This paper presents the first results from model-based TanDEM-X tomography of a structurally complex temperate forest in Tasmania. Two tomographic profiles are shown and heights estimated using three-level model inversion are compared with similar metrics from airborne lidar scanning.

OGSA-DAI is an open-source middleware designed to facilitate the access and integration of distributed heterogeneous data resources. It provides a ready-made framework that promotes locality and product transparency to connect data resources to the grid environment. This paper reports on the evaluation of OGSA-DAI for bioinformatics grid applications by the BioDA project. We based our study on dialogue with OGSA-DAI and grid project developers as well as through practical case studies involving current projects. We also reveal the challenges and strategies in developing an OGSA-DAI exemplar for one of these projects, BiodiversityWorld - a grid-based problem solving environment that specializes in the exploration and analysis of patterns in global biodiversity. Finally, we summarize the key issues for projects considering adopting OGSA-DAI.

In the Biodiversity World (BDW) project we have created a flexible and extensible Web services-based grid environment for biodiversity researchers to solve problems in biodiversity and analyse biodiversity patterns. In this environment, heterogeneous and globally distributed biodiversity-related resources such as data sets and analytical tools are made available to be accessed and assembled by users into workflows to perform complex scientific experiments. One such experiment is bioclimatic modelling of the geographical distribution of individual species using climate variables in order to predict past and future climate-related changes in species distribution. Data sources and analytical tools required for such analysis of species distribution are widely dispersed, available on heterogeneous platforms, present data in different formats and lack interoperability. The BDW system brings all these disparate units together so that the user can combine tools with little thought as to their availability, data formats and interoperability. We describe the architecture of the BDW problem solving environment (PSE) consisting of a number of components providing uniform access to heterogeneous resources and analytical tools. Architectural components of the BDW system include a workflow management tool, resource wrappers, a communications layer, BDW datatypes and a metadata repository.

We demonstrate a prototype toolkit that uses constraints and constraint violation repair techniques to enable the automated detection and, where possible, the automated resolution of conflicts in taxonomic databases.

The LITCHI project is concerned with the integration and maintenance of databases of biological knowledge organised by species. We use constraints pertaining to good taxonomic practice in order to identify taxonomix conflicts in individual species databases and in databases formed by merging species databases from distinct sources. The LITCHI system can be used to resolve such conflicts incrementally. As the project has progressed, we have identified a number of distinctive features of the problem domain, and needs of the intended users, which have had a significant impact on the techniques and modes of operation that we found to be appropriate, especially in contrast with applications that handle rapidly-accumulating 'raw' data. It is upon these aspects of LITCHI that we concentrate in the present paper viewing LITCHI as an example of the more general problem of merging scientific data sets in which conflicts between the terminology used can occur.

Over recent years, international initiatives such as the 1993 UN Convention on Biological Diversity have highlighted the need for information about species diversity on a global scale. However, attempts to build global information systems by integrating smaller, independently created biodiversity databases have been hampered by differences in the sets of species names used. Some databases use different names to refer to the same species, while in other cases the same name can be applied to differing definitions of a species, or even entirely different species. The LITCHI project aims to assist biologists in the integration of databases by searching for conflicts within taxonomic checklists (i.e. lists of the species names used in a database and the relationships between them). In order to detect such conflicts, we have created a formal model of taxonomic practice, which describes (amongst other things) what it means for a checklist to be consistent and well-specified. This model has been used as the basis for a prototype tool that uses Prolog to search for naming conflicts within a relational database of checklists. We describe the background to our formal model and show how it has been used to implement the LITCHI system. Our prototype tool is already proving its worth by detecting conflicts and errors within real taxonomic checklists.

Summary form only given. The LITCHI project (Logic-based Integration of Taxonomic Conflicts in Heterogeneous Information Systems) aims to develop software to enable the automated detection and, where possible, resolution of conflicts in taxonomic checklists. A taxonomic checklist is a list of the names of species (and other taxa) used within a particular biological database. Since species names are typically used to gain access to data within biological databases, checklists provide a concise representation of the data values that can act as keys when querying such databases. More importantly, species names are also typically used as the join attribute when integrating several biological databases. However, naming of species is a subjective activity, and different scientific communities will have different ideas about the names that should be used for particular species. These conflicts of opinion arise as a result of the subjective nature of the classification process and geographical or historical differences in background knowledge. Some communities may use different names for the same species, while other groups of scientists may use the same name to refer to different species. Often, there is no one right naming scheme, but some consistent set of names must be used if biological databases are to be integrated. Therefore, there is a real need for a tool which will assist biologists in the integration of checklists, prior to the integration of species databases, so that these differences of opinion can be resolved.