---
esco_uri: http://data.europa.eu/esco/skill/fa642fc3-5cc5-41cf-8926-6617a8b976a7
esco_label: "promote the participation of citizens in scientific and research activities"
relation: essential
onet_soc: 15-2051.00
source: model-knowledge
confidence: high
qa_count: 0
generator: gemma3:27b (prompt-designed and spot-checked by Claude)
generated: 2026-07-10
---

# promote the participation of citizens in scientific and research activities — Data Scientist

For a Data Scientist, 'promoting citizen participation' isn’t about running public science experiments directly – it’s about *designing* data solutions that inherently enable broader input and understanding. This often manifests as building accessible dashboards (Tableau, Power BI, Streamlit apps) visualizing complex findings in plain language, or creating interactive tools where non-technical users can explore datasets themselves (e.g., using filtering/aggregation features). It also means proactively considering how data collection methods might introduce bias *and* involving diverse stakeholders in defining what questions the data should even be asking – a crucial step often overlooked. Think 'user-centered data science'.

Typical tasks include translating technical results into actionable insights for non-experts, documenting code and methodologies clearly (think literate programming with Jupyter notebooks), and actively soliciting feedback on model outputs from subject matter experts *outside* the core data team. Tools extend beyond standard DS libraries to include communication platforms (Slack, Teams) for collaborative analysis and documentation tools like Confluence or wikis. A key pitfall is 'solutionizing' – building something technically impressive but unusable or incomprehensible to those who need it most; another is assuming you *know* what stakeholders want without asking.

Good looks like demonstrably increased data literacy amongst collaborators, a clear audit trail of how citizen input influenced analysis (e.g., documented changes in feature engineering based on SME feedback), and ultimately, more impactful decisions informed by data because those decisions are understood and trusted by a wider group. It’s about shifting from 'we the experts analyze' to 'we facilitate understanding *with* stakeholders'. This isn't just ethical; it improves model robustness and reduces unintended consequences.

Finally, consider open-source contributions. Sharing code, datasets (where privacy allows), and analytical approaches fosters wider participation in validating and extending your work. Platforms like GitHub become crucial for this – not just as a version control system but as a collaborative space where others can contribute to the scientific process.

## Weitere Anreicherung

Stage-2 source for future practitioner grounding: arXiv cs.LG/stat.ML (ML preprints).
