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'''"[[Journal:Ten simple rules for cultivating open science and collaborative R&D|Ten simple rules for cultivating open science and collaborative R&D]]"'''
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


How can we address the complexity and cost of applying science to societal challenges?
[[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 />
 
''Recently featured'':
Open science and collaborative R&D may help. Open science has been described as "a research accelerator." Open science implies open access but goes beyond it: "Imagine a connected online web of scientific knowledge that integrates and connects data, computer code, chains of scientific reasoning, descriptions of open problems, and beyond ... tightly integrated with a scientific social web that directs scientists' attention where it is most valuable, releasing enormous collaborative potential."
{{flowlist |
 
* [[Journal:A blockchain-driven IoT-based food quality traceability system for dairy products using a deep learning model|A blockchain-driven IoT-based food quality traceability system for dairy products using a deep learning model]]
Open science and collaborative approaches are often described as open-source, by analogy with open-source software such as the operating system Linux which powers Google and Amazon — collaboratively created software which is free to use and adapt, and popular for internet infrastructure and scientific research. ('''[[Journal:Ten simple rules for cultivating open science and collaborative R&D|Full article...]]''')<br />
* [[Journal:Effect of good clinical laboratory practices (GCLP) quality training on knowledge, attitude, and practice among laboratory professionals: Quasi-experimental study|Effect of good clinical laboratory practices (GCLP) quality training on knowledge, attitude, and practice among laboratory professionals: Quasi-experimental study]]
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* [[Journal:GitHub as an open electronic laboratory notebook for real-time sharing of knowledge and collaboration|GitHub as an open electronic laboratory notebook for real-time sharing of knowledge and collaboration]]
''Recently featured'':  
}}
: ▪ [[Journal:Ten simple rules to enable multi-site collaborations through data sharing|Ten simple rules to enable multi-site collaborations through data sharing]]
: ▪ [[Journal:Ten simple rules for developing usable software in computational biology|Ten simple rules for developing usable software in computational biology]]
: ▪ [[Journal:The effect of the General Data Protection Regulation on medical research|The effect of the General Data Protection Regulation on medical research]]

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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