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285 results for “evolution models”
Drainage reorganisation and species evolution: model sensitivity analysis data
<p>Data description:</p> <ul> <li><strong>‘trial_factor_values.csv’:</strong> The factor values for experiment trials were generated using a quasi-random Sobol sequence (Sobol, 1967). The table field, ‘initial_landscape_id’ is the identifier for unique combinations of the following factor values that controlled the landscape elevation in the initial conditions phase of the model: initial elevation seed, <span class="math-tex">\(U\)</span>, <span class="math-tex">\(K\)</span>, and <span class="math-tex">\(k_d\)</span>. The factors, <span class="math-tex">\(U\)</span>, <span class="math-tex">\(K\)</span>, <span class="math-tex">\(k_d\)</span>, <span class="math-tex">\(P_m\)</span>, and allopatric wait time varied logarithmically. The values of these factors in the file are the exponent of base 10.</li> <li><strong>‘trial_response_values_initial_conditions_phase.csv’:</strong> Topographic relief at steady state along with the model time to initial steady state are the trial model responses included in the file. Values are listed for each initial landscape ID rather than trial because many trials had the same combinations of the factors that controlled the topography of the initial landscape. </li> <li><strong>‘trial_response_values_perturb_phase_base_level_fall_scenario.csv’ and ‘trial_response_values_perturb_phase_fault_throw_scenario.csv’:</strong> Model responses of the perturb phase for base level fall and fault throw scenario along with the initial landscape ID, species count values, and the model time back to steady state.</li> <li><strong>The files beginning with `sobol`</strong>: the sensitivity analysis results output by the software, ‘SALib’ (Herman and Usher, 2017). ‘S1’, ‘S2’, and ‘ST’ in the file name indicates if the file contains data of the Sobol first, second, or total order effect, respectively.</li> </ul>
Model output used in the manuscript "The evolution of a non-autonomous chaotic system under non-periodic forcing: a climate change example"
<p>This *.zip file contains the model output from ensemble simulations for the Lorenz 84-Stommel 61 model (<a href="https://doi.org/10.1034/j.1600-0870.2001.00241.x" target="_blank" rel="noopener">Van Veen et al, 2001</a>; <a href="https://dx.doi.org/10.1088/1748-9326/8/3/034021" target="_blank" rel="noopener">Daron and Stainforth, 2013</a>). To run these simulations, we used the Low-EFFourth ensemble generator (<a href="https://doi.org/10.48550/arXiv.2506.03313" target="_blank" rel="noopener">de Melo Viríssimo, 2025a</a>; <a href="https://doi.org/10.5281/zenodo.15566109" target="_blank" rel="noopener">de Melo Viríssimo, 2025b</a>), which is a MATLAB-based framework that allows for large ensembles of low-dimensional dynamical systems to be run and studied in a systematic way (<a href="https://doi.org/10.5194/egusphere-egu23-14755" target="_blank" rel="noopener">de Melo Viríssimo and Stainforth, 2023</a>).</p> <p>These model outputs are presented and discussed in the article "<em>The evolution of a non-autonomouys chaotic system under non-periodic forcing: a climate change example</em>", published by Chaos (<a href="https://doi.org/10.1063/5.0180870" target="_blank" rel="noopener">de Melo Viríssimo et al., 2024</a>). The manuscript describes the experiments performed, the parameter values used and the modifications done to the original L84-S61 model. For this matter, we also refer you to <a href="https://dx.doi.org/10.1088/1748-9326/8/3/034021" target="_blank" rel="noopener">Daron and Stainforth (2013)</a>.</p> <p>All files uploaded were generated from simulations run by the authors.</p> <p>For specific information about each file uploaded, please refer to the README file. If you have any questions, please feel free to contact me.</p> <p><strong>Note:</strong> This version (v1.1) is the same version as v1.0 but with the correct README file.</p>
Supplementary data - Modelling the role of dynamic topography and eustasy in the evolution of the Great Artesian Basin
<p>This data repository contains the supplementary data for the paper:</p> <p><strong>Modelling the role of dynamic topography and eustasy in the evolution of the Great Artesian Basin.</strong></p> <p>Carmen Braz<sup>1</sup>, Sabin Zahirovic<sup>1</sup>, Tristan Salles<sup>1</sup>, Nicolas Flament<sup>2</sup>, Lauren Harrington<sup>1</sup>, R. Dietmar Müller<sup>1</sup></p> <p><sup>1</sup> EarthByte Group, School of Geosciences, The University of Sydney, Sydney, Australia</p> <p><sup>2</sup> GeoQuEST Research Centre, School of Earth and Environmental Sciences, University of Wollongong, Wollongong, NSW, Australia</p> <p><em>Basin Research, https://doi.org/10.1111/bre.12606</em></p> <p>Included in this supplement are:</p> <ul> <li> All input files required for running Badlands models M1-M4</li> <li> Badlands digital output for preferred model M4</li> <li> Animations of topography and erosion-deposition through time for all four models presented in the paper </li> <li> Sediment layers for all time steps for preferred model M4 provided as netcdf grids.</li> </ul>
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>
Stellar Evolution Models from "Finding the Fuse: Prospects for the Detection and Characterization of Hydrogen-Rich Core-Collapse 5 Supernova Precursor Emission with the LSST"
<p>These data consist of all runs from the Modules for Experiments in Stellar Astrophysics (MESA; Paxton et al. 2011, 2013, 2015, 2018, 2019) code, used to construct radius priors for modeling supernova precursor emission in<em> <a href="https://arxiv.org/abs/2408.13314">Finding the Fuse: Prospects for the Detection and Characterization of Hydrogen-Rich Core-Collapse 5 Supernova Precursor Emission with the LSST</a></em> (Gagliano+2024, submitted). </p> <p>The contents of the data files are detailed in the file <strong>ReadmeMESA.txt</strong>. Additional detail concerning the simulations can be found in Section 2.2 of the linked publication. </p>
Model outputs for the article "Modelling the evolution of Arctic multiyear sea ice over 2000–2018"
<p><em>icemod_monthly.tar.gz </em>contains the gridded monthly averaged quantities used in the manuscript "Modelling the evolution of Arctic multiyear sea ice over 2000-2018" for each year between 2000 and 2018.</p> <p>Multiyear ice variables are conc_myi (concentration of multiyear ice in a grid cell) and thick_myi (cell average thickness of multiyear ice in a grid cell, in metres), along with source and sink terms (units per day) for multiyear concentration (dci_mlt_myi, dci_ridge_myi and dci_rplnt_myi, for melt, ridging and replenishment) and volume (dvi_mlt_myi and dvi_rplnt_myi, for melt and replenishment).</p> <p><em>transports_monthly_sections.zip </em>contains the transports of multiyear ice through the sections defining each region in Figure 8 of the paper. MYIsiaXport indicates multiyear ice area transport, while myiXport indicates multiyear ice volume transport.</p> <p>In case information is missing, do not hesitate to contact heather.regan@nersc.no, guillaume.boutin@nersc.no, or einar.olason@nersc.no.</p>
Evolution of model and geological inconsistencies during inversion
<p>Supplementary material to: </p> <p>Giraud, J., Caumon, G., Grose, L., Ogarko, V., and Cupillard, P.: Integration of automatic implicit geological modelling in deterministic geophysical inversion, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-129, 2023</p> <p>The GIF shows a 3D view of the inverted model and its geological inconsistencies during inversion when geological correction is applied at each iteration. </p>
A Linked Application of Discrete Differential Evolution Algorithm Coupled with Simulation- Optimization Model and Comparative Analysis by Genetic Algorithm for Discrete Groundwater Management Problems
<p>Complete dataset of publication name as "The complete publication dataset is "A Discrete Differential Evolution- Linear Programming Algorithm for Groundwater Management Problems." You can find all the written codes in the zip file.</p>
DNA loss model explains the evolution of the neuropeptide LWamide, APGWamide, APGW/AKH, RPCH, AKH, ACP, CRZ, and GnRH families
<p><strong>R1: Establishment and purification of neuropeptide sequences</strong></p> <p>The LW, APGW, RPCH, AKH, CRZ, and GnRH neuropeptide families were searched in the GenBank database using 10 keywords: the neuropeptide name, the precursor abbreviation, the full name of the precursor, the full name of the precursor with the word “prepropeptide,” and the combinations of these terms. The candidate sequences were downloaded in FASTA format using the appropriate commands in the GenBank database. The AKH neuropeptide family was classified according to the groups published in the literature, as well as the amino acid number and sequence. Furthermore, the ACP hybrid family was identified in the GenBank database using BLAST alignments.</p> <p><strong>C00: Neuropeptide Precursor. </strong>Eight folders were named with the initials of each neuropeptide family. The AKH family folder was the only one containing four subfolders. All of the folders contained the same type of files: three text files named after the neuropeptide initials and the obtained result. The files identified with the words “<em>with codes</em>” contained the sequences with the codes generated for this study, whereas the documents with the word “<em>Full</em>” contained the GenBank database search results obtained with the 10 aforementioned keywords. These files were located in a folder named “<em>Fasta Keywords.</em>” Each file contained the results from each respective keyword. The files with the words “<em>selected EA</em>” contained the sequences that were selected for evolutionary analyses.</p> <p><strong>C01: BLAST ACP</strong>. The text file named “00 BLAST ACP” contains the BLAST alignment results obtained from the NCBI database generated with the Adipokinetic Hormone/Corazonin-related peptide from the transcriptome of <em>Callinectes toxotes</em>. The file named “01 ACP Selected” contains the precursors selected for this study. All sequences were in FASTA format and contained the codes summarized in Supplementary Material 3 “<em>Database Sequences.</em>”</p> <p>The file named “<em>02 ACP selected EA</em>” contains the ACP precursors of other species, which were used for the evolutionary analyses of <em>C. toxotes</em> ACP. The PDF file titled “<em>03 ACP ProP 1.0 Serv</em>” contains the results of the proteolytic cleavage sites of the precursors indicated in the file named “<em>02 ACP selected EA,</em>” which were generated using the aforementioned software.</p> <p><strong>C02: BLAST VP.</strong> The folder contains the results of the BLAST alignment against the NCBI database, which were generated with the virtual peptide sequences reported by Martinez-Perez et al. (2007). This folder contains seven text files. The name of each file corresponds to the precursor and species in which it was identified. Moreover, the PDF document named “<em>Virtual peptides ProP 1.0 Serv</em>” contains the results of the proteolytic cleavage sites generated with the aforementioned software.</p> <p><strong>C03: Debugging sequences with software.</strong> This folder contains three subfolders containing the results obtained with each software used in this study for the detection of each of the neuropeptide sequences using the appropriate keywords.</p> <p>The folder named “<em>BioDataToolKit</em>” contains six subfolders with the abbreviated name of each neuropeptide. Additionally, there is a file containing the sequences downloaded from the GenBank database, as well as a Microsoft Excel file containing the details generated by the software. The name of each file corresponds to the keywords used for each search. The software used in this study can be found in the following repository: <a href="https://github.com/rduarte24/BiodataToolkit">https://github.com/rduarte24/BiodataToolkit</a>.</p> <p>The folder named “<em>Pro1.0Server</em>” was organized in the same way as the results derived for the “<em>BioDataToolKit</em>” for each neuropeptide family. However, each of the neuropeptide folders contained a file with the pertinent sequences whereas another file contained the endoproteolytic cleavage sites of the neuropeptide precursors obtained with the software.</p> <p>The folder named “Proteios” contains seven files. The file names indicate the precursor analyzed with the software and the identified sequences in FASTA format. The Proteios software is available in the following website: <a href="https://github.com/Martin-Munive/Proteios">https://github.com/Martin-Munive/Proteios</a>.</p> <p><strong>C04: Neuropeptide precursors for evolutionary analysis.</strong> Files with the sequences of the neuropeptide precursors used for the generation of the phylogenetic trees in Supplementary Materials 4 and 7. The name of each file corresponds to the name of each of the analyzed neuropeptides.</p> <p><strong>R2: Transcriptome BLAST</strong></p> <p>Microsoft Excel file containing the BLAST alignments conducted using the sequences of the AKH/CRZ-related peptide (ACP) from <em>C. toxotes</em> and Corazonin (CRZ) from <em>C. arcuatus</em>. The following information is summarized in the spreadsheets named <em>C. toxotes</em> and <em>C. arcuatus</em>: Column A, neuropeptide name; Column B, species name; Columns C–G, BLAST alignment results; Column H, GenBank protein accession number; Column I, precursor sequence.</p> <p><strong>R3: </strong><strong>Construction of neuropeptide database</strong></p> <p>Microsoft Excel file with information pertaining to the database and a detailed description of each of the neuropeptide precursors analyzed in this study. The Excel file contains seven spreadsheet tabs. Each of the tabs contains the following columns:</p> <p><strong>Neuropeptides.</strong> Column A, sequence numbering in descending order; Column B, neuropeptide name; Column C, identification code used in this study; Column D, accession number; Columns E–G, species taxonomy; Columns H–L, GenBank sequence description; Columns M–N, literature reference and link. <strong>Taxonomy.</strong> Taxonomic description of each of the examined species derived from the NCBI database. <strong>Sequences evolutionary anal</strong>. This tab contains the code developed for this work in Column C; the GenBank accession codes of each neuropeptide are summarized in Column D and species taxonomy details are summarized in Columns E y F. <strong>Table of differences.</strong> Column B shows the codes of identical sequences and Column C shows the code of the sequence selected for this study. <strong>Codes deleted. </strong>This tab contains the accession codes of the species and the species name but contains no details on the properties of the neuropeptide precursors. <strong>Sequences Paper</strong>. Neuropeptide sequences reported in previous studies that were later reported in the GenBank database. The sequences marked with asterisks have not been previously reported in public databases. The codes used in this study to designate the sequences are also included. <strong>Keywords. </strong>Keywords used to conduct the GenBank database searches to obtain the members of each neuropeptide family.</p> <p><strong>R4: <em>In silico</em> validation, alignments, and phylogenetic relationships</strong></p> <p>Generated phylogenetic trees and results obtained from individual runs for each of the neuropeptide families with the DNA-LM and Kalign parameters using the IQ-TREE software.</p> <p>The folder named “<em>RUN</em>” contains the “<em>DNALM and kalign 2.0 default parameters</em>” subfolder. Both folders contain 11 subfolders with the names of each of the neuropeptide families, as well as the results obtained with the IQ-TREE software. The folder named “<em>Trees</em>” contains the folder “<em>DNALM and kalign 2.0 default parameters</em>” containing the phylogenetic trees for each of the neuropeptide families, which were created with the Itol software.</p> <p><strong>R5: BLAST alignment of the virtual peptide precursors</strong></p> <p>Results of the BLAST alignment of the virtual peptides described by Martinez-Perez et al. (2007) with respect to the sequences in the GenBank database. The files follow the same nomenclature as in the folder named “<em>Carpeta 02 BLAST VP</em><strong>”</strong> in Repository 1.</p> <p><strong>R6: Alignment of neuropeptide precursors</strong></p> <p>“<em>DNALM and Kalign 2.0 default parameter</em>” folders. Each of these folders contains the alignments of the examined neuropeptide precursors from each family and each folder is named after the corresponding neuropeptide. The remaining files contain the alignments in ascending order in the evolutionary scale and are appropriately named after the corresponding neuropeptide. The file named “<em>All Sequence FASTA</em>” contains the sequences used in our study in FASTA format.</p> <p><strong>R7: Phylogenetic clustering of the precursors </strong></p> <p> “<em>DNALM and Kalign 2.0 default parameter</em>” folders. Both folders contain the phylogenetic tree clustering results from Supplementary Material 6, which were obtained using the DNA-LM y Kalign parameters and the IQ-TREE software. All analyses were conducted using the GUANE-1 supercomputer (Universidad Industrial de Santander). The phylogenetic clustering results of all of the precursors are contained in the folders with the respective precursor name. The folder also contains Figure 6, which was included in our main manuscript.</p> <p>Additionally, a folder entitled "Orthofinder and Robinson-Foulds" is included, which corresponds to the analyses carried out for: the Robinson-Foulds metric and the Orthofinder software.</p>
The SPOTS Models: A Grid of Theoretical Stellar Evolution Tracks and Isochrones For Testing The Effects of Starspots on Structure and Colors
<p><strong>The SPOTS Models: A Grid of Theoretical Stellar Evolution Tracks and Isochrones For Testing The Effects of Starspots on Structure and Colors</strong></p> <p>This repository contains the Stellar Parameters of Tracks with Starspots (SPOTS) grid of theoretical stellar evolutionary tracks and isochrones, presented in Somers, Pinsonneault, and Cao (2020, in prep). Our models were calculated with the Yale Rotating Evolution Code (e.g. van Saders & Pinsonneault, 2013, ApJ 776, 67), including updated which incorporate a treatment of surface starspots (Somers & Pinsonneault, 2015, ApJ 807, 174S). Modelling details can be found in these references. The purpose of this evolutionary suite is to provide the community with state-of-the-art predictions for the influence of starspots and magnetic activity on the structure of stars.</p> <p>The grid includes both isochrones and tracks. They can be downloaded individually from this repository, or in bulk by downloading the .zip files.</p> <p><strong>Isochrones (.isoc):</strong></p> <p>Each isochrone file contains a series of isochrones (stellar properties for a range of masses at fixed age) for ages between 1 Myrs and 4 Gyrs. Each file contains these isochrones for a different surface starspot covering fraction, given by the name of the file -- f000.isoc = 0% covering fraction, f017.isoc = 17% covering fraction, etc. Each isochrone contains several columns with different information, including,</p> <ol> <li>Fundamental properties: mass, age, luminosity, radius, logg, Teff, convective overturn timescale (TauCZ), lithium abundance relative to initial (Li/Li0).</li> <li>Starspot properties: Covering fraction (Fspot), ratio of spot temperature to ambient temperature (Xspot), the temperatures of hot and cool regions (T_hot, T_cool).</li> <li>Two-temperature colors, including Johnson BV, Cousins RI, 2MASS JHK, WISE W1, and Gaia G, BP, RP.</li> </ol> <p>Colors that fell outside of the calibrated range are listed as -99.0.</p> <p><strong>Tracks (.track):</strong></p> <p>We also include individual tracks for every combination of Fspot and Mass considered in the paper. Each .track file lists the mass and starspot covering fraction in the filename -- i.e. m055_f034.track is the model of mass 0.55Msun and with a 34% surface covering fraction. In addition to all the properties included in the isochrones, the track files also include:</p> <ol> <li>The total moment of interia of the model (total_I) and the moment of interia of the surface convection zone (CZ_I)</li> <li>The central and surface hydrogen abundances (X_cen, X_surf) and the surface metallicity (Z/X_surf)</li> <li>The deuterium abundance relative to initial (H2/H2_0)</li> </ol>
Model, data, and analysis for Negative Niche Construction Favors the Evolution of Cooperation
<p>This repository contains the model, data, and analysis corresponding to <em>Negative Niche Construction Favors the Evolution of Cooperation</em> as submitted for review by Brian D. Connelly, Katherine J. Dickinson, Sarah P. Hammarlund, and Benjamin Kerr. Contents are released to the public domain under the Creative Commons CC0 License.</p>
Model, Data, and Analysis Scripts for The Evolution of Cooperation by the Hankshaw Effect
<p>Model, Data, and Analysis Scripts for The Evolution of Cooperation by the Hankshaw Effect as submitted</p>
Data from: In vivo functional phenotypes from a computational epistatic model of evolution
<p><span>Computational models of evolution are valuable for understanding the dynamics of sequence variation, to infer phylogenetic relationships or potential evolutionary pathways, and for biomedical and industrial applications. Despite these benefits, few have validated their propensities to generate outputs with <em>in vivo </em>functionality, which would enhance their value as accurate and interpretable evolutionary algorithms. Utilizing the Hamiltonian of the joint probability of sequences in the family as fitness metric, we sampled and experimentally tested for <em>in vivo</em> beta-lactamase activity in E. coli TEM-1 variants. These variants retain family-like functionality while being more active than their WT predecessor. We found that depending on the inference method used to generate the epistatic constraints, different parameters simulate diverse selection strengths. Under weaker selection, local Hamiltonian fluctuations reliably predict relative changes to variant fitness, recapitulating neutral evolution. In this dataset, we include input datasets, simulation trajectories as well as experimental data to support the publication: "In vivo functional phenotypes from a computationa epistatic model of evolution".</span></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>
Datasets related to the study "Spatial variability and future evolution of surface solar radiation over Northern France and Benelux: a regional climate model approach"
<p>This dataset contains the data of the manuscript "Spatial variability and future evolution of surface solar radiation over Northern France and Benelux: a regional climate model approach" under publication in Atmospheric Chemistry and Physics. <br>It includes CNRM-ALADIN64 simulations of surface solar radiation, cloud fraction, aerosol optical depth and water vapor content. <br>A directory is dedicated to HINDCAST simulations. It includes all datasets involved in the evaluation of CNRM-ALADIN64 simulations, as well as all datasets used for the analysis of the spatial variability of surface solar irradiance over the recent past. <br>Another directory is dedicated to future climate simulations. In this case, several sub directories can be found, representing either the simulations over the historical period (2005-2014, i.e. HIST directory), or simulations at mid (2045-2054, "mid" suffix) and long term (2091-2100, "end" suffix) horizons for SSP1-1.9 and SSP3-7.0. Each set of climate simulations is composed of three members (r1f, r2f, r3f), which were used collectively to increase the statistical significance of our analysis. </p>
Planetary Atmosphere-Interior Model Outputs from Time-Evolution Movie
<p>This array contains the outputs from the movie version of Fig. 2 in Krissansen-Totton et al. (2021, Nature Astronomy). The numpy array can be loaded as follows</p> <p>Outputs = np.load("Output_arrays.npy",allow_pickle=True)</p> <p>The array has format Outputs[i][k][j]</p> <p>where i = 0, ... 5 (total_time, pO2, pCO2, pH2O, Tsurf, and AU)</p> <p>k = 0 , ..., 1437 is the iteration number</p> <p>j = 0 , ... , 2000 is the time index</p> <p>(AU is just a scalar providing orbital separation of that iteration)</p>
The excess of cool supergiants from contemporary stellar evolution models defies the metallicity-independent Humphreys-Davidson limit
<p>Input files and simulation results for stellar evolution tracks computed for the paper "The excess of cool supergiants from contemporary stellar evolution models defies the metallicity-independent Humphreys-Davidson limit", as well as synthetic populations generated and catalogues of observed cool supergiants in the Magellanic Clouds used in the analysis. Version 10398 of MESA was used for the simulations. More details in the README.txt file and in the paper.</p>
Raw data for: A model for the formation and evolution of structure of initial loess deposits
<p>The dataset includes the monitoring results of volumetric water content and matric suction during wetting and drying processes of initial loess deposits. The data are used for Figure 3 in the manuscript “A model for the formation and evolution of structure of initial loess deposits” (submitted to Geophysical Research Letters).</p>
Model data repository of "Styles of Trench-parallel Mid-ocean Ridge Subduction Affect Cenozoic Geological Evolution in circum-Pacific Continental Margins"
<p>This dataset contains the data used in Wu et al. (2022): "Styles of Trench-parallel Mid-ocean Ridge Subduction Affect Cenozoic Geological Evolution in circum-Pacific Continental Margins".</p>
Chronogram or phylogram for ancestral state estimation? Model-fit statistics indicate the branch lengths underlying a binary character's evolution: R scripts and simulated trees
<p>All R scripts used in this study, and the set of simulated phylogenetic trees used in the study.</p> <p>1. Modern methods of ancestral state estimation (ASE) incorporate branch length information, and it has been demonstrated that ASEs are more accurate when conducted on the branch lengths most correlated with a character's evolution; however, a reliable method for choosing between alternate branch length sets for discrete characters has not yet been proposed.<br><br>2. In this study, we simulate paired chronograms and phylograms, and generate binary characters that evolve in correlation with one of these. We then investigate (1) the effect of alternate branch lengths on ASE error, and (2) whether phylogenetic signal statistics and/or model-fit statistic can be used to select the branch lengths most correlated with a binary character.<br><br>3. In agreement with previous studies, we find that ASEs are more accurate when conducted on the branch lengths most correlated with the character. Phylogenetic signal statistics show limited utility for selecting the correct branch lengths, but model-fit statistics are found to be more accurate, with the correct branch lengths generally returning greater model-fit (lower AICc and BIC values). Using this method to choose between alternate branch length sets is more accurate when tree and character properties are more favorable for model optimization, and when shape differences between alternate phylogenies are greater.<br><br>4. Our results indicate that researchers conducting ASEs on discrete characters should carefully consider which branch lengths are appropriate, and, in the absence of other evidence, we suggest estimating model-fit values over alternate branch length sets and evolutionary models and choosing the branch length/model combination that returns better model fit.</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.