1. Writing a paper introduction 

The goal of this module is to help you write a compelling and clear introduction to your research. You will gain the skills to effectively explain your methodology, connect your work to broader literature, and clearly showcase the unique contribution your project makes to knowledge. By the end of this course, you’ll be able to engage your audience and set the stage for the impact of your research.

 

2. Data vizualization

A significant portion of communicating economic research results—whether in presentations or written papers—relies on graphs and visuals. This session aims to help you develop essential skills for effective data visualization. We will cover fundamental visualization principles and general best practices before examining the specific challenges of data visualization for causal inference. While it can be a powerful rhetorical tool for causal inference, data visualizations can also be misleading if not handled carefully.

 

3. Data Collection  

Theses two modules introduces automated approaches to collecting novel data, drawing on both historical archives and high-resolution satellite imagery. Participants will gain an overview of state-of-the-art tools—from web scraping, OCR, and AI-based extraction of text, tables, images, and maps, to the use of satellite data to study environmental change, agriculture and food security, and natural resources. The modules highlight key trade-offs in time, cost, skills, legality, and data quality, helping participants assess what different techniques can realistically achieve. Students have the opportunity to choose either one of the two thematic modules or to take both.

 

4. LLMs for academic research

This module introduces you to the practical applications of large language models (LLMs) throughout the research pipeline. You will learn how to use LLMs to support brainstorming,literature review, and academic writing, as well as how to leverage LLMs' APIs for empirical tasks such as automated data collection, text analysis, and coding. The module will also cover critical aspects including algorithmic biases, ethical considerations, and best practices for ensuring quality and reliability. 

 

5. Replication and data management

This module introduces the key concepts of reproducibility and replicability in empirical research. It emphasizes their role in promoting transparency and credibility in Economics by addressing issues such as publication bias and p-hacking. We will also discuss why replication matters and how replication studies are conducted. We will also review guidelines for setting up reproducible workflows, data management and version control, and for creating replication packages from day one.

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6. Discussing a research paper

​This module provides you with a methodology and tips for constructively discussing research papers in two academic settings. First, you will learn how to deliver a paper discussion at academic events such as workshops and conferences— highlighting contributions, identifying caveats, and offering forward-looking suggestions in a concise way. Second, the module guides you through the steps of writing a referee report for a journal, including how to structure your evaluation, phrase your criticism and comments, and communicate clearly with editors.