A great deal of our work starts off with rigorous demand side research; knowing what, how and why users engage with digital technologies provides us with a solid evidence-base to make our policy recommendations. For example, the nationally representative data that we had collected through our AfterAccess surveys in six Asian countries, provided us with a solid evidence base to argue for various policy changes as soon as the pandemic hit. https://lirneasia.net/AfterAccess-COVID19. But going forward with current projects getting off the ground, and in the midst of designing new ones, we’ve had to think about what the pandemic, lockdowns, social distancing, etc.
How can peer review be effective when the underlying data cannot be shared? When scientists publish their research, they also make the underlying data available so the results can be verified by other scientists. At least that is how the system is supposed to work. But lately social scientists have come up against an exception that is, true to its name, huge. It is “big data,” the vast sets of information gathered by researchers at companies like Facebook, Google and Microsoft from patterns of cellphone calls, text messages and Internet clicks by millions of users around the world.