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Open information: Documenting data and methods

Open data is great, Open data is amazing, but unless your users understand your data they can't use it.

Do you share or would like to share open data? Is your data adjusted in anyway? Have you cleaned it or removed outliers? Have you added a random spatial offset to anonymise it? Have you used age adjustment, or seasonal adjustment to reveal underlying patterns? Do your users know that you have done this? Do your users need an advanced degree to understand your documentation? Would a journalist picking up you data be reasonably expected to understand what it is telling them without talking to you?

Are there biases in your data? Are you catching every case or do you think that they are some cases that are not captured? Do your users understand how those missing cases and biases effect their use of your data?

Based on my work in medical statistics, I will talk about how to share data with methods and documentation to make data relevant and results reproducible and accessible. In open source we know a lot about documentation, UX design, technical debt and onboarding time. I want to ask how we can apply these ideas to developing open data

Rhydwyn Mcguire

Rhydwyn is a statistician currently working in the healthcare system, working with large and rapidly changing data sets, and presenting and communicating these to non-statisticians. Rhydwyn is passionate about open source technology that makes science easier and gets meaningful results into scientists’ and policymakers’ hands.

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