Collecting, analyzing, and interpreting big data may just be a job that requires more scientists:

For projects like NEON, interpreting the data is a complicated business. Early on, the team realized that its data, while mid-size compared with the largest physics and biology projects, would be big in complexity. “NEON’s contribution to big data is not in its volume,” said Steve Berukoff, the project’s assistant director for data products. “It’s in the heterogeneity and spatial and temporal distribution of data.”

Unlike the roughly 20 critical measurements in climate science or the vast but relatively structured data in particle physics, NEON will have more than 500 quantities to keep track of, from temperature, soil and water measurements to insect, bird, mammal and microbial samples to remote sensing and aerial imaging. Much of the data is highly unstructured and difficult to parse — for example, taxonomic names and behavioral observations, which are sometimes subject to debate and revision.

And, as daunting as the looming data crush appears from a technical perspective, some of the greatest challenges are wholly nontechnical. Many researchers say the big science projects and analytical tools of the future can succeed only with the right mix of science, statistics, computer science, pure mathematics and deft leadership. In the big data age of distributed computing — in which enormously complex tasks are divided across a network of computers — the question remains: How should distributed science be conducted across a network of researchers?

Two quick thoughts:

1. There is a lot of potential here for crossing disciplinary boundaries to tackle big data projects. This isn’t just about parceling out individual pieces of the project; bringing multiple perspectives together could lead to an improved final outcome.

2. I wonder if sociologists aren’t particularly well-suited for this kind of big data work. Given our emphasis on theory and methods, we both emphasize the big picture as well as how to effectively collect, analyze, and interpret data. Sociology students could be able to step into such projects and provide needed insights.