In response to Rutger Vos's question I've started to add GBIF taxon ids to the iPhylo Linkout website. If you've not come across iPhylo Linkout, it's a Semantic Mediawiki-based site were I maintain links between the NCBI taxonomy and other resources, such as Wikipedia and the BBC Nature Wildlife finder. For more background seePage, R. D. M. (2011). Linking NCBI to Wikipedia: a wiki-based approach. PLoS Currents, 3, RRN1228.

This post arose from an ongoing email conversation with Tony Rees about extracting and annotating taxonomic names. In BioStor I use the GBIF classification to display the taxonomic names found in the OCR text in the form of a tree.

When I think of the Biodiversity Heritage Library (BHL) or GBIF I tend to think of taxonomy and biodiversity. Folk wisdom has it that BHL is full of old books, mostly pre-1923. Great for finding old taxonomic names, or nice artwork, but not exactly "modern" biology. GBIF is mainly about displaying organism distributions based on museum specimens, the primary data of taxonomic research.

One reason I'm pursuing the theme of specimen identifiers (and identifiers in general) is the central role they play in annotating databases. To give a concrete example, I (among others) have argued for a wiki-style annotation layer on top of GenBank to capture things such as sequencing errors, updated species names, etc. Annotation is a lot easier if we have consistent identifiers for the things being annotated.

Brief update on yesterday's post about finding specimens in BioStor. BioStor has some 66,000 articles from BHL, from which I've extracted 143,000 cases of a specimen code being cited in the text.

Following on from exploring links between GBIF and GenBank here I'm going to look at links between GBIF and the primary literature, in this case articles scanned by the Biodiversity Heritage Library (BHL). The OCR text in BHL can be mined for a variety of entities. BHL itself has used uBio's tools to identity taxonomic names in the OCR text, and in my BioStor project I've extracted article-level metadata and geographic co-ordinates.

Duplicate records are the bane of any project that aggregates data from multiple sources.

As part of my mantra that it's not about the data, it's all about the links between the data, I've started exploring matching GenBank sequences to GBIF occurrences using the specimen_voucher codes recorded in GenBank sequences. It's quickly becoming apparent that this is not going to be easy.

Given various discussions about identifiers, dark taxa, and DNA barcoding that have been swirling around the last few weeks, there's one notion that is starting to bug me more and more.

I'm in the midst of rebuilding iSpecies (my mash-up of Wikipedia, NCBI, GBIF, Yahoo, and Google search results) with the aim of outputting the results in RDF. The goal is to convert iSpecies from a pretty crude "on-the-fly" mash-up to a triple store where results are cached and can be queried in interesting ways. Why?