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3,139 results for “fits”
Indoor Wireless Deterministic Anycast Transmissions Data from the FIT IoT-Lab testbed
<p>This dataset contains the raw openwsn results generated by indoor experiments.</p> <p>The data was collected on the <a href="https://www.iot-lab.info">FIT IoT-Lab</a> platform, using the m3 motes with a AT86RF231 radio chip, on the Grenoble's site.</p> <p>We rely on the following workflow:</p> <ul> <li>a modified version of openwsn that implements anycast transmissions at the link layer (CCA branch, <a href="https://github.com/ftheoleyre/openwsn-fw/releases/tag/duocast-mswim21">https://github.com/ftheoleyre/openwsn-fw/releases/tag/duocast-mswim21</a>). The firmware is implemented in C, and is executed by the m3 motes;</li> <li>a modified version of openvisualizer (<a href="https://github.com/ftheoleyre/openvisualizer/releases/tag/mswim21">https://github.com/ftheoleyre/openvisualizer/releases/tag/mswim21</a>)</li> <li>a tool to process the dataset and compute the metrics: end-to-end reliability, number of transmissions, CCA events, etc. (<a href="https://github.com/ftheoleyre/openwsn-data/releases/tag/mswim21-duocast">https://github.com/ftheoleyre/openwsn-data/releases/tag/mswim21-duocast</a>)</li> </ul> <p> </p> <p> </p> <p> </p>
Surfalex HF formability study - Workflow 3 - Fit yield functions
<p>This MatFlow workflow is the third in a set of eight workflows developed during our formability study of the Surfalex HF (AA6016A) material. In this workflow, we fitted three anisotropic yield functions to yield stress data obtained from a large set of multiaxial crystal plasticity simulations. The fitted yield functions were: the Hill1948 yield criterion, the Barlat Yld91 (6-parameter) yield function, and the Barlat Yld2004-18p (18-parameter) yield function. DAMASK was used to perform these simulations.</p> <p>This workflow can be downloaded and explored in a Jupyter notebook, as explained in the <a href="https://github.com/LightForm-group/surfalex_data_explorer">GitHub repository here</a>.</p>
Surfalex HF formability study - Workflow 2 - Fit single crystal parameters
<p>This MatFlow workflow is the second in a set of eight workflows developed during our formability study of the Surfalex HF (AA6016A) material. In this workflow, we calibrated crystal plasticity (CP) parameters using experimental data. The hardening coefficient, the initial and maximum CRSS values, and the hardening exponent, as defined in the CP phenomenological power law adopted by DAMASK were optimised. Starting from trial guesses for these parameters, we optimised the parameters with respect to the experimental stress-strain curve of the Surfalex HF material, using a Levenberg-Marquardt procedure. At each iteration of the process, N+1 CP simulations were performed (for N fitting parameters). Five iterations were used in total.</p> <p>This workflow can be downloaded and explored in a Jupyter notebook, as explained in the <a href="https://github.com/LightForm-group/surfalex_data_explorer">GitHub repository here</a>.</p>
The Quest for the Missing Dust: New Herschel Maps of Local Group Galaixes (LMC, SMC, M31, M33) that Restore Previously-Missed Extended Emission, Along With SED-Fitting Results, Hydrogen Gas Maps, and Swift UV Observations
<p>Here we provide the data products from publications:</p> <p>Clark, C.J.R., et al., <em>The Quest for the Missing Dust: I – Restoring Large Scale Emission in Herschel Maps of Local Group Galaxies</em>, ApJ 921 35</p> <p>Clark, C.J.R., et al., <em>The Quest for the Missing Dust: II – Two Orders of Magnitude of Evolution in the Dust-to-Gas Ratio Resolved Within Local Group Galaxies</em>, ApJ 946 42</p> <p>This data concerns four Local Group galaxies: the Large Magellanic Cloud (LMC), the Small Magellanic Cloud (SMC), M31, and M33.</p> <p> </p> <p>For each galaxy, we provide our new Herschel maps, as described in the above publications, which were combined in Fourier space ('feathered') with Planck, IRAS, and COBE data, in order to restore extended emission that was removed from previous Herschel reductions for these galaxies.</p> <p>For each galaxy, we provide this new Herschel data for 5 Hershcel bands: the PACS 100 and 160 <span>\(\mu\)</span>m bands, and the SPIRE 250, 350, and 500 <span>\(\mu\)</span>m bands. This data is provided in FITS format, with one FITS file for each band for each galaxy. Each of these files contains 4 extensions. Extension 1 (IMAGE) provides the standard feathered map. Extension 2 (UNC) provides the uncertainty map. Extension 3 (MASK) provides a binary mask map indicating the portion of the data where reliable, fully-feathered high-resolution coverage is available. Extension 4 provides the foreground-subtracted version of the feathered map (FGND_SUB), the header of which also describes the uncertainty on that subtraction. All maps are in units of MJy/sr (except the MASK extension, which is boolean).</p> <p> </p> <p>We also provide the outputs of our Spectral Energy Distribution (SED) fitting to this data, as described in the publications. For each galaxy, we provide FITS files giving the median value of each parameter in each pixel, and maps of the uncertainties on those medians (being the 68.3% quantile around the median). The parameters are dust mass surface density (SED_Sigma_Mass.fits), dust temperature (SED_Temp.fits), beta 1 (SED_Beta1.fits), beta 2 (SED_Beta2.fits), break wavelength (SED_Break.fits), and 500 <span>\(\mu\)</span>m excess (SED_Excess500.fits). Each of these files contain 2 extensions. Extension 1 (median) provides the map of pixel parameter median values. Extension 2 (uncert) provides the map of uncertainties on those medians.</p> <p>Additionally, we provide the full posterior probability distribution for all SED parameters, consisting of 1000 posterior samples, for all pixels, in the form of a FITS file containing a 4-dimensional hypercube, with axes corresponding to right ascension, declination, parameters (in order: dust mass surface density, dust temperature, beta 1, beta 2, break wavelength, and 500 <span>\(\mu\)</span>m excess), and samples. This is provided as a gzip compressed FITS file for each galaxy.</p> <p>Furthermore, provide the Swift-UVOT maps used in Paper II. This data is provided for Swift-UVOT bands W1, W2, and M2. For each band, we provide a FITS file containing 3 extensions. Extension 1 (SURF_BRI) provides the map of surface brightness in MJy/sr (converted using the Swift-UVOT zero points given in Breeveld et al., 2011). Extension 2 (RATE) provides the map of count rate (in photons/sec). Extension 3 (EXP) provides the map of exposure time (in sec). The maps for the LMC and SMC are those presented in Hagen et al. (2017). The maps for M31 and M33 are were reduced following the same process as those in Hagen et al. (2017), and will be fully presented in Decleir et al. (in prep.), but are provided here for the purposes of reproducibility.</p> <p>Lastly, for each galaxy, we provide our maps of the hydrogen surface density (Sigma_H.fits), and dust-to-gas ratio (DtG.fits). None of the maps presented have had deprojection corrections applied</p> <p> </p>
Supplementary Data: Global fits if simplified models for dark matter with GAMBIT II. Vector dark matter with an s-channel vector mediator
<p>This record contains the YAML files, data files, and some of the plotting scripts for: "Global fits of simplified models for dark matter with GAMBIT II. Vector dark matter with an s-channel vector mediator".</p> <p>Samples have been created using GAMBIT and figures can be reproduced with pippi. Plotting scripts (*.pip) are designed to work with either the original version of pippi 2.1 or the forked unreleased version. The provided scripts do not reproduce all the figures in the paper exactly.</p> <p>To save storage space, all samples have been compressed using <code>tar</code>. To inflate each dataset after downloading run <code>tar -zxvf <samples>.hdf5.gz</code>.</p> <p>To facilitate uploading to Zenodo, several of the data files have been thinned to only include enough points to reproduce plots.</p>
Datasets used for the manuscript: "Sibling competition, dispersal and fitness outcomes in humans"
<p>Datasets used for the manuscript: “Sibling competition, dispersal and fitness outcomes in humans”, 10.1038/s41598-023-33700-3</p>
Diffuse Emission of High-Energy Neutrinos from a Global Fit to Cosmic Rays
<p>Model of diffuse emission of high-energy neutrinos from a global fit of cosmic rays and model of high-energy neutrino emission from unresolved pulsar-powered sources.</p> <p>The maps presented in the form of <em>HEALPix </em>maps (Gorski et al 2005, ApJ, 622, 759) of per-flavor intensity in units of GeV<sup>-1</sup> cm<sup>-2</sup> s<sup>-1</sup> sr<sup>-1</sup> at 50 logarithmically spaced energies between 10 GeV and 10<sup>8</sup> GeV. We use a value of NSIDE=256 and the RING binning scheme.</p> <p>We here make available our fiducial model, which is calculated assuming the <em>Ferrière 2001</em> cosmic ray source distribution, the <em>AAfrag</em> hadronic production cross sections and the <em>GALPROP</em> gas maps. We calculated the emission from unresolved sources following Vecchiotti et al. 2022, ApJ, 928, 19.</p> <p>In Version 2 of this dataset, we also make available the local cosmic ray fluxes of our fiducial model obtained from a global fit to cosmic ray data together with the corresponding 68% and 95% uncertainty bands. These are shown in figure 6 of <a href="https://arxiv.org/abs/2211.15607">arXiv:2211.15607</a>. The nuclear fluxes are in (GeV/n)<sup>-1</sup> m<sup>-2</sup> s<sup>-1</sup> sr<sup>-1</sup>, the fluxes of electrons and positrons are in GeV<sup>-1</sup> m<sup>-2</sup> s<sup>-1</sup> sr<sup>-1</sup> . The fluxes are local interstellar fluxes without solar modulation.</p> <p>In Version 3 of this dataset, we add the fiducial diffuse gamma ray model calculated assuming the <em>Ferrière 2001</em> cosmic ray source distribution, the <em>AAfrag</em> hadronic production cross sections as well as the <em>GALPROP</em> gas maps and ISRF model. We separately make available 3 maps: The hadronic emission on neutral atomic gas, the hadronic emission on molecular gas and the leptonic emission from Inverse Compton Scattering. </p> <p>Similar to the dataset of the fiducial neutrino model, the maps are presented in the form of <em>HEALPix </em>maps (Gorski et al 2005, ApJ, 622, 759) in units of GeV<sup>-1</sup> cm<sup>-2</sup> s<sup>-1</sup> sr<sup>-1</sup>. We use a value of NSIDE=256 and the RING binning scheme. For the hadronic maps, the intensity is given at 50 logarithmically spaced energies between 10 GeV and 10<sup>8</sup> GeV. For the leptonic maps from Inverse Compton Scattering, the intensity is given at 48 logarithmically spaced energies between 1 GeV and 10<sup>6</sup> GeV.</p> <p>The structure of the files is somewhat different from the file containing the fiducial neutrino model. This is to allow for easy use of the gamma ray maps with the <em>gammapy</em> package (Deil et al. 2017, <a href="https://arxiv.org/abs/1709.01751"> arXiv:1709.01751</a>).</p> <p>Also available in Version 3 are the full spatio-spectral cosmic ray distributions in the Milky Way as predicted by our fiducial model. The nuclear fluxes are given for each species in (GeV/n)<sup>-1</sup> m<sup>-2</sup> s<sup>-1</sup> sr<sup>-1</sup> at 63 energies between 1 GeV and 10<sup>9</sup> GeV. The leptonic fluxes are given for each species in GeV<sup>-1</sup> m<sup>-2</sup> s<sup>-1</sup> sr<sup>-1</sup> at 36 energies between 1 GeV and 10<sup>5</sup> GeV. </p> <p>All fluxes are given on a spatial grid at 81 galactocentric radii from 0 kpc to 20 kpc and 61 distances perpendicular to the galactic plane between -6 kpc and 6 kpc.</p> <p>Finally, a word of caution about the extra component of cosmic ray leptons included in our model: This component is contained in the last <em>HDUnit</em> of the <em>fits</em> file containing the leptonic cosmic ray distributions. It is there denoted as a flux of electrons. It must, however, also be added to the flux of positrons to achieve correct results.</p> <p>Please refer to <a href="https://arxiv.org/abs/2211.15607">arXiv:2211.15607</a> for further details.</p> <p>When using these models in your research work, please refer to this Zenodo dataset and the publication.</p>
Supplementary Data: Fast and accurate AMS-02 antiproton likelihoods for global dark matter fits
<p>The files in this record contain supplementary data for the study, "Fast and accurate AMS-02 antiproton likelihoods for global dark matter fits". Samples have been created using <a href="https://gambitbsm.org/" target="_blank" rel="noopener">GAMBIT</a> and figures can be reproduced with <a href="http://github.com/patscott/pippi" target="_blank" rel="noopener">pippi</a>.</p>
Parameters for Statistical Evaluation of Time to Fixate Efectiveness for Assessment of Fitness to Drive
<p>This repository contains a table with parameters (including time to fixate - TTF parameter) and an R script for statistical analysis. This is supplementary material for the paper titled "<a href="https://doi.org/10.3758/s13428-023-02177-3">Effectiveness of a Time to Fixate for Fitness to Drive Evaluation in Neurological Patients</a>" and authored by Nadica Miljković and Jaka Sodnik (published in <a href="https://www.springer.com/journal/13428">Behavior Research Methods</a> and previously shared on <a href="https://arxiv.org/ftp/arxiv/papers/2205/2205.08942.pdf">arXiv</a>).</p> <p>TTF parameter was calculated in overall 56 patients during selected scenario with pedestrian collision in a driving simulator produced by <a href="https://www.nervtech.com/">Nervtech</a>. Together with other parameters, ST parameters are stored in <a href="https://zenodo.org/record/7963337/files/tableParametersAll.csv?download=1">tableParametersAll.csv</a>, while R programming code for statistical analysis of all parameters is placed in <a href="https://zenodo.org/record/7963337/files/statisticalAnalysisAll.R?download=1">statisticalAnalysisAll.R</a>.</p> <p><strong>Dataset contents</strong></p> <ol> <li><a href="https://zenodo.org/record/7963337/files/tableParametersAll.csv?download=1">tableParametersAll.csv</a>, table with parameters, csv (comma-separated values) format</li> <li><a href="https://zenodo.org/record/7963337/files/statisticalAnalysisAll.R?download=1">statisticalAnalysisAll.R</a>, code in R programming language for statistical analysis</li> </ol> <p><strong>Table with parameters has the following structure</strong></p> <ol> <li>column - no which is ordinary number in consecutive order from 1 to 56</li> <li>column - id presents an internal patient's id</li> <li>column - ttf presents TTF parameter in ms</li> <li>column - fitness presents a categorical variable and can be either fit-, unfit-, or conditionally-fit-to-drive (cond fit)</li> <li>column - speed at the collision onset in km/h</li> <li>column - ttc presents time-to-collision in s</li> <li>column - manual_correction is categorical variable: 0 means that no manual correction was required for ST calculation, while 1 means that manual correction was required</li> <li>column - igd presents initial gaze distance in pixels</li> <li>column - R1 presents the first measurement of perception response time (PRT)</li> <li>column - R2 presents the second measurement of PRT</li> <li>column - R3 presents the third measurement of PRT</li> </ol> <p>Missing data are presented with NA (Not Available).</p> <p>NOTE: Python code for ST calculation and sample eye tracker video are available on GitHub repository <a href="https://github.com/NadicaSm/Time-To-Fixate-Calculation-from-the-Eye-Tracker-Videos">https://github.com/NadicaSm/Time-To-Fixate-Calculation-from-the-Eye-Tracker-Videos</a> under GNU GPL license and released on Zenodo with doi (<a href="https://doi.org/10.5281/zenodo.6560419">https://doi.org/10.5281/zenodo.6560419</a>).</p> <p>If you find these parameters and R code useful for your own research and teaching class, please cite the following references:</p> <ol> <li>Miljković, N., & Sodnik, J. (2023). NadicaSm/Time-To-Fixate-Calculation-from-the-Eye-Tracker-Videos: v2. [Software code], Zenodo. <a href="https://doi.org/10.5281/zenodo.6560419">https://doi.org/10.5281/zenodo.6560419</a></li> <li>Miljković, N., & Sodnik, J. (2023). Effectiveness of a time to fixate for fitness to drive evaluation in neurological patients. Behavior Research Methods. <a href="https://doi.org/10.3758/s13428-023-02177-3">https://doi.org/10.3758/s13428-023-02177-3</a></li> <li>Motnikar, L., Stojmenova, K., Štaba, U. Č., Klun, T., Robida, K. R., & Sodnik, J. (2020). Exploring driving characteristics of fit-and unfit-to-drive neurological patients: A driving simulator study. Traffic Injury Prevention, 21(6), 359-364. <a href="https://doi.org/10.1080/15389588.2020.1764547">https://doi.org/10.1080/15389588.2020.1764547</a></li> </ol> <p><strong>Acknowledgements</strong></p> <p>J.S. kindly acknowledges University Rehabilitation Institute Soča employees and the Nervtech team. Authors gratefully appreciate the support from Nenad B. Popović, PhD from University of Belgrade – School of Electrical Engineering for his valuable assistance in design of illustrations and for provided feedback for the initial manuscript structure. Also, both Authors thank Nebojša Jovanović, MSc from University of Belgrade - School of Electrical Engineering for his kind contribution to earlier stages of the project, especially for his work on developing Python code to capture time to fixate parameter. Last but not least, we are very thankful to Damjan Krstajić, founder and director of the Research Centre for Cheminformatics for his precious advices on statistical analysis in a retrospective study and to student Gregor Kovač from Faculty of Electrical Engineering, University of Ljubljana for his diligent work on YOLO application in driving simulation.</p>
Community-level flowering & fitness data across an elevational gradient, Rocky Mountain Biological Lab, 2021-2022
We collected data at three sites in Washington Gulch near the Rocky Mountain Biological Laboratory (RMBL, Gothic, Colorado, USA) from June to August 2021 and 2022. RMBL is located in the East River valley of the West Elk mountains, approximately 10 kilometers from Crested Butte, Colorado. Study sites were located at 2815 m (38°53'50"N, 106°58'43"W), 3165 m (38°57'38"N, 107°01'53"W) and 3380 m (38°58'10"N, 107°01'53"W) in elevation. All sites are approximately 50 m2 and have similar slope and aspect (Sloat et al. 2015). We randomly defined five 1.2 m x 1.2 m plots within each site. Within the plots, we marked all newly open flowering units once per week from early June to mid-August. We counted flowering units as either a single flower (most species), a flowering head (Asteraceae), or an umbel (Apiaceae). We only counted flowering units once as they opened, so counts were not cumulative. We quantified fitness as successful fruit and seed development at the end of the season. When flowers began to produce fruits, we counted the number of units with fruit, the number of developed fruits produced by each flowering unit, and the number of seeds per fruit. We also recorded the week of flowering for each fruit. We could not count fruits and seeds for all species because they disappeared, were consumed too quickly, or did not produce seeds during our field season.
Initial conditions to reproduce figures of the article "On the impact of tides on the transit-timing fits to the TRAPPIST-1 system"
<p>The directory includes the initial conditions of the simulations needed to reproduce the figures of the article "On the impact of tides on the transit-timing fits to the TRAPPIST-1 system". </p> <p>The simulations were done with Posidonius version v2019.07.30. The information to download and use Posidonius can be found here:<br><a href="https://www.google.com/url?q=https://www.blancocuaresma.com/s/posidonius&sa=D&source=hangouts&ust=1580820901945000&usg=AFQjCNEmxwuCiIgh9d-aTfx-bqB0_u_9BA">https://www.blancocuaresma.com/s/posidonius</a>. <br>That version was only slightly modified to use a maximum timestep size for the IAS15 algorithm. </p> <p>The directory includes:</p> <ul> <li>a README giving all the information defining the test directories</li> <li>test directories with a file called trappist1.json, which can be used to launch Posidonius:<br>posidonius start --silent trappist1.json data_dir/trappist1.bin data_dir/trappist1_history.bin</li> </ul>
Constraining properties of the next nearby core-collapse supernova with multi-messenger signals: gravitational wave frequency fits
<p>1D FLASH simulations with STIR, for alpha_lambda = 1.23, 1.25, and 1.27. Run with SFHo EOS, M1 with 12 energy groups.</p> <p>For more information on these simulations, see Warren, Couch, O'Connor, & Morozova (arXiv:1912.03328) and Couch, Warren, & O'Connor (2020).</p> <p>Includes fit to the gravitational wave peak frequency versus time post-bounce, for a functional fit of the form f = A*sqrt(t) + B*t + C, where the frequency f is in Hz and the time t is in seconds. The columns are: progenitor mass [M_sun], fit coefficient A, fit coefficient B, fit coefficient C, and the R^2 of the fit.</p>
Genetic variation in early fitness traits across European populations of silver birch (Betula pendula)
<p>Early life phenotypic data from three <em>Betula pendula</em> common garden experiments spread across the species latitudinal range in Europe.</p>
The Experimental Data for the Study "Frequency Fitness Assignment: Making Optimization Algorithms Invariant under Bijective Transformations of the Objective Function Value"
<p>The Experimental Data for the Study "Frequency Fitness Assignment: Making Optimization Algorithms Invariant under Bijective Transformations of the Objective Function Value"</p> <p><strong>1. Introduction</strong></p> <p>Frequency Fitness Assignment (FFA) replaces the objective value in the selection step of an optimization method with its encounter frequency in any selection step so far. It turns static problems into dynamic ones. Here we experimentally investigated this approach in two important contexts: First, we integrated it into a basic (1+1)-EA, obtaining the (1+1)-FEA. We applied both algorithms to several well-known benchmark problems with bit-string based search spaces, including the OneMax, LeadingOnes, TwoMax, Jump, Plateau, and W-Model functions. We also applied them to the Max-3-Sat instances from SATLib. We then also integrated FFA into a Memetic Algorithm for the Job Shop Problem.</p> <p><strong>2. Paper</strong></p> <p>This data is used as the basis for the following article: Thomas Weise, Zhize Wu, Xinlu Li, and Yan Chen. Frequency Fitness Assignment: Making Optimization Algorithms Invariant under Bijective Transformations of the Objective Function Value, originally submitted to <a href="https://arxiv.org/abs/2001.01416">arxiv</a> on 2020-01-06 (under the title Frequency Fitness Assignment: Making Optimization Algorithms Invariant under Bijective Transformations of the Objective Function), updated with the new data in June 2020, and submitted to the IEEE Transactions on Evolutionary Computation.</p> <p><strong>3. Data</strong></p> <p>This data set contains all the results of these experiments, the source codes used in the experiments (i.e., the algorithm implementations), as well as the scripts used for evaluating the results.</p> <p><strong>4. Version History</strong></p> <p>This is the second version of the data set, including extended experiments and more evaluation results. Most importantly, data for larger scales of OneMax and LeadingOnes has been added. The original version is at <a href="http://dx.doi.org/10.5281/zenodo.3598172">10.5281/zenodo.3598172</a>.</p> <p><strong>5. Contact</strong></p> <p>If you have any questions or suggestions, please contact <a href="http://iao.hfuu.edu.cn/team/director">Prof. Dr. Thomas Weise</a> of the <a href="http://iao.hfuu.edu.cn/">Institute of Applied Optimization</a> at <a href="http://www.hfuu.edu.cn">Hefei University</a> in Hefei, Anhui, China via email to <a href="mailto:tweise@hfuu.edu.cn">tweise@hfuu.edu.cn</a> with CC to <a href="mailto:tweise@ustc.edu.cn">tweise@ustc.edu.cn</a>.</p>
Physical Fitness in Subacute Stroke (Phys-Stroke)
<p>Raw data of efficacy endpoint data and analyses scripts</p> <p> </p> <p>In this version a '.csv' File with the baseline data is added to enable more open access to the provided data.</p>
SIR Model Fitting for COVID-19 Dataset
<p>SIR Model for COVID-19 Dataset. The dataset is described in "Analytical parameter estimation of the SIR epidemic model. Applications to the COVID-19 pandemic" https://arxiv.org/abs/2010.07000</p> <p>The primary data source is ECDC.</p>
Evolution under pH stress and high population densities leads to increased density-dependent fitness in the protist Tetrahymena thermophila
<p>Abiotic stress is a major force of selection that organisms are constantly facing. While the evolutionary effects of various stressors have been broadly studied, it is only more recently that the relevance of interactions between evolution and underlying ecological conditions, that is, eco-evolutionary feedbacks, have been highlighted. Here, we experimentally investigated how populations adapt to pH-stress under high population densities. Using the protist species <em>Tetrahymena thermophila</em>, we studied how four different genotypes evolved in response to stressfully low pH conditions and high population densities. We found that genotypes underwent evolutionary changes, some shifting up and others shifting down their intrinsic rates of increase (<em>r<sub>0</sub></em>). Overall, evolution at low pH led to the convergence of <em>r<sub>0</sub></em> and intraspecific competitive ability (<em>α</em>) across the four genotypes. Given the strong correlation between <em>r<sub>0</sub></em> and <em>α</em>, we argue that this convergence was a consequence of selection for increased density-dependent fitness at low pH under the experienced high density conditions. Increased density-dependent fitness was either attained through increase in <em>r<sub>0</sub></em> , or decrease of <em>α</em>, depending on the genetic background. In conclusion, we show that demography can influence the direction of evolution under abiotic stress.</p> <p> </p>
Time spent in distinct life-history stages has sex-specific effects on reproductive fitness in wild Atlantic salmon
<p><span>In species with complex life cycles, life history theory predicts that fitness is affected by conditions encountered in previous life history stages. Here, we use a four-year pedigree to investigate if time spent in two distinct life history stages has sex-specific reproductive fitness consequences in anadromous Atlantic salmon (<i>Salmo salar</i>). We determined the amount of years spent in fresh water as juveniles (freshwater age, FW, measured in years), and years spent in the marine environment as adults (sea age, SW, measured in sea winters) on 264 sexually mature adults collected on a river spawning ground. We then estimated reproductive fitness as the number of offspring (reproductive success) and the number of mates (mating success) using genetic parentage analysis (>5000 offspring). Sea age is significantly and positively correlated with reproductive and mating success of both sexes whereby older and larger individuals gained the highest reproductive fitness benefits (females: 62.2% increase in offspring/SW and 34.8% increase in mate number/SW; males: 201.9% offspring/SW and 60.3% mates/SW). Younger freshwater age was significantly related to older sea age and thus increased reproductive fitness, but only among females (females: -33.9% offspring/FW and -32.4% mates/FW). This result implies that females can obtain higher reproductive fitness by transitioning to the marine environment earlier. In contrast, male mating and reproductive success was unaffected by freshwater age and more males returned at a younger age than females despite the reproductive fitness advantage of later sea age maturation. Our results show that the timing of transitions between juvenile and adult phases has a sex-specific consequence on female reproductive fitness, demonstrating a life-history trade-off between maturation and reproduction in wild Atlantic salmon.</span></p>
Complete Hydrogen Column Density Fit Data for NH/AV Relation Calculation
<p>Xspec fit summaries (and graphics of fits) for multiple regions within 17 supernova remnants. Complete data is included on html pages (which make browsing the data easier). Hydrogen column density (NH) fit values were combined with published optical extinction values (AV) to determine the slope of the linear relationship between the two measurements. The NH data alone is included in a spreadsheet format.</p>
Standard Model effective field theory global fit using electroweak data
<p>These are files that can be used to reproduce the fit results in 10.1007/JHEP09(2016)157 and arXiv:1610.01783<strong>.</strong></p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.