More fictional taxa and the myth of the expert taxonomic database

I know I'm starting to sound like a broken record, but the more I look, the more taxonomic databases seem to be full of garbage. Databases such as the Catalogue of life, which states that it is a "quality-assured checklist" have records that are patently wrong. Here's yet another example.

If you search for the genus Raymondia in the Catalogue of Life you get multiple occurrences of the same species names, e.g.:

• Raymondionymus fossor Ganglebauer, L., 1906
• Raymondionymus fossor Hustache, A., 1930

Both of these are listed as "provisionally accepted names", supplied by WTaxa: Electronic Catalogue of Weevil names (Curculionoidea). Clearly we can't have two species with the same name, so what's happening?

Firstly, Hustache, A., 1930 is:

Hustache A (1930) Curculionidae Gallo-Rhénans. Annales de la Société entomologique de France 99: 81-272. http://gallica.bnf.fr/ark:/12148/bpt6k6112240j/f3

On p. 246 Hustache refers to Raymondionymus fossor Aubé, 1864 (see below).



So, Raymondionymus fossor Hustache, A., 1930 is not a new species but simply the citation of a previously published one (it's a chresonym). Hustache cites the author of the name as Aubé, 1864, and you can see the original description by Aubé in BioStor (Description de six espèces nouvelles de Coléoptères d'Europe dont deux appartenant a deux genres nouveaux et aveugles, http://biostor.org/reference/104589). So, if the taxonomic authority should be Aubé, 1864, what about Raymondionymus fossor Ganglebauer, L., 1906? Again, if we track down the original publication (Revision der Blindrüsslergattungen Alaocyba und Raymondionymus, http://biostor.org/reference/104591) it's simply Ganglebauer citing (on p. 142) Aubé's paper, not describing a new species.

Note that the nomenclature of this weevil species is further complicated because Aubé originally described the species as Raymondia fossor, but Raymondia was already in use for a fly (see Über eine neue Fliegengattung: Raymondia, aus der Familie der Coriaceen, nebst Beschreibung zweier Arten derselben, http://biostor.org/reference/104588). To resolve this homonymy Wollaston proposed the name Raymondionymus:

Wollaston, T. V. (1873). XVIII. On the Genera of the Cossonidae. Transactions of the Royal Entomological Society of London, 21(4), 427–652. doi:10.1111/j.1365-2311.1873.tb00645.xhttp://biostor.org/reference/51301

So, we have a bit of a mess. Unfortunately this mess percolates up through other databases, for example EOL has three different pages for Raymondionymus fossor.

For me the lesson here is that relying on acquiring data from "trusted" sources, curated by "experts" is simply not a tenable strategy for building lists of taxa. If names are essential bits of biodiversity infrastructure upon which we hang other data, then these lists need to be cleaned, which means exposing them to scrutiny, and providing an easy means for errors to be flagged and corrected. Trust is something that is earned, not asserted, and it's time taxonomic databases stop claiming to be authoritative simply because they rely on expert sources. Expertise is no guarantee that you won't make errors.

For me this is one of the key reasons projects like BHL are so important. As more and more of the original literature becomes available, we lessen our reliance on "expertise". We can start to see for ourselves. In other words, "Nullius in verba" ("take nobody's word for it").

70,000 articles extracted from the Biodiversity Heritage Library

Just noticed that BioStor now has just over 70,000 articles extracted from the Biodiversity Heritage Library. This number is a little "soft" as there are some duplicates in the database that I need to clean out, but it's a nice sounding number. Each article has full text available, and in most cases reasonably complete metadata.

Most of the articles in BioStor have been added using semi-automated methods, but there's been rather more manual entry than I'd like to admit. One task that does have to be done manually is attaching plates to papers. This is largely an issue for older publications, where printing text and figures required different processes, resulting in text and figures often being widely separated in the publication. Technology evolved, and the more recent literature doesn't have this problem.

Future plans include adding the ability to download the articles as searchable PDFs, and to support OCR correction, amongst other things. BioStor also underpins some of my other projects, such as the EOL Challenge entry, which as of now has around 80,000 animal names linked to their original description in BioStor (and some 300,000 in total linked to some form of digital identifier). One day I may also manage to get the article locations into BHL itself, so that when you browse a scanned item in BHL you can quickly find individual articles. Oh, and it would be cool to have all this on the iPad...

BHL and text-mining: some ideas

Some quick notes on possibilities for text-mining BHL (in rough order of priority). Any text-mining would have to be robust to OCR errors. I've created a group of OCR-related papers on Mendeley:

OCR - Optical Character Recognition is a group in Computer and Information Science on Mendeley.

Improve finding taxonomic names in text in face of OCR errors

There is some published research on OCR errors that could be used to develop a tool to improve our ability to index OCR text. The outcome would be improved search in BHL (and other archives). I've touched on some of these issues earlier). One approach that looks interesting is using anagram hashing (see Reynaert, 2008), which may be a cheap way to support approximate string matching in OCR text.

Reynaert, M. (2008). Non-interactive OCR Post-correction for Giga-Scale Digitization Projects. Lecture Notes in Computer Science, 4919:617-630. doi:10.1007/978-3-540-78135-6_53 (PDF here).


Recognition and extraction of literature cited

Given an article extract all the references it cites. There's a fair amount of literature on automated citation extraction, but again we need to do this in the face of OCR errors, and enormous variability in citation styles. The outputs could help build citation indexes, and also serve as data for the "bibliography of life". The citations could also be used to help locate further articles in BHL (e.g., using BioStor's OpenURL resolver).


Improved extraction of named entities (e.g., museum specimen codes) and localities (e.g., latitude and longitudes, place names)

This would enable better geographic searches, and help start to link literature to museum specimen databases.

Automated recognition of articles within scanned volumes

My own approach to finding articles has focussed on finding articles based on citation metadata, e.g. based on article title, journal, volume, and pagination, find corresponding article in BHL:

Page, R. D. (2011). Extracting scientific articles from a large digital archive: BioStor and the Biodiversity Heritage Library. BMC Bioinformatics, 12(1), 187. doi:10.1186/1471-2105-12-187

An alternative is to infer articles from just the scanned pages. There has been some limited work on this in the context of BHL:

Lu, X., Kahle, B., Wang, J. Z., & Giles, C. L. (2008). A metadata generation system for scanned scientific volumes. Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries - JCDL ’08 (p. 167). Association for Computing Machinery (ACM). doi:10.1145/1378889.1378918 (PDF here)

The NLM has some cool stuff on automatically labelling the parts of a document, see Automated Labeling in Document Images and Ground truth data for document image analysis. See also Distance Measures for Layout-Based Document Image Retrieval.

Other links
Should also note that there's a relevant question on StackOverflow about OCR correction, which has links to tools like OCRspell:

Taghva, K., & Stofsky, E. (2001). OCRSpell: an interactive spelling correction system for OCR errors in text. International Journal on Document Analysis and Recognition, 3(3), 125–137. doi:10.1007/PL00013558

Code is on github.