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This wiki was created for the participants of the Responsible Data Forum on Data visualization. We will use this for ongoing creating, sharing and collaborating.

Information for participants

Please make sure to send all notes and materials that are not already captured in the wiki to notes@responsibledata.io

Hashtags and Twitter accounts

Hashtags: #RDFviz and #responsibledata

Twitter list of participants

Spectrogram statements


Polarizing statements we used to spark discussion

  • Misleading viz for advocacy can be justified
  • Anyone, regardless of background, should be free and empowered to visualize data and share widely
  • Data visualization should always be able to be interpreted without accompanying narrative
  • Potential impact is more important than marginal risk
  • Bad dataviz is better than no dataviz
  • Only rigorous statistical inference should be visualized at all

Other polarizing statements

  • Dataviz should provoke empathy/concern
  • It's ok to simplify data in visualization
  • Visualization without uncertainty is useless
  • The better the visualization, the less it has a point of view
  • Pie charts can be useful
  • Dataviz should be fact-checked
  • Dataviz should have an emotional impact to be meaningful in storytelling
  • Your axes should always be labelled
  • Dataviz is the best way of making an argument
  • The more people visualize data, the better
  • People know how to read dataviz
  • Aesthetics are critical to good dataviz
  • Indigenous voices don't need to be visualized in this project
  • Objectivity is the holy grail of dataviz
  • The process of construction of a dataviz is more important than the outcome
  • The eudcational value of a viz/report/data is wholly determined by its impact on future events
  • Infographics are not responsible data visualization
  • Ethical data visualization discussions should only focus on the visual
  • There should be no figurative representation in data visualization
  • Convenience samples should not be visualized
  • Web mercator is great
  • All data should be open for viz
  • A good dataviz opens its data and code
  • All things can be visualized
  • Majority of dataviz is ultimately useless, if not harmful
  • Visualization always has a point of view
  • It is possible to anonymize data and still have it be useful
  • Everyone should learn how to visualize data

Theme clustering


Working groups