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How Data Visualization Supports the Formation of Better Hypotheses

Since Exaptive launched in 2011, we’ve worked with many researchers, particularly in medicine and the natural sciences. PubMed®, a medical journal database, pops up repeatedly as a key tool for these researchers to develop hypotheses. It’s a tool built in a search-and-find paradigm with which we’re all familiar. Execute a keyword search. Get a list of results. Visualization can make search - and, therefore, research - much more meaningful.

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Machine Learning Helps Humans Perform Text Analysis

The rise of Big Data created the need for data applications to be able to consume data residing in disparate databases, of wildly differing schema. The traditional approach to performing analytics on this sort of data has been to warehouse it; to move all the data into one place under a common schema so it can be analyzed.

This approach is no longer feasible with the volume of data being produced, the variety of data requiring specific optimized schemas, and the velocity of the creation of new data. A much more promising approach has been based on semantic link data, which models data as a graph (a network of nodes and edges) instead of as a series of relational tables.

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How To Use PubMed®: The New Way

The Researchers, Principal Investigators, and Science Writers we’ve talked to seem to have a love-hate relationship with PubMed. They love that a simple search can get them quick access to the latest articles but they hate the limiting interface and how much reading is required to find good articles. They told us they aren’t always sure they got all the right articles and that they want a more efficient and customized system that meets more of their needs and gets better over time as it learns from their behavior.

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