Data from: Salinity tolerance loci revealed in rice using high-throughput non-invasive phenotyping
Nature Communications, ISSN: 2041-1723
2016
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Dataset Description
High-throughput phenotyping produces multiple measurements over time, which require new methods of analyses that are flexible in their quantification of plant growth and transpiration, yet are computationally economic. Here we develop such analyses and apply this to a rice population genotyped with a 700k SNP high-density array. Two rice diversity panels, indica and aus, containing a total of 553 genotypes, are phenotyped in waterlogged conditions. Using cubic smoothing splines to estimate plant growth and transpiration, we identify four time intervals that characterize the early responses of rice to salinity. Relative growth rate, transpiration rate and transpiration use efficiency (TUE) are analysed using a new association model that takes into account the interaction between treatment (control and salt) and genetic marker. This model allows the identification of previously undetected loci affecting TUE on chromosome 11, providing insights into the early responses of rice to salin...
Bibliographic Details
https://datadryad.org/dataset/doi:10.5061/dryad.3118j; https://zenodo.org/record/4936155; http://datadryad.org/resource/doi:10.5061/dryad.3118j/1; http://datadryad.org/resource/doi:10.5061/dryad.3118j/3; https://zenodo.org/records/4936155; http://datadryad.org/resource/doi:10.5061/dryad.3118j/2; http://dx.doi.org/10.5061/dryad.3118j/3; https://dx.doi.org/10.5061/dryad.3118j/3; https://datadryad.org/stash/dataset/doi:10.5061/dryad.3118j; http://dx.doi.org/10.5061/dryad.3118j/1; https://dx.doi.org/10.5061/dryad.3118j/1; http://dx.doi.org/10.5061/dryad.3118j/2; https://dx.doi.org/10.5061/dryad.3118j/2; http://dx.doi.org/10.5061/dryad.3118j; https://dx.doi.org/10.5061/dryad.3118j
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