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:NIH Master Logo Vertical 2Color.png|160px]]</div>
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The '''[[National Institutes of Health]]''' ('''NIH''') is a biomedical research facility primarily located in Bethesda, Maryland, USA, operating as an agency of the [[United States Department of Health and Human Services]]. The NIH is the U.S. agency most responsible for biomedical and health-related research, primarily through its Intramural Research Program (IRP), which claims to be "the largest institution for biomedical science on earth." In addition to conducting its own research, the agency provides major biomedical research funding to non-NIH research facilities through its Extramural Research Program (ERP). For example, in 2003 the NIH and its extramural arm provided 28% of biomedical research funding spent annually in the U.S., or about $26.4 billion.
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


The NIH comprises 27 separate institutes and centers that conduct research in different disciplines of biomedical science. The IRP is responsible for many scientific accomplishments, including the discovery of fluoride to prevent tooth decay, the use of lithium to manage bipolar disorder, and the creation of vaccines against hepatitis, ''Haemophilus influenzae'' (HIB), and human papillomavirus. The funding of NIH has at times been a source of contention in Congress, serving as a proxy for the political currents of the time. In fiscal year 2010, NIH spent $10.7 billion (not including temporary funding from the ARRA) on clinical research, $7.4 billion on genetics-related research, $6.0 billion on prevention research, $5.8 billion on cancer, and $5.7 billion on [[biotechnology]]. ('''[[National Institutes of Health]]''')<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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