eReefs is a comprehensive interoperable information platform that has been developed for the Great Barrier Reef (GBR) region to provide users with access to improved environmental intelligence allowing them to assess past, present, and future conditions, as well as management options to mitigate the risks associated with multiple and sometimes competing uses of the GBR. eReefs is built upon an integrated system of data, catchment and marine models, visualisation, reporting and decision support tools that span the entire GBR area. This communication briefly describes eReefs architecture and components and provides examples of applications that have been used to inform policy and management decisions, and finally discusses challenges and key learnings and considers future developments and applications.

The need to identify both digital and physical objects is ubiquitous in our society. Past and present persistent identifier (PID) systems, of which there is a great variety in terms of technical and social implementation, have evolved with the advent of the Internet, which has allowed for globally unique and globally resolvable identifiers. PID systems have, by in large, catered for identifier uniqueness, integrity, and persistence, regardless of the identifier’s application domain. Trustworthiness of these systems has been measured by the criteria first defined by Bütikofer (2009) and further elaborated by Golodoniuc 'et al'. (2016) and Car 'et al'. (2017). Since many PID systems have been largely conceived and developed by a single organisation they faced challenges for widespread adoption and, most importantly, the ability to survive change of technology. We believe that a cause of PID systems that were once successful fading away is the centralisation of support infrastructure – both organisational and computing and data storage systems. In this paper, we propose a PID system design that implements the pillars of a trustworthy system – ensuring identifiers’ independence of any particular technology or organisation, implementation of core PID system functions, separation from data delivery, and enabling the system to adapt for future change. We propose decentralisation at all levels — persistent identifiers and information objects registration, resolution, and data delivery — using Distributed Hash Tables and traditional peer-to-peer networks with information replication and caching mechanisms, thus eliminating the need for a central PID data store. This will increase overall system fault tolerance thus ensuring its trustworthiness. We also discuss important aspects of the distributed system’s governance, such as the notion of the authoritative source and data integrit

The Location Index (LocI) project is building a national and authoritative, also federated, index for Australian spatial data using Semantic Web technologies. It will be used to link observation and measurement data (social, economic and environmental) to spatial objects identified in any one of multiple, interoperable, datasets. Its goal is to improve efficiency and reliability of data integration to support government decision making.

Point time series are a key data-type for the description of real or modelled environmental phenomena. Delivering this data in useful ways can be challenging when the data volume is large, when computational work (such as aggregation, subsetting, or re-sampling) needs to be performed, or when complex metadata is needed to place data in context for understanding. Some aspects of these problems are especially relevant to the environmental domain: large sensor networks measuring continuous environmental phenomena sampling frequently over long periods of time generate very large datasets, and rich metadata is often required to understand the context of observations. Nevertheless, timeseries data, and most of these challenges, are prevalent beyond the environmental domain, for example in financial and industrial domains. A review of recent technologies illustrates an emerging trend toward high performance, lightweight, databases specialized for time series data. These databases tend to have non-existent or minimalistic formal metadata capacities. In contrast, the environmental domain boasts standards such as the Sensor Observation Service (SOS) that have mature and comprehensive metadata models but existing implementations have had problems with slow performance. In this paper we describe our hybrid approach to achieve efficient delivery of large time series datasets with complex metadata. We use three subsystems within a single system-of-systems: a proxy (Python), an efficient time series database (InfluxDB) and a SOS implementation (52 North SOS). Together these present a regular SOS interface. The proxy processes standard SOS queries and issues them to the either 52 North SOS or to InfluxDB for processing. Responses are returned directly from 52 North SOS or indirectly from InfluxDB via Python proxy where they are processed into WaterML. This enables the scalability and performance advantages of the time series database to be married with the sophisticated metadata handling of SOS. Testing indicates that a recent version of 52 North SOS configured with a Postgres/PostGIS database performs well but an implementation incorporating InfluxDB and 52 North SOS in a hybrid architecture performs approximately 12 times faster.