# Rseng Storytelling > Covers telling the story of research data, software and projects to broad audiences: narrative structure for data stories, turning milestones into human-centered stories, and citizen-science engagement - recruiting contributors, closing the feedback loop with data stories, honest narrative that never oversells. Use when the user wants to explain a project, dataset or tool to non-specialists, mentions storytelling, outreach, public engagement or citizen science, or needs project stories for websites, funders or volunteers. (Research-facing outputs - software papers, talks, announcements: rseng-science-communication; in-repo docs: rseng-documentation.) - Skill: `fdiblen/rseng-storytelling` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add fdiblen/rseng-storytelling` - Raw SKILL.md: https://api.skillmd.com/api/skills/fdiblen/rseng-storytelling/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: CC-BY-4.0 - Author: fdiblen (https://skillmd.com/u/fdiblen) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/fdiblen/rseng-storytelling --- # Storytelling for research data, software and projects Facts inform specialists; stories move everyone else. A research project that can only speak in methods loses the audiences that sustain it - volunteers, funders, institutions, the public. The craft is honest narrative: real people, real stakes, real uncertainty, built on the same verified artifacts the rest of this pack maintains. rseng-science-communication covers research-facing outputs (papers, talks, announcements); this skill covers the narrative layer for everyone else - and the citizen-science setting where storytelling is not decoration but the engine of participation. ## Narrative structure that works for research - Lead with a person or a question, not an institution: "Maria photographs the same hedgerow every week - here is what 40,000 photos like hers revealed" beats "the consortium collected image data". - The story arc is the research arc, honestly told: question, struggle (failed approaches are relatable - and true), turning point, what we know now, what remains open. Uncertainty told plainly builds trust; certainty theater destroys it when it unravels. - One story, one idea: pick the single finding or moment; link the rest. The paper holds everything; the story holds one thing. - Concrete beats abstract: a number needs a comparison ("enough water to fill 300 pools"), a dataset needs a face, software needs a user whose day it changed. ## Data stories Data storytelling turns datasets into narratives with evidence: - Structure: context (why this data), the reveal (the pattern, shown not told), the meaning (what changes because we know). Build the reveal on honest visualization - the colormap and axis discipline from rseng-scientific-visualization applies doubly for lay audiences, who cannot defend themselves against a truncated axis. - Every number in the story traces to the pipeline (rseng-reproducibility): a story that cites its data (with the DOI - rseng-citation-metadata) and links "explore it yourself" (a live view or notebook) respects the audience and models open science (rseng-open-science-practices). - Accessibility is part of the craft: alt text that tells the story of the figure, plain language, translated summaries where the community needs them (rseng-ux-accessibility). ## Software and project stories - Software stories are user stories: what could someone do the day after the release that they could not before? Frame releases, milestones and even bugfixes through the person affected (rseng-science-communication handles the announcement mechanics; this skill supplies its narrative spine). - Project stories need characters: the PhD student whose analysis went from weeks to hours, the museum volunteer whose transcription surfaced in a paper. Get consent for every named appearance, and share credit generously - people amplify stories they are in (rseng-community-governance's recognition habits). - The failure story is underused and powerful: what broke, what it taught, what changed - engineering credibility for the project and normalized error for the field (rseng-trainer's errors-are-curriculum, told outward). ## Citizen science: storytelling as infrastructure In citizen science the story IS the recruitment, retention and ethics layer (the ECSA ten principles set the frame - genuine science, mutual benefit, acknowledged contributors): - Recruit with purpose, not tasks: "help us track the spring earlier every year" outperforms "classify images". State honestly what participation contributes to the science. - Close the loop relentlessly: contributors who never learn what their data became stop contributing. Data stories back to the community - "your observations did THIS" - are the retention mechanism, and platforms (SciStarter, EU-Citizen.Science) expect them. - Acknowledge visibly: contributors in publications and releases per the project's stated policy, milestones celebrated with the community, results announced to participants BEFORE or with the press. - Keep the science honest in translation: simplification may not become distortion; the project's own findings, limits and data practices (rseng-data-management) are told truthfully - trust is the citizen-science currency, spent once. - AI-assisted storytelling is disclosed like any other AI contribution (rseng-ai-declaration) - audiences increasingly ask, and the honest answer is cheap. ## Working with this skill This skill is source-independent: its authority is the citizen science community's principles and platforms linked below, combined with the verified artifacts of the project itself. It is the broad-audience narrative layer over rseng-science-communication. Learn more (verified): - https://www.ecsa.ngo - European Citizen Science Association (ten principles of citizen science) - https://citizenscience.eu - EU-Citizen.Science platform - https://scistarter.org - SciStarter project platform - https://participatorysciences.org - Association for Advancing Participatory Sciences - https://riojournal.com/article/21283/ - principles for citizen-science apps and platforms ## Related skills Check whether any of these applies before moving on: - rseng-ai-declaration - disclose AI-assisted storytelling - rseng-community-governance - consent and recognition habits - rseng-data-management - truthful data practices in stories - rseng-science-communication - research-facing communication mechanics - rseng-scientific-visualization - honest figures for lay audiences - rseng-ux-accessibility - alt text and plain language