (PC) inferred deformation grid and histogram of individual (symmetry) in Surigao City, Surigao del Norte.
Fig. 2 in Fluctuating Asymmetry And Length-Weight Relationship Of The Three Populations Of Sardinella Lemuru (Clupeiformes, Dorosomatidae) From Surigao Del Norte, Philippines
Fig. 2. Principal components (PC) inferred deformation grid and histogram of individual (symmetry) in Malimono, Surigao del Norte.
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.