Putting meaning into NETMAR – the open service network for marine environmental data

The open service network for marine environmental data (NETMAR) project uses semantic web technologies in its pilot system which aims to allow users to search, download and integrate satellite, in situ and model data from open ocean and coastal areas. The semantic web is an extension of the fundamental ideas of the World Wide Web, building a web of data through annotation of metadata and data with hyperlinked resources. Within the framework of the NETMAR project, an interconnected semantic web resource was developed to aid in data and web service discovery and to validate Open Geospatial Consortium Web Processing Service orchestration. A second semantic resource was developed to support interoperability of coastal web atlases across jurisdictional boundaries. This paper outlines the approach taken to producing the resource registry used within the NETMAR project and demonstrates the use of these semantic resources to support user interactions with systems. Such interconnected semantic resources allow the increased ability to share and disseminate data through the facilitation of interoperability between data providers. The formal representation of geospatial knowledge to advance geospatial interoperability is a growing research area. Tools and methods such as those outlined in this paper have the potential to support these efforts

Applying Two-Level Modelling to Remote Sensor Systems Design to Enable Future Knowledge Generation

Geographical Information Scientists have a need to combine data from many sources and in various ways to synthesize new understanding, producing new knowle­dge [1]. Remote sensor deployments, monitoring environmental phenomena, are a huge provider of valuable data. Often, observation systems are built in isolation, and the data representations are not adequately designed for re-use and higher order knowledge generation. There are many standards that allow syntactic interoperability and sharing of remote sensor systems observational data, such as the OGC’s suite of standards [2]. However, semantic interoperability remains a work in progress [3] [4]. This presentation describes how system design techniques used in the health informatics domain [5] to tackle similar problems of how data, information and knowledge concepts are modelled and managed can be applied to remote sensing applications. Much like the health domain, remotely sensed data is traditionally modelled from a computer science perspective. Traditional object-oriented techniques typically used to model complex data are insufficient in a geographical data context, as they are too stringent during the early stages of knowledge acquisition. Standards such as O&M on their own precipitate a codifying effect as systems are developed, constraining rapidly evolving information [6]. The authors have investigated the OGC’s O&M standard as a reference model to underpin a two-level modelling approach. An augmented O&M model has been developed and is presented along with a worked example of how a two-level modelling approach using O&M as the reference model can be applied to modelling a marine data buoy. [1] M. Gahegan and W. Pike, A situated knowledge representation of geographical information, Transactions in GIS, vol. 10, pp. 727-749, 2006. [2] M. Botts, G. Percivall, C. Reed and J. Davidson, OGC® sensor web enablement: Overview and high level architecture, in GeoSensor Networks Springer, 2008, pp. 175-190. [3] S. Cox, An explicit OWL representation of ISO/OGC observations and measurements. in Ssn@ Iswc, 2013, pp. 1-18. [4] A. M. Leadbetter, R. K. Lowry and D. O. Clements, Putting meaning into NETMAR–the open service network for marine environmental data, International Journal of Digital Earth, pp. 1-18, 2013. [5] T. Beale, Archetypes: Constraint-based domain models for future-proof information systems, in OOPSLA 2002 Workshop on Behavioural Semantics, 2002. [6] M. F. Goodchild, GIScience ten years after Ground Truth, Transactions in GIS, vol. 10, pp. 687-692, 2006

Enhancing Water Quality Data Service Discovery And Access Using Standard Vocabularies

There is a growing need for consistency across the publishing, discovering, integrating and access to scientific datasets, such as water quality data. Such datasets may have varying formats and service interfaces. The Network Common Data Form (NetCDF) is both a software package and a data format for producing array-oriented scientific data, which is commonly used to exchange data, including water quality data. NetCDF datasets are also published through service interfaces using the THREDDS data server. Alternatively water quality datasets can be encoded with standard XML formats such as WaterML 2.0, which can be published with services such as the Open Geospatial Consortium (OGC) community\u27s Web Feature Service interface standard (WFS). However, appropriate interpretation of the content, discovery and interoperability of data depends on common models, schemas and vocabularies, though these may not always be available. Using the water quality vocabulary we have developed, formalized using the Resource Description Framework (RDF) language, and published as Linked Data, we demonstrate the use of such standard vocabularies in existing data services for providing service capability metadata. We also present methods for augmenting existing metadata fields for water quality data specifically in formats such as NetCDF, WaterML 2.0 using standard vocabularies. We show how using standard vocabularies that are encoded and published using semantic technologies can enhance discovery, integration and access to existing data services delivering water quality datasets

Bringing dark data into the light : a case study of the recovery of Northwestern Atlantic zooplankton data collected in the 1970s and 1980s

© The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in GeoResJ 6 (2015): 195-201, doi:10.1016/j.grj.2015.03.001.Data generated as a result of publicly funded research in the USA and other countries are now required to be available in public data repositories. However, many scientific data over the past 50+ years were collected at a time when the technology for curation, storage, and dissemination were primitive or non-existent and consequently many of these datasets are not available publicly. These so-called “dark data” sets are essential to the understanding of how the ocean has changed chemically and biologically in response to the documented shifts in temperature and salinity (aka climate change). An effort is underway to bring into the light, dark data about zooplankton collected in the 1970s and 1980s as part of the cold-core and warm-core rings multidisciplinary programs and other related projects. Zooplankton biomass and euphausiid species abundance from 306 tows and related environmental data including many depth specific tows taken on 34 research cruises in the Northwest Atlantic are online and accessible from the Biological and Chemical Oceanography Data Management Office (BCO-DMO).This is a contribution from the Biological and Chemical Oceanography Data Management office (BCO-DMO) that is funded by the United States National Science Foundation Grants OCE-1031253 and OCE-1435578

Semantic linking of complex properties, monitoring processes and facilities in web-based representations of the environment

Where a virtual representation of the Earth must contain data values observed within the physical Earth system, data models are required that allow the integration of data across the silos of various Earth and environmental sciences domains. Creating a mapping between the well-defined terminologies of these silos is a stubborn problem. This paper presents a generalised ontology for use within Web 3.0 services, which builds on European Commission spatial data infrastructure models. The presented ontology acknowledges that there are many complexities to the description of environmental properties which can be observed within the physical Earth system. The ontology is shown to be flexible and robust enough to describe concepts drawn from a range of Earth science disciplines, including ecology, geochemistry, hydrology and oceanography. This paper also demonstrates the alignment and compatibility of the ontology with existing systems and shows applications in which the ontology may be deployed

Technologies for a FAIRer use of Ocean Best Practices

The publication and dissemination of best practices in ocean observing is pivotal for multiple aspects of modern marine science, including cross-disciplinary interoperability, improved reproducibility of observations and analyses, and training of new practitioners. Often, best practices are not published in a scientific journal and may not even be formally documented, residing solely within the minds of individuals who pass the information along through direct instruction. Naturally, documenting best practices is essential to accelerate high-quality marine science; however, documentation in a drawer has little impact. To enhance the application and development of best practices, we must leverage contemporary document handling technologies to make best practices discoverable, accessible, and interlinked, echoing the logic of the FAIR data principles [1]

Bayesian participatory-based decision analysis : an evolutionary, adaptive formalism for integrated analysis of complex challenges to social-ecological system sustainability

Includes bibliographical references (pages. 379-400).This dissertation responds to the need for integration between researchers and decision-makers who are dealing with complex social-ecological system sustainability and decision-making challenges. To this end, we propose a new approach, called Bayesian Participatory-based Decision Analysis (BPDA), which makes use of graphical causal maps and Bayesian networks to facilitate integration at the appropriate scales and levels of descriptions. The BPDA approach is not a predictive approach, but rather, caters for a wide range of future scenarios in anticipation of the need to adapt to unforeseeable changes as they occur. We argue that the graphical causal models and Bayesian networks constitute an evolutionary, adaptive formalism for integrating research and decision-making for sustainable development. The approach was implemented in a number of different interdisciplinary case studies that were concerned with social-ecological system scale challenges and problems, culminating in a study where the approach was implemented with decision-makers in Government. This dissertation introduces the BPDA approach, and shows how the approach helps identify critical cross-scale and cross-sector linkages and sensitivities, and addresses critical requirements for understanding system resilience and adaptive capacity

Putting meaning into NETMAR – the open service network for marine environmental data

The open service network for marine environmental data (NETMAR) project uses semantic web technologies in its pilot system which aims to allow users to search, download and integrate satellite, in situ and model data from open ocean and coastal areas. The semantic web is an extension of the fundamental ideas of the World Wide Web, building a web of data through annotation of metadata and data with hyperlinked resources. Within the framework of the NETMAR project, an interconnected semantic web resource was developed to aid in data and web service discovery and to validate Open Geospatial Consortium Web Processing Service orchestration. A second semantic resource was developed to support interoperability of coastal web atlases across jurisdictional boundaries. This paper outlines the approach taken to producing the resource registry used within the NETMAR project and demonstrates the use of these semantic resources to support user interactions with systems. Such interconnected semantic resources allow the increased ability to share and disseminate data through the facilitation of interoperability between data providers. The formal representation of geospatial knowledge to advance geospatial interoperability is a growing research area. Tools and methods such as those outlined in this paper have the potential to support these efforts