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:Fig2 Heinen BMCBioinfo2020 21.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''"[[Journal:HEnRY: A DZIF LIMS tool for the collection and documentation of biospecimens in multicentre studies|HEnRY: A DZIF LIMS tool for the collection and documentation of biospecimens in multicentre studies]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


Well-characterized biological specimens (biospecimens) of high quality have great potential for the acceleration of and quality improvement in [[Translational research|translational biomedical research]]. To improve accessibility of local [[Sample (material)|specimen]] collections, efforts have been made to create central repositories ([[biobank]]s) and catalogues. Available technical solutions for creating professional local specimen catalogues and connecting them to central systems are cost intensive and/or technically complex to implement. Therefore, the HIV-focused Thematic Translational Unit (TTU) of the German Center for Infection Research (DZIF) developed a [[laboratory information management system]] (LIMS) called HIV Engaged Research Technology (HEnRY) for implementation into the HIV Translational Platform (TP-HIV) at the DZIF and other research networks. ('''[[Journal:HEnRY: A DZIF LIMS tool for the collection and documentation of biospecimens in multicentre studies|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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