Social Data School: June 2023 (Cambridge)

Social Data School 2023 in Cambridge (26-30 June)

You will learn new methods and tools for data-intensive public interest investigations and academic research, from leading academics and practitioners in the field. You will also have the possibility to use the Library and visit relevant places at the University of Cambridge.

We aim to bring together participants from journalism, academia, publicity, and civil society to explore the methods used to create, visualise and analyse digital data and its dissemination in society.

At the SDS you will learn by doing during in-person sessions and other activities. The school is intensive, but extensive online resources will be available to students to help them follow up on what they learned in the taught sessions during and after the Data School. We encourage anyone working with social data to apply!

Q&A

Structure

The SDS is an application only intensive teaching programme structured around the life-cycle of a digital research project, covering principles of research design, data collection, cleaning and preparation, methods of analysis and visualisation, and data management and preservation practices.

Students will gain hands-on experience of data-intensive research methods from leading researchers and practitioners, as well as critically addressing theoretical assumptions about these practices. They will be equipped with tools, methods, and ideas to study the social in the ever-changing digital environment today.

As part of the application process, participants will propose a project to work on during the five days of the Data School. You may apply with a pre-existing project or propose a new project which you will develop during the course of the Data School. In either case, projects should show great potential to have an impact in the public interest. Taught sessions, workshops, keynotes, and other activities will provide opportunities for these projects to be developed with teachers and peers. Participants will be expected to present their progress and what they learned at the end of the school. See more details about projects and their relation to applications in the ‘How to apply’ section below.

Modules will cover the following content in the context of discussions about Machine Learning, Social Media and Decoloniality which will run across the whole curriculum: 

• Methodology for Digital Investigations
• Introduction to Computer Vision and Machine Learning for Investigations
• Basic Automations for Investigations 
• Geolocation and Open Source Investigations
• Social Network Analysis 
• Critical Approaches to Data Visualisation 

We will have three greatly stimulating keynotes from practitioners and/or academics working around those topics:

• Jacopo Ottaviani, from Code4Africa, on the work his organisation has done with communities, local journalists, and NGOs in Africa.
• Edgar Gomez Cruz, from the University of Texas at Austin, on ‘Decentering Knowledge’.
• Sofija Stefanovic, PhD student at the Centre for Doctoral Training in Artificial Intelligence for Environmental Risk.

In addition, students will be able to use the Cambridge University Library, have hands-on exercises with teachers available on-site to help, have participatory activities with peers, and go out for dinner in Cambridge.

You can view the full programme and module descriptions here: Social Data School Programme

Note: content and timings may be subject to change.