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'''[[Evolutionary informatics]]''' is a sub-branch of [[informatics]] that addresses the algorithmic and technological tools (like information and analytical systems) needed to better manage data from research in ecology and evolutionary biology and answer evolutionary questions. As in [[bioinformatics]] and [[genomics]], scientists studying biological evolution have gathered an increasingly large volume of information, resulting in information management problems. Additionally, as bioinformatics and genomics are pertinent to the study of evolution, utilization of information from those areas is of concern in evolutionary informatics.
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


Evolutionary informatics has evolved out of a wide variety of scientific, mathematical, and computational endeavors, including evolutionary biology, evolutionary computation, algorithmic and evolutionary algorithmic research, and software development. It can help tackle problems and tasks such as reducing "the growing number of lineages that lack formal taxonomic names," digitizing and semantically enhancing legacy biodiversity data while also making it more portable, and building "sustainable digital community repositories that provide access to rich data and metadata" in the field of evolutionary biology. ('''[[Evolutionary informatics|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'': [[Scientific data management system]], [[Centers for Disease Control and Prevention]], [[Histopathology]]
{{flowlist |
* [[Journal:A blockchain-driven IoT-based food quality traceability system for dairy products using a deep learning model|A blockchain-driven IoT-based food quality traceability system for dairy products using a deep learning model]]
* [[Journal:Effect of good clinical laboratory practices (GCLP) quality training on knowledge, attitude, and practice among laboratory professionals: Quasi-experimental study|Effect of good clinical laboratory practices (GCLP) quality training on knowledge, attitude, and practice among laboratory professionals: Quasi-experimental study]]
* [[Journal:GitHub as an open electronic laboratory notebook for real-time sharing of knowledge and collaboration|GitHub as an open electronic laboratory notebook for real-time sharing of knowledge and collaboration]]
}}

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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