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:Fig1 Navale F1000Research2020 8.gif|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''"[[Journal:Development of an informatics system for accelerating biomedical research|Development of an informatics system for accelerating biomedical research]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


The Biomedical Research Informatics Computing System (BRICS) was developed to support multiple disease-focused research programs. Seven service modules are integrated together to provide a collaborative and extensible web-based environment. The modules—Data Dictionary, Account Management, Query Tool, Protocol and Form Research Management System, Meta Study, Data Repository, and Globally Unique Identifier—facilitate the management of research protocols, including the submission, processing, curation, access, and storage of clinical, imaging, and derived [[genomics]] data within the associated data repositories. Multiple instances of BRICS are deployed to support various biomedical research communities focused on accelerating discoveries for rare diseases, traumatic brain injuries, Parkinson’s disease, inherited eye diseases, and symptom science research. No personally identifiable [[information]] is stored within the data repositories. Digital object identifiers (DOIs) are associated with the research studies. ('''[[Journal:Development of an informatics system for accelerating biomedical research|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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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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