How can we make open-source tools reach a larger audience and have a bigger impact in our society? Localization is not the only way but it is a necessary step in every project for it to reach different communities and allow them to cross the language barriers. This in turn provides a way for a more diverse and inclusive group of users to become contributors. This is how JupyterLab does it!
How can we make open-source tools and have them reach a larger audience? How should user interfaces work to provide a native and inclusive experience for the vast majority of users? The aim of this talk is to explain and clarify the difference between the terms of translation, localization, internationalization and globalization. It will cover some background on the topics around the initial questions, but specifically will talk about in the topic of localization, highlighting the importance of providing localization for the tools and the open-source ecosystem in general. Examples of how JupyterLab solves these issues and how developers authors can provide localization for their extensions will be discussed at a high level. We will show some examples in Python and TypeScript of how tools handle the specificities of different languages (not programming languages!) and finally invite the community to join forces in helping localize JupyterLab to more languages using crowdsourcing technologies. The section on localizing extensions will include some examples with TypeScript and Python but no prior experience with either of the languages is necessary to profit from this talk.
Open-source needs to further democratize the tools, the documentation, and the overall ecosystem around them, to the non-English speaking world. By doing this a larger and more diverse audience is reached which in turn provides a door for a more diverse group of users to become contributors. In this context, localization is a necessary step to improve the diversity and inclusion of Open-source projects.
The JupyterLab project is used as an example of how this can be approached and the available tools for localizing extensions are presented.
gettext, ICU, Fluent and how they are used.