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Our lab

What is the CS2 DataLab?

CS2 DataLab is an interdisciplinary laboratory where professors, researchers, and MSc/PhD students coming from different disciplines and diverse methodological background tackle similar social problems through the use of computational methods and a complex systems perspective. CS2 DataLab is an innovative research environment, which is aimed to promote computational social science research and doctoral training, but also transference and social impact activities.

Who we are?

Our team is composed by professors, researchers, and students from different departments that are part of the University Institute of Research for Social Sustainable Development (INDESS) at the University of Cadiz (UCA). CS2 DataLab has been conceived as an open-access multidisciplinary research laboratory on computational social science, where social and behavioural scientists, mathematicians, health researchers, humanities scientists, physicists, and computer scientists share knowledge and cooperate side-by-side to come up with innovative solutions and theory- grounded models to explain complex social phenomena.

What we do?

The role of the CS2 DataLab aims to address the Big Problems of contemporary society, but also to increase social equity and efficiency through better policy planning and delivery of community services. Our team brings together a new collaboration from across the INDESS and other external research institutions, with expertise in computational social science and decision analytics. We facilitate evidence-based decision making across all societal levels, including local, regional, national and international. CS2 DataLab applies innovative approaches to social data analysis and visualisation for advancing social and health research and policy planning. We also develop research tools to help policy makers and private companies in complex social issues and advanced problem solving.

Fondo futurista abstracto

Main research lines

Computational social science

We use cutting-edge computational techniques oriented to the study of the Social and Behavioural Sciences (such as social network analysis, simulations, machine learning, text mining and sentiment analysis, among other tools), including a particular focus on social dynamics that may affect population health and wellbeing. 

Social and health data science

Our team combines theoretical concepts from the social and health sciences with statistics and computer science to find new answers and uncover actionable insights hidden in complex, unstructured, and high-dimensional data.

Social networks and analysis of complex systems

We use complex systems and network science approaches to explain social and health phenomena in contemporary societies, paying special attention to the structure and dynamics of urban environments and health information systems.


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