Journal
JOURNAL OF SUPERCOMPUTING
Volume -, Issue -, Pages -Publisher
SPRINGER
DOI: 10.1007/s11227-023-05815-x
Keywords
Data driven; Group collaboration; Indicator construction; Machine learning; Adaptive enhancement algorithm; Tenfold cross; Hyperparameter
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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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