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README.md

Binder

All Contributors

Looking for how to get involved? Click here.

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 individuals 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 referred to and included.
  • In order to maintain 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 interesting 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.

Contributing

This repository is always a work in progress and everyone is encouraged to help us build something that will be useful to the many.

How can I get involved?

  • Story ideas: Have an idea for an interesting story that could be told if you had the data, or knew how to analyse it? We can help.
  • Data: Stumbled across an interesting dataset, or perhaps mashed together several sources of data yourself? We want to hear about it.
  • Code: Are you an expert in Bayesian analysis? Do you have sick matplotlib skills? Put that knowledge to work!
  • Peer Review: Know a bit about data analysis? Good at communicating that knowledge? Interested in learning about it can be applied to understanding society? We need reviews to make sure our stories are the best they can be.
  • Communication: Are you an amazing writer? Help us with the story telling side of our stories.
  • Community: Don't fit in any of the above categories, but still want to hang out and be involved? We've got you, drop us a line.

The process for proposing a story and reviewing a story can be found in our submission and review guidelines. All contributors are asked to follow our code of conduct and to checkout our contributing guidelines for more information on how to get started.

How to Read Stories

Our stories are published online via fastpages. You can check them out here.

Alternatively, click the binder badge at the top of this README to load an interactive version of our stories.

Another option is to run the notebooks locally yourself. To do this, we recommend installing conda, cloning this repository, and then setting up an environment using

git clone git@github.com:alan-turing-institute/TuringDataStories.git
cd TuringDataStories
conda env create -f binder/environment.yml

Any problems, open an issue!

The team

The team is currently composed of four members:

We currently meet every Wednesday afternoon

Citing TuringDataStories

Beavan, D., C. Rangel Smith, S. Van Stroud, and K. Xu. Turing Data Stories, 2020. https://github.com/alan-turing-institute/TuringDataStories.

@misc{beavan_turing_2020,
	title = {Turing {Data} {Stories}},
	url = {https://github.com/alan-turing-institute/TuringDataStories},
	author = {Beavan, D. and Rangel Smith, C. and Van Stroud, S. and Xu, K.},
	year = {2020}
}

Get in touch

You can join our community at Slack 🏡 (turingdatastories.slack.com) by opening an issue here along with your email id. We virtually meet on Wednesday afternoons to work collaboratively.

Contributors


kevinxufs

🤔 ⚠️ 🖋 💻 📖 📆

Camila Rangel Smith

🤔 ⚠️ 🖋 💻 📖 📆

David Beavan

🤔 ⚠️ 🖋 💻 📖 📆

Sam Vs

🤔 ⚠️ 🖋 💻 📖 📆

Yo Yehudi

📖 🤔

Louise Bowler

👀

nbarlowATI

👀

Martin O'Reilly

🤔

Eric Daub

📝 💻 🤔 🖋

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