The authors of a new white paper on trends in big data research in the social sciences posited that because ‘big data’ is new, interdisciplinary and, well, big, that it would present “unique problems” to researchers. In fact, a lot of the biggest problems faced by researches will ring true in any academic endeavor –elusive funding, elusive data and that elusive perfect collaborator.
Among big data researchers surveyed, 42 percent identified funding as a “big problem,” followed by 32 percent who cited gaining access to commercial or proprietary data and 30 percent “finding collaborators with the right skills and knowledge.” (Multiple answers were allowed.) Of course, the nature of those challenges may have a different complexion for social data researchers, and the respondents also identified challenges that definitely had a big data cast. For example, 30 percent cited learning new software as a major challenge, and 27 percent “learning new analytic methods for myself.”
All told, 2,273 self-identified big data researchers answered that question for the authors of Who Is Doing Computational Social Science? Trends in Big Data Research, a white paper produced by SAGE Publishing. The survey team initially reached out to more than a half million social science contacts around the world, and 9,412 of them fully completed the survey. A third of those self-reported that they had conducted research using big data recently.
Here at MethodSpace, we’re unpacking those findings in three posts. The first, available here, looked at who is doing computational social science, while the second, available here, examined the tools being used for computational research. This post examines the challenges the survey respondents identified. The white paper itself was authored by Katie Metzler, publisher for SAGE Research Methods; David A. Kim, in the Department of Emergency Medicine at Stanford University; Nick Allum, a professor of sociology and research methodology at the University of Essex; and Angella Denman of the University of Essex.
Looking at that funding question a little more deeply, a plurality of big data researchers – 30 percent –said that university or institutional funding was their main source, followed by government or NGO sources (25 percent) and a science-funding body (15 percent). Some 12 percent said they had self-funded their work and 7 percent said they had tapped a private company.
When asked to name specific problems they had encountered in doing big data research, funding and data access remained the biggest issues. However, some new wrinkles developed, including developing effective research designs, establishing a successful career in an interdisciplinary field, and choosing a suitable journal for publishing findings. “Interestingly,” the authors write, “those who reported that most or all of their research was big data were more likely to say that ‘choosing a suitable journal’ was a problem for them compared to those whose research is less focused on big data.” Some 48 percent of respondents who already did big data research said their work had been published in a journal, including medical, social science, science, and methods journals – but rarely journals dedicated to publishing computational social science research, in large part because those are currently few and far between.
The survey asked researchers who weren’t doing big data work – but who were interested — what was staying their hand. Finding collaborators with the right skills and the amount of time required to learn anew field were cited as the biggest impediments, listed as “big problems” by 29 and 21 percent of the 4,894 answering that question. Big data research is a new-ish field for social scientists, and despite the term’s currency, that novelty still creates roadblocks: Some 12 percent said “big data not recognized or used in my field” was a big problem, and an additional 40 percent said it was somewhat of a problem.
That state of affairs is diminishing over time, the white paper’s authors conclude:
“… [A]t SAGE Publishing we believe that social research is at a turning point. However, the successful collection and rigorous analysis of this data require new skills, new collaborations, new research methods, and new computational tools. The findings of the survey suggest that many social scientists are already rising to some of the challenges posed by big data, and that a large number of social scientists are looking to engage in this kind of research in the future.”