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

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'''"[[Journal:Data without software are just numbers|Data without software are just numbers]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


Great strides have been made to encourage researchers to [[Archival informatics|archive]] data created by research and provide the necessary systems to support their storage. Additionally, it is recognized that data are meaningless unless their provenance is preserved, through appropriate [[metadata]]. Alongside this is a pressing need to ensure the [[Software quality|quality]] and archiving of the software that generates data, through simulation and control of experiment or data collection, and that which [[Data analysis|analyzes]], modifies, and draws value from raw data. In order to meet the aims of reproducibility, we argue that [[Information management|data management]] alone is insufficient: it must be accompanied by [[Systems development life cycle|good software practices]], the training to facilitate it, and the support of stakeholders, including appropriate recognition for software as a research output. ('''[[Journal:Data without software are just numbers|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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