Analyse

Description¶

The Analyse stage centres on exploring trends and relationships in your data. This can be done through creation of visualisations and/or statistical analysis.

📔 Recommended documentation¶

Describe your analysis process and workflow.

→ Include what tools, code and data were used to come to your analysis.¹
→ Describe the shortcomings and benefits of your chosen form of analysis.
→ Outline other possible ways your data can be read and used, and whether or not the outcomes may be different.

Tasks¶

Analysing and Interpreting Data¶

When undertaking this task, what should you consider?

Unintended Use¶

Can the visualisation/analysis be misinterpreted?²

Representation¶

What does the chosen visualisation style highlight and under- and/or misrepresent?³

Reproducibility¶

Have you documented your analysis process sufficiently for others to reproduce it?
How are you handling intersectionality in your analysis?
Do your interpretations risk essentializing complex identities?

Methodology (including algorithms)¶

How might your analytical methods themselves introduce bias?
What assumptions underlie your interpretive frameworks?

What are good practices in relation to this task?

Document the shortcomings and possibilities of your analysis
Highlight these are not the only conclusions that can be drawn from your data, and describe other potential outcomes and uses.
Include different forms of visualisation, such as word-concept distance or statistics.
Develop strategies for communicating uncertainty alongside findings

Resources¶

Data Feminism https://data-feminism.mitpress.mit.edu/

Generous Interfaces for Digital Cultural Collections https://www.digitalhumanities.org/dhq/vol/9/1/000205/000205.html

Reflect: Assess sufficiency of data collection¶

When undertaking this task, what should you consider?

Silences¶

Are there gaps and/or silences in your data collection process?

What are good practices in relation to this task?

Rethink your data collection practices. Adjust if needed.
Document decisions made in adjusting (or not adjusting) data collection practice.
Document implications of the data collection process on output and the subsequent use of the output.

Resources¶

See Set Up: Select and Curate Sources and/or Data

Adapted from RDMkit, Analysing (accessed 21 August 2025). ↩
Adapted from Dataschool Utrecht, DEDA Dataschool (2022). ↩
Adapted from Dataschool Utrecht, DEDA Dataschool (2022). ↩