Towards a Global Names Architecture: The Future of Indexing Scientific Names

For more than 250 years, the taxonomic enterprise has remained almost unchanged. Certainly, the tools of the trade have improved: months-long journeys aboard sailing ships have been reduced to hours aboard jet airplanes; advanced technology allows humans to access environments that were once utterly inaccessible; GPS has replaced crude maps; digital hi-resolution imagery provides far more accurate renderings of organisms that even the best commissioned artists of a century ago; and primitive candle-lit microscopes have been replaced by an array of technologies ranging from scanning electron microscopy to DNA sequencing. But the basic paradigm remains the same. Perhaps the most revolutionary change of all - which we are still in the midst of, and which has not yet been fully realized - is the means by which taxonomists manage and communicate the information of their trade. The rapid evolution in recent decades of computer database management software, and of information dissemination via the Internet, have both dramatically improved the potential for streamlining the entire taxonomic process. Unfortunately, the potential still largely exceeds the reality. The vast majority of taxonomic information is either not yet digitized, or digitized in a form that does not allow direct and easy access. Moreover, the information that is easily accessed in digital form is not yet seamlessly interconnected. In an effort to bring reality closer to potential, a loose affiliation of major taxonomic resources, including GBIF, the Encyclopedia of Life, NBII, Catalog of Life, ITIS, IPNI, ICZN, Index Fungorum, and many others have been crafting a "Global Names Architecture" (GNA). The intention of the GNA is not to replace any of the existing taxonomic data initiatives, but rather to serve as a dynamic index to interconnect them in a way that streamlines the entire taxonomic enterprise: from gathering specimens in the field, to publication of new taxa and related data.