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:US-DeptOfHHS-Logo.svg|180px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
The '''[[United States Department of Health and Human Services]]''' ('''HHS'''), also known as the '''Health Department''', is a cabinet-level department of the Federal government of the United States with the goal of protecting the health of all Americans and providing essential human services. Its motto is "Improving the health, safety, and well-being of America". Before the separate federal Department of Education was created in 1979, it was called the Department of Health, Education, and Welfare (HEW).
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


HHS is administered by the Secretary of Health and Human Services, who is appointed by the President with the advice and consent of the Senate. The United States Public Health Service (PHS) is the main division of the HHS and is led by the Assistant Secretary for Health. The current Secretary, Kathleen Sebelius is the Vice-Chair of the United States Interagency Council on Homelessness. The Department of Health and Human Services is a member of the Council, which is dedicated to preventing and ending homelessness in America.
[[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'':
As of May 2013, CMS administers 115 programs over 11 operating divisions, including the [[Centers for Disease Control and Prevention]] (CDC), the [[Centers for Medicare and Medicaid Services]] (CMS), and the [[National Institutes of Health]] (NIH). ('''[[United States Department of Health and Human Services|Full article...]]''')<br />
{{flowlist |
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* [[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'': [[Bioimage informatics]], [[Biobank]], [[Translational research]]
* [[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]]
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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...)
Recently featured:

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