The New England Journal of Statistics in Data Science (NEJSDS)
The NEJSDS is the official journal of the New England Statistical Society (NESS). The aims of the journal are to serve as an interface between statistics and other disciplines in data science, to encourage researchers to exchange innovative ideas, and to promote data science methods to the general scientific community. The journal publishes high quality original research, novel applications, and timely review articles in all aspects of data science, including (but not limited to) all areas of statistical methodology, methods of machine learning, and artificial intelligence, novel algorithms, computational methods, data management and manipulation, applications of data science methods, among others. We encourage authors to submit collaborative work driven by real life problems posed by researchers, administrators, educators, or other stakeholders, and which require original and innovative solutions from data scientists.
Our mission is to harness the power of statistics and data science to contribute to society through the advancement of scientific research and the creation of long-term collaborations, which includes:
- Posing research questions, subject-matter expert hypotheses or aims, and suggestions for data sources and collection methods.
- Proposed designs and methods based on past research, or new methodologies when existing ones are shown to be inadequate.
- Data analysis - application of the proposed methods, introducing new software tools, proposing insightful and effective visualization methods, addressing computational challenges, and providing innovative solutions.
- Discussions - conclusions and refinements of the research aims or hypotheses, highlighting policy or societal implications, and critiques of the proposed methods.
A submission is not required to include all of these components. Our vision for the journal is to breed innovation and forge collaborations between researchers and data scientists through an ongoing scholarly, peer-reviewed discussion. Authors are also encouraged to submit traditional manuscripts, including methodological papers, applications, software and visualization tools, or reviews, as long as they are motivated by real-life problems.