Datum:
09.03.2026, 13:00Uhr - 10.03.2026,17:00 Uhr
Ort:
DOR 24 | House 1 | 4th Floor | Rooms 1.401–1.405
Sprache:
English
Organisiert durch:
IZ D2MCM in cooperation with BERD@NFDI and the Digital Research Academy (DRA)
Anmeldung:
Link to registration DFN Booking Tool by the 4th of March.
Kontakt:
iz-d2mcm.contact@hu-berlin.de
iCal-Download
EconData Workshop
About
Struggling to turn data into valuable insights?
Wondering where to find the right research data for your topic?
Finding it tough to choose the right algorithms and methods?
Or maybe you just have a data-related question that has been on your mind for a while, but you have not had the right space to ask it?
You are a PhD candidate, early-career researcher, or advanced Master’s student in business, economics, or the social sciences, and you are facing questions like these? Then join our EconData Workshop and bring any data question you have not yet had the chance to ask. Choose the topics you want to focus on, and we will provide practical advice you can use right away in your research. No prerequisites are needed. Just come with your questions, and leave with fresh strategies.
We offer:
- Free attendance
- Refreshments and Catering
Event Highlights
Day 1 (Half Day) – Starts at 1:00 PM
Kick off with your most pressing questions and dive straight into hands-on sessions. Collaborate with peers who share your challenges or offer fresh solutions. Explore methods, tools, and approaches together – this is your chance to experiment, learn, and build from real-life problems.
Day 2 (Full Day) – Starts at 9:00 AM
Continue working on your data challenges and refine your solutions. Wrap up with concrete outcomes, such as best practice guides, code snippets, or new strategies. Share your insights and take away practical ideas to support your future research.
How to Participate?
Just register here: DFN Booking Tool, by *4th of March*. to get your free ticket and join us on March 9-10 in Berlin.
About the Organizers
IZ D2MCM (Host)
Sonja Greven is Professor of Statistics at Humboldt-Universität zu Berlin. Professor Greven's expertise is particularly in statistics, data science, statistical learning, machine learning, statistical modeling, statistical inference, biostatistics, and their applications in various fields (economics, social sciences, medicine, epidemiology, engineering, linguistics, etc.). She is also well-versed in functional data analysis and statistics for object data (curves, images, shapes, etc.).
Eliza Mandieva is project coordinator of DesBi and member of the management team of the Interdisciplinary Centre Digitality and digital Methods Campus Mitte, with a profile in content and communication (both HU Berlin). Her research is placed in economics, methods on surveys and data analyitcs.
Kleio Chrysopoulou Tseva is a student assistant at the Interdisciplinary Centre Digitality and digital Methods Campus Mitte and studies economics.
Digital Research Academy (DRA)
Ilona Lipp is the Open Science Officer at the University of Leipzig, promoting and supporting transparency and integrity in research. She has over 12 years of research experience in interdisciplinary neuroscience in Austria, the UK, and Germany. With additional training in coaching, mediation and didactics, Ilona now helps scientists navigate academia and optimize their research practices, for example as a trainer for the Digital Research Academy.
Carolina Natel is a postdoctoral researcher at the Karlsruhe Institute of Technology (KIT). Her work combines process-based models, data science, and machine learning to study how climate and land-use change affect ecosystem functioning. She is also a trainer with the Digital Research Academy, focusing on open science, data science, and reproducible research.
BERD@NFDI
Jan Klostermann is a postdoctoral researcher at University of Cologne, Chair in Marketing Science and Analytics, since March 2022. His research focuses on the intersection of brand management and social media marketing with an emphasis on topics such as word-of-mouth, influencer marketing, and brand positioning. He analyzes unstructured textual and visual user-generated content from social networks and online review platforms using methods of natural language processing and computer vision.