Open Science, Open Data, Open Source
Free

Open Science, Open Data, Open Source

By Pedro L. Fernandes
Free
Book Description

The goal of these resources is to give a bird's eye view of the developments in open scientific research. That is, we cover both social developments (e.g. the culture in various communities) as well as technological ones. As such, no part of the contents are especially in-depth or geared towards advanced users of specific practices or tools. Nevertheless, certain sections are more relevant to some people than to others. Specifically:


  • The most interesting sections for Graduate students will be about navigating the literature,managing evolving projects, and publishing and reviewing.

  • Lab technicians may derive the most benefit from the sections about capturing data, working with reproducibility in mind and sharing data.

  • For data scientists, the sections on organizing computational projects as workflows, managing versions of data and source code, open source software development, and data representation will be most relevant.

  • Principal investigators may be most interested in the sections on data management, data sharing, and coping with evolving projects.

  • Scientific publishers may be interested to know how scientists navigate the literature, what the expectations are for enhanced publications, and the needs for data publishing.

  • Science funders and policy makers may easily find value in the capturing data, data management,data sharing and navigating the literature.

  • Science communicators may be more interested in exploring the content by starting with navigating the literature, working with reproducibility in mind and sharing data.

Table of Contents
  • Front matter
  • Who is this for?
  • How to navigate the scientific record
  • How to cope with evolving research outputs
  • How to make your research reproducible
  • How to publish your research with impact
  • How to capture data to produce reliable records
  • How to manage data and plan for it
  • How to share research data fairly and sensibly
  • How to do analyses in a workflow-oriented manner
  • How to be formally explicit about data and concepts
  • How to improve scientific source code development
  • References
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