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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Day 253 - West Midlands Police - Forensic Science Lab (7969822920).jpg|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''[[Forensic science]]''' (often shortened to '''forensics''') is the application of a broad spectrum of sciences — from anthropology to toxicology — to answer questions of interest to a legal system. During the course of an investigation, forensic scientists collect, preserve, and analyze scientific evidence using a variety of special [[laboratory]] equipment  and special techniques for such interests. In addition to their laboratory role, the forensic scientists may also testify as an expert witness in both criminal and civil cases and can work for either the prosecution or the defense.
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


Much of the work of forensic science is conducted in the forensic laboratory. Such a laboratory has many similarities to a traditional [[clinical laboratory|clinical]] or research lab in so much that it contains various lab instruments and several areas set aside for different tasks. However, it differs in other ways. Windows, for example, represent a point of entry into a forensic lab, which must be secure as it contains evidence to crimes. ('''[[Forensic science|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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