Researchers working at the intersection of science and data science have several new publishing opportunities through Data Science in Science, the open-access journal published in association with the American Statistical Association. The journal is currently seeking submissions for three major special issues focused on environmental health, particle and nuclear physics, and ecology.
A Journal Built at the Science-Data Science Interface
Data Science in Science is an international, open-access journal dedicated to publishing original research and review articles that advance both scientific discovery and data science methodology. The journal's mission is to promote new approaches to experimental and observational learning, uncertainty quantification, reproducibility, and interdisciplinary collaboration across scientific domains. It emphasizes research that combines scientific questions with innovative statistical, computational, and machine learning methods.
According to the journal's overview, it seeks to foster collaboration at three levels: deep domain-specific research, broader inter-domain collaborations, and trans-disciplinary scientific partnerships. Special issues are a key part of this strategy, highlighting emerging areas where advances in data science can accelerate scientific understanding.
Special Issue: Data Science at the Intersection of Health and the Environment
One of the journal's newest calls for papers focuses on the growing connections among environmental change, public health, and data-driven decision making. The special issue,"Data Science at the Intersection of Health and the Environment," invites both original research and review articles addressing climate-related health risks, environmental exposures, and coupled human-environment systems. The submission deadline has been extended to December 31, 2026.
Topics of interest include:
- Climate and environmental data fusion
- Spatiotemporal modeling of climate-sensitive systems
- Human adaptation and decision analytics
- Uncertainty quantification for climate impacts
- Machine learning for climate extremes
- Equity-focused environmental analytics
- Network and systems modeling
- Advanced monitoring and sensor design
The editors note that advances in remote sensing, environmental monitoring, and large-scale demographic datasets have created opportunities for new analytical approaches capable of addressing complex health and environmental challenges.
Special Issue: Data Science for Particle and Nuclear Physics
The journal is also calling for papers for a special issue on"Data Science for Particle and Nuclear Physics," with submissions accepted through December 31, 2026 following an extension of the original deadline.
The issue aims to support the next generation of discoveries in particle and nuclear physics, particularly as major research facilities such as the High-Luminosity Large Hadron Collider and the Electron-Ion Collider generate increasingly large and complex datasets.
Suggested research areas include:
- High-dimensional inference and uncertainty quantification
- Surrogate modeling and simulation emulation
- Simulation-based and likelihood-free inference
- Anomaly and novelty detection
- Foundation models and AI applications
- Fairness and interpretability in scientific algorithms
- Experimental design and detector optimization
- Model validation and comparison of measurements across experiments
Editors encourage submissions that bring state-of-the-art data science, machine learning, and artificial intelligence methods to experimental, theoretical, and phenomenological physics applications. [think.tayl...rancis.com]
Special Issue: Data Science in Ecology
A third special issue,"Data Science in Ecology," is accepting submissions through February 28, 2027. The call responds to growing demand for analytical methods capable of handling massive ecological datasets generated by remote sensing, automated sensors, genomics, and large-scale monitoring networks.
The editors are particularly interested in work involving:
- Data integration and multi-source ecological modeling
- Citizen science and opportunistic datasets
- Machine learning and AI for ecological prediction
- Bayesian methods and uncertainty quantification
- Mechanistic and agent-based models
- Causal inference in ecological systems
- Spatiotemporal modeling of ecological change
The special issue is being developed in partnership with the 2026 ENVR Workshop of the American Statistical Association and the International Statistical Ecology Conference (ISEC), although participation in those events is not required for submission.
Growing Momentum for Interdisciplinary Research
The three calls for papers reflect the journal's broader vision of advancing scientific discovery through modern data science methods. From climate resilience and environmental health to high-energy physics and biodiversity conservation, the special issues highlight fields where increasingly complex data require new statistical and computational tools.
Researchers interested in contributing can submit original research articles or review papers through the journal's submission portal. The journal also notes that article processing charge waivers may be available in certain circumstances.
For more information: Visit Data Science in Science and the individual special issue pages for detailed submission requirements and author guidelines.