Compilation of riverine water quality data from the Great Barrier Reef catchment area, northeastern Australia.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-02-18 DOI:10.1038/s41597-025-04534-7
Cassandra James, Zoe Bainbridge, Stephen Lewis, Celine Clech-Goods, Reinier Mann, Raethea Huggins, David Orr, Kylee Welk, Shuci Liu, Gordon Agnew, Rebecca Bartley, Robert Bramley, Alicia Buckle, Lex Cogle, Matthew Cross, Aaron Davis, Michelle Devlin, Bradley D Eyre, John Faithful, Miles Furnas, Paul Godfrey, Renee Gruber, Aaron Hawdon, Christina Howley, Heather Hunter, Mark Kennard, Emma Laxton, Stephen Mackay, David McJannet, Michael Nash, Dominique O'Brien, Fred Oudyn, Richard Pearson, Ken Rohde, Michelle Tink, Andrew Moss
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引用次数: 0

Abstract

This manuscript describes the collation of available water quality data from the freshwater reaches of surface streams within the Great Barrier Reef catchment area, northeastern Australia. This compilation represents one of the most comprehensive online datasets for historical tropical and subtropical freshwater quality around the world. We document the criteria for selection of the data and associated publications as well as the processes of data cleaning used to produce a qualitative assessment of the datasets. The final compilation includes 41 individual datasets that collectively report 466 sites and contain over 26,000 discrete water quality sample records totaling more than 350,000 unique water quality results. Finally, we outline the nuances of the data that end users need to take into account when combining them for spatial and temporal analyses. The dataset ensures that these valuable water quality data collected over the past four decades are preserved for the next generations of researchers, practitioners, management agencies and policy makers.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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