The “Scientific colour map” Initiative: Version 7 and its new additions
Bibliographic record
Abstract
Does visualisation hinder scientific progress? Is visualisation widely misused to tweak data? Is visualisation intentionally used for social exclusion? Is visualisation taken seriously by academic leaders? Using scientifically-derived colour palettes is a big step towards making it obsolete to even ask such brutal questions. Their perceptual uniformity leaves no room to highlight artificial boundaries, or hide real ones. Their perceptual order visually transfers data effortlessly and without delay. Their colour-vision deficient friendly nature leaves no reader left wondering. Their black-and-white readability leaves no printer accused of being not good enough. It is, indeed, the true nature of the data that is displayed to all viewers, in every way. The “Scientific colour map” initiative (Crameri et al., 2020) provides free, citable colour palettes of all kinds for download for an extensive suite of software programs, a discussion around data types and colouring options, and a handy how-to guide for a professional use of colour combinations. Version 7 of the Scientific colour maps (Crameri, 2020) makes crucial new additions towards fairer and more effective science communication available to the science community. Crameri, F., G.E. Shephard, and P.J. Heron (2020), The misuse of colour in science communication, Nature Communications, 11, 5444. Crameri, F. (2020). Scientific colour maps. Zenodo. http://doi.org/10.5281/zenodo.1243862
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.813 | 0.832 |
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".