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Crops In Silico: Generating Virtual Crops Using an Integrative and Multi-scale Modeling Platform

Overview of attention for article published in Frontiers in Plant Science, May 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (94th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

blogs
3 blogs
twitter
43 X users

Citations

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116 Dimensions

Readers on

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177 Mendeley
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Title
Crops In Silico: Generating Virtual Crops Using an Integrative and Multi-scale Modeling Platform
Published in
Frontiers in Plant Science, May 2017
DOI 10.3389/fpls.2017.00786
Pubmed ID
Authors

Amy Marshall-Colon, Stephen P. Long, Douglas K. Allen, Gabrielle Allen, Daniel A. Beard, Bedrich Benes, Susanne von Caemmerer, A. J. Christensen, Donna J. Cox, John C. Hart, Peter M. Hirst, Kavya Kannan, Daniel S. Katz, Jonathan P. Lynch, Andrew J. Millar, Balaji Panneerselvam, Nathan D. Price, Przemyslaw Prusinkiewicz, David Raila, Rachel G. Shekar, Stuti Shrivastava, Diwakar Shukla, Venkatraman Srinivasan, Mark Stitt, Matthew J. Turk, Eberhard O. Voit, Yu Wang, Xinyou Yin, Xin-Guang Zhu

Abstract

Multi-scale models can facilitate whole plant simulations by linking gene networks, protein synthesis, metabolic pathways, physiology, and growth. Whole plant models can be further integrated with ecosystem, weather, and climate models to predict how various interactions respond to environmental perturbations. These models have the potential to fill in missing mechanistic details and generate new hypotheses to prioritize directed engineering efforts. Outcomes will potentially accelerate improvement of crop yield, sustainability, and increase future food security. It is time for a paradigm shift in plant modeling, from largely isolated efforts to a connected community that takes advantage of advances in high performance computing and mechanistic understanding of plant processes. Tools for guiding future crop breeding and engineering, understanding the implications of discoveries at the molecular level for whole plant behavior, and improved prediction of plant and ecosystem responses to the environment are urgently needed. The purpose of this perspective is to introduce Crops in silico (cropsinsilico.org), an integrative and multi-scale modeling platform, as one solution that combines isolated modeling efforts toward the generation of virtual crops, which is open and accessible to the entire plant biology community. The major challenges involved both in the development and deployment of a shared, multi-scale modeling platform, which are summarized in this prospectus, were recently identified during the first Crops in silico Symposium and Workshop.

X Demographics

X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 177 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 1 <1%
Chile 1 <1%
Unknown 175 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 41 23%
Student > Ph. D. Student 36 20%
Student > Master 12 7%
Student > Doctoral Student 12 7%
Student > Bachelor 11 6%
Other 29 16%
Unknown 36 20%
Readers by discipline Count As %
Agricultural and Biological Sciences 67 38%
Biochemistry, Genetics and Molecular Biology 19 11%
Engineering 9 5%
Computer Science 7 4%
Environmental Science 5 3%
Other 21 12%
Unknown 49 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 46. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 December 2019.
All research outputs
#959,200
of 26,461,995 outputs
Outputs from Frontiers in Plant Science
#247
of 25,294 outputs
Outputs of similar age
#18,469
of 329,481 outputs
Outputs of similar age from Frontiers in Plant Science
#5
of 610 outputs
Altmetric has tracked 26,461,995 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 25,294 research outputs from this source. They receive a mean Attention Score of 4.0. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 329,481 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 610 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.