Find research datasets worth reusing

opencc-by-4.0Apr 2020View details →

Virulence mismatches in index hosts shape the outcomes of cross-species transmission

Supplemental data and code for the paper Virulence mismatches in index hosts shape the outcomes of cross-species transmission. <ul> <li>Dataset 1 is an R Shiny app allowing the estimation of rabies disease progression parameters for all observed combinations of virus source (reservoir) and recipient species, including within-species inoculations.</li> <li>Dataset 2 contains the original data and the analysis code used in this study.</li> </ul> See the README files in each dataset folder for further information and usage instructions.

openother-openJan 2021View details →

Code and data for: Is habitat selection in the wild shaped by individual-level cognitive biases in orientation strategy?

This repository is a companion to the manuscript "Is habitat selection in the wild shaped by individual-level cognitive biases in orientation strategy?" and is linked to <a href="https://github.com/CBeardsworth/Pheasant_OrientStrat_Habitat">Github</a>. For any questions about the code please contact Christine at <a href="mailto:c.e.beardsworth@gmail.com">c.e.beardsworth@gmail.com</a> To use any data contained in this repository contact Joah at <a href="mailto:j.r.madden@exeter.ac.uk">j.r.madden@exeter.ac.uk</a> for permission. In this repository, we have included a run-through of the R analysis <a href="https://cbeardsworth.github.io/Pheasant_OrientStrat_Habitat/">here</a> to show the outputs of the analysis without the need to run the code. For those that might want to run the code themselves, we have included three R scripts (<a href="https://github.com/CBeardsworth/NavigationHabitat/blob/master/R">/R</a>) and their accompanying datasets (<a href="https://github.com/CBeardsworth/NavigationHabitat/blob/master/Data">/Data</a>). A description of the code and the data needed to run them is below: Cognition analysis and figs.R = Run the cognition analysis for the first section of the manuscript and create the figures. For this, the datasets mazeData.csv (the learning trials) and mazeRotationResults.csv (the probe trial) are required.  iSSA analysis and bootstrapping.R = Run iSSA models and bootstrapping. This produces the datasets required for the next stage of analysis. For this code, the datasets habitat.grd (habitat information), atlas2018-strategy.csv (atlas data + id and strategy data for each bird) and FeederCoords2017_27700.csv (coordinates of feeder locations from 2017-2018) are required. The produced datasets are included in <a href="https://github.com/CBeardsworth/NavigationHabitat/blob/master/Data">/Data</a> therefore to run subsequent analyses, this code does not need to be run. To develop this code we relied heavily on the code included in the supplementary material of <a href="https://doi.org/10.1002/ece3.4823">Signer et al. (2019)</a> as well as an <a href="https://bsmity13.github.io/log_rss">online tutorial</a> from Brian J. Smith for calculating log-RSS. Habitat analysis and Figs.R = Run the statistical models for the final section of the manuscript and create the figures. For this code, the datasets produced in the previous R script are required (habitatOrientation_coefs.csv and habitatOrientation_avail.csv). We have included <a href="https://github.com/CBeardsworth/NavigationHabitat/blob/master/Data">these datasets</a> so users do not need to run the iSSA analysis and bootstrapping.R script themselves.

Data from: Gene flow, ancient polymorphism, and ecological adaptation shape the genomic landscape of divergence among Darwin's finches

Genomic comparisons of closely related species have identified "islands" of locally elevated sequence divergence. Genomic islands may contain functional variants involved in local adaptation or reproductive isolation and may therefore play an important role in the speciation process. However, genomic islands can also arise through evolutionary processes unrelated to speciation, and examination of their properties can illuminate how new species evolve. Here, we performed scans for regions of high relative divergence (FST) in 12 species pairs of Darwin's finches at different genetic distances. In each pair, we identify genomic islands that are, on average, elevated in both relative divergence (FST) and absolute divergence (dXY). This signal indicates that haplotypes within these genomic regions became isolated from each other earlier than the rest of the genome. Interestingly, similar numbers of genomic islands of elevated dXY are observed in sympatric and allopatric species pairs, suggesting that recent gene flow is not a major factor in their formation. We find that two of the most pronounced genomic islands contain the ALX1 and HMGA2 loci, which are associated with variation in beak shape and size, respectively, suggesting that they are involved in ecological adaptation. A subset of genomic island regions, including these loci, appears to represent anciently diverged haplotypes that evolved early during the radiation of Darwin's finches. Comparative genomics data indicate that these loci, and genomic islands in general, have exceptionally low recombination rates, which may play a role in their establishment.

opencc-zeroMay 2020View details →

Data from: DCDC2 READ1 regulatory element: how temporal processing differences may shape language

Classic linguistic theory ascribes language change and diversity to population migrations, conquests, and geographic isolation, with the assumption that human populations have equivalent language processing abilities. We hypothesize that spectral and temporal characteristics make some consonant manners vulnerable to differences in temporal precision associated with specific population allele frequencies. To test this hypothesis, we modeled association between RU1-1 alleles of DCDC2 and manner of articulation in 51 populations spanning five continents, and adjusting for geographic proximity, genetic and linguistic relatedness. RU1-1 alleles, acting through increased expression of DCDC2, appear to increase auditory processing precision that enhances stop-consonant discrimination, favoring retention in some populations and loss by others. These findings enhance classical linguistic theories by adding a genetic dimension, which until recently, has not been considered to be a significant catalyst for language change.

Data from: Hierarchical social networks shape gut microbial composition in wild Verreaux's sifaka

In wild primates, social behaviour influences exposure to environmentally acquired and directly transmitted microorganisms. Prior studies indicate that gut microbiota reflect pairwise social interactions among chimpanzee and baboon hosts. Here, we demonstrate that higher-order social network structure—beyond just pairwise interactions—drives gut bacterial composition in wild lemurs, which live in smaller and more cohesive groups than previously studied anthropoid species. Using 16S rRNA gene sequencing and social network analysis of grooming contacts, we estimate the relative impacts of hierarchical (i.e. multilevel) social structure, individual demographic traits, diet, scent-marking, and habitat overlap on bacteria acquisition in a wild population of Verreaux's sifaka (Propithecus verreauxi) consisting of seven social groups. We show that social group membership is clearly reflected in the microbiomes of individual sifaka, and that social groups with denser grooming networks have more homogeneous gut microbial compositions. Within social groups, adults, more gregarious individuals, and individuals that scent-mark frequently harbour the greatest microbial diversity. Thus, the community structure of wild lemurs governs symbiotic relationships by constraining transmission between hosts and partitioning environmental exposure to microorganisms. This social cultivation of mutualistic gut flora may be an evolutionary benefit of tight-knit group living.

opencc-zeroApr 2015View details →

Perception of shape and space across rigid transformations

Dataset relative to the following publication: Schmidt, F., Spröte, P., & Fleming, R. W. (2016). Perception of shape and space across rigid transformations. Vision Research, 126, 318-329. <a href="http://dx.doi.org/10.1016/j.visres.2015.04.011"> http://dx.doi.org/10.1016/j.visres.2015.04.011 </a> Each folder contains the data relative to one experiment and a text file with comments.

FIGURE 3 in Identifying Neogobius species from the southern Caspian Sea by otolith shape (Teleostei: Gobiidae)

FIGURE 3. Sagittal otoliths of: A – B: N. pallasi (TL: 95 mm); A, male; B, female. C – D: N. caspius (TL: 95 mm); C, male; D, female. E – F: N. melanostomus (TL: 95 mm); E, male; F, female.

FIGURE 2 in Identifying Neogobius species from the southern Caspian Sea by otolith shape (Teleostei: Gobiidae)

FIGURE 2. SEM micrograph of sagittal otolith of a 60 mm specimen of Neogobius pallasi and its features.

opencc-by-4.0Apr 2017View details →

Mountain landscape connectivity and subspecies appurtenance shape genetic differentiation in natural plant populations of the snapdragon (Antirrhinum majus L.)

This dataset provides the raw data for the population genetic analyses for the article: "Mountain landscape connectivity and subspecies appurtenance shape genetic differentiation in natural plant populations of the snapdragon (Antirrhinum majus L.)" by Benoit Pujol; Juliette Archambeau; Aurore Bontemps; Mylène Lascoste; Sara Marin; and Alexandre Meunier found in the journal "Botany Letters", Vol 164 pp. 111-119 (DOI: 10.1080/23818107.2017.1310056). Link to journal open access article: http://www.tandfonline.com/doi/pdf/10.1080/23818107.2017.1310056 Link to Zenodo article reporsitory: https://zenodo.org/record/801169 The datafile includes three data sheets: Data, which contains for each plant : the name of the population, the name of the sampled individual, the subspecies, the latitude of the population, the longitude of the population, the altitudinal elevation of the population in meters, and the microsatellite genotype of each plant. Genotype data is recorded by locus (two columns for the two alleles at one locus). Locus name is found as the title of the column. The record for each allele is its allele size. valleys 1 and valleys 2, which contains the association between populations and valleys following the two scenarios that we analyzed in the paper. Microsatelite loci were developed during previous work: see the following paper for more details: Debout, G., E. Lhuillier, P.-J. Malé, B. Pujol, and C. Thébaud. 2012. Development and characterization of 24 polymorphic microsatellite loci in two Antirrhinum majus subspecies (Plantaginaceae) using pyrosequencing technology. Conservation Genetics Resources 4:75-79.

opencc-by-4.0Nov 2024View details →

Strong-field quantum control in the extreme ultraviolet using pulse shaping

Dataset for supporting the findings of the paper 'Strong-field quantum control in the extreme ultraviolet using pulse shaping' (https://doi.org/10.1038/s41586-024-08209-y)

opencc-by-4.0May 2020View details →

RAW DATA - The role of aborescent octocorals in shaping the diversity and composition of benthic communities

Spreadsheets with the raw data from the Mediterranean and Antarctic remotely operated vehicle dives used for the Spearman rank correlation analyses, linear regression models, Principal coordinates analyses and PERMANOVA. R code use to carry out all these analyses is also included.

opencc-by-4.0Oct 2024View details →

Equilibrium Dynamics Shape Diversity Patterns Across Terrestrial Tetrapod Clades

This repository contains all scripts, data, and documentation supporting the analyses in our study. The materials are organized into folders corresponding to specific steps of the workflow. This README provides a detailed guide to the structure, contents, and usage of each folder. <h2>Folder Structure and Contents</h2> <h3>1. Environmental Variables (<code>Grid_level_environment</code>)</h3> <ul> <li> Contains grid-cell level environmental variables in <code>environmental_data.rds</code>. <ul> <li> Includes temperature (°C), precipitation (mm), and net primary productivity (NPP). </li> <li> Includes grid cell IDs and latitude/longitude coordinates. </li> </ul> </li> </ul> <h3>2. Evolutionary Rates Across Species and Grid Cells (<code>Grid_level_speciation</code>)</h3> <ul> <li> Contains present-day speciation rate estimates for each species. <ul> <li> Includes DR, BAMM, and ClaDS estimates. </li> <li> Maps each species’ speciation rate to its corresponding grid cells. </li> </ul> </li> </ul> <h3>3. Evolutionary Time Across Grid Cells (<code>Grid_level_assemblage_age</code>)</h3> <ul> <li> Contains files used for BioGeoBEARS DEC model integration at the grid-cell level for each tetrapod clade (amphibians, reptiles, birds, mammals). </li> <li> Each clade is organized in a separate folder with the following files: <ul> <li> Assemblage age: <code>arrival_time_clade*.csv</code> </li> <li> Most likely biogeographic areas per grid cell: <code>clade*_biogeo_area.csv</code> </li> <li> Presence/absence matrix: <code>clade*_PAM.csv</code> </li> <li> Clade phylogenetic tree: <code>clade*_tree.tre</code> </li> <li> DEC area file: <code>geo_area_clade*.data</code> </li> <li> DEC outputs: <code>results_DEC_clade*.Rdata</code> </li> <li> Estimated geographic area plots across the phylogeny: <code>DEC_plot_clade*.pdf</code> </li> </ul> </li> </ul> <h3>4. Path Analysis Example (<code>Path_model</code>)</h3> <ul> <li> Contains an example R script: <code>path_analysis_clades.R</code>. <ul> <li> Illustrates path models applied to tetrapod clades. </li> <li> Example uses mammalian clades; replace the dataset to run on other clades. </li> </ul> </li> </ul> <h3>5. Path Analysis Outputs (<code>Path_outputs_&_clade_traits</code>)</h3> <ul> <li> Contains outputs from path analyses for each tetrapod clade. <ul> <li> <code>Path_all_effects.csv</code> consolidates all path outputs and includes clade-level traits (see Methods in the main paper). </li> <li> Other files include: <ul> <li> <code>Path_direct_effects.csv</code> – direct effects across clades </li> <li> <code>Path_indirect_via_productivity.csv</code> – indirect effects via productivity </li> <li> <code>Path_indirect_via_speciation.csv</code> – indirect effects via speciation </li> <li> <code>Path_indirect_via_time.csv</code> – indirect effects via evolutionary time </li> </ul> </li> </ul> </li> </ul> <h3>6. Path Output Figures (<code>Path_outputs_figures</code>)</h3> <ul> <li> Contains R scripts to visualize path model outputs across tetrapod clades: <ul> <li> Direct effects: <code>Path_direct_effects.R</code> </li> <li> Indirect effects: <code>Path_indirect_via_productivity.R</code>, <code>Path_indirect_via_speciation.R</code>, <code>Path_indirect_via_time.R</code> </li> <li> Total effects: <code>Path_total_effects.R</code> </li> </ul> </li> </ul> <h3>7. Clade-Level Trait Effects on Richness (<code>Clade_level_effects.R</code>)</h3> <ul> <li> Contains the R script <code>Clade_level_effects.R</code>. </li> <li> Explores whether the effects of the tested predictors on species richness depend on clade-level traits, including: <ul> <li> Physiological traits: endothermy vs ectothermy </li> <li> Spatial–historical traits: climate origin, range size, centroid displacement, displacement rate </li> <li> Temporal/size-related traits: clade age, species richness </li> </ul> </li> </ul>

Raw landmarks related to the paper, "Evolution under intensive industrial breeding: skull size and shape comparison between historic and modern pig lineages "

PLEASE NOTE: This dataset has been superseeded by an updated version which has the correct number of specimens as referred to in the below article. It can be accesssed at: https://doi.org/10.5281/zenodo.14262754     Raw coordinates (p x k = 82 x 3) of domestic and wild pig skulls that form the dataset for the paper, "Evolution under intensive industrial breeding: skull size and shape comparison between historic and modern pig lineages "

opencc-by-4.0Feb 2014View details →

FIGURE 6. Inflorescence shape. A–E in A new species of stonecrop (Sedum section Gormania, Crassulaceae) from northern California

FIGURE 6. Inflorescence shape. A–E. Sedum obtusatum subsp. boreale cylindrical flowering shoots, with lower branches suppressed; A–B. Zika 26289, Siskiyou Co.; C. Zika 25687, Trinity Co.; D–E. Zika 26294, Shasta Co., California; F–H. S. citrinum (Zika 26185); F. Flat-topped flowering shoots, with well-developed lower branches; G–H. Plants with 2–3 fertile shoots from one rosette; I. S. obtusatum subsp. boreale several fertile shoots, typically unbranched at base (Zika 26289). Scale bars 1 cm.

Fig. 1. Seta shape terminologies. 1. Fine. 2–3. Stout and truncated. 4–5. Plank-like. 6–7. Acicular. 8. Narrowly elliptic. 9. Elliptic. 10. Linear. 11 in Further additions to the knowledge of Strumigenys (Formicidae: Myrmicinae) within South East Asia, with the descriptions of 20 new species

Fig. 1. Seta shape terminologies. 1. Fine. 2–3. Stout and truncated. 4–5. Plank-like. 6–7. Acicular. 8. Narrowly elliptic. 9. Elliptic. 10. Linear. 11. Short linear or short subspatulate. 12–13. Subspatulate. 14–15. Spatulate. 16–17. Oblanceolate. 18–19. Small obovate. 20. Large obovate. 21. Suborbicular / orbicular. 22. Orbicular. 23. Remiform. 24. Remiform / narrowly claviform (red arrow). 25–26. Claviform. 27. Shoehorn-shaped. 28. Spoon-shaped.

opencc-by-nc-4.0Nov 2021View details →

A statistical shape model of craniosynostosis patients and 100 model instances of each pathology

This dataset is part of the publication "A statistical shape model for radiation-free assessment and classification of craniosynostosis" (M. Schaufelberger et al.).  It includes several 3D head models constructed of surface scans of craniosynostosis patients: The full shape model, a texture model, and submodels of four classes: sagittal suture fusion (scaphocephaly), metopic suture fusion (trigonocephaly), coronal suture fusion (brachycephaly and anterior plagiocephaly), and a control model (normocephaly and positional plagiocephaly).  Each of the models is available in an .h5 file.  We also include 100 mesh instances as a .ply file in a zip file.  The model's statistical information can be incorporated into the [Liverpool-York child head model (Dai et al. 2019)](https://doi.org/10.1007/s11263-019-01260-7) as it uses the same vertex order and IDs (starting from index 0).  If you want to synthesize new models, take a look a the demo.py file.  For information about the hierarchy in the h5-file, take a look at documentation.md.

The supplemental data for the paper: "Methodology of generation of CFD meshes and 4D shape reconstruction of coronary arteries from patient-specific dynamic CT"

The supplemental data for the paper: "Methodology of generation of CFD meshes and  4D shape reconstruction of coronary arteries from patient-specific dynamic CT"A video file (minimum play resolution is HD to see the mesh) showing the movement of the LCA throughout the heart cycle and .STL files for 10--100% (increment of 10\%) of the heart cycle phase.

Training and test data, plus saved models for the upcoming paper `Top-down perceptual inference shaping the activity of early visual cortex'

Each .pkl file contains a training or test dataset in the form of a Python dictionary (generated with Python 3.8.5) with the following fields:<ul><li>'train_images': 640,000 float32 images used for model training. These are 40px images that contain 1600 pixel intensities each.</li><li>'train_labels': float32 labels for each image in 'train_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family.</li><li>'test_images': 64,000 float32 images used for model testing. These are 40px images that contain 1600 pixel intensities each.</li><li>'test_labels': float32 labels for each image in 'test_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family.</li></ul>The .zip file contains a saved model snapshot and various intermediate evaluative data. Details on these are coming soon.

opencc-zeroNov 2023View details →

Genomic landscape of introgression from the ghost lineage in a gobiid fish uncovers the generality of forces shaping hybrid genomes

Extinct lineages can leave legacies in the genomes of extant lineages through ancient introgressive hybridization. The patterns of genomic survival of these extinct lineages provide insight into the role of extinct lineages in current biodiversity. However, our understanding of the genomic landscape of introgression from extinct lineages remains limited due to challenges associated with locating the traces of unsampled "ghost" extinct lineages without ancient genomes. Herein, we conducted population genomic analyses on the East China Sea (ECS) lineage of Chaenogobius annularis, which was suspected to have originated from ghost introgression, with the aim of elucidating its genomic origins and characterizing its landscape of introgression. By combining phylogeographic analysis and demographic modeling, we demonstrated that the ECS lineage originated from ancient hybridization with an extinct ghost lineage. Forward simulations based on the estimated demography indicated that the statistic γ of the HyDe analysis can be used to distinguish the differences in local introgression rates in our data. Consistent with introgression between extant organisms, we found reduced introgression from extinct lineage in regions with low-recombination rates and with functional importance, thereby suggesting a role of linked selection that has eliminated the extinct lineage in shaping the hybrid genome. Moreover, we identified enrichment of repetitive elements in regions associated with ghost introgression, which was hitherto little-known but was also observed in the reanalysis of published data on introgression between extant organisms. Overall, our findings underscore the unexpected similarities in the characteristics of introgression landscapes across different taxa, even in cases of ghost introgression.