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3,139 results for “fits”
Database of fitted spectra for: Changing-Look AGNs - I. Tracking the transition on the main sequence of quasars
<h3>Results from the spectral fitting for a sample of changing-look active galactic nuclei (AGNs) with SDSS spectroscopy using PyQSOFit.</h3>
Murine norovirus virulence factor 1 (VF1) protein contributes to viral fitness during persistent infection [Primary data]
<p>Primary data underlying journal article titled "<strong>Murine norovirus virulence factor 1 (VF1) protein contributes to viral fitness during persistent infection</strong>"</p>
Posterior samples of a dynamical fit for planetary system HD128311
<p>This dataset contains the posterior samples from a dynamical fit of the multi-planetary system HD128311. See <a href="https://doi.org/10.1093/mnrasl/slu202">https://doi.org/10.1093/mnrasl/slu202</a> for a description of the fit. The dataset is one numpy array, consisting of</p> <ul> <li>500 walkers </li> <li>1250 generations</li> <li>3 bodies (1 star + 2 planets)</li> <li>7 parameters per body (mass, x, y, z, vx, vy, vz)</li> </ul>
MCMC chains for demographic fits presented in "NICMOS Kernel-Phase Interferometry II: Demographics of Nearby Brown Dwarfs"
<p>These files are the data behind the figure for Figure 3 (and the corresponding Figure Set) as well as other fits presented in Table 5. They are saved in <a href="https://numpy.org/doc/stable/reference/generated/numpy.lib.format.html">npy</a> format which can be read into python using numpy according to the code snippet below.</p> <p>The files are flattened and trimmed MCMC chains produced by running emcee (Foreman-Mackey et al. 2013) using 64 walkers for 10,000 steps. The first 1,000 steps were trimmed for burn in and the remaining chains were thinned by 40 steps.</p> <p>The files are named according to the following convention: flatSamples<malm cor><age><prior>.npy where:</p> <p><malm cor> is either 'Malm' or '' (nothing) if the model population was or was not corrected for Malmquist bias (before comparing to the observed population while fitting).</p> <p><age> is '0p9', '1p2', '1p5', '1p9', '2p4', or '3p1' according to that assumed field age (in Gyr).</p> <p><prior> is 'U' or 'I' for uninformed or informed (incorporating the information from Blake et al. 2010 on the unresolved population).</p> <p>The true underlying population corresponds to the flatSamplesMalm<age>I.npy files while the others are included for context and comparison to populations fit to the observed (not Malmquist corrected) population. The uninformed prior chains are dominated by a significant population of unresolved companions which is not consistent with previous RV studies.</p> <p>The files can be read into python using:</p> <pre><code class="language-python">import numpy as np flat_samples0p9I = np.load('flatSamples0p9I.npy') </code></pre> <p>which produces an array with shape 14400 x 4. The rows are the samples and the four columns are the parameters <span class="math-tex">\(F, \gamma, \overline{\log(\rho)}\)</span>, and <span class="math-tex">\(\sigma_{\log(\rho)}\)</span>, respectively.</p>
deGeco genomic compartments model fit results on whole genome at resolution of 50kb
<p>These files contain the fitted parameters for the <a href="https://www.biorxiv.org/content/10.1101/2022.10.01.510432v1.article-info">deGeco</a> model for genomic compartments. Fits were done at 50kb on four cell lines: GM12878 (from Rao, et al., 2014), H1, HFF (both from Krietenstein, et al., 2020) and mESC (from Bonev, et al., 2017). Each Hi-C file was zoomified again using cooler, to prevent duplicate entries in the pixel table.</p> <p>The file format is NumPy's npz object that has two main keys:</p> <ol> <li>Metadata - an object containing various information on the run: command line parameters, duration of run, etc</li> <li>Parameters - an object containing the actual fitted parameters: <ol> <li>state_probabilities - an NxS matrix of state probabilities, where N is the number of bins and S the number of states the model was run with</li> <li>cis_weights - an SxS matrix of cis state affinities</li> <li>trans_weights - an SxS matrix of trans state affinities</li> <li>cis_dd_power - the exponent of the power law decay of interaction intensity in cis (also denoted as alpha)</li> <li>trans_dd - the constant background level of trans interaction (also denoted as beta)</li> <li>cis_lengths - Number of bins for each chromosome. Sum of cis_lengths is N, the total number of bins.</li> </ol> </li> </ol> <p>To read using numpy:</p> <pre><code class="language-python">import numpy as np fit = np.load(filename, allow_pickle=True) metadata = fit['metadata'][()] parameters = fit['parameters'][()]</code></pre> <p>or use the gc_datafile module from the deGeco <a href="https://github.com/KaplanLab/deGeco">repository</a>:</p> <pre><code class="language-python">import gc_datafile parameters = gc_datafile.load_params(filename)</code></pre> <p> </p>
An estimate of fitness reduction from mutation accumulation in a mammal allows assessment of the consequences of relaxed selection: Dataset
<p>Supplementary files (data and analysis) for "An estimate of fitness reduction from mutation accumulation in a mammal allows assessment of the consequences of relaxed selection"</p> <p>Supplementary File 1: C3H_pheno_fix_Jun7_2023_nolowmut.csv</p> <p>Data for all mice in MA experiment including: mouse ID, sire, dam, generation, mating ID, sex, weight at 3 weeks, weight at 6 weeks, tail length, litter size, litter ID, line ID</p> <p> </p> <p>Supplementary File 2: C3H_pheno_Kontrol_June2023.csv</p> <p>Data for all control mice including: mouse ID, sire, dam, generation, mating ID, sex, weight at 3 weeks, weight at 6 weeks, tail length, litter size, litter ID, line ID</p> <p> </p> <p>Supplementary File 3: C3H_birthdates.csv</p> <p>Data for all C3H mice including: mouse ID, birthdate</p> <p> </p> <p>Supplementary File 4: MA_pheno.R</p> <p>R code for visualising trait data, running linear regressions, and comparing control and MA experiment data</p> <p> </p> <p>Supplementary File 5: C3H_pheno_burnin20_Jun7_2023_nolowmut.csv</p> <p>Data for all mice in MA experiment including a 20 generation burn-in to simulate mutation-drift balance for Animal model analyses: mouse ID, sire, dam, generation, mating ID, sex, weight at 3 weeks, weight at 6 weeks, tail length, litter size, litter ID, line ID</p> <p> </p> <p>Supplementary File 6: asreml_C3H_ALL.R</p> <p>R code for estimating mutational heritabilities using mixed model analysis</p> <p> </p> <p>Supplementary File 7: C3H_ped_rekey_Jun2023.csv</p> <p>Pedigree data for all mice in MA experiment</p> <p> </p> <p>Supplementary File 8: C3H_ped_rekey_KEY.csv</p> <p>Key for pedigree data file</p> <p> </p> <p>Supplementary File 9: plot_pedigree_tree_MS_final.R</p> <p>R code for visualising pedigree of mice in MA experiment</p>
Dataset of report "A.2.2.6: Validation of the fitness of purpose of the performance assessment protocol developed in A2.1.4 by demonstrating its applicability for 2 terpenes using TD-GC/MS/FID and the static standards produced in A1.1.2."
<p>Dataset of report "A.2.2.6: Validation of the fitness of purpose of the performance assessment protocol developed in A2.1.4 by demonstrating its applicability for 2 terpenes using TD-GC/MS/FID and the static standards produced in A1.1.2."</p>
Supplementary Data: Axion global fits with Peccei-Quinn symmetry breaking before inflation using GAMBIT
<p><strong>Description of Supplementary Data</strong></p> <p>This record contains the samples used to create the figures (excluding validation and prior dependence plots) and to derive most of the results in Hoof et al., <em>“Axion global fits with Peccei-Quinn symmetry breaking before inflation using GAMBIT”</em> (available on the <a href="https://arxiv.org/abs/1810.07192">arXiv</a>). Please contact the authors if you are interested in other samples, YAML files or plotting scripts.<br> <br> This record consists of</p> <ul> <li>21 <code>YAML</code> files (6 for <code>T-Walk</code>, 15 for <code>Diver</code>). Running <code>./gambit -f path/to/YAML/file.yaml</code> in the GAMBIT directory will start the scan. However, most users might want to adjust the output file name and directory as well as the settings for the samplers to their systems.</li> <li>21 <code>hdf5</code> files (6 for <code>T-Walk</code>, 15 for <code>Diver</code>). These files contain the actual samples and were compressed using the <code>tar</code> format.</li> <li>Two example <code>pip</code> files (<code>2_QCDAxion_10M1.pip</code> for <code>Diver</code> samples, <code>2_QCDAxion_3041.pip</code> for <code>T-Walk</code> samples) for producing plots from the corresponding <code>hdf5</code> files, using <a href="https://github.com/patscott/pippi"><code>pippi</code></a> and <code>functions.py</code>.</li> </ul> <p>The files follow the naming scheme <code>V_ModelName_[S][C][I][R][E]</code> plus one of the extensions <code>.yaml</code>, <code>.hdf5.tar.gz</code>, or <code>.pip</code>.</p> <ul> <li><code>V</code>: This internal version number can be ignored, but should be quoted when asking for help with the plotting scripts</li> <li><code>ModelName</code>: Corresponds to the axion models in the paper (<em>GeneralALP</em>, <em>QCDAxion</em>, <em>DFSZAxion_I</em>, <em>DFSZAxion_II</em>, <em>KSVZAxion</em>)</li> <li><code>S</code>: Scanner (<code>S=1</code>: <code>Diver</code>, <code>S=3</code>: <code>T-Walk</code>)</li> <li><code>C</code>: Switch to include (<code>C=1</code>) or exclude (<code>C=0</code>) the White Dwarf cooling hints</li> <li><code>I</code>: Setting for the initial misalignment angle <em>θ<sub>i</sub></em> (<code>I=4</code>: flat prior on <em>θ<sub>i</sub></em> with values in [-3.1415, 3.1415]). <code>I=M</code> is used to indicate that the file includes merged samples from other scans in addition to the corresponding <code>I=4</code> scan.</li> <li><code>R</code>: Setting for the DM relic density likelihood (<code>R=1</code>: upper limit, <code>R=2</code>: matching the DM density)</li> <li><code>E</code>: Extra digit for the anomaly ratio <em>E/N</em>; only for <em>KSVZAxion</em> models (<code>E=1</code>: 0, <code>E=2</code>, 2/3, <code>E=3</code>: 5/3, <code>E=4</code>: 8/3), <em>DFSZAxion-I</em> models (<code>E=1</code>: 8/3), <em>DFSZAxion-II</em> models (<code>E=2</code>: 2/3), or some <em>GeneralALP</em> files (<code>E=a</code>: “QCD-like setting” with <em>β</em> = 7.94, <em>T<sub>crit</sub></em> = 147 MeV; <code>E=b</code>: “Simple ALP-like setting” with <em>β</em> = 0, <em>T<sub>crit</sub></em> irrelevant)</li> </ul> <p>For convenience, we provide a mapping between the figures in the paper and the <code>hdf5</code> files:</p> <ul> <li>Fig. 1: none</li> <li>Figs 2 - 11: Validation plots</li> <li>Figs 12 + 13: 2_GeneralALP_10M2</li> <li>Fig. 14: 2_GeneralALP_10M2a, 2_GeneralALP_10M2b</li> <li>Fig. 15: 2_QCDAxion_10M1, 2_QCDAxion_10M2</li> <li>Fig. 16: 2_QCDAxion_3041, 2_QCDAxion_3042</li> <li>Figs 17 + 18: 2_QCDAxion_10M1, 2_QCDAxion_10M2, 2_QCDAxion_30M1, 2_QCDAxion_30M2</li> <li>Fig. 19: 3_KSVZAxion_10M11, 3_KSVZAxion_10M12, 3_KSVZAxion_10M13, 3_KSVZAxion_10M14, 3_DFSZAxion_I_10M11, 3_DFSZAxion_II_10M12</li> <li>Fig. 20: 2_QCDAxion_10M1, 3_KSVZAxion_10M11, 3_KSVZAxion_10M12, 3_KSVZAxion_10M13, 3_KSVZAxion_10M14, 3_DFSZAxion_I_10M11, 3_DFSZAxion_II_10M12</li> <li>Fig. 21: 2_QCDAxion_11M1, 2_QCDAxion_11M2</li> <li>Fig. 22: 2_QCDAxion_3141, 2_QCDAxion_3142</li> <li>Figs 23 + 24: 2_QCDAxion_3041, 2_QCDAxion_3042, 2_QCDAxion_3141, 2_QCDAxion_3142</li> <li>Fig. 25: 2_QCDAxion_3141, 2_QCDAxion_3142</li> <li>Fig. 26: 2_QCDAxion_11M1, 3_DFSZAxion_I_11M11, 3_DFSZAxion_II_11M12</li> <li>Fig. 27: 2_QCDAxion_3141, 3_DFSZAxion_I_31411, 3_DFSZAxion_II_31412</li> <li>Fig. 28: Validation plot</li> <li>Fig. 29: Prior dependence plot</li> </ul> <p>A few caveats to keep in mind:</p> <ul> <li>The YAML files are designed to work with <code>GAMBIT 1.3.1</code>, and the pip files are tested with <code>pippi 2.1</code>, commit 1a08644. They may or may not work with later versions of either software (these working versions/commits can always be obtained via the <code>git</code> history).</li> <li>The <code>pip</code> files will produce an approximately complete, but very basic version of plots in the paper. Re-creating all the plots in the paper requires various manual, undocumented interventions such as additions, deletions and combination of the plotting scripts created by <code>pippi</code>. Users wishing to reproduce the more advanced plots in the paper should contact the authors for tips, scripts, or experiment for themselves.</li> </ul>
Datasets of fitted toy Monte Carlo samples for "Gaussian Process-based calculation of look-elsewhere trials factor"
<p>For the paper "Gaussian Process-based calculation of look-elsewhere trials factor", we generated and fitted a number of toy Monte Carlo samples which can be used to reproduce figures from the paper or do tests of hypotheses inspired by the method suggested in the article.</p> <p>Together with the hdf5 files containing the samples, we also provide a Jupyter notebook which was used to produce the figures.</p>
Best-fitting Sea Level Curves generated from the Tidal Notch Generator model
<p>The following dataset contains the data produced by the TidalNotch Generator model found at: https://zenodo.org/badge/latestdoi/700386384 and is part of the publication entitled: <strong>Decoding the interplay between tidal notch geometry and sea-level variability during the Last Interglacial (Marine Isotopic Stage 5e) high stand.</strong></p> <p>Each folder name describes the Erosion Rate used for each simulation, the Linear Regression of each curve group, and the number of peaks: e.g. Filename: 05mm_Negative_3peak. </p> <p>Each of the subfolders contains the final clusters grouped based on the methodology followed, extensively described in the manuscript.</p> <p>Each txt file contains 15 columns, while the content of each one is described below:</p> <p>Column 1: Random Sea Level Curve (Years)</p> <p>Column 2: Random Sea Level Curve (Elevation)</p> <p>Column 3: Modeled Notch Geometry (Notch Depth)</p> <p>Column 4: Modeled Notch Geometry (Notch Elevation)</p> <p>Column 5: Measured Notch Geometry (Notch Depth)</p> <p>Column 6: Measured Notch Geometry (Notch Elevation)</p> <p>Column 7: Fitting score (e.g. 0.17 --> 1-0.17=0.83-->83%)</p> <p>Column 8: Polynomial Order used to Interpolate the Randomly generated Sea Level points</p> <p>Column 9: Erosion Rate used for the simulation</p> <p>Column 10: ID of measured notch profile</p> <p>Column 11: number of simulation </p> <p>Column 12: Inclination of the measured notch</p> <p>Column 13: Category of Inclination</p> <p>Column 14: second ID of measured notch profile</p> <p>Column 15: Linear Regression value</p>
Models and source data for MuML dipole fitting
<p>Source data, models, and scripts necessary to reproduce the results of: "Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles" (M. Veit, D. M. Wilkins, Y. Yang, R. A. DiStasio Jr., M. Ceriotti, arXiv: 2003.12437). The model is a combination of symmetry-adapted Gaussian process regression (SA-GPR) for atomic dipoles and scalar GPR for atomic partial charges, which are fit together to reproduce the molecule's total dipole moment. Source data, kernel matrices, weights, residuals, and scripts for fitting and plotting the results are included.</p>
Supplementary Data - MORTALITY RATE DUE TO PULMONERY FIBROSIS ASSOCIATED WITH SARS- COV-2 INFECTION: SCOPE OF BEST FIT REGRESSION
<p>The dataset contains number of infected pateints - Death Frquencies - Mortality rate globally due to pulmonary fibrosis associated with SARS-COV-2 infection with effect from 21st Jan to 28 th April ,2020 . Data analysis report by best fit regression software Curve Expert V.1.4 supported with Spreadsheet ( Excel , Office 2007 ) are included for computation of statistical significance .</p>
Data and materials from: "Towards Auditory Profile-based Hearing-aid Fitting: Fitting Rationale and Pilot Evaluation"
<p>This repository contains materials and data used and described in:</p> <p><strong>Sanchez-Lopez, R., Fereczkowski, M., Santurette, S., Dau, T., Neher, T. (2021). Towards Auditory Profile-based Hearing-aid Fitting: Fitting Rationale and Pilot Evaluation. <em>Audiol. Res.</em> 11, no. 1: 10-21. <a href="https://doi.org/10.3390/audiolres11010002 ">https://doi.org/10.3390/audiolres11010002 </a></strong></p> <p> </p> <p><strong>Abstract</strong></p> <p><strong>Background</strong> - The clinical characterization of hearing deficits for hearing-aid fitting purposes is typically based on the pure-tone audiogram only. In a previous study, a group of hearing-impaired listeners completed a comprehensive test battery designed to tap into different dimensions of hearing abilities. A data-driven analysis of the data yielded four clinically relevant patient subpopulations or “auditory profiles”. The purpose of the current study was to propose and pilot-test profile-based hearing-aid settings to explore their potential for providing more targeted hearing-aid treatment.<br> <strong>Methods </strong>- Four candidate hearing-aid settings were developed and evaluated by a subset of the participants tested previously. The evaluation consisted of multi-comparison preference ratings carried out in realistic sound scenarios.<br> <strong>Results </strong>- Listeners belonging to the different auditory profiles showed different patterns of preference for the tested hearing-aid settings that were largely consistent with the expectations.<br> <strong>Conclusion</strong> - The results of this pilot evaluation support further investigations into stratified, profile-based hearing-aid fitting with wearable hearing aids.</p> <p>Please cite this article when using the data</p> <p> </p> <p><strong>Description of the files:</strong></p> <ul> <li><strong>APBHAF_Audiofiles.zip: </strong>Audio files used in the SenseLabOnline environment. Each folder corresponds to one participant.</li> <li><strong>APBHAF_MUSHA.xlsx: </strong>Raw Data of the MUSHA experiment.</li> <li><strong>APBHAF_MUSHA_Analysis.R: </strong>R code for the data analysis.</li> <li><strong>MUS_SoundScenes.mat</strong>: Mat file with a structure 1x9 MUS with the raw acoustic signals before processing with the hearing-aid simulator. <ul> <li>CurrentMixture: matrix consisting of the simulated acoustic signal recorded by the 4 microphones of the hearing-aid satellites: 1) front-left, 2) back-left, 3) front-right, 4) back-right.</li> <li>CurrentAnchor: matrix consisting of the simulated acoustic signal recorded by the 4 microphones of the hearing-aid satellites: 1) front-left, 2) back-left, 3) front-right, 4) back-right. The Anchor is -6 dB SNR.</li> <li>SampleLabel: Either "Kantine", "Traffic" or "Quiet"</li> <li>ConditionLabel: Either "Cond1", "Cond2" or "Cond3"</li> <li>fsmix: sampling frequency. For all signals must be 32000Hz</li> </ul> </li> <li><strong>MUSHA_Instruction: </strong>Instructions used for explaining the task and the environment.</li> </ul> <p>* The participant IDs in each of the files has been assigned randomly to ensure the anonymization of the data. The pseudo-anonymized data might be shared under request by direct correspondence with the authors.</p>
Raw male and female fitness data
<p>Raw male and female fitness data for 223 hemiclonal genotypes sampled from the LH<sub>M</sub> laboratory adapted population. See Gilks et al (2017; https://f1000research.com/articles/5-2644/v3) for full details on how these lines were established. Assays were designed to measure total adult lifetime fitness for both males and females from each line, under conditions that match as close as possible those experienced by adults in the base population (Chippindale <em>et al.</em>, 2001; Rice, 2005; Rice <em>et al.</em>, 2006).</p> <p><em>Male fitness assay</em><br> 5 hemiclonal males per line were combined in adult competition vials with 10 competitor <em>bw</em><sup>-</sup> males and 15 virgin <em>bw</em><sup>-</sup> females. After 2 days, each <em>bw</em><sup>- </sup>female was isolated into individual oviposition test-tubes (containing the cornmeal-molasses-agar media but with no additional dried yeast) and left to oviposit for 18 hours. On Day 12, progeny were scored for eye-colour, in two observation rounds to allow ensure that as many eclosing offspring were included. Hemiclonal males were assigned paternity to progeny with wild-type red eyes (progeny of competitors are homozygous for the <em>bw</em><sup>-</sup> allele and therefore have brown eyes), giving an average fitness score (number of offspring sired) for the 5 hemiclonal males that were assayed per line. This assay was independently replicated 5 times, representing data from a total of 25 hemiclonal males per line. Male fitness was calculated as the proportion of offspring sired per assayed male, which accounts for instances where less than 5 hemiclonal males were included (6 out of 1105 assays).</p> <p><em>Female fitness assays</em><br> Assays of female fitness followed a similar protocol to the male assays, again to match as close as possible the timing and conditions experienced by individuals in the base population. In this case, 5 virgin hemiclonal females were combined in adult competition vials with 10 competitor <em>bw</em><sup>- </sup>females and 15 <em>bw</em><sup>- </sup>males for 2 days. After 2 days, the 5 hemiclonal females were isolated into individual test-tubes and left to oviposit for 18hrs. The tubes were immediately chilled (4°C) to halt embryo development and the number of eggs per female was counted to provide a measure of fecundity. Data was excluded for tubes in which the female was either dead or not present. Since unmated females are known to produce eggs at a low rate, we also excluded data from females where egg counts were 0 or 1 as these are likely to represent output from unmated females (see Supplementary figure 1). By averaging fecundity across all 5 females this provided an average female fitness score for that line. This assay was independently replicated 5 times, representing a total of 25 hemiclonal females per line.</p> <p><strong>Dataset Column headings:</strong></p> <p><strong><em>Male</em></strong><br> sex - all male (value = 1)<br> rep - replicate (values from 1 to 5)<br> line - hemiclonal line (223 different lines, values from 1 to 230 with 7 lines missing)<br> red_1 - number of wild-type red-eyed offspring in first round of counting<br> red_2 - number of wild-type red-eyed offspring in second round of counting<br> brown_1 - number of brown-eyed offspring in first round of counting<br> brown_2 - number of brown-eyed offspring in second round of counting<br> total_red - number of offspring counted with wild-type red eyes (genotype bw<sup>+</sup>/bw<sup>-</sup>)<br> total_brown - number of offspring counted with brown eyes (genotype bw<sup>-</sup>/bw<sup>-</sup>)<br> male_density - number of hemiclonal males per vial (value usually 5, but may be less due to missing males)</p> <p>note: NA - missing value</p> <p><em><strong>Female</strong></em><br> sex - all female (value = 2)<br> rep - replicate (values from 1 to 5)<br> line - hemiclonal line (223 different lines, values from 1 to 230 with 7 lines missing)<br> f1 - fecundity of female 1<br> f2 - fecundity of female 2<br> f3 - fecundity of female 3<br> f4 - fecundity of female 4<br> f5 - fecundity of female 5</p> <p>note: NA - missing value</p> <p><strong>References</strong></p> <p>Chippindale, A.K., Gibson, J.R. & Rice, W.R. 2001. Negative genetic correlation for adult fitness between sexes reveals ontogenetic conflict in Drosophila. <em>Proc. Natl. Acad. Sci.</em> <strong>98</strong>: 1671–1675.</p> <p>Gilks WP, Pennell TM, Flis I et al. Whole genome resequencing of a laboratory-adapted <em>Drosophila melanogaster</em> population sample [version 3; referees: 2 approved]. F1000Research 2016, 5:2644 (doi: 10.12688/f1000research.9912.3)</p> <p>Rice, W.R. 2005. Inter-locus antagonistic coevolution as an engine of speciation: Assessment with hemiclonal analysis. <em>Proc. Natl. Acad. Sci.</em> <strong>102</strong>: 6527–6534.</p> <p>Rice, W.R., Stewart, A.D., Morrow, E.H., Linder, J.E., Orteiza, N. & Byrne, P.G. 2006. Assessing sexual conflict in the Drosophila melanogaster laboratory model system. <em>Philos. Trans. R. Soc. B Biol. Sci.</em> <strong>361</strong>: 287–299.</p>
Phenotype data for Sussex LHM Drosophila melanogaster reproductive fitness GWAS
<p>Input data, code, logs, graphs and output data for the Sussex LHM Drosophila melanogaster hemiclones.</p> <p>Aim is to generate single, standardised values of female and male reproductive fitness for each hemiclone genome, for using in genome-wide association test using Plink software.</p> <p>Notes on how to run are provided in the code.</p>
Supplementary Data: A global fit of the MSSM with GAMBIT (arXiv:1705.07917)
<p><strong>Supplementary Data</strong></p> <p><em>A global fit of the MSSM with GAMBIT </em><br> <em>arXiv:1705.07917 </em></p> <p>The files in this record contain data for the MSSM7 model considered in the GAMBIT “Round 1” weak-scale SUSY paper.</p> <p>The files consist of</p> <ul> <li>A number of YAML files corresponding to different sets of sampling parameters and/or priors</li> <li>MSSM7.yaml, a YAML file used for postprocessing</li> <li>StandardModel_SLHA2_scan.yaml, a universal YAML fragment included from other YAML files</li> <li>StandardModel_SLHA2_postprocessing.yaml, a YAML fragment included from MSSM7.yaml</li> <li>A final hdf5 file, containing the combined results of all sampling runs</li> <li>An example pip file, for producing plots from the hdf5 file using pippi</li> <li>gambit_preamble.py, a collection of python functions used for in-line data processing in the pip file</li> <li>SLHA1 and SLHA2 files for the best-fit point in each subregion of the fit. These can found inside the tarball best_fits_SLHA.tar.gz.</li> </ul> <p>The different YAML files corresponding to different samplers and/or priors follow the naming scheme MSSM7_[scanner]_[prior]_[slice]_[special].yaml , where</p> <ul> <li>scanner = Diver, MN</li> <li>prior = log, flat</li> <li>slice = nM2, pM2, Afunnel, hZfunnel, sqcoann, slcoann (positive or negative M2, A/H funnel, h/Z funnel, squark co-annihilation, slepton co-annihilation)</li> <li>special = jDE, [blank] (used pure jDE, or used the default lambdajDE)</li> </ul> <p>A few caveats to keep in mind:</p> <ol> <li> <p>The final hdf5 results file included here was generated in the following way:</p> <ul> <li>carry out initial runs using YAML files following the naming scheme above</li> <li>combine the resulting hdf5 output files into a single file, using<br> gambit/Printers/scripts/combine_hdf5.py</li> <li>postprocess the samples to remove all points more than 5 sigma from the current best fit, using MSSM7_strip.yaml</li> <li>postprocess the samples to include a new likelihood term for LHC Run II searches, and to recompute the FlavBit likelihoods (these were buggy in a pre-release version of GAMBIT), using MSSM7.yaml .</li> </ul> </li> <li> <p>It is not necessary to repeat the steps listed in point 1 when running new scans; the LHC Run II likelihoods can be included in the original YAML file, so that no postprocessing step is required.</p> </li> <li> <p>The YAML files that we give here are updated compared to the ones that we used when generating the hdf5 file, in order to match the set of available options in the release version of GAMBIT 1.0.0. The included physics and numerics are however identical.</p> </li> <li> <p>The YAML files are designed to work with the tagged release of GAMBIT 1.0.0, and the pip file is tested with pippi 2.0, commit 2ab061a8. They may or may not work with later versions of either software (but you can of course always obtain the version that they do work with via the git history).</p> </li> <li> <p>The pip file is an example only. Users wishing to reproduce the more advanced plots in any of the GAMBIT papers should contact us for tips or scripts, or experiment for themselves. Many of these scripts are in multiple parts and require undocumented manual interventions and steps in order to implement various plot-specific customisations, so please don’t expect the same level of polish as for files provided here or in the GAMBIT repo.</p> </li> </ol>
Supplementary Data: Global fits of GUT-scale SUSY models with GAMBIT (arXiv:1705.07935)
<p>Supplementary Data</p> <p><em>Global fits of GUT-scale SUSY models with GAMBIT</em><br> <em>arXiv:1705.07935</em></p> <p>The files in this record contain data for the CMSSM, NUHM1 and NUHM2 models considered in the GAMBIT "Round 1" GUT-scale SUSY paper.</p> <p>For each model, there are</p> <ul> <li>A number of YAML files, each corresponding to a different set of sampling parameters and/or priors</li> <li>A set of YAML files used for postprocessing: CMSSM_intermediate.yaml, CMSSM.yaml, NUHM1.yaml and NUHM2.yaml</li> <li>A final hdf5 file, containing the combined results of all sampling runs</li> <li>An example pip file, for producing plots from the hdf5 file using pippi</li> <li>SLHA1 and SLHA2 files for the best-fit point in each subregion of the fit. These can be found inside the tarball best_fits_SLHA.tar.gz.</li> </ul> <p>The record also contains</p> <ul> <li>StandardModel_SLHA2_scan.yaml and StandardModel_SLHA2_postprocessing.yaml, two universal YAML fragments included from other yaml files</li> <li>gambit_preamble.py, a collection of python functions used for in-line data processing in the pip files</li> </ul> <p>The different YAML files corresponding to different samplers and/or priors follow the naming scheme [model]_[scanner]_[prior]_[slice]_[special].yaml, where</p> <ul> <li>model = CMSSM, NUHM1, NUHM2</li> <li>scanner = Diver, MN</li> <li>prior = log, flat</li> <li>slice = pmu, nmu (positive or negative mu)</li> <li>special = sqcoann, slcoann, [blank] (squark co-annihilation, slepton co-annihilation, or bulk)</li> </ul> <p>A few caveats to keep in mind:</p> <ol> <li> <p>For each model, the final hdf5 results file included here was generated in the following way:</p> <ul> <li>carry out initial runs using YAML files following the naming scheme above</li> <li>combine the resulting hdf5 output files into a single file, using gambit/Printers/scripts/combine_hdf5.py</li> <li>postprocess the samples to remove all points more than 5 sigma from the current best fit, using [model]_strip.yaml</li> <li>postprocess the samples to include a new likelihood term for LHC Run II searches, and to recompute the FlavBit likelihoods (these were buggy in a pre-release version of GAMBIT). For the CMSSM, this happened in two steps, due to persistent flavour bugs, using CMSSM_intermediate.yaml and CMSSM.yaml. For the NUHM1 and NUHM2, this was done in a single step each, using NUHM1.yaml and NUHM2.yaml.</li> </ul> </li> <li> <p>It is not necessary to repeat the steps listed in point 1 when running new scans; the LHC Run II likelihoods can be included in the original YAML file, so that no postprocessing step is required.</p> </li> <li> <p>The YAML files that we give here are updated compared to the ones that we used when generating the hdf5 file, in order to match the set of available options in the release version of GAMBIT 1.0.0. The included physics and numerics are however identical.</p> </li> <li> <p>The YAML files are designed to work with the tagged release of GAMBIT 1.0.0, and the pip files are tested with pippi 2.0, commit 2ab061a8. They may or may not work with later versions of either software (but you can of course always obtain the version that they do work with via the git history).</p> </li> <li> <p>The pip file for each model is an example only. Users wishing to reproduce the more advanced plots in any of the GAMBIT papers should contact us for tips or scripts, or experiment for themselves. Many of these scripts are in multiple parts and require undocumented manual interventions and steps in order to implement various plot-specific customisations, so please don't expect the same level of polish as for files provided here or in the GAMBIT repo.</p> </li> </ol>
Bivariate GWAS for female and male fitness in Drosophila melanogaster (Sussex, LHm)
<p>Code, logs, results and graphs for genome-wide association study of reproductive fitness in D.melanogaster hemiclone lines, using the R package 'Mulitphen'.</p>
Mutual Induced Fit Transition Structure Stabilization of Corannulene's Bowl-to-Bowl Inversion in a Perylene Bisimide Cyclophane
<p>Additional data to report <a href="https://doi.org/10.1039/D3SC05341E">https://doi.org/10.1039/D3SC05341E</a>:<br><br>Corannulene is known to undergo a fast bowl-to-bowl inversion at r.t. <em>via</em> a planar transition structure (TS). Herein we present the catalysis of this process within a perylene bisimide (PBI) cyclophane composed of chirally twisted, non-planar chromophores, linked by <em>para</em>-xylylene spacers. Variable temperature NMR studies reveal that the bowl-to-bowl inversion is significantly accelerated within the cyclophane template despite the structural non-complementarity between the binding site of the host and the TS of the guest. The observed acceleration corresponds to a decrease in the bowl-to-bowl inversion barrier of 11.6 kJ mol<sup>−1</sup> compared to the uncatalyzed process. Comparative binding studies for corannulene (20 π-electrons) and other planar polycyclic aromatic hydrocarbons (PAHs) with 14 to 24 π-electrons were applied to rationalize this barrier reduction. They revealed high binding constants that reach, in tetrachloromethane as a solvent, the picomolar range for the largest guest coronene. Computational models corroborate these experimental results and suggest that both TS stabilization and ground state destabilization contribute to the observed catalytic effect. Hereby, we find a “mutual induced fit” between host and guest in the TS complex, such that mutual geometric adaptation of the energetically favored planar TS and curved π-systems of the host results in an unprecedented non-planar TS of corannulene. Concomitant partial planarization of the PBI units optimizes noncovalent TS stabilization by π–π stacking interactions. This observation of a “mutual induced fit” in the TS of a host–guest complex was further validated experimentally by single crystal X-ray analysis of a host–guest complex with coronene as a qualitative transition state analogue.</p>
Data from: Evolutionary potential and constraints in an aposematic species: Genetic correlations between warning coloration and fitness components in wood tiger moths
<p>Phenotypic data and pedigrees of two laboratory populations of wood tiger moths (<em>Arctia plantaginis</em>) of Finnish (=FIN) and Estonian (=EST) ancestry.</p> <p><strong>Pedigree: </strong><br>ID: individual identifier<br>sire = Father<br>dam=mother</p> <p><strong>Pheno.data: </strong><br>ID: individual identifier<br>Sex: 1=male; 2=female<br>hatchingdate: date when larva hatched<br>pupadate: date of pupation<br>adultdate: date of exclusion<br>Pupa.Weight: weight of pupa [mg]<br>Female.Colour = hindwing colour of females. In this species hindwing colour in females varies continuously from yellow to red. It was quantified by visual matching of hinwdings against a colour scale ranging from 1 = yellow to 6 = red. <br>Signal.Size = larva signal size. Larvae show an orange patch of variable size on the back of their black body. The size is given as number of segments<br>Egg.N = egg number produced by the individual<br>Off.N = offspring number. Larvae were counted 2-3 weeks after egg laying</p> <p> </p>
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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.