This collection of resources rounds up blog posts, research and resources related to how-to’s and advice for researchers from 2021. It includes our SAGE Perspectives Blog, webinars, Social Science Space and Methodspace, our online community for social and behavioral research methods.
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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.
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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.
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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) somehow doesn’t 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.
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With COVID-19 at the forefront of our global consciousness, research communities across the globe are putting their full force into collecting, analyzing and sharing data to help us better understand the pandemic. These free data mapping and visualization platforms provide insights into the numbers in a format that’s digestible both for the research community and the general public.
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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.
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We all know that today’s economic inequality is bad—we can feel it—but we don’t have a clear way to articulate why this is, unless we appeal to morality. The field of economics doesn’t offer a compelling narrative, which is a major shortcoming. Don’t get me wrong, there are many brilliant economists out there doing important work and publishing insightful individual studies. But the overarching paradigms of the field of economics can’t pull together the results of these individual works in a way that convincingly condemns massive and persistent economic inequality.
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Images contain information absent in text, and this extra information presents opportunities and challenges. It is an opportunity because one image can document variables with which text sources (newspaper articles, speeches or legislative documents) struggle or on datasets too large to feasibly code manually. It has been a challenge because of the technical difficulty of identifying the objects and concepts encoded in an image, requiring researchers to rely on manual coding. Because human coders are slow, expensive and have different interpretations of the same images, studies using images have historically been small. The falling cost of computing, coupled with the availability of large datasets, means these techniques will become mainstream shortly. Our research focuses on hastening that process, and the purpose of this blog post is to provide a very high-level overview of images as data
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Unfortunately the residual effects of some significant surgery this summer prevented me from attending the recent launch of the Research on Research Institute (RoRi). This initiative has the laudable aim of better informing funders and policymakers about “how research is funded, practiced and evaluated, and… how research cultures and systems can be made more efficient, open, inclusive and impactful.” It is receiving significant support from the Wellcome Trust and Digital Science “to build an international consortium of funders, academics and technologists committed to transformative & translational RoR.”
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