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

From LIMSWiki
Jump to navigationJump to search
(Updated article of the week text)
(Updated article of the week text)
(86 intermediate revisions by the same user not shown)
Line 1: Line 1:
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Yeste AdvLabMed2021 2-3.jpg|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''"[[Journal:Management of post-analytical processes in the clinical laboratory according to ISO 15189:2012: Considerations about the management of clinical samples, ensuring quality of post-analytical processes and laboratory information management|Management of post-analytical processes in the clinical laboratory according to ISO 15189:2012: Considerations about the management of clinical samples, ensuring quality of post-analytical processes and laboratory information management]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


[[ISO 15189|ISO 15189:2012 ''Medical laboratories — Requirements for quality and competence'']] establishes the requirements for clinical specimen management, ensuring the [[Quality (business)|quality]] of processes and [[laboratory]] [[information management]]. ENAC (Entidad Nacional de Acreditación), the sole accreditation authority in Spain, established the requirements for the authorized use of the ISO 15189 accreditation label in reports issued by accredited laboratories. These recommendations are applicable to the lab's post-analytical processes and the professionals involved. The standard requires laboratories to define and document the duration and conditions of specimen [[Retention period|retention]]. Laboratories are also required to design an internal [[quality control]] scheme to verify whether post-analytical activities attain the expected standards. Information management requirements are also established, and laboratories are required to design a contingency plan to ensure the communication of laboratory results  ... ('''[[Journal:Management of post-analytical processes in the clinical laboratory according to ISO 15189:2012: Considerations about the management of clinical samples, ensuring quality of post-analytical processes and laboratory information management|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 />
<br />
''Recently featured'':
''Recently featured'':
{{flowlist |
{{flowlist |
* [[Journal:A survival guide for the rapid transition to a fully digital workflow: The Caltagirone example|A survival guide for the rapid transition to a fully digital workflow: The Caltagirone example]]
* [[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:From biobank and data silos into a data commons: Convergence to support translational medicine|From biobank and data silos into a data commons: Convergence to support translational medicine]]
* [[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:Quality management system implementation in human and animal laboratories|Quality management system implementation in human and animal laboratories]]
* [[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...)
Recently featured:

Retrieved from "https://www.limswiki.org/index.php?title=Template:Article_of_the_week&oldid=63298"