The Practice Of Reproducible Research: Case Studies And Lessons From The Data-intensive Sciences
by Justin Kitzes /
2017 / English / Kindle
3.9 MB Download
The Practice of Reproducible Research
The Practice of Reproducible Research presents
concrete examples of how researchers in the data-intensive
sciences are working to improve the reproducibility of their
research projects. In each of the thirty-one case studies in this
volume, the author or team describes the workflow that they used
to complete a real-world research project. Authors highlight how
they utilized particular tools, ideas, and practices to support
reproducibility, emphasizing the very practical how, rather than
the why or what, of conducting reproducible research.
presents
concrete examples of how researchers in the data-intensive
sciences are working to improve the reproducibility of their
research projects. In each of the thirty-one case studies in this
volume, the author or team describes the workflow that they used
to complete a real-world research project. Authors highlight how
they utilized particular tools, ideas, and practices to support
reproducibility, emphasizing the very practical how, rather than
the why or what, of conducting reproducible research.
Part 1 provides an accessible introduction to reproducible
research, a basic reproducible research project template, and a
synthesis of lessons learned from across the thirty-one case
studies. Parts 2 and 3 focus on the case studies
themselves.
Part 1 provides an accessible introduction to reproducible
research, a basic reproducible research project template, and a
synthesis of lessons learned from across the thirty-one case
studies. Parts 2 and 3 focus on the case studies
themselves.The Practice of Reproducible
Research
The Practice of Reproducible
Research is an invaluable resource for students and
researchers who wish to better understand the practice of
data-intensive sciences and learn how to make their own research
more reproducible.
is an invaluable resource for students and
researchers who wish to better understand the practice of
data-intensive sciences and learn how to make their own research
more reproducible.
