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

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(Updated article of the week text.)
(Updated article of the week text.)
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 ZhuSciProg2018 2018-2018.png|240px]]</div>
'''"[[Journal:Evidence-based design and evaluation of a whole genome sequencing clinical report for the reference microbiology laboratory|Evidence-based design and evaluation of a whole genome sequencing clinical report for the reference microbiology laboratory]]"'''
'''"[[Journal:Big data management for cloud-enabled geological information services|Big data management for cloud-enabled geological information services]]"'''


Microbial genome [[sequencing]] is now being routinely used in many [[Clinical laboratory|clinical]] and [[Public health laboratory|public health laboratories]]. Understanding how to [[Reporting|report]] complex genomic test results to stakeholders who may have varying familiarity with genomics — including clinicians, laboratorians, epidemiologists, and researchers — is critical to the successful and sustainable implementation of this new technology; however, there are no evidence-based guidelines for designing such a report in the pathogen genomics domain. Here, we describe an iterative, human-centered approach to creating a report template for communicating tuberculosis (TB) genomic test results. ('''[[Journal:Evidence-based design and evaluation of a whole genome sequencing clinical report for the reference microbiology laboratory|Full article...]]''')<br />
[[Cloud computing]] as a powerful technology of performing massive-scale and complex computing plays an important role in implementing [[Geoinformatics|geological information services]]. In the era of big data, data are being collected at an unprecedented scale. Therefore, to ensure successful data processing and analysis in cloud-enabled geological information services (CEGIS), we must address the challenging and time-demanding task of big data processing. This review starts by elaborating the system architecture and the requirements for big data management. This is followed by the analysis of the application requirements and technical challenges of big data management for CEGIS in China. This review also presents the application development opportunities and technical trends of big data management in CEGIS, including collection and preprocessing, storage and management, analysis and mining, parallel computing-based cloud platforms, and technology applications. ('''[[Journal:Big data management for cloud-enabled geological information services|Full article...]]''')<br />
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''Recently featured'':  
''Recently featured'':  
: ▪ [[Journal:Evidence-based design and evaluation of a whole genome sequencing clinical report for the reference microbiology laboratory|Evidence-based design and evaluation of a whole genome sequencing clinical report for the reference microbiology laboratory]]
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: ▪ [[Journal:Moving ERP systems to the cloud: Data security issues|Moving ERP systems to the cloud: Data security issues]]
: ▪ [[Journal:Method-centered digital communities on protocols.io for fast-paced scientific innovation|Method-centered digital communities on protocols.io for fast-paced scientific innovation]]
: ▪ [[Journal:Method-centered digital communities on protocols.io for fast-paced scientific innovation|Method-centered digital communities on protocols.io for fast-paced scientific innovation]]
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Revision as of 17:22, 9 April 2018

Fig1 ZhuSciProg2018 2018-2018.png

"Big data management for cloud-enabled geological information services"

Cloud computing as a powerful technology of performing massive-scale and complex computing plays an important role in implementing geological information services. In the era of big data, data are being collected at an unprecedented scale. Therefore, to ensure successful data processing and analysis in cloud-enabled geological information services (CEGIS), we must address the challenging and time-demanding task of big data processing. This review starts by elaborating the system architecture and the requirements for big data management. This is followed by the analysis of the application requirements and technical challenges of big data management for CEGIS in China. This review also presents the application development opportunities and technical trends of big data management in CEGIS, including collection and preprocessing, storage and management, analysis and mining, parallel computing-based cloud platforms, and technology applications. (Full article...)

Recently featured:

▪ Evidence-based design and evaluation of a whole genome sequencing clinical report for the reference microbiology laboratory
▪ Moving ERP systems to the cloud: Data security issues
▪ Method-centered digital communities on protocols.io for fast-paced scientific innovation
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