Ginninderra Catchment - Upper Murrumbidgee Waterwatch

Quality Control

Water Bug Sampling data quality description: Data quality assurance methods: dataownercurated, systemsupported, subjectexpertverification Data quality assurance description: Data collectors are trained in methodology by professional coordinators. Data is collected using supplied kits. Measuring devices are calibrated and collectors are trained in the calibration process. Community collectors enter data into the database and records are embargoed until checked by their local coordinator. Once checked, data is released to the public domain and made available for aggregation and subsequent analysis. Data Management policy description: Data management is described in section 2.6 of this publication: DOI=10.3389/fenvs.2023.1270247 AUTHOR=O’Reilly Woo, Starrs Danswell TITLE=Science citizen: shifting to a “science-first” approach and recognising the trade-offs between objectives in a long-term citizen science program
JOURNAL=Frontiers in Environmental Science
VOLUME=11
YEAR=2023 Citizen science is a powerful force for scientific enquiry, allowing volunteers to collect data over broader spatial and temporal scales than could be achieved using traditional methods. Many citizen science programs, however, have suffered from a lack of confidence in the data due to a range of both real and perceived issues, resulting in low recognition and a lack of data uptake by researchers and policymakers. This in turn can threaten the very existence of citizen science programs that are dependent on external funding. In this paper, we explore the shift to a ‘science-first’ approach undertaken by a long-term citizen science program, Upper Murrumbidgee Waterwatch. The ‘science-first’ approach has enabled Upper Murrumbidgee Waterwatch to better partition and appropriately manage the program’s key areas of data collection, education and awareness raising. Moving towards a contributory typology, whilst recognising the positive elements that come with the original collaborative model, has enabled more strategic data collection and better data quality. This has resulted in greater data uptake in catchment management decision-making as well as secure, long-term funding for the program. Data Management policy url: https://www.frontiersin.org/articles/10.3389/fenvs.2023.1270247 Water Quality Monitoring data quality description: Data quality assurance methods: dataownercurated, systemsupported, subjectexpertverification Data quality assurance description: Community members are trained in methods by professional Waterwatch coordinators, with regular retraining and upskilling opportunities provided. Records are verified by the coordinators. The database provides controlled data type protocols to ensure appropriate standards-based formats are applied to stored data, as well as controlled vocabularies, mandatory obligations on specific fields, range constraint parametisation where appropriate, pre-defined site selection for consistent site naming and geo-referencing, etc. Calculated values are dynamically calculated in-form by the database at the time of recording and stored as calculated values. Data Management policy description: Data management is described in section 2.6 of this publication: DOI=10.3389/fenvs.2023.1270247 AUTHOR=O’Reilly Woo, Starrs Danswell TITLE=Science citizen: shifting to a “science-first” approach and recognising the trade-offs between objectives in a long-term citizen science program
JOURNAL=Frontiers in Environmental Science
VOLUME=11
YEAR=2023 Citizen science is a powerful force for scientific enquiry, allowing volunteers to collect data over broader spatial and temporal scales than could be achieved using traditional methods. Many citizen science programs, however, have suffered from a lack of confidence in the data due to a range of both real and perceived issues, resulting in low recognition and a lack of data uptake by researchers and policymakers. This in turn can threaten the very existence of citizen science programs that are dependent on external funding. In this paper, we explore the shift to a ‘science-first’ approach undertaken by a long-term citizen science program, Upper Murrumbidgee Waterwatch. The ‘science-first’ approach has enabled Upper Murrumbidgee Waterwatch to better partition and appropriately manage the program’s key areas of data collection, education and awareness raising. Moving towards a contributory typology, whilst recognising the positive elements that come with the original collaborative model, has enabled more strategic data collection and better data quality. This has resulted in greater data uptake in catchment management decision-making as well as secure, long-term funding for the program. Data Management policy url: http://www.act.waterwatch.org.au/Waterwatch Manual.html Rapid Appraisal of Riparian Condition - RARC data quality description: Data quality assurance methods: dataownercurated, systemsupported, subjectexpertverification Data quality assurance description: This survey is only conducted by professional Waterwatch coordinators on a bi-annual basis. The database provides controlled data type protocols to ensure appropriate standards-based formats are applied to stored data, as well as controlled vocabularies, mandatory obligations on specific fields, range constraint parametisation where appropriate, pre-defined site selection for consistent site naming and geo-referencing, etc. Calculated values are dynamically calculated in-form by the database at the time of recording and stored as calculated values. Data Management policy description: Data management is described in section 2.6 of this publication: DOI=10.3389/fenvs.2023.1270247 AUTHOR=O’Reilly Woo, Starrs Danswell TITLE=Science citizen: shifting to a “science-first” approach and recognising the trade-offs between objectives in a long-term citizen science program
JOURNAL=Frontiers in Environmental Science
VOLUME=11
YEAR=2023 Citizen science is a powerful force for scientific enquiry, allowing volunteers to collect data over broader spatial and temporal scales than could be achieved using traditional methods. Many citizen science programs, however, have suffered from a lack of confidence in the data due to a range of both real and perceived issues, resulting in low recognition and a lack of data uptake by researchers and policymakers. This in turn can threaten the very existence of citizen science programs that are dependent on external funding. In this paper, we explore the shift to a ‘science-first’ approach undertaken by a long-term citizen science program, Upper Murrumbidgee Waterwatch. The ‘science-first’ approach has enabled Upper Murrumbidgee Waterwatch to better partition and appropriately manage the program’s key areas of data collection, education and awareness raising. Moving towards a contributory typology, whilst recognising the positive elements that come with the original collaborative model, has enabled more strategic data collection and better data quality. This has resulted in greater data uptake in catchment management decision-making as well as secure, long-term funding for the program. Data Management policy url: http://www.act.waterwatch.org.au/Vol Resources.html

Method steps

1. Water Bug Sampling method: systematic Water quality - Macroinvertebrate survey - SIGNAL2 method Water Quality Monitoring method: systematic Standardised physical/chemical attribute measurements http://www.act.waterwatch.org.au/Waterwatch Manual.html Rapid Appraisal of Riparian Condition - RARC method: systematic Rapid Appraisal of Riparian Condition (RARC)