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E-raamat: Research Software Engineering: A Guide to the Open Source Ecosystem

(KOF Swiss Economic Institute, Zurich, Switzerland)
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Research Software Engineering: A Guide to the Open Source Ecosystem strives to give a big-picture overview and an understanding of the opportunities of programming as an approach to analytics and statistics. The book argues that a solid "programming" skill level is not only well within reach for many but also worth pursuing for researchers and business analysts. The ability to write a program leverages field-specific expertise and fosters interdisciplinary collaboration as source code continues to become an important communication channel. Given the pace of the development in data science, many senior researchers and mentors, alongside non-computer science curricula lack a basic software engineering component. This book fills the gap by providing a dedicated programming-with-data resource to both academic scholars and practitioners.

Key Features

  • overview: breakdown of complex data science software stacks into core components
  • applied: source code of figures, tables and examples available and reproducible solely with license cost-free, open source software
  • reader guidance: different entry points and rich references to deepen the understanding of selected aspects


Research Software Engineering: A Guide to the Open Source Ecosystem strives for a big picture overview and to give an understanding of the opportunities of programming as an approach to analytics and statistics.

Arvustused

"Covering the broad and evolving field of research software engineering ... is an ambitious task, and this book makes a commendable effort in doing so. It starts with a general overview, introducing key concepts such as development toolkits, programming basics, and interactive environments. It then delves into core areas like Git version control, data management, infrastructure, and automation. A dedicated chapter on community is a welcome inclusion, highlighting the importance of collaboration in research software development. ...it succeeds in raising awareness of best practices and encouraging researchers to adopt a more structured approach to software development." - Nathan Green, Journal of the Royal Statistical Society, Series A

1. Introduction
2. Stack - A Developers Toolkit
3. Programming 101
4.
Interaction Environment
5. Git Version Control
6. Data Management
7.
Infrastructure
8. Automation
9. Community
10. Publishing & Reporting
11. Case
Studies Appendix
Matthias Bannert, Ph.D. gained his hands-on data science and data engineering at ETH Zürich in more than a decade of working for the KOF Swiss Economic Institute. Today, he works as a data engineering expert advisor at cynkra and supports ETH as a section lead in the innovation-minded KOF Lab. In 2021, he was a co-chair of useR!, the annual user conference of the R Project for Statistical Computing. He remains an active contributor to extension packages of the R language and the open source community in general.
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Püsilink: https://www.kriso.ee/db/97810400051252e.html
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