Global health dashboards love averages. But averages hide geography, and geography hides scale. This note pulls live programmatic data from The Global Fund's API — focusing on ART coverage (the percentage of people on treatment among all people living with HIV) across nationally representative grants closing in H2 2023. The goal: move beyond a single headline number. To do that, we borrow from psychometrics. The Wright Map — typically used to plot test-takers against item difficulty — turns out to be a surprisingly natural fit for performance data. Here, countries replace test-takers, and performance against target replaces ability scores. The twist: label size scales with the size of the national target, so the visual weight of each country reflects its actual weight in the global portfolio. The result is a single chart that answers three questions simultaneously: How is performance distributed? Who are the outliers? And whose results move the needle most?

I spent years fighting RStudio's data viewer. Eventually I gave up and started dumping dataframes to CSV just to look at them properly. When Positron came out, I did what any reasonable person would do: I opened a dataframe, tried to filter it, and took notes. What I found was a neat little product paradox — more steps, better experience. As a certified Product Owner, I had thoughts.

The famous Iris dataset isn't FAIR—it's just a CSV. No persistent IDs, no ontology links, no provenance, no license. This proof-of-concept fixes that in an afternoon using Airtable and R. We structure the data relationally with taxonomic metadata (NCBI Taxonomy) and measurement ontologies (Plant Ontology), then auto-generate JSON-LD that meets all four FAIR principles. Each of 150 records gets a unique identifier and machine-readable semantics. The approach scales to clinical trials, genomics, or any tabular data. Code included. No enterprise tools required.

We used the mistral-large-latest large language model to automatically map AELLT (Lowest Level Term) entries to their corresponding MedDRA System Organ Class (SOC) using adverse event data from the PhUSE CS Working Group 5 CDISC pilot submission.