
Shenglong developed a Virtual Chinrest Plugin for the revisit-studies/study repository, focusing on enhancing data quality in online experiments by enabling precise control of viewing distance and stimulus scaling. Using React and TypeScript, Shenglong implemented a dynamic ball and fixed square interface to estimate user viewing distance, and designed a card size calibration feature that matches an image to a credit card, determining pixels per millimeter for consistent scaling across devices. The work demonstrated depth in frontend development and UI/UX design, addressing reproducibility challenges in web-based research. Over the month, Shenglong delivered a complete feature with careful attention to experimental accuracy.

Concise monthly summary for 2025-10: Delivered the Virtual Chinrest Plugin with viewing distance calibration and card size calibration for the revisit-studies/study repository. This work enables precise control of viewing distance in online experiments and consistent stimulus scaling across devices, leading to improved data quality and reproducibility.
Concise monthly summary for 2025-10: Delivered the Virtual Chinrest Plugin with viewing distance calibration and card size calibration for the revisit-studies/study repository. This work enables precise control of viewing distance in online experiments and consistent stimulus scaling across devices, leading to improved data quality and reproducibility.
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