A sequence diagram can serve as a fat-marker sketch of a happy-path checklist for a spike. Let’s unpack that mouthful. First, the fat-marker sketch is an idea well-described in Ryan Singer’s book Shape Up, based on software design practices at Basecamp. It’s a way to roughly shape a two-dimensional layout for a user interface when consideration of those two spatial dimensions is essential to articulate the intended user experience.

This is a brief note on my perception of the ISO 11179 Metadata Registry (MDR) standard, including my perception of its relation to W3C Semantic Web standards. Examples are taken from this Wikipedia article. In ISO 11179, there are concepts . There are relations of concepts to wider and more narrow concepts.

I enjoyed Rich Hickey’s talk on deconstructing the database, whereby he shows an unbundling of the concerns of transaction processing and durable storage from the concerns of a query engine, of live indexing, and of caching.

Eliot is a Python logging system that outputs a stream of JSON objects detailing causal chains of actions. Scientific computing is identified in its documentation as an ideal use case for this style of logging, in part because feedback cycles can be slow for such long-running processes where interactive step-debugging is inconvenient. It seems that such logs would also be appropriate for event sourcing.

What matters for application of knowledge – what substantiates it? I think two things do: shared conceptualizations, and use cases.

A headless content management system (CMS) is one that is API-first, decoupled from any particular presentation layer on the front-end. Such a service can support multiple channels for working with and presenting content, given the type of user and their context. Strapi is one example of an open-source headless CMS.

I was asked about a positioning statement for my business, to describe who I work with and what outcome I help them with: something like “Helping X with Y”. I gave this: The response I got was “Great start. What type of researchers would be an ideal fit?” I had thought I had specialized my audience sufficiently: I’m not helping “people”, I’m helping researchers.

The four components of FAIR can be mapped to successive stages of reusability: possible, plausible, probable, and perishable. The foundational principles of FAIR are Findability, Accessibility, Interoperability, and Reusability. ¹ Ultimately, FAIR is all about reuse, and it’s helpful for me to think of the principles as successive stages of increasing the chance that successful reuse can happen.

I like Cynthia Arnold’s approach to the problem of data discovery for a materials R&D organization. In a podcast episode for Citrine, she says ¹ : An inventory, understanding what you have, is all about finding . Nothing is accessible yet, just identifiable. She goes on: This is backtracking a FAIR ² pipeline.

Microsoft Excel is known to, by default, convert gene names to dates and floating-point numbers.

Some queries can be optimized; others can’t. This is due to the structure of the query itself, which in turn is due to the structure of the underlying data. “Sargable” is database-speak for Search ARGument ABLE . ¹ A query is sargable if it can be optimized, e.g. by creating and using an index. It’s a good idea to write sargable queries.