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Data Science in Science is an open access, international journal publishing original research and reviews at the intersection of Science and Data Science. Its aim is to advance: new ideas for experimental and observational data-driven learning and discovery that help address fundamental questions at the frontiers of Science and scientific inference; quantification and summarization of uncertainty from data-driven theories and complex Data Science models, algorithms, and workflows; and new practices for scientific reproducibility and replicability enabled through Data Science.
Data Science in Science is an open access, international journal publishing original research and reviews at the intersection of Science and Data Science. Its aim is to advance: new ideas for experimental and observational data-driven learning and discovery that help address fundamental questions at the frontiers of Science and scientific inference; quantification and summarization of uncertainty from data-driven theories and complex Data Science models, algorithms, and workflows; and new practices for scientific reproducibility and replicability enabled through Data Science.