Begin with a simple classification: public, controlled, or private. Map each document, dataset, and conversation to that scale before anything is uploaded. Use embargoes for sensitive timelines, redact identifiers aggressively, and maintain an internal ledger describing what was withheld and why, so future you—and collaborators—can revisit choices transparently and responsibly.
Treat each visible change as a narrative moment. Write commit messages that explain intent, link to issues that motivated the work, and tag releases when analyses stabilize. Pair repositories with persistent identifiers and a changelog readable by non-specialists. Your future readers will trust the process because they can follow the breadcrumbs effortlessly.
Write short overviews first, prioritizing outcomes, uncertainty, and practical relevance. Replace jargon with everyday comparisons, but never hide limitations or confidence intervals. Tools like readability checks, glossaries, annotated diagrams, and short screencasts help clarify meaning while linking to detailed notes, protocols, and code for readers seeking deeper engagement and verification.
Experts expect complete provenance: data dictionaries, environment files, deterministic seeds, and exact commands. Provide a minimal working example and a replication script that rebuilds figures from raw or derived data. Cross-reference issues with methods decisions, cite prior art, and explain deviations from preregistered plans with respectful, succinct, evidence-based reasoning.
Teachers appreciate modular notebooks with guiding questions, starter datasets, and suggested extensions. Practitioners value checklists, quickstart guides, and risk notes highlighting deployment constraints. Offer both, derived from the same work, so classroom learners gain confidence while decision-makers can responsibly adapt insights under constraints typical of real-world environments.