Our first bioRxiv submission
Our recent manuscript on image-based phenotyping in sorghum has been in the review process for a while now, so we decided to post it on the bioRxiv preprint server (after we received permission from the journal peer review manager and editor) to get it out in the wild prior to publication. The premise of the bioRxiv (and its inspiration in the physical sciences, the arXiv) is to make scientific findings immediately available, and Needhi Bhalla wrote a useful primer on the pros and cons of submitting your work to a preprint server prior to publication.
I'm not sure if we'll always choose to post a preprint, but I think it makes sense this time since (1) we know the manuscript has already been shared with others outside of our intended circle of collaborators, and (2) this contribution is more timely than our past findings given the goals of the ARPA-E TERRA initiative. Even though pre-prints don't hold the same prestige as peer-reviewed journal articles, there will at least be timestamped documentation and source code that can be used by others and can be shown to potential future employers.
Without further ado, the following text links to a preprint documenting an image-based phenotyping platform we developed and quantitative trait loci that we identified using it: "3D sorghum reconstructions from depth images enable identification of quantitative trait loci regulating shoot architecture" by McCormick, Truong, and Mullet.
The software we developed can be found on GitHub by following this link. Many previous posts on this blog were written in the context of developing that code.
And most importantly, here are some 3D sorghum plants.
I'm not sure if we'll always choose to post a preprint, but I think it makes sense this time since (1) we know the manuscript has already been shared with others outside of our intended circle of collaborators, and (2) this contribution is more timely than our past findings given the goals of the ARPA-E TERRA initiative. Even though pre-prints don't hold the same prestige as peer-reviewed journal articles, there will at least be timestamped documentation and source code that can be used by others and can be shown to potential future employers.
Without further ado, the following text links to a preprint documenting an image-based phenotyping platform we developed and quantitative trait loci that we identified using it: "3D sorghum reconstructions from depth images enable identification of quantitative trait loci regulating shoot architecture" by McCormick, Truong, and Mullet.
The software we developed can be found on GitHub by following this link. Many previous posts on this blog were written in the context of developing that code.
And most importantly, here are some 3D sorghum plants.
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