GESIS Training News

The GESIS Training Team is very concerned about the health of participants and lecturers of our courses. However, we know that many of you (like us) are looking forward to meeting in person again. Thus we are happy to announce that we will offer in-person courses beginning in June. The GESIS Summer School will be carried out as a hybrid event. Please respect that local restrictions due to the coronavirus may apply. At the moment, masks are mandatory in all of our in-person courses. Because we know that some of you prefer online classes altogether, we have added digital formats to our regular program. More information here.

Stay well and safe!

We are excited to announce the program of the GESIS Fall Seminar in Computational Social Science 2022: Join us at the new GESIS premises in Mannheim from 05 September to 23 September and choose from a variety of introductory and advanced courses on computational social science methods! The Fall Seminar targets social scientists, data scientists, and researchers in the digital humanities that want to collect and analyze data from the web, social media, or digital text archives. Its courses are taught by GESIS and international experts and cover methods and techniques for working with digital behavioral data (“big data”). Week 1 comprises courses on the foundations of working with digital behavioral data, courses in Week 2 focus on the collection and management of big data, and courses in Week 3 cover different techniques for analyzing these data. Lectures in each course are complemented by hands-on exercises giving participants the opportunity to apply these methods to data. All courses are held in English.

Below, you can find an overview of this year's courses:

Week 1 (05 - 09 September): Foundations of Working with Digital Behavioral Data

Introduction to Computational Social Science with R

Dr. Aleksandra Urman (University of Zurich), Dr. Max Pellert (Sony Computer Science Lab Rome)   

Introduction to Computational Social Science with Python

Prof. Dr. Milena Tsvetkova (London School of Economics), Dr. Patrick Gildersleve (London School of Economics)    

Tools for Efficient Workflows, Smooth Collaboration and Optimized Research Outputs

Dr. Julia Schulte-Cloos (University of Munich), Lukas Lehner (University of Oxford)   

Week 2 (12 - 16 September): Collection and Management of Digital Behavioral Data

Automated Web Data Collection with R

Dr. Theresa Gessler (University of Zurich), Dr. Hauke Licht (University of Cologne)   

Automated Web Data Collection with Python

Felix Soldner (GESIS Cologne), Dr. Jun Sun (GESIS Cologne), Leon Fröhling (GESIS Cologne)   

Big Data Management and Analytics

Prof. Dr. Rainer Gemulla (University of Mannheim), Adrian Kochsiek (University of Mannheim)   

Week 3 (19 - 23 September): Analyzing Digital Behavioral Data

Network Analysis in R

Dr. David Schoch (GESIS Cologne), TBA    

Introduction to Machine Learning for Text Analysis with Python

Prof. Dr. Damian Trilling (University of Amsterdam), Prof. Dr. Anne Kroon (University of Amsterdam)    

Automated Image and Video Data Analysis with Python

Prof. Dr. Andreu Casas (Vrije Universiteit Amsterdam), Felicia Loecherbach (Vrije Universiteit Amsterdam)    

ECTS Credits & More

Thanks to our cooperation with the a.r.t.e.s. Graduate School for the Humanities at the University of Cologne, participants of the GESIS Fall Seminar can obtain 2 ECTS credit points per one-week course.

For those without any prior experience in R or Python and those who’d like a refresher, we’re additionally offering two pre-courses, “R 101” and “Python 101” (two days, online) in the week before the start of the Fall Seminar.

All courses are stand-alone and can be booked separately – feel free to mix and match to build your own personal Fall Seminar experience that perfectly suits your needs and interests. There is no registration deadline, but places are limited and allocated on a first-come, first-served basis. To secure a place in the course(s) of your choice, we strongly recommend registering early.

Please visit our website for detailed course descriptions and registration and sign up here!

The GESIS Summer School 2022 will take place from 03 to 26 August 2022. The Summer School will be organized as a hybrid event for the first time: Some courses will be held on-site in Cologne and some online.

Go to www.gesis.org/summerschool for the entire program, including detailed descriptions of this year’s courses on Directed Acyclic Graphs (DAGs), Data Science Techniques, Factorial Survey Design, Web Surveys, Mixed-Mode Surveys, Longitudinal Social Network Data, Questionnaire Design, Survey Design, Stata or R, Sampling and Weighting, (Non-)Probability Samples, and Multiple Imputation.

ECTS Credits & More

Thanks to our cooperation with the Center for Doctoral Studies in Social and Behavioral Sciences at the University of Mannheim, participants can obtain a certificate acknowledging a workload worth 4 ECTS credit points per one-week course (24hrs or 30hrs). More information is available here.

There is no registration deadline, but places are limited and allocated on a first-come, first-served basis. You will find the full program, detailed course descriptions, and more information here.

KonsortSWD is happy to announce their workshop introducing a standardized data management plan for educational research (Stamp - Standardisierter Datenmanagementplan für die Bildungsforschung). The event will take place (virtually) on 22 June 2022 and will be repeated on 7 September 2022, from 10-11:30 am. The workshop language will be German!

Researchers are increasingly confronted with requirements to manage their research data systematically. For many, research data management is a challenge. The Stamp is an easy solution to systematically manage data in line with the FAIR Data Principles and the idea of Open Science. It was developed within the BMBF-funded project Domain-Data-Protocols for Empirical Educational Research in a consortium of thirteen research infrastructure institutions. This workshop, funded by KonsortSWD, primarily targets researchers, research data management experts, and representatives of funding institutions in educational research and related disciplines. If you are interested in participating, please register here.

After registering, you will receive further information on the event and access details by e-mail. If you have any questions, please do not hesitate to contact ddp-bildung@gesis.org.

Please Note: For a general introduction to research data management (not focused on educational research) in English, look at the workshop Data Management and Open Science with Dr. Anja Perry and Dr. Sebastian Netscher (11 to 13 October 2022).

Andreu Casas is an Assistant Professor in the Department of Communication Science at the Vrije Universiteit Amsterdam and a Faculty Associate in the Center for Social Media and Politics at New York University. His research interests encompass the areas of political communication, public policy processes, and computational social sciences. His methodological interests and strengths are more generally natural language processing (text as data), computer vision (images as data), machine learning and artificial intelligence. With Felicia Loecherbach, he will teach the course “Automated Image and Video Data Analysis with Python” at the GESIS Fall Seminar in September 2022. Felicia is a PhD candidate in the Department of Communication Science at the Vrije Universiteit Amsterdam. Her research focuses on the diversity of (online) news consumption to build a better understanding of the impact that changes in online environments have on the understanding and usage of news. She applies and develops innovative computational methods to automatically analyze large amounts of media and trace data.

How did you become interested in your subject?

Andreu: During my PhD, I was doing research on the role of social media in political mobilization, and I realized that, although many had claimed in the past that some iconic images were essential to understanding support for many historical mobilizations (e.g., civil rights movements in the United States), most existing work on social media and protests ignored the visual content of the messages being spread through social media. For this reason, I started exploring ways in which I could automatically analyze images in social media messages related to relevant protests at the time, such as Black Lives Matter. Eventually, I started learning about many computer vision techniques -- and later, I started putting together workshops and courses to transfer what I had learned to other social scientists interested in the automatic analysis of images and videos.

Felicia: During my master’s program in communication science, I had a class about using computational methods as a social scientist. I started learning Python for automated text analysis to apply it to social media and news data. I got fascinated by the opportunities it gives for researching digital trace data. When starting my PhD (which involves analyzing online news consumption), I participated in a workshop with Andreu on using Python for image analysis. I immediately could see how it would benefit the work that I am doing: Especially on social media, the information we see is often visual, with Instagram and TikTok, two of the most prominent players, being focussed on photos and videos. So to study the content users see online, it is crucial to learn more about the automated analysis of visual content.

What lessons can participants draw from your GESIS course?

Andreu: The course will first give an introduction into using images as data in the social sciences and why they are such a central data source. In a very hands-on approach, we will then cover the workflow of collecting, handling, storing, and processing images - as this often includes many challenges that researchers more used to textual data are not familiar with. But of course, we will also go into the “fun” parts of working with images, such as image classification, face detection/recognition, and the role deep learning and CNNs play in this.

What do you enjoy most about being a social scientist?

Andreu: I have always been interested in politics and understanding the strategies many groups in society can use to influence policy. Then I also became very interested in how social and digital media have shaped political processes. For me, the joy comes from studying societally relevant problems and from studying issues that are not easy to disentangle. Running externally valid experiments where we can genuinely control all factors is really difficult in the social sciences. We usually need to get very creative in terms of research design.

Felicia: I really like how my research relates to everyday life; wherever I go, I can see examples of how my study topics impact society. When I talk about the studies that I am doing with friends and family, everyone has a story of how social media or online news influences them, often leading to interesting conversations. Researching how we get our information online and the impact it has on how we see and think about the world is just an exciting topic - although it is something that will never have a definitive answer and constantly evolves. So my work never gets boring!

We thank Felicia and Andreu for their interesting insights and look forward to their class.