Coming from a social science background, I have had very limited exposure to data science. I was therefore excited to learn about the emerging field of computational social science and the Summer Institute in Computational Social Science (SICSS) presented the right opportunity. I applied to the 2019 SICSS and I was accepted for the Cape Town partner site. I went in not knowing what to expect but by the end of the first day I knew the experience at the two-week Summer Institute was going to be truly worthwhile.

It’s all about incentives. The current academic ecosystem incentivises publication in high impact factor journals and grant capture above all else, but there is more to being an academic than producing journal articles and winning grants. Luckily there are an increasing number of initiatives that are helping academics get credit for more of the work they do and increase their broader impact. This post rounds up some of the most interesting efforts.

The beginning of term is nearing. You’re teaching a new module on Computational Social Science (CSS). The field is developing rapidly and so are best practices around teaching the theory, methods and techniques to students.

Where do you start when you’re putting together your teaching materials? Do you visit the websites and blogs of academics who are experienced in teaching CSS to look for resources? Do you search online for syllabi, reading lists and tutorials? Maybe you scour YouTube for videos to include in your slides?

Together with a group of UK academics, the SAGE Ocean team have been digging into where academics go to find teaching materials and what the barriers are for academics who want to share, reuse and give and get credit for the materials they produce for teaching. This post includes thoughts from the group on what’s needed to promote a stronger culture of sharing teaching materials in CSS. And we’ve curated a list of our favorite resources for you too!

I would argue that computational social science necessitates collaboration, and indeed is tamed by it. A collaborative approach provides the necessary structure, goals, and a critical approach to research methods. In response to the question of what computational social science has helped me achieve, it may seem obvious to mention the concrete projects, the outputs, the measurable outcomes. However, for me computational social science has achieved something more substantial and enduring—a new way of working, a new way of thinking, and a new kind of enthusiasm for research.

It’s an exciting time to be in social science. Social media, digital identities and the world of big data has opened up new ways for social scientists to study and examine social phenomenon.

Some examples include using online search patterns to predict the spread of disease, tracking near real-time Twitter data to understand political movements or using location data to understand interpersonal interactions.

The move to a digital world has created a innovative new area of social science called computational social science (CSS).

Calling all social scientists. How were you trained? How are you keeping up (or not) with new developments in this rapidly changing digital world? How are you training your students?

This was the subject of an event sponsored by SAGE Ocean as part of the ESRC’s 2018 Festival of Social Science. In case you are not aware, Sage, who have been at the forefront of publishing qualitative work, have now launched SAGE Ocean – an initiative “to help social scientists to navigate vast datasets and work with new technologies”.

Watch these short videos as leading academics present at this year’s inaugural Social Science Foo Camp discuss the opportunities and challenges presented by big data and the move to more computational methods.

Being a data scientist with a sociological background is extremely valuable in trying to answer research questions to advance contemporary humanity. It goes beyond programming skills or just applying algorithms to data.

A little over a week ago, I posted a blog celebrating 39 women in computational social science. We knew there would be so many more amazing researchers to add, and the social science community duly delivered, suggesting plenty of women that should also be celebrated. Therefore, rather fittingly on #AdaLovelaceDay we have  published an updated list. The number has now more than doubled, and we hope that it is a good start for anyone looking for a supervisor for their PhD, or just wanting to see what other doctoral fellows are working on.

I want to share with you this list of 39 female researchers that are all crushing it in the social sciences and humanities with their innovative use of computational methods and very cool explorations of cutting edge tech. Follow them, read their papers and collaborate!

Teaching and learning resources from the 2018 Summer Institute for Computational Social Science have been made free to access online, allowing more people to explore in depth the field of computational social science.

With so much diverse data to dig into, the future of quantitative social science is exciting, particularly for those studying the granularities of individual-level behavior. In doing so, we must make sure that this research is ethical, robust and ultimately useful

The Economic and Social Research Center hosted the biennial Research Methods Festival at the University of Bath last week.

"Positions in data science require a unique set of job skills that many professionals simply don’t possess.  The level of programming knowledge, understanding of statistics and business sense make for a difficult position to fill. Because of this, many businesses find it difficult to hire appropriately for the position of data scientist." Kayla Matthews gives pointers on ways that companies, looking for data scientists, could stand out in this demanding market for data engineers.