What does a journal look like when you design it for today’s research world, along with the one we’re building? On a recent episode of the Practical Significance podcast, cohosts Donna LaLonde and Ron Wasserstein welcomed the editorial leadership team behind ASA Discoveries, the American Statistical Association’s new open-access journal. Editor-in-Chief Bo Li and co-editors Sebastien Haneuse, Galin Jones, Shujie Ma, and Abel Rodriguez shared the journal’s vision and goals.
Donna LaLonde: Bo and Galin, tell us about your day jobs and share your thoughts on the gap in the publishing landscape you believe ASA Discoveries will fill?
Bo Li: I’m a professor in statistics and data science at Washington University in St. Louis. I’m also the co-director of a transdisciplinary institute in applied data science at Washington University, and I’m the editor-in-chief of ASA Discoveries.
ASA already has a very strong portfolio of journals, including flagship journals and highly specialized ones. We picture ASA Discoveries not as filling a gap in subject matter, but rather a gap in how we accommodate the pace and breadth of modern research.
Our field has expanded dramatically. Statisticians and data scientists produce far more high-quality work than even a decade ago, and at the same time, machine learning and AI are reshaping research in many directions at once.
Innovation is happening across traditional boundaries—between theory and application, statistics and AI, and across domains.
Galin Jones: I’m a professor of statistics at the University of Minnesota. I’m the director of the School of Statistics and chair of the university’s data science and AI hub.
Bo is exactly right. There was a need for a journal that can showcase broad, emerging, and cross-cutting innovations. Papers that may not fit neatly into a single traditional category but may be statistically or scientifically important and forward-looking.
ASA Discoveries is that home, complementing existing ASA journals by highlighting impactful ideas that reflect where the field is going.
Donna LaLonde: Sebastien and Abel, if you had to distill the soul of ASA Discoveries into a few sentences, what is the core impact you want it to have? But first, tell us about your day jobs.
Sebastien Haneuse: I’m a professor in the department of biostatistics at the Harvard Chan School of Public Health, and I also serve as director of the PhD program.
The “soul” is a tricky one, so I’m going to let Abel speak to that directly. But I’ll say that the landscape in which statistics, data science, and AI operate is growing and evolving rapidly, and that certainly wasn’t lost on any of us.
We hope to serve as a venue for publishing high-quality and impactful work at the intersection of these fields. One core ingredient that distinguishes us is that we’re fully open access, meaning all published papers will be universally available.
Abel Rodriguez: I’m a professor of statistics at the University of Washington and serve as department chair.
Picking up on threads already mentioned, what counts as statistics research is evolving rapidly.
For example, contributions involving infrastructure—benchmark data sets, data repositories, or broadly useful research software—have historically been hard to publish. ASA Discoveries will expand the frontier of what counts as statistical research, while maintaining rigor and high standards.
These frontier areas need publication standards, and defining them is ongoing work. We’ll work closely with authors, especially in the early years, to help shape those standards.
Ron Wasserstein: Shujie, please introduce yourself and tell us what makes ASA Discoveries fundamentally different from existing publications. What will readers and authors find here that they can’t get elsewhere?
Shujie Ma: I’m a professor of statistics and graduate adviser in the Department of Statistics at the University of California, Riverside.
ASA Discoveries is uniquely positioned as a fully open-access, interdisciplinary journal that integrates statistics, data science, and artificial intelligence into a single venue. Unlike traditional journals that emphasize theory or application in isolation, ASA Discoveries promotes work that connects methodological innovation with real-world impact.
All articles are freely available, removing barriers to dissemination and enabling broader societal impact. Articles are published on a rolling basis and can be immediately cited. The journal also emphasizes ethical, transparent, and responsible data science, encouraging authors to address fairness, accountability, and transparency.
Ron Wasserstein: Bo, would you walk us through your editorial philosophy. How does that philosophy shape what you will accept and how you’ll work with authors?
Bo Li: My editorial philosophy is to establish ASA Discoveries as a home for rigorous, innovative, and forward-looking research and education. We aim to cover a broad scope, but we are very intentional about maintaining a high bar for quality. That philosophy shapes both what we accept and how we work with authors.
On the acceptance side, we’re looking for work that is technically sound, intellectually novel, and genuinely impactful—especially research that pushes the field in a new direction or connects ideas across areas.
On the process side, we care deeply about how authors experience the journal. We strive to provide timely and clear feedback, starting with thoughtful editorial screening. If a paper passes that initial stage, it will receive careful and serious attention from expert referees.
We are committed to ensuring reviews are fair, balanced, and constructive. Even when a paper is not accepted, we want authors to feel the feedback helped improve their work.
Ron Wasserstein: Sebastien and Shujie, what will success look like in the first year of ASA Discoveries, beyond standard metrics like impact factors?
Shujie Ma: We view success in the first year primarily in terms of community engagement, content quality, and visibility.
A successful first year would include attracting a diverse set of high-quality submissions across statistics, data science, and AI; publishing impactful articles that span both methodological innovation and real-world applications; and establishing the journal as a trusted, accessible venue for interdisciplinary research.
The first year will be successful if authors, readers, and practitioners recognize ASA Discoveries as a welcoming platform for timely, relevant, and ethically grounded work, supported by open-access and rapid dissemination.
Sebastien Haneuse: Success means ASA Discoveries becomes a go-to journal at the intersection of statistics, data science, and AI. We envision a publication that is widely read and cited, known for both rigorous scholarship and practical relevance.
We’re keenly aware that success must also reflect strong global participation. We’re very interested in submissions from all over the world and hope to foster sustained interdisciplinary collaboration.
Ultimately, the goal is to shape the future of the field through ASA Discoveries—highlighting impactful discoveries, fostering innovation, and making high-quality research accessible to a broader scientific community and the public at large.
Donna LaLonde: Galin and Abel, with AI as the great equalizer, what’s the policy going to be on using AI to polish work before submission?
Galin Jones: We will follow Taylor & Francis’s publicly available policy. The basic idea is that it’s acceptable to use AI to help polish writing, assist with idea generation, or help with code, but that use must be acknowledged. Generative AI cannot be listed as an author, and it must be used responsibly.
Text or code generated by AI cannot be included without significant revision and engagement by the authors. AI cannot be used for synthetic data generation or to substitute missing data without a robust methodology. Generating inaccurate content—including in supplemental materials—is prohibited.
That said, we also recognize the reality that many of us, me included, use AI tools to help polish writing or generate ideas, and that is permissible within these guidelines.
Abel Rodriguez: Another important aspect for authors is that image generation and image manipulation are prohibited under Taylor & Francis policies. That’s different from text, and authors should keep that in mind when preparing manuscripts.
These AI policies apply not only to authors, but also to the editorial board and associate editors. We must be mindful of confidentiality, proprietary data, and privacy concerns. Editors and reviewers are not allowed to put manuscripts into generative AI tools to assist with the review process—that’s a clear no-go zone.
It is permissible for reviewers or editors to use AI to polish the language of their reviews, but they must ensure that no confidential or proprietary information is shared with AI systems.
Donna LaLonde: Bo and Shujie, reproducibility is a concern across many fields. How are you approaching code and data sharing?
Bo Li: Reproducibility is a serious issue that the editorial team discussed at length before launching ASA Discoveries. We all agree that reproducibility of methods, code, and results is essential for scientific progress.
Ideally, a journal would have the resources to formally verify reproducibility for every accepted paper. However, at this stage, like most journals, we simply do not have the bandwidth to conduct full reproducibility checks ourselves.
Rather than overpromising, we are transparent about what we can and cannot do. Because ASA Discoveries is an open-access journal, the broader community is naturally positioned to evaluate, test, and validate published work.
We strongly encourage authors to share code and data, typically through supplementary materials or public repositories.
Shujie Ma: I agree that sharing code has become increasingly important, especially from the author’s perspective. When a paper introduces a new method without accompanying code, it becomes much harder for others to use, extend, or adopt the work. In practice, this can significantly limit the paper’s visibility and long-term influence.
While we don’t mandate code or data sharing, we view it as a strong positive signal for reproducibility, trust, and impact. Looking ahead, ASA Discoveries is committed to exploring additional ways to strengthen reproducibility.
This may include encouraging standardized documentation, clear data descriptions, reproducible workflows, and the use of persistent public repositories. We also aim to highlight exemplary reproducible papers as models for the community and to promote a culture where transparency and openness are recognized as core elements of high-quality research.
Ultimately, our goal is to foster an ecosystem in which reproducibility is supported not only by policies, but also by community norms.
Donna LaLonde: Abel, would you elaborate about emerging research methods, formats, or types of scholarship you’re excited to publish—especially work that might not fit the traditional journal model?
Abel Rodriguez: One area that has come up repeatedly is work at the intersection of statistics and AI. I believe there are many important foundational questions for modern AI methods, particularly generative AI—where statistical thinking can lead to important advances. We hope to be a venue for statisticians engaging in that work.
We publish data notes and registered reports, which is another way we differentiate ourselves. As a discipline, method development has traditionally been valued more than assembling data repositories or resources that benefit the broader community, even though those resources can significantly advance the field.
These publication mechanisms allow us to address that imbalance. Another area I’m excited about is methodology for reproducibility itself—not just reproducibility of individual papers, but advances in how reproducibility is achieved more broadly.
Finally, ethics in data science is an important area. These submissions need to be substantive and grounded in statistical methodology, not just case studies. This kind of work often lacks a clear home in the statistics community, and we believe ASA Discoveries can provide one.
Ron Wasserstein: Galin, what advice do you have for early-career researchers looking to publish their most boundary-pushing work in ASA Discoveries?
Galin Jones: The biggest piece of advice I can give is simple: submit it to ASA Discoveries. Do it immediately.
We’re looking for impactful, boundary-pushing work in statistics, machine learning, and AI—whether theoretical, methodological, or applied. We’re also open to novel article types that don’t have a clear home elsewhere, such as data reports, preregistrations, and work on ethics.
Ron Wasserstein: Bo, what opportunities are there for people who want to be involved in ASA Discoveries?
Bo Li: We want people to get involved; that would be a success.
First and foremost, we encourage researchers to submit their work. We’re especially excited to see innovative, forward-looking papers that reflect where the field is headed. Another important way to contribute is by serving as a reviewer. Thoughtful, fair, and timely reviews are essential to building a strong journal, and we truly value the expertise and time of our reviewers. We also welcome ideas and feedback, especially in these early stages.
While the editorial board is set for now, we do expect it to evolve over time. As the journal grows, there will be opportunities to serve in more formal roles. We very much see ASA Discoveries as a community-driven journal and hope many people will grow with it.
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