There exist today a multitude of biological databases containing a wide array of information regarding different species including specimens in museum databases, occurrence information, genome sequence and expression data and image data to name a few. A common feature of these databases is that the information normally corresponds to a particular species (or taxa) and therefore the databases tend to employ some taxonomy to structure the information and access the data. However as yet there is no common taxonomy which is used across these databases to enable reliable linking across the databases. Matching species across databases is challenging. Different databases can and do use different classifications which use different names to represent the same underlying species or taxa. Tools that aid integration of data from these sources will be of benefit to biologists allowing them to incorporate additional data into their analyses and ensure the quality of the data and the accuracy of results are improved. The utility of visualising data is well established for tasks such as presentation of information. Visualisations are effective for a range of other tasks such as acting as ad-hoc error-checks for data e.g. spotting a record of a lion placed in the middle of the Pacific Ocean in a geographical information visualisation plot clearly suggests an error in the data. However, the true advantage of visualisation isn't in static presentation but in allowing users to interactively explore and view the effects of changes to constraints and variables, although suitable tools are frequently not available to biologists where they could be most useful. This project will build on the biological standards developed for taxonomic information and develop a set of web-based visualisation tools for use by a wide range of biologists and end-users of these databases to support them clean, explore and compare the data contained within. The resulting tools will have a wide ranging impact on the quality of data made available and the accessibility of the data to a wide range of users.

Biological taxonomy involves the classification and naming of plants, animals and all the other living organisms on earth, however taxonomists often disagree over how the organisms should be classified, so we end up with many different taxonomies. Generally, individual taxonomists can only manage to classify a small group of organisms, and so many taxonomists' work is joined together to form larger taxonomies. When we have many alternative taxonomies, combining them into one large hierarchy to picture the relationships between organisms is difficult and problems also arise when the taxonomies get too large to comprehend. To compound these problems, different classifications often result in different names for what some people may consider the same organism. This results in one name in one classification meaning something else in another classification, so taxonomists also have to handle and process this information if they are to understand the overall relationship between taxonomies.This project aims to help taxonomy users through 'Information Visualization', a technique which allows data to be displayed and manipulated graphically, rather than as pages of raw text. This approach is effective because people perceive information and relationships more easily when they are displayed diagrammatically, in terms of shapes, sizes, colours and positions. In this project we will create and develop techniques that graphically show taxonomic hierarchies and their associated relationships, to allow taxonomists to intuitively interact with and query the data.This should allow taxonomists to focus on the questions they need to ask of the data and the answers that are returned, rather than the process of how to ask the questions or how to cope with untangling the relationships of the information that they are faced with.Not only will the project result in effective tools for the taxonomists but we will advance data visualisation techniques applicable to any area where it is important to compare large alternative hierarchies of information.