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Journal of Data Science, 24(3): 2025 GASP Conference

  • 1.  Journal of Data Science, 24(3): 2025 GASP Conference

    Posted 11 days ago

    Dear Colleagues,

    The third issue of Volume 24 of the Journal of Data Science (https://jds-online.org/journal/JDS/issue/99) is a special issue featuring contributions from the 2025 Government Advances in Statistical Programming (GASP) Conference. The articles demonstrate how modern statistical and data science methods, including machine learning, privacy-preserving analysis, data integration, and uncertainty quantification, are being applied to government data and official statistics. We extend our sincere thanks to guest editors Lisa M. Frehill, Peter B. Meyer, and José Bayoán Santiago Calderón for their leadership throughout the editorial process. We also thank the review team and all contributing authors for their efforts in making this special issue possible.

    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 in which knowledge and insights are extracted from data. We welcome submissions to all sections of the journal: 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,

    Yichen Qin and Jun Yan
    Co-Editors, Journal of Data Science

    ===

    Journal of Data Science
    Volume 24, Issue 3, 2026

    Frehill, Lisa M., Meyer, Peter B., and José Bayoán Santiago Calderón. "Editorial: Government Advances in Statistical Programming (GASP) 2025." Journal of Data Science 24 no. 3 (2026):477-481. https://doi.org/10.6339/26-JDS243EDI.

    Belyaeva, Irina, Carino, Christopher, and Liang-Chi Wang. "Leveraging Survey Metadata for LLM Reasoning via Knowledge Graphs." Journal of Data Science 24 no. 3 (2026):482-503. https://doi.org/10.6339/26-JDS1230.

    Preiss, Alexander J., Konet, Amanda, Chew, Robert, Williams, Matthew R., Segarra, Elan A., Oh, David H., Boon, Erin, and Terrance D. Savitsky. "A Practical Guide to Differentially Private Deep Learning Using the Pseudo Posterior Mechanism." Journal of Data Science 24 no. 3 (2026):504-522. https://doi.org/10.6339/26-JDS1237.

    Yin, Xiaohui, Xie, Yingfa, Yan, Jun, Wang, Siyan, Liu, Pang-Yu, Gagnon, Jeffrey, Thompson, Elton, Walsh, Lawrence, Fuerst, Nathan, and Ming-Hui Chen. "Prediction Intervals and Group Variable Importance for Classification Models in University Enrollment Yield." Journal of Data Science 24 no. 3 (2026):523-543. https://doi.org/10.6339/26-JDS1241.

    Elkasabi, Mahmoud, Lewis, Taylor, and Matthew Williams. "An Estimation Framework for Combining Probability and Non-probability Samples." Journal of Data Science 24 no. 3 (2026):544-563. https://doi.org/10.6339/26-JDS1234.

    Williams, Matthew R., McGuire, F. Hunter, and Terrance D. Savitsky. "Uncertainty Quantification for Multi-Level Models Using the Survey-Weighted Pseudo-Posterior." Journal of Data Science 24 no. 3 (2026):564-583. https://doi.org/10.6339/26-JDS1238.

    Rhodes, Sean, Johnson, David M., Sartore, Luca, Garber, Samuel C., Miller, Darcy, and Denise A. Abreu. "Use of Farm Equipment Machine-Logged Data to Inform Crop Production Statistics." Journal of Data Science 24 no. 3 (2026):584-599. https://doi.org/10.6339/26-JDS1235.

    Champney, Timothy F., and Hongxun Qin. "Maximizing Linkage in Address Data: Spatial, Exact, and Fuzzy Matching." Journal of Data Science 24 no. 3 (2026):600-615. https://doi.org/10.6339/26-JDS1236.

    Saluja, Rashi, Sun, Hanyu, Korkmaz, Gizem, Carle, Jill, Hubbard, Ryan, Edwards, Brad, and Rick Dulaney. "Predicting "Yes": Machine Learning and Diverse Data to Boost Respondent Cooperation." Journal of Data Science 24 no. 3 (2026):616-629. https://doi.org/10.6339/26-JDS1242.



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