Data Quality Strategy at the National Computational Sample Clauses

Data Quality Strategy at the National Computational. Infrastructure (Australia) NCI manages 10+ PB data collections from climate, coasts, oceans and geophysics through to astronomy, bioinformatics and the social sciences. It wants to enable transdisciplinary access to its data holdings. Therefore it has implemented a Data Quality Strategy (DQS) to simplify access to data. Key elements of the DQS are to maximize benefit of NCI’s collections and computational capabilities and to ensure seamless interoperable access to these datasets. The goal is to combine data and to visualize them. Collections are being accessed and utilised from a broad range of options: direct access on filesystem, Web and data services, data portals, and virtual labs. The data concern many disciplines and within these disciplines span a wide range of features: gridded, non-gridded (i.e., trajectories/profiles, point data), coordinate reference projections, resolutions. A major challenge was application of community-agreed data standards to the broad set of Earth systems and environmental data that are being used. Image: Data Quality Strategy (DQS) at NCI The DQS provides processes for: 1) underlying High Performance Data (HPD) file format, 2) close collaboration with data custodians and managers (planning, designing, and assessing the data collections), 3) quality control through compliance with recognised community standards, 4) Data assurance through demonstrated functionality across common platforms, tools, and services. The following image gives the architecture that was established at NCI for the National Environmental Research Data Interoperability Platform (NERDIP). Image: Architecture of the National Environmental Research Data Interoperability Platform (NERDIP)