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10,767 results for “Contribution”
Smithsonian Contributions Series: Smithsonian Contributions to Zoology
<p></p>https://repository.si.edu/handle/10088/796<p></p><p></p>https://repository.si.edu/handle/10088/5097/browse?type=dateissued
Additional files for Hegyi and Lexa, 2024. Y chromosome-coded HSATII repeats may contribute to higher incidence of cancer in men.
<p>Additional data for Hegyi and Lexa, 2024 (scripts, data, supplementary figures, tables and files).</p>
Data Set "Protein-Ligand Interaction Energies from Quantum-Chemical Fragmentation Methods: Upgrading the MFCC-Scheme with Many-Body Contributions"
<p>This data set accompanies the publication "Protein-Ligand Interaction Energies from Quantum-Chemical Fragmentation Methods: Upgrading the MFCC-Scheme with Many-Body Contributions" by Johannes Vornweg and Christoph R. Jacob (TU Braunschweig, Germany) </p> <p>It contains the following files:</p> <p><br>Directory 1_structures:</p> <p> PDB files of all structures used for the test calculations. <br> The PDB files correspond to the protonated structures obtained <br> as described in the main text.</p> <p><br>Directory 02_figure_scripts:</p> <p> Jupyter Notebook for generating all plots included in the manuscript, <br> including raw numerical data.</p> <p><br>Directory 03_input_scripts:</p> <p> - min_congrad.mdp: input file for partial optimization of protonated <br> protein--ligand complexes with Gromacs</p> <p> PyADF input scripts:</p> <p> - sp_single.pyadf: single-point calculations of separate protein and ligand<br> - sp_complex.pyadf: single-point calculation of protein-ligand complex<br> - mfccmbe3.pyadf: MFCC and MFCC-MBE(2) calculations of P-L interaction energy</p> <p> These scripts can be used with PyADF v1.5 (DOI: 10.5281/zenodo.13236550)</p>
CLONALITY CONTRIBUTES TO THE SPREAD OF AVRAINVILLEA LACERATA (BRYOPSIDALES, CHLOROPHYTA) IN HAWAIʻI
<p>The excel file includes all multilocus genotypes. The first sheet includes all raw multilocus genotypes prior to allele dosage estimation. The second sheet is in GenAPoPop format and includes all multilocus genotypes after allele dosage correction using allele peak height. </p> <p>Population acronyms:</p> <p>ewaA – ʻEwa Beach, Lagoon East</p> <p>mlb – Maunalua Bay</p> <p>ewaB – ʻEwa Beach, Kaloi</p> <p>The code for each individual sampled during 2018, 2019, and 2021 is Av_sample # (01-260).</p> <p>The code for each individual sampled during 2022 is Av_3-digit sample # (001-040).</p> <p>The code for each individual sampled during 2023 is Av mound #_H holdfast #_B blade #.</p> <p> </p> <p>This project was supported by Alabama Academy of Science student research award (to BMT), the Phycological Society of America Grant In Aid of Research (to BMT), the UAB Graduate Student Government (GSG) Professional Development and Travel Award (to BMT), the UAB Harold Martin Outstanding Student Development Award (to BMT), CLONIX-2D (ANR-18-CE32-0001 to SS and SAKH), start-up funds from the College of Arts and Sciences at UAB (to SAKH and MLH), the National Science Foundation (NSF) DEB-2113745 (to SAKH), and National Fish and Wildlife Foundation (#74235 to SAKH and HLS). SAKH was supported by the NSF CAREER award DEB-2141971 and the Norma Lang Early Career Fellowship from the Phycological Society of America.</p>
Partitioning species contributions to ecological stability - Data and Code
<p>This repository contains all R code and data used for the manuscript entitled "Partitioning species contributions to ecological stability in disturbed communities".</p> <p>Authors: Charlotte Kunze, Dominik Bahlburg, Pablo Urrutia-Cordero, Maren Striebel, Egle Kelpsiene, Silke Langenheder, Ian Donohue & Helmut Hillebrand</p>
Contribution of surface and cloud radiative feedbacks to Greenland Ice Sheet meltwater production during 2002-2023
<p>These datasets accompany the paper titled "Contribution of surface and cloud radiative feedbacks to Greenland Ice Sheet meltwater production during 2002-2023". </p>
Earth BioGenome Project: Authors and author contributions for publication "The Earth BioGenome Project Phase II: Illuminating the Eukaryotic Tree of Life"
<p>The zipped file cntains three tab-separated datasets (worksheets). These worksheets list</p> <p>The contributing authors for the manuscript "The Earth BioGenome Project Phase II: Illuminating the Eukaryotic Tree of Life" and the roles of these authors in the manuscript.</p> <p>The funding sources for these authors</p> <p>A list of the authors contributing to the "EBP Community of Scientists" collective authorship for the same manuscript.</p>
Whole-genome sequencing reveals contribution of rare and common variation to structural kidney and urinary tract malformations
<p>Supplementary tables detailing analysis of whole-genome sequencing data from 992 patients with congenital anomalies of the kidneys and urinary tract (CAKUT). </p>
Contributions of mirror-image hair cell orientation to mouse otolith organ and zebrafish neuromast function
<p>Dataset for Ono et al., 2024.<br>Contains all source data from the Tarchini laboratory, including illustrative images and raw data for quantification, as well as the data file containing statistical analysis. It also contains the data file produced by the Cullen laboratory reporting behavioral data. </p>
Figura. 1 in Contribution to the knowledge of Copeocoris truncaticornis (Stål) (Heteroptera: Pentatomidae)
Figura. 1. Copeocoris truncaticornis (Stål). 1. Habitus, specimen from Entre Ríos. Scale: 1 mm.
Data - The contribution of boreal wetlands to the Northern hemisphere carbonyl sulfide sink
<p>COS fluxes in pmolm-2s-1, LAI data and model output data used in the article "The contribution of boreal wetlands to the Northern hemisphere carbonyl sulfide sink". </p>
MD data for Ionizable cationic lipids and helper lipids synergistically contribute to RNA packing and protection in lipid-based nanomaterials
<p>The data stored in this repository is part of the journal article: Zimmer, D. N., Schmid, F., & Settanni, G. (2024). Ionizable Cationic Lipids and Helper Lipids Synergistically Contribute to RNA Packing and Protection in Lipid-Based Nanomaterials. <em>The Journal of Physical Chemistry B</em> <a href="https://doi.org/10.1021/acs.jpcb.4c05057" target="_blank" rel="noopener">https://doi.org/10.1021/acs.jpcb.4c05057</a></p> <p> </p> <p>Data of multiscale simulations of DLinDMA:DOPE:Cholesterol, DLinDMA:DSPC:Cholesterol, DLinDAP:DOPE:Cholesterol and DLinDAP:DSPC:Cholesterol in the presence of RNA. For each formulation, data is provided with different coarse-grained parameterizations (generic, adapted) and differents treatments of the RNA (ELN, noELN). Provided are the first and the final frame of each run, the associated topologies, and the respective gromacs input files.</p> <p><strong>> M_PE, M_PC, P_PE, P_PC</strong></p> <p>DLinDMA:DOPE:Cholesterol, DLinDMA:DSPC:Cholesterol, DLinDAP:DOPE:Cholesterol and DLinDAP:DSPC:Cholesterol in presence of a 40mer RNA fragment. </p> <ul> <li>cg_<strong>generic</strong>+aa: <ul> <li>cg: 2 microsecond production run based on a generic MARTINI parametrization <ul> <li>md_0.gro: first frame</li> <li>md_10.gro: final frame </li> <li>cg_rna_bilayer.top: Topology of the system</li> <li>cg_DLD{M/P}_lipid.itp: generic MARTINI topology of DLinDMA/DLinDAP</li> <li>martini_v2.0_CHOL_02.itp, martini_v2.0_DSPC_01.itp, martini_v2.0_ions, martini_v2.1.itp, martini_v2.1-dna.itp: Several MARTINI topology files for molecules not included in MARTINI</li> <li>Nucleic_A.itp or Nucleic_A_eln.itp: Topology of the RNA fragment for MARTINI</li> </ul> </li> <li>aa: 300/600 nanosecond production run based on CHARMM36 <ul> <li>md_0.gro: first frame</li> <li>md_60.gro: final frame </li> <li>backmapped.top: Topology of the system (including the parametrization of DLinDMA/DLinDAP)</li> <li>CHOL.itp, DOPE.itp, DSPC.itp, 40mer_autopsf.itp: topology files for Cholesterol, DOPE, DSPC and RNA fragment as they are not part of the standard molecules in CHARMM36.</li> </ul> </li> <li>ELN and noELN indicate presence or absence of an elastic network to fix the structure of the RNA during the cg runs. </li> <li>cgmdp: Gromacs input files for the cg runs</li> <li>aamdp: Gromacs input files for the aa runs</li> </ul> </li> <li>cg_<strong>adapted</strong>+aa: <ul> <li>cg: starting and ending frame of a 2 microsecond production run based on an adapted MARTINI parametrization <ul> <li>md_0.gro: first frame</li> <li>md_10.gro: final frame </li> <li>cg_rna_bilayer.top: Topology of the system</li> <li>martini_v2.0_DIDMA_20 or martini_v2.0_DIDAP_20: generic MARTINI topology of DLinDMA/DLinDAP</li> <li>martini_v2.0_CHOL_02.itp, martini_v2.0_DSPC_01.itp, martini_v2.0_ions, martini_v2.1-dna_cr1_POL_NACL.itp: Several MARTINI topology files for molecules not included in MARTINI</li> <li>Nucleic_A.itp or Nucleic_A_eln.itp: Topology of the RNA fragment for MARTINI</li> </ul> </li> <li>aa: 300/600 nanosecond production run based on CHARMM36 <ul> <li>md_0.gro: first frame</li> <li>md_60.gro: final frame </li> <li>backmapped.top: Topology of the system (including the parametrization of DLinDMA/DLinDAP)</li> <li>CHOL.itp, DOPE.itp, DSPC.itp, 40mer_autopsf.itp: topology files for Cholesterol, DOPE, DSPC and RNA fragment as they are not part of the standard molecules in CHARMM36.</li> </ul> </li> <li>ELN and noELN indicate presence or absence of an elastic network to fix the structure of the RNA during the cg runs. </li> <li>cgmdp: Gromacs input files for the cg runs</li> <li>aamdp: Gromacs input files for the aa runs</li> </ul> </li> </ul>
Processed data supporting the manuscript "Cutting the sap: first molecular phylogeny of twig-girdler longhorn beetles (Coleoptera: Cerambycidae: Lamiinae: Onciderini) suggests shifts in host plant attack behaviors contributed to morphological evolution"
<div><strong>Processed data supporting the manuscript: </strong>Cutting the sap: first molecular phylogeny of twig-girdler longhorn beetles (Coleoptera: Cerambycidae: Lamiinae: Onciderini) suggests shifts in host plant attack behaviors contributed to morphological evolution</div> <div> </div> <div><strong>By:</strong> Diego de S. Souza 1, 2, Rowan L. K. French 3, José O. Silva Júnior 4, Eugenio H. Nearns 5, Luciane Marinoni 4, Ian P. Swift 6, Kelly B. Miller 7, Felix A. H. Sperling 2 & Marcela L. Monné 1</div> <div> </div> <div>1 Department of Entomology, National Museum, Federal University of Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil.</div> <div>2 Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada.</div> <div>3 Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada.</div> <div>4 Department of Zoology, Federal University of Paraná, Curitiba, Paraná, Brazil.</div> <div>5 National Museum of Natural History, Smithsonian Institution, Washington, DC, USA.</div> <div>6 California State Collection of Arthropods, Sacramento, California, USA.</div> <div>7 Department of Biology and Museum of Southwestern Biology, University of New Mexico, Albuquerque, New Mexico, USA.</div> <div> </div> <div>Corresponding author: Diego de S. Souza, dsouza@fieldmuseum.org. Current affiliation: Field Museum of Natural History, Chicago, Illinois, USA.</div> <div> </div> <div> </div> <div><strong>List of Contents: </strong></div> <div> </div> <div><strong>Onciderini_concat_matrix.phy</strong></div> <div>Concatenated matrix (cox1, Wg and CPS) used for the phylogenetic analyses of Onciderini (Coleoptera: Cerambycidae: Lamiinae: Onciderini). </div> <div> </div> <div><strong>PartitionFinder_AICc_best_scheme.txt</strong></div> <div>Results from PartitionFinder v2.1.1, containing the best partitioning scheme for the concatenated matrix of Onciderini, identified using the corrected Akaike Information Criterion (AICc), with model definitions for use in the phylogenetic analyses.</div> <div> </div> <div><strong>RAxML_Onciderini_concat_matrix (zip file)</strong></div> <div>- Onciderini_concat_matrix.phy: concatenated matrix (cox1, Wg and CPS) used in the RAxML phylogenetic analyses of Onciderini (Coleoptera: Cerambycidae: Lamiinae: Onciderini).</div> <div>- Partitions_AICc_RAxML.txt: partitioning scheme used in the RAxML analysis as predefined by PartitionFinder v2.1.1 using the corrected Akaike Information Criterion (AICc).</div> <div>- RAxML_bestTree.Onciderini_concat_matrix_ML: best-scoring maximum likelihood tree inferred by RAxML for the concatenated matrix of Onciderini.</div> <div>- RAxML_bipartitions.Onciderini_concat_matrix_final: bipartitions (clades) of the maximum likelihood tree inferred by RAxML with support values estimated from 1,000 pseudoreplicates.</div> <div>- RAxML_bipartitionsBranchLabels.Onciderini_concat_matrix_final: final maximum likelihood tree inferred by RAxML for the concatenated matrix of Onciderini, with labeled branches showing bootstrap support values.</div> <div>- RAxML_bootstrap.Onciderini_concat_matrix_bootstrap: bootstrap trees generated from a non-parametric bootstrap analysis in RAxML based on 1,000 pseudoreplicates.</div> <div>- RAxML_info.Onciderini_concat_matrix_bootstrap: log file containing details of the bootstrap analysis, including the settings and parameters used in the non-parametric bootstrap runs in RAxML.</div> <div>- RAxML_info.Onciderini_concat_matrix_final: log file summarizing the RAxML analysis, including settings and convergence statistics for the final maximum likelihood tree.</div> <div>- RAxML_info.Onciderini_concat_matrix_ML: log file containing details of the maximum likelihood tree search, including the parameters and models applied during the maximum likelihood analysis conducted by RAxML.</div> <div>- RAxML_log.Onciderini_concat_matrix_ML: log file of the maximum likelihood tree search for the concatenated matrix of Onciderini.</div> <div>- RAxML_parsimonyTree.Onciderini_concat_matrix_ML: parsimony starting tree used by RAxML during the maximum likelihood analysis for the concatenated matrix of Onciderini.</div> <div>- RAxML_result.Onciderini_concat_matrix_ML: maximum likelihood tree inferred by RAxML from the concatenated matrix of Onciderini, summarizing the tree topology and likelihood score for the best tree obtained.</div> <div> </div> <div><strong>BI_AICc_Onciderini_concat_matrix (zip file)</strong></div> <div>- BI_AICc_Onciderini_concat_matrix.nex: nexus file containing the concatenated matrix of Onciderini used for Bayesian Inference (BI), including the best-fit model scheme identified by PartitionFinder and MCMC parameters for running the analysis in MrBayes.</div> <div>- BI_AICc_Onciderini_concat_matrix.nex_r1_r2_combined_consensus.tree: consensus tree from two combined independent Bayesian Inference (BI) runs based on the concatenated matrix of Onciderini, after discarding the first 25% of initial generations as burn-in.</div> <div>- BI_AICc_Onciderini_concat_matrix.nex.run1.p: log file containing parameter values and likelihood scores from the first run of the Bayesian Inference (BI) based on the concatenated matrix of Onciderini.</div> <div>- BI_AICc_Onciderini_concat_matrix.nex.run2.p: log file containing parameter values and likelihood scores from the second run of the Bayesian Inference (BI) based on the concatenated matrix of Onciderini.</div> <div> </div> <div><strong>BEAST2_Onciderini_BD_lognormal (zip file)</strong></div> <div>- BEAUTi_Onciderini_BD_lognormal.xml: XML file generated by BEAUTi for running BEAST2, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a lognormal distribution.</div> <div>- BEAST2_Onciderini_BD_lognormal_run[1-8].log: log files from eight independent runs of BEAST2, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a lognormal distribution.</div> <div>- TreeAnnotator_Onciderini_BD_lognormal_run1-run8_consensus.out: TreeAnnotator output file combining the results of eight BEAST2 runs based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a lognormal distribution.</div> <div>- TreeAnnotator_Onciderini_BD_lognormal_run1-run8_consensus.tre: consensus tree from eight combined BEAST2 runs, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a lognormal distribution, after discarding the first 10% of initial generations as burn-in.</div> <div> </div> <div><strong>BEAST2_Onciderini_BD_exponential (zip file)</strong></div> <div>- BEAUTi_Onciderini_BD_exponential.xml: XML file generated by BEAUTi for running BEAST2, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and an exponential distribution.</div> <div>- BEAST2_Onciderini_BD_exponential_[1-8].log: log files from eight independent runs of BEAST2, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and an exponential distribution.</div> <div>- TreeAnnotator_Onciderini_BD_exponential_run1-run8_consensus.out: TreeAnnotator output file combining the results of eight BEAST2 runs based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and an exponential distribution.</div> <div>- TreeAnnotator_Onciderini_BD_exponential_run1-run8_consensus.tre: consensus tree from eight combined BEAST2 runs, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and an exponential distribution, after discarding the first 10% of initial generations as burn-in.</div> <div> </div> <div><strong>BEAST2_Onciderini_BD_uniform (zip file)</strong></div> <div>- BEAUTi_Onciderini_BD_uniform.xml: XML file generated by BEAUTi for running BEAST2, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a uniform distribution.</div> <div>- BEAST2_Onciderini_BD_uniform_[1-8].log: log files from eight independent runs of BEAST2, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a uniform distribution.</div> <div>- TreeAnnotator_Onciderini_BD_uniform_run1-run8_consensus.out: TreeAnnotator output file combining the results of eight BEAST2 runs based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a uniform distribution.</div> <div>- TreeAnnotator_Onciderini_BD_uniform_run1-run8_consensus.tre: consensus tree from eight combined BEAST2 runs, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a uniform distribution, after discarding the first 10% of initial generations as burn-in.</div> <div> </div> <div><strong>Comparative_analyses (zip file)</strong></div> <div><strong>RawData (folder):</strong> raw morphometric and girdling data, plus tree that was later pruned for downstream comparative analyses; these data were used as input for the OncidHeadDimorphism-DatasetPREP-FINAL.R data cleaning script. </div> <div>- Onciderini_BD_lognormal_run1-run8_consensus.nwk: newick version of TreeAnnotator_Onciderini_BD_lognormal_run1-run8_consensus.tre (outputted as a newick file by importing the .tre file into FigTree and exporting in newick format).</div> <div>- Measurements_Onciderini_Raw_Final.csv: individual-level raw morphometric data for Onciderini.</div> <div>- Behav_Matrix_2_states_trimmed_2022-12-15.csv: species-level data on girdling status for Onciderini species, with all Lochmaeocles species classified as girdlers (2 behavioral states across Onciderini species). </div> <div>- Matrix_3_states_trimmed_final.csv: species-level data on girdling status for Onciderini species, with all Lochmaeocles species classified as facultative girdlers (3 behavioral states across Onciderini species). </div> <div>- Matrix_2_states_trimmed_1LochGirdler_Final.csv: species-level data on girdling status for Onciderini species, with only one Lochmaeocles species (L. tessellatus) classified as a girdler (2 behavioral states across Onciderini species). </div> <div> </div> <div><strong>ProcessedData (folder): </strong>filtered data and pruned trees outputted by the OncidHeadDimorphism-DatasetPREP-FINAL.R script</div> <div>- oncid_f_36spp_clean.csv: dataset of species means and log-ratios for morphometric traits in females, plus girdling data; only includes species that have girdling data and are in the phylogenetic tree</div> <div>- oncid_m_42spp_clean.csv: dataset of species means and log-ratios for morphometric traits in males, plus girdling data; only includes species that have girdling data and are in the phylogenetic tree</div> <div>- oncid_sd_35spp_clean.csv: dataset of species means for sexual dimorphism in morphometric traits, plus girdling data; only includes species that have girdling data and are in the phylogenetic tree</div> <div>- oncid_girdlingbehav_allingroupspp_clean.csv: full dataset of girdling behavior for 56 Onciderini species that are in the phylogenetic tree; includes separate columns for the three alternative girdling classification schemes</div> <div>- oncid_tree_behavfull_56spp.nwk: pruned phylogenetic tree for the full girdling dataset (56 species)</div> <div>- oncid_tree_f_36spp.nwk: pruned phylogenetic tree for the female morphometric dataset (36 species)</div> <div>- oncid_tree_m_42spp.nwk: pruned phylogenetic tree for the male morphometric dataset (42 species)</div> <div>- oncid_tree_mf_43spp.nwk: pruned phylogenetic tree for all species with morphometric data for males or females; used for the stochastic character map next to the heatmap plot (Fig 4)</div> <div>- oncid_tree_sd_35spp.nwk: pruned phylogenetic tree for the sexual dimorphism dataset (35 species)</div> <div> </div> <div><strong>FittedModels (folder): </strong>fitted models (mvgls, model comparison analyses, OUM models, simmaps) outputted by the OncidHeadDimorphism-Analysis-FINAL.R script </div> <div>- MacroModelFits_logRtraits_f_36spp-2024-10-12.Rdata: summary of model comparison results for Brownian Motion (BM), single-peak Ornstein-Uhlenbeck (OU), multipeak OU (OUM), and multi-rate Brownian motion (BMM) models (univariate and multivariate) fitted to female morphometric data across 100 stochastic character maps of girdling behavior</div> <div>- MacroModelFits_logRtraits_m_42spp-2024-10-12.Rdata: summary of model comparison results for Brownian Motion (BM), single-peak Ornstein-Uhlenbeck (OU), multipeak OU (OUM), and multi-rate Brownian motion (BMM) models (univariate and multivariate) fitted to male morphometric data across 100 stochastic character maps of girdling behavior</div> <div>- MacroModelFits-SDDI-35spp_2024-10-11.Rdata: summary of model comparison results for Brownian Motion (BM), single-peak Ornstein-Uhlenbeck (OU), multipeak OU (OUM), and multi-rate Brownian motion (BMM) models (univariate and multivariate) fitted to sexual dimorphism data across 100 stochastic character maps of girdling behavior</div> <div>- mvgls-results-headsize-mf-2024-10-12.Rdata: fitted mvgls regression models for male and female traits (analyzed separately)</div> <div>- mvgls-results-sddi-2024-10-12.Rdata: fitted mvgls regression models for sexual dimorphism</div> <div>- OUM_headtraits_f_36spp-2024-10-12.Rdata: fitted OUM models and summary statistics for female head traits</div> <div>- OUM_headtraits_m_42spp-2024-10-12.Rdata: fitted OUM models and summary statistics for male head traits</div> <div>- OUM-SDDI-35spp-2024-10-12.Rdata: fitted OUM models and summary statistics for sexual dimorphism in head traits</div> <div>- simmaps_ard_full_2state.RDS: stochastic character maps of girdling behaviour for all 56 species with girdling data, with two behavioral states (girdling or non-girdling) - all Lochmaeocles species are classified as girdlers</div> <div>- simmaps_ard_full_3state.RDS: stochastic character maps of girdling behaviour for all 56 species with girdling data, with three behavioral states (girdling, non-girdling, or facultative girdling)</div> <div>- simmaps_ard_full_1Loch.RDS: stochastic character maps of girdling behaviour for all 56 species with girdling data, with two behavioral states (girdling or non-girdling) - only one Lochmaeocles species (L. tessellatus) is classified as a girdler</div> <div>- simmaps_ard_m.RDS: stochastic character map for the 42 species used in the analyses of male morphometric traits</div> <div>- simmaps_ard_f.RDS: stochastic character map for the 36 species used in the analyses of female morphometric traits</div> <div>- simmaps_ard.RDS: stochastic character map for the 35 species used in the sexual dimorphism analyses; 2 behavioral states.</div> <div> </div> <div><strong>Rscripts (folder): </strong>R scripts used to process data and run phylogenetic comparative analyses of head size and girdling behavior.</div> <div>- OncidHeadDimorphism-DatasetPREP-FINAL.R: R script used to filter data and prune trees from the RawData folder for downstream phylogenetic comparative analyses; outputs of this script are in the ProcessedData folder.</div> <div>- OncidHeadDimorphism-Analysis-FINAL.R: R script used to analyze data in the ProcessedData folder to answer questions about the origin and evolution of girdling behavior and the relationship between girdling and head size or head size sexual dimorphism; fitted models outputted by this script are in the FittedModels folder</div> <div>- OncidHeadDimorphism-Plots-FINAL.R: R script used to generate plots for the manuscript<br><br></div>
Annex B – Occurrence data on brominated phenols and their derivatives in food submitted to EFSA, dietary surveys per country and age group available in the EFSA Comprehensive Database considered in the exposure assessment, and the detailed results of the chronic dietary exposure assessment to 2,4,6-TBP and the contribution of different food groups to the dietary exposure
<p>This Annex contains the occurrence data submitted to EFSA, the dietary surveys per country and age group, and the detailed results of the chronic dietary exposure assessment to 2,4,6-TBP and the contribution of different food groups to the dietary exposure related to the Update of the risk assessment of brominated phenols and their derivatives in food.</p>
Dataset for the paper: What Do Contribution Guidelines Say About Software Testing?
<p>Dataset for the paper: What Do Contribution Guidelines Say About Software Testing? This dataset contains the raw data for the guidelines of 200 Python and JavaScript open-source software projects.</p>
Data from: A combination of sexual and ecological divergence contributes to rearrangement spread during initial stages of speciation
Chromosomal rearrangements between sympatric species often contain multiple loci contributing to assortative mating, local adaptation, and hybrid sterility. When and how these associations arise during the process of speciation remains a subject of debate. Here, we address the relative roles of local adaptation and assortative mating on the dynamics of rearrangement evolution by studying how a rearrangement co-varies with sexual and ecological trait divergence within a species. Previously, a chromosomal rearrangement that suppresses recombination on the Z (sex) chromosome was identified in European corn borer moths (Ostrinia nubilalis). We further characterize this recombination suppressor and explore its association with variation in sex pheromone communication and seasonal ecological adaptation in pairs of populations that are divergent in one or both of these characteristics. Direct estimates of recombination suppression in pedigree mapping families indicated that more than 39% of the Z chromosome (encompassing up to ~10 megabases and ~ 300 genes) resides within a non-recombining unit, including pheromone olfactory receptor (OR) genes and a major quantitative trait locus (QTL) that contributes to ecotype differences (Pdd). Combining direct and indirect estimates of recombination suppression, we found that the rearrangement was occasionally present between sexually isolated strains (E versus Z) and between divergent ecotypes (univoltine versus bivoltine). However, it was only consistently present when populations differed in both sexual and ecological traits. Our results suggest that independent of the forces that drove the initial establishment of the rearrangement, a combination of sexual and ecological divergence is required for rearrangement spread during speciation.
Polygenic and major-locus contributions to sexual maturation timing in Atlantic salmon
<p>Sexual maturation timing is a life-history trait central to the balance between mortality and reproduction. Maturation may be triggered when an underlying compound trait, called liability, exceeds a threshold. In many different species and especially fishes, this liability is approximated by growth and body condition. However, environmental vs. genetic contributions either directly or via growth and body condition to maturation timing remain unclear. Uncertainty exists also because the maturation process can reverse this causality and itself affect growth and body condition. In addition, disentangling the contributions of polygenic and major loci can be important. In many fishes, males mature before females, enabling the study of associations between male maturation and maturation-unbiased female liability traits. Using 40 Atlantic salmon families, longitudinal common-garden experimentation, and quantitative genetic analyses, we disentangled environmental from polygenic and major locus (vgll3) effects on male maturation, and sex-specific growth and condition. We detected polygenic heritabilities for maturation, growth, and body condition, and vgll3 effects on maturation and body condition but not on growth. Longitudinal patterns for sex-specific phenotypic liability, and for genetic variances and correlations between sexes suggested that early growth and condition indeed positively affected maturation initiation. However, towards spawning time, causality appeared reversed for males whereby maturation affected growth negatively and condition positively via both the environmental and genetic effects. Altogether, the results indicate that growth and condition are useful traits to study liability for maturation initiation, but only until maturation alters their expression, and that vgll3 contributes to maturation initiation via condition.</p>
Dataset for: Using plant traits to understand the contribution of biodiversity effects to annual crop community productivity
<p>Dataset for: Using plant traits to understand the contribution of biodiversity effects to annual crop community productivity</p>
Contributions of wild and provisioned foods to the diets of domestic cats that depredate wild animals
<p>Predation of wildlife by domestic cats <i>Felis catus</i> presents a threat to biodiversity conservation in some ecological contexts. The proportions of wild prey captured and eaten by domestic cats and thus the contributions of wild prey to cat diets are hard to quantify. This limits understanding of any impacts of cats may have on wild animal populations and confounds analyses of the effects of interventions aimed at reducing wildlife killing.</p> <p>We used stable isotope analyses to quantify the relative contributions of wild and provisioned foods to the diets of domestic cats kept as companion animals and which frequently captured wild prey. We tested the effects of treatments aimed at reducing killing upon stable isotope ratios of cat whiskers and, where treatments had significant effects, we estimated variation in the contributions of wild prey to cats' diets before and during treatment. We evaluated bells, Birdsbesafe collar covers, provision of food in a 'puzzle feeder', provision of food in which meat was the principal source of protein, object play and a control group.</p> <p><span>As expected, cat diets consisted primarily of provisioned foods, though the contribution of wild animals to the diets of these cats, all of which regularly caught wild animals, was low (cat food ~96%, wild animals ~3</span>–<span>4%). Compared to the pre-treatment period and control group, cats with a Birdsbesafe collar cover, exhibited significant reduction in nitrogen stable isotope ratios in their whiskers and consumed less wild prey, most likely attributable to effective inhibition of hunting, particularly for birds. Fitting cats with a Birdsbesafe collar cover, therefore, reduced both returns of wild birds and consumption of wild prey.</span></p> <p><span>While multiple interventions can significantly affect the numbers of wild animals that cats capture and return home, the remarkably small dietary contributions made by wild animal prey mean dietary change is harder to discern. Domestic cats rely almost exclusively on food provided by people even when they frequently kill wild animals. This suggests that the hunting behavior of domestic cats may be driven by behavioral motivations, or by a need to address micronutrient requirements, but is unlikely to alter macronutrient intake.</span></p>
RNA-seq data and results from in-vitro experiments for "Genetic control of fetal placental genomics contributes to development of health and disease" (Bhattacharya et al 2021)
<p>This dataset includes data and results from RNA-seq data collected from in-vitro experiments presented in "Genetic control of fetal placental genomics contributes to development of health and disease" (Bhattacharya et al 2021). Please check the README.txt file for more details.</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.