A Pilot Study into the Usability of a Scientific Workflow Construction Tool
Bibliographic record
Abstract
We describe a recent pilot study into the usability of the scientific workflow creation and enactment tool called Taverna. Both programmers and non-programmers were used as subjects for a defined programming task. We used a combination of user observation and questionnaires to determine programming efficiency roadblocks in the tool. More generally, differences between the roadblocks encountered by programmers and nonprogrammers suggest that pilot studies are crucial to inform the proper evaluation of novice programming tools. The study also suggests that there is a high demand for reusable Life Sciences workflows, due to both their ability to facilitate human-human communication about data analysis, and their ability to simplify repetitive operations used by bench scientists. Most roadblocks to Taverna programming are interface related, but a more fundamental issue is related to data input and type enforcement. Despite UI issues, we discovered users willingness to re-use and modify workflows, which leads us to suggest that programs first be created in simpler tools as a stepping stone in end-user development for the Life Sciences.
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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 | 0.019 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".