A Data Quality Strategy to Enable FAIR, Programmatic Access across Large, Diverse Data Collections for High Performance Data Analysis

• DOI: 10.3390/informatics4040045
• Publication Date: 2017-12-14T09:30:55Z
• Authors: Ben Evans; Kelsey Druken; Jingbo Wang; Rui Yang; Clare Richards; Lesley Wyborn
• Keywords: Benchmarking; Data access

To ensure seamless, programmatic access to data for High Performance Computing (HPC) and analysis across multiple research domains, it is vital have a methodology standardization of both services. At the Australian National Computational Infrastructure (NCI) we developed Data Quality Strategy (DQS) that currently provides processes for: (1) Consistency structures needed (HPD) platform; (2) Control (QC) through compliance with recognized community standards; (3) Benchmarking cases operational performance tests; (4) Assurance (QA) demonstrated functionality common platforms, tools By implementing NCI DQS, seen progressive improvement in quality usefulness datasets different subject ease by which modern methods can be used data, either situ or via web services, uses ranging from traditional emerging machine learning techniques. help increase re-usability broader communities, particularly high environments, DQS also identify need any extensions relevant international standards interoperability and/or access.