From the post:
The phrase "open source” evokes an egalitarian, welcoming niche where programmers can work together towards a common purpose — creating software to be freely available to the public in a community that sees contribution as its own reward. But for data scientists who are just entering into the open source milieu, it can sometimes feel like an intimidating place. Even experienced, established open source developers like Jon Schlinkert have found the community to be less than welcoming at times. If the author of more than a thousand projects, someone whose scripts are downloaded millions of times every month, has to remind himself to stay positive, you might question whether the open source community is really the developer Shangri-la it would appear to be!
And yet, open source development does have a lot going for it:
- Users have access to both the functionality and the methodology of the software (as opposed to just the functionality, as with proprietary software).
- Contributors are also users, meaning that contributions track closely with user stories, and are intrinsically (rather than extrinsically) motivated.
- Everyone has equal access to the code, and no one is excluded from making changes (at least locally).
- Contributor identities are open to the extent that a contributor wants to take credit for her work.
- Changes to the code are documented over time.
So why start a blog post for open source noobs with a quotation from an expert like Jon, especially one that paints such a dreary picture? It's because I want to show that the bar for contributing is… pretty low.
Ask yourself these questions: Do you like programming? Enjoy collaborating? Like learning? Appreciate feedback? Do you want to help make a great open source project even better? If your answer is 'yes' to one or more of these, you're probably a good fit for open source. Not a professional programmer? Just getting started with a new programming language? Don't know everything yet? Trust me, you're in good company.
Becoming a contributor to an open source project is a great way to support your own learning, to get more deeply involved in the community, and to share your own unique thoughts and ideas with the world. In this post, we'll provide a walkthrough for data scientists who are interested in getting started in open source — including everything from version control basics to advanced GitHub etiquette.
Two of Rebecca’s points are more important than the rest:
- the bar for contributing is low
- contributing builds community and a sense of ownership
Will 2017 be the year you move from the sidelines of open source and into the game?