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.

Modelling taxonomic names in databases

Quick notes on modelling taxonomic names in databases, as part of an ongoing discussion elsewhere about this topic.

Simple model

One model that is widely used (e.g., ITIS, WoRMS) and which is explicit in Darwin Core Archive is something like this:

We have a table for taxa and we don't distinguish between taxa and their names. the taxonomic hierarchy is represented by the parentID field, which points to your parent. If you don't have a (non NULL) value for parentID you are not an accepted taxon (i.e., you are a synonym), and the field acceptedID points to the accepted taxon. Simple, fits in a single database table (or, let's be honest, and Excel spreadsheet).

The tradeoff is that you conflate names and taxa, you can't easily describe name-only relationships (e.g., homonyms, nomenclatural synonyms) without inventing "taxa" for each name.

Separating names and taxa

The next model, which I've drawn rather clunky below as if you were doing this in a relational database, is based on the TDWG LSID vocabularies. One day someone will explain why the biodiversity informatics community basically ignored this work, despite the fact that all the key nomenclators use it.

In this model we separate out names as first-class objects with globally unique identifiers. The taxa table refers to the names table when it mentions a name. Any relationships between names are handled separately from taxa, so we can easily handle things like replacement names for homonyms, basionyms, etc. Not that we can also remove a lot of extraneous stuff from the taxa table. For example, if we decide that Poissonia heterantha is the accepted name for a taxon, we don't need to create taxa for Coursetia heterantha or Tephrosia heterantha, because by definition those names are synonyms of Poissonia heterantha.

The other great advantage of this model is that it enables us to take the work the nomenclators have done straight without having to first shoe-horn it into the Darwin Core format, which assumes that everything is a taxon.

The Biodiversity Data Journal is not machine readable

In my (previous post ) I discussed the potential for the Biodiversity Data Journal (BDJ) to be a venue for nano (or near-nano publications). In this post I want to draw attention to what I think is a serious stumbling block, which is the lack of machine readable statements in the journal.

Given that the journal is probably the most progressive in the field (indeed, is suspect that there are few journals in any field as advanced in publishing technology as BDJ) this may seem an odd claim to make. The journal provides XML of its text, and typically provides data in Darwin Core Archive format, which is harvested by GBIF. The article XML is marked up to flag taxonomic names, localities, etc. Surely this is the very definition of machine readable?

The problem becomes apparent when you ask "what claims or assertions are the papers making?", and "how are those assertions reflected in the article XML and/or the Darwin Core Archive?".

For example, consider the following paper:

Gil-Santana, H., & Forero, D. (2015, June 16). Aristathlus imperatorius Bergroth, a newly recognized synonym of Reduvius iopterus Perty, with the new combination Aristathlus iopterus (Perty, 1834) (Hemiptera: Reduviidae: Harpactorinae) ​. BDJ. Pensoft Publishers. http://doi.org/10.3897/bdj.3.e5152

The title gives the key findings of this paper: Aristathlus imperatorius = Reduvius iopterus, and Reduvius iopterus = Aristathlus iopterus. Yet these statements are no where to be found in the Darwin Core Archive for the paper, which simply lists the name Aristathlus iopterus. The XML markup flags terms as names, but says nothing about the relationships between the names.

Here is another example:

Starkevich, P., & Podenas, S. (2014, December 30). New synonym of Tipula (Vestiplex) wahlgrenana Alexander, 1968 (Diptera: Tipulidae). BDJ. Pensoft Publishers. http://doi.org/10.3897/bdj.2.e4237

Indeed, I've yet to find an example of a paper in BDJ where a synonomy asserted in the text is reflected in the Dawrin Core Archive!

The issue here is that neither the XML markup nor the associated data files are capturing the semantics of the paper, in the sense of what the paper is actually saying. The XML and DwCA files capture (some) of the names, and localities mentioned, but not the (arguably) most crucial new pieces of information.

There is a disconnect between what the papers are saying (which a human reader can easily parse) and what the machine-readable files are saying, and this is worrying. Surely we should be ensuring that the Darwin Core Archives and/or XML markup are capturing the key facts and/or assertions made by the paper? Otherwise databases down stream will remain none the wiser about the new information the journal is publishing.

Nanopublications and annotation: a role for the Biodiversity Data Journal?

I stumbled across this intriguing paper:

Do, L., & Mobley, W. (2015, July 17). Single Figure Publications: Towards a novel alternative format for scholarly communication. F1000Research. F1000 Research, Ltd. http://doi.org/10.12688/f1000research.6742.1

The authors are arguing that there is scope for a unit of publication between a full-blown journal article (often not machine readable, but readable) and the nanopublication (a single, machine readable statement, not intended for people to read), namely the Single Figure Publications (SFP):

The SFP, consisting of a figure, the legend, the Material and Methods section, and an optional Results/Discussion section, reduces the unit of publication to a more tractable size. Importantly, it results in a markedly decreased time from data generation to publication. As such, SFPs represent a new means by which to communicate scientific research. As with the traditional journal article, the content of the SFPs is readily understandable by the scientist. Coupled with additional tools that aid in structuring content (e.g. describing in detail the methods using pre-defined steps from protocols), the SFP represents a “bottom-up” means by which scholars can structure the content of their findings in a modular and piece-wise fashion wedded to everyday laboratory life.

It seems to me that this is something that the Biodiversity Data Journal is potentially heading towards. Some of the papers in that journal are short, reporting say, new occurence records for a single species e.g.:

Ang, Y., Rohner, P., & Meier, R. (2015, June 26). Across the Baltic: a new record for an enigmatic black scavenger fly, Zuskamira inexpectata (Pont, 1987) (Sepsidae) in Finland. BDJ. Pensoft Publishers. http://doi.org/10.3897/bdj.3.e4308

Imagine if we have even shorter papers that are essentially a series of statements of fact, or assertions (linked to supporting evidence). These could potentially be papers that annotated and/or clarified data in an external database, such as GBIF. For example, let's imagine we find two names in GBIF that GBIF treats as being different taxa, but a recent publication asserts are actually synonyms. We could make that information machine readable (say, using Darwin Core Archive format), link it to the source(s) of the assertion (i.e., the DOI of the paper making the synonymy), then publish that as a paper. As the Darwin Core Archive is harvested by GBIF, GBIF then has access to that information, and when the next taxonomic indexing occurs it can make use of that information.

One reason for having these "micropublications" is that sometimes resolving an issue in a dataset can take some time. I've often found errors in databases and have ended up spending a couple of hours finding names, literature, etc. to figure out what is going on. As fun as that is, in a sense it's effort that is wasted if it's not made more widely available. But if I can wrap that couple of hours scholarship into a citable unit, publish it, and have it harvested and incorporated into, say, GBIF, then the whole exercise seems much more rewarding. I get credit for the work, and GBIF users get (hopefully) a tiny bit of improvement, and they can see the provenance of that improvement (i.e., it is evidence-based).

This seems like a simple mechanism for providing incentives for annotating databases. In some ways the Biodiversity Database Journal could be though of as doing this already, however as I'll discuss in the next blog post, there's an issue that is preventing it being as useful as it could be.

Purposeful Games and the Biodiversity Heritage Library

.@BioDivLibrary Had a quick play of the Beanstalk OCR correction game http://t.co/fjUAFssKQu, lack of context is off-putting #bhlib

— Roderic Page (@rdmpage) July 23, 2015

These are some quick thoughts on the games on the BHL site, part of the Purposeful Gaming and BHL Project. As mentioned on Twitter, I had a quick play of the Beanstalk game and got bored pretty quickly. I should stress that I'm not a gamer (although my family includes at least one very serious gamer, and a lot of casual players). Personally, if I'm going to spend a large amount of time with a computer I want to be creating something, so gaming seems like a big time sink. Hence, I may not be the best person to review the BHL games. Anyhow...

It seems to me that there are a couple of ways games like this might work:

1. You want to complete the game so you can do something you really want to do. This is the essence of solving CAPTCHAs, I'll solve your stupid puzzle so that I can buy my tickets.
2. The game itself is engaging, and what you are asked to do is a natural part of the game's world. When you swipe candy, bits of candy may explode, or fall down (this is a good thing, apparently), or when you pull back a slingshot and release the bird, you get to break stuff).

The BHL games are trying to get you to do one activity (type in the text shown in a fragment of a BHL book) and this means, say, a tree grows bigger. To me this feels like a huge disconnect (cf. point 2 above), there is no connection between what I'm doing and the outcome.

Worse, BHL is an amazing corpus of text and images, and this is almost entirely hidden from me. If I see a cool looking word, or some old typeface, there's no way for me to dig deeper (what text did that come from?, what does that phrase mean?). I get no sense of where the words come from, or whether I'm doing anything actually useful. For things like ReCAPTCHA (where you helped OCR books) this doesn't matter because I don't care about the books, I want my tickets. But for BHL I do care (and BHL should want at least some of the players to care as well).

So, remembering that I'm not a gamer, here are some quick ideas for games.

Find that species

One reason BHL is so useful is it contains original taxonomic descriptions. Sometimes the OCR is too poor for the name to extracted from the description. Imagine a game where the player has a list of species (with cute pictures) and is told to go find them in the text. Imagine that we have a pretty good idea where they are (from bibliographic data we could, for example, know the page the name should occur on), the player hunts for the word on the page, and when they find it and mark it. BHL then gets corrected text and confirmation that the name occurs on that page. Players could select taxa (e.g., birds, turtles, mosses) that they like.

Find lat/longs

BHL text is full of lat/long pairs, often the OCR is not quite good enough to extract them. Imagine that we can process BHL to find things that look like lat/long pairs. Imsgine that we can read enough of the text to get a sense of where in the world the text refers to. Now, have a game where we pick a spot on a map and find things related to that spot. Say we get presented with OCR text that may refer to that locality, we fix it, and the map starts get populated. A bit like Yelp and Four Square, we could imagine badges for the most articles found about a place.

Find the letter/font

There are lots of cool symbols and fonts in BHL, someone might be interested collecting these. Simple things might be diphthongs such as æ. Older BHL texts are full of these, often misinterpreted. Other examples are male and female symbols. Perhaps we could have a game where we try and guess what symbol the OCR text actually matches - in other words, show the OCR text first, player tries to guess actual symbol, then the image appears, and then player types in actual symbol. Goal is to get good at predicting OCR errors.

Games like this would really benefit if the player could see (say, on the side) the complete text. Imagine that you correct a word, then you see it comes from a gorgeous plate of a bird. Imagine you could then correct any of the there words on that page.

Word eaters

Imagine the layer is presented with a page with text and, a bit like Minecraft's monsters, things appear which start to "eat" the words. You need to check as many words as possible before the text is eaten. Perhaps structure things in such a way that checked words form a barrier to the word-eating creatures and buy you some time, or like Minecraft, fixing a bad OCR word blasts a radius free of the word eaters. As an option (again, like Minecraft) turn off the eaters and just correct the words at your leisure.

Countdown

Based on the UK game show, present a set of random letters (as images), player makes longest word they can, then check against dictionary, this tells you what letters they think the images represent.

Falling words

Have page fragments fall from the top of the screen, and have a key word displayed (say, "sternum", or enable player to type a word in) then display images of words whose OCR text resembles this (in other words, have a bunch of OCR text indexed using methods that allow for errors). As the word images fall, the player taps on an image that matches the word and they are collected. Maybe add features such as a timeline to show when the word was used (i.e., the date of the BHL text), give the meaning of the word, lightly chide players who enter words like "f**k" (that'd be me), etc.

Summary

Like comedy, I imagine that designing games is really, really hard. But the best games I've seen create a world that the player is immersed in and which makes sense within the rules of that world. Regardless of whether these ideas are any good, my concern is that the BHL games seem completely divorced from context, and the game play bears no relation to outcomes in the game.

Altmetrics, Disqus, GBIF, JSTOR, and annotating biodiversity data

Browsing JSTOR's Global Plants database I was struck by the number of comments people have made on individual plant specimens. For example, for the Holotype of Scorodoxylum hartwegianum Nees (K000534285) there is a comment from Håkan Wittzell that the "Collection number should read 1269 according to Plantae Hartwegianae". In JSTOR the collection number is 1209.

Now, many (if not all) of these specimens will also be in GBIF. Indeed, K000534285 is in GBIF as http://www.gbif.org/occurrence/912442645, also with collection number 1209. A GBIF user will have no idea that there is some doubt about one item of metadata about this specimen.

So, an obvious thing to do would be to make the link between the JSTOR and GBIF records. Implementing this would need so fussing because (sigh) unlike DOIs for articles we don't have agreed upon identifiers for specimens. So we'd need to do some mapping between the specimen barcode K000534285, the JSTOR URL http://plants.jstor.org/stable/10.5555/al.ap.specimen.k000534285, and the GBIF record http://www.gbif.org/occurrence/912442645.

In addition to providing users with more information, it might also be useful in kickstarting annotation on the GBIF site. At the moment GBIF has no mechanism for annotating data, and if it did, then it would have to start from scratch. Imagine that a person visiting occurrence 912442645 sees that it has already attracted attention elsewhere (e.g., JSTOR). They might be encouraged to take part in that conversation (because at least one person cared enough to comment already). Likewise, we could feed annotations on the GBIF site to JSTOR.

A variation on this idea is to think of annotations such as those in the JSTOR database as being analogous to the tweets, blog posts, and bookmarking that altmetric tracks for academic papers. Imagine if we applied the same logic to GBIF and had a way to show users that a specimen has been commented on in JSTOR Plants? Thinking further down the track, we could image adding other sorts of "attention", such as citations by papers, vouchers for DNA sequences, etc.

It would be a fun project to see whether the Disqus API enabled us to create a tool that could match JSTOR Global Plants comments to GBIF occurrences.

Steve Baskauf on RDF and the "Rod Page Challenge"

Steve Baskauf has concluded a thoughtful series of blog posts on RDF and biodiversity informatics with http://baskauf.blogspot.co.uk/2015/07/confessions-of-rdf-agnostic-part-7.html. In this post he discussed the "Rod Page Challenge", which was a series of grumpy posts I wrote (starting with this one) where I claimed RDF basically sucked, and to illustrate this I issued a challenge for people to do something interesting with some RDF I provided. Since this RDF didn't have a stable home I've put it on GitHub and it has a DOI http://dx.doi.org/10.5281/zenodo.20990 courtesy of GitHub's integration with Zenodo.

I argued that the RDF typically available was basically useless because it wasn't adequately linked (see Reflections on the TDWG RDF "Challenge"). Two of the RDF files I provided were created specifically created to tackle this problem (derived from my projects iPhylo Linkout http://dx.doi.org/10.1371/currents.RRN1228 and the precursor to BioNames http://dx.doi.org/10.7717/peerj.190). This marked pretty much the end of any interest I had in pursuing RDF.

Towards the end of Steve's post he writes:

At the close of my previous blog post, in addition to revisiting the Rod Page Challenge, I also promised to talk about what it would take to turn me from an RDF Agnostic into an RDF Believer. I will recap the main points about what I think it will take in order for the Rod Page Challenge to REALLY be met (i.e. for machines to make interesting inferences and provide humans with information about biodiversity that would not be obvious otherwise):

1. Resource descriptions in RDF need to be rich in triples containing object properties that link to other IRI-identified resources.
2. "Discovery" of IRI-identified resources is more likely to lead to interesting information when the linked IRIs are from Internet domains controlled by different providers.
3. Materialized entailed triples do not necessarily lead to "learning" useful things. Materialized entailed triples are useful if they allow the construction of more clever or meaningful queries, or if they state relationships that would not be obvious to humans.

Steve's point 1 is essentially the point I was making with the challenge. At the time of the challenge, RDF from major biodiversity informatics projects was in silos, with few (if any) links to external resources (the kinds of things Steve refers to in his point 2). As a result, the promised benefits from RDF simply haven't materialised. The lesson I took from this is that we need rich, dense cross-links between data sources (the "biodiversity knowledge graph"), and that's one reason I've been obsessed with populating BioNames, which links animal names to the primary literature (I'm planning to extend this to plants as well). Turns out , creating lots of cross links is really hard work, much harder than simply pumping out a bunch of RDF and waiting for it to automagically coalesce into an all-connected knowledge graph.

I posed the challenge back in 2011, and since then I think the landscape has changed to the extent that I wonder if trying to "fix" RDF is really the way forward.

XML is dead

Anyone (sane) developing for the web and wanting to move data around is using JSON, XML is hideous and best avoided. Much of the early work on RDF used XML, which only made things even harder than they already were. JSON beats XML, to the extent that RDF itself now has a JSON serialisation, JSON-LD. But JSON-LD is about more than the semantic web (see JSON-LD and Why I Hate the Semantic Web), and has the great advantage that you can actually ignore all the RDF cruft (i.e., the namespaces) and simply treat the data as key-value pairs (yay!). Once you do that, then you can have fun with the data, especially with databases such as CouchDB ("fun" and "database" in the same sentence, I know!).

Key-value pairs, document stores, and graph databases

The NoSQL "movement" has thrown up all sorts of new ways to handle data and to think about databases. We can think of RDF as describing a graph, but it carries the burden of all the namespaces, vocabularies, and ontologies that come with it. Compare that with the fun (there's that word again) of graph databases such as Neo4J with its graph gists. The Neo4J folks have made a great job of publicising their approach, and making it easy and attractive to play with.

So, we're in a interesting time when there are a bunch of technologies available, and I think maybe it's time to ask whether the community's allegiance to RDF and the Semantic Web has been somewhat misplaced...