ALA species sightings and OzAtlas

Quality Control

Individual sighting data quality description: Data quality assurance methods: systemsupported, recordannotation, crowdsourcedverification Data quality assurance description: This dataset comprises opportunistic ad-hoc sightings contributed by members of the public via both web app and mobile apps. Both the web and mobile apps employ in-built software mechanisms to maximise recording accuracy, but there are still opportunities for human decisions to cause data inaccuracies, particularly in respect to species identifications. These are primary data which are not directly validated or curated in this application before before being harvested into the Atlas of Living Australia (ALA). Therefore, whilst most of the data is likely to be reliably accurate and of high quality, they do contain some variability in the accuracy of species identifications and also potentially temporal and spatial accuracy. Once these records are harvested into the ALA, they can have accuracy issues flagged via the Atlas annotation service, through which the record owner is notified and can correct any errors in this application. Corrected records are then re-harvested to update the Atlas record. Test sighting data quality description: Data quality assurance methods: dataownercurated, crowdsourcedverification Data quality assurance description: This dataset comprises opportunistic ad-hoc sightings contributed by members of the public via both web app and mobile apps. Both the web and mobile apps employ in-built software mechanisms to maximise recording accuracy, but there are still opportunities for human decisions to cause data inaccuracies, particularly in respect to species identifications. These are primary data which are not directly validated or curated in this application before before being harvested into the Atlas of Living Australia (ALA). Therefore, whilst most of the data is likely to be reliably accurate and of high quality, they do contain some variability in the accuracy of species identifications and also potentially temporal and spatial accuracy. Once these records are harvested into the ALA, they can have accuracy issues flagged via the Atlas annotation service, through which the record owner is notified and can correct any errors in this application. Corrected records are then re-harvested to update the Atlas record.

Method steps

1. Individual sighting method: opportunistic Opportunistic/ad-hoc observation recording Test sighting method: opportunistic Opportunistic/ad-hoc observation recording