Research data, tracked

Our own analyses of open research data: how much of it actually gets shared, how complete it is, and how that’s changing over time. They show us where the gaps are, and help us decide what to build next.

Every sample SeqDesk handles ends up — if it’s shared — in a public read archive, stamped with the center that submitted it. So we turn that stamp around: who submits the most, where they are, and which platforms they run. It’s a map of archive submission activity (ENA-centric, surveillance-shaped), not total capacity — read it as “who feeds the commons.” Each card opens one slice of the analysis. ENA snapshot 2026-06-29.

SeqDesk integrates sequencing tools so researchers don’t have to stitch them together themselves. Tracking how each tool’s adoption moves across the field tells us which ones matter most — and that is how we prioritize what to integrate next.

Beyond individual tools, most sequencing analysis now runs through whole pipelines. We track which workflow technologies the field is adopting — by the citation curves of their papers — how many registered workflows each carries, and, for the two engines with a GitHub-backed registry or repository population, which individual pipelines the community actually uses. That tells us which end-to-end workflows matter most, and where SeqDesk should plug in next.

A handful of influential papers put hard numbers on how fast research data would grow — and most are now several years old. We take the chance to rebuild their inputs from public archives, check each forecast against what actually happened, and revise the prediction where reality diverged.