SICSS-ODISSEI

Pre-arrival

The Summer Institute will bring together people from many fields and backgrounds. In order to use our time together efficiently, there are a number of things that you should do before participating in SICSS-ODISSEI 2023.

• Complete the pre-institute readings
• Complete coding bootcamp (if needed)
• Prepare your computing environment

TAs will host office hours through Slack to support you as you work through these pre-arrival materials.

Reading

In order to prepare for SICSS-ODISSEI 2023, we recommend reading Matt Salganik’s book, Bit by Bit: Social Research in the Digital Age (Read online or purchase from IndieBound, Princeton University Press), or Amazon, Barnes & Noble. Parts of this book, which is a broad introduction to computational social science, will be review for most of you, but if we all read this book ahead of time, then we can use our time together for more advanced topics.

Obligatory reading material:

(Closer to the Summer School, a few titles may be added to this list)

• Hofman, Jake M., et al. “Integrating explanation and prediction in computational social science.” Nature 595.7866 (2021): 181-188.
• Liu, David M., and Matthew J. Salganik. “Successes and struggles with computational reproducibility: lessons from the fragile families challenge.” Socius 5 (2019): 2378023119849803.
• Salganik, Matthew J., Ian Lundberg, Alexander T. Kindel, Caitlin E. Ahearn, Khaled Al-Ghoneim, Abdullah Almaatouq, Drew M. Altschul et al. “Measuring the predictability of life outcomes with a scientific mass collaboration.” Proceedings of the National Academy of Sciences 117, no. 15 (2020): 8398-8403.
• Breznau, Nate, Eike Mark Rinke, Alexander Wuttke, Muna Adem, Jule Adriaans, Amalia Alvarez-Benjumea, Henrik Kenneth Andersen et al. “Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty.” (2021).
• van der Laan, Jan, Marjolijn Das, Saskia te Riele, Edwin de Jonge, and Tom Emery. “Using a network of the whole population of the Netherlands to measure exposure to differing educational backgrounds.” (2021).
• Lugtig, Peter. “Panel attrition: separating stayers, fast attriters, gradual attriters, and lurkers.” Sociological Methods & Research 43, no. 4 (2014): 699-723.

Recommended material:

• Introduction to Data Science for Social Scientists
• Machine learning versus Statistics
• Statistics versus Machine learning

Training

We strongly recommend that participants follow the course Data Science & Society Introduction course which is provided by Chris Bail. In the course the emphasis is more on text and unstructured data than the content covered in the SICSS-ODISSEI summer school which will be more oriented towards complex, structured administrative and linked data. However, many of the principles are the same and it will provide excellent preparation for our two weeks in Rotterdam.

Participants are expected to be familiar with either R or Python before the start of the summer school. We expect participants to bring their own laptops with R and Python installed and ready to use. If bringing your own laptop is not possible or is impractical for any reason, please let us know and we will make alternative arrangements for you.

In addition to the Data Science & Society Introductory Course, there are a large number of resources for learning R that are available:

Coding Boot Camp

The SICSS Boot Camp is an online training program created by Chris Bail to provide you with beginner level skills in coding so that you can follow the more advanced curriculum we teach at SICSS. The videos and materials are designed for complete beginners and are best viewed as a sequence since each video builds upon content introduced in previous tutorials. If you are already familiar with the topics in these videos, you do not need to complete them.

If you would like more practice after completing the Boot Camp videos, some other materials that we can recommend are:

• RStudio Primers, which can be supplemented by the open access book R for Data Science by Garrett Grolemund and Hadley Wickham
• R for Social Science, Data Carpentry.
• Introduction to R for Social Scientists, Taught by Charles Lanfear at University of Washington. This course includes videos of lectures, slides, and assignments.
• Learn R, Code Academy.

Please note that the majority of the coding work presented at SICSS-ODISSEI will employ R. You are welcome to employ a language of your choice, such as Python, Julia, or other languages that are commonly used by computational social scientists. However, we cannot support those languages.

Computing environment

R
Some of the activities will require coding, and we will support R. You are welcome to use other languages, but we cannot guarantee that we can support them. Before SICSS you should install a modern, stable-release version of R and RStudio.

Slack
Before participating at SICSS-ODISSEI 2023, you should have an account in the SICSS Slack workspace. If you have not used Slack before, you should review these getting started materials. Slack can be hard to use at first, but we’ve found that it is the best way to enable everyone to collaborate.

GitHub
Many participants at SICSS use GitHub to collaborate. If you do not yet have one, you should create a GitHub account. If you are a student, we recommend that you apply for a GitHub Student Developer Pack.