I spent quite a bit of my career operating in the not-for-profit sector, which included a lot of technology-challenged, yet mission critical operations, from the point of view of those they served. This was in the closing decades of the previous century, when free and open software was just getting established. Whereas many of these non-profits were using proprietary software, we were starting to see the downward pressure on prices exerted by such packages as Open Office, later rename Office Libre.
The grant-giving organizations, at the core of this sector, were especially frustrated to see their limited funds financing copy after copy of the same office software. Databases were proportionately more expensive. Meyer Memorial Trust, in our neighborhood, was especially keen to have applicants use copyleft tools whenever possible, in the name of doing the greatest good. To this end, the trust undertook to “eat its own dog food” which meant replacing a proprietary grant applicant processing system in Filemaker, with something more relational, written in Perl or one of those “P-languages” from the LAMP stack days (LAMP stands for Linux Apache MySQL, and any P-language: Perl, Python and of course PHP).
The same pressure on the non-profits to avoid expensive software was operative in academia as well. Those with a technical and engineering background were especially singled out, as surely these people would be a part of the open software revolution. They could write their own tools, right? Many of them did. That’s how free and open source software got started: people who coded for a living decided to stop paying for at least some of the tools, and instead work together on making and sharing their own. With digital IP, such licensing arrangements are especially sane, given the infinite copyability, without degradation, of the original.
When I flew to Baltimore that time, to train the Hubble space telescope instrumentation team in Python, the economics became clear to me. The astronomers wanted to share raw data and the signal processing pipelines that resulted in more meaningful visualizations. However, their code was written in a proprietary language which university departments could scarcely afford. Spreading their computation intensive infrastructure in some affordable form, to collaborators and peers…