I have been recently thinking about dataset discovery, most recently on the possible impact of Google Dataset search.

It seems like just a short time last year in May where I blogged about the new iteration of Microsoft's academic search dubbed "Microsoft Academic" which was then in beta and it eventually left beta at the end of the year.

I’ve recently come across an idea known as inversion.

It has been a very eventful couple of weeks in the academic, publisher and library related worlds with regards to the push towards open.

Update Aug 2020 - The main thrust of this response is to point out RA21 type solutions do not handle the appropriate copy problem.

Open access is a complicated business. Everytime I think I understand it (and I've blogged a lot on it, trying too get to gripes with it - in particular this post), some new nuance appears to make me realize I don't really understand  at all. In this case, my mind was blown when I learnt that there was  wall of shame for posting preprints on Bioarxiv!   But let's back up a bit and talk a bit about preprints first.

In the heyday of Web Scale Discovery (2009 to the early to mid 2010s), library discovery was a big issue that was front and center in our profession's sights.

4 years ago in 2014, I wrote about the coming disruption to academic libraries due to Open Access.

I've been reading up lately about the idea of making research reproducible and replicable.

2018 appears to be the year of the voice assistants and smart speakers.

Update 2023: Since I wrote this, Knowtro was discontinued. The closest tool I know of that is similar is System Pro, though as of time of writing (May 2023) it extracts only from PubMed. Another tool that can create a research matrix of papers with preset and even custom variables is Elict.org.