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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Evans Informatics2017 4-4.png|240px]]</div>
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'''"[[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]]"'''
'''"[[Journal:Ten simple rules for managing laboratory information|Ten simple rules for managing laboratory information]]"'''
 
[[Information]] is the cornerstone of [[research]], from experimental data/[[metadata]] and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging [[laboratory information management system]]s (LIMS) to transform this large information load into useful scientific findings. The development of [[mathematical model]]s that can predict the properties of biological systems is the holy grail of [[computational biology]]. Such models can be used to test biological hypotheses, guide the development of biomanufactured products, engineer new systems meeting user-defined specifications, and much more ... ('''[[Journal:Ten simple rules for managing laboratory information|Full article...]]''')<br />


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 18:03, 10 June 2024

Fig2 Berezin PLoSCompBio23 19-12.png

"Ten simple rules for managing laboratory information"

Information is the cornerstone of research, from experimental data/metadata and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging laboratory information management systems (LIMS) to transform this large information load into useful scientific findings. The development of mathematical models that can predict the properties of biological systems is the holy grail of computational biology. Such models can be used to test biological hypotheses, guide the development of biomanufactured products, engineer new systems meeting user-defined specifications, and much more ... (Full article...)

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