Experiment data: Human-in-the-loop decision support in process control rooms
Bibliographic record
Abstract
These datasets contain measures from multi-modal data sources. They include objective and subjective measures commonly used to determine cognitive states of workload, situational awareness, stress, and fatigue using data collection tools such as NASA-TLX, SART, eye tracking, EEG, Health Monitoring Watch, a survey to assess training, and a think-aloud situational awareness assessment following the SPAM methodology. Also, data from a simulation formaldehyde production plant based on the interaction of the participants in a controlled control room experimental setting is included. The interaction with the plant is based on a human-in-the-loop alarm handling and process control task flow, which includes Monitoring, Alarm Handling, Recovery planning, and intervention (Troubleshooting, Control and Evaluation). Data was collected from 92 participants, split into four groups while they underwent the described task flow. Each participant tested three scenarios lasting 15-18 min with a -10-min survey completion and break period in between using different combinations of decision support tools. The decision support tools tested and varied for each group include alarm prioritisation vs. none, paper-based vs. Digitised screen-based procedures, and an AI recommendation system. This is relevant to compare current practices in the industry and the impact on operators' performance and safety. It is also applicable to validate proposed solutions for the industry. A statistical analysis was performed on the dataset to compare the outcomes of the different groups. Decision-makers can use these datasets for control room design and optimisation, process safety engineers, system engineers, human factors engineers, all in process industries, and researchers in similar or close domains.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".