The Internet Archive, Project Gutenberg, and Google Books are wonderful sources of historical books, but the finished products of their digitization efforts, while thorough and functional, lack that last bit of polish. For example, one of my interests is historical cooking, specifically Georgian and Regency British cookery and the contemporary period in American cookery, but the PDF versions of the relevant cookbooks are usually just basic black and white scans with no features that aid findability or searchability. The plain text versions, while more searchable, are not aesthetically pleasing and often contain numerous optical character recognition errors...

Open source isn't just about opening up your code—it's also about building a supporting infrastructure that invites people to contribute. In order to create a vibrant, growing, and exciting project, the community needs to be able to participate in the governance, the documentation, the code, and the actual structures that keep the project alive. If the overall "hive" is doing well, it attracts more individuals with diverse skills to the project. Although many projects strive for "open everything," infrastructure is often closed to contribution. Usually, only a few people run the infrastructure and keep the lights on. They're sometimes unable to recruit help because, well, you can't really give the keys to the kingdom to everyone. A certain level of trust is needed before granting a contributor access to project infrastructure...

Machine learning and data analysis are two areas where open source has become almost the de facto license for innovative new tools. Both the Python and R languages have developed robust ecosystems of open source tools and libraries that help data scientists of any skill level more easily perform analytical work. The distinction between machine learning and data analysis is a bit fluid, but the main idea is that machine learning prioritizes predictive accuracy over model interpretability, while data analysis emphasizes interpretability and statistical inference. Python, being more concerned with predictive accuracy, has developed a positive reputation in machine learning. R, as a language for statistical inference, has made its name in data analysis...

The European-funded IoT open source platform FIWARE has matured significantly in the past two years according to developers, and is now being used in industrial production cases, pilot smart city, and utilities projects. Two projects using the FIWARE platform include a city water quality pilot and an early warning system to identify and prevent pest risks to agricultural crops. To further support industry uptake, FIWARE has recently formalized a foundation to lead community efforts. The Foundation is expected to see a new wave of community participation in the open source platform, which already has significant links with other open source projects...

We announced earlier this month that Open States—a project covered on Opensource.com in 2011—is now being maintained by the original creators of the project, a community of Sunlight Foundation alumni and other volunteers. After a year of scant staffing culminating in the closure of Sunlight Labs, we expect that getting Open States fully operational again will take a significant effort, and we know from experience that maintaining the menagerie of scrapers into the future isn't easy either. (That's why we're looking for volunteers and donations.) So why, despite the challenges, are we taking on this project? Why do we think that Open States matters so much? And where do we plan on taking this tool going forward?...