Difference between revisions of "Rdviz-culture"
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== Outputs == | == Outputs == | ||
''Description of a minimum viable product, aspirational output, stretch goals, etc.'' | ''Description of a minimum viable product, aspirational output, stretch goals, etc.'' | ||
+ | Collaborative website: The Inclusive Data Visualization Project | ||
+ | |||
+ | Tabs | ||
+ | * What is inclusive data visualization? | ||
+ | ** Here we ask visitors to submit their comments about what they think of when it comes to having an inclusive data visualization field | ||
+ | |||
+ | * Exclusion by Access | ||
+ | ** Problem: Data visualization can exclude due to issues of access, including | ||
+ | *** bandwidth and connectivity | ||
+ | *** language/layout of tools and resources | ||
+ | *** discriminatory hiring practices | ||
+ | |||
+ | * Exclusion by Misrepresentation | ||
+ | ** Problem: Data visualization can exclude by misrepresenting people, along the dimensions of | ||
+ | *** gender | ||
+ | *** ethnicity | ||
+ | *** location | ||
+ | |||
+ | * Exclusions by Extraction | ||
+ | ** Problem: Data visualization can exclude by taking and not giving back | ||
+ | *** form is not legible to the community | ||
+ | |||
+ | |||
+ | * Each page has space for case studies, tools, resources, and other links | ||
== Notes == | == Notes == |
Revision as of 22:01, 15 January 2016
Outputs
Description of a minimum viable product, aspirational output, stretch goals, etc. Collaborative website: The Inclusive Data Visualization Project
Tabs
- What is inclusive data visualization?
- Here we ask visitors to submit their comments about what they think of when it comes to having an inclusive data visualization field
- Exclusion by Access
- Problem: Data visualization can exclude due to issues of access, including
- bandwidth and connectivity
- language/layout of tools and resources
- discriminatory hiring practices
- Problem: Data visualization can exclude due to issues of access, including
- Exclusion by Misrepresentation
- Problem: Data visualization can exclude by misrepresenting people, along the dimensions of
- gender
- ethnicity
- location
- Problem: Data visualization can exclude by misrepresenting people, along the dimensions of
- Exclusions by Extraction
- Problem: Data visualization can exclude by taking and not giving back
- form is not legible to the community
- Problem: Data visualization can exclude by taking and not giving back
- Each page has space for case studies, tools, resources, and other links
Notes
Culture (producers and consumers)
Possible Outputs
- Cultural checklist or guide
- Mentorship program (physical/virtual meetup space)
- Human-centered design for dataviz
- Manifesto
- Active discussion tool/platform (wiki?)
- Open source tools
- “The Manifesto of the Missing People”
- kids, African, Asian, etc.
- here is the list of people who are not here
- how do we start to include them?
- Resources of/for people who have agreed to abide by it
- “I’ve agreed to commit to this as part of my practice”
- Tap into featured people who are already in the conversation
- The Inclusive Data Visualization Project
- Tell by showing—case studies; blog posts
- Tools
- Link dump/Resource page
- Checklist
- Access—low bandwidth, no English
- Not seeing themselves in the data—gender, race
- Seeing themselves in the eyes of others
- “a sketchpad for people to say, ‘this is what I think inclusive data visualization looks like.’” ← as a sheet on the science project
- Who is this for?
- Practitioners
- What do we want them to do?
- This is what we know; what do you know?
- Mission statement
- Series of blog posts
- Research agenda (wiki?)
- Early ed. Curriculum
- Baseline data/graph literacy
- Exercises targeted to different cultures
- Treat data visualization as a core standard
- Post-secondary education
- Interdisciplinary?
- Different dataviz mediums and how they are used and understood by different groups
- most efficient engagement tools for different groups
Audience
Personas, use cases, context