Blog

Building better evidence: How smart study design drives real-world change

At our latest Brown Bag seminar, Drew Dimmery, PhD, Professor of Data Science for the Common Good at the Hertie School, invited us to rethink how we approach solving methodological problems.
Blog

Playing games with AI: What behavioural game theory reveals about large language models

Exploring how AI learns to cooperate: insights from Elif Akata’s research.
Blog

How can data help shape the future of cycling in our cities?

That was the guiding question of the workshop Data-Driven Approaches for Understanding and Advancing Urban Cycling, organised by PhD students Silke Kaiser and Carol Sobral, together with Professor Lynn Kaack at the Hertie School. 
Blog

Inside the experiment: comparing pre- and post-moderation in online comment sections on reducing toxicity in digital discourse

Online comment sections are a microcosm of public debate – lively, diverse, and at times, toxic. Managing the balance between open expression and respectful dialogue has become a key challenge for digital publishers. To understand what works, researchers are now turning to field experiments with real-world partners.
Research

How central bank communication shapes public opinion

A study co-authored by Senior Research Scientist Will Lowe shows that communication style, social group adherence and public trust significantly influence how citizens evaluate messages from the European Central Bank.
Research

New study in Nature Cities maps machine learning research for urban climate change mitigation

Over 70% of global greenhouse gas (GHG) emissions come from cities, mainly due to fossil fuel-dependent transport systems and carbon-intensive infrastructure.
Research

Our areas of focus

The key research focus of the Hertie School as a whole and Data Science Lab in particular is contribution to the common good. Core research areas involve experimental survey methods, causal inference, and natural language processing. 
Teaching

Educating future leaders

The Lab builds capacity through training and educating the new generation of public policy professionals and practitioners that utilize Data Science and AI to tackle complex societal challenges of today.
Research Consulting

Technical and substantive support

Our Research Consulting team provides resources and advice on methodological and technical issues for graduate students, researchers, and faculty at the Hertie School and the SCRIPTS Cluster of Excellence. 
Outreach

Data science events and workshops

We organise regular data science events and meet-ups, such as the Brown Bag series with researchers, to enrich campus life at the Hertie School and foster dialogue across Berlin’s academic, start-up, and industrial communities.
Research area

Machine Learning

Theoretical and practical applications of machine learning methods to solve interesting policy and social questions. 

Research area

Experimental Survey Methods

Applications of techniques that combine random sampling with random assignment in conducting modern surveys. 

Research area

Causal Inference

Applications of causal inference methodologies in research into legislative politics, political economy, and public policy.

Research area

Political Behaviour and Communication

Research into opinion formation and how political behavior is shaped in the digital age, public opinion, and the use of online data in social research.

Research area

Climate Change and AI Policy

Methods from statistics and machine learning to inform climate mitigation policy across the energy sector.

Project highlight

Climate Action to Advance Healthy Societies in Europe (CATALYSE)

CATALYSE is a five-year initiative that seeks to deepen and disseminate knowledge on the health impacts of climate change. It aims to identify the connections between environmental hazards, ecosystems, and human health, explore the health co-benefits of climate action, and understand the role of health evidence in decision-making processes using machine learning and other computational tools. Prof. Lynn Kaack and Slava Jankin are principle investigators for this project. 
Project highlight

Media exposure and opinion formation

A research project led by Prof. Simon Munzert and supported by the Volkswagen Foundation, Paying Attention to Attention: Media Exposure and Opinion Formation in an Age of Information Overload studies the consequences of online media exposure for political preferences and behavior. 
Project highlight

Climate Change AI

Co-founded by Prof. Lynn Kaack, Climate Change AI (CCAI) is a international initiative to catalyze impactful work at the intersection of climate change and machine learning. Since its establishment in June 2019, CCAI has led the creation of a global movement in climate change and machine learning, encompassing researchers, engineers, entrepreneurs, investors, policymakers, companies, and NGOs.
Project highlight

Lancet Countdown on health and climate change

Prof. Slava Jankin and Prof. Simon Munzert are regular contributors to the Lancet Countdown to help monitor the evolving health profile of climate change, and providing an independent assessment of the delivery of commitments made by governments worldwide under the Paris Agreement
Project highlight

Agent-based modelling of infectious diseases

To help find the most effective methods to utilize when modelling epidemics, the research team from the Centre for Digital Governance and the Data Science Lab conducts interviews with leaders in the field of infectious disease modelling and studying novel ways of using models to help improve policy.
Project highlight

Machine learning enables global solar-panel detection

In this publication in Nature, Prof. Lynn Kaack analyses the policy implications of recent research using machine learning to grasp scale of global solar installations through satellite images and what is coming up next for this field of research. 
International outreach

CIVICA Data Science Seminar Series

A multi-disciplinary series on applications and methodologies of data science for the social, political, and economic world. Each session focuses on a particular academic or industry question that can be answered by the help of data science. 

 
International outreach

Data Science Summer School

A series of theoretical and practical workshops on the exciting quantitative methods and technologies currently employed by industry, government, and civil society to address the world's most complex problems. Organised every summer free-of-charge to participants worldwide. 
International outreach

Data4Good Festival

An annual hackathon for the common good bringing together bachelors students from across Europe, fostering creativity, collaboration, and innovative solutions to address pressing societal issues using data-driven approaches.
Local outreach

Data Science Brownbag Seminar

An informal and interactive research talk series where participants bring their own brown bag lunch and engage in discussions on research and insights the field of data and computational social science.

See our events

Master of Data Science for Public Policy (MDS)

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The MDS brings together passionate students from both technical and social science backgrounds to deepen their analytical skills and develop an in-depth understanding of the policy world. During this two-year master's they learn how to leverage algorithmic decision-making to tackle some of the most complex challenges of our time with data-driven solutions. The Data Science Lab is proud to help manage and support for this new programme. 

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High-performance computing server

Together with the IT Service Desk, the Data Science Lab manages the high performance computing (HPC) cluster at the Hertie School. The multi-A100 GPU server is accessible to faculty and researchers across the school’s various programmes. The server allows researchers to run compute-intensive experimentation and build prototypes for digital products, opening up new avenues for scientific exploration in data-driven research. In keeping with our mission for sustainability, the heavy electricity demand of supercomputing is offset by utilising completely carbon-neutral energy sources. 

Please contact [email protected] if you would like to experiment with and implement data science research on the server.

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