Webinar "The promising role of synthetic data to enable responsible innovation"
Good quality FAIR data is fundamental for enhancing data reuse. When we discuss data quality in the FAIR context, we often focus on the metadata level quality attributes like accessibility and reuse conditions rather than the semantic ones like imbalances, outliers, and duplicates. In practice, ensuring both the metadata and semantic levels of data quality is crucial but also challenging. One solution for this challenge is synthetic data. MIT technology review names synthetic data as one of the
An exploration of how synthetic data can address data-quality challenges and enable responsible innovation, covering both metadata-level and semantic-level quality attributes such as imbalances, outliers, and duplicates in FAIR data.
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