Erasing the Gap Between Statistics and Imaging:
A Q&A with Marina Vannucci
Statistics and Data Science in Imaging, an open-access journal launched by the American Statistical Association in 2024, serves as a dedicated platform for quantitative scientists to discover, discuss, and disseminate research findings, methodological advancements, and applications related to the statistical analysis of all types of imaging data. Editor Marina Vannucci, the journal’s first, responds to a few questions below about how things are going so far.
How does Statistics and Data Science in Imaging distinguish itself from other journals in statistical or imaging research?
Statistics and Data Science in Imaging is an open-access journal published on behalf of the ASA with support from its Section on Statistics in Imaging. Its primary mission is to provide a dedicated platform for disseminating research findings, methodological innovations, and applications related to the statistical analysis of imaging data across all domains. While forums exist for neuroscientists and other domain experts, the statistical community has no such outlet. Thus, we believe SDSI addresses a significant gap in the current journals available to researchers working on statistical methods and data science techniques for imaging data analysis.
What have been your biggest challenges so far?
A key challenge has been building the journal’s visibility and establishing its position within both the statistical and imaging communities. Launched in October 2024, the inaugural issue of the journal featured an editorial piece and several review papers by experts in diverse imaging domains, including solar imaging, forensic statistics, remote sensing, and cancer imaging, to showcase the breadth of the journal. The second issue has continued this momentum with a review on brain connectivity alongside several original research articles.
Ongoing efforts have centered on curating special issues about emerging topics in imaging. These special issues, supported by various ASA sections and interest groups, are led by invited guest editors and aim to highlight timely research themes. Currently, a call for a special issue on spatial statistics in imaging is open until September 2026, and calls for special issues on astrostatistics and forensics statistics will be posted soon on the journal’s website.
How do you hope SDSI helps connect statisticians with researchers in other fields?
The rapid growth of imaging technologies has created exciting new opportunities for interdisciplinary collaboration. Imaging is now central to research in neuroscience, medicine, astronomy, engineering, environmental science, and many other domains. As imaging modalities evolve and new data types emerge, there is a growing need for specialized statistical, machine-learning, and deep-learning methods to analyze increasingly complex data. SDSI strives to serve as a bridge between quantitative researchers and domain experts. By providing a forum in which methodological advancements and domain-specific applications are presented side-by-side, the journal encourages cross-disciplinary dialogue and fosters collaborations that can drive innovation in imaging science.
If you could look five years into the future, what would success for SDSI look like?
I hope SDSI will become a leading venue for rigorous, innovative statistical and data-science research in imaging and a valuable outlet for interdisciplinary research. A constant pipeline of high-quality submissions, influential special issues, and broad engagement from the global imaging community would indicate SDSI has become a central platform for advancing the field.
What do successful paper submissions look like?
SDSI welcomes a wide range of high-quality contributions. Successful submissions may include the following:
- Methodological research papers that introduce innovative statistical or data-science methods for imaging
- Discussion papers that invite concise responses from the community, stimulating scientific dialogue
- Comprehensive review papers by leading experts that synthesize current knowledge, challenges, and opportunities in key imaging topics
- Case-study papers coauthored by quantitative scientists and domain experts that illustrate the impact of statistical methods on real-world imaging problems
- Short communications highlighting emerging issues or timely developments of interest to the statistical imaging community
- Best-practice papers detailing workflows for data preprocessing, analysis, reproducibility, or software tools, including guidance on accessing public data repositories
Together, these submission types reflect SDSI’s mission to support methodological advancement, interdisciplinary collaboration, and practical application across the imaging sciences.