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3,139 results for “Fitting”
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>
Reflectometry curves (XRR and NR) and corresponding fits for machine learning
<p>This is a compiled dataset of raw X-ray reflectivity (XRR, reflectometry) measurements together with corresponding fit parameters, intentionally published to use as training or test data for machine learning models. (The authors aim to include NR data in further versions of this dataset and plan to include other substrates and materials for XRR. Contributions welcome!)</p> <p><br> <strong>An interactive documentation can be found in <em>"README.html"</em> or at <a href="https://schreiber-lab.github.io/reflectometry-dataset">https://schreiber-lab.github.io/reflectometry-dataset</a>.</strong></p> <ul> <li>Data structure</li> </ul> <p>All data is provided in an hdf5 file, following <a href="https://www.nexusformat.org/">NeXus</a> convention with respect to the provided metadata in the hdf5 attributes. Some datesets have been measured in-situ and therefore there are stacks of curves that correspond to the different layer thicknesses of the same material on top of SiOx. The measured data is provided under experimental and the corresponding fit parameters under fit. Additional information is collected in metadata.</p> <ul> <li>Where to find the dataset and how to contribute</li> </ul> <p>Have a look at <a href="https://github.com/schreiber-lab/reflectometry-dataset">github</a> and <a href="https://doi.org/10.5281/zenodo.6497438">zenodo</a>. In case you wish to contribute further curves to this dataset or have ideas how to improve the dataset or where else to deposit it, please contact the authors at softmatter AT ifap.uni-tuebingen.de.</p>
Fit for 55 MIX H2 2030 scenario for PyPSA
<p>Country-level PyPSA model based on the Fit for 55 MIX-H2 scenario for the year 2030. The model includes both electricity and hydrogen. </p> <p>Following the methodology described here: https://data.jrc.ec.europa.eu/dataset/d4d59b89-89f7-4275-801a-45ea8957e973</p> <p>Each NetCDF contains a "solved" scenario with a different climate year that can be imported into PyPSA with `pypsa.Network.import_from_netcdf()`</p>
Separate-sex GWAS for reproductive fitness in Drosophila melanogaster (Sussex LHM sample)
<p>Code, data, logs, and graphs for GWAS on seperate-sex reproductive fitness in Drosophila melanogaster, Sussex LHM population sample.</p> <p>The shell script, code_drive_basic_gwas.sh, downloads input data files from the internet, drives Plink to select LD-independent SNPs, and then perform a genome-wide association test against female and male fitness, separately. Plink is also used to assign functions and gene names to SNPs. Bash/Unix code is used for formatting/compatibility adjustments, and also to add NCBI-dbSNP IDs to results. The shell script starts an R script that generates basic diagnostic graphs. This updated version differs from the first in that three large unconfirmed snRNA genes have been omitted to improve assignment of SNPs to genes.</p> <p>See https://f1000research.com/articles/5-2644/v3 and http://www.sussex.ac.uk/lifesci/morrowlab/</p>
False discovery rate calculations for genome-wide association study of reproductive fitness in Drosophila melanogaster (Sussex LHM sample)
<p>R code and results of applying false discovery (FDR) rate calculations to establish statistical signficance in a genome-wide association study of reproductive fitness in Drosophila melanogaster. Phenotype values were generated on hemiclone female and male lines from an outbred, laboratory adapted population. Thus, GWAS were previously performed seperately on the phenotype values for each sex, and also using a bivariate GWAS implemented in the R package multiPhen.</p> <p>FDR calculations were performed using the R package 'fdrtool' on all SNPs, and on LD-independent SNPs, the latter of which was used to determine p-value thresholds for genome-wide significance when all SNPs were considered.</p> <p>This version differs from the original in that: i) Some gene positions/names have been reassigned for accuaracy in the input data. ii) A file containing the p-value thresholds corresponding to an FDR of 0.1 has been added. The 95% credible intervals for each SNP association have been added to the results data files.</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>
Supplementary Data: Impact of vacuum stability, perturbativity and XENON1T on global fits of Z2 and Z3 scalar singlet dark matter (arXiv:1806.11281)
<p> </p> <p><strong>Supplementary Data</strong></p> <p> </p> <p><em>Impact of vacuum stability, perturbativity and XENON1T on global fits of Z<sub>2</sub> and Z<sub>3</sub> scalar singlet dark matter</em> <a href="https://arxiv.org/abs/1806.xxxxx"><em>arXiv:</em></a><em><a href="https://arxiv.org/abs/1806.11281">1806.11281</a></em></p> <p>The files in this record contain data for the scalar singlet dark matter models considered in the <a href="http://gambit.hepforge.org">GAMBIT</a> "Scalar singlet Mark II" paper.</p> <p>The files consist of</p> <ul> <li>30 regular YAML files</li> <li><code>StandardModel_SLHA2_scan.yaml</code>, a universal YAML fragment included from the other YAML files</li> <li>14 hdf5 files. 8 of these correspond to the complete set of combined samples for each fit. These 8 fits are generated from all binary permutations of three run properties: Z2 or Z3 model, with or without absolute vacuum stability demanded, and with constraints from the 2017 or 2018 XENON1T data. These 8 hdf5 files are used to generate the profile likelihood plots in the paper. The other 6 hdf5 files are the results of T-Walk runs, and are used to generate the posterior pdfs in the paper.</li> <li>Some example pip files for producing plots from the hdf5 files using <a href="github.com/patscott/pippi">pippi</a></li> <li>A tarball <code>best_fits_yaml.tar.gz</code> containing YAML files of the best-fit point in each of the 8 fits.</li> </ul> <p>The files follow the naming scheme <code>SingletDM_[model]_[slice]_[vacuum]_[xenon]_[prior]_[scanner].yaml</code>.</p> <ul> <li>model: <code>Z2</code> or <code>Z3</code></li> <li>slice: <code>full</code>, <code>lowmass</code>, <code>neck</code> or absent (for hdf5 files)</li> <li>vacuum: <code>ms</code> (metastable) or <code>vs</code> (absolute vacuum stability)</li> <li>prior: <code>logmu3</code>, <code>flatmu3</code> or absent (for Z<sub>2</sub> scans)</li> <li>scanner: <code>TWalk</code> or absent (implies Diver scans in the case of YAML files, and indicates merged samples potentially from both Diver and T-Walk in the case of hdf5 files)</li> </ul> <p>A few caveats to keep in mind:</p> <ol> <li> <p>The YAML files are designed to work with GAMBIT 1.2.0, commit e4d3f739, and the pip files are tested with pippi 2.1, commit c094b8c8. 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 files are examples 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> <p> </p>
SCIAMACHY NO regression fit MCMC samples
<p><strong>SCIAMACHY mesosphere NO data and regression model samples</strong></p> <p>SCIAMACHY mesosphere daily zonal mean NO data and Markov-Chain Monte-Carlo samples from the regression coefficient distributions, derived from and for use with the <a href="https://github.com/st-bender/sciapy"><code>sciapy</code></a> regression module.</p> <p>This data set contains the following files:</p> <ul> <li><code>NO_regress_output_pGM_Lya_ltcs_exp1dscan60d_km32_float32.nc</code>, <code>NO_regress_output_pGM_Lya_ltcs_exp1dscan60d_km32_float64.nc</code> - samples from the regression coefficient distributions (single and double precision)</li> <li><code>NO_regress_quantiles_pGM_Lya_ltcs_exp1dscan60d_km32.nc</code> - the 0.1, 2.5, 16, 50, 84, 97.5, and 99.9 percentiles of the sampled distributions</li> <li><code>scia_nom_dzmNO_2002-2012_v6.2.1_2.2_akm0.002_geomag10_nw.nc</code> - the SCIAMACHY daily zonal mean NO data</li> <li><code>sciapy_regress_tutorial.ipynb</code> - example ipython notebook</li> </ul> <p><strong>MCMC Samples</strong></p> <p>The files <code>NO_regress_output..._float32.nc</code> and <code>NO_regress_output..._float64.nc</code> contain MCMC samples of the model as single and double precision floats. The file <code>NO_regress_quantiles....nc</code> contains the 0.1, 2.5, 16, 50, 84, 97.5, and 99.9 percentiles of the sampled distributions and is provided for convenience. The files contain the following parameters:</p> <ul> <li><code>kernel:log_sigma</code>, <code>kernel:log_rho</code> - the "strength" and "lengthscale" of the Matérn-3/2 Gaussian Process kernel</li> <li><code>mean:offset:value</code> - the constant offset of the NO model in [<span class="math-tex">\(10^6\)</span> cm<span class="math-tex">\(^{-3}\)</span>]</li> <li><code>mean:Lya:amp</code> - the Lyman-<span class="math-tex">\(\alpha\)</span> coefficient of the mean model in [<span class="math-tex">\(10^6\)</span> cm<span class="math-tex">\(^{-3}\)</span> / Lyman-<span class="math-tex">\(\alpha\)</span>]</li> <li><code>mean:GM:amp</code> - the geomagnetic coefficient (AE) in [<span class="math-tex">\(10^6\)</span> cm<span class="math-tex">\(^{-3}\)</span> / nT]</li> <li><code>mean:GM:tau0</code> - the constant lifetime of the geomagnetic lifetime in [d]</li> <li><code>mean:GM:taucos1</code>, <code>mean:GM:tausin1</code> - cosine and sine amplitudes of the yearly geomagnetic lifetime variation in [d]</li> </ul> <p><strong>Daily zonal mean NO data</strong></p> <p>The model was trained on the <a href="http://doi.org/10.5281/zenodo.1009078">SCIAMACHY mesosphere NO dataset</a>, binned into 10° geomagnetic latitude bins using the provided <code>gm_lat</code> variable and using the standard error of the mean as data uncertainties. The data are uploaded as <code>scia_nom_dzmNO_2002-2012_v6.2.1_2.2_akm0.002_geomag10_nw.nc</code> and were prepared by running (after installing <code><a href="https://github.com/st-bender/sciapy">sciapy</a>)</code>:</p> <pre><code class="language-bash">bash> scia_daily_zonal_mean.py -g -b'-90:90:10' -o <daily_zonal_mean_NO.nc> </path/to/SCIAMACHY_NO_NOM_orbits_20??_v6.2.1.nc></code></pre> <p><strong>Regression sampling</strong></p> <p>The samples were generated by running the following command:</p> <pre><code class="language-bash">bash> python -m sciapy.regress <daily_zonal_mean_NO.nc> --proxies Lya:<Lyman-alpha_file.dat>,GM:<AE_file.dat> -A <altitude> -L <geomag_latitude_bin> -w 14 -b 800 -p 1400 -F \"\" -I GM --fit_annlifetimes GM --positive_proxies GM --lifetime_scan=60 --lifetime_prior exp -k -K Mat32 -O0 -m "nom_pGM_Lya_ltcs_exp1dscan60d_km32" -P</code></pre> <p> </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 log linear wavelength solution
<p>This file was wavelength calibrated using IRAF and written to a FITS file using a log linear wavelength solution.</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>
Sample FITS file with linear wavelength solution
<p>This file was wavelength calibrated using IRAF and written to a FITS file using a linear wavelength solution which means it has been resample.</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>
Raw data for: "Altered trophic interactions in warming climates: consequences for predator diet breadth and fitness"
<p><strong>Raw data for the article:</strong> Bestion, E, Soriano-Redondo, A, Cucherousset, J, Jacob, S, White, J, Zinger, L, Fourtune, L, Di Gesu, L, Teyssier, A, Cote, J. Altered trophic interactions in warming climates: consequences for predator diet breadth and fitness. Proceedings of the Royal Society: B. 2019. 286:20192227. https://doi.org/10.1098/rspb.2019.2227</p> <p><strong>This data should be cited as</strong>: Bestion, E, Soriano-Redondo, A, Cucherousset, J, Jacob, S, White, J, Zinger, L, Fourtune, L, Di Gesu, L, Teyssier, A, Cote, J (2019). Raw data for: "Altered trophic interactions in warming climates: consequences for predator diet breadth and fitness", Bestion et al 2019 Proceedings B. (Version 1). Zenodo. https://doi.org/10.5281/zenodo.3475402</p> <p><strong>This data is composed of</strong> one dataset with 21 columns and a README file</p> <p>Composition of the Bestion_2019_isotopy_dataset_for_zenodo.csv dataset</p> <p>- Individual: numerical index corresponding to each of the 327 individuals in the dataset<br> - Age: age class, J = juvenile (<1 year old), A = adult (1 and 2+ year old)<br> - Sex: F (female) or M (male)<br> - Climate: Present-day climate or Warm climate<br> - Enclosure: enclosure number (10 enclosures, 5 per climatic treatment)<br> - delta13C_september: stable isotope values for delta13C in september<br> - delta15N_september: stable isotope values for delta15N in september<br> - delta13C_september_corrected: stable isotope values for delta13C in september corrected for the stable isotope value of the three invertebrate prey categories<br> - delta15N_september_corrected: stable isotope values for delta15N in september corrected for the stable isotope value of the three invertebrate prey categories<br> - Prop_predator_eaten: proportion of predatory invertebrates eaten by each individual derived from the corrected stable isotope values<br> - Prop_phytophagous_eaten: proportion of phytophagous invertebrates eaten by each individual derived from the corrected stable isotope values<br> - Prop_detritivorous_eaten: proportion of detritivorous invertebrates eaten by each individual derived from the corrected stable isotope values<br> - Levins_diet_index: levins' dietary index corresponding to lizard diet specialization (with 3 = completely generalist and 1 = completely specialist lizard)<br> - Body_Size_september: lizard body size (snout-vent length in mm)<br> - Body_Mass_september: lizard body mass (in g)<br> - Body_Condition_september: lizard body condition (residuals of body mass by body size)<br> - Microbiota_shannon_index: shannon index representing gut microbial bacteria community diversity<br> - Survival_winter: survival during the winter (1 = survived, 0 = died)<br> - Abundance_predator_enclosure: abundance of predatory invertebrates within the enclosure<br> - Abundance_phytophagous_enclosure: abundance of phytophagous invertebrates within the enclosure<br> - Abundance_detitivorous_enclosure: abundance of detritivorous invertebrates within the enclosure</p> <p> </p> <p> </p>
Article fit data for selected journals (2015-2019)
<p>This dataset contains metadata and article fit scores for articles published in selected scholarly journals from 2015-2019 (inclusive), as well as a random sample of articles assigned to random journal IDs, and their fit scores therein. Article metadata is drawn from OpenAlex using the openalexR package ca. April 2024.</p>
Stemflow fitting equations at OAL-UK
<p>Dataset containing fitting equations from linear regression models for predicting stemflow from gross rainfall for multiple woody plant species -i.e. sycamore, ash, goat willow and basket willow. The equations were fitted per tree species and individual. </p> <p>Stemflow is a relevant eco-hydrological mechanism related to rainfall partitioning at the tree's canopy. Stemflow consists in the funnelling of rainfall around the tree stem, concentrating water and nutrients on its way to the root zone. More information on stemflow can be found here: <a href="https://doi.org/10.1016/j.jhydrol.2019.124448">https://doi.org/10.1016/j.jhydrol.2019.124448</a> </p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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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.