Incorporating human experiences into the design process of a visualization tool: A case study from bioinformatics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Visualization tools are helpful in the analysis of large and complex information such as genomics data and biological phenomena. However, there exists a conceptual gap between how the tools actually work and the user experiences, tasks and behaviors. To design more human-centered tools that fully support the biologist's experiences, we have defined a framework, called UX-P (user experiences to patterns). The framework leverages the complicity of personas, a technique to capture user experiences, and design patterns - allowing us to narrow the gap between user experiences and the tool's design and features. First, HCI experts need to capture the user's needs, interaction behavior and task flow. This information can then be used to derive design patterns which are composed to create a conceptual design. To test and further improve this framework, we carried out an empirical study with users of a bioinformatics visualization tool called protein explorer. Results of our empirical study will be presented.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it