Data science

This repo contains thoughts and activities around research data, with a focus on

• Analysis of different kinds of open models for use in research funding.
  • How can research funding be opened up to the benefit of research?
  • How can peer review be made more efficient?
• Interfacing of the Commons with activities across institutional, disciplinary and international boundaries.
  • How to conceptualize and implement a Commons infrastructure for research?
  • What policies govern open access to research outputs?
  • How does free and open-source software fit into the picture?
  • How can data best be described in order to lay the groundwork for interoperability across data sources?
• Quantifying the reuse of various research objects, so as to gather information on what needs to be sustained.
  • How are research objects being used and reused, and how can reuse be encouraged and quantified?
  • How best to cite data and software?
  • How to make data accessible for people with disabilities?
  • How to sustain infrastructure for data sharing?
• Engaging communities with data, both experts and in the framework of citizen science projects.
  • How best to integrate crowdsourcing components into data management?
  • How to make data management plans machine readable?
  • How can quality data journalism be supported?

as well as a collection of further relevant resources in the reading room.

For a quick overview of some of the key elements of data science, see this infographic.