2026-10-27 –, LUMC03
You've assigned PIDs to your datasets, your people, your publications. But where did that data actually come from? The data acquisition layer, the instruments, sensors, and control software that generate raw measurements, is almost always a blind spot in PID infrastructure. Open frameworks like PyLabRobot are tackling this from the software side, building the driver layer where instrument identity and configuration can finally be captured and stamped onto the data it produces. Come explore why DAQs are a missing link in connected knowledge, where the real barriers are, vendor lock-in, no open drivers, no persistent identity, no metadata that travels with the data, and what PID-connected instrumentation could actually look like. Leave with a clearer sense of what it would take to get there, and who else is working on the same problem.
Research data has a birth certificate problem. We track who analysed it, who published it, which grant funded it, but the moment the measurement was made, on which instrument, in which configuration, running which firmware, is almost never recorded in a way that travels with the data. The data acquisition system is where knowledge begins, and it is the layer least connected to our PID infrastructure.
What would it mean for a DAQ to be truly PID-ready?
Instruments are not passive objects. They make decisions: gain settings, sampling rates, calibration states, software versions. Two datasets that look identical on the surface may be incomparable because the instrument that generated them was in a different state. Without a persistent identifier anchored to a rich instrument record, one that captures not just identity but configuration, this context is silently lost.
The barriers are not just technical, they are structural. Especially in the life sciences and biomedical domain, most instruments are vendor-locked. Their control software does not expose the metadata needed for a FAIR instrument record. The firmware version is not machine-readable. The calibration log lives in a PDF. And critically, there is no open software layer sitting between the instrument and the data that could capture and stamp this information automatically.
PyLabRobot is an open-source Python framework for lab automation that addresses exactly this gap. By building open, hardware-agnostic drivers for liquid handlers, plate readers, centrifuges, and other lab instruments, PyLabRobot creates the software layer where instrument identity, configuration, and state can be captured programmatically and stamped onto the data the instrument produces. It is a concrete example of what it looks like when the DAQ layer is designed with traceability in mind from the start, rather than retrofitted afterward.
Through the PyLabRobot project, we have been developing the PLR Readiness Level, a three-axis maturity scale that scores any instrument on software connectivity, instrument transparency including PID integration, and hardware openness. It is one attempt to make the problem legible: to move from "we know this instrument exists somewhere" to "every dataset knows exactly which instrument made it, and that instrument is findable and fully described." But this session is not about PyLabRobot or any single project. It is about a shared blind spot that affects everyone building PID infrastructure, and a conversation that the instrumentation world and the PID world have mostly not had together yet.
You will:
Map the DAQ layer in your own domain: what does the instrument-to-data handoff actually look like?
Identify the specific barriers that prevent instruments in your field from being PID-connected
Discuss what community standards, vendor engagement, or policy levers could actually move the needle
Whether you work in repositories, publishing, research infrastructure, or active data collection, the DAQ layer affects the quality and connectedness of everything downstream. This is a chance to start closing that gap, together.
Vincent de Boer is an Assistant Professor in Human and Animal Physiology at Wageningen University & Research. His research addresses fundamental questions in physiology, how muscles age, how metabolism fails, and how mitochondria respond to disease and stress. He combines wet lab expertise with data science and software engineering, developing open-source tools and methods that make physiological data more reproducible, transparent, and reusable. He is a core contributor to PyLabRobot, an open-source framework for lab automation. Alongside his research, he is committed to teaching the next generation of life scientists to work with data rigorously and openly. He is also involved in initiatives at the intersection of open hardware and sustainability, including efforts to reduce electronic waste in research infrastructure. He trained at Harvard Medical School and has a background in analytical chemistry and molecular biology.