Oral Presentation Australian Society for Limnology Conference 2017

Aquatic Sciences Data Reference Model (#116)

Adrian T Dickson 1 , Asif Q Gill
  1. GHD, Parramatta, NSW, Australia

Context: To inform effective decision making, aquatic ecosystem scientists are required to integrate and interpret information from a variety of sources and domains such as biology, chemistry, hydrology, geology, meteorology, climate science and geophysics.

Problem: Preparation, analysis, interpretation and communication of data from disparate sources in different formats is a challenging task. Data from different domains can often be stored differently and is subject to multiple interpretations. This suggests a need to define a taxonomy of aquatic ecosystem data.

Solutions: To address the problem in hand, this paper proposes the Data Reference Model (DRM) to facilitate the understanding of data entities, topics and relationships of data within the Aquatic Science domain.

Research Method: A series of brainstorming exercises with experienced aquatic ecologists identified a range of data entities and an analysis of existing data standards provided clarity to the entity selection process. Adherence to the Open Geospatial Consortium (OGC) WaterML-WQ Best Practice and the data standards of the Atlas of Living Australia provided guidance to the development of the Aquatic Sciences DRM. The DRM is documented as a tree structure diagram and presented as a poster of data entity taxonomy, to enhance communication. Finally, it is applied to a ten-year aquatic ecosystem monitoring dataset, to demonstrate implementation.

Impact: The DRM is aimed to be an important tool for the facilitation of communication between practitioners of aquatic ecosystem science and information systems specialists. It establishes a vocabulary that the two, almost opposing parties, can comprehend and provides a structured and tested approach to data interpretation and governance.

Conclusion: The DRM is not intended to be a static document that covers the entirety of its subject matter, but rather an evolving model that, through ongoing collaboration, will facilitate communication and understanding, ultimately leading to improved data driven ecological outcomes.