Results 11 to 20 of about 12,687,490 (344)
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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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 Art and Practice of Data Science Pipelines: A Comprehensive Study of Data Science Pipelines In Theory, In-The-Small, and In-The-Large [PDF]
ICSE 2022: The 44th International Conference on Software
Engineering, 2021 Increasingly larger number of software systems today are including data science components for descriptive, predictive, and prescriptive analytics. The collection of data science stages from acquisition, to cleaning/curation, to modeling, and so on are referred to as data science pipelines. To facilitate research and practice on data science pipelines,
arxiv +1 more source
Designing Data Science Workshops for Data-Intensive Environmental Science Research
Journal of Statistics and Data Science Education, 2021 Over the last 20 years, statistics preparation has become vital for a broad range of scientific fields, and statistics coursework has been readily incorporated into undergraduate and graduate programs.
Allison S. Theobold +2 more
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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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Towards Ordinal Data Science [PDF]
Transactions on Graph Data and Knowledge, 2023 Order is one of the main instruments to measure the relationship between objects in (empirical) data. However, compared to methods that use numerical properties of objects, the amount of ordinal methods developed is rather small.
Stumme, Gerd +2 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 DATA CATALOGUE [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2023 Open Science is a catalyst for innovation. Across the Earth Observation value chain, from R&D to prototyping new products and development of commercial applications, openness can play an important role by promoting long-term sustainable, community ...
F. Schindler +5 more
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Risk Assessment for Scientific Data
Data Science Journal, 2020 Ongoing stewardship is required to keep data collections and archives in existence. Scientific data collections may face a range of risk factors that could hinder, constrain, or limit current or future data use.
Matthew S. Mayernik +6 more
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