New ASA Journal a Dedicated Platform
for Quantitative Scientists

Over the past decade, there has been an increase in the use of statistical methods for analyzing imaging data across various disciplines. Currently, imaging data is used in areas such as neuroimaging (using various modalities), medical imaging (including radiomics), satellite imaging, computational imaging, forensic imaging, astronomy, remote sensing, and materials science.

Statistics and Data Science in Imaging, a new open access journal published by the American Statistical Association, will serve as a dedicated platform for quantitative scientists (statisticians and data scientists) to discover, discuss, and disseminate research findings, methodological advancements, and applications related to the statistical analysis of all types of imaging data.

Along with methodological research papers, the journal welcomes the following:

• Discussion papers
• In-depth reviews of specific topics that provide readers with a comprehensive understanding of the current research landscape and challenges and opportunities in imaging research
• Case-study papers, written jointly by quantitative scientists and domain experts, that highlight applications of statistical methods in imaging data analysis to address real-world questions
• Short communications highlighting emerging issues of interest to the statistical community
• Best practice papers for pipelines of data pre-processing and analysis that provide practical guidance for researchers and facilitate access to public data repositories and use of statistical software

SDSI just completed its first volume year. Following are selected articles that provide the depth and breadth of the journal:

• Statistics and Data Science in Imaging: A Message from the Founding Editors, by Marina Vannucci et al.
• Solar Imaging Data Analytics: A Selective Overview of Challenges and Opportunities, by Yang Chen et al.
• Mapping Conservation Practices via Deep Learning: Improving Performance via Hillshade Imagery, Sampling Design, and Centerline Dice Loss, by Charles J. Labuzzetta and Zhengyuan Zhu
• Challenges in Modeling, Interpreting, and Drawing Conclusions from Images as Forensic Evidence, by Karen Kafadar and Alicia Carriquiry
• Statistical Analysis of Quantitative Cancer Imaging Data, by Shariq Mohammed et al.