Reflections from the Monday contributed session “Using big data to understand marine social-ecological systems: challenges, opportunities, and frontiers”

The word cloud below was generated by the audience as a response to, “what comes to mind when you hear the term big data?” The session organizers defined ‘Big Data’ as ‘any data that is as big of a problem as is asking the scientific question itself’. Because I have a deep appreciation for data of all size, I think it is important to note that this definition is one of many, and may be limiting in some situations.

Quick presentations by Beatrice Crona (Stockholm Resilience Centre, Sweden), Michael Cox (Dartmouth College, US), Maricela de la Torre Castro, (Stockholm University, Sweden) and Larry Crowder (Stanford University, US) ranged from data collection project overviews such as the Social-Ecological Systems Meta-Analysis Database to the role of data aggregation in the dynamic management techniques of oceans and fisheries.

One of the common themes in the Big Data session was the utilization of the data itself. Typically, researchers go into a system with a question, or a problem, collect data, and interpret their findings (i.e., answer the question). For answering problems related to processes that span the planet, or very large areas and across many years (think climate change), obtaining data is difficult. Often times we will recycle data – that is, we will use data that someone else has collected and made available – to attempt to answer our research question(s).

Presenters in Monday’s session promoted the use of multiple datasets to answer questions. Larry Crowder suggested that the scaling issues that come along with combining various datasets “is the most challenging part.” As we move forward into a world of ever-increasing technological capabilities, the novel approaches to the use of data sets will be necessary to understand the patterns occurring across large swaths of space and over many generations.

Your Resilience2017 correspondent:

Jessica Burnett is a Ph.D. student at the University of Nebrsaka-Lincoln in Nebraska, U.S. Her research explores the use of statistical and modelling techniques for identifying rapid changes in wildlife communities across space and time. She is also interested in using existing data to identify areas and systems vulnerable to the effects of climate change and globalisation.

 

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