About the Gas Trends databrowser

This page gives advice on using the Gas Trends databrowser and interpreting results. You may show or hide any section by clicking on the section header.

Background

This databrowser extends some of the ideas from the Energy Export databrowser and was created for the 2010 Peak Oil conference. Data from the BP Statistical Review are combined with gas data from the US Energy Information Administration and population data from the US Census Bureau. Additional plot options are available that give users more specific control of the output graphics to better tell the stories in the data.

This databrowser also showcases the possibility of connecting exploratory graphics with explanatory text by linking specific user-generated graphics to related posts in an associated Energy Trends blog.

More about databrowsers

A databrowser is a web based interface that allows non-technical users to interact with scientific data.

Making sense of complex datasets depends upon two very different kinds of machines. Silicon based number crunchers (computers) perform complex mathematical calculations at lightning speed with essentially zero errors while carbon based pattern recognizers (our brains) detect visual patterns much faster than any computer and use these patterns to develop further questions about the data.

People enjoy looking at informative scientific graphics if the barrier to creating them is low. When this happens, our species' extraordinary capabilities as pattern recognizers enable us to convert what we see in excellent scientific graphics into a deeper understanding. The problem in many fields of science is that the barriers to creating excellent graphics are discouragingly high.

A bottleneck exists where information is transferred between number crunchers and pattern recognizers. It can take a large amount of time to organize, format and analyze data before generating the graphics that tell the story of the data. Often, the role of data management and analysis is handed over to computer experts rather than the scientist end users with a real interest in the data. With no easy way to create the graphics that they need, the ability of scientits, managers and interested members of the public to develop their intuition about a dataset is greatly impaired.

Scientific databrowsers attempt to solve this problem by hiding the details of data management and analysis while providing simple, intuitive interfaces to the kinds of analysis that are appropriate for a particular dataset. These analyses are typically vetted statistical routines that are written in code in such a way as to be driven by input from a web browser user interface. In this manner, end users including both experts and non-experts can harness the power of (server side) number crunchers as well as their own (client side) pattern recognizers without having to learn the arcana of data management and scientific analysis software.

Building a databrowser.

The process of building a databrowser involves several steps:

  1. cleaning up any problems with the source data so that they are consistent and well organized
  2. writing code that allows vetted statistical analyses to be run interactively
  3. writing code to create high quality scientific graphics based on the results of the analysis
  4. embedding the analysis and visualization code in a web-server based databrowser engine
  5. creating a user interface that allows users to quickly and easily send requests to the analysis and visualization engine running on the server

When properly designed, the code behind a good databrowser can encapsulate a huge amount of institutional memory about the scientific process. Ideally, databrowser graphics should be of high enough quality that they are immediately ready to be included in scientific publications.