Epistemic status : I have been reading on and off technical papers on large language models since 2020, mostly get the gist but don't understand the deepest technical details. I have written and published on academic discovery search for most of my librarianship career since 2008. Since the 2000s the way search engines have worked has not changed.

I've started seriously studying and blogging about Open Access since 2012, starting off by reading books by Walt Crawford and Peter Suber  and since then I've continued to read and muse about the issue covering everything from How academic libraries may change when Open Access becomes the norm

I recently came across "Automated citation recommendation tools encourage questionable citations" (which was first brought to my attention by this blog post) an exceedingly thought-provoking article about bias in discovery tools, particularly new ones that can suggest what to reference based on text in a paper. Leaving aside the issues such recommenders might bring, you don't have to think extremely hard to realize such tools will

With open scholarly data (metadata and full-text) becoming increasingly available, it is natural to see attempts to apply the latest deep learning techniques to try to see if one can ease the burden of doing literature review. In my view, Elicit.org's attempt to apply the famous large language model GPT-3 on academic papers to help discovery and extract data is one of the most interesting efforts currently.

My blog usually takes the view that most of our users do not start at the library homepage as a truism and since the beginning of the blog in 2009, I have covered tools that help users "get back" to library resources or at least easily check for availability via their library catalogs or library discovery service. The latest iteration of such tools are sometimes dubbed as Access Broker Browser Extensions, currently popular ones include

I've been writing about the rise in availability of open Scholarly metadata (in particular Open Citations) as far back as 2018 and how they might impact academia.  Clearly, the readily availability of open scholarly metadata (which is also related to PID providers and academic knowledge graphs) and to a lesser extent full text has led to the rise of numerous tools designed to exploit such data.

I've always had an interest in bibliometrics, and while I have served in teams supporting bibliometrics in the past (2010-2015) and enjoy reading and skimming bibliometrics papers, I have never been officially given the title of a librarian supporting bibliometrics. Still based on

In late 2018, I gave a talk at  OCLC Asia Pacific Regional Conference Meeting 2018 and I gave my thoughts on how I see the game changing for libraries in the years to come based on 3 fundamental trends.

Note: This is based off my contribution to the Upstream blog (plus some major additions)- Aaron Tay is Keeping Tabs on Open Research I tend to find interesting articles via Twitter or from following references of such articles. For articles that look potentially interesting I will usually put the link in my Google Keep and tag them with “Professional Development” , which I try to clear every week.

Note: This is adapted from an internal talk I gave at my place of work. It is written for generalists who want to have an idea about DOIs and related services and hence does not go into specific details (e.g. how to actually deposit metadata, fields used etc). The thesis here is that if you are an academic librarian you could probably do well to learn more about DOIs.

If there is one academic discovery search that dominates it is Google Scholar.