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Evaluating, ranking, and clustering (ERC) stand as fundamental tasks in scientific research, each requiring a mathematical foundation. This study presents an ERC system anchored in the CCHZ-DISO (Chen, Chen, Hu, and Zhou-Distance between Indices of Simulation and Observation) system. Previous research underscores the optimality achieved by the CCHZ-DISO system (Hu et al., 2022). Since the inception of CCHZ- DISO-series research by Hu et al. (2019), DISO has found extensive applications across various domains including geography, hydrology, and economics. Analogous to the CCHZ-DISO system's construction, the ERC system employs the Euclidean distance to perform evaluating, ranking, and clustering tasks. Furthermore, illustrative examples are provided to elucidate the application of the ERC system. In fact, the ERC system unified the evaluating, ranking, and clustering tasks in one simple equation which is more flexible and simpler than the present system. It will have a more widely application than CCHZ-DISO in diverse scientific domains. [ABSTRACT FROM AUTHOR]

With the rapid development of big data, assessment of data quality or model performance has become a hot scientific question. However, most existing lots of metrics focus on specific aspects of the assessment, and comprehensive assessment is rare. Therefore, it is very necessary to develop new assessment system. To address this problem, a new assessment system is constructed which is named after Chen, Chen, Hu, and Zhou (CCHZ)‐distance between indices of simulation and observation (DISO) according to the contributions of Xi Chen, Deliang Chen, Zengyun Hu, and Qiming Zhou. CCHZ‐DISO system builds on the Euclidean Distance and flexible determination of statistical metrics and their numbers. Due to its simplicity and flexibility, CCHZ‐DISO can be readily and widely applied to any subject of science. Therefore, it follows the principle of the Chinese philosopher Lao Zi's Da Dao Zhi Jian which means that the most basic truth is very simple. Plain Language Summary: With the rapid development of big data, assessment of data quality or model performance has become a hot scientific question and plays an essential and key role in any science subject. However, most existing lots of metrics focus on specific aspects of the assessment, and integrative and comprehensive assessment is rare. Therefore, it is very necessary and high urgency to develop new assessment system. To address this problem, in this study, a new assessment system is constructed which is named after Chen, Chen, Hu, and Zhou (CCHZ)‐distance between indices of simulation and observation (DISO) according to the contributions of Xi Chen, Deliang Chen, Zengyun Hu, and Qiming Zhou. CCHZ‐DISO system builds on the Euclidean Distance and flexible determination of statistical metrics and their numbers. Two examples are provided to illustrate the CCHZ‐DISO application. Moreover, we have a brief comparison between CCHZ‐DISO and other approaches to prove the advantage of our new system. Due to its simplicity and flexibility, CCHZ‐DISO can be readily and widely applied to any subject of science. Therefore, it follows the principle of the Chinese philosopher Lao Zi's Da Dao Zhi Jian which means that the most basic truth is very simple. Key Points: A Timely New Assessment System for data quality or model performance is establishedChen, Chen, Hu, and Zhou (CCHZ)‐distance between indices of simulation and observation (DISO) has the advantage to evaluate the overall performance of different models for multiple variables and add the weights of metricsCCHZ‐DISO can be widely used to quality the model performance in any subjects [ABSTRACT FROM AUTHOR]