Working across U.S. federal agencies, international agencies, and multiple worldwide data centers, and spanning seven international network organizations, the Earth System Grid Federation (ESGF) allows users to access, analyze, and visualize data using a globally federated collection of networks, computers, and software. Its architecture employs a system of geographically distributed peer nodes that are independently administered yet united by common federation protocols and application programming interfaces (APIs). The full ESGF infrastructure has now been adopted by multiple Earth science projects and allows access to petabytes of geophysical data, including the Coupled Model Intercomparison Project (CMIP)—output used by the Intergovernmental Panel on Climate Change assessment reports. Data served by ESGF not only include model output (i.e., CMIP simulation runs) but also include observational data from satellites and instruments, reanalyses, and generated images. Metadata summarize basic information about the data for fast and easy data discovery.

This paper describes the open access graph dataset that shows the connections between Dryad, CERN, ANDS and other international data repositories to publications and grants across multiple research data infrastructures. The graph dataset was created using the Research Graph data model and the Research Data Switchboard (RD-Switchboard), a collaborative project by the Research Data Alliance DDRI Working Group (DDRI WG) with the aim to discover and connect the related research datasets based on publication co-authorship or jointly funded grants. The graph dataset allows researchers to trace and follow the paths to understanding a body of work. By mapping the links between research datasets and related resources, the graph dataset improves both their discovery and visibility, while avoiding duplicate efforts in data creation. Ultimately, the linked datasets may spur novel ideas, facilitate reproducibility and re-use in new applications, stimulate combinatorial creativity, and foster collaborations across institutions.

The National Computational Infrastructure (NCI) at the Australian National University (ANU) has co-located a priority set of over 10 PetaBytes (PBytes) of national data collections within a HPC research facility. The facility provides an integrated high-performance computational and storage platform, or a High Performance Data (HPD) platform, to serve and analyse the massive amounts of data across the spectrum of environmental collections – in particular from the climate, environmental and geoscientific domains. The data is managed in concert with the government agencies, major academic research communities and collaborating overseas organisations. By co-locating the vast data collections with high performance computing environments and harmonising these large valuable data assets, new opportunities have arisen for Data-Intensive interdisciplinary science at scales and resolutions not hitherto possible.

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.

In recent years, magnetotelluric (MT) processing has become computationally intensive as the scale and size of MT surveys being run increases. Consequently, High Performance Computing (HPC) is now becoming a valuable tool for timely processing and modelling of these large MT datasets. As part of the MT component of the 2017-2019 Australian Research Data Commons (ARDC) funded Geoscience Data Enhanced Virtual Laboratory (DeVL) continuity project, The National Computational Infrastructure (NCI) at the Australian National University will enable MT datasets from The University of Adelaide to be added to the NCI HPC platform with the goal of creating a more Findable, Accessible, Interoperable, Reusable (FAIR) and open public resource. A focus will be on making the time series datasets more suitable for use on HPC and more interoperable with other Earth science disciplines, where High Performance Data (HPD) formats will allow for better scalability and performance. Metadata attributes, as defined by the Australian MT research community, will be added directly to the time series data files. Additionally, time series processing and 3D inversion codes are being optimised for HPD/HPC, with the end goal of rapid time series processing and 3D inversion. Making FAIR MT time series available on HPC can lead to a transformative change in the way MT data analysis is routinely conducted and such a change has the capacity to create new ways of doing collaborative and transparent MT analysis.