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5,828 results for “traits”
Wikipedia: Wikipedia English - traits (inferred records)
Wikipedia is a multilingual, web-based, free-content encyclopedia project supported by the Wikimedia Foundation and based on a model of openly editable content. EOL harvests articles from wikipedia that are indexed as species or higher taxa.<p></p>
Dataset and R code: Above and belowground functional trait coordination in the Neotropical understory genus Costus
<p>Dataset and R code accompanying the paper "Above and belowground functional trait coordination in the Neotropical understory genus <em>Costus</em>" published by AoB Plants. </p>
Leaf reflectance and traits of floating and emergent macrophytes
<p>This dataset includes leaf samples from six floating and emergent macrophyte species common in temperate areas, covering different phenological stages, seasons, and environmental conditions, and measured leaf reflectance (400-2500 nm) and leaf traits (dealing with photophysiology, pigments, and structure). Data were collected along three years (2016-2018) from three temperate shallow lakes surrounded by wetlands and hosting abundant macrophyte communities, located in central and southern Europe: Lake Hídvégi or Kis-Balaton (Hungary), Mantua lakes system (Italy), and Lake Varese (Italy).</p> <p>Leaf photophysiological parameters derived from chlorophyll fluorescence measured with a PAM-2500 chlorophyll fluorometer (Heinz Walz GmbH, Germany).</p> <p>Leaf pigments were derived from spectrophotometric readings of absorbance of leaf extracts in acetone 80%.</p> <p> </p>
Potential Metabolic Activity, Catalase Activity, Performance traits and Morphological variables of 94 individuals belonging to Podarcis muralis species used in the analysis
<p>Potential Metabolic Activity (ETS26_P, ETS31_P, ETS36_P), Catalase Activity (CAT_P), Performance traits (BITE, SPRINT,CLIMB, MANO) and Morphological variables (snout-vent length (SVL), trunk length (TRL), pileus length (PL), head length (HL), head width (HW), head height (HH), fore limb length (FLL) and hind limb length (HLL) of 94 individuals belonging to <em>Podarcis muralis</em> species. The data was used in the analysis of the paper entitled: Is It Function or Fashion? An Integrative Analysis of Morphology, Performance, and Metabolism in a Colour Polymorphic Lizard, by authors Verónica Gomes, Anamarija Žagar, Guillem Pérez i de Lanuza, Tatjana Simčič and Miguel A. Carretero, published in the journal Diversity 2022, 14, 116. <a href="https://doi.org/10.3390/d14020116">https://doi.org/10.3390/d14020116</a></p>
Supporting data for review article: The Global Distribution, Formation, and Fate of Mineral-Associated Soil Organic Matter Under a Changing Climate – A Trait-Based Perspective
<p>Supporting data and code for review article: Sokol N.W., Whalen E.D., Kallenbach C., Pett-Ridge J., Georgiou K. The Global Distribution, Formation, and Fate of Mineral-Associated Soil Organic Matter Under a Changing Climate – A Trait-Based Perspective. <em>Functional Ecology, </em>2022.</p> <p>We leveraged data from a global synthesis of soil fractionation measurements (DOI: 10.5281/zenodo.5987415). For this review article, we specifically focused on measurements of bulk and mineral-associated soil organic carbon concentrations (reported in units of gC/kg soil) and the proportion of bulk soil organic carbon that is mineral-associated (reported as a %). This subset also includes auxiliary data regarding climate and biome characteristics extracted from the synthesized papers; for more variables, see the original full dataset. Köppen-Geiger climate zones were extracted from a georeferenced global database (using R package 'kgc' v1.0.0.2) with site coordinates, where available. Three files are provided in this repository: (1) data file, (2) metadata file, and (3) code for manuscript figures and summary statistics.</p>
Data for article: A quantitative framework to infer the effect of traits, diversity and environment on dispersal and extinction rates from fossils
<p>Supplementary information for:</p> <p><strong>A quantitative framework to infer the effect of traits, diversity and environment on dispersal and extinction rates from fossils</strong></p> <p>Torsten Hauffe, Mathias M. Pires, Tiago B. Quental, Thomas Wilke, and Daniele Silvestro</p> <p> </p><ul> <li> Simulations <ul> <li>Scripts <ul> <li>Scenario1_SamplingHeterogeneity.R: Script to simulate biogeographic histories with sampling heterogeneity</li> <li>Scenario3_SealevelInvasion.R: Script to simulate biogeographic histories where sea level facilitates dispersal and invasion induces extinction</li> <li>Scenario3_DiversityDependence.R: Script to simulate diversity-dependent biogeographic histories</li> <li>Scenario4_TraitDependence.R: Script to simulate trait-dependent biogeographic histories</li> <li>Scenario5_CategoricalTraitDependence.R: Script to simulate trait-dependent biogeographic histories</li> </ul> </li> <li>Results <ul> <li>Scenario1_SamplingHeterogenetiy_alpha05.txt: Results of simulations scenario 1 with a sampling heterogeneity of alpha = 0.5</li> <li>Scenario1_SamplingHeterogenetiy_alpha1.txt: Results of simulations scenario 1 with a sampling heterogeneity of alpha = 1</li> <li>Scenario1_SamplingHeterogenetiy_alpha2.txt: Results of simulations scenario 1 with a sampling heterogeneity of alpha = 2</li> <li>Scenario1_SamplingHeterogenetiy_alpha10.txt: Results of simulations scenario 1 with a sampling heterogeneity of alpha = 10</li> <li>Scenario2_Independent_dispersal_and_extinction.txt: Results of simulation scenario 2 with sea-level independent dispersal and no invasion induced extinction</li> <li>Scenario2_Sealevel_dependent_dispersal_and_independent_extinction.txt: Results of simulation scenario 2 with sea-level dependent dispersal and no invasion induced extinction</li> <li>Scenario2_Sealevel_independent_dispersal_and_invasion_induced_extinction.txt: Results of simulation scenario 2 with sea-level independent dispersal and invasion induced extinction</li> <li>Scenario2_Sealevel_dependent_dispersal_and_invasion_induced_extinction.txt: Results of simulation scenario 2 with sea-level dependent dispersal and invasion induced extinction</li> <li>Scenario3_Independent_dispersal_and_extinction.txt: Results of simulations scenario 3 with diversity-independent dispersal and extinction</li> <li>Scenario3_Diversity_dependent_dispersal_and_independent_extinction.txt: Results of simulations scenario 3 with diversity-dependent dispersal and diversity-independent extinction</li> <li>Scenario3_Independent_dispersal_and_Diversity_dependent_extinction.txt: Results of simulations scenario 3 with diversity-dependent dispersal and diversity-independent extinction</li> <li>Scenario3_Diversity_dependent_dispersal_and_extinction.txt: Results of simulations scenario 3 with diversity-dependent dispersal and extinction</li> <li>Scenario4_Independent_dispersal_and_extinction.txt: Results of scenario 4 with trait-independent dispersal and extinction</li> <li>Scenario4_Trait_dependent_dispersal_and_independent_extinction.txt: Results of scenario 4 with trait-dependent dispersal and independent extinction</li> <li>Scenario4_Independent_dispersal_and_trait_dependent_extinction.txt: Results of scenario 4 with independent dispersal and trait-dependent extinction</li> <li>Scenario4_trait_dependent_dispersal_and_extinction.txt: Results of scenario 4 with trait-dependent dispersal and extinction</li> <li>Scenario5_CatTrait_dependent_dispersal_and_independent_extinction.txt: Results of model 2 with categorical traits (e.g family) influence dispersal but no influence of a category-specific continuous traits</li> </ul> </li> </ul> </li> <li>Carnivora <ul> <li>BinnedOccurrence: Folder with 100 replicates of binned occurrences of max. 330 carnivoran genera throughout the Neogene</li> <li>BodyMass: Folder with 100 replicates of body mass for 330 carnivoran genera</li> <li>Sealevel: Folder with sea level through the Neogene</li> <li>Temperature: Folder with the temperature record of the Neogene</li> <li>Families: Folder with families as taxonomic proxy for phylogeny. FamilyGeneraNumeric.txt is the numeric coding used for the Bayesian analyses of carnivoran biogeography</li> </ul> </li> </ul> <p></p>
Ancient Reef Traits Database
<p>This is the first official release of the Ancient Reef Traits Database. </p> <p>Data descriptor can be found [Here].</p> <p>The complete list of data sources can be accessed <a href="https://osf.io/f3u9k/">here</a>.</p>
Sharkipedia: A Curated Open Access Database of Shark and Ray Life History Traits and Abundance Time-series
<p>This dataset represent the intial launch of Sharkipedia: a curated open access database of shark and ray life history traits and abundance time-series. A curated database of shark and ray biological data is increasingly necessary both to support fisheries management and conservation efforts, and to test the generality of hypotheses of vertebrate macroecology and macroevolution. Sharks and rays are one of the most charismatic, evolutionary distinct, and threatened lineages of vertebrates, comprising around 1,250 species. To accelerate shark and ray conservation and science, we developed Sharkipedia as a curated open-source database and research initiative to make all published biological traits and population trends accessible to everyone. Sharkipedia hosts information on 58 life history traits from 264 sources, for 170 species, from 39 families, and 12 orders related to length (n=9 traits), age (8), growth (12), reproduction (19), demography (5), and allometric relationships (5), as well as 871 population time-series from 202 species. Sharkipedia relies on the backbone taxonomy of the IUCN Red List and the bibliography of Shark-References. Sharkipedia has profound potential to support the rapidly growing data demands of fisheries management, international trade regulation as well as anchoring vertebrate macroecology and macroevolution.</p>
UK Biobank release and systematic evaluation of optimised polygenic risk scores for 53 diseases and quantitative traits
<p>Summary-level GWAS data for 53 traits generated by <a href="https://www.genomicsplc.com/">Genomics plc</a> as presented in:</p> <p>Thompson D. et al. UK Biobank release and systematic evaluation of optimised polygenic risk scores for 53 diseases and quantitative traits (<a href="https://doi.org/10.1101/2022.06.16.22276246">https://doi.org/10.1101/2022.06.16.22276246</a>)</p> <p>If you have any questions or comments regarding these files, please contact Genomics plc at <a href="mailto:research@genomicsplc.com">research@genomicsplc.com</a></p> <p><strong>NOTES</strong></p> <p>These analyses were carried out using the full UK Biobank (UKB) imputation data release (v3b). After removal of exclusions and withdrawals, a subset of 337,151 UKB individuals, the White British Unrelated (WBU) subgroup, was defined as the intersection of two sample groups created by Bycroft et al 2018 (Nature 562, 203-209): the ‘White British ancestry’ group (UKB Data Field 22006) and the ‘used in genetic principal components’ group (UKB Data Field 22020), the latter being high quality samples that were filtered to avoid closely related individuals. All GWAS analyses were performed on the WBU subgroup.</p> <p>Phenotypes were defined as described in Supplementary Table 1 ‘Phenotype definitions’ using a combination of Hospital Episode Statistics, Cancer Registry reports (where applicable) and self-report responses, with the exception of coronary artery disease (CAD). GWAS data was generated for both a “narrow” and a “broad” definition of CAD. The former was used as part of the training data for the Enhanced CAD PRS, the latter was used as part of the training data for the Enhanced CVD PRS. The phenotype definitions for “narrow” and a “broad” CAD are as follows:</p> <table> <tbody> <tr> <td>Narrow CAD<br> (includes angina)</td> <td>ICD10 codes (where .X indicates all subcodes) from both hospital and death records: I21, I22, I23, I24.1, I25.2, I20.X. ICD9 codes: 410-412, 42979, 413.X. OPCS-4 codes (K40.1–40.4, K41.1–41.4, K45.1–45.5,K49.1–49.2, K49.8–49.9, K50.2, K75.1–75.4, K75.8–75.9), self-reported heart attack (UKB codes 1075 in field 20002; code 1 in field 6150), self-reported coronary angioplasty (ptca) or coronary artery bypass graft (UKB codes 1070 and 1095 in field 20004), self-reported angina.</td> </tr> <tr> <td>Broad CAD<br> (includes angina and all ischaemic heart disease)</td> <td>As for Narrow CAD, plus ICD10 codes I24.X, I25X, and ICD9 codes 414.X (where .X indicates all subcodes).</td> </tr> </tbody> </table> <p>Note that there is no GWAS for cardiovascular disease (CVD) per se. This is because the UKB training data for the Enhanced CVD PRS consisted of separate GWASs for “narrow” CAD and ischaemic stroke.</p> <p>All analyses included Age at assessment, sex (for non-sex specific traits), genotyping chip, and 10 principal components as covariates.</p> <p>GWAS summary statistics for each trait were generated by applying PLINK 2.0 to the WBU subgroup, using a logistic regression for disease traits, and a linear regression model for quantitative traits. For chromosome X variants males were treated as having 0 or 2 alternative alleles.</p> <p>The results are not adjusted for genomic control.</p> <p><strong>DATA FILE CONTENT DESCRIPTION (DISEASE TRAITS)</strong></p> <table> <tbody> <tr> <td>cpra</td> <td>Variant ID in ‘CPRA’ format. Position reflects position in b37</td> </tr> <tr> <td>chrom</td> <td>Chromosome</td> </tr> <tr> <td>pos</td> <td>Position in base pairs (b37, 1-based)</td> </tr> <tr> <td>alt</td> <td>Alternative allele (effect allele)</td> </tr> <tr> <td>beta</td> <td>Effect size (log odds ratio)</td> </tr> <tr> <td>standard_error</td> <td>Standard error of beta</td> </tr> <tr> <td>minus_log10_p</td> <td>Minus log(base 10) of P-value</td> </tr> <tr> <td>ref</td> <td>Reference allele (non-effect allele)</td> </tr> <tr> <td>ncase</td> <td>Number of cases</td> </tr> <tr> <td>ncontrol</td> <td>Number of controls</td> </tr> </tbody> </table> <p><strong>DATA FILE CONTENT DESCRIPTION (QUANTITATIVE TRAITS)</strong></p> <table> <tbody> <tr> <td>cpra</td> <td>Variant ID in ‘CPRA’ format. Position reflects position in b37</td> </tr> <tr> <td>chrom</td> <td>Chromosome</td> </tr> <tr> <td>pos</td> <td>Position in base pairs (b37, 1-based)</td> </tr> <tr> <td>alt</td> <td>Alternative allele (effect allele)</td> </tr> <tr> <td>beta</td> <td>Effect size</td> </tr> <tr> <td>standard_error</td> <td>Standard error of beta</td> </tr> <tr> <td>minus_log10_p</td> <td>Minus log(base 10) of P-value</td> </tr> <tr> <td>ref</td> <td>Reference allele (non-effect allele)</td> </tr> <tr> <td>ntotal</td> <td>Total sample size</td> </tr> </tbody> </table> <p><strong>FILE NAMES</strong></p> <p>The following is a list of traits and their corresponding file names.</p> <p><em><strong>DISEASE TRAITS</strong></em></p> <table> <tbody> <tr> <td>Age-related macular degeneration</td> <td>amd_strict_UKB_WBU.csv.gz</td> </tr> <tr> <td>Alzheimer's disease</td> <td>alzheimers_disease_UKB_WBU.csv.gz</td> </tr> <tr> <td>Asthma</td> <td>asthma_UKB_WBU.csv.gz</td> </tr> <tr> <td>Atrial fibrillation</td> <td>atrial_fibrillation_UKB_WBU.csv.gz</td> </tr> <tr> <td>Bipolar disorder</td> <td>bipolar_disorder_UKB_WBU.csv.gz</td> </tr> <tr> <td>Bowel cancer</td> <td>CRC_UKB_WBU.csv.gz</td> </tr> <tr> <td>Breast cancer</td> <td>BC_UKB_WBU_women.csv.gz</td> </tr> <tr> <td>Coeliac disease</td> <td>celiac_disease_UKB_WBU.csv.gz</td> </tr> <tr> <td>Narrow coronary artery disease</td> <td>NARROW_CAD_UKB_WBU.csv.gz</td> </tr> <tr> <td>Broad coronary artery disease</td> <td>BROAD_CAD_UKB_WBU.csv.gz</td> </tr> <tr> <td>Crohn's disease</td> <td>crohns_disease_UKB_WBU.csv.gz</td> </tr> <tr> <td>Epithelial ovarian cancer</td> <td>OC_UKB_WBU.csv.gz</td> </tr> <tr> <td>Hypertension</td> <td>HT_UKB_WBU.csv.gz</td> </tr> <tr> <td>Ischaemic stroke</td> <td>IS_stroke_UKB_WBU.csv.gz</td> </tr> <tr> <td>Melanoma</td> <td>melanoma_UKB_WBU.csv.gz</td> </tr> <tr> <td>Multiple sclerosis</td> <td>multiple_sclerosis_UKB_WBU.csv.gz</td> </tr> <tr> <td>Osteoporosis</td> <td>OP_WBU_training.csv.gz</td> </tr> <tr> <td>Prostate cancer</td> <td>PC_UKB_WBU.csv.gz</td> </tr> <tr> <td>Parkinson's disease</td> <td>parkinsons_disease_UKB_WBU.csv.gz</td> </tr> <tr> <td>Primary open angle glaucoma</td> <td>POAG_WBU_training.csv.gz</td> </tr> <tr> <td>Psoriasis</td> <td>psoriasis_UKB_WBU.csv.gz</td> </tr> <tr> <td>Rheumatoid arthritis</td> <td>rheumatoid_arthritis_UKB_WBU.csv.gz</td> </tr> <tr> <td>Schizophrenia</td> <td>schizophrenia_UKB_WBU.csv.gz</td> </tr> <tr> <td>Systemic lupus erythematosus</td> <td>lupus_UKB_WBU.csv.gz</td> </tr> <tr> <td>Type 1 diabetes</td> <td>t1d_UKB_WBU.csv.gz</td> </tr> <tr> <td>Type 2 diabetes</td> <td>T2D_UKB_WBU.csv.gz</td> </tr> <tr> <td>Ulcerative colitis</td> <td>ulcerative_colitis_UKB_WBU.csv.gz</td> </tr> <tr> <td>Venous thromboembolic disease</td> <td>VTE_UKB_WBU.csv.gz</td> </tr> </tbody> </table> <p><em><strong>QUANTITATIVE TRAITS</strong></em></p> <table> <tbody> <tr> <td>Age at menopause</td> <td>age_at_menopause_UKB_WBU.csv.gz</td> </tr> <tr> <td>Apolipoprotein A1</td> <td>apolipoprotein_a1_UKB_WBU.csv.gz</td> </tr> <tr> <td>Apolipoprotein B</td> <td>apolipoprotein_b_UKB_WBU.csv.gz</td> </tr> <tr> <td>Body mass index</td> <td>bmi_UKB_WBU.csv.gz</td> </tr> <tr> <td>Calcium</td> <td>calcium_UKB_WBU.csv.gz</td> </tr> <tr> <td>Docosahexaenoic acid</td> <td>docosahexaenoic_acid_UKB_WBU.csv.gz</td> </tr> <tr> <td>Estimated bone mineral density T-score</td> <td>BMD_WBU_training.csv.gz</td> </tr> <tr> <td>Estimated glomerular filtration rate (creatinine based)</td> <td>egfr_UKB_WBU.csv.gz</td> </tr> <tr> <td>Estimated glomerular filtration rate (cystatin based)</td> <td>egfr_cys_UKB_WBU.csv.gz</td> </tr> <tr> <td>Glycated haemoglobin</td> <td>hba1c_UKB_WBU_nodiabetes.csv.gz</td> </tr> <tr> <td>High density lipoprotein cholesterol</td> <td>hdl_cholesterol_UKB_WBU.csv.gz</td> </tr> <tr> <td>Height</td> <td>height_UKB_WBU.csv.gz</td> </tr> <tr> <td>Intraocular pressure</td> <td>iop_WBU_training.csv.gz</td> </tr> <tr> <td>Low density lipoprotein cholesterol</td> <td>ldl_UKB_WBU_nostatins.csv.gz</td> </tr> <tr> <td>Omega-6 fatty acids</td> <td>omega_6_fatty_acids_UKB_WBU.csv.gz</td> </tr> <tr> <td>Omega-3 fatty acids</td> <td>omega_3_fatty_acids_UKB_WBU.csv.gz</td> </tr> <tr> <td>Phosphatidylcholines</td> <td>phosphatidylcholines_UKB_WBU.csv.gz</td> </tr> <tr> <td>Phosphoglycerides</td> <td>phosphoglycerides_UKB_WBU.csv.gz</td> </tr> <tr> <td>Polyunsaturated fatty acids</td> <td>polyunsaturated_fatty_acids_UKB_WBU.csv.gz</td> </tr> <tr> <td>Resting heart rate</td> <td>resting_heart_rate_UKB_WBU.csv.gz</td> </tr> <tr> <td>Remnant cholesterol (Non-HDL, Non-LDL cholesterol)</td> <td>remnant_cholesterol__UKB_WBU.csv.gz</td> </tr> <tr> <td>Sphingomyelins</td> <td>sphingomyelins_UKB_WBU.csv.gz</td> </tr> <tr> <td>Total cholesterol</td> <td>total_cholesterol_UKB_WBU.csv.gz</td> </tr> <tr> <td>Total fatty acids</td> <td>total_fatty_acids_UKB_WBU.csv.gz</td> </tr> <tr> <td>Total triglycerides</td> <td>total_triglycerides_UKB_WBU.csv.gz</td> </tr> </tbody> </table>
Phenotypic variation and quantitative trait loci for resistance to southern anthracnose and clover rot in red clover
<p>Red clover (<em>Trifolium pratense</em> L.) is an important forage legume of temperate regions, particularly valued for its high yield potential and its high forage quality. Despite substantial breeding progress during the last decades, continuous improvement of cultivars is crucial to ensure yield stability in view of newly emerging diseases or changing climatic conditions. The high amount of genetic diversity present in red clover ecotypes, landraces and cultivars provides an invaluable, but often unexploited resource for the improvement of key traits such as yield, quality, and resistance to biotic and abiotic stresses.</p> <p>A collection of 397 red clover accessions was genotyped using a pooled genotyping-by-sequencing approach with 200 plants per accession. Resistance to the two most pertinent diseases in red clover production, southern anthracnose caused by <em>Colletotrichum trifolii</em>, and clover rot caused by <em>Sclerotinia trifoliorum, </em>was assessed using spray inoculation. The mean survival rate for southern anthracnose was 22.9% and the mean resistance index for clover rot was 34.0%. Genome-wide association analysis revealed several loci significantly associated with resistance to southern anthracnose and clover rot. Most of these loci are in coding regions. One quantitative trait locus (QTL) on chromosome 1 explained 16.8% of the variation in resistance to southern anthracnose. For clover rot resistance we found eight QTL, explaining together 80.2% of the total phenotypic variation. The SNPs associated with these QTL provide, once validated, a promising resource for marker-assisted selection in existing breeding programs, facilitating the development of novel cultivars with increased resistance against two devastating fungal diseases of red clover.</p>
Melon pan-genome and multi-parental framework for highresolution trait dissection
<p>Gff3 annotation files for 25 de-novo melon genomes discussed in publication.<br> These are draft annotations based on lif-over from "Harukei-3" and "HS" melon genomes.<br> Genome fasta files can be found in NCBI PRJNA726743 </p>
Data set: Variations in water economy traits in two Sphagnum species across their distribution boundaries
<p><em>Sphagnum</em> trait data collected (2016-2017) across a climatic gradient in Sweden. Trait data for both shoot and canopy traits. Data for <em>Sphagnum cuspidatum</em> and <em>Sphagnum lindbergii</em>. Also contains data on species occurrence records in Sweden and output from speceis distribution modelling. See published paper for more information.</p> <p>Files contain (i) processed data ("calculated_trait_data...cvs"), (ii) raw data ("Campbell_etal_clim_traits_...cvs"), (iii) their readme files, and (iv) R-scripts to run the analyses. Note that you need the files in the zip-file to run the analyses in the R-script. The zip-file contains all raw data (climate, traits, species occurences), MaxEnt output, and raster files from photogrammetry.</p> <p>More info in paper: <a href="https://doi.org/10.1002/ajb2.16347" target="_blank" rel="noopener">https://doi.org/10.1002/ajb2.16347</a></p>
Salmonella enterica serovar Derby isolated from eggs show genomic and phenotypic traits that may be linked to inability to produce human infection.
<p><em><span>Salmonella enterica</span></em><span> serovar Derby causes foodborne disease (FBD) outbreaks worldwide, mainly from contaminated pork but also from chickens. During a major epidemic of FBD in Uruguay due to <em>S</em>. Enteritidis from poultry, we conducted a large survey of commercially available eggs, where we isolated many <em>S.</em> Enteritidis strains but surprisingly also a much larger number (ratio 5:1) of <em>S</em>. Derby strains. No single case of <em>S</em>. Derby infection was detected in that period, suggesting that the <em>S</em>. Derby egg strains were impaired for human infection. We sequenced fourteen of these egg isolates, as well as fifteen isolates from pork or human infection that were isolated in Uruguay before and after that period, and all sequenced strains had the same sequence type <span>(ST40). Phylogenomic genomic analysis was conducted using more than 3500 genomes from the same sequence type (ST), revealing that Uruguayan isolates clustered into four distantly related lineages. Population structure analysis (BAPS) suggested the division of the analyzed genomes into nine different BAPS1 groups, with Uruguayan strains clustering within four of them. </span>All egg isolates clustered together as a monophyletic group and showed marked differences in gene content with the strains in the other clusters. <span>Differences included the absence of a C-terminal fragment of the <em>speF</em> gene, as well as variations in the composition of mobile genetic elements, such as plasmids, insertion sequences, transposons, and phages, between egg isolates and human/pork isolates.</span></span> <span>Egg isolates showed an acid susceptibility phenotype, reduced ability to reach the intestine after oral inoculation of mice, and reduced induction of SPI-2 <em>ssaG</em> gene, compared to human isolates from other monophyletic groups. Mice challenge experiments showed that mice infected intraperitoneally with human/pork isolates died between 1-7 days p.i., while all animals infected with the egg strain survived the challenge. Altogether, our results suggest that loss of gene functions and the absence of plasmids in egg isolates may explain why these <em>S</em>. Derby were not capable of producing human infection despite being at that time, the main serovar recovered from eggs countrywide.</span></p>
Taxonomy, occurrences, phylogeny, traits and uses of the entire plant genus Scleria (Cyperaceae)
<p>This resource includes several datasets:</p> <p>(1) Taxonomy (261 species): Updated taxonomy of the genus Scleria at the species level based on Bauters et al. 2016 and 2019.</p> <p>(2) Occurrences (latitude/longitude): data was compiled using observations from the Global Biodiversity Information Facility, Red List, research grade identifications from iNaturalist (accessed 13/08/2023) and records for collections from BR, K, GENT, L, MO, NY, P, US and WAG which were georeferenced using Google Earth. The dataset includes 22,759 observations from 248 species. Methodology follows Larridon et al. (2021).</p> <p>(3) Phylogeny of the genus based on three markers (ITS, ndhF, rps16) from Larridon et al. (2021) (136 species).</p> <p>(4) Traits. (i) We measured maximum height, maximum blade length, maximum blade width, stem width, nutlet length and nutlet width from 1,254 specimens of 209 species housed at Royal Botanic Gardens, Kew and the Muséum National d'Histoire Naturelle in Paris. (ii) We also compiled another dataset of 16 continuous and categorical traits for all 261 Scleria species derived from protologues and descriptions from regional floras. (iii) We measured nutlet weight for 141 species.</p> <p>(5) Uses & ecology: ethnobotany (mostly medicinal uses) and references to its ecology in several ecosystems (e.g., pollination, dispersal, ecological role). This data was gathered from several bibliographical sources, also provided.</p> <p>(6) Pictures of nutlets from 141 species.</p>
CarniTraits: Functional traits of the worlds Late Quaternary terrestrial mammalian predators
<p><em>CarniTraits, </em>a comprehensive functional trait database of all late Quaternary (~130,000 ybp) terrestrial mammalian carnivore species (149 species, >1kg body mass). The database contains the body mass, diet, locomotion, cooperative hunting, hunting habitat, hunting method, bone use, and hunting temporal activity patterns of all carnivores over the last ~130,000 years. We also include the IUCN status for all extant species and ground the database in a modern phylogeny and is thus compatible and easily interlinked with range maps published in PHYLACINE v1.2.1. CarniTraits is broadly applicable to assisting in local and macroecological studies, meta-analytic research, global syntheses, and paleoecological research.</p> <p><em>CarniTraits</em> includes data on:</p> <ul> <li>Body Mass</li> <li>Diet</li> <li>Scavenging behaviour</li> <li>Bone Consumption</li> <li>Locomotion</li> <li>Cooperative hunting</li> <li>Hunting habitat</li> <li>Hunting method</li> <li>Hunting time</li> <li>Brain mass</li> <li>Encephalisation Quotient</li> </ul> <p>Each of these traits is fundamental to the ecological impact that carnivores have on terrestrial ecosystems. Trait data compiled represents the best available knowledge on the functional traits of late Quaternary hypercarnivorous mammals. As such, <em>CarniTraits</em> provides a tool for the analysis of carnivore functional diversity both past and present, as well as their effects on ecosystem dynamics.</p> <p>Each trait is accompanied by columns that describes the confidence in the data (notes), whether it was inferred and what level it was inferred from and the reference that the data was collected from.</p>
Rethinking the fundamental unit of ecological remote sensing: Estimating individual level plant traits at scale
<p>derived data of leaf and plant structural traits for two National Ecological Observatory Network (NEON) Airborne Observatory Platform (AOP) sites. Dataset contains spatial explicit information for 4.5 million trees, and include: Nitrogen (%mass), Phosphorus (%mass), Leaf mass per area (g m<sup>-2</sup>), diameter at breast height (cm), crown area (m2), tree height (m) and other physical topographic variables (Albedo, Elevation, Slope, Aspect). data are associated to the </p>
Contemporary phenotypic change in plant quantitative traits
<p>This is a new version of the Gorné & Díaz 2017 database (doi:10.5281/zenodo.580095). We cheked the categorization of each case, fixed of some mistakes. Also, we disambiguated the trait type moderator and add a new (mean based) measure of change.</p> <p>This database included studies that provide data of changes in quantitative traits of angiosperms within a known temporal framework (<300 years). The search was performed by Scopus (www.scopus.com), up to 22 December 2015 (search strings in Gorné and Díaz 2017). The database includes studies that measured intraspecific change in a quantitative trait and which report the elapsed time when the phenotypic change occurred. The studies recorded a single population before and after a change in the environment or compared two (or more) populations by measuring a quantitative trait across two situations, where one of them was a new condition of known age. Both, by measuring change directly in the field or by performing common condition experiments (e.g. common garden experiments or reciprocal transplants). Studies reporting results from artificial selection or interspecific hybridization were excluded. The environmental changes included expansions of distributional range, soil or air pollution, exposure to herbicides, changes in salinity, pH, climate, disturbance or irrigation regime, and addition or loss of species in the local community. All data available in each study were recorded, including several observations of the same species. These procedures resulted in a database containing 1716 observations from 128 studies, with changes in populations of 152 species from 34 families, in elapsed times of < 260 years, and covering a wide range of traits, lifespan, growth forms and environmental situations.</p> <p>All data points were categorized according to biological properties of the study system (lifespan, growth form, trait type) and methodological ones. The amount and rate of phenotypic change is expresed as the standardized mean difference Hedges <em>g</em> (Hedges 1981, 1982), a rate of change which is the Hedges <em>g</em> over the elapsed time in years, and the log-transformation of both of them. The standardized mean difference is equal to the <em>haldane</em> numerator, which is a standard rate of evolution (Haldane 1949; Gingerich 1993). In addition, we upgraded the Díaz and Gorné (2017) database, computing the response ratio effect size (<em>logRR</em>) (Hedges et al. 1999) whenever possible. The response ratio is a mean-scaled metric equal to the <em>darwins</em> numerator (Haldane 1949). So that we compute a rate of change similar to <em>darwins</em> (time expressed as years instead of million years).</p> <p> </p> <p>contact email address: gorneld@gmail.com</p>
Trait records sample: Carnivora traits from traitdb (ZIP of CSV files)
This file is part of an API design process and is placed here for internal review. It contains trait records for Carnivora, and neighboring files.<p></p>This is going to similar to the previous set of CSV files, but with fewer tables and fewer columns, since it is derived from the neo4j traitdb instead of the whole back-end SQL database
Trait records sample: Carnivora traits (JSON tarball)
This file is part of an API design process and is placed here for internal review. It contains trait records for Carnivora, and neighboring files.<p></p>This is a gzipped tar file containing a single .json file
Trait records sample: Carnivora traits tarball
This file is part of an API design process and is placed here for internal review. It contains trait records for Carnivora, and neighboring files.<p></p>This tarball contains CSV files for pages, traits, and metadata.
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.