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Accelerating AI for science: open data science for science. [PDF]
R Soc Open SciAspirations for artificial intelligence (AI) as a catalyst for scientific discovery are growing. High-profile successes deploying AI in domains such as protein folding have highlighted AI’s potential to unlock new frontiers of scientific knowledge. However, the pathway from AI innovation to deployment in research is not linear. Those seeking to drive a
Lawrence ND, Montgomery J.
europepmc +4 more sources
OAE – Organizational Architect and Engineer Journal, 2019 Published by Gesellschaft für Informatik e.V ...
Scholtes, Ingo, Strohmaier, Markus
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The science is in the data [PDF]
IUCrJ, 2017 Understanding published research results should be through one's own eyes and include the opportunity to work with raw diffraction data to check the various decisions made in the analyses by the original authors. Today, preserving raw diffraction data is technically and organizationally viable at a growing number of data archives, both centralized and ...
John R. Helliwell +3 more
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Why geographic data science is not a science [PDF]
Geography Compass, 2020 Abstract“Data Science” has taken many disciplines by storm. And for a good reason: New forms and unseen quantities of data enter nearly every scientific field, substantially changing the ways how scientists do science, and potentially allowing them to answer old questions or to pose them in novel ways.
Scheider, Simon +3 more
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Science and data science [PDF]
Proceedings of the National Academy of Sciences, 2017 Data science has attracted a lot of attention, promising to turn vast amounts of data into useful predictions and insights. In this article, we ask why scientists should care about data science. To answer, we discuss data science from three perspectives: statistical, computational, and human.
Padhraic Smyth, David M. Blei
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Proceedings of the National Academy of Sciences, 2020 Building and expanding on principles of statistics, machine learning, and scientific inquiry, we propose the predictability, computability, and stability (PCS) framework for veridical data science. Our framework, composed of both a workflow and documentation, aims to provide responsible, reliable, reproducible, and transparent results across the data ...
Yu, Bin, Kumbier, Karl
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Data science: a game changer for science and innovation [PDF]
International Journal of Data Science and Analytics, 2021 AbstractThis paper shows data science’s potential for disruptive innovation in science, industry, policy, and people’s lives. We present how data science impacts science and society at large in the coming years, including ethical problems in managing human behavior data and considering the quantitative expectations of data science economic impact.
Grossi V +5 more
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Communications of the ACM, 2022 Given the complexity of data science projects and related demand for human expertise, automation has the potential to transform the data science process.
De Bie, Tijl +5 more
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The Role of Data Science in Web Science [PDF]
IEEE Intelligent Systems, 2016 Web science relies on an interdisciplinary approach that seeks to go beyond what any one subject can say about the World Wide Web. By incorporating numerous disciplinary perspectives and relying heavily on domain knowledge and expertise, data science has emerged as an important new area that integrates statistics with computational knowledge, data ...
Phethean, Christopher +4 more
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Open Science and Data Science [PDF]
Data Intelligence, 2021 Data Science (DS) as defined by Jim Gray is an emerging paradigm in all research areas to help finding non-obvious patterns of relevance in large distributed data collections. “Open Science by Design” (OSD), i.e., making artefacts such as data, metadata, models, and algorithms available and re-usable to peers and beyond as early as possible, is a pre ...
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