Tangible Chromatin: Tangible and Multi-surface Interactions for Exploring Datasets from High-Content Microscopy Experiments
Bibliographic record
Abstract
In biology, microscopy data from thousands of individual cellular events presents challenges for analysis and problem solving. These include a lack of visual analysis tools to complement algorithmic approaches for tracking important but rare cellular events, and a lack of support for collaborative exploration and interpretation. In response to these challenges, we have designed and implemented Tangible Chromatin, a tangible and multi-surface system that promotes novel analysis of complex data generated from high-content microscopy experiments. The system facilitates three specific approaches to analysis: it (1) visualizes the detailed information and results from the image processing algorithms, (2) provides interactive approaches for browsing, selecting, and comparing individual data elements, and (3) expands options for productive collaboration through both independent and joint work. We present three main contributions: (i) design requirements that derive from the analytical goals of DNA replication biology, (ii) tangible and multi-surface interaction techniques to support the exploration and analysis of datasets from high-content microscopy experiments, and (iii) the results of a user study that investigated how the system supports individual and collaborative data analysis and interpretation tasks.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".