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:Fig6 Argento EMBOReports2020 21-3.jpg|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''"[[Journal:Institutional ELN-LIMS deployment: Highly customizable ELN-LIMS platform as a cornerstone of digital transformation for life sciences research institutes|Institutional ELN-LIMS deployment: Highly customizable ELN-LIMS platform as a cornerstone of digital transformation for life sciences research institutes]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


The systematic recording and management of experimental data in academic life science research remains an open problem. École Polytechnique Fédérale de Lausanne (EPFL) engaged in a program of deploying both an [[electronic laboratory notebook]] (ELN) and a [[laboratory information management system]] (LIMS) six years ago, encountering a host of fundamental questions at the institutional level and within each [[laboratory]]. Here, based on our experience, we aim to share with research institute managers, principal investigators (PIs), and any scientists involved in a combined ELN-LIMS deployment helpful tips and tools, with a focus on surrounding yourself with the right people and the right software at the right time. In this article we describe the resources used, the challenges encountered, key success factors, and the results obtained at each phase of our project. Finally, we discuss the current and next challenges we face, as well as how our experience leads us to support the creation of a new position in the research group: the laboratory data manager. ('''[[Journal:Institutional ELN-LIMS deployment: Highly customizable ELN-LIMS platform as a cornerstone of digital transformation for life sciences research institutes|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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