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:Datacenter-telecom.jpg|220px]]</div>
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A '''[[data center]]''' is a facility used to house computer systems and associated components such as telecommunications and storage systems. A data center generally includes redundant or backup power supplies, redundant data communications connections, environmental controls (e.g. air conditioning and fire suppression), and various security devices. Data centers vary in size, with some large data centers capable of using as much electricity as a "medium-size town."
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


The main purpose of many data centers is for running applications that handle the core business and operational data of one or more organizations. Often those applications will be composed of multiple hosts, each running a single component. Common components of such applications include databases, file servers, application servers, and middleware. Such systems may be proprietary and developed internally by the organization or bought from enterprise software vendors. However, a data center may also solely be concerned with operations architecture or other services. ('''[[Data center|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'': [[Carrier cloud]], [[Forest informatics]], [[International Electrotechnical Commission]]
{{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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