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- Make the presentation of the data understandable by anyone. Enough so that it is not necessary for you to be there to describe the data
- Use a shared vocabulary so people can understand your work without doing too much learning
- Make sources of information clear and available. Be clear about where the data is coming from and how you collected it (Klein and D’Ignazsio)
- Protect any personal information provided by people that you used for your data
- Be mindful of the purposes of your research
- Create visual / easy to understand methodology along with data (Drucker)
- Always cite our sources to ensure credibility & give credit to people/services that are typically overlooked
- Make sure any visualizations accurately represents the data
- Being transparent enough for replication purposes *(Force 11, Ramsey and Rockwell, Pew, Eviction Lab)
- Consider context when looking at data (Klein and D’Ignazsio)
- Being overall aware of potential subjectivity of data and how you yourself are able to skew the data.