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

Abstract

Abstract: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. By implementing the NCI DQS, we have seen progressive improvement in the quality and usefulness of the datasets across the different subject domains, and demonstrated the ease by which modern programmatic methods can be used to access the data, either in situ or via web services, and for uses ranging from traditional analysis methods through to emerging machine learning techniques. To help increase data re-usability by broader communities, particularly in high performance environments, the DQS is also used to identify the need for any extensions to the relevant international standards for interoperability and/or programmatic access.

Quality Management Framework for Climate Datasets

“…When the evaluator looks for the metadata standard compliance (e.g., ACDD convention), here the evaluator is checking that the attributes describing the files are according to a set of communityrecognised metadata characteristics (e.g., specific date-time format). There is no check of the data content, no scientific evaluation in this case, but a metadata conformity check; it is a purely technical assessment (Evans et al 2017;Stockhause et al 2012). The physical consistency checks are borderline in this distinction.…”

International Community Guidelines for Sharing and Reusing Quality Information of Individual Earth Science Datasets

“…As an example, when the evaluator looks for the metadata standard compliance such as compliance against the Attribute Convention for Data Discovery (ACDD), 20 here the evaluator is checking that the attributes describing the files are according to a set of community-recognized metadata characteristics (e.g., specific date-time format). There is no check of the file content, no scientific evaluation in this case, but a metadata conformity check, it is a purely technical assessment (Stockhause et al 2012, Evans et al 2017. One the other hand, when the evaluator plots the dataset variable and checks for reproducibility of El Niño events against skill metrics, here the scientific soundness of the data content is considered (e.g., Haiden et al 2019).…”

Quality Issues of CRIS Data: An Exploratory Investigation with Universities from Twelve Countries

“…More recently, ref [6] developed a methodology for the standardization of data and services, for large computational research data infrastructures. The "fitness for (all) purpose(s)" is again in the heart of the model, as the data quality is assessed through the control of the compliance with recognized community standards, and as the quality assurance includes all common research data platforms, services and tools.…”

“…The "fitness for (all) purpose(s)" is again in the heart of the model, as the data quality is assessed through the control of the compliance with recognized community standards, and as the quality assurance includes all common research data platforms, services and tools. Moreover, [6] highlight the importance of having a consistent data structure and metadata, two crucial aspects for the evaluation of research data in CRIS [7].…”