Journal overview
Statistics and Data Science in Imaging is an open access, international journal publishing original research and reviews on Statistical Analysis of Imaging Data.
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.
Read full aims and scopeThe 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.