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What is the Data Fair?

The data fair is an event that brings together our Master students in Design Informatics from the University of Edinburgh (School of Informatics and College of Art) and external partners (you!). We invite external partners to share their real-world data with our students, collaborate on data analysis and visualization, and ideally end up with a project that you can share publicly.

The collaboration is open to everyone with data: individuals or organizations, academics or not. Projects from past years included cultural heritage collections, document collections, social networks, twitter data etc.

The goal for the students is to chose a real-world dataset and an associated challenge for their course Data Science for Design that runs from October to December. Within that course, students will learn the basics of data analysis and visualization. Their assignment requires them to analyze a real-world data set and work on a visualization project that focuses on data exploration and the communication of findings through data visualization. Students will work in groups of three. Projects from past years can be found here.

_This year, due to ongoing distancing measured Covid19, the course will mix offline and online teaching. You can meet with students physically or online. We will provide platforms for collaboration and meetings. _

For references to the datafair, please cite this website and our research.

Why should I participate?

You will have the chance to work with motivated and creative students from a variety of backgrounds: graphic design, media, computer science, product design, etc. You will define a data challenge, i.e., an urgent problem or project you require help with around data analysis and visualization. You are invited to work with the students as close as you wish and attend our lectures and lab sessions (Thursdays 9am-1pm).

We hope that novel collaborations between you and the students and us will emerge. In the following semester (Jan-April), students towards their masters project in the summer; if the data fair project works well, there you are welcome to propose further projects and collaborate on supervision.

How can I participate?

The commitment from your side will be:

  1. Submit a data brief and data challenge before September 20 by fill this form (approx. 10min). Please, also read How to write a good Data Challenge?
  2. From all submissions, we will seek those ones we believe will best fit the course and students skills and let you know by September 24th (Friday).
  3. You present your challenge in 3min on Thursday, 30th September 2021, 10:00am, online: pitch your data brief for 3min, with the support of slides and any other material you would like to show and ideally stay for 1-2h to discuss with students. We organize a speed-dating session, then we will have an open discussion between you and the students. In case you cannot attend this event, leave a note in your data brief and we will contact you. You can bring along any colleagues you wish.
  4. Then, students have 1 week to decide on a challenge. Make it easy for students to decide for your challenge.
  5. Once they have chosen a challenge, you shold provide your data to students by mid October in an accessible format (e.g., a CSV, Excel, Online API, data base dump) and eventually help them get going. If you need, we can sign non-disclosure contracts. However, data and respective visualizations and analyses will still be visible to other students on the course and the course organizers and tutors for marking and feedback.
  6. During the semester, you consult with the students as much or little as you want and you’re welcome to attend our lectures and lab sessions.
  7. Students will get regular feedback from tutors and course organizers on their project in the form of discussions and marking feedback.
  8. Students will first submit a report on the data analysis (~November 6th, 2021). Then will work on a final visualization project.
  9. Final presentations will happen on December 2nd 2021, same location. Students are required to deliver their python code and an extensive data report with measures and visualizations. You are welcome to attend this event and give public feedback.

What are students doing with my data?

Analysis report

Students have 3 weeks to come up with an an individual analysis report (each student, hence 3 per group). Each report consists of an IPython notebook that includes python code and results of an exploratory analysis. Each report should investigate something different and will contain:

  1. 4-5 exploratory data visualisations, presented in a readable way and provided with explanation about what you have found: e.g., distributions, relationships, correlations, simple statistics, etc.
  2. 1-2 relationships between variables analysed: Trends, outliers, clusters, some high-level statistics.
  3. 2-3 hypotheses for further investigation

Visualization project

After these three weeks, students have 3 weeks to come up with some engaging form of presenting the data (group work). This end-piece should help communicating insights from or around the data to a specific audience. This will be shaped by what you think makes most sense for you and the data, but the students will have the final say over their brief. Some outputs might be:

Course Organizers: