Difference between revisions of "Template:Article of the week"
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'''"[[Journal: | <div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Evans Informatics2017 4-4.png|240px]]</div> | ||
'''"[[Journal:A data quality strategy to enable FAIR, programmatic access across large, diverse data collections for high performance data analysis|A data quality strategy to enable FAIR, programmatic access across large, diverse data collections for high performance data analysis]]"''' | |||
To ensure seamless, programmatic access to data for high-performance computing (HPC) and [[Data analysis|analysis]] across multiple research domains, it is vital to have a methodology for standardization of both data and services. At the Australian National Computational Infrastructure (NCI) we have developed a data quality strategy (DQS) that currently provides processes for: (1) consistency of data structures needed for a high-performance data (HPD) platform; (2) [[quality control]] (QC) through compliance with recognized community standards; (3) benchmarking cases of operational performance tests; and (4) [[quality assurance]] (QA) of data through demonstrated functionality and performance across common platforms, tools, and services. ('''[[Journal:A data quality strategy to enable FAIR, programmatic access across large, diverse data collections for high performance data analysis|Full article...]]''')<br /> | |||
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Revision as of 18:45, 15 October 2018
To ensure seamless, programmatic access to data for high-performance computing (HPC) and analysis across multiple research domains, it is vital to have a methodology for standardization of both data and services. At the Australian National Computational Infrastructure (NCI) we have developed a data quality strategy (DQS) that currently provides processes for: (1) consistency of data structures needed for a high-performance data (HPD) platform; (2) quality control (QC) through compliance with recognized community standards; (3) benchmarking cases of operational performance tests; and (4) quality assurance (QA) of data through demonstrated functionality and performance across common platforms, tools, and services. (Full article...)
Recently featured:
- ▪ How big data, comparative effectiveness research, and rapid-learning health care systems can transform patient care in radiation oncology
- ▪ Wireless positioning in IoT: A look at current and future trends
- ▪ Password compliance for PACS work stations: Implications for emergency-driven medical environments