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)
(156 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:Fig3 Bonvoisin JOfOpenHard2017 1-1.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''"[[Journal:What is the "source" of open-source hardware?|What is the "source" of open-source hardware?]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


What “open source” means once applied to tangible products has been so far mostly addressed through the light of licensing. While this approach is suitable for software, it appears to be over-simplistic for complex hardware products. Whether such a product can be labelled as open-source is not only a question of licence but a question of documentation, i.e. what is the information that sufficiently describes it? Or in other words, what is the “source” of open-source hardware? To date there is no simple answer to this question, leaving large room for interpretation in the usage of the term. Based on analysis of public documentation of 132 products, this paper provides an overview of how practitioners tend to interpret the concept of open-source hardware. It specifically focuses on the recent evolution of the open-source movement outside the domain of electronics and DIY to that of non-electronic and complex open-source hardware products. The empirical results strongly indicate the existence of two main usages of open-source principles in the context of tangible products: publication of product-related documentation as a means to support community-based product development and to disseminate privately developed innovations. ('''[[Journal:What is the "source" of open-source hardware?|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:From command-line bioinformatics to bioGUI|From command-line bioinformatics to bioGUI]]
{{flowlist |
: ▪ [[Journal:ChromaWizard: An open-source image analysis software for multicolor fluorescence in situ hybridization analysis|ChromaWizard: An open-source image analysis software for multicolor fluorescence in situ hybridization analysis]]
* [[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:Haves and have nots must find a better way: The case for open scientific hardware|Haves and have nots must find a better way: The case for open scientific hardware]]
* [[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"