Rseng Storytelling

---
name: rseng-storytelling
description: >-
  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.)
license: CC-BY-4.0
metadata:
  version: 0.1.0
---

# 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

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## 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

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