As the leader of a data science team at the Urban Institute, I get to work on interesting issues that intersect data science and social science every day. By data science, I mean technical tools, architectures, and processes that are borrowed from computer science and are atypical in the social sciences. This is a slightly more limited definition than most would have for the term data science, but because so much of what defines a data scientist at Urban also defines a researcher — cleaning data, analyzing it, visualizing results, etc. — my definition draws a finer line.
Earlier this year Allen AI were announced as the winners of the NYU Coleridge Initiative’s Rich Context Competition. The goal of the competition was to automate the discovery of research datasets and the associated research methods and fields of social science research publications. You can find out about all the finalists and their work here.
We caught up with Allen AI to talk about the work and their involvement in this year’s competition.
Watch our panel event from the ESRC Festival of Social Science on what skills social scientists will need to be able to do the social research of the future.
SAGE Research Methods has launched a new Data Science video collection, with hours of educational material for researchers of all levels and backgrounds.
At the end of February we ran a most enthralling event experience. Three panelists, two hosts and about 20 attendees all put their headsets on from their labs, offices and homes to join a virtual classroom decorated with trees, a castle, a slightly scary tiger and a hippo, to talk about the future of VR in social science research.
Find out more about the field of collective intelligence by tuning into these vox pops, filmed
The second annual Social Science Foo Camp took place at Facebook’s headquarters in Menlo Park at the start of this month, convening an eclectic mix of more than 200 social scientists, technologists, funders, policy makers, businesspeople and writers.
We are excited to announce that the finalists for the NYU Coleridge Initiative’s Rich Context Competition have been selected. The competition challenged computer scientists to find ways of automating the discovery of research datasets, fields and methods behind social science research publications. 20 teams from 8 countries submitted letters of intent and four finalists have been chosen. We will be live webcasting the finalists’ presentations as well as the announcement of the winner on February 15.
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”.
Last week, a mix of PhD students, early career and tenured researchers met in Cologne to discuss their latest projects around bias and discrimination on social media, and the algorithms underpinning many of the most pervasive services we use today.
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.
I recently got jazzed about two findings coming out of the world of computational social science, primarily because they hit so close to my home (hello, junior faculty feeling the pressure to produce)
This month the Product Innovation team at SAGE have launched the SAGE Labs site that will showcase the experiments and projects that the team are currently working on.
In June, I attended the second iteration of the Summer Institute for Computational Social Science (SICSS), an intensive two-week program held at Duke that was intended to bring together researchers from across the social science and data science disciplines to learn and discuss topics in computational social science (CSS). Each day, the organizers Chris Bail and Matt Salganik taught mini-lectures on different CSS topics, we split into groups to work on activities together, and a speaker came in to present their research.
This project is a competition for researchers to build tools to help automate the discovery of data sets in the social sciences. The competition comes with prizes of $2,000 to each of the finalists, with up to $20,000 to be awarded to the winning team. Find out more and apply today.
Ahead of this year’s APSA general meeting, we attended the Politics and Computational Social Science (PaCSS) pre-conference, hosted at Northeastern University. The event brought together political scientists working with large-scale data sets and emerging computational methods.
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