Our vision

Our aim is to help people understand the data driven world around us. We want to inspire an open community around a central platform. One that encourages us all to harness the potential of open data by creating ‘data stories’. These ‘data stories’ will mix computer code, narrative, visuals and real world data to document an insightful result. They should relate to society in a way that people care about, and be educational. They must maintain a high standard of openness and reproducibility and be approved by the community in a peer review process. The stories will develop data literacy and critical thinking in the general readership.

About the project

This project was initially formed by a desire to contribute and advance to the analysis of government COVID-19 data.

As part of this process we recognised that government reporting of COVID-19 data was not always in the most accessible format. We also recognised that especially during these times, many invididuals may be interested in developing their technical skills in an impactful way, but not know where to start.

Our goal was therefore to help provide educational data science content that would guide the user through the process of making the data accessible, to using the data for analysis.

We hope that by using the story telling medium, we can bring people along the data science journey and showcase how these techniques can answer both fascinating and socially relevant questions.

What is a Turing Data Story?

The Turing Data Stories should be detailed and pedagogic Jupyter notebooks that document an interesting insight or result using real world data. The aim of the Turing Data Stories is to spark curiosity and motivate more people to play with data.

We expect that the notebook of a data story takes the reader through each step of the analysis done to create the data story results. Turing Data Stories should follow these principles:

The story should be told in a pedagogical way, describing both the context of the story and the methods used in the analysis. The analysis must be fully reproducible (the notebooks should be able to be ran by others using a defined computer environment). The results should be transparent, all data sources are correctly refered to and included. In order to mantain the quality of the results, the Turing Data Story should be peer-reviewed by other contributors before published. We don’t expect sophisticated analyses, just insteresting stories told with data. If you have an idea of a Turing Data Story you want to develop please follow our contributing guidelines to make sure your contributions can be easily integrated in the project.

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