The ALA recognises the importance of ensuring high quality, well annotated and described data for its community and is investing in the Data Quality Project over the 2020 calendar year to respond to feedback from stakeholder and user groups.
The project includes software development and other activities (e.g. training, workshops or other engagements) to enhance understanding of the quality characteristics of data available via the ALA.
What is the Data Quality Project
The Data Quality Project is being led by Miles Nicholls (former ALA Data Manager) supported by ALA senior management and the development team. The project aims to:
- Improve the ways users can assess data quality or fitness-for-purpose, improve reliability of data (e.g. taxonomic names, quality of species identifications)
- Enhance ALA users’ understanding of the type of data in the ALA, and their attributes, and ways to assess fitness-for-use.
- Enhance the ALA stakeholder community’s understanding of the type and quality of data in the ALA.
- Provide clear information on what constitutes high quality data and support data providers to improve collection and curation of data.
How you can contribute to the Data Quality Project and help shape the ALA
The Data Quality Project is being run as a transparent and agile project with many opportunities for our users and stakeholders to contribute:
- Email email@example.com to sign up to receive project updates and invitations to beta test
- Take our survey and help us decide priorities and shape the work in our Data Quality Project. The survey will remain open until Tuesday 10 March 2020. We greatly value your feedback.
- Check the ALA Data Quality Project page for project updates
- View the Data Quality Project on GitHub to keep track of activities
- Use the Data Quality repository to add and comment on issues
- Follow us on Twitter
Why the ALA is focussing on data quality?
Data in the ALA is used by over 45,000 users to better understand the environment in areas such as biodiversity, conservation, biosecurity and agriculture. High quality, well annotated and well described data is essential for the community to undertake meaningful research and to inform decision-making.
Issues around data quality have been consistently raised in workshops, support enquiries and general feedback as an area for improvement in the ALA. Examples include: poor visibility of data quality attributes, confusion in regards to how to filter data, uncertainty about taxonomic and spatial accuracy of ALA data, insufficient metrics to assess whether data is fit for purpose, visibility of occurrence record metadata.
Data quality was raised as a weakness, threat and accordingly as a key opportunity in the national consultation process (read ALA Future Directions National Consultation Findings Report) .
The data quality project is a high priority strategic activity that will provide significant value to the ALA user community.
Back to ALA Newsletter February 2020