Baiyun District Boosts RD Data Quality with Training

Baiyun District, Guangzhou, will hold a training session on R&D investment statistics for research institutions on January 29, 2026. The aim is to improve the standardization and accuracy of R&D investment statistics within the district's research institutions. This will provide reliable data support for government decision-making and contribute to the technological innovation development of Baiyun District. The training is targeted at research institutions participating in the overall social R&D investment statistics.
Baiyun District Boosts RD Data Quality with Training

A specialized training session will be held in January 2026 to enhance the precision of research and development (R&D) expenditure reporting among scientific institutions in Guangzhou’s Baiyun District. The event, organized by the district’s Bureau of Science, Technology, Industry, Commerce, and Information Technology in collaboration with the High-Tech Enterprise Association, seeks to standardize data collection methodologies and strengthen coordination between government agencies and research organizations.

Scheduled for January 29, 2026, at the Baiyun Beauty Bay Plaza conference facility, the program will provide participants with detailed guidance on R&D statistical reporting requirements and standardized measurement approaches. Representatives from research institutions involved in district-wide R&D investment tracking are encouraged to attend, particularly those responsible for statistical documentation and research operations.

The initiative underscores the growing emphasis on data reliability in regional innovation policy formulation. By improving reporting consistency and reducing measurement discrepancies, the training aims to furnish policymakers with more robust empirical foundations for strategic decisions regarding technological advancement and resource allocation.

Organizers anticipate that the workshop will contribute to broader efforts to elevate Baiyun District’s scientific innovation capacity through enhanced data transparency and methodological rigor.