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 Green NIST221-22.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''"[[Journal:A roadmap for LIMS at NIST Material Measurement Laboratory|A roadmap for LIMS at NIST Material Measurement Laboratory]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


Over the past decade, emerging technology in [[laboratory]] and computational science has changed the landscape for research by accelerating the production, processing, and exchange of data. The NIST Material Measurement Laboratory community recognizes that to keep pace with the transformation of measurement science to a digital paradigm, it is essential to implement [[laboratory information management system]]s (LIMS). Effective introduction of LIMS early in the research life cycle provides direct support for planning and execution of experiments and accelerating research productivity. From this perspective, LIMS are not passive entities with isolated interaction, but rather key resources supporting collaboration, scientific integrity, and transfer of knowledge over time. They serve as a delivery system for organizational contributions to the broader federated data community, supporting both controlled and open access, determined by the sensitivity of the research ... ('''[[Journal:A roadmap for LIMS at NIST Material Measurement Laboratory|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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