Codevis
Initial stage in which research aims, questions, scope, and ethical orientations are defined. More in glossary.
Stage in which conceptual ideas are translated into concrete methodological, ethical, and organizational procedures. More in glossary.
Stage in which visual data are produced, collected, selected, and documented in the field. More in glossary.
Stage in which visual materials are examined, contextualized, and interpreted to generate research findings. More in glossary.
Stage in which research findings are prepared and shared with academic or public audiences. More in glossary.
Stage in which visual data are secured, documented, and prepared for long-term storage. More in glossary.
Stage in which archived visual materials are accessed again for new research or educational purposes. More in glossary.
Visual data are increasingly central to social research. Photos, videos, screenshots, and other visual materials can offer rich insights into social life, culture, and everyday practices. At the same time, making visual data open raises specific ethical, legal, and methodological challenges that cannot be addressed by applying generic open data principles alone. Visual data raises distinctive challenges for open data practices. They are highly context-dependent and difficult to anonymize. Once shared openly, visual materials can also be easily reinterpreted, reused, or amplified, increasing the risk of misrepresentation or unintended harm. Therefore, visual data cannot be treated like any other research data. Responsible openness requires early, reflexive, and context-sensitive decisions that balance transparency, accessibility, and ethical responsibility.
Use this website to navigate a user-friendly model for open data practices in visual social research. Drawing on the Recommendations for open data practices in visual social research, the site guides you through practical, context-sensitive decisions to be considered at each stage of the research process, from the conceptual phase to data reuse. In addition, the website invites you to contribute to the ongoing development of the model by sharing examples of open data practices from your own visual research or from work you see significant. You may also suggest complementary perspectives that can further enrich and extend the recommendations presented here.