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ShareScore release 0.7.1
Dataset results
74 results for “Environment Prediction”
Data from: How feathered are birds? environment predicts both the mass and density of body feathers
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Data from: Nonlinear averaging of thermal experience predicts population growth rates in a thermally variable environment
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Data from: Genome-environment associations in sorghum landraces predict adaptive traits
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Data from: Prenatal stress effects in a wild, long-lived primate: predictive adaptive responses in an unpredictable environment
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Data from: The predictive adaptive response: modeling the life history evolution of the butterfly, Bicyclus anynana, in seasonal environments.
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Data from: Adaptive value of phenological traits in stressful environments: predictions based on seed production and laboratory natural selection
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Data from: Chaos and the (un)predictability of evolution in a changing environment
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Data from: Amphibian species' traits, evolutionary history, and environment predict Batrachochytrium dendrobatidis infection patterns, but not extinction risk
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Data from: Predicting biotic interactions and their variability in a changing environment
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Antisense transcription predicts a distinct chromatin environment at mammalian promoters
GEO Series GSE74308. Homo sapiens. 7 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing; Other.
Data from: Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials
Breeding for drought tolerance is a challenging task that requires costly, extensive and precise phenotyping. Genomic selection (GS) can be used to maximize selection efficiency and the genetic gains in maize (Zea mays L.) breeding programs for drought tolerance. Here we evaluated the accuracy of genomic selection of additive (A) against additive+dominance (AD) models to predict the performance of untested maize single-cross hybrids for drought tolerance in multi-environment trials. Phenotypic data of five drought-tolerance traits were measured in 308 hybrids in eight trials under water-stressed (WS) and well-watered (WW) conditions over two years and two locations in Brazil. Hybrids' genotypes were inferred based on their parents' genotypes (inbred lines) using single nucleotide polymorphism data obtained via genotyping-by-sequencing. GS analyses were performed using genomic best linear unbiased prediction by fitting a factor analytic (FA) multiplicative mixed model. Results showed differences in the predictive accuracy between A and AD models for the five traits under consideration in both water conditions. For grain yield (GY), the AD model doubled the predictive accuracy in comparison to the A model. FA framework allowed for investigating the stability of additive and dominance effects across environments, as well as the additive- and dominance-by-environment interactions, with interesting applications for parental and hybrid selection. Prediction performance of untested hybrids using GS that benefit from borrowing information from correlated trials increased 40% and 9% for A and AD models, respectively. These results highlighted the importance of multi-environment trial analysis with GS that incorporate dominance effects into genomic predictions of GY in maize single-cross hybrids.
Predicting the solvation of organic compounds in aqueous environments: from alkanes and alcohols to pharmaceuticals
<p>Please refer to the article entitled "Predicting the solvation of organic compounds in aqueous environments: from alkanes and alcohols to pharmaceuticals" for full detail.</p>
Data from: Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials
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Complementary patterns of gene expression by human oligodendrocyte progenitors and their environment predict determinants of progenitor maintenance and differentiation.
GEO Series GSE26535. Homo sapiens. 6 samples. Type: Expression profiling by array.
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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.