Difference between revisions of "Template:Article of the week"

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'''"[[Journal:Developing a bioinformatics program and supporting infrastructure in a biomedical library|Developing a bioinformatics program and supporting infrastructure in a biomedical library]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


Over the last couple decades, the field of [[bioinformatics]] has helped spur medical discoveries that offer a better understanding of the genetic basis of disease, which in turn improve public health and save lives. Concomitantly, support requirements for molecular biology researchers have grown in scope and complexity, incorporating specialized resources, technologies, and techniques.
[[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 />
 
''Recently featured'':
To address this specific need among [[National Institutes of Health]] (NIH) intramural researchers, the NIH Library hired an expert bioinformatics trainer and consultant with a PhD in biochemistry to implement a bioinformatics support program. This study traces the program from its inception in 2009 to its present form. Discussion involves the particular skills of program staff, development of content, collection of resources, associated technology, assessment, and the impact of the program on the NIH community. ('''[[Journal:Developing a bioinformatics program and supporting infrastructure in a biomedical library|Full article...]]''')<br />
{{flowlist |
<br />
* [[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]]
''Recently featured'':  
* [[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:Big data and public health systems: Issues and opportunities|Big data and public health systems: Issues and opportunities]]
* [[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]]
: ▪ [[Journal:Generating big data sets from knowledge-based decision support systems to pursue value-based healthcare|Generating big data sets from knowledge-based decision support systems to pursue value-based healthcare]]
}}
: ▪ [[Journal:Characterizing and managing missing structured data in electronic health records: Data analysis|Characterizing and managing missing structured data in electronic health records: Data analysis]]

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...)
Recently featured:

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