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:Tab1 Li RivItal21 3-1.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''"[[Journal:Cross-border data transfer regulation in China|Cross-border data transfer regulation in China]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


With the growing participation of emerging countries in global data governance, the traditional legislative paradigm dominated by the European Union and the United States is constantly being analyzed and reshaped. It is of particular importance for China to establish the regulatory framework of cross-border data transfer, for not only does it involve the rights of Chinese citizens and entities, but also the concepts of cyber-sovereignty and national security, as well as the framing of global cyberspace rules. China continues to leverage data sovereignty to persuade lawmakers to support the development of critical technology in digital domains and infrastructure construction. This paper aims to systematically and chronologically describe Chinese regulations for cross-border data exchange. Enacted and draft provisions—as well as binding and non-binding regulatory rules—are studied, and various positive dynamic developments in the framing of China’s cross-border data regulation are shown ... ('''[[Journal:Cross-border data transfer regulation in China|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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