I was recently asked a question - how do you find seminal papers/work/research? My first thought was just to read (articles, reference works etc)! But my second thought was, that's actually quite an interesting question, as I throw around the term "seminal paper" all the time in workshops.

As academic librarians helping early-stage researchers (Masters, Phds students), we are often asked to provide guidance on the literature review process in one shot classes. One thing we tend to focus on during such sessions is the keyword search technique, though many of us also cover alternative to keyword techniques like citation searching, starting off with review articles etc.

As I write this, OpenAI has just unleashed ChatGPT - their GPT3.5 Large Language Model(LLMs) for about a month, and the online world is equal parts hype and confusion. In my corner of Twitter with educators & librarians there is worry about how LLMs might make detecting plagiarism difficult. I've written my take about this elsewhere but it occurs to me that this isn't the first time I've seen someone worry about automated tools.

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.