Data Science in Science

Dear Colleagues,

This Special Issue of Data Science in Science invites original manuscripts that bring together novel data science methods for, and applications in, the analysis of data in the federal government, the use of federally held and federally collected data, and the production of federal statistics. We welcome interdisciplinary collaborations, original research, and reviews. We evaluate submissions on the basis of scientific rigor, technical depth, and ethical standards, regardless of perceived novelty.

Papers in this special issue will conform to the regular standards and scope of the journal.

We welcome original, unpublished and innovative submissions including, but not limited to, the following areas of research:

• The use of data science, machine learning, and artificial intelligence in data collection and the production of federal statistics.
• The use of data science, machine learning, and artificial intelligence in generating linkages across survey, administrative, and other data sources.
• The applications and limitations of data science, machine learning, and artificial intelligence in federal agencies.
• Organizational, managerial, and ethical challenges in data science, machine learning, and artificial intelligence in the context of federal data and federal agencies.
• Balancing data utility and data privacy in an era of artificial intelligence.

Sincerely yours,

Snigdhansu (Ansu) Chatterjee, Ellen Galantucci, Emily Hadley, David Haziza, David S. Matteson, Linden McBride, Tucker McElroy, Saeid Molladavoudi, and Paul A. Parker

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Special Issue Editors

Snigdhansu (Ansu) Chatterjee, Department of Mathematics and Statistics, University of Maryland at Baltimore County, USA, [email protected]

Ellen Galantucci, Bureau of Trade Analysis, Federal Maritime Commission, USA, [email protected]

Emily Hadley, Center for Data Science and AI, RTI International, USA, [email protected]

David Haziza, Department of Mathematics and Statistics, University of Ottawa, Canada, [email protected]

David S. Matteson, Department of Statistics and Data Science, Cornell University, USA, [email protected]

Linden McBride, Center for Economic Studies, US Census Bureau, USA, [email protected]

Tucker McElroy, Research and Methodology Directorate, US Census Bureau, USA, [email protected]

Saeid Molladavoudi, Data Science Division, Statistics Canada, Canada, [email protected]

Paul A. Parker, Department of Statistics, University of California, Santa Cruz, USA, [email protected]

• All manuscripts must be in English and written in accordance with the "Instructions for Authors" which can be found on the Journal's homepage.
• All submissions will be peer-reviewed and meet the same requirements and standards as that of a regular paper submission.
• Submissions must not have been previously published in other journals or conferences; submissions that have already been uploaded to preprints such as arXiv are allowed.
• Please select "Data Science in the Federal Government" when submitting your paper to Data Science in Science in the author  Submission Portal.
• Taylor & Francis are currently supporting a 100% APC discount for all authors. An APC will not apply to any new submissions (free and open access).
• For inquiries about this Special Issue, please contact the editors by e-mail.

Instructions for AuthorsSubmit an Article