A generalized classification and coding system of Human Disease Animal Model Resource data with a case study to show improving database retrieval efficiency

Abstract

Background Currently there is no unified data classification and coding standard for the existing human disease animal model resource data worldwide. Different data classification and coding systems produce different retrieval methods. Some of these methods are inefficient and difficult to use. This research investigated the rules for the classification and coding of such data based on the Replication Methodology of Animal Models for Human Disease, the Classification and Coding Rules for Health Information Data Set (WS/T 306–2009), the Science and Technology Resource Identification (GB/T 32843–2016), the Scientific Data Management Measures (000014349/2018-00052), and The Generic Description Specification for Natural Science and Technology Resources. This research aimed to develop a classification and coding system for data obtained from human disease animal model resource based on the Internet environment to provide a standardized and unified foundation for the collection, saving, retrieval, and sharing of data from this resource. Results A complete data classification and coding table compiled in the form of letters and numbers was produced, with a classification infrastructure that expanded layer by layer according to the three dimensions (namely, system diseases, animal species, and modeling methods) and essential attributes. When necessary, it adopted the hierarchy of major, intermediate, and minor categories for certain layer and also one-to-one matched the code and classification result. Conclusion Through this study, a sharing and joint construction mechanism for data from this resource can be developed by all research institutes in this field. As a case study, this research also offered technical support for constructing the database for the National Human Disease Animal Model Resource Center. The technological innovation of this paper is to derive a research oriented retrieval method, which provides technical support for the research on the current COVID-19 epidemic and on possible future epidemics.