The ESIP Information Quality Cluster (IQC) has formally defined information quality as a combination of the following four aspects of quality, spanning the full life cycle of data products: scientific quality, product quality, stewardship quality, and service quality. The IQC has been expanding its scope to focus on additional sources of Earth science data, including citizen science, which has been offering increasingly important contributions to Earth science research. Information quality is critical to the successful collection and use of citizen science data. Quality aspects of citizen science data need to be identified and documented to ensure that the data can be useful for a variety of disciplinary applications. The purpose of this session is to advance citizen science data and information quality and continue the discussion on citizen science data and information quality initiated by IQC at the 2020 ESIP winter meeting. At this session, the IQC will collaborate with the Citizen Science Association (CSA) Data and Metadata Working Group (DMWG). The objectives are to:
- share experiences, practices, and state-of-the-art of research on citizen science data quality
- identify opportunities to improve and document the quality of citizen science data
- develop an approach to determine the data quality requirements for policy makers, community leaders, and other non-academic scientists to use data products in decision-making
- develop a roadmap toward a rubric for the evaluation of quality levels attained by citizen science data products and services
- foster a collaborative interchange of technical information intended to help advance the assessment, improvement, capture, conveyance, and use of quality information associated with citizen science data
This session will first share invited presentations that address data quality issues important for citizen science data, then followed by a discussion of these issues among participants.
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Takeaways- Platforms have an important role for helping with CS workflow and increasing confidence in CS data products. The more we can include documentation of quality in all stages of CS projects the better.
- The draft survey is useful to capture data quality requirements from different types of users; target at not just downstream decision makers, but also those in the middle that could improve use of CS data in science that generates useful end products.
- Rubrics for the evaluation of quality levels attained by citizen science data products and services are useful and should be pursued, though there are challenges that need continued discussion.