In 2018, I started noticing an increasing number of startups that focused on applying machine learning in the Libraries and Scholarly Communication arena. There were tools such as Scholarcy, Paper Digest and Get the Research that auto summarizes articles, while companies like UNSILO were applying machine learning to publisher manuscript submission systems like ScholarOne.

In academia, the print paradigm still holds sway. For instance, I've argued that our citation practices makes no sense in our largely digital era. That said, quite a bit of the content, we read, cite and use are available only in hard copy , or failing that content in PDF that can't be easily manipulated in digital formats.

It has been over a year in April 2018 since I had the opportunity to present at two panels in  conferences alongside experts such  as Lisa Hinchliffe, Johan Tilstra (Founder Lean Library), Jason Priem (Cofounder Unpaywall), Ben Kaube (Cofounder Kopernio) on the topic of browser extensions that help users gain quick access to the full text of articles while browsing the web.

The news that University of California system cancelled their deal with Elsevier seemed to have caused a bit of a stir all over the world, including here in Singapore and I was asked to do a talk to brief faculty on the latest trends in this area. The more I researched the more interested I got. What were institutions and consortiums doing to better their bargaining position with the big publishers?

Most people agree with Open Access is a good thing to have, but  a lot of the debate resolves over business models and the cost we should pay for it. Fundamentally, the question is how much is the output worth? While it isn't perfect, Librarians use cost per use figures in negotiations and discussions on whether to renew something but how are cost per use figures usually calculated?

Yes, I made it. 10 whole years of blogging about librarianship. It has been a long road and it's hard to believe 10 years on I'm still here blogging. If you are curious, this is  the posts that started it all , 10 years ago in 2009! My very first blog post on my old Wordpress platform Looking back it seems incredibly long ago. I was then a very new librarian with over a year of experience, very much wet behind the ears.

As I write this, Open Access week is still many months away. But it never hurts to plan ahead right? Sure you could organize talks to promote open access but why not switch it up and try to educate users with a fun interactive game?

I was recently invited to the Changing Landscape of Science and Technology Libraries (CLSTL) 2019 conference. This would have been my second time speaking there but unfortunately due to personal reasons I had to give it a miss. Still I contributed a recorded talk for the event . My talk was about how compared to a decade ago,  the tons of Scholarly metadata that is now freely and openly available to be leveraged by API.

There is something in the air right now around datasets. Increasingly, datasets are being made visible and shared online in data repositories. Undoubtedly getting researchers to deposit their datasets whether open or even closed to data repositories is a difficult task, but once that is achieved the next challenge lies with making the datasets discoverable.

Note : Jan 2019 - I wrote the bulk of this in July 2018, when Scopus announced a deal with Unpaywall.

Historical note : This was written before the launch of APA 7. Some of the specific examples about APA 6 like the need to add place of publication no longer hold for APA 7, but the general arguments in this essay do not hinge on any specifics. People outside academia are often struck by how odd things are in academia.