Thoughts on BHL, ALA, GBIF, and Plazi

If you compare the impact that BHL and Plazi have on GBIF, then it's clear that BHL is almost invisible. Plazi has successfully in carved out a niche where they generate tens of thousands of datasets from text mining the taxonomic literature, whereas BHL is a participant in name only. It's not as if BHL lacks geographic data. I recently added back a map display in BioStor where each dot is a pair of latitude and longitude coordinates mentioned in an article derived from BHL's scans.

This data has the potential to fill in gaps in our knowledge of species distributions. For example, the Atlas of Living Australia (ALA) shows the following map for the cladoceran (water flea) Simocephalus:

Compare this to the localities mentioned in just one paper on this genus:

Timms, B. V. (1989). Simocephalus Schoedler (Cladocera: Daphniidae) in tropical Australia. The Beagle, 6, 89–96. Retrieved from https://biostor.org/reference/241776

There are records in this paper for species that currently have no records at all in ALA (e.g., Simocephalus serrulatus):

As it stands BioStor simply extracts localities, it doesn't extract the full "material citation" from the text (that is, the specimen code, date collected, locality, etc. for each occurrence). If it did, it would then be in a position to contribute a large amount of data to ALA and GBIF (and elsewhere). Not only that, if it followed the Plazi model this contribution would be measurable (for example, in terms of numbers of records added, and numbers of data citations). Plazi makes some of its parsing tools available as web services (e.g., http://tb.plazi.org/GgWS/wss/test and https://github.com/gsautter/goldengate-webservices), so in principle we could parse BHL content and extract data in a form usable by ALA and GBIF.

Notes on Plazi web service

The endpoint is http://tb.plazi.org/GgWS/wss/invokeFunction and it accepts POST requests, e.g. data=Namibia%3A%2058%20km%20W%20of%20Kamanjab%20Rest%20Camp%20on%20road%20to%20Grootberg%20Pass%20%2819%C2%B038%2757%22S%2C%2014%C2%B024%2733%22E%29&functionName=GeoCoordinateTaggerNormalizing.webService&dataUrl=&dataFormat=TXT and returns XML.

Visualising article coverage in the Biodiversity Heritage Library

It's funny how some images stick in the mind. A few years ago Chris Freeland (@chrisfreeland), then working for Biodiversity Heritage Library (BHL), created a visualisation of BHL content relevant to the African continent. It's a nice example of small multiples.

For more than a decade (gulp) I've been extracting articles from the BHL and storing them in BioStor. My approach is to locate articles based on metadata (e.g., information on title, volume, and pagination) and store the corresponding set of BHL pages in BioStor. BHL in turn regularly harvests this information and displays these articles as "parts" on their web site. Most of this process is semi-automated, but still requires a lot of manual work. One thing I've struggled with is getting a clear sense of how much progress has been made, and how much remains to be done. This has become more pressing given work I'm doing with Nicole Kearney (@nicolekearney) on Australian content. Nicole has a team of volunteers feeding me lists of article metadata for journals relevant to Australian biodiversity, and it would be nice to see where the remaining gaps are.

So, motivated by this, and also a recent comment by Nicky Nicolson (@nickynicolson) about the lack of such visualisations, I've put together a crude tool to try and capture the current state of coverage. You can see the results here: https://rdmpage.github.io/bhl-article-coverage/.

As an example, here is v.25:pt.2 (1988) of Memoirs of the Queensland Museum. Each contiguous block of colour highlights an article in this volume:

This scanned item-level view is constructed for each item (typically a volume or an issue). I then generate a PNG bitmap thumbnail of this display for each volume, and display them together in a page for the corresponding journal (e.g., Memoirs of the Queensland Museum):

So at a glance we can see the coverage for a journal. Gray represents pages that have not been assigned to an article, so if you want to add articles to BHL those are the volumes to focus on.

There's an argument to be made that it is crazy to spend a decade extracting some 226,000 articles semi-automatically. Ideally we could use a tool like machine learning to identify articles in BHL. It would be a huge time saver if we could simply run a BHL article through a tool that could (a) extract article-level metadata and (b) associate that with the corresponding set of scanned pages. Among the possible approaches would be to develop a classifier that would assign each page in a scanned volume to a category such as "article start", "article end", "article", "plate", etc. In effect, we want a tool that can could segment scans into articles (and hence reproduce the coverage diagrams shown above) simply based on attributes of the page images. This doesn't solve the entire problem, we still need to extract metadata (e.g., article titles), but it would be a start. However, it poses the classic dilemma, do I keep doing this manually, or do I stop adding articles and take the time to learn a new technology in the hope that eventually I will end up adding more articles than if I'd persisted with the manual approach?

(by Bruno Oliveir, see Manual tasks of today should be the Automated tasks of tomorrow)

New feature for BioStor: extracting literature cited from OCR text

At present BioStor provides a simple display of an article extracted from BHL. You get the page images, and sometimes a map and an altmetric "donut". But we can do better than this. For example, I'm starting to experiment with displaying a list of literature cited by the article. Below is a screenshot of the article A remarkable new species of Homalomena (Araceae) from Peninsular Malaysia showing the two references this article cites:

These references have been extracted using some simple regular expressions written in Javascript and wrapped up in a CouchDB view. They are extracted as simple text strings, I've not made any further attempt to parse the string into authors, title, journal, etc.

We don't need no stinkin' parser- a guide to resolving free-form citations with #CrossRef Metadata Search -> goo.gl/f9a4e

— Geoffrey Bilder (@gbilder) October 18, 2012

Of course, what we really want is to be able to convert these strings into clickable links to the actual reference. In the spirit of "We don't need no stinkin' parser" (see also Resolving free-form citations) I've added a little search icon that when you click on it attempts to find the reference in BioStor. In the screenshot above we've found both references in BioStor.

Obvious next steps are to add other resolvers (such as CrossRef for DOIs), do the resolution before the references are displayed (rather than wait for the user to click on the search icon), and even more usefully, display a list of articles that cite each article in BioStor (in the example above, both cited articles should "know" that they have been cited).

Whether an article in BioStor has a list of citations depends on the success of the regular expressions in extracting them, and whether the database has the OCR text. The current version of BioStor didn't originally store the OCR text, so I'm slowly adding that to the references. Other examples of articles with citations include Northeast African racers of the Platyceps rhodorachis complex (Reptilia: Squamata: Colubrinae) and Synopsis of the Neotropical mantid genus Pseudacanthops Saussure, 1870, with the description of three new species (Mantodea: Acanthopidae).

Long term adding linked citations to BioStor means we get a step closer to being able to offer readers an experience like PubMed Central (PMC), where articles in PMC are linked to articles in PMC that either cite, or a cited by that article. I think there's a case for a PubMed Central-like service for biodiversity literature (see Possible project: A PubMed Central for taxonomy) that rescues that literature from the ghetto much of it currently resides in, and instead makes it a first class citizen of the wider digital biodiversity landscape.

The Biodiversity Heritage Library meets Wikidata via Wikispecies: adding author identifiers to BioStor

I've added an experimental feature to BioStor that uses data from Wikidata and Wikispecies to augment what information BioStor displays on authors. This is a crude first step towards the goal of representing all the data in BioStor as a "knowledge graph" where articles, journals, and authors are all treated as entities, all have identifiers, and we can explore relationships between those entities (e.g., citation, co-authorship, etc.). At the moment this is true of articles, which have Biostor URLs (and in many cases DOIs), and for most journals which are identified by their ISSN. Using identifiers helps reduce ambiguity, especially if there are multiple ways to represent the same thing (e.g., all the alternative ways to write a journal name can be circumvented by using the journal's ISSN).

However, BioStor doesn't have a way to identify authors beyond simply searching for a name. As a first step to tackling this problem I've added a little widget that displays information about an author based on the name you are searching for. For example, searching for George Albert Boulenger will give you a list of publications where the author name is "George Albert Boulenger", as well as a picture of the author and some identifiers (from sources such as VIAF, ISNI, IPNI, and Wikidata):

For now this widget is independent of the data in BioStor. I don't link an article to its author(s) using identifiers for those authors, nor have I tackled the problem of clustering all the variations in people's names together into one set of names that share the same identifier (see Equivalent author names) nor do I attempt to match names to identifiers (see Reconciling author names using Open Refine and VIAF) other than by an exact text search (for details see below). At this stage I just want to get a sense of what identifiers exist for an author, and what I can learn from those identifiers. I also want to explore the potential of Wikispecies as a source of data on people and publications, and how this relates to Wikidata (for earlier thoughts on using Wikipedia for the same goal see Thoughts on Wikipedia, Wikidata, and the Biodiversity Heritage Library).

Wikispecies

I confess I've never really "got" Wikispecies (e.g., Wikispecies is not a database), it seems to exist in isolation from Wikipedia, which is arguably more informative about many species. But there are a couple of things Wikispecies does very well. Firstly, it is building a rich, crowd-sourced bibliography of papers on the taxonomy of many different species. Readers of iPhylo will recall how many times I've expressed frustration at the nearly evidence-free nature of many online taxonomic databases that simply have lists of names unconnected to the primary literature. Many Wikispecies pages have long lists of papers, making it a potential goldmine. Recently there is a lot of interest in extracting bibliographic data from Wikipedia (see WikiCite). Wikispecies could also be harvested, although a major obstacle any such project faces is the lack of a consistent format for references in Wikispecies.

The other nice thing about Wikipecies is that it has articles on taxonomic authorities, and these often list publications by those authors, and also list external identifiers for those authors, such as the VIAF and ISNI identifiers used in the library world, IPNI and ZooBank identifiers used in taxonomic databases, and ORCID which is becoming the de-facto identifier for academic researchers. This information also ends up in Wikidata.

Using Wikidata to glue things together

Wikidata is an interesting project that, like Wikispecies, I've been in two minds about (see Wikidata, Wikipedia, and #wikisci). However, I've started to make more use of it recently. Inspired by the Wikidata:SPARQL query service/2016 SPARQL Workshop I decided to explore the SPARQL query interface to Wikidata. I was struck by one of the example queries involving Wikispecies, and so after a little bit of messing about came up with a query that takes the name of an author and returns some identifiers from Wikidata, as well as an image of that person if one is available. I restrict the results to people that have an article about them in Wikispecies, because I want start exploring using those articles to make assertions about authorship. Here is a query to search for "George Albert Boulenger":

SELECT *
WHERE
{
  ?item rdfs:label "George Albert Boulenger"@en .
  ?article schema:about ?item .
  ?article schema:isPartOf  .
  OPTIONAL {
   ?item wdt:P213 ?isni .
	}
  OPTIONAL {
   ?item wdt:P214 ?viaf .
	}
  OPTIONAL {
   ?item wdt:P18 ?image .
	}
  OPTIONAL {
   ?item wdt:P496 ?orcid .
	}
  OPTIONAL {
   ?item wdt:P586 ?ipni .
	}
  OPTIONAL {
   ?item wdt:P2006 ?zoobank .
	}
}

This query simply asks whether Wikidata has an item on this person, whether that item is linked to Wikispecies, what identifiers Wikidata has, and whether there is an image of the person. You can see the query "live" here:

I've added some code to BioStor to do this query on the fly, and display the results. So, for Boulenger we get: Here is the result for noted carcinologist Jocelyn Crane who currently lacks identifiers: A nice surprise was Bernard Landry: Note the ORCID 0000-0002-6005-1067. Interestingly, Bernard Landry's ORCID profile doesn't list any publications, whereas we can see lists of these in BioStor and Wikispecies.

Where next?

There are several obstacles to mapping the names of authors to identifiers. One is simply the lack of identifiers. This seems to be rapidly becoming less of a problem with the efforts of the library community around VIAF, the rise of ORCID for living researchers, and the creation of Wikidata items for every taxonomist in Wikispecies. The next challenge is clustering the different ways of writing the same person's name into sets that represent the same person. As discussed above, there are tools for this. Furthermore, with Wikipedia and Wikispecies we have sources of lists of publications linked to a person and their identifiers, which should simplify the task considerably. What is nice about this is that it relies on a crowd-sourcing effort which is already well-established, namely those people who in adding articles to Wikispecies and Wikipedia are created a curated database of publications linked to authors. In many cases those publications are linked to BHL (the source that BioStor extracts its articles from), so many of the links between publications and people are essentially lying there, just waiting for some skilful harvesting.

Taxonomic name timelines for BHL

Given a big corpus of literature one of the fun things to do is look at how the use of a term has changed over time. When did people first use a particular word? When did one word start to replace another, etc.? Google's Ngram Viewer is perhaps the best known tool for exploring these questions.

In the context of biodiversity doing something similar for BHL is an obvious thing to do. I've made various clunky attempts in the past (e.g., Biodiversity Heritage Library sparklines) but these all died.

Ryan Schenk (who did a lot of the user interface for my BioNames project) wrote a very stylish tool to display changes in names over time. Called "Synynyms" his tool is now defunct, but you can read about it here and the source code is on github. Ryan would take a name, find synonyms, then graph the changes in use of all those names over time.

The death of Synynyms has not gone unnoticed:

@rdmpage Is there a service simiar to synynyms to check for nomen usage? synynyms is not available anymore :(

— JCAM (@PhyloJCAM) December 12, 2016

I've had a tool for my own use that searches BHL for a name and displays the results after first trying to aggregate the hits in a sensible way. For example, if there is more than one hit in a scanned volume, and those hits al fall on pages in the same article in BioStor, then I display the BioStor article, instead of a list of each hit separately. Inspired by @PhyloJCAM's question I've built a simple tool to explore the use of one or more name over time.

Located in the "labs" section of BioStor, the BHL timeline takes one or more names and searches for those names in BHL, displaying the results as a chart and a list of hits. I often use it simply to search BHL for a particular name, but you can also use it to compare names, e.g. Aspidoscelis costata and Cnemidophorus costatus:

The timeline tool is pretty crude, and it's slow if there are lots of hits in BHL. So, it's not as slick as Synynyms (Ryan Schenk is a clever programmer than I am). Still, it is a useful way to explore BHL and discover articles that you might not have known existed.

@rdmpage @ryanschenk working great with uninomials, one improvement could be to allow for regular expressions, but already awesme as it is! pic.twitter.com/W6TTRXwszM

— JCAM (@PhyloJCAM) December 15, 2016

BioStor updates: nicer map, reference matching service

BioStor now has 150,000 articles. When I wrote a paper describing how BioStor worked it had 26,784 articles, so things have progressed somewhat!

I continue to tweak the interface to BioStor, trying different ways to explore the articles.

Spatial search

I've tweaked spatial search in BioStor. As blogged about previously I replaced the Google Maps interface with Leaflet.js, enabling you to draw a search area on the map and see a set of articles that mention that area. I've changed the base map to the prettier "terrain" map from Stamen, and added back the layer showing all the localities in BioStor. This gives you a much better sense of the geographic coverage in BioStor. This search interface still needs work, but is a fun way to discover content.

Reference matching

In the "Labs" section of the web site I've added a demonstration of BioStor's reconciliation service. This service is based on the Freebase reconciliation service used by tools such as OpenRefine, see Reconciliation Service API. The goal is to demonstrate a simple way to locate references in BioStor, simply paste references, one per line, click Match and BioStor will attempt to find those references for you.

This service is really intended to be used by tools like OpenRefine, but this web page helps me debug the service.

Suggestions?

BioStor is part labour of love, part albatross around my neck. I'm always open to suggestions for improvements, or for articles to add (but remember that all content must first have been scanned and in the Biodiversity Heritage Library). If you are involved in publishing a journal and are interested in getting it into BHL, please get in touch.

Spatial search in BioStor

I've been experimenting with simple spatial search in BioStor, as shown in the demo below. If you go to the map on BioStor you can use the tools on the left to draw a box or a polygon on the map, and BioStor will search it's database for articles that mention localities that occur in that region. If you click on a marker you can see the title of the article, clicking on that title takes you to the article itself.

This is all rather crude (and a little slow), but it provides another way to dive into the content I've been extracting from the BHL. One thing I've been doing is looking at protected areas (often marked on Open Street Map), drawing a simple polygon around that area, then seeing if BioStor knows anything about that area.

For the technically minded, this tool is an extension of the Searching GBIF by drawing on a map demo, and uses the geospatial indexing offered by Cloudant.

Hypothes.is revisited: annotating articles in BioStor

Over the weekend, out of the blue, Dan Whaley commented on an earlier blog post of mine (Altmetrics, Disqus, GBIF, JSTOR, and annotating biodiversity data. Dan is the project lead for hypothes.is, a tool to annotate web pages. I was a bit dismissive as hypothes.is falls into the "stick note" camp of annotation tools, which I've previously been critical of.

However, I decided to take another look at hypothes.is and it looks like a great fit to another annotation problem I have, namely augmenting and correcting OCR text in BioStor (and, by extension, BHL). For a subset of BioStor I've been able to add text to the page images, so you can select that text as you would on a web page or in a PDF with searchable text. If you can select text, you can annotate it using hypothes.is. Then I discovered that not only is hypothes.is a Chrome extension (which immediately limits who will use it), you can add it to any web site that you publish. So, as an experiment I've added it to BioStor, so that people can comment on BioStor using any modern browser.

So far, so good, but the problem is I'm relying on the "crowd" to come along and manually annotate the text. But I have code that can take text and extract geographic localities (e.g., latitude and longitude pairs), specimen does, and taxonomic names. What I'd really like to do is be able pre-process the text, locate these features, then programmatically add those as annotations. Who wants to do this manually when a computer can do most of it?

Hypothesi.is, it turns out, has an API that, while a bit *cough* skimpy on documentation, enables you to add annotations to text. So now I could pre-process the text, and just ask people to add things that have been missed, or flag errors on the automated annotations.

This is all still very preliminary, but as an example here's a screen shot of a page in BioStor together with geographic annotations displayed using hypothes.is (you can see this live here: http://biostor.org/reference/147608/page/1 (make sure you click on the widgets at the top right of the page to see the annotations):

The page shows two point localities that have been extracted from the text, together with a static Google Map showing the localities (hypothesis.is supports Markdown in annotations, which enables links and images to be embedded).

Not only can we write annotations, we can also read them, so if someone adds an annotation (e.g., highlights a specimen code that was missed, or some text that OCR has messed up) we could retrieve that and, for example, index the corrected text to improve findability.

Lots still to do, but these early experiments are very encouraging.

Towards a new BioStor

One of my pet projects is BioStor, which has been running since 2009 (gulp). BioStor extracts articles from the Biodiversity Heritage Library (details here: http://dx.doi.org/10.1186/1471-2105-12-187), and currently has over 110,000 articles, all open access. The site itself is showing its age, both in terms of performance and design, so I've wanted to update it for a while now. I made a demo in 2012 of BioStor in the Cloud, but other stuff got in the way of finishing it, and the service that it ran on (Pagodabox) released a new version of their toolkit, so BioStor in the Cloud died.

At last I've found the time to tackle this again, motivated in part because I've had to move BioStor to a new server, and it's performance has been pretty poor. The next version of BioStor is currently sitting at http://biostor.gopagoda.io (the images and map views are good ways to enter the site). It's still being populated, and there is code to tweak, but it's starting to look good enough to use. It has a cleaner article display, built in search (making things much more findable), support for citation styles using citeproc-js, and display of altmetrics (e.g., Another variation on the gymnure theme: description of a new species of Hylomys (Lipotyphla, Erinaceidae, Galericinae).

Once all the data has been moved across and I've cleaned up a few things I plan to make bistor.org point to this new version.

BioStor and JournalMap: a geographic interface to articles from the Biodiversity Heritage Library

Great to see @JournalMap jump from ~11000 to ~17000 articles: http://t.co/bVarqDGGVU

— Ken Mankoff (@mankoff) September 27, 2014

The recent jump from ~11000 to ~17000 articles in JournalMap is mostly due to JournalMap ingesting content from my BioStor database. BioStor extracts articles from the Biodiversity Heritage Library (BHL), and in turn these get fed back into BHL as "parts" (you can see these in the "Table of Contents" tab when viewing a scanned volume in BHL).

In addition to extracting articles, BioStor pulls out latitude and longitude pairs mentioned in the OCR text and creates little Google Maps for articles that have geotagged content. Working with Jason Karl (@jwkarl), JournalMap now talks to BioStor and grabs all its geotagged articles so that you can browse them in JournalMap. As a consequence, journals such as Proceedings of The Biological Society of Washington now appear on their map (this journal is third most geotagged journal in JournalMap).

As an example of what you can do in JournalMap, here's a screenshot showing localities in Tanzania, and an article from BioStor being displayed:


JournalMap is an elegant interface to the biodiversity literature, and adding BioStor as a source is a nice example of how the Biodiversity Heritage Library's content is becoming more widely used. BioStor would only be possible if BHL made its content and metadata available for easy downloading. This is a lesson I wish other projects would learn. Instead of focussing on building flash-looking portals, make sure (a) you have lots of content, and (b) make it easy for developers to get that content so they can do cool things with it. BHL does well in this regard — other projects, such as BHL-Europe, not so much.

Geotagging stats for BioStor

Note to self for upcoming discussion with JournalMap.

As of Monday August 25th, BioStor has 106,617 articles comprising 1,484,050 BHL pages. From the full text for these articles, I have extracted 45,452 distinct localities (i.e., geotagged with latitude and longitude). 15,860 BHL pages in BioStor pages have at least one geotag, these pages belong to 5,675 BioStor articles.

In summary, BioStor has 5,675 full-text articles that are geotagged. The largest number of geotags for an article is 2,421, for Distribución geográfica de la fauna de anfibios del Uruguay (doi:10.5479/si.23317515.134.1).

The SQL for the queries is here.

New look Biodiversity Heritage Library launched

Tomorrow the new & improved #bhlib launches!! ow.ly/iVeZb Explore the changes in our Guide! ow.ly/iVf1W

— BHL (@BioDivLibrary) March 17, 2013

The new look Biodiversity Heritage Library has just launched. It's a complete refresh of the old site, based on the Biodiversity Heritage Library–Australia site. If you want an overview of what's new, BHL have published a guide to the new look site. Congrats to involved in the relaunch.

One of the new features draws on the work I've been doing on BioStor. The new BHL interface adds the notion of "parts" of an item, which you can see under the "Table of Contents" tab. For example, the scanned volume 109 of the Proceedings of the Entomological Society of Washington now displays a list of articles within that volume:


This means you can now jump to individual articles. Before you had to scroll through the scan, or click through page numbers until you found what you were after. The screenshot above shows the article "Three new species of chewing lice (Phthiraptera: Ischnocera: Philopteridae) from australian parrots (Psittaciformes: Psittacidae)". The details of this article have been extracted from BioStor, where this article appears as http://biostor.org/reference/55323. You can go directly to this article in BHL using the link http://www.biodiversitylibrary.org/part/69723. As an aside, I've chosen this article because it helps demonstrate that BHL has modern content as well as pre-1923 literature, and this article names a louse, Neopsittaconirmus vincesmithi after a former student of mine, Vince Smith. You're nobody in this field unless you've had a louse named after you ;)

BioStor has over 90,000 articles, but this is a tiny fraction of the articles contained in BHL content, so there's a long way to go until the entire archive is indexed to article level. There will also be errors in the article metadata derived from BioStor. If we invoke Linus's Law ("given enough eyeballs, all bugs are shallow") then having this content in BHL should help expose those errors more rapidly.

As always, I have a few niggles about the site, but I'll save those for another time. For noe, I'm happy to celebrate an extraordinary, open access archive of over 40 million pages. BHL represents one of the few truly indispensable biodiversity resources online.

BioStor in the cloud

Quick note on an experimental version of BioStor that is (mostly) hosted in the cloud. BioStor currently runs on a Mac Mini and uses MySQL as the database. For a number of reasons (it's running on a Mac Mini and my knowledge of optimising MySQL is limited) BioStor is struggling a bit. It's also gathered a lot of cruff as I've worked on ways to map article citations to the rather messy metadata in BHL.

So, I've started to play with a version that runs in the cloud using my favourite database, CouchDB. The data is hosted by Cloudant, which now provides full text search powered by Lucene. Essentially, I simply take article-level metadata from BioStor in BibJSON format and push that to Cloudant. I then wrote a simple wrapper around querying CouchDB, couple that with the Documentcloud Viewer to display articles and citeproc-js to format the citations (not exactly fun, but someone is bound to ask for them), and a we have a simple, searchable database of literature.

If you want to try the cloud-based version go to http://biostor-cloud.pagodabox.com/ (code on Github).



I've been wanting to do this for a while, partly because this is how I will implement my entry in EOL's computational data challenge, but also because CrossRef's Metadata search shows the power of finding references simply by using full text search (I've shamelessly borrowed some of the interface styling from Karl Ward's code). David Shorthouse demonstrates what you can do using CrossRef's tool in his post Conference Tweets in the Age of Information Overconsumption. Given how much time I spend trying to parse taxonomic citations and match them to articles in CrossRef's database, or BioStor, I'm looking forward to making this easier.

There are two major limitations of this cloud version of BioStor (aprt from the fact it has only a subset of the articles in BioStor). The first is that the page images are still being served from my Mac Mini, so they can be a bit slow to load. I've put the metadata and the search engine in the cloud, but not the images (we're talking a terabyte or two of bitmaps).

The other limitation is that there's no API. I hope to address this shortly, perhaps mimicking the CrossRef API so if one has code that talks to CrossRef it could just as easily talk to BioStor.

Dear GBIF, please stop changing occurrenceIDs!

If we are ever going to link biodiversity data together we need to have some way of ensuring persistent links between digital records. This isn't going to happen unless people take persistent identifiers seriously.

I've been trying to link specimen codes in publications to GBIF, with some success, so imagine my horror when it started to fall apart. For example, I recent added this paper to BioStor:

A remarkable new asterophryine microhylid frog from the mountains of New Guinea. Memoirs of The Queensland Museum 37: 281-286 (1994) http://biostor.org/reference/105389

This paper describes a new frog (i>Asterophrys leucopus) from New Guinea, and BioStor has extracted the specimen code QM J58650 (where "QM" is the abbreviation for Queensland Museum), which according to the local copy of GBIF data that I have, corresponds to http://data.gbif.org/occurrences/363089399/. Unfortunately, if you click on that link GBIF denies all knowledge (you get bounced to the search page). After a bit of digging I discover that specimen is now in GBIF as http://data.gbif.org/occurrences/478001337/. At some point GBIF has updated its data and the old occurrenceID for QM J58650 (363089399) has been deleted. Noooo!

Looking at the old record I have there is an additional identifier:

urn:catalog:QM: Herpetology:J58650

This is a URN, and it's (a) unresolvable and (b) invalid as it contains a space. This is why URNs are useless. There's no expectation they will be resolvable hence there's no incentive to make sure they are correct. It's as much use as writing software code but not bothering to run it (because surely it will work, no?).

The GBIF record http://data.gbif.org/occurrences/478001337/ contains a UUID as an alternative identifier:

bc58ce6b-3cc3-459a-9f5b-4a70a026afbe

If you Google this you discover a record in the Atlas of Living Australia http://biocache.ala.org.au/occurrences/bc58ce6b-3cc3-459a-9f5b-4a70a026afbe, which also lists the URN from the now deleted GBIF record http://data.gbif.org/occurrences/363089399/.

I'm guessing that at some point the OZCAM data provided to GBIF was updated and instead of updating data for existing occurrenceIDs the old ones were deleted and new ones created (possibly because OZCAM switched from URNs to UUIDs as alternative identifiers). Whatever the reason, I will now need to get a new copy of GBIF occurrence data and repeat the linking process. Sigh.

If we are ever going to deliver on the promise of linking biodiversity data together we need to take identifiers seriously. Meantime I need to think about mechanisms to handle links that disappear on a whim.

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...

The GBIF classification is broken — how do we fix it?

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. The idea is to give the reader a sense of "what the paper is about". I also use the classification to help link to GBIF occurrence records.

The GBIF backbone classification ("nub") is probably the single largest classification of life that has been assembled, and provides GBIF users with a way to navigate through GBIF's collection of specimen and observation records. Given the scale of the undertaking it is inevitable that there will be issues with the classification, and this post provides one example.

On the page for the article "Further additions to the known marine Molluscan fauna of St. Helena" (http://biostor.org/reference/88554, see also http://dx.doi.org/10.1080/00222939208677383) part of the classification looks like this:

└Animalia
 └Annelida
  └Polychaeta
   └Sabellida
    └Serpulidae
     └Hipponyx

Tony points out that "Hipponyx" is a mollusc, yet in the GBIF classification appears in the annelid worms.

Like a fool I started to investigate further. First off, what is "Hipponyx"? Browsing the GBIF classification there are species of Hipponyx and Hipponix under the genus Hipponix, so it looks like we have two alternative spellings of this genus name. Nomenclator Zoologicus has both spellings, Hipponix credited to DeFrance 1819 Journ. de Physique, 88, 217, and Hipponyx credited to Defrance 1819 Bull. Sci. Soc. philom. Paris, 8. Gotta love those cryptic citations. After some digging around in BHL I found Journ. de Physique, 88, 217 (Mémoire sur un nouveau genre de mollusque) and Bull. Sci. Soc. philom. Paris, 8. (Sur un nouveau genre de coquilles (Hipponix)). Both papers are by Jacques Louis Marin DeFrance, and both use the spelling Hipponix (no 'y'). I'm guessing the second paper is actually the original description of the genus, but my French is abysmal (Google Translate to the rescue).

OK, so we have two spellings of what is probably the same thing (and I've no idea why we have two spellings). Both spellings seem in use (see Google NGrams chart below).



So, bit of a mess, but this still doesn't deal with Hipponyx being a worm in GBIF. After a bit of Googling on "Serpulidae" and "Hipponyx" I came across a specimen record from Te Papa labelled "Worm, Temporaria inexpectata (Mestayer, 1929); holotype; holotype of Hipponyx inexpectata Mestayer, 1929". I then came across this paper:

Fleming, C. A. (1971). A preliminary list of New Zealand fossil polychaetes. New Zealand Journal of Geology and Geophysics, 14(4), 742–756. doi:10.1080/00288306.1971.10426332

with the following abstract:

An annotated list of fossil “worm tubes” from New Zealand includes both published and new records from Mesozoic and Cenozoic deposits.

The binomen Zoophycos plicatus (Hutton) is proposed for the trace fossil long known as the Amuri fucoid, of unknown zoological affinity.

The following living species are recorded as New Zealand fossils for the first time: Protula bispiralis (Savigny), Salmacina dysteri (Huxley), Hydroides norvegicus Gunnerus, Pomatoceras cariniferus (Gray), P. aff. terranovae (Benham), Galeolaria hystrix (Moerch), Boccardia ? polybranchia (Haswell); new records of fossil species are Ditrupa cf. plana (Sowerby), Dorsoserpula lumbricalis (Schlotheim), and Neomicrorbis crenatostriatus (Münster). The name Hipponyx inexpectata Mestayer 1929, applied to a serpulid operculum, is used in the combination Temporaria inexpectata for a tubeworm common in deep water off New Zealand that has also been identified, with associated operculum, from the bathyal Waitotaran (Pliocene) sediments of Palliser Bay. Serpula wharjensis Wilkens and S. ougenensis Chapman are placed in Sclerostyla Moerch. Two species of Vermiliopsis and two of Spirorbis are figured but not named specifically.

The author of the paper (Charles Fleming) argues that Hipponyx inexpectata, regarded as a mollusc by its describer (Marjorie K. Mestayer, see Notes on New Zealand Mollusca. No. 4.) is actually a worm, and he moves it to the genus Temporaria.

So it seems that the reason Hipponyx has ended up being a worm in the GBIF classification is due to this synonymy.

Now, this little investigation was "fun", but took a couple of hours. Much of that was spent tracking down the literature and adding it to BioStor, which is a one-time cost. Not every issue with the GBIF classification will take this long to resolve, some cases may take longer. So there's a problem of scalability. Then there's the issue of how this information gets into the GBIF classification so we fix it (and so that people don't think Hipponyx is a worm). As has been said several times before, most eloquently by David Shorthouse, isn't it time we started using software development tools such as version control to help build, annotate, and correct classifications such as the one that underpins GBIF? That way when somebody spots an error it can be flagged, and someone with the time (and curiosity) can fix it.

GBIF specimens in BioStor: who are the top ten museums with citable specimens?

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. Of these 143,000 occurrences, 81,000 have been matched to an occurrence in GBIF.

The top ten collections with specimens in BioStor are:

Dataset	Number of specimens
NMNH Vertebrate Zoology Herpetology Collections (National Museum of Natural History)	11194
Herpetology Collection (University of Kansas Biodiversity Research Center)	9619
Herpetology Collection (University of Kansas Biodiversity Research Center)	9328
NMNH Invertebrate Zoology Collections (National Museum of Natural History)	9061
CAS Herpetology Collection Catalog (California Academy of Sciences)	6720
MCZ Herpetology Collection (Museum of Comparative Zoology, Harvard University)	5818
NMNH Vertebrate Zoology Fishes Collections (National Museum of Natural History)	4642
MCZ Herpetology Collection - Reptile Database (Museum of Comparative Zoology, Harvard University)	4380
FMNH Herpetology Collections (Field Museum)	2110
FMNH Fishes Collections (Field Museum)	2061

This is pretty much what I expected. Virtually complete runs of publications from The Field Museum at Chicago, the University of Kansas, and the Biological Society of Washington are available in BHL, and many of these have been added to BioStor. These journals have extensive taxonomic treatments of vertebrate taxa, particularly frogs, hence herpetology collections dominate the rankings.

There will inevitably be errors in the mapping between specimen codes and GBIF occurrences. I've tried to minimise these by mapping codes within taxonomic groups, but it's clear that there are duplicate codes even within some collections. There is also all manner of variation in the way people cite museum specimens, and these are often different from the codes that appear in GBIF. There will also be issues with extracting specimen codes, and I'm also discovering a few *cough* duplicates of articles in BioStor, so the numbers I present above are liable to change as I clean things up.

But one could imagine a "league table" of museum collections, where we can measure both the extent to which those collections have been digitised, and the extent to which material from those collections have been cited. We could use this to compute measures of the impact of a collection.

But for now I'm browsing the results trying to get a sense of how successful the mapping has been. There are some interesting examples. The specimen codes extracted from the article Review Of The Chewing Louse Genus Abrocomophaga (Phthiraptera : Amblycera), With Description Of Two New Species are those for the mammalian hosts of the lice. Hence someone viewing the records for these specimens and following the link to this paper would discover that these mammals had parasitic lice. If we add other sorts of links to the mix, such as between specimens and DNA sequences, then we can start to build a rich network of connections between the basic data of biodiversity.

Linking GBIF and the Biodiversity Heritage Library

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. Given that many articles in BioStor list museum specimens I wrote some code to extract these (see Extracting museum specimen codes from text) and applied this to the OCR text for those articles.

Having a list of specimens is nice, but in this digital age I want to be able to find out more about these specimens. An obvious solution is try and match these specimen codes to the specimen records held by GBIF. Linking to GBIF is complicated by the fact that museum codes are not unique. For example, "FMNH 147942" could refer to a bird, an amphibian, or a mammal. To tackle the non uniqueness I use the taxonomic names extracted from each page by BHL to work out what taxon an article is mainly "about". To do this I use the Catalogue of Life classification to get "paths" for each name (i.e., the lineage of each taxon down to the root of the classification) and then find the majority-rule path. You can see these paths in the "Taxonomic classification" displayed on a page for a BioStor article. If there are multiple GBIF specimens for the same code I test whether the taxon or rank "class" in the GBIF record is in the majority-rule path for the article. If so, I accept that specimen as the match to the code.

There are also issues where the specimen codes in GBIF have been modified during input (e.g., USNM 730715 has become USNM 730715.457409). There are also the inevitable OCR errors that may cause museum codes to be missed or otherwise corrupted. Bearing all this in mind, BioStor now has specimen pages (these are still being generated as I write this). For example, the page for FMNH 147942 lists the three articles in BioStor that cite this specimen code:



All three specimens have been mapped on to GBIF occurrence http://data.gbif.org/occurrences/61846037/. When BioStor displays the articles it now lists the specimen codes that have been extracted from the article, together with the GBIF logo if the specimen has been matched to a GBIF record. For example, here is a screenshot from Deep-water octopods (Mollusca: Cephalopoda) of the northeastern Pacific:


The map has been extracted from the OCR text (an obvious next step would be to add localities associated with the specimen records). Below the map are the specimen codes. The lack of some USNM specimens is probably due to misinterpreted specimen codes, whereas the CAS specimens don't seem to be online (the California Academy of Sciences has some of its collections in GBIF, but not its molluscs).

Where next?
Once these links between BioStor (and hence, BHL) and GBIF are created then we can do some interesting things. If you visit BioStor and want to learn more about a specimen you can click on the link an view the record in GBIF. We could also envisage doing the reverse. GBIF could augment the information it displays about a specimen by displaying a link to the content in BioStor (e.g., "this specimen is cited by these articles"). Those articles may contain further information about that specimen (for example, the habitat it was collected from, how secure is its identification, and so on).

We could also start to compute the "impact" of different museum collections based on the number of citations of specimens from their collections (this idea is explored further in this paper: http://dx.doi.org/10.1093/bib/bbn022, free preprint available here: hdl:10101/npre.2008.1760.1).

All of this works because we are linking objects (in this case articles and specimens) via their identifiers. Consequently, the links are as stable as their identifiers, which is why I've been pursuing the issue of specimen identifiers recently (see here, here, and here). If GBIF maintains the URLs for the specimens I've linked to, then links I've created could persist. If these URLs are likely to change (e.g., because the metadata from the host institution has changed) then the links (and any associated value we get from them) disappear. This is why I want globally unique, resolvable, persistent identifiers for specimens.

Adding article-level metadata to BHL

Recently I've been thinking about the best ways to make article-level metadata from BioStor more widely available. For example, for someone visiting the BHL site there is no easy way to find articles, which are the basic unit for much of the scientific literature. How hard would it be to add articles to BHL? In the past I've wanted an all-singing all dancing article-level interface to BHL content (sort of BioStor on steroids), but that's a way off, and ideally would have a broader scope than BHL. So instead I've been thinking of ways to add articles to BHL without requiring a lot of re-engineering of BHL itself.

Looking at other digital archive projects like Gallica and Google Books it strikes me that if the BHL interface to a scanned item had a "Contents" drop down menu then users would be able to go to individual articles very easily. Below is a screen shot of how Gallica does this (see http://gallica.bnf.fr/ark:/12148/bpt6k61331684/f57).



There's also a screen shot of something similar in Google Books (see http://books.google.co.uk/books?id=PkvoRnAM6WUC)



The idea would be that if BioStor had found articles within a scanned item, they would be listed in the contents menu (title, author, starting page), and if the user clicked on the article title then the BHL viewer would jump to that page. If there were no known articles, but the scanned item had a table of contents flagged (e.g., http://www.biodiversitylibrary.org/item/25703) then the menu could function as a button that takes you to that page. If there are no articles or contents, then the menu could be grayed out, or simply not displayed. This way the interface would work for books, monographs, and journal volumes.

Now, admittedly this is not the most elegant interface, and it treats articles as fragments of books rather than individual units, but it would be a start. It would also require minimal effort both on the part of BHL (who need to add the contents button), and myself (it would be easy to create a dump of the article titles indexed by scanned item).

Linking taxonomic names to literature: beyond digitised 5×3 index cards

Tomorrow is the Anchoring Biodiversity Information: From Sherborn to the 21st century and beyond meeting. It should be an interesting gathering, albeit overshadowed by the sudden death of Frank Bisby.

I'm giving a talk entitled "Open Taxonomy", in which I argue that most taxonomic databases are little more than digitised collections of 5×3 index cards, where literature is treated as dumb citation strings rather than as resources with digital identifiers. To make the discussion concrete I've created a mapping between the Index to Organism Names (ION) database and a range of bibliographic sources, such as CrossRef (for DOIs), BioStor, JSTOR, etc.

This mapping is online at http://iphylo.org/~rpage/itaxon/.

So far I've managed to link some 200,000 animal names to a literature identifier, and a good fraction of these articles are freely available, either as images in BioStor and Gallica (for I've created a simple viewer) or as PDFs (which are displayed using Google Docs.

Some examples are:

• Geothelphusa marmorata (BioStor)
• Rhopalione (Gallica)
• Potamotrygon garouaensis (PDF)
• Endacusta kirrimurra (Google Books)

The site is obviously a work in progress, and there's a lot to be done to the interface, but I hope it conveys the key point: a significant fraction of the primary taxonomic literature is online, and we should be linking to this. The days of digitised 5×3 index cards are past.