A call for clean code to effectively communicate science
Bibliographic record
Abstract
Abstract Effective coding is fundamental to the study of biology. Computation underpins most research, and reproducible science can be promoted through clean coding practices. Clean coding is crafting code design, syntax and nomenclature in a manner that maximizes the potential to communicate its intent with other scientists. However, computational biologists are not software engineers, and many of our coding practices have developed ad hoc without formal training, often creating difficult‐to‐read code for others. Hard‐to‐understand code can thus be limiting our efficiency and ability to communicate as scientists with one another. The purpose of this paper is to provide a primer on some of the practices associated with crafting clean code by synthesizing a transformative text in software engineering along with recent articles on coding practices in computational biology. We review past recommendations to provide a series of best practices that transform coding into a human‐accessible form of communication. Three common themes shared in this synthesis are the following: (a) code has value and you are responsible for its organization to enable clear communication , (b) use a formatting style to guide writing code that is easily understandable and consistent and (c) apply abstraction to emphasize important elements and declutter. While many of the provided practices and recommendations were developed with computational biologists in mind, we believe there is wider applicability to any biologist undertaking work in data management or statistical analyses. Clean code is thus a crucial step forward in resolving some of the crisis in reproducibility for science.
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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 | 0.179 | 0.466 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.008 | 0.048 |
| Scholarly communication | 0.024 | 0.050 |
| Open science | 0.009 | 0.018 |
| Research integrity | 0.011 | 0.028 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".