Big Data in the Geosciences: 3

We are inundated with environmental data – Earth observing satellites stream terabytes of data back to us daily; ground-based sensor networks track weather, water quality and air pollution, taking readings every few minutes; and community scientists log hundreds and thousands of observations every day, recording everything from bird sightings to road closures and accidents. But this very richness of data has created a new set of problems.
This third post in our four-part series gives a brief summary of how deep learning is being used in the geosciences today – loosely based on the Earth and Space Science Informatics sessions and town halls at the AGU fall meeting in Dec 2016.

Deep learning
Artificial Neural Networks (ANNs) are already being widely used in domains ranging from stock price predictions to image recognition; from genetic sequencing to targeted marketing. Deep learning neural networks – that is, networks that have multiple layers of neurons between the input and output neurons – are also beginning to be used in the Geosciences to address a range of problems.

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Big Data in the Geosciences: 2

We are inundated with environmental data – Earth observing satellites stream terabytes of data back to us daily; ground-based sensor networks track weather, water quality, and air pollution, taking readings every few minutes; and community scientists log hundreds and thousands of observations every day, recording everything from bird sightings to road closures and accidents. But this very richness of data has created a new set of problems.
This second post in our four-part series gives a high-level view of the challenges of portraying and communicating big data in the geosciences – and how these challenges are being addressed – loosely based on the Earth and Space Science Informatics sessions and town halls at the AGU fall meeting in Dec 2016.

Data Visualization
One of the challenges facing geoscientists is simply how to wrangle meaning from big data and effectively communicate their findings to other interested scientists, communities, students, planners or policy-makers. Big data is challenging as it can have a large number of variables with complex, non-linear relationships among them. Scientists are turning to data visualization – which leverages the incredible pattern-recognition power of the human eye – to design graphics that effectively convey complex information.

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Big Data in the Geosciences: 1

We are inundated with environmental data – Earth observing satellites stream terabytes of data back to us daily; ground-based sensor networks track weather, water quality, and air pollution, taking readings every few minutes; and community scientists log hundreds and thousands of observations every day, recording everything from bird sightings to road closures and accidents.  But this very richness of data has created a new set of problems.
This four-part post gives a high-level view of some of the challenges of big data in the geosciences – and how they might be solved – loosely based on the Earth and Space Science Informatics sessions and town halls at the AGU fall meeting in Dec 2016.

Data discovery
With so much environmental data, looking for a specific dataset for a research project can sometimes feel like looking for a needle in a haystack. How can data discovery, that is, finding the right dataset or sharing one’s own dataset with the larger research community, be made more efficient?

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