Difference between revisions of "Template:Article of the week"

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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Kindler F1000Res2017 5.gif|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''"[[Journal:Method-centered digital communities on protocols.io for fast-paced scientific innovation|Method-centered digital communities on protocols.io for fast-paced scientific innovation]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


The internet has enabled online social interaction for scientists beyond physical meetings and conferences. Yet despite these innovations in communication, dissemination of methods is often relegated to just academic publishing. Further, these methods remain static, with subsequent advances published elsewhere and unlinked. For communities undergoing fast-paced innovation, researchers need new capabilities to share, obtain feedback, and publish methods at the forefront of scientific development. For example, a renaissance in virology is now underway given the new metagenomic methods to sequence viral DNA directly from an environment. Metagenomics makes it possible to “see” natural viral communities that could not be previously studied through culturing methods. Yet, the knowledge of specialized techniques for the production and analysis of viral metagenomes remains in a subset of labs. ('''[[Journal:Method-centered digital communities on protocols.io for fast-paced scientific innovation|Full article...]]''')<br />
[[Artificial intelligence]] (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful, and usable ways to integrate, compare, and [[Data visualization|visualize]] large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in [[Information management|data management]] that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of [[machine learning]] (ML), which holds much promise for this domain ... ('''[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Full article...]]''')<br />
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Revision as of 17:50, 15 April 2024

Tab1 Williamson F1000Res2023 10.png

"Data management challenges for artificial intelligence in plant and agricultural research"

Artificial intelligence (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful, and usable ways to integrate, compare, and visualize large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in data management that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of machine learning (ML), which holds much promise for this domain ... (Full article...)
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