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.

I was recently invited to the OCLC Asia Pacific Regional Conference Meeting 2018 in Bangkok to speak.

I expect that most readers of this blog are familar with Lorcan Dempsey's idea of distinguishing between "outside-in" vs "inside-out" activities for libraries.

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It's not big secret that Machine learning and AI is an increasingly important topic to study.

As the battle for open access , or more accurately the route taken to open access rages on, I have become aware of the rise of a new type of "open" - the quest for "open infrastructure".

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.