I was looking at the different NFDI consortia in the Research Organization Registry (ROR), and found that the only two that have a parent relations to the NFDI (ror:05qj6w324) are NFDI4DS (ror:00bb4nn95) and MaRDI (ror:04ncnzm65). This felt strange to me, so I started looking around Wikidata to see if I could automatically make a curation sheet to send along to them.

I became aware of just while watching Hynek’s second video on uv a few months ago. I immediately fell in love with its elegance and simplicity, so I have begun replacing task running in my repositories that relied on tox with just. This post gives a bit of background, context, and walks through making the switch on one of my repositories that has some annoying dependencies.

Earlier this week at the sixth NFDI4Chem consortium meeting, Torsten Schrade from the NFDI4Culture consortium gave a lovely and whimsical talk entitled A Data Alchemist’s Journey through NFDI which explored ways that we might federate and jointly query both consortia’s knowledge via their respective SPARQL endpoints.

Using standard CURIE prefixes and URI prefixes in semantic web artifacts such as Resource Description Framework (RDF) promotes interoperability, enables reuse in downstream data integration, and makes data more FAIR. The Bioregistry defines a set of standard CURIE prefixes and URI prefixes against which RDF files can be validated/standardized.

I’ve recently submitted an article to the Journal of Open Source Software (JOSS) describing chembl-downloader, a Python package for automating downloading and using ChEMBL data in a reproducible way. In this post, I use chembl-downloader to show how the number of compounds, assays, activities, and other entities in ChEMBL have changed over time.

The Bioregistry is a database and toolchain for standardization of prefixes, CURIEs, and URIs that appear in linked (open) data. While I created it in 2019 as a component of PyOBO in order to support parsing database cross-references appearing in biomedical ontologies, it has since become an independent project with a community-driven governance model and much broader applications. This post is a first attempt to quantify its usage and impact.

The Bioregistry is a community-driven registry of semantic spaces and their metadata. When I learned about BiomarkerKB at the International Society for Biocuration’s 18th Annual International Biocuration Conference, I was excited to curate new records (and prefixes) in the Bioregistry to cover BiomarkerKB’s semantic spaces on biomarkers.

The Ontology Lookup Service (OLS) is now indexing dense embeddings for ontology terms constructed from term labels, synonyms, and descriptions using LLMs. I maintain a Python client library for the OLS (ols-client) and was recently asked to implement a wrapper to the OLS’s API endpoint that exposes these embeddings.

Assembling and inferring missing semantic mappings is a timely problem in biomedical data and knowledge integration. I’ve been developing the Semantic Mapping Assembler and Reasoner (SeMRA) as a generic toolkit for this. In this blog post, I highlight its inference capabilities. SeMRA implements the chaining and inference rules described in the SSSOM specification.

Following the theme of my previous two posts, I’ve run into another typing conundrum where I want to unpack a pre-existing Callable into a class with Generic[P, T] where P is a parameter specification type (i.e. ParamsSpec) After figuring out the right way to declare a generic featuring a ParamSpec, I updated the class-resolver package to use the shiny new (and more accurate) annotations.

I’ve been working on applying strict static typing to my Python package class-resolver and ran into an interesting way of using generics in combination with parameter specification variables (i.e., ParamSpecs). Normally, if you want to type annotate a function, you use the Callable, which works like the following: from collections.abc import Callable #: the [int] represents a function that takes in a single integer, #: and returns a single