Data visualisation has been defined as: The set of techniques used to turn a set of data into visual insight. It aims to give the data a meaningful representation by exploiting the powerful discerning capabilities of the human eye. Part 1 of this briefing paper will highlight some examples of new collaborative web services using Web 2.0 technologies which venture into the numeric data visualisation arena. These mashups allow researchers to upload and analyse their own data in ‘open’ and dynamic environments. Broadly speaking the numeric data being referred to could be micro-data (data about the individual), macro-data or country-level data, derived or summary data.

Part 2 will investigate and showcase examples of spatial (or geographic) data mashups using Web 2.0 technologies and how they can be utilised in a research environment. This paper does not intend to conduct an investigation into the definitive merits of each utility but rather compare the functionality, ‘openness’ and usability of such utilities from the perspective of a researcher willing to share or analyse their data.

A word of warning - researchers will have to account for the inconstant nature of the web - resources such as those described above may not be around in two, five or ten years. Not only will there be further advances in web technologies but services merge, are bought out or indeed cease to exist. Services that start off open or free may become ‘closed’. Resources may start up with a particular rationale but may evolve into a completely different service or resource. Thus it would be unwise for users to deposit the only copy of a dataset within such a utility, i.e. use it as an archive or trusted repository. Certain data held within such utilities may often lack any authority or obvious provenance. Indeed data deposited may well be open to edits and uses unforeseen by the depositor. Many issues regarding the legal, cultural and technical aspects surrounding the sharing and publishing of data produced from personal or academic research abound. This paper does not purport to find solutions but highlights possibilities!

Utilities covered
There is a range of proprietary and academic domain-specific data visualisation tools, such as Nesstar (social science), CORDA Centerview (business intelligence), GGobi (high-dimensional data visualisation), Crystal Xcelsius (business data), Ferret (oceanography / meteorology), Giovanni (earth sciences), to name but a few. There are also Open Source utilities such as the Prefuse visualisation toolkit which is a set of tools for creating interactive data models, visualisations and animations.

The data visualisation tools described in this briefing paper can be regarded as Open Data utilities in that they retain the ethos of other ‘Open’ initiatives such as Open Source and Open Access by allowing anyone to upload or use the data. They are generally commercial services embracing a Web 2.0 business model in which users pay to access additional features such as keeping data private. The tools described here have emerged on the World Wide Web in the last two years, namely: Data360, Many Eyes, Swivel, Gapminder, StatCrunch, Graphwise. Note: Gapminder World has been included in this paper because the interactive data features are to be included in the forthcoming version of the utility.

Download pdf Numeric and Spatial Data Mashups