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Even though it has only entered public perception relatively recently, the term "data science" already means many things to many people. This chapter explores both top-down and bottom-up views on the field, on the basis of which we define data science as
Paul McNicholas
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Accelerating AI for science: open data science for science
Aspirations 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 ...
Neil D. Lawrence, Jessica Montgomery
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Re-engineering Clinical Trial Management System Using Blockchain Technology
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: The Foundation of Science [PDF]
A guiding principle of Genetics, the peer-edited journal of the Genetics Society of America, is to make geneticists' work accessible and available to the community, with the goal of recording scientific progress and catalyzing further advances.
Lauren M. McIntyre
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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 — definition and structural representation
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]
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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Why geographic data science is not a science [PDF]
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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The science is in the data [PDF]
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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