Illustrating your research: design basics for junior clinicians and scientists
Bibliographic record
Abstract
Communication in the fields of science, technology, engineering, and mathematics has historically been dominated by text based mediums. Scientific articles, textbooks, reviews, conference proceedings, and posters rely heavily on text to communicate scientific findings. Our current method of communication is framed primarily for those who conduct research, and we generally make little use of visual aids, which are arguably more effective.1 However, scientists and clinicians may struggle to translate scientific data into clear and informative graphics. As a group of biomedical postsecondary students and scientific illustrators interested in graphic design, we have consolidated and summarised the eight steps we use for creating eye-catching illustrations. These steps are intended as a practical resource for junior clinicians and scientists to use when creating scientific graphics, including manuscript figures, scientific poster presentations, and slides for oral presentations. Most people interested in finding information now use online resources. The internet is full of relevant search results, so being able to capture an audience’s attention is crucial for knowledge translation. Some high impact journals, including Cell , now feature and request graphical abstracts, creating a prime opportunity to incorporate visual communication into scientific data presentation.2 Data suggest that clinicians and medical trainees respond more favourably to visual communication methods such as infographics rather than to traditional text based information. For trainees and junior clinicians, designing effective and clear visuals improves communication, knowledge dissemination, and application to real-life scenarios. Visual illustrations are more memorable than words, as stated in the “picture superiority effect.” Several hypotheses have been developed as to why visuals are more memorable.3 Catching readers’ attention is especially crucial when sharing scientific pieces on social media platforms, a key part of knowledge dissemination in today’s society. Scientific illustrations on social media are more accessible than text-heavy articles; a thorough illustration enables …
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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 | 0.039 | 0.111 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.072 | 0.037 |
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".