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:Fig5 Kayser TechInnoManRev2018 8-3.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''"[[Journal:Data science as an innovation challenge: From big data to value proposition|Data science as an innovation challenge: From big data to value proposition]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


Analyzing “big data” holds huge potential for generating business value. The ongoing advancement of tools and technology over recent years has created a new ecosystem full of opportunities for data-driven innovation. However, as the amount of available data rises to new heights, so too does complexity. Organizations are challenged to create the right contexts, by shaping interfaces and processes, and by asking the right questions to guide the [[data analysis]]. Lifting the innovation potential requires teaming and focus to efficiently assign available resources to the most promising initiatives. With reference to the innovation process, this article will concentrate on establishing a process for analytics projects from first ideas to realization (in most cases, a running application). The question we tackle is: what can the practical discourse on big data and analytics learn from innovation management? ('''[[Journal:Data science as an innovation challenge: From big data to value proposition|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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