Is open article data too big to ignore (for Text data mining)? | Musings about librarianship

"4 years ago in 2014, I wrote about the coming disruption to academic libraries due to Open Access. In How academic libraries may change when Open Access becomes the norm , I wrote "The trend I am increasingly convinced that is going to have a great impact on how academic libraries will function is the rise of Open Access.  As Open Access takes hold and eventually becomes the norm in the next 10-15 years, it will disrupt many aspects of academic library operations and libraries will need to rethink the value-add they need to provide to universities. The events of the past year have convinced me that the momentum for open access is nearly unstoppable and the tipping point for open access has or will occur soon. " How do we know the tipping point has been reached? Simple, discovery tools which had for years ignored or gave cursory attention to discovery of free to read articles started to get serious about open access discovery, as the pool of open access articles has become too large to ignore and being able to reliably detect and point to them is a critical issue. From 2017 onwards, whether it is moves from existing players in the discovery space like Summon/Primo (Proquest/Exlibris), Scopus (Elsevier), Web of Science (Clairvate) or from new disrupters like Kopernio, Unpaywall, Lean Library browser, Anywhere Access (Digital Science), 1Findr(1Science), Open Access button etc we saw this happen.  This is a fast moving space, but here's a fairly recent summary by me at CNI Spring 2018 on some of the issues. So what's next? While the first phrase of open access benefited human researchers who could get access and read articles, the next phase I think might belong to the machines...."