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:Fig3 Schröder JofBioSem22 13.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''"[[Journal:Structure-based knowledge acquisition from electronic lab notebooks for research data provenance documentation|Structure-based knowledge acquisition from electronic lab notebooks for research data provenance documentation]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


[[Electronic laboratory notebook]]s (ELNs) are used to document experiments and investigations in the wet [[Laboratory|lab]]. Protocols in ELNs contain a detailed description of the conducted steps, including the necessary [[information]] to understand the procedure and the raised research data, as well as to reproduce the research investigation. The purpose of this study is to investigate whether such ELN protocols can be used to create [[wikipedia:Semantics|semantic]] documentation of the provenance of research data by the use of [[Ontology (information science)|ontologies]] and linked data methodologies. Based on an ELN protocol of a biomedical wet lab experiment, a retrospective [[wikipedia:Provenance#Data provenance|provenance model]] of the raised research data describing the details of the experiment in a machine-interpretable way is manually engineered. Furthermore, an automated approach for knowledge acquisition from ELN protocols is derived from these results. This structure-based approach exploits the structure in the experiment’s description—such as headings, tables, and links—to translate the ELN protocol into a semantic knowledge representation ... ('''[[Journal:Structure-based knowledge acquisition from electronic lab notebooks for research data provenance documentation|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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''Recently featured'':
''Recently featured'':
{{flowlist |
{{flowlist |
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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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