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:Fig4 SprengholzQuantMethSci2018 14-2.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''"[[Journal:Welcome to Jupyter: Improving collaboration and reproduction in psychological research by using a notebook system|Welcome to Jupyter: Improving collaboration and reproduction in psychological research by using a notebook system]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


The reproduction of findings from psychological research has been proven difficult. Abstract description of the data analysis steps performed by researchers is one of the main reasons why reproducing or even understanding published findings is so difficult. With the introduction of [[Jupyter Notebook]], a new tool for the organization of both static and dynamic [[information]] became available. The software allows blending explanatory content like written text or images with code for preprocessing and analyzing scientific data. Thus, Jupyter helps document the whole research process from ideation over data analysis to the interpretation of results. This fosters both collaboration and scientific quality by helping researchers to organize their work. This tutorial is an introduction to Jupyter. It explains how to set up and use the notebook system. While introducing its key features, the advantages of using Jupyter Notebook for psychological research become obvious. ('''[[Journal:Welcome to Jupyter: Improving collaboration and reproduction in psychological research by using a notebook system|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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