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:Fig3 Giray Sustain21 13-13.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Wong DataSciJourn22 21-1.png|240px]]</div>
'''"[[Journal:Design of a data management reference architecture for sustainable agriculture|Design of a data management reference architecture for sustainable agriculture]]"'''
'''"[[Journal:Development and governance of FAIR thresholds for a data federation|Development and governance of FAIR thresholds for a data federation]]"'''


Effective and efficient [[Information management|data management]] is crucial for smart farming and precision agriculture. To realize operational efficiency, full automation, and high productivity in agricultural systems, different kinds of data are collected from operational systems using different sensors, stored in different systems, and processed using advanced techniques, such as [[machine learning]] and deep learning. Due to the complexity of data management operations, a data management [[reference architecture]] is required. While there are different initiatives to design data management reference architectures, a data management reference architecture for sustainable agriculture is missing. In this study, we follow domain scoping, domain modeling, and reference architecture design stages to design the reference architecture for sustainable agriculture. Four case studies were performed to demonstrate the applicability of the reference architecture. This study shows that the proposed data management reference architecture is practical and effective for sustainable agriculture ... ('''[[Journal:Design of a data management reference architecture for sustainable agriculture|Full article...]]''')<br />
The [[Journal:The FAIR Guiding Principles for scientific data management and stewardship|FAIR]] (findable, accessible, interoperable, and re-usable) principles and practice recommendations provide high-level guidance and recommendations that are not research-domain specific in nature. There remains a gap in practice at the data provider and domain scientist level, demonstrating how the FAIR principles can be applied beyond a set of generalist guidelines to meet the needs of a specific domain community. We present our insights developing FAIR thresholds in a domain-specific context for self-governance by a community (in this case, agricultural research). "Minimum thresholds" for FAIR data are required to align expectations for data delivered from providers’ distributed data stores through a community-governed federation (the Agricultural Research Federation, AgReFed) ... ('''[[Journal:Development and governance of FAIR thresholds for a data federation|Full article...]]''')<br />
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Revision as of 17:11, 9 January 2023

Fig1 Wong DataSciJourn22 21-1.png

"Development and governance of FAIR thresholds for a data federation"

The FAIR (findable, accessible, interoperable, and re-usable) principles and practice recommendations provide high-level guidance and recommendations that are not research-domain specific in nature. There remains a gap in practice at the data provider and domain scientist level, demonstrating how the FAIR principles can be applied beyond a set of generalist guidelines to meet the needs of a specific domain community. We present our insights developing FAIR thresholds in a domain-specific context for self-governance by a community (in this case, agricultural research). "Minimum thresholds" for FAIR data are required to align expectations for data delivered from providers’ distributed data stores through a community-governed federation (the Agricultural Research Federation, AgReFed) ... (Full article...)

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