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 Huang ICTExpress2017 3-2.jpg|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''"[[Journal:Energy informatics: Fundamentals and standardization|Energy informatics: Fundamentals and standardization]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


Based on international standardization and power utility practices, this paper presents a preliminary and systematic study on the field of energy [[informatics]] and analyzes boundary expansion of information and energy systems, and the convergence of energy systems and ICT. A comprehensive introduction of the fundamentals and standardization of energy informatics is provided, and several key open issues are identified.
[[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'':
With the changing of global climate and a world energy shortage, a smooth transition from conventional fossil fuel-based energy supplies to renewable energy sources is critical for the sustainable development of human society. Meanwhile, the energy domain is experiencing a paradigmatic change by integrating conventional energy systems with advanced [[information]] and communication technologies (ICT), which poses new challenges to the efficient operation and design of energy systems. ('''[[Journal:Energy informatics: Fundamentals and standardization|Full article...]]''')<br />
{{flowlist |
<br />
* [[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]]
''Recently featured'':  
* [[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]]
: ▪ [[Journal:Electronic laboratory notebooks in a public–private partnership|Electronic laboratory notebooks in a public–private partnership]]
* [[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]]
: ▪ [[Journal:Laboratory information system – Where are we today?|Laboratory information system – Where are we today?]]
}}
: ▪ [[Journal:Clinical note creation, binning, and artificial intelligence|Clinical note creation, binning, and artificial intelligence]]

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...)
Recently featured:

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