期刊
JOURNAL OF SUPERCOMPUTING
卷 -, 期 -, 页码 -出版社
SPRINGER
DOI: 10.1007/s11227-023-05815-x
关键词
Data driven; Group collaboration; Indicator construction; Machine learning; Adaptive enhancement algorithm; Tenfold cross; Hyperparameter
This paper presents a data-driven framework for evaluating collaboration efficiency within scientific research teams. The framework introduces a team efficiency evaluation system consisting of 40 specific indicators, which are analyzed and modeled using statistical methods. The adaptive enhancement algorithm model achieves the highest accuracy, recall, and F1 values. These findings demonstrate the feasibility of the proposed data-driven research team collaboration model and offer theoretical support for enhancing the effectiveness of group collaboration.
This paper presents a data-driven framework for evaluating collaboration efficiency within scientific research teams. The framework introduces a team efficiency evaluation system consisting of 40 specific indicators, which are analyzed and modeled using statistical methods. The adaptive enhancement algorithm model achieves the highest accuracy, recall, and F1 values, with scores of 0.852, 0.530, and 0.620, respectively. These findings demonstrate the feasibility of the proposed data-driven research team collaboration model, offering theoretical support for enhancing the effectiveness of group collaboration. Moreover, the study is significant for further research on group collaboration in diverse fields.
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