Comprehensive 3D phenotyping reveals continuous morphological variation across genetically diverse sorghum inflorescences is a research paper published in New Phytologist (2020). On theSindex it has a DataRank of 0. It has been cited 50 times.
Scored on demand from live citation data
Linked data & code
DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
We only score data papers we can read in full — never from an abstract alone.
National Science Foundation
Grant: DBI‐1759796
National Science Foundation
Grant: IOS‐1638507
National Institutes of Health
Grant: CA233303‐1
NCI NIH HHS
Grant: U2C CA233303
National Science Foundation
Grant: IOS‐1822330
National Science Foundation
Grant: RI‐1618685
National Science Foundation
Grant: DEB‐1457748
National Science Foundation
Grant: 1822330
RESEARCH-PGR: PanAnd - Harnessing convergence and constraint to predict adaptations to abiotic stress for maize and sorghum
National Science Foundation
Grant: 1759796
Collaborative Research: ABI Innovation: Algorithms for recovering root architecture from 3D imaging
National Science Foundation
Grant: 1457748
Evolution of dispersal and pollination in ecologically dominant grasses
National Institutes of Health
Grant: 5U2CCA233303-03
Washington University Human Tumor Atlas Research Center
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals
Additional file 1 of TopoRoot: a method for computing hierarchy and fine-grained traits of maize roots from 3D imaging
Additional file 1 of TopoRoot: a method for computing hierarchy and fine-grained traits of maize roots from 3D imaging
Additional file 4 of Detection and characterization of spike architecture based on deep learning and X-ray computed tomography in barley
Additional file 2 of Detection and characterization of spike architecture based on deep learning and X-ray computed tomography in barley
Additional file 2 of Detection and characterization of spike architecture based on deep learning and X-ray computed tomography in barley
Additional file 4 of Detection and characterization of spike architecture based on deep learning and X-ray computed tomography in barley
Additional file 1 of Detection and characterization of spike architecture based on deep learning and X-ray computed tomography in barley
Additional file 3 of Detection and characterization of spike architecture based on deep learning and X-ray computed tomography in barley
Additional file 1 of Detection and characterization of spike architecture based on deep learning and X-ray computed tomography in barley
Additional file 3 of Detection and characterization of spike architecture based on deep learning and X-ray computed tomography in barley