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Patterns, 2020 Most data science is about people, and opinions on the value of human data differ. The author offers a synthesis of overly optimistic and overly pessimistic views of human data science: it should become a science, with errors systematically studied and ...
DL Oberski
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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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Re-engineering Clinical Trial Management System Using Blockchain Technology
Blockchain in Healthcare Today, 2022 The annual ConV2X is a leading international health tech symposium driving real world evidence, strategy, research, operations and trends to create a blueprint for a new digital health era.
Yan Zhuang, PhD, National Institute of Health Data Science, Peking University +2 more
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Data Science — definition and structural representation
Sistemnì Doslìdženâ ta Informacìjnì Tehnologìï, 2021 This article is a continuation of the discussion on the existing meanings and formalization of the definition of “Data Science” as an autonomous discipline, field of knowledge, clarification of its defining components, integration, and interaction ...
Pavlo Maslianko, Yevhenii Sielskyi
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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.
David M. Blei, Padhraic Smyth
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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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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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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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The Computer Journal, 2016 The field of Data Science concerns techniques for extracting knowledge from diverse data, with a particular focus on ‘big’ data exhibiting ‘V’ attributes such as volume, velocity, variety, value and veracity. The field of data science is becoming increasingly influential in the public, private and voluntary sectors, with its overarching aim of ...
Maneth, Sebastian +1 more
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