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:Fig1 Sun JBiosafetyBiosecurity2021 3-1.jpg|120px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''"[[Journal:Laboratory information management system for the biosafety laboratory: Safety and efficiency|Laboratory information management system for the biosafety laboratory: Safety and efficiency]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


The [[laboratory information management system]] (LIMS) has been widely used to facilitate [[laboratory]] activities. However, the current LIMS does not contain functions to improve the [[Laboratory#Laboratory safety|safety of laboratory work]], which is a major concern of [[biosafety]] laboratories (BSLs). With significant biosafety [[information]] needing to be managed and an increasing number of biosafety-related research projects underway, it is worthy of expanding the current framework of LIMS and building a system that is more suitable for BSL usage. Such a system should carefully trade off between the safety and efficiency of regular lab activities, allowing laboratory staff to conduct their research as freely as possible while also ensuring their and the environment’s safety. In order to achieve this goal, relevant information on the type of research, laboratory personnel, experimental materials, and experimental equipment must be collected and fully utilized by a centralized system and its databases. ('''[[Journal:Laboratory information management system for the biosafety laboratory: Safety and efficiency|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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