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128 results for “model fit”
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>
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: 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>
Example code and data for ubms: An R package for fitting hierarchical occupancy and N-mixture abundance models in a Bayesian framework
<p>This repository contains an R script (grouse_example.R) and data (grouse_data.csv) used to reproduce the grouse abundance analysis described in Kellner, K. F., et al. (2021) ubms: An R package for fitting hierarchical occupancy and N-mixture abundance models in a Bayesian framework. Methods in Ecology and Evolution. The R script requires installation of the ubms R package, which can be obtained from CRAN (https://cran.r-project.org/package=ubms).</p> <p>The repository also contains an additional example occupancy analysis (occupancy_example.R) using the crossbill dataset included with the unmarked R package.</p>
Geosci. Model Dev. paper data for Flipo et al., "Regional coupled surface-subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency-domain discharge data"
<p>Data and associated user guide, as part of the paper :</p> <p>Flipo N., Gallois N., Schuite J. Regional coupled surface-subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency-domain discharge data, Geoscientific Model Development.</p> <p>In consistency with the “Code and data availability” sub-section of the paper, all data necessary for the reproduction of<br> Figs. 7, 8c, 8d, 9, 10 and 11 are here provided.</p>
Science ready spectra of star clusters and their best-fitting models described in the research paper "Using Star Clusters as Tracers of Star Formation and Chemical Evolution: the Chemical Enrichment History of the Large Magellanic Cloud" by Chilingarian & Asa'd
<p>Science ready spectra of star clusters in the Large Magellanic Cloud and their best-fitting templates (alpha-enhanced MILES based simple stellar population models) obtained using the NBursts full spectrum fitting code. Each spectrum is presented as a binary FITS table, which contains a spectrum (wavelength, flux, uncertainties), best-fitting template, best-fitting parameters (radial velocity, age, metallicity), and a pixel mask used in the fitting procedure. For each cluster, 5 spectra are provided, which correspond to [alpha/Fe] values from 0.0 to 0.4 dex with a step of 0.1 dex. The only exception is NGC2249, for which only 3 models are provided. The alpha-enhancement value of a model grid used in the fitting procedure is given in the FITS keyword MGFEGRID.</p>
Sample FITS file with non-linear wavelength solution using a Chebyshev model
<p>This file was wavelength calibrated using IRAF and written to a FITS file using a non-linear wavelength solution using a Chebyshev model.</p> <p>This data is in its original shape.</p>
Sample FITS file with non-linear wavelength solution using a cubic spline model
<p>This file was wavelength calibrated using IRAF and written to a FITS file using a non-linear wavelength solution using a cubic spline model.</p> <p>This data is in its original shape.</p>
Sample FITS file with non-linear wavelength solution using a Legendre model
<p>This file was wavelength calibrated using IRAF and written to a FITS file using a non-linear wavelength solution using a Legendre model.</p> <p>This data is in its original shape.</p>
Sample FITS file with non-linear wavelength solution using a linear spline model
<p>This file was wavelength calibrated using IRAF and written to a FITS file using a non-linear wavelength solution using a linear spline model.</p> <p>This data is in its original shape.</p>
Analysis of correlation-based biomolecular networks from different omics data by fitting stochastic block models
<p><strong>Baum_et_al_2019_Supplementary_Figures.pdf: </strong>Supplementary Figures S1-S4. Legends are included under each figure.</p> <p><strong>sbm-for-correlation-based-networks-master.zip: </strong>Archived source code of R and Python functions for the analyses and example workflow description at time of publication. Files are maintained at https://gitlab.com/biomodlih/sbm-for-correlation-based-networks and https://gitlab.com/kabaum/sbm-for-correlation-based-networks.</p>
Science ready spectra and their best-fitting models described in the research paper ``Internal dynamics and stellar content of nine ultra-diffuse galaxies in the Coma cluster prove their evolutionary link with dwarf early-type galaxies'' by Chilingarian et al.
<p>Science ready spectra of nine ultra-diffuse galaxies in the Coma cluster collected with the Binospec multi-object spectrograph and their best-fitting PEGASE.HR templates obtained using the NBursts full spectrum fitting code. These spectra were presented in the paper ``Internal dynamics and stellar content of nine ultra-diffuse galaxies in the Coma cluster prove their evolutionary link with dwarf early-type galaxies'' by Chilingarian et al. accepted for publication in the Astrophysical Journal on Sep/3/2019 (arXiv:1901.05489).</p> <p>Each spectrum is presented as a binary FITS table, which contains a spectrum (wavelength, flux, uncertainties), best-fitting template, best-fitting parameters (radial velocity, age, metallicity), and a pixel mask used in the fitting procedure. For six galaxies there are two files provided: (i) one-dimensional optimally extracted integrated spectrum and (ii) two dimensional spectrum for spatially resolved radial velocity information. For the remaining three galaxies, only spatially resolved spectra are provided.</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>
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>
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>
Toward greater realism in inclusive fitness models: the case of caste fate conflict in insect societies
<p>In the field of social evolution, inclusive fitness theory has been successful in making a wide range of qualitative predictions on expected patterns of cooperation and conflict. Nevertheless, outside of sex ratio theory, inclusive fitness models that make accurate quantitative predictions remain relatively rare. Past models dealing with caste fate conflict in insect societies, for example, successfully predicted that if female larvae can control their own caste fate, an excess should opt to selfishly develop as queens. Available models, however, were unable to accurately predict levels of queen production observed in <em>Melipona</em> bees – a genus of stingless bees where caste is self-determined – as empirically observed levels of queen production are ca. two times lower than the theoretically predicted ones. Here, we show that this discrepancy can be resolved by explicitly deriving the colony-level cost of queen overproduction from a dynamic model of colony growth, requiring incorporation of parameters of colony growth and demography, such as the per-capita rate at which new brood cells are built and provisioned, the percentage of the queen's eggs that are female, costs linked with worker reproduction and worker mortality. Our revised model predicts queen overproduction to more severely impact colony productivity, resulting in an evolutionarily stable strategy (ESS) that is ca. half that of the original model, and is shown to accurately predict actual levels of queen overproduction observed in different <em>Melipona</em> species. Altogether, this shows how inclusive fitness models can provide accurate quantitative predictions, provided that costs and benefits are modelled in sufficient detail and are measured precisely.</p>
Main model fits and substitution rate predictions for: A quantitative genetic model of background selection in humans
<p>Across the human genome, there are large-scale fluctuations in genetic diversity caused by the indirect effects of selection. This can be thought of as a "linked selection signal" that reflects the impact of selection varying according to the placement of functional regions and recombination rates along the genome. Previous work has shown that negative selection against the steady influx of new deleterious mutations into conserved regions is the predominant mode of selection in humans. However, the theoretic model that underpins these results, classic Background Selection theory, is only applicable when new mutations are so deleterious that they cannot fix in the population. Here, we develop a statistical method based on a quantitative genetics view of the linked selection, which models the effects of weak draft created according to how polygenic additive fitness variance is distributed along the genome. We use a recent model that jointly predicts the equilibrium fitness variance and substitution rates due to both strong and weakly deleterious mutations, we estimate the distribution of fitness effects (DFE) and mutation rate across three human populations. While our model can accommodate weaker selection, we initially find evidence across three human populations of very strong selection against deleterious mutations consistent with previous work. However, the corollary predicted substitution rates for conserved regions are unreasonably low, and in disagreement with observed rates. We hypothesize this could be due to selected sites experiencing a further diminished population size due to selective interference. When we account for this in our method, we find evidence of weakly deleterious mutations in conserved regions which brings the predicted substitution rate into agreement with observations. However, these models lead to implausibly large mutation rate estimates. Overall, while our model of the genomic linked selection signal brings us a step towards uniting population and quantitative genetic selection models with the substitution process, our work suggests considerable uncertainty remains about the processes generating fitness variance in humans.</p>
Complex models of sequence evolution improve fit, but not gene tree discordance, for tetrapod mitogenomes
<p>Variation in gene tree estimates is widely observed in empirical phylogenomic data and is often assumed to be the result of biological processes. However, a recent study using tetrapod mitochondrial genomes to control for biological sources of variation due to their haploid, uniparentally inherited, and non-recombining nature found that levels of discordance among mitochondrial gene trees were comparable to those found in studies that assume only biological sources of variation. Additionally, they found that several of the models of sequence evolution chosen to infer gene trees were doing an inadequate job of fitting the sequence data. These results indicated that significant amounts of gene tree discordance in empirical data may be due to poor fit of sequence evolution models and that more complex and biologically realistic models may be needed. To test how the fit of sequence evolution models relates to gene tree discordance, we analyzed the same mitochondrial datasets as the previous study using two additional, more complex models of sequence evolution that each model a different biologically realistic aspect of the evolutionary process: a covarion model to incorporate heterotachy, and a model partitioned model to incorporate variable evolutionary patterns by codon position. Our results show that both additional models fit the data better than the models used in the previous study, with the covarion being consistently and strongly preferred as tree size increases. However, even these more preferred models still inferred highly discordant mitochondrial gene trees, thus deepening the mystery around what we label the "Mito-Phylo Paradox" and leading us to ask whether the observed variation could be biological after all.</p>
When does antimicrobial resistance increase bacterial fitness? Effects of dosing, social interactions and frequency dependence on the benefits of AmpC β-lactamases in broth, biofilms and a gut infection model.
<p><span>One of the longstanding puzzles of antimicrobial resistance is why the frequency of resistance persists at intermediate levels.<span> </span>Theoretical explanations for the lack of fixation of resistance include cryptic costs of resistance or negative frequency-dependence but are seldom explored experimentally. <span> </span><em>β</em>-lactamases, which detoxify penicillin-related antibiotics, have well-characterized frequency-dependent dynamics driven by cheating and cooperation.<span> </span>However, bacterial physiology determines whether <em>β</em>-lactamases are cooperative and we know little about the sociality or fitness of <em>β</em>-lactamase producers in infections.<span> </span>Moreover, media-based experiments constrain how we measure fitness, and ignore important parameters such as infectivity and transmission among hosts.<span> </span>Here, we investigated the fitness effects of broad-spectrum AmpC <em>β</em>-lactamases in <em>Enterobacter cloacae</em> in broth, biofilms and gut infections in a model insect. <span> </span>We quantified frequency- and dose-dependent fitness using cefotaxime, a third-generation cephalosporin.<span> </span>We predicted that infection dynamics would be similar to those observed in biofilms, with social protection extending over a wide dose range.<span> </span>We found evidence for the sociality of <em>β</em>-lactamases in all contexts with negative frequency-dependent selection ensuring the persistence of wild-type bacteria although cooperation was less prevalent in biofilms, contrary to predictions.<span> </span>While competitive fitness in gut infections and broth had similar dynamics, incorporating infectivity into measurements of fitness in infections<em> </em>significantly affected conclusions. <span> </span>Resistant bacteria had reduced infectivity which limited the fitness benefits of resistance to infections challenged with low antibiotic doses and having low initial frequencies of resistance. <span> </span>The fitness of resistant bacteria in more physiologically tolerant states (in biofilms, in infections) could be constrained by the presence of wild-type bacteria, high antibiotic doses and limited availability of <em>β</em>-lactamases.<span> </span>One conclusion is that increased tolerance of <em>β</em> -lactams does not necessarily increase selection pressure for resistance.<span> </span>Overall, both cryptic fitness costs and frequency-dependence curtailed the fitness benefits of resistance in this study.<span> </span></span></p> <p><span> </span></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
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.