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 environmental sciences are witnessing a data revolution as large amounts of data are being made available at an increasing rate. Many datasets are being published through operational monitoring programs, research activities and global earth observation virtual laboratories. An important aspect is the ability to query relevant metadata which can potentially provide useful information to discover, access and interpret environmental datasets, information about the data providers themselves, data services, data encodings, observation and measurement properties and data service endpoints. However, support for producing and accessing metadata descriptions in a flexible, extensible, easily integrated and easily discovered manner is lacking as current methods require interpreting multiple standards and formalisms. In this paper, we propose components to streamline discovery and access of hydrological and environmental data: a Data Provider Node ontology (DPN-O) which allows precise descriptions to be captured about datasets, data services and their interfaces; and a Data Brokering Layer which provides an Application Programming Interface (API) for registering metadata for discovery and query of registered DPN datasets. We discuss this work in the context of the eReefs project which is developing an integrated information platform for discovery and visualization of observational and modelled data of the Great Barrier Reef.
Part 5: Architectures, Infrastructures, Platforms and ServicesInternational audiencePoint 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
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.