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5,828 results for “Traits”
Trait Spreadsheet to DwCA: Trophic guild data for microbial eukaryotes
<p></p>https://eol-jira.bibalex.org/browse/DATA-1882<p></p>Updated: 2022-05-31 02:59
Trait Spreadsheet to DwCA: Fungi ecomorphological trait data
<p></p>https://eol-jira.bibalex.org/browse/DATA-1882<p></p>Created: 2023-05-05 15:46
Trait Spreadsheet to DwCA: Orthopteroid Body Size
<p></p>https://eol-jira.bibalex.org/browse/DATA-1882<p></p>Updated: 2023-03-28 13:01
Trait Spreadsheet to DwCA: Habitat data for fungi
<p></p>https://eol-jira.bibalex.org/browse/DATA-1882<p></p>Created: 2023-04-10 21:33
Trait Spreadsheet to DwCA: Habitat data for microbial eukaryotes
<p></p>https://eol-jira.bibalex.org/browse/DATA-1882<p></p>Updated: 2022-09-28 15:15
DeepRVAT gene-trait association testing results on the 470k UK Biobank WES dataset
<p>Association testing results from DeepRVAT on the 470k UK Biobank WES dataset, covering all tested genes and traits. Tests were performed on the full dataset and on Caucasian individuals ("cohort" column). "Significant" indicates significance after multiple testing correction (FWER < 5%).</p>
Predicting Phenotypic Traits Using Conglomerate RNA-seq Datasets -- Datasets
<p>Phenotype Annotations and Corresponding Gene Expression Matrices created for the paper Hadish et al. [REF]. Raw Phenotypic Annotations were downloaded and processed from NCBI BioProject and raw RNA-seq data was downloaded from NCBI SRA. All annotations are contained within a single file for either Tissue or Age. Gene expression matrices are for each of the 12 species used in this project in their unproccessed form. </p> <p>Scripts used in this project are located here: </p> <p>GitLab Repository: </p> <p>https://gitlab.com/ficklin-lab/predicting-phenotypic-traits-using-conglomerate-rna-seq-datasets</p> <p>Paper Citation: </p> <p>[REF]</p>
Invader defense traits in France, Japan, and the USA: dataset and R code
<p>This record includes leaf trait data for 44 species sampled in France, Japan, and the USA. R code is associated with analyses for publication.</p>
Trait Networks: Assessing Marine Community Resilience and Extinction Recovery
<p>Data and R code for Trait networks. From the paper:</p> <p> </p> <p><strong>Trait Networks: Assessing Marine Community Resilience and Extinction Recovery</strong></p> <p><strong>Summary</strong></p> <p><strong>Extensive global habitat degradation and the climate crisis are tipping the biosphere towards a “sixth” mass extinction and marine communities will not be spared from this catastrophic loss of biodiversity. The resilience of marine communities following large-scale disturbances or extinction events is mediated by the life-history traits of species and their interplay within communities. The presence and abundance of traits in communities provide proxies of function, but whether the breakdown of their associations with species loss can delineate functional loss remains unclear. Here, we propose that relationships between traits within trait networks provide unique perspectives on the importance of specific traits, trait combinations, and their role in supporting the stability of communities, whilst allowing quantification of the vulnerability of both past deep time and present-day marine communities.</strong></p>
Genetic instruments for blood cell traits
<p>This dataset includes summary statistics for genetic instruments for 9 blood cell trait phenotypes. These genetic instruments were developed in the UK Biobank and were used in several Mendelian randomization studies, including an analysis of childhood acute lymphoblastic leukemia (Kachuri et al. 2021; PMID 34469753) and glioma subtypes (Kachuri et al. 2024). Details of the GWAS and instrument development process are described in: https://pubmed.ncbi.nlm.nih.gov/34469753/</p>
POST-FIRE-GERMINATIVE-RESPONSE,-FRUITS-AND-SEEDS-TRAITS-OF-SOME-SPECIES-OF-THE-TDF-IN-COLOMBIA
<p>In fire-prone ecosystems, certain plant species possess diaspores with morphological and physiological traits that enable them to persist and thrive following fire events. Postfire germination has been associated with various fruit and seed traits, including fruit lignification, seed size, seed weight and seed dormancy. However, the collective role and interactions of these traits in determining germination tolerance after wildfires remain unclear. We characterized ten key traits (fruit type and size (length and width), seed mass and size (length and width), dispersal mechanism, storage behavior, seed dormancy, and ecological guild) across eighteen species exhibiting three post-fire germination responses (tolerant, sensitive, and stimulated). Multinomial logistic regression was used to determine the relationship between these traits. Our findings suggest practical implications for restoration and enrichment strategies in Colombian tropical dry forests by prioritizing species with protective fruits, dormant and orthodox seeds to increase the probability of sexual regeneration after fires, and to contribute to the functional diversity and resilience of these ecosystems.</p>
Table 2 in Effects of different combinations of N, P and K at different time interval on vegetative, reproductive, yield and quality traits of mango (Mangifera Indica. L) cv. Dusehri
<p><b>Table 2.</b> Effect of different fertilizer combinations of N, P and K on reproductive physiology of mango cv. Dusehri.</p><table><tbody><tr><th><b>Treatments</b></th><th><b>Growth size (mm)</b></th><th><b>Total No. of Panicle/Tree</b></th><th><b>Total no. of flowers / Panicle</b></th><th><b>Sex Ratio (%)</b></th><th><b>Fruit Drop (%)</b></th><th><b>Fruit Retention (%)</b></th><th><b>Total no. of fruit/tree</b></th><th><b>Yield (Kg/Tree)</b></th><th><b>Fruit Length (cm)</b></th><th><b>Fruit Weight (g)</b></th><th><b>Pulp Weight (g)</b></th><th><b>Stone Weight (g)</b></th><th><b>Peel Weight (g)</b></th><th><b>TSS (%)</b></th><th><b>Total Acidity (%)</b></th><th><b>TSS/Acid Ratio</b></th><th><b>Vit. C (mg/100 mL)</b></th><th><b>Total Sugar (%)</b></th></tr></tbody><tbody><tr><th>T1 (Control)</th><td>149.34d ± 3.89</td><td>397.67h ± 3.51</td><td>543.21h ± 3.61</td><td>51.17c ± 2.10</td><td>94.85a ± 1.40</td><td>1.83e ± 0.15</td><td>186.72g ± 4.51</td><td>40.01e ± 4.51</td><td>15.4b ± 3.17</td><td>155.15e ± 6.34</td><td>76.30g ± 2.22</td><td>24.14f ± 2.02</td><td>28.61f ± 1.86</td><td>20.29d ± 1.05</td><td>0.52a ± 0.005</td><td>22.43</td><td>31.26f ± 0.92</td><td>14.52c ± 0.25</td></tr><tr><th>T2 (N)</th><td>166.67b ± 4.47</td><td>508.57f ± 4.51</td><td>612.47f ± 4.58</td><td>54.42bc ± 1.05</td><td>94.86a ± 2.41</td><td>5.57d ± 0.57</td><td>213.34f ± 3.06</td><td>52.70cd ± 4.50</td><td>16.4b ± 3.11</td><td>175.50bc ± 5.50</td><td>82.41f ±1.90</td><td>30.04de ± 2.21</td><td>33.70e ± 1.38</td><td>21.06cd ± 0.95</td><td>0.49b ± 0.004</td><td>25.16</td><td>42.22c ± 1.18</td><td>15.24bc ± 0.41</td></tr><tr><th>T3 (P)</th><td>156.26cd ± 4.85</td><td>467.33g ± 4.04</td><td>593.34g ± 3.61</td><td>53.57bc ± 1.01</td><td>91.72ab± 2.51</td><td>8.82bc ± 0.72</td><td>241.40e ± 4.04</td><td>50.14d ± 5.03</td><td>18.3ab ± 2.75</td><td>169.24cd ± 5.41</td><td>85.23f ± 1.96</td><td>28.50e ± 2.00</td><td>35.62de ±1.94</td><td>22.07bc ± 1.10</td><td>0.45c ± 0.005</td><td>29.13</td><td>39.37d ± 0.98</td><td>15.82bc ± 0.29</td></tr><tr><th>T4 (K)</th><td>164.80bc ± 4.95</td><td>634.57c ± 4.51</td><td>730.19c ± 4.56</td><td>57.39bc ± 2.38</td><td>94.26ab ± 1.79</td><td>5.83d ± 0.48</td><td>278.33d ± 3.51</td><td>55.23cd ± 4.50</td><td>19.3ab ± 2.99</td><td>182.01b ± 5.47</td><td>99.92e ± 1.89</td><td>31.26e ± 1.76</td><td>40.84c ± 1.35</td><td>21.41cd ± 1.25</td><td>0.37d ± 0.002</td><td>43.27</td><td>36.62e ± 1.21</td><td>16.89b ± 0.55</td></tr><tr><th>T5 (NP)</th><td>166.48b ± 4.98</td><td>584.47d ± 3.51</td><td>639e.46 ± 4.04</td><td>56.61bc ± 1.59</td><td>93.34ab ± 2.15</td><td>6.92cd ± 0.33</td><td>288.62c ± 4.51</td><td>57.31c ± 5.03</td><td>17.2b ± 3.29</td><td>160.46de ±4.86</td><td>107.34c ± 2.11</td><td>35.63bc ± 2.02</td><td>37.81d ± 1.88</td><td>23.43ab ± 0.93</td><td>0.35d ± 0.001</td><td>40.48</td><td>42.09b ± 0.47</td><td>16.65b ± 0.61</td></tr><tr><th>T6 (NK)</th><td>160.75bc ± 5.05</td><td>684.66b ± 4.50</td><td>810.62b ± 4.59</td><td>60.17b ± 2.53</td><td>90.28cd ± 2.71</td><td>9.74ab ± 0.66</td><td>320.32b ± 3.05</td><td>66.61b ± 4.49</td><td>18.5ab ± 2.73</td><td>180.32b ± 5.35</td><td>118.04b ± 1.94</td><td>37.21b ± 1.81</td><td>46.92b ± 1.65</td><td>23.30ab ± 0.08</td><td>0.32e ± 0.002</td><td>51.28</td><td>51.48b ±1.07</td><td>16.07b ± 0.71</td></tr><tr><th>T7 (PK)</th><td>159.42bc ± 4.98</td><td>559.71e ± 3.49</td><td>701.17d ± 3.61</td><td>55.31bc ± 1.02</td><td>92.96ab ± 2.56</td><td>7.49bcd ± 0.52</td><td>274.37d ± 2.52</td><td>54.85cd ± 4.51</td><td>18.1b ± 3.01</td><td>178.24bc ± 6.53</td><td>103.51d ± 1.79</td><td>33.07cd ± 1.95</td><td>42.14c ± 1.43</td><td>2.35bc ± 0.05</td><td>0.31e ± 0.002</td><td>52.41</td><td>51.55b ± 0.95</td><td>15.01b ± 0.37</td></tr><tr><th>T8 (NPK)</th><td>177.51a ± 4.92</td><td>845.64a ± 3.61</td><td>974.52a ± 4.58</td><td>69.18a ± 2.87</td><td>86.10e ± 2.85</td><td>13.85a ± 0.43</td><td>379.05a ± 3.00</td><td>82.35a ± 3.51</td><td>23.3a ± 3.10</td><td>197.05a ± 5.62</td><td>135.32a ± 2.09</td><td>43.53a ± 2.07</td><td>52.09a ± 1.77</td><td>24.53a ± 0.06</td><td>0.26f ± 0.001</td><td>73.53</td><td>57.63a ± 0.07</td><td>20.48a ± 0.53</td></tr></tbody></table><p>Values within each column followed by the same letter are not significantly different at P <0.5 level.</p><p>Values within each column followed by the same letter are not significantly different at <i>P</i> <0.05 level.</p><p>Values within each column followed by the same letters are not significantly different at <i>P</i> <0.05 level.</p>
The influence of food web structure and foraging behaviour on visual system traits in a predatory freshwater fish
<p>Dataset used in the manuscript titled "The influence of food web structure and foraging behaviour on visual system traits in a predatory freshwater fish" Dataset includes lake trout visual system traits, body size, and food web structural attributes sampled from four different lakes in Algonquin, ON, Canada.</p>
What's the matter in phytoplankton? Highlighting the importance of stoichiometric traits in lake ecosystem models
<p>Code to run lake ecosystem simulations for Woodstoich 5 paper: <strong><em>What’s the matter in phytoplankton? Highlighting the importance of stoichiometric traits in lake ecosystem models</em></strong></p> <p> </p>
Supplementary Table S1. Combined analysis of variance containing the degrees of freedom (DF), mean squares (MS), P value (P val.), mean, coefficient of experimental variation (CEV%) and selective accuracy (SA) for the traits of luminosity (L*), chromaticity a* (a*), chromaticity b* (b*), grain length (length, mm), grain width (width, mm), grain thickness (thickness, mm), mass of 100 grains (Mass, g), normal grains (Ng, %), water absorption (absorption, %), cooking time (Ct, min:s), and concentrations of potassium (K, g kg-1 dry matter - DM), phosphorus (P, g kg-1 DM), calcium (Ca, g kg-1 DM), magnesium (Mg, g kg-1 DM), iron (Fe, mg kg-1 DM), zinc (Zn, mg kg-1 DM), and copper (Cu, mg kg-1 DM) obtained in 25 common bean cultivars evaluated in four experiments carried out from 2019 to 2021
<p><strong><span>Table S1.</span></strong><span> Combined analysis of variance.</span></p> <p><strong><span>Indirect selection for multiple technological and nutritional traits in common bean cultivars under different degrees of multicollinearity</span></strong></p> <p><strong><span>Bragantia, 2024.</span></strong></p>
Data from: Assessing the effect of tissue and fire-response traits on plant growth rates post-disturbance in Eastern Australia
<p>Here is the necessary code and data to reproduce results published in 'Assessing the effect of tissue and fire-response traits on plant growth rates post-disturbance in Eastern Australia'.</p>
100 Years of Penaeids Domestication and Meta-analysis of Breeding Traits
<p><strong>Additional files</strong></p> <p><strong>Additional file 1 Table S1</strong></p> <p>Format: csv file</p> <p>Title: Quantitative genetic publications for penaeids genetic improvement programs from 1997 to 2024.</p> <p><strong>Additional file 2 Table S2</strong></p> <p>Format: csv file</p> <p>Title: Summary of heritability estimates on breeding traits in penaeids selective breeding programs.</p> <p><strong>Additional file 3 Table S3</strong></p> <p>Format: csv file</p> <p>Title: Analysis of heritability estimates in penaeids selective breeding programs for disaese resistance, feeding efficiency, growth traits, morphological traits, quality traits, reproductive traits, stress tolerance and survival.</p>
Data from: Genome-wide search for quantitative trait loci controlling important plant and flower traits in petunia using an interspecific recombinant inbred population of Petunia axillaris and Petunia exserta
A major bottleneck in plant breeding has been the much limited genetic base and much reduced genetic diversity in domesticated, cultivated germplasm. Identification and utilization of favorable gene loci or alleles from wild or progenitor species can serve as an effective approach to increasing genetic diversity and breaking this bottleneck in plant breeding. This study was conducted to identify quantitative trait loci (QTL) in wild or progenitor petunia species that can be used to improve important horticultural traits in garden petunia. An F7 recombinant inbred population derived between Petunia axillaris and P. exserta was phenotyped for plant height, plant spread, plant size, flower counts, flower diameter, flower length, and days to anthesis, in Florida in two consecutive years. Transgressive segregation was observed for all seven traits in both years. The broad-sense heritability estimates for the traits ranged from 0.20 (days to anthesis) to 0.62 (flower length). A genome-wide genetic linkage map consisting 368 single nucleotide polymorphism bins and extending over 277 cM was searched to identify QTL for these traits. Nineteen QTL were identified and localized to five linkage groups. Eleven of the loci were identified consistently in both years; several loci explained up to 34.0% and 24.1% of the phenotypic variance for flower length and flower diameter, respectively. Multiple loci controlling different traits are co-localized in four intervals in four linkage groups. These intervals contain desirable alleles that can be introgressed into commercial petunia germplasm to expand the genetic base and improve plant performance and flower characteristics in petunia.
Optical traits perform equally well as directly-measured functional traits in explaining the impact of an invasive plant on litter decomposition
<p>1. Functional traits can help elucidate and predict the impact of invasive plant species on ecosystem functioning. Yet, this approach requires comprehensive and labor-intensive trait collection campaigns, covering intraspecific trait variation of both the invader and native species in the invaded community. One potential way to overcome these logistic constraints is using hyperspectral remote sensing technology to efficiently quantify functional trait values. Although such spectrally derived or 'optical' traits are known to closely link to directly-measured functional traits, little research has explored how well these optical traits perform in assessing invader-induced ecosystem impact. 2. Here, we explored the trait-mediated impact of the invasive Rosa rugosa on litter decomposition and evaluated whether optical traits perform equally well as directly-measured traits in predicting litter decomposition variation. We collected data on species-specific functional traits, leaf hyperspectral reflectance and standardized 'tea bag index' litter decomposition across 25 invaded and 25 uninvaded coastal grassland plots. The selected traits were all potentially related to litter decomposition and covered the leaf economics spectrum, additional leaf structural components and competitive ability. Optical traits were quantified through a combination of a physical radiative transfer model inversion and vegetation indices calculations. 3. Invasion significantly increased the stabilization factor, i.e. the amount of resulting recalcitrant litter. Invader impact on litter decomposition could be entirely explained by changes it induced in the functional traits of the native community, rather than by the invader's traits itself. More specifically, the invader pushed the invaded community towards traits associated with high litter quality. Optical traits performed equally well as directly-measured traits in explaining the invasion impact on the stabilization factor (R2= 41.9% vs. 38.5%). Furthermore, the interpretation of the results based on optical traits resulted in a similar functional understanding of the invader impact. 4. Synthesis: Our results indicate the potential of hyperspectral data to explain changes in ecosystem functioning. The combination of radiative transfer models and vegetation indices allowed to extract all relevant trait information from the hyperspectral data. This framework thus presents a practical short-cut to assess relevant leaf traits, requiring only a limited amount of field trait measurements.</p>
Data from: Functional response of subordinate species to intraspecific trait variability within dominant species
1. Dominant species can act as a biotic filter in structuring plant communities by constraining the establishment and survival of subordinate species. The effect of intraspecific trait variability of dominant species on the functional response of subordinate species, however, is not well understood. 2. We quantified intraspecific variation in four functional traits of 26 subordinate species established in an experimental grassland established with two population sources (i.e., cultivars and local ecotypes) of three dominant grasses (Sorghastrum nutans, Andropogon gerardii, and Schizachyrium scoparium) and three pools of subordinate species (each from one origin) within each of the dominant grass source treatments. 3. Twenty of the 26 subordinate species exhibited intraspecific trait variability for one trait or more in response to dominant species population source, and variation among population sources of the dominant species was non-random. Dominant grass population source affected intraspecific variability in functional traits of multiple subordinate species. Cultivar sources of the dominant grasses and some of the subordinate species that established with them had higher and generally more variable functional leaf area and leaf nitrogen content compared to local ecotypes of the dominant grasses and the subordinate species that established with them. 4. Synthesis. This study provides evidence that intraspecific trait variability in dominant species acts as an inner, biotic filter to constrain niche availability and dimensionality affecting trait variation of subordinate species during community assembly.
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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.