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Journal of Data Science 2025, 23(1)

  • 1.  Journal of Data Science 2025, 23(1)

    Posted 3 days ago
    Dear Colleagues,

    The 1st issue of Volume 23 of the Journal of Data Science has just been released (https://jds-online.org/journal/JDS/issue/93). This issue features a comprehensive review on power priors for leveraging historical data by Ming-Hui Chen, Zhe Guan, Min Lin, and Max Sun. The article is accompanied by seven insightful discussions from leading experts in the field, along with a rejoinder from the authors. You will also find a fascinating conversation with Dr. David Salsburg, the renowned statistician and author of The Lady Tasting Tea, conducted by Haim Bar and Naitee Ting. The rest of the issue includes one article in Computing in Data Science, three articles in Data Science in Action, and five articles in Statistical Data Science.

    All articles are published as open access under the CC-BY license (https://creativecommons.org/licenses/by/4.0/) to ensure the widest dissemination. Thanks to funding from the School of Statistics and the Center for Applied Statistics at Renmin University of China, there are no Article Processing Charges. The journal is known for its fast review process and rigorous reproducibility checks.

    Established in 2003, the Journal of Data Science aims to advance and promote data science methods, computing, and applications across all scientific fields where knowledge and insights are to be extracted from data. We welcome submissions to all sections of the journal, including 1) Philosophies of Data Science; 2) Statistical Data Science; 3) Computing in Data Science; 4) Data Science in Action; 5) Data Science Review; 6) Education in Data Science; and 7) Data Science Conversation.

    Best regards,
    Jun Yan
    Editor, Journal of Data Science
    ===
    Journal of Data Science
    Volume 23, Issue 1, 2025

    Chen, M., Guan, Z., Lin, M., & Sun, M. (2025). Power Priors for Leveraging Historical Data: Looking Back and Looking Forward. Journal of Data Science, 23(1), 1-30. doi:10.6339/24-JDS1161


    Chen, F. (2025). Discussion of "Power Priors for Leveraging Historical Data: Looking Back and Looking Forward". Journal of Data Science, 23(1), 31-37. doi:10.6339/25-JDS1161D

    Gamalo, M., Shi, H., Zhao, Y., & Kudela, M. (2025). Discussion of "Power Priors for Leveraging Historical Data: Looking Back and Looking Forward". Journal of Data Science, 23(1), 38-47. doi:10.6339/25-JDS1161C

    Nie, L. (2025). Discussion of "Power Priors for Leveraging Historical Data: Looking Back and Looking Forward". Journal of Data Science, 23(1), 48-51. doi:10.6339/25-JDS1161A

    Wang, C. (2025). Discussion of "Power Priors for Leveraging Historical Data: Looking Back and Looking Forward". Journal of Data Science, 23(1), 52-55. doi:10.6339/25-JDS1161G

    Wu, G. (2025). Discussion of "Power Priors for Leveraging Historical Data: Looking Back and Looking Forward". Journal of Data Science, 23(1), 56-58. doi:10.6339/25-JDS1161F

    Xie, M. (2025). Discussion of "Power Priors for Leveraging Historical Data: Looking Back and Looking Forward". Journal of Data Science, 23(1), 59-61. doi:10.6339/25-JDS1161E

    Zhang, P. (2025). Discussion of "Power Priors for Leveraging Historical Data: Looking Back and Looking Forward". Journal of Data Science, 23(1), 62-63. doi:10.6339/25-JDS1161B

    Chen, M., Guan, Z., Lin, M., & Sun, M. (2025). Rejoinder: "Power Priors for Leveraging Historical Data: Looking Back and Looking Forward",. Journal of Data Science, 23(1), 64-69. doi:10.6339/25-JDS1172

    Bar, H., & Ting, N. (2025). A Conversation with Dr. David S. Salsburg. Journal of Data Science, 23(1), 70-89. doi:10.6339/25-JDS1171

    Wang, Z. (2025). Unified Robust Boosting. Journal of Data Science, 23(1), 90-108. doi:10.6339/24-JDS1138

    Halladay, J., Cullen, D., Briner, N., Miller, D., Primeau, R., Avila, A., Watson, W., Basnet, R., & Doleck, T. (2025). BIE: Binary Image Encoding for the Classification of Tabular Data. Journal of Data Science, 23(1), 109-129. doi:10.6339/24-JDS1122

    Bian, Y., Shi, Y., Guo, H., Yi, G. Y., & He, W. (2025). Physician Effects in Critical Care: A Causal Inference Approach Through Propensity Weighting with Parametric and Super Learning Methods. Journal of Data Science, 23(1), 130-148. doi:10.6339/24-JDS1143

    Bastin, K., & Healey, C. G. (2025). Visual Analytics for NASCAR Motorsports. Journal of Data Science, 23(1), 149-170. doi:10.6339/24-JDS1141

    Riffenburgh, R. H., & Wang, L. (2025). A Joint Equivalence and Difference (JED) Test for Practical Use in Controlled Trials. Journal of Data Science, 23(1), 171-187. doi:10.6339/24-JDS1142

    Lee, J. W., & Harel, O. (2025). A Two-Stage Classification for Dealing with Unseen Clusters in the Testing Data. Journal of Data Science, 23(1), 188-207. doi:10.6339/24-JDS1140

    Asai, M., Chu, A. M., & So, M. K. (2025). Dynamic Network Poisson Autoregression with Application to COVID-19 Count Data. Journal of Data Science, 23(1), 208-224. doi:10.6339/24-JDS1124

    Jiang, L., Kennedy, C., & Matloff, N. (2025). An S-Curve Method for Abrupt and Gradual Changepoint Analysis. Journal of Data Science, 23(1), 225-242. doi:10.6339/24-JDS1137

    Sikdar, S., Hooker, G., & Kadiyali, V. (2025). Variable Importance Measures for Multivariate Random Forests. Journal of Data Science, 23(1), 243-263. doi:10.6339/24-JDS1152