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About this journal
Aims and scope
The primary aim of the journal is to serve as a forum for discussing methodological challenges encountered in the analysis of imaging data and for presenting statistically sound solutions to those challenges. The journal covers a broad spectrum of statistical methods and data science techniques applicable to various imaging domains, including, but not limited to, neuroimaging, medical imaging, satellite imaging, physics, forensic imaging, astronomy, remote sensing, and materials science. The target audience comprises quantitative researchers, including statisticians, engineers, computer scientists, and data scientists, along with imaging researchers in fields such as brain science, radiology, satellite, forensic imaging, environmental studies, who are involved in developing and investigating methods for analyzing imaging data. Through this journal, a platform is provided for quantitative scientists (statisticians and data scientists) and field investigators to discover and discuss innovative methods in statistical imaging, promoting a collaborative environment for interdisciplinary research and harnessing the power of data analytics in imaging.
Along with methodological research papers, we publish discussion papers, soliciting concise feedback from the statistical imaging community, including the members of the ASA SI section, together with the authors’ rejoinders. Additionally, we feature in-depth reviews of specific topics by leading statisticians and data scientists, to ensure that the journal becomes an important reference for all investigators dealing with imaging data, and to generate further interest in the journal. We also include case-study papers that highlight applications of statistical methods in imaging data analysis to address real world questions. We invite submissions from both quantitative scientists and field investigators to encourage collaborations and facilitate cross-disciplinary discussions. The papers will follow the successful template of the case studies presented at the annual ASA/SI Statistical Methods in Imaging conference and will be reviewed by experts in both statistics and the relevant fields. We believe that case studies will be a valuable addition to the journal, providing readers with practical examples of the use of statistical methods in imaging data analysis.
We are also planning to provide a platform to discuss the best practices for pipelines of data pre-processing and analysis and provide practical guidance for researchers, facilitate access to public data repositories and use of statistical software even from single investigators and small groups. These pipelines are essential for ensuring the accuracy and reliability of statistical imaging analyses. The pre-processing stage is critical in imaging data analysis as it involves various steps, such as noise reduction, artifact removal, and image registration. When data is from publicly available repositories, the pipelines may also vary greatly according to the repository (e.g., UK Biobank vs ADNI vs ABCD), providing a barrier to access for investigators. We invite experts in the field to share their knowledge and experience on the most effective pre-processing methods for different types of imaging data. With this effort, we hope to facilitate better standardization of imaging data analysis, increased use of public data repositories and promote reproducible research.
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Editorial board
Editor-in-Chief
Marina Vannucci, Department of Statistics, Rice University
Co-Editors
Michele Guindani, Department of Biostatistics, UCLA
Martin Lindquist, Department of Biostatistics, John Hopkins University
Hernando Ombao, Statistics Program, Kaust University, Saudi Arabia
Associate Editors
Veera Balandayuthapani, University of Michigan
David Banks, Duke University
Moo Chung, University of Wisconsin-Madison
Emily Kang, University of Cinncinati
Claudia Kirch, Otto-van-Guericke University Magdeburg
Suprateek Kundu, UT MD Anderson Cancer Center
Sebastian Kurtek, Ohio State University
Nicole Lazar, Penn State University
Qiwei Li, UT Dallas
Amanda Mejia, Indiana University
Danica Ommen, Iowa State University
Anqi Qiu, National University of Singapore
Claudia Redenbach, University of Kaiserslautern-Landau (RPTU)
Sean Simpson, Wake Forest University
Chee-Ming Ting, Monash University
Dana Tudorascu, University of Pittsburgh
Zhaoxia Yu, UC Irvine
Open access
Statistics and Data Science in Imaging is an open access journal and only publishes open access articles. Publishing open access means that your article will be free to access online immediately on publication, increasing the visibility, readership, and impact of your research.
Why choose open access?
- Increase the discoverability and readership of your article
- Make an impact and reach new readers, not just those with easy access to a research library
- Freely share your work with anyone, anywhere
- Comply with funding mandates and meet the requirements of your institution, employer or funder
- Rigorous peer review for every open access article
Article Publishing Charges (APC)
To publish open access in this journal you may be asked to pay an Article Publishing Charge (APC). You may be able to publish your article at no cost to yourself or with a reduced APC if your institution or research funder has an open access agreement or membership with Taylor & Francis. Discounts and waivers may also be available for researchers in selected countries when publishing in open access journals.
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continuous publication
The American Statistical Association and our publisher Taylor & Francis make every effort to ensure the accuracy of all the information (the "Content") contained in our publications. However, The American Statistical Association and our publisher Taylor & Francis, our agents (including the editor, any member of the editorial team or editorial board, and any guest editors), and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by The American Statistical Association and our publisher Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. The American Statistical Association and our publisher Taylor & Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to, or arising out of the use of the Content. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions .