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

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'''"[[Journal:Next steps for access to safe, secure DNA synthesis|Next steps for access to safe, secure DNA synthesis]]"'''
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


The [[DNA synthesis]] industry has, since the invention of gene-length synthesis, worked proactively to ensure synthesis is carried out securely and safely. Informed by guidance from the U.S. government, several of these companies have collaborated over the last decade to produce a set of best practices for customer and sequence screening prior to manufacture. Taken together, these practices ensure that synthetic DNA is used to advance research that is designed and intended for public benefit. With increasing scale in the industry and expanding capability in the synthetic biology toolset, it is worth revisiting current practices to evaluate additional measures to ensure the continued safety and wide availability of DNA synthesis. Here we encourage specific steps, in part derived from successes in the [[cybersecurity]] community, that can ensure synthesis screening systems stay well ahead of emerging challenges, to continue to enable responsible research advances. [[Artificial gene synthesis|Gene synthesis]] companies, science and technology funders, policymakers, and the scientific community as a whole have a shared duty to continue to minimize risk and maximize the safety and security of DNA synthesis to further power world-changing developments in advanced biological manufacturing, agriculture, drug development, healthcare, and energy. ('''[[Journal:Next steps for access to safe, secure DNA synthesis|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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