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This post provides a good explanation why and how the data collection process should be part of the implementation of an open data project rather than just a once-executed-and-then-forgotten step. Ideally the data collection building scripts plus various processing steps, e.g., conversion to various data formats, dataset merging, or parsing of meta data are made a open source project themselves. See this open-source project for an example.

I am interesting in other such projects for various reasons:

  • Learn how to interpret various types of open data collections
  • Best practices for open data "build" systems
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I started the open football project - that collects public domain football data (e.g. World Cup in Brazil, English Premier League, Bundesliga, Champions League etc.). Nothing new other than having public domain (license-free, no rights reservied) datasets in plain old text.

What's different is that all tools and scripts including, of course, the build scripts are public domain (and open sourced) too and, of course, part of the project.

For example, to build a single-file SQLite database e.g. worldcup.db that includes all World Cups from 1930 to 2014 use the Ruby make tool, that is, rake. Example:

$ rake build DATA=worldcup

That's it. Cheers.

PS: The same "system" works, for sure, for other topics. See, the open beer and brewery project as another "real-world" example that includes build scripts that let you build "The Free World Beer Book" (e.g. $ rake book).

  • thanks for pointing me to those projects. Sounds like good examples for what I am intending to todo. – georg Aug 3 '14 at 10:32
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I just stumbled upon another good example: swiss-maps

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