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 Pillai FrontBioengBiotech2022 10.jpg|120px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''"[[Journal:Practical considerations for laboratories: Implementing a holistic quality management system|Practical considerations for laboratories: Implementing a holistic quality management system]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


A [[quality management system]] (QMS) is an essential element for the effective operation of [[research]], clinical, testing, or production/manufacturing [[Laboratory|laboratories]]. As technology continues to rapidly advance and new challenges arise, laboratories worldwide have responded with innovation and process changes to meet the continued demand. It is critical for laboratories to maintain a robust QMS that accommodates laboratory activities (e.g., basic and applied research; regulatory, clinical, or proficiency testing), records management, and a path for [[Continual improvement process|continuous improvement]] to ensure that results and data are reliable, accurate, timely, and reproducible. A robust, suitable QMS provides a framework to address gaps and risks throughout the laboratory's [[workflow]] that could potentially lead to a critical error, thus compromising the integrity and credibility of the institution. While there are many QMS frameworks (e.g., a model such as a consensus standard, guideline, or regulation) that may apply to laboratories, ensuring that the appropriate framework is adopted based on the type of work performed and that key implementation steps are taken is important for the long-term success of the QMS and for the advancement of science ... ('''[[Journal:Practical considerations for laboratories: Implementing a holistic quality management system|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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