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

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(Updated article of the week text)
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Ishizuki SciTechAdvMatMeth2023 3-1.jpeg|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Lehmann Sensors23 23-1.png|240px]]</div>
'''"[[Journal:Autonomous experimental systems in materials science|Autonomous experimental systems in materials science]]"'''
'''"[[Journal:Establishing reliable research data management by integrating measurement devices utilizing intelligent digital twins|Establishing reliable research data management by integrating measurement devices utilizing intelligent digital twins]]"'''


The emergence of [[Laboratory automation|autonomous experimental systems]] (AESs) integrating [[machine learning]] (ML) and robots is ushering in a paradigm shift in [[materials science]]. Using computer algorithms and robots to decide and perform all experimental steps, these systems require no human intervention. A current direction focuses on discovering unexpected materials and theories with unconventional [[research]] approaches. This article reviews the latest achievements and discusses the impact of AESs, which will fundamentally change the way we understand research. Moreover, as AESs continue to develop, the need to think about the role of human researchers becomes more pressing ... ('''[[Journal:Autonomous experimental systems in materials science|Full article...]]''')<br />
One of the main topics within [[research]] activities is the [[Information management|management of research data]]. Large amounts of data acquired by heterogeneous scientific devices, sensor systems, measuring equipment, and experimental setups have to be processed and ideally managed by [[Journal:The FAIR Guiding Principles for scientific data management and stewardship|FAIR]] (findable, accessible, interoperable, and reusable) data management approaches in order to preserve their intrinsic value to researchers throughout the entire data lifecycle. The symbiosis of heterogeneous measuring devices, FAIR principles, and [[digital twin]] technologies is considered to be ideally suited to realize the foundation of reliable, sustainable, and open research data management ... ('''[[Journal:Establishing reliable research data management by integrating measurement devices utilizing intelligent digital twins|Full article...]]''')<br />
''Recently featured'':
''Recently featured'':
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Revision as of 17:32, 4 December 2023

Fig1 Lehmann Sensors23 23-1.png

"Establishing reliable research data management by integrating measurement devices utilizing intelligent digital twins"

One of the main topics within research activities is the management of research data. Large amounts of data acquired by heterogeneous scientific devices, sensor systems, measuring equipment, and experimental setups have to be processed and ideally managed by FAIR (findable, accessible, interoperable, and reusable) data management approaches in order to preserve their intrinsic value to researchers throughout the entire data lifecycle. The symbiosis of heterogeneous measuring devices, FAIR principles, and digital twin technologies is considered to be ideally suited to realize the foundation of reliable, sustainable, and open research data management ... (Full article...)
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