Beyond FAIR Data: Instrument Traces for Active and Autonomous Scientific Experimentation

2026-08-24

AI Summary

论文提出通过同步记录样本、仪器和决策历史的方法，解决科学实验中数据记录不全的问题，以支持可重复的自主实验。

Abstract

Artificial intelligence is turning scientific instruments into active systems in which observations can determine what is measured next. We argue that this creates an additional scientific record, the experimental trajectory, complementing sample provenance, acquired data and metadata, and analysis workflows. Instrument Traces should ultimately be synchronized with Sample Traces describing specimen evolution and Decision Traces recording human or algorithmic choices. We reconstruct an Instrument Trace retrospectively from a longitudinal AFM/PFM archive containing 118,000 timestamped events from 2023-2026. Conventional saved files reveal material campaigns, latent probe and calibration states, session-level complexity, experimental decision grammar, and composite tuning actions. They also expose what is missing, including unsaved tuning and failures, explicit sample/probe identities, complete timing, exogenous state, and decision rationale. We therefore propose a prospective trace architecture that records synchronized sample, instrument, and decision histories, enabling reproducible autonomy, predictive maintenance, counterfactual analysis, operator training, and transfer across facilities.

Problem

• Active Systems
• Experimental Trajectory
• Synchronized Traces
• Reproducibility
• Autonomy

Innovation

• Instrument Traces
• Autonomous Scientific Experimentation
• Synchronized Records
• Reproducible Autonomy
• Predictive Maintenance