Observations are not neutral. They can extract attention and data from places without contributing to local stewardship or respecting cultural contexts.
Practice guidance
- Learn local ecological and cultural context before intensive observation.
- Share findings with local groups when appropriate.
- Avoid creating high-volume datasets that are disconnected from local knowledge.
Indicators of extractive practice
- Observations focus on a region without engagement or feedback to local stakeholders.
- Species lists are exported or published without acknowledging local expertise.
- Data are used to justify interventions without local consultation.
Indigenous and local knowledge considerations
- Respect Indigenous data sovereignty and local governance structures.
- Avoid publishing sensitive locations tied to cultural practices or traditional harvesting.
- Treat local ecological knowledge as a co-equal source of evidence, not anecdote.
What goes wrong if you do this poorly
Extractive observing erodes trust, increases resistance to citizen science, and can lead to data that are scientifically questionable because they ignore local context.
Sources
- Cooper et al. (2014) on ethics in citizen science: https://doi.org/10.1007/s13280-014-0565-5
- CARE Principles for Indigenous Data Governance: https://www.gida-global.org/care
- OCAP® principles (First Nations data governance): https://fnigc.ca/ocap-training/
Consent and collaboration guidance
- Seek permission before intensive surveying on Indigenous or locally governed lands.
- Offer data access and review before public release when requested.
- Co-design observation goals with local partners when possible.
Participatory frameworks
- Follow CARE Principles (Collective Benefit, Authority to Control, Responsibility, Ethics).
- Use OCAP® principles where applicable for First Nations data governance.
Locally governed data-sharing examples
- Memoranda of understanding defining where data can be shared.
- Project-level agreements that keep precise locations private.
Data minimization
Only collect and share what is necessary for the stated purpose. Excessively detailed location or cultural context can increase risk without improving data utility.
See also
- Sensitive species and obscured locations
- Civic and open science resources