Revise and apply the Data Management Plan

A good Data Management Plan (DMP) supports this phase by making the analytical workflow explicit: what happens to the visual materials during analysis, how decisions are documented, and how the dataset is prepared (already now) for possible future reuse. 

What should your DMP cover? 

1. How you structure and organize the dataset. 

Make your structure clear and consistent. For example:

• How files are grouped (by case, date, platform, participant, theme, etc.) 
• Naming conventions and folder structure 
• Versioning rules (what counts as a new version of an image or dataset) 

2. What counts as “data” during analysis 

Not only images are data. Your dataset may also include: 

• coding sheets, codebooks, annotation files
• interpretive memos and reflexive notes (positionality, assumptions, interpretive choices)
• documentation of analytical steps (how categories were built, how comparisons were made) 

3. How you document interpretation

Make your analytical process traceable. 

• explain how interpretive decisions are made and recorded
• ensure that others can understand how you moved from data to findings 

4. Metadata and contextual documentation 

Context is essential for understanding visual data. Your DMP should specify: 

• which metadata you create (e.g., how and in which conditions the visual was produced and used)
• how you capture key context (who produced the visual, when and where, under which conditions, and for what purpose in the study)
• how this documentation is stored with the visuals, so it does not get lost or separated