Peer Reviewed Special Issue

The American Statistical Association journal Data Science in Science will publish a special issue featuring work presented at CoDA. All poster presenters and invited speakers can submit a manuscript by June 13 June 30, 2025 to be considered for this peer reviewed special issue.

Note that Taylor & Francis plans to waive the article publishing charge for the CoDA special issue.

See the journal's call for papers for more details and a submission link.

 

Links to past special issues:

  • CoDA 2023, Statistical Analysis and Data Mining, Volumes 17, 2024 and 18, 2025
  • CoDA 2020, Statistical Analysis and Data Mining, Volume 14, 2021
  • CoDA 2018, Part I and Part II, Statistical Analysis and Data Mining, Volume 12, 2019
  • CoDA 2016, Part I, Part II, Part III, Statistical Analysis and Data Mining, 2017
  • CoDA 2014, Statistical Analysis and Data Mining, Volume 8, 2015
  • CoDA 2012, Technometrics, Volume 55, 2013

 

Guidance for Authors

We  welcome articles written for a general technical audience that will include both “data scientists” and “scientist scientists” (subject matter experts). To reach this broad audience, we ask that authors discuss both the context of their problem and the methods they've chosen as discussed below.

 

Context

If your analyses arise in the context of a particular application or problem, provide background for that application in a way that’s accessible to a non-expert. At a minimum this means defining all your terms and spelling out your acronyms the first time you use them.

Even better would be to provide the motivation that’s leading you to conduct your analyses. For instance, what questions are you trying to answer? What makes these questions hard to answer? How will your data help answer those questions? How were your data collected or generated or observed?

Assume that your readers will know nothing about your area, so provide enough detail for them to follow the logic and recognize the success of your analyses.

 

Methods

While we don’t require you to develop new methodology for this special issue, we do want you to demonstrate that you’ve chosen your methods in a thoughtful way that’s appropriate for your data and application.

To that end, present plots of your data or other types of exploratory data analysis that demonstrate why the method you chose is appropriate. What are the characteristics of your data? Do the characteristics of your data match the assumptions of the method you chose? If not, how are you justifying your use of the method, or how are you modifying your interpretation of the results?

As with the context, assume that your readers will know nothing about the methods you chose and give enough information for a layperson to understand what you’re doing and why. Provide definitions and references. You don’t need to replicate an entire textbook entry in your paper, just provide the intuition and motivation for your choices.