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 Fuertes GIMDS2018 7-1.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''"[[Journal:CÆLIS: Software for assimilation, management, and processing data of an atmospheric measurement network|CÆLIS: Software for assimilation, management, and processing data of an atmospheric measurement network]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


Given the importance of atmospheric aerosols, the number of instruments and measurement networks which focus on its characterization is growing. Many challenges are derived from standardization of protocols, monitoring of instrument status to evaluate network [[Data integrity|data quality]], and manipulation and distribution of large volumes of data (raw and processed). CÆLIS is a software system which aims to simplify the management of a network, providing the scientific community a new tool for monitoring instruments, processing data in real time, and working with the data. Since 2008, CÆLIS has been successfully applied to the photometer calibration facility managed by the University of Valladolid, Spain, under the framework of the Aerosol Robotic Network (AERONET). Thanks to the use of advanced tools, this facility has been able to analyze a growing number of stations and data in real time, which greatly benefits network management and data quality control. The work describes the system architecture of CÆLIS and gives some examples of applications and data processing. ('''[[Journal:CÆLIS: Software for assimilation, management, and processing data of an atmospheric measurement network|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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