Heatmapper2: web-enabled heat mapping made easy
Bibliographic record
Abstract
First released in 2016, Heatmapper provided the first comprehensive, web-based platform for easily visualizing and manipulating heat maps for a wide range of applications in biology, epidemiology, ecology, and many other areas of science and social science. However, as Heatmapper's popularity grew, limitations in its performance and functionality became more apparent, necessitating the development of a new version: Heatmapper2 (https://heatmapper2.ca/). Heatmapper2 represents a substantial upgrade to the original Heatmapper web server, with much of the code being completely rewritten to improve performance, enhance capabilities and integrate new web technologies. Among the key changes are the conversion of the back-end code from R to Python (for better processing speed), the migration away from R Shiny to Shiny Python, and the use of WebAssembly. WebAssembly enables high performance, graphically intense applications to be run client-side in a web browser. Moving computationally intense calculations away from a central server and on to client computers eliminates server congestion and significantly improves performance. In addition to its significantly improved performance, Heatmapper2 now supports a wider range of heat mapping options including: time-series or animated heat maps (for geospatial applications), 3D heat maps (for mapping data on organisms or body parts); protein structure heat maps (for mapping molecular dynamic processes), molecular spatial heat maps (for spatial omics applications), and spectrometric heat maps (for mass spectrometry applications). Heatmapper2's redesigned interface also supports much more extensive customization, more easily editable tables, and more efficient handling of large datasets. These enhancements should make Heatmapper2 much more appealing for a wider range of researchers and research applications.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.093 | 0.044 |
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".