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'''"[[Journal:Defending our public biological databases as a global critical infrastructure|Defending our public biological databases as a global critical infrastructure]]"'''
'''"[[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 />
Progress in modern biology is being driven, in part, by the large amounts of freely available data in public resources such as the International Nucleotide Sequence Database Collaboration (INSDC), the world's primary database of biological sequence (and related) [[information]]. INSDC and similar databases have dramatically increased the pace of fundamental biological discovery and enabled a host of innovative therapeutic, diagnostic, and forensic applications. However, as high-value, openly shared resources with a high degree of assumed trust, these repositories share compelling similarities to the early days of the internet. Consequently, as public biological databases continue to increase in size and importance, we expect that they will face the same threats as undefended cyberspace. There is a unique opportunity, before a significant breach and loss of trust occurs, to ensure they evolve with quality and security as a design philosophy rather than costly “retrofitted” mitigations. This perspective article surveys some potential quality assurance and security weaknesses in existing open [[Genomics|genomic]] and [[Proteomics|proteomic]] repositories, describes methods to mitigate the likelihood of both intentional and unintentional errors, and offers recommendations for risk mitigation based on lessons learned from [[cybersecurity]]. ('''[[Journal:Defending our public biological databases as a global critical infrastructure|Full article...]]''')<br />
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Revision as of 15:16, 23 July 2019

"Defending our public biological databases as a global critical infrastructure"

Progress in modern biology is being driven, in part, by the large amounts of freely available data in public resources such as the International Nucleotide Sequence Database Collaboration (INSDC), the world's primary database of biological sequence (and related) information. INSDC and similar databases have dramatically increased the pace of fundamental biological discovery and enabled a host of innovative therapeutic, diagnostic, and forensic applications. However, as high-value, openly shared resources with a high degree of assumed trust, these repositories share compelling similarities to the early days of the internet. Consequently, as public biological databases continue to increase in size and importance, we expect that they will face the same threats as undefended cyberspace. There is a unique opportunity, before a significant breach and loss of trust occurs, to ensure they evolve with quality and security as a design philosophy rather than costly “retrofitted” mitigations. This perspective article surveys some potential quality assurance and security weaknesses in existing open genomic and proteomic repositories, describes methods to mitigate the likelihood of both intentional and unintentional errors, and offers recommendations for risk mitigation based on lessons learned from cybersecurity. (Full article...)

Recently featured:

Determining the hospital information system (HIS) success rate: Development of a new instrument and case study
Smart information systems in cybersecurity: An ethical analysis
Chemometric analysis of cannabinoids: Chemotaxonomy and domestication syndrome