A major challenge facing biodiversity informatics is integrating data stored in widely distributed databases. Initial efforts have relied on taxonomic names as the shared identifier linking records in different databases. However, taxonomic names have limitations as identifiers, being neither stable nor globally unique, and the pace of molecular taxonomic and phylogenetic research means that a lot of information in public sequence databases is not linked to formal taxonomic names. This review explores the use of other identifiers, such as specimen codes and GenBank accession numbers, to link otherwise disconnected facts in different databases. The structure of these links can also be exploited using the PageRank algorithm to rank the results of searches on biodiversity databases. The key to rich integration is a commitment to deploy and reuse globally unique, shared identifiers (such as DOIs and LSIDs), and the implementation of services that link those identifiers.

Future biodiversity research will make increased use of distributed data networks, scientific workflows, and powerful mechanisms for resolving a broad spectrum of primary data. This paper outlines the anatomy of an ecological niche modeling workflow and concomitant needs for taxonomic resolution. Contemporary Linnaean names and synonymy relationships are shown to be too imprecise too support these needs. Taxonomic concepts (i.e., the meanings of names as specified in a particular source) and a new vocabulary for expressing their semantic interrelationships are introduced as a more reliable long-term solution. The concept approach has so far been implemented with success in select taxonomic databases and regional floristic treatments. Quantitative analyses have added further weight to the claim that taxonomic concepts are suitable to overcome the problem of name/meaning disjunction inherent in conventional nomenclature. Therefore, full documentation of the taxonomic process will depend on a wider adoption of concept taxonomy. The concept approach will improve communication about nature without compromising any of the useful properties of the Linnaean system.

Marine scientists face an opportunity and a challenge in the volume of data available from various ocean observing systems. The MMI Project can help make it easier for you to find, access, and use other data sets. If you create marine science data sets, MMI can help make your data easier to advertise, distribute, reuse, and combine with other data sets. Our goal is to promote collaborative research in the marine science domain, by simplifying the incredibly complex world of metadata into specific, straightforward guidance. MMI hopes to encourage scientists and data managers at all levels to apply good metadata practices from the start of a project, by providing the best advice and resources for data management. MMI maintains this website to foster communication and collaboration among its hundreds of members, and provides forums for discussion of diverse topics related to marine data management. MMI is also developing web applications and stand-alone tools to enable sophisticated interactions across marine data systems.

This paper proposes a novel metadata solution to allow applications to intelligently use science data in an automated fashion. The solution provides rich syntactic and semantic metadata, where the semantic metadata is linked with an ontology to define the semantic terms. This solution allows applications to exploit the syntactic metadata to read the data and the semantic metadata to infer the content and the meaning of the data. The solution presented in this paper leverages the Earth Science Markup Language for providing the syntactic metadata and adds a semantic metadata component along with links to the appropriate ontology. This new semantic component is orthogonal to the syntactic metadata, so it does not perturb the existing design. An example application was designed and built that integrates this syntactic and semantic metadata via an ontology to perform a data processing operation.

Life Science Identifiers (LSIDs) offer an attractive solution to the problem of globally unique identifiers for digital objects in biology. However, I suggest that in the context of taxonomic names, the most compelling benefit of adopting these identifiers comes from the metadata associated with each LSID. By using existing vocabularies wherever possible, and using a simple vocabulary for taxonomy-specific concepts we can quickly capture the essential information about a taxonomic name in the Resource Description Framework (RDF) format. This opens up the prospect of using technologies developed for the Semantic Web to add �taxonomic intelligence� to biodiversity databases. This essay explores some of these ideas in the context of providing a taxonomic framework for the phylogenetic database TreeBASE.