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)
(188 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:Fig4 Ebnehoseini OAccessMacJofMedSci2019 7-9.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''"[[Journal:Determining the hospital information system (HIS) success rate: Development of a new instrument and case study|Determining the hospital information system (HIS) success rate: Development of a new instrument and case study]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


A [[hospital information system]] (HIS) is a type of health information system which is widely used in clinical settings. Determining the success rate of a HIS is an ongoing area of research since its implications are of interest for researchers, physicians, and managers. In the present study, we develop a novel instrument to measure HIS success rate based on users’ viewpoints in a teaching [[hospital]]. The study was conducted in Ibn-e Sina and Dr. Hejazi Psychiatry Hospital and education center in Mashhad, Iran. The instrument for data collection was a self-administered structured questionnaire based on the information systems success model (ISSM), covering seven dimensions, which includes system quality, [[information]] quality, service quality, system use, usefulness, satisfaction, and net benefits. The verification of content validity was carried out by an expert panel. The internal consistency of dimensions was measured by Cronbach’s alpha. Pearson’s correlation coefficient was calculated to evaluate the significance of associations between dimensions. The HIS success rate on users’ viewpoints was determined. ('''[[Journal:Determining the hospital information system (HIS) success rate: Development of a new instrument and case study|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'':
: ▪ [[Journal:Smart information systems in cybersecurity: An ethical analysis|Smart information systems in cybersecurity: An ethical analysis]]
{{flowlist |
: ▪ [[Journal:Chemometric analysis of cannabinoids: Chemotaxonomy and domestication syndrome|Chemometric analysis of cannabinoids: Chemotaxonomy and domestication syndrome]]
* [[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:National and transnational security implications of asymmetric access to and use of biological data|National and transnational security implications of asymmetric access to and use of biological data]]
* [[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...)
Recently featured:

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