What is the field of statistical analysis? So begins Gary King’s first online course in the Harvard Government Dept graduate methods sequence. King, the Albert J. Weatherhead III University Professor at Harvard University -- one of 25 with Harvard's most distinguished faculty title -- and Director of the Institute for Quantitative Social Science has just recorded all his lectures and made them free to access online. The videos range in length from 30 minutes to an hour and half and you can watch them all on YouTube here.

My journey into text mining started when the institute of Digital Humanities (DH) at the University of Leipzig invited students from other disciplines to take part in their introductory course. I was enrolled in a sociology degree at the time, and this component of data science was not part of the classic curriculum; however, I could explore other departments through course electives and the DH course sounded like the perfect fit.

The latest edition of the SAGE Ocean newsletter shares tips and resources for moving your research online and making the shift to online teaching.

As a D-Lab and Data Science Education Program Fellow at the University of California, Berkeley in Spring 2020, I helped to ensure and enhance the quality of more than 40 Data Science Discovery Projects, working with community partners and undergraduate research assistants. The goal of these projects was to connect undergraduates with community impact groups, entrepreneurship ventures, and educational initiatives across UC Berkeley and provide them with hands-on and team-based research opportunities outside the classroom.

For the past few years in June, the Summer Institutes in Computational Social Science (SICSS) have seen students gather across the world at partner locations and in the designated primary location to begin a two-week program of collaboration, workshops, lectures, and participant-led research projects in computational social science (CSS). The strange times of COVID have somewhat altered these plans with some partner locations postponing until 2021 and some opting to move online. Whether virtual or postponed the fourth iteration of SICSS set a new record for partner locations—a total of 22 locations signed up to take part. Founders Matt Salganik and Chris Bail, allow participants to only attend once but as attendance has grown so have graduates returning to their institutions and setting up new partner locations.

This blog by Sam Gilbert explains how internet search data is being used in responses to the Covid-19 pandemic, and what search datasets and tools are available to researchers.

There’s a lot of uncertainty about how higher education will be taught in the age of COVID-19. How should professors and instructors of qualitative methods courses re-think their curriculums for online classrooms or cohorts? How can students conduct observations if they’re sheltered at home? How will students work in teams to analyze data if they’re distributed across the world? Here are some tips for alternative data collection methods, and collaborative tools for remote analysis.

COVID-19 has affected research all over the world. With universities closing their campuses and governments issuing restrictions on social gatherings, behavioral research in the lab has ground to a halt. This situation is urgent. Ongoing studies have been disrupted and upcoming studies cannot begin until they are adapted to the new reality. At Volunteer Science, we’re helping researchers around the world navigate these changes. In this post, I’ll condense the most important recommendations we’re giving to researchers for translating their studies into an online format and recruiting virtual participants.

We will emerge from this pandemic with a better understanding of the world and an improved ability to teach others about it. For now, we need to be continuously analyzing the data and thinking about the lessons we can learn and apply. Here’s how you can join in!

At SAGE, we have been working with academics around improving and sharing teaching resources, especially for quantitative and computational methods in social sciences. Besides the mass remote and emergency teaching experiment happening right now, one of the positive things we can already identify and reuse to improve learning in methods courses is the glut of data visualizations. The absolute advantage here is that all these visualizations are produced (almost always) with the same raw input, telling a variety of different stories. What better way to explain the different uses and impact of visualizations and the use of different tools to students than examples based on the same data?

Around the world, higher education faculty and students have been grappling with the mammoth task of flipping from face-to-face teaching to online learning, practically overnight. As teaching faculty scramble to figure out how to use Zoom for online learning and the debate continues as to whether universities should cancel exams or switch to home-based open book or open Google exams, it’s becoming clear that the impact of COVID-19 on academic research could be just as profound as the impact on teaching. In-person lab experiments, face-to-face interviews, focus groups, fieldwork and other data collection may be impossible for much of 2020. Where possible, researchers will switch modes from face-to-face to virtual or telephone data collection, and where that’s not possible or desirable for practical or methodological reasons, university research offices and funders are issuing guidance for academics who need to delay their data collection or fieldwork.

As we all adjust to the new normal things can’t and won’t simply revert to a pre-COVID-19 world. Here in the UK we are only a few weeks into our new socially distant lives, blue Monday 2020 (January 18th) doesn’t somehow seem so depressing now. As Matt Reynolds of Wired has noted, ‘this is only the grim first act of the coronavirus crisis’. With this in mind, it is extremely important that we hear from experts right across the academic spectrum.

Nesta launched the Centre for Collective Intelligence Design back in 2018 at an event jointly hosted by SAGE Publishing. The event featured talks, workshops and discussions exploring the development of collective intelligence as its own field, bridging the worlds of academia and industry together to create a new look domain. October 2019 saw the return of this one day event, jam packed with interactive sessions and an array of attendees from tech to the arts, data science to critical thinking and beyond.

The rise of music streaming platforms, such as Spotify and Apple Music, has contributed to an explosion of new forms of digital data about music consumption practices. As the digital platforms through which consumers access and engage with recorded music and creators distribute it, they are uniquely positioned to create immense volumes of data about what and how people consume music, individually and at scale. From data about what music people search for and skip, to demographic information about who is consuming what, music streaming platforms generate data about almost every micro-interaction with music, amassing enormous databases ripe for further value-extraction.

Now is the time for social scientists to take responsibility for guiding societal improvement.

Twenty-first-century societies are rapidly changing. We’re witnessing historic levels of partisan discord and institutional breakdown, and multiple simultaneous sea changes in norms around gender and ethnic identity, sexual expression, and the definitions of criminality. These political and cultural shifts, often amplified and accelerated through Internet platforms, are occurring alongside major economic upheavals, including the deaths and births of entire industries, renewed international trade wars, and inequality levels rivaling those of feudal times. Worse, there is no end in sight for these tumultuous trends. What are people to do? How are we to make sense of all this turmoil and find some working consensus about social reality (if not a social contract) allowing more of us to find a stable and comfortable way in the world?

Text mining techniques have become critical for social scientists working with large scale social data, be it Twitter collections to track polarization, party documents to understand opinions and ideology, or news corpora to study the spread of misinformation. In the infographic shown in this blog, we identify more than 80 different apps, software packages, and libraries for R, Python and MATLAB that are used by social science researchers at different stages in their text analysis project. We focused almost entirely on statistical, quantitative and computational analysis of text, although some of these tools could be used to explore texts for qualitative purposes.

I’ve just got back from a fantastic workshop looking at infrastructure for research data discovery. I’ll blog about the workshop in due course, but I was asked to comment on Google Dataset Search (GDS). I had the chance to meet with Natasha Noy from Google who is behind the service. 

As with many Google services, it has been created by a small team, but with the underlying web-scale infrastructure of Google to build on top of. They look for data sets on the web that have been identified using Home - schema.org tags. Data repositories that expose these tags will get indexed by GDS (this includes both Figshare and DataDryad).