T/JNBDA 0009-2025 Active Group standards

T/JNBDA 0009-2025 Construction specifications for high-quality datasets in reproductive medicine

T/JNBDA 0009-2025 Construction specifications for high-quality datasets in reproductive medicine

Publish Date: 2025-11-13 Implement Date: 2025-11-13 For services related to genuine standard inquiry, procurement, translation, and other related services in China, please Contact Us
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Basic Information

Standard Code: T/JNBDA 0009-2025
Standard Type: Group standards
Standard Status: Active
is_force_gb: no
CCS Name: Urban transportation facilities
ICS Name: Comprehensive Application of Information Technology
Publish Date: 2025-11-13
Implement Date: 2025-11-13

Scope

This document stipulates the general requirements, construction requirements, construction content, safety requirements, and verification methods for high-quality datasets in reproductive medicine. This document applies to the collection, storage, governance, and sharing of high-quality datasets in reproductive medicine. It specifies the overall requirements for the construction of high-quality datasets in reproductive medicine, the full-process construction requirements, the specific construction content, safety norms, and quality evaluation methods. The core of it is to build a standardized data resource system covering the entire cycle of reproductive medicine, which is suitable for data collection, governance, and sharing. The standard details the entire process from data planning, collection, preprocessing, labeling, testing to application, and clearly defines the complete data classification including baseline information, clinical diagnosis and treatment, traditional Chinese medicine diagnosis and treatment, assisted reproductive technology cycles, laboratory examinations, imaging, and outcome follow-up. It particularly emphasizes data security and ethical compliance, requiring the implementation of classified and graded protection and privacy security measures. Finally, by establishing an evaluation index system including completeness, standardization, and accuracy, it ensures that the quality of the evidence set meets the needs of AI model training and industry applications.

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