Ever since I joined Twitter in 2011, and then moved to Mastodon in 2022, I have been unhappy with the timeline view proposed by both of these communication platforms as their main interface. Now I have finally done something about it: I wrote my own Mastodon client. Or perhaps rather a non-client, because the concept of "the client" is a big part of what I disliked.
Over at the work blog, I’m discussing what knowledge means for large language models (LLMs), and the ways in which we can leverage this knowledge dividend for better inference. Read the full post here.
The Unified Medical Language System (UMLS) is a widely used biomedical and clinical vocabulary maintained by the United States National Library of Medicine. However, it is notoriously difficult to access and work with due to licensing restrictions and its complex download system. In the same vein as my previous posts about DrugBank and ChEMBL, this post describes open source software I’ve developed for downloading and working with this data.
I’ve been working on improving reproducibility in the field of cheminformatics for some time now. For example, I’ve written posts about making data from DrugBank and ChEMBL more actionable. Over the last year, I’ve been preparing a concept with the editors of the Journal of Cheminformatics on how to include an assessment of reproducibility to reviews of manuscripts submitted to the journal.
Today’s short post is about three SPARQL queries I wrote to get bibliometric information about journals and publishers out of Wikidata. Each of the following queries can be readily copy-pasted into the Wikidata Query Service and run in the browser.
I was recently nominated for the International Society for Biocuration’s Excellence in Biocuration Early Career Award (results will be announced on June 14 th !). This made me curious about how to model nominations and awards on Wikidata. In this post, I’ll describe how to curate awards, nominations, recipients, and how to make SPARQL queries to get them.
Don’t archive you assets 1 — frontier them. Research is a living process. 2 Even when a research project is “finished”, is it really?
Archival Resource Keys (ARKs) are flavor of persistent identifiers like DOIs, URNs, and Handles that have the benefit of being free, flexible with what metadata gets attached, and natively able to resolve to web pages. Name-to-Thing (N2T) implements a resolver for a variety of ARKs, so this blog post is about how that resolver can be re-implemented with the curies Python package. In a lot of ways, ARKs look and act like CURIEs.
This blog is normally about very serious science , but I’m taking a break from that for the evening to advertize my band’s upcoming show on April 8 th in the SPH Music Masters Finale (aka, the German Battle of the Bands). We need your support!
Over at the work blog, I’m discussing what knowledge means for large language models (LLMs), and the ways in which we can leverage this knowledge dividend for better inference. Read the full post here.