Friday, November 10, 2017

Exploring images in the Biodiversity Literature Repository

A post by on the Plaza blog Expanded access to images in the Biodiversity Literature Repository has prompted me to write up a little toy I created earlier this week.

The Biodiversity Literature Repository (BLR) is a repository of taxonomic papers hosted by Zenodo. Where possible Plazi have extracted individual images and added those to the BLR, even if the article itself is not open access. The justification for being able to do this is presented here: DOI:10.1101/087015. I'm not entirely convinced by their argument (see Copyright and the Use of Images as Biodiversity Data) but rather than rehash that argument I decide dit would be much more fun to get a sense of what is in the BLR. I built a tool to scrape data from Zenodo and store it in CouchDB, put a simple search engine on top (using the search functionality in Cloudant) to search within the figure captions, and wrote some code to use a cloud-based image server to generate thumbnails for the images in Zenodo (some of which are quite big). The tool is hosted at Heroku, you can try it out here:

Screenshot 2017 11 10 11 03 30

This is not going to win any design awards, I'm simply trying to get a feel for what imagery BLR has. My initial reactions was "wow!". There's a rich range of images, including phylogenies, type specimens, habitats, and more. Searching by museum codes, e.g. NHMUK is a quick way to discover images of specimens from various collections.

Screenshot 2017 11 10 11 22 05

Based on this experiment there are at least two things I think would be fun to do.

Adding more images

BLR already has a lot of images, but the biodiversity literature is huge, and there's a wealth of imagery elsewhere, including journals not in BLR, and of course the Biodiversity Heritage Library (BHL). Extracting images from articles in BHL would potentially add a vast number of additional images.

Machine learning

Machine learning is hot right now, and anyone using iNaturalist is probably aware of their use of computer vision to suggest identifications for images you upload. It would be fascinating to apply machine learning to images in the BLR. Even basic things such as determining whether an image is a photo or a drawing, how many specimens are included, what the specimen orientation is, what part of the organism is being displayed, is the image a map (and of what country) would be useful. There's huge scope here for doing something interesting with these images.

The toy I created is very basic, and merely scratches the surface of what could be done (Plazi have also created their own tool, see But spending a few minutes browsing the images is well worthwhile, and if nothing else is a reminder of both how diverse life is, and how active taxonomists are in trying to discover and describe that diversity.

Friday, October 06, 2017

Notes on finding georeferenced sequences in GenBank

Notes on how many georeferenced DNA sequences there are in GenBank, and how many could potentially be georeferenced.

BCT	Bacterial sequences
PRI	Primate sequences
ROD	Rodent sequences
MAM	Other mammalian sequences
VRT	Other vertebrate sequences
INV	Invertebrate sequences
PLN	Plant and Fungal sequences
VRL	Viral sequences
PHG	Phage sequences
RNA	Structural RNA sequences
SYN	Synthetic and chimeric sequ
UNA	Unannotated sequences
?db=nucleotide nucleotides
&term=ddbj embl genbank with limits[filt]
NOT transcriptome[All Fields] ignore transcriptome data
NOT mRNA[filt] ignore mRNA data
NOT TSA[All Fields] ignore TSA
NOT scaffold[All Fields] ignore scaffold
AND src lat lon[prop] include records that have source feature "lat_lon"
AND 2010/01/01:2010/12/31[pdat] from this date range
AND gbdiv_pri[PROP] restrict search to PRI division (primates)
AND srcdb_genbank[PROP] Need this if we query by division, see NBK49540

Numbers of nucleotide sequences that have latitude and longitudes in GenBank for each year.


Numbers of nucleotide sequences that don't have latitude and longitudes in GenBank for each year but do have the country field and hence could be georeferenced.


Wednesday, October 04, 2017

TDWG 2017: thoughts on day 3

Day three of TDWG 2017 highlighted some of the key obstacles facing biodiversity informatics.

After a fun series of "wild ideas" (nobody will easily forget David Bloom's "Kill your Darwin Core darlings") we had a wonderful keynote by Javier de la Torre (@jatorre) entitled "Everything happens somewhere, multiple times". Javier is CEO and founder of Carto, which provides tools for amazing geographic visualisations. Javier provided some pithy observations on standards, particularly the fate of official versus unofficial "community" standards (the community standards tend to be simpler, easier to use, and hence win out), and the potentially stifling effects standards can have on innovation, especially if conforming to standards becomes the goal rather than merely a feature.

The session Using Big Data Techniques to Cross Dataset Boundaries - Integration and Analysis of Multiple Datasets demonstrated the great range of things people want to do with data, but made little progress on integration. It still strikes me as bizarre that we haven't made much progress on minting and reusing identifiers for the same entities that we keep referring too. Channeling Steve Balmer:

Identifiers, identifiers, identifiers, identifiers

It's also striking to compare Javier de la Torre's work with Carto where there is a clear customer-driven focus (we need these tools to deliver this to users so that they can do what they want to do) versus the much less focussed approach of our community. Many of the things we aspire to won't happen until we identify some clear benefits for actual users. There's a tendency to build stuff for our own purposes (e.g., pretty much everything I do) or build stuff that we think people might/should want, but very little building stuff that people actually need.

TDWG also has something of an institutional memory problem. Franck Michel gave an elegant talk entitled A Reference Thesaurus for Biodiversity on the Web of Linked Data which discussed how the Muséum national d'Histoire naturelle's taxonomic database could be modelled in RDF (see for example There's a more detailed description of this work here:

This browser does not support PDFs. Please download the PDF to view it: Download PDF.

What struck me was how similar this was to the now deprecated TDWG LSID vocabulary, still used my most of the major taxonomic name databases (the nomenclatures). This is an instance where TDWG had a nice, workable solution, it lapsed into oblivion, only to be subsequently reinvented. This isn't to take anything away from Frank's work, which has a thorough discussion of the issues, and has a nice way to handle the the difference between asserting that two taxa are the same (owl:equivalentClass) and that a taxon/name hybrid (which is what many databases serve up because they don't distinguish between names and taxa) and a taxon might be the same (linking via the name they both share).

The fate of the RDF served by the nomenclators for the last decade illustrates a point I keep returning too (see also EOL Traitbank JSON-LD is broken). We tend to generate data and standards because it's the right thing to do, rather than because there's actually a demonstrable need for that data and those standards.

Bitcoin, biodiversity, and micropayments for open data

I gave a "wild ideas" talk at TDWG17 suggesting that the biodiversity community use Bitcoin to make micropayments to use data.

The argument runs like this:

  1. We like open data because it's free and it makes it easy to innovate, but we struggle to (a) get it funded and (b) it's hard to demonstrate value (hence pleas for credit/attribution, and begging for funding).
  2. The alternative of closed data, such as paying a subscription to access a database limits access and hence use and innovation, but generates an income to support the database, and the value of the database is easy to measure (it's how much money it generates).
  3. What if we have a "third model" where we pay small amounts of money to access data (micropayments)?

Micropayments as a way to pay creators is an old idea (it was part of Ted Nelson's Xanadu vision). Now that we have cryptocurrencies such as Bitcoin, micropayments are feasible. So we could imagine something like this:

  1. Access to raw datasets is free (you get what you pay for)
  2. Access to cleaned data comes at a cost (you are paying someone else to do the hard, tedious work of making the data usable)
  3. Micropayments are made using Bitcoin
  4. To help generate funds any spare computational capacity in the biodiversity community is used to mine Bitcoins

After the talk Dmitry Mozzherin sent me a link to Steem, and then this article about Steemit appeared in my Twitter stream:

Clearly this is an idea that has been bubbling around for a while. I think there is scope for thinking about ways to combine a degree of openness (we don't want to cripple access and innovation) with a way to fund that openness (nobody seems interested in giving us money to be open).

Tuesday, October 03, 2017

TDWG 2017: thoughts on day 1

Some random notes on the first day of TDWG 2017. First off, great organisation with the first usable conference calendar app that I've seen (

I gave the day's keynote address in the morning (slides below).

It was something of a stream of consciousness brain dump, and tried to cover a lot of (maybe too much) stuff. Among the topics I covered were Holly Bik's appeal for better links between genomic and taxonomic data, my iSpecies tool, some snarky comments on the Semantic Web (and an assertion that the reason that GenBank succeeded was due more to network effects than journals requiring authors to submit sequences there), a brief discussion of Wikidata (including using d3sparql to display classifications, see here), and the use of Hexastore to query data from BBC Wildlife. I also talked about Ted Nelson, Xanadu, using to annotate scientific papers (see Aggregating annotations on the scientific literature: a followup on the ReCon16 hackday), social factors in building knowledge graphs (touching on ORCID and some of the work by Nico Franz discussed here), and ended with some cautionary comments on the potential misuse of metrics based on knowledge graphs (using "league tables" of cited specimens, see GBIF specimens in BioStor: who are the top ten museums with citable specimens?).

TDWG is a great opportunity to find out what is going on in biodiversity informatics, and also to get a sense of where the problems are. For example, sitting through the Financial Models for Sustaining Biodiversity Informatics Products session you couldn't help being struck by (a) the number of different projects all essentially managing specimen data, and (b) the struggle they all face to obtain funding. If this was a commercial market there would be some pretty drastic consolidation happening. It also highlights the difficulty of providing services to a community that doesn't have much money.

I was also struck by Andrew Bentley's talk Interoperability, Attribution, and Value in the Web of Natural History Museum Data. In a series of slides Andrew outlined what he felt collections needed from aggregators, researchers, and publishers, e.g.:

Chatting to Andrew at the evening event at the Canadian Museum of Nature, I think there's a lot of potential for developing tools to provide collections with data on the use and impact of their collections. Text mining the biodiversity literature on a massive scale to extract (a) mentions of collections (e.g., their institutional acronyms) and (b) citations of specimens could generate metrics that would be helpful to collections. There's a great opportunity here for BHL to generate immediate value for natural history collections (many of which are also contributors to BHL).

Also had a chance to talk to Jorrit Poelen who works on Global Biotic Interactions (GloBI). He made some interesting comparisons between Hexastores (which I'd touched on in my keynote) and Linked Data Fragments.

The final session I attended was Towards robust interoperability in multi-omic approaches to biodiversity monitoring. The overwhelming impression was that there is a huge amount of genomic data, much of which does not easily fit into the classic, Linnean view of the world that characterises, say, GBIF. For most of the sequences we don't know what they are, and that might not be the most interesting question anyway (more interesting might be "what do they do?"). The extent to which these data can be shoehorned into GBIF is not clear to me, although doing so may result in some healthy rethinking of the scope of GBIF itself.

Monday, September 18, 2017

Guest post: Our taxonomy is not your taxonomy

Bob mesibov The following is a guest post by Bob Mesibov.

Do you know the party game "Telephone", also known as "Chinese Whispers"? The first player whispers a message in the ear of the next player, who passes the message in the same way to a third player, and so on. When the last player has heard the whispered message, the starting and finishing versions of the message are spoken out loud. The two versions are rarely the same. Information is usually lost, added or modified as the message is passed from player to player, and the changes are often pretty funny.

I recently compared ca 100 000 beetle records as they appear in the Museums Victoria (NMV) database and in DarwinCore downloads from the Atlas of Living Australia (ALA) and the Global Biodiversity Information Facility (GBIF). NMV has its records aggregated by ALA, and ALA passes its records to GBIF. The "Telephone" effect in the NMV to ALA to GBIF comparison was large and not particularly funny.

Many of the data changes occur in beetle names. ALA checks the NMV-supplied names against a look-up table called the National Species List, which in this case derives from the Australian Faunal Directory (AFD). If no match is found, ALA generalises the record to the next higher supplied taxon, which it also checks against the AFD. ALA also replaces supplied names if they are synonyms of an accepted name in the AFD.

GBIF does the same in turn with the names it gets from ALA. I'm not 100% sure what GBIF uses as beetle look-up table or tables, but in many other cases their GBIF Backbone Taxonomy mirrors the Catalogue of Life.

To give you some idea of the magnitude of the changes, of ca 85000 NMV records supplied with a genus+species combination, about one in five finished up in GBIF with a different combination. The "taxonRank" changes are summarised in the overview below, and note that replacement ALA and GBIF taxon names at the same rank are often different:


Of the species that escaped generalisation to a higher taxon, there are 42 names with genus triples: three different genus names for the same taxon in NMV, ALA and GBIF.

Just one example: a paratype of the staphylinid Schaufussia mona Wilson, 1926 is held in NMV. The record is listed under Rytus howittii (King, 1866) in the ALA Darwin Core download, because AFD lists Schaufussia mona as a junior subjective synonym of Tyrus howitti King, 1866, and Tyrus howittii in AFD is in turn listed as a synonym of Rytus howittii (King, 1866). The record appears in GBIF under Tyraphus howitti (King, 1865), with Rytus howittii (King, 1866) listed as a synonym. In AFD, Rytus howittii is in the tribe Tyrini, while Tyraphus howitti is a different species in the tribe Pselaphini.

ALA gives "typeStatus" as "paratype" for this record, but the specimen is not a paratype of Rytus howittii. In the GBIF download, the "typeStatus" field is blank for all records. I understand this may change in future. If it does, I hope the specimen doesn't become a paratype of Tyraphus howitti through copying from ALA.

There are lots of "Telephone" changes in non-taxonomic fields as well, including some geographical howlers. ALA says that a Kakadu National Park record is from Zambia and another Northern Territory record is from Mozambique, because ALA trusts the incorrect longitude provided by NMV more than it does the NMV-supplied locality text. GBIF blanks this locality text field, leaving the GBIF user with two African records for Australian specimens and no internal contradictions.

ALA trusts latitude/longitude to the extent of changing the "stateProvince" field for localities near Australian State borders, if a low-precision latitude/longitude places the occurrence a short distance away in an adjoining State.

Manglings are particularly numerous in the "recordedBy" field, where name strings are reformatted, not always successfully. Complex NMV strings suffer worst, e.g. "C Oke; Charles John Gabriel" in NMV becomes "Oke, C.|null" in ALA, and "Ms Deb Malseed - Winda-Mara Aboriginal Corporation WMAC; Ms Simone Sailor - Winda-Mara Aboriginal Corporation WMAC" is reformatted as in ALA "null|null|null|null"

Most of the "Telephone" effect in the NMV-ALA-GBIF comparison appears in the NMV-ALA stage. I contacted ALA by email and posted some of the issues on the ALA GitHub site; I haven't had a response and the issues are still open. I also contacted Tim Robertson at GBIF, who tells me that GBIF is working on the ALA-GBIF stage.

Can you get data as originally supplied by NMV to ALA, through ALA? Well, that's easy enough record-by-record on the ALA website, but not so easy (or not possible) for a multi-record download. Same with GBIF, but in this case the "original" data are the ALA versions.

Monday, August 28, 2017

Let’s rise up to unite taxonomy and technology

Holly Bik (@hollybik) has an opinion piece in PLoS Biology entitled "Let’s rise up to unite taxonomy and technology" (thanks to @sjurdur for bringing this to my attention).

Journal pbio 2002231 g001

It's a passionate plea for integrating taxonomic knowledge and "omics" data. In her article Bik includes a mockup of the kind of tool she'd like to see (based in part on Phinch), and writes:

Step 2: Clicking on a specific data point (e.g., an OTU) will pull up any online information associated with that species ID or taxonomic group, such as Wikipedia entries, photos, DNA sequences, peer-reviewed articles, and geolocated species observations displayed on a map.

This sort of plea has been made any times, and reminds me very much of PLoS's own efforts when they wanted to build a "Biodiversity Hub" and biodiversity informatics basically failed them. The hub itself later closed down.. There's clearly a need for a simply way to summarise what we know about a species, but we've yet to really tackle this (on the face of it) fairly simple task.

Quickly summarising the available information about a species was the motivation behind my little tool iSpecies, which I recently reworked to use DBpedia, GBIF, CrossRef, EOL, TreeBASE and OpenTreeofLife as sources. For the nematode featured in Bik's figure (Desmoscolex) there's not a great deal of easily available information (see We can get a little more form other sources not queried by iSpecies, such as BioNames, which aggregates the primary taxonomic literature, see

Part of the problem is that taxonomy is fundamentally a "long tail" field, both in terms of the subject matter (a few very well know species, then millions of poorly known species) and our knowledge of those species (a large, scattered taxonomic literature, much of it not yet digitised, although progress is being made). Furthermore, the names of species (and our conception of them) can change, adding an additional challenge.

But I think we can do a lot better. Simple web-based tools like iSpecies can assemble reasonable information from multiple sources (and in multiple languages) on the fly. It would be nice to expand those sources (the more primary sources the better). The current iSpecies tool searches on species name. This works well if the sources being queried mention that name (e.g., in the title of a paper that has a DOI and is indexed by CrossRef). Given that many of the "omics" datasets Bik works with are likely to have dark taxa, what we'll also need is the ability to search, say, using NCBI taxon ids, and retrieve literature linked to sequences for those taxa

It would also be useful to package those up in a simple API that other tools could consume. For example, if I wanted to improve the utility of iSpecies, one approach would be to package up the results in a JSON object. Perhaps even use JSON-LD (with global identifiers for taxa, documents, etc.) to make it possible for consumers to easily integrate that data with their own.

Taxonomy could be on the brink of another golden age—if we play our cards right. As it is reinvented and reborn in the 21st century, taxonomy needs to retain its traditional organismal-focused approaches while simultaneously building bridges with phylogenetics, ecology, genomics, and the computational sciences.

Taxonomy is, of course, doing just this, albeit not nearly fast enough. There are some pretty serious obstacles, some of them cultural, but some of them due to the nature of the problem. Taxonomic knowledge is massively decentralised, mostly non-digital, and many of the key sources and aggregations are behind paywalls. There is also a fairly large "technical debt" to deal with. Ian Mulvany was recently interviewed by PLoS and he emphasised that because academic publishers had been online from early on they were pioneers, but at the same time this left them with a legacy of older technologies and approaches that can sometimes get in the way of new idea. I think taxonomy suffers from some of the same problems. Because taxonomy has long been involved with computers, sometime we needed up betting on the "wrong" solutions. For example, at one time XML was the new hotness, and people invested a lot of effort in developing XML schema, and then ontologies and RDF vocabularies. Meantime much of the web has moved to simple data formats such as JSON, many specialist vocabularies are gathering dust as takes off, and projects like Wikidata force us to rethink the need to topic-specific databases.

But these are technical details. For me the key point of "Let’s rise up to unite taxonomy and technology" is that it's a symptom of the continued failure of biodiversity informatics to actually address the needs of its users. People keep asking for fairly simple things, and we keep ignoring them (or explaining why it's MUCH harder than people think, which is another way of ignoring them).

Sunday, August 20, 2017

Notes on displaying big trees using Google Maps/Leaflet

Notes to self on web map-style tree viewers. The basic idea is to use Google Maps or Leaflet to display a tree. Hence we need to compute tiles. One approach is to use a database that supports spatial queries to store the x,y coordinates of the tree. When we draw a tile we compute the coordinates of that tile, based on position and zoom level, do a spatial query to extract all lines that intersect with the rectangle for that tile, and draw those.

A nice example of this is Lifemap (see also De Vienne, D. M. (2016). Lifemap: Exploring the Entire Tree of Life. PLOS Biology, 14(12), e2001624. doi:10.1371/journal.pbio.2001624).

It occurs to me that for trees that aren't too big we could do this without an external database. For example, what if we used a Javascript implementation of an R-tree, such as imbcmdth/RTree or its fork leaflet-extras/RTree. So, we could compute the coordinates of the nodes in the tree in "geographic" space, store the bounding box for each line/arc in an R-tree, then query that R-tree for lines that intersect with the bounding box of the relevant tile. We could use a clipping algorithm to only draw the bits of the lines that cross the tile itself.

Web maps, at least in my experience, make trips to a tile server to fetch a tile, we would want instead to call a routine within our web page, because all the data would be loaded into that page. So we'd need to modify the tile creating code.

The ultimate goal would be to have a single page web app that accepts a Newick-style tree and converts into a browsable, zoomable visualisation.

Tuesday, July 04, 2017

Apple's Knowledge Navigator concept video (1987): we are still a long way from this vision

I've been viewing Apple's Knowledge Navigator concept video from 1987 and it's striking how much of this we have today, and yet how far away we are from the complete vision. For some background on this promotional video see The Making of Knowledge Navigator. The computer scientist Alan Kay provided some advice to the makers (who put the video together for a presentation by then Apple CEO John Sculley). Kay is a true visionary, he's currently working on children, computers, and education, motivated by the realisation that, like the printing press before it, computing will change the way people think, and how children learn using computers could have a profound impact on our future.

The Knowledge Navigator video looked futuristic when it came out, but now we have ubiquitous touch interfaces, video chat, and can talk to computers (albeit not with the level of sophistication shown in the video). But there are a couple of things in the concept video that are in many ways even more impressive.

Early on, our professor is trying to track down a paper, and he can't quite remember the name of the author. His visual assistant (a more sophisticated version of Siri) finds it, which of itself isn't too exciting (Google supports searching for things when you don't quite know what it is you're looking for). What is more impressive is that the professor can access and play with the data in the paper, and compare the predictions made in that paper with more recent data.

This requires that we have access to the data and models from a published paper, and a way to easily add new data and redo the analyses. This is related to "reproducible science" doi:10.1038/s41562-016-0021 and the notion of "executable papers" doi:10.1016/j.procs.2011.04.074, but goes beyond that because we don't just reproduce the results in the original paper, we can add to them. And it's all seamless and effortless. Anyone who has tried to get adta from a paper and do something with it will recognise that we are a long way from this.

The second interesting example is when our professor is chatting online with a colleague about deforestation in South America, and she sends him her graphical model of the spread of the Sahara. They then view these side-by-side. Note that this is not two separate videos, the simulations merge together and their timeline syncs so that they play together simultaneously. The parameters of the simulations can also be changed on the fly.

This ability to collaborate in real time in the same space with both data and analysis is something that we don't really have, at least I'm not aware of it. Yes, we can work together on editing a Google Document, but throwing together two data sets or visualisations and have them align themselves automatically is pretty cool.

While some aspects of the Knowledge Navigator video look quaint, it's striking that the actual core of the video - a researcher redoing an analysis published by another researcher, or collaborating with a colleague with different but related data is still something we haven't been able to achieve yet (for some related work on collaboratively viewing evolutionary trees see "Interactive Tree Comparison for Co-located Collaborative Information Visualization" doi:10.1109/TVCG.2007.70568). In this respect the Knowledge Navigator is still a vision of the future.

Friday, June 30, 2017

Response to To Increase Trust, Change the Social Design Behind Aggregated Biodiversity Data

Nico Franz and Beckett W. Sterner recently published a preprint entitled "To Increase Trust, Change the Social Design Behind Aggregated Biodiversity Data" on bioRxiv Below is the abstract:

Growing concerns about the quality of aggregated biodiversity data are lowering trust in large-scale data networks. Aggregators frequently respond to quality concerns by recommending that biologists work with original data providers to correct errors "at the source". We show that this strategy falls systematically short of a full diagnosis of the underlying causes of distrust. In particular, trust in an aggregator is not just a feature of the data signal quality provided by the aggregator, but also a consequence of the social design of the aggregation process and the resulting power balance between data contributors and aggregators. The latter have created an accountability gap by downplaying the authorship and significance of the taxonomic hierarchies ≠ frequently called "backbones" ≠ they generate, and which are in effect novel classification theories that operate at the core of data-structuring process. The Darwin Core standard for sharing occurrence records plays an underappreciated role in maintaining the accountability gap, because this standard lacks the syntactic structure needed to preserve the taxonomic coherence of data packages submitted for aggregation, leading to inferences that no individual source would support. Since high-quality data packages can mirror competing and conflicting classifications, i.e., unsettled systematic research, this plurality must be accommodated in the design of biodiversity data integration. Looking forward, a key directive is to develop new technical pathways and social incentives for experts to contribute directly to the validation of taxonomically coherent data packages as part of a greater, trustworthy aggregation process.

Below I respond to some specific points that annoyed me about this article, at the end I try and sketch out a more constructive response. Let me stress that although I am the current Chair of the GBIF Science Committee, the views expressed here are entirely my own.

Trust and social relations

Trust is a complex and context-sensitive concept...First, trust is a dependence relation between a person or organization and another person or organization. The first agent depends on the second one to do something important for it. An individual molecular phylogeneticist, for example, may rely on GenBank (Clark et al. 2016) to maintain an up-to-date collection of DNA sequences, because developing such a resource on her own would be cost prohibitive and redundant. Second, a relation of dependence is elevated to being one of trust when the first agent cannot control or validate the second agent's actions. This might be because the first agent lacks the knowledge or skills to perform the relevant task, or because it would be too costly to check.

Trust is indeed complex. I found this part of the article to be fascinating, but incomplete. The social network GBIF operates in is much larger than simply taxonomic experts and GBIF, there are relationships with data providers, other initiatives, a broad user community, government agencies that approve it's continued funding, and so on. Some of the decisions GBIF makes need to be seen in this broader context.

For example, the article challenges GBIF for responding to errors in the data by saying that these should be "corrected at source". This a political statement, given that data providers are anxious not to ceed complete control of their data to aggregators. Hence the model that GBIF users see errors, those errors get passed back to source (the mechanisms for tis is mostly non-existent), the source fixes it, then the aggregator re-harvests. This model makes assumptions about whether sources are either willing or able to fix these errors that I think are not really true. But the point is this is less about not taking responsibility, but instead avoiding treading on toes by taking too much responsibility. Personally I think should take responsibility for fixing a lot of these errors, because it is GBIF whose reputation suffers (as demonstrated by Franz and Sterner's article).


A third step is to refrain from defending backbones as the only pragmatic option for aggregators (Franz 2016). The default argument points to the vast scale of global aggregation while suggesting that only backbones can operate at that scale now. The argument appears valid on the surface, i.e., the scale is immense and resources are limited. Yet using scale as an obstacle it is only effective if experts were immediately (and unreasonably) demanding a fully functional, all data-encompassing alternative. If on the other hand experts are looking for token actions towards changing the social model, then an aggregator's pursuit of smaller-scale solutions is more important than succeeding with the 'moonshot'.

Scalability is everything. GBIF is heading towards a billion occurrence records and several million taxa (particularly as more and more taxa from DNA-barcoding taxa are added). I'm not saying that tractability trounces trust, but it is a major consideration. Anybody advocating a change has got to think about how these changes will work at scale.

I'm conscious that this argument could easily be used to swat away any suggestion ("nice idea, but won't scale") and hence be a reason to avoid change. I myself often wish GBIF would do things differently, and run into this problem. One way around it is to make use of the fact that GBIF has some really good APIs, so if you want GBIF to do something different you can build a proof of concept to show what could be done. If that is sufficiently compelling, then the case for trying to scale it up is going to be much easier to make.

Multiple classifications

As a social model, the notion of backbones (Bisby 2000) was misguided from the beginning. They disenfranchise systematists who are by necessity consensus-breakers, and distort the coherence of biodiversity data packages that reflect regionally endorsed taxonomic views. Henceforth, backbone-based designs should be regarded as an impediment to trustworthy aggregation, to be replaced as quickly and comprehensively as possible. We realize that just saying this will not make backbones disappear. However, accepting this conclusion counts as a step towards regaining accountability.

This strikes me as hyperbole. "They disenfranchise systematists who are by necessity consensus-breakers". Really? Having backbones in no way prevents people doing systematic research, challenging existing classifications, or developing new ones (which, if they are any good, will become the new consensus).

We suggest that aggregators must either author these classification theories in the same ways that experts author systematic monographs, or stop generating and imposing them onto incoming data sources. The former strategy is likely more viable in the short term, but the latter is the best long-term model for accrediting individual expert contributions. Instead of creating hierarchies they would rather not 'own' anyway, aggregators would merely provide services and incentives for ingesting, citing, and aligning expert-sourced taxonomies (Franz et al. 2016a).

Backbones are authored in the sense that they are the product of people and code. GBIF's is pretty transparent (code and some data on github, complete with a list of problems). Playing Devil's advocate, maybe the problem here is the notion of authorship. If you read a paper with 100's of authors, why does that give you any greater sense of accountabily? Is each author going to accept responsibility for (or being to talk cogently about) every aspect of that paper? If aggregators such as GBIF and Genbank didn't provide a single, simple way to taxonomically browse the data I'd expect it would be the first thing users would complain about. There are multiple communities GBIF must support, including users who care not at all about the details of classification and phylogeny.

Having said that, obviously these backbone classifications are often problematic and typically lag behind current phylogenetic research. And I accept that they can impose a certain view on how you can query data. GenBank for a long time did not recognise the Ecdysozoa (nematodes plus arthropods) despite the evidence for that group being almost entirely molecular. Some of my research has been inspired by the problem of customising a backbone classification to better more modern views (doi:10.1186/1471-2105-6-208).

If handling multiple classifications is an obstacle to people using or contributing data to GBIF, then that is clearly something that deserves attention. I'm a little sceptical, in that I think this is similar to the issue of being able to look at multiple versions of a document or GenBank sequence. Everyone says it's important to have, I suspect very few people ever use that functionality. But a way forward might be to construct a meaningful example (in other words an live demo, not a diagram with a few plant varieties).

Ways forward

We view this diagnosis as a call to action for both the systematics and the aggregator communities to reengage with each other. For instance, the leadership constellation and informatics research agenda of entities such as GBIF or Biodiversity Information Standards (TDWG 2017) should strongly coincide with the mission to promote early-stage systematist careers. That this is not the case now is unfortunate for aggregators, who are thereby losing credibility. It is also a failure of the systematics community to advocate effectively for its role in the biodiversity informatics domain. Shifting the power balance back to experts is therefore a shared interest.

Having vented, let me step back a little and try and extract what I think the key issue is here. Issues such as error correction, backbones, multiple classifications are important, but I guess the real issue here is the relationship between experts such as taxonomists and systematists, and large-scale aggregators (note that GBIF serves a community that is bigger than just these researchers). Franz and Sterner write:

...aggregators also systematically compromise established conventions of sharing and recognizing taxonomic work. Taxonomic experts play a critical role in licensing the formation of high-quality biodiversity data packages. Systems of accountability that undermine or downplay this role are bound to lower both expert participation and trust in the aggregation process.

I think this is perhaps the key point. Currently aggregation tends to aggregate data and not provenance. Pretty much every taxonomic name has at one point or other been published by somebody. For various reasons (including the crappy way most nomenclature databases cite the scientific literature) by the time these names are assembled into a classification by GBIF the names have virtually no connection to the primary literature, which also means that who contributed the research that led to that name being minted (and the research itself) is lost. Arguably GBIF is missing an opportunity to make taxonomic and phylogenetic research more visible and discoverable (I'd argue this is a better approach than Quixotic efforts to get all biologists to always cite the primary taxonomic literature).

Franz and Sterner's article is a well-argued and sophisticated assessment of a relationship that isn't working the way it could. But to talk in terms of "power balance" strikes me as miscasting the debate. Would it not be better to try and think about aligning goals (assuming that is possible). What do experts want to achieve? What do they need to achieve those goals? Is it things such as access to specimens, data, literature, sequences? Visibility for their research? Demonstrable impact? Credit? What are the impediments? What, if anything, can GBIF and other aggregators do to help? In what way can facilitating the work of experts help GBIF?

In my own "early-stage systematist career" I had a conversation with Mark Hafner about the Louisiana State University Museum providing tissue samples for molecular sequencing, essentially a "project in a box". Although Mark was complaining about the lack credit for this (a familiar theme) the thing which struck me was how wonderful it would be to have such a service - here's everything you need to do your work, go do some science. What if GBIF could do the same? Are you interested in this taxonomic group, well here's the complete sum of what we know so far. Specimens, literature, DNA sequences, taxonomic names, the works. Wouldn't that be useful?

Franz and Sterner call for "both the systematics and the aggregator communities to reengage with each other". I would echo this. I think that the sometimes dysfunctional relationship between experts and aggregators is partly due to the failure to build a community of researchers around GBIF and its activities. The focus of GBIF's relationship with the scientific community has been to have a committee of advisers, which is a rather traditional and limited approach ("you're a scientist, tell us what scientists want"). It might be better served if it provided a forum for researchers to interact with GBIF, data providers, and each other.

I stated this blog (iPhylo) years ago to vent my frustrations about TreeBASE. At the time I was fond of a quote from a philosopher of science that I was reading, to the effect that we only criticise those things that we care about. I take Franz and Sterner's article to indicate that they care about GBIF quite a bit ;). I'm looking forward to more critical discussion about how we can reconcile the needs of experts and aggregators as we seek to make global biodiversity data both open and useful.

Friday, June 16, 2017

GBIF Challenge 2017: Liberating species records from open data repositories for scientific discovery and reuse

Ebbe v5 300

GBIF is running its Ebbe Nielsen Challenge for the third successive year. This year the title is Liberating species records from open data repositories for scientific discovery and reuse. To quote from the Challenge background on Devpost:

This year's Challenge will seek to leverage the growth of open data policies among scientific journals and research funders, which require researchers to make the data underlying their findings publicly available. Adoption of these policies represents an important first step toward increasing openness, transparency and reproducibility across all scientific domains, including biodiversity-related research.

To abide by these requirements, researchers often deposit datasets in public open-access repositories. Potential users are then able to find and access the data through repositories as well as data aggregators like OpenAIRE and DataONE. Many of these datasets are already structured in tables that contain the basic elements of biodiversity information needed to build species occurrence records: scientific names, dates, and geographic locations, among others.

However, the practices adopted by most repositories, funders and journals do not yet encourage the use of standardized formats. This approach significantly limits the interoperability and reuse of these datasets. As a result, the wider reuse of data implied if not stated by many open data policies falls short, even in cases where open licensing designations (like those provided through Creative Commons) seem to encourage it.

In essence, the 2017 Challenge is to develop tools to discover these biodiversity-relevant datasets, and make them available to GBIF. In other words, we want tools to enable us to do this:


As an example of the impact that external data can have on GBIF, last year I wrote a blog post (The Zika virus, GBIF, and the missing mosquitoes) describing how I took published data (doi:10.1038/sdata.2015.35) from the Dryad repository and added it to GBIF. The effect was dramatic:




1651430 updated

This is just one example. I suspect that there is a lot of biodiversity data gathering digital dust sitting in repositories that could be more widely reused if we just had the tools to discover it, and convert it into a form that GBIF can use. Prove me right, and win cash prizes! Details at

Wednesday, May 31, 2017

Programming with Glitch: microservices and serverless computing

LgbNpkq 400x400Yes, this post is indeed an attempt to fit as many buzzwords that I don't really understand into the title. I've been playing around with Glitch, which is a delightful project from Fog Creek (makers of Trello and co-creators of Stack Overflow).

On first glance Glitch looks weirdly retro, and it took a little while for me to get the hang of things. Bit it's fun and very powerful. Basically it's a place where you can start creating web apps in your browser, and each app is automatically hosted online. If you see an app that you like you can see the source code (just like you can see HTML using "view source" in your browser). if you want to hack on the code you can simply create a copy and it's yours to play with (this is called "remixing", like forking on GitHub). Your copy gets a cute name (possibly annoyingly cute) and away you go.

If you're a developer, then at this point you're probably wondering what is actually happening under the hood. Each Glitch app is a node.js app, which means you're programming in Javascript (you can just use HTML and client side Javascript if you want to avoid node.js). I'm very new to node.js, so Glitch has been a fun way to experiment.

There are two things which make Glitch very powerful. The first is the "remix" feature. Don't know where to start? Find an app that looks like it might do something you want to do, remix it, and hack away. The code is edited online, and the editor works very well. It also checks your code for Javascript errors as you type, which is helpful (usually).

The second great feature is that you get built in hosting for free. As soon as you remix an app you have a functioning web site. Remixing is very like forking in GitHub, and if you're running node.js on your local machine then the benefits of Glitch might not seem obvious. But hosting is often a pain, either you need to set up your own servers, or use a hosting service. Glitch takes care of this for you, so your app is instantly available for others to use.

So, what can you do with Glitch? There's some great examples on the Glitch site, but I want to show an almost trivial example. I've created an app called "enchanting-bongo" (yes, the name is a bit irritating) that does one simple thing. You give it a DOI for an article and enchanting-bongo tells you whether any of the authors of that work have an ORCID. For example, try the DOI 10.3897/zookeys.555.6173. Why did I write this? I'm interested in ways to link people to the work that they've done, especially work that ends up being aggregated in large-scale biodiversity databases like GBIF (see Possible project: #itaxonomist, combining taxonomic names, DOIs, and ORCID to measure taxonomic impact).

Screenshot 2017 05 31 17 09 32

The app does one thing. It takes the DOI and calls the ORCID API to see if anyone has claimed authorship of the paper with that DOI. You can use the app with a web browser, or you can use an HTTP client and call the API (e.g.,

Glitch is an example of servers computing, where you don't have to worry about physical servers or the software infrastructure that runs on them (e.g., the web server itself), you just write code. Like any buzzword, there is some pushback, see for example What Is “Serverless”? An Alternative Take, but for a fascinating essay I recommend Why the fuss about serverless?. But the notion that I can simply hack away on some code and have an instantly available web app is very attractive.

The other buzzword is "microservices". I'm forever needing to do tasks such as find a DOI for a paper, match a "microcitation" to the enclosing article, locate a specimen in GBIF based on catalogue number in a paper, parse some text into structured data, such as a reference, geographic coordinates, etc. These are tools that I need in lots of contexts, and I've written software to do this on my machine, often as part of larger projects. "Microservices" is the idea that instead of large, monolithic apps we write a series of minimal tools that typically do one thing, and do it well. We then chain the together to do various tasks. Having small tools means that we can treat each problem independently, and if the tools communicate over the web (HTTP) then it doesn't matter what programming language we use. I've started thinking more and more about adopting this model and developing a bunch of small services to perform many of the tasks I need. Hosting these services then becomes in issue, I have web servers in my office but they are a pain to maintain (my university is forever insisting that I upgrade their software), so cloud-based hosting seems the obvious way forward. Free-hosting looks ideal, so Glitch is looking very attractive.

So, I'm hoping to experiment more with this approach. One thing I might do is create a series of services very like enchanting-bongo, have a simple web interface and an API that the web interface calls. That way users can play with it in their web browser, then call the service via the API if it does something useful. As a more sophisticated example of a service, I'm working on tools to parse Wikispecies reference strings, and link specimen codes to records in GBIF.

One reason I'm enthusiastic about Glitch is that it is fun!. Some of the best shifts in technology that I've made have been because a tool made something easy and fun to do. For example, CouchDB made working with structured data fun, and that was a revelation (databases, fun, surely not). Fun is a much neglected characteristic of the tools we use.

Querying Wikidata

For my own use more than anything else I've started creating a list of Wikidata SPARQL queries here. I personally don't find Wikidata's data model particularly easy to grasp, so one way to learn is to take the example queries on the Wikidata Query site and mess about with them.

For those interested in taxonomic data Wikidata is quite rich in content. For example, you can find the author of a taxonomic names, or find taxon names an author is responsible for creating.

It is also fairly straightforward to search for content by identifier, e.g.

  ?work wdt:P356 "10.2476/ASJAA.62.33" .
will find the article with the DOI 10.2476/ASJAA.62.33. One minor gotcha is that Wikidata has all DOIs in UPPERCASE, so you either need to sera for uppercase version of the DOI, or use a filter to convert the case, which is slow.

As I come across interesting or useful queries I'll add them to the list in GitHub.

Wikidata, WikiCite, and the "bibliography of life"

3hhZSGOn 400x400Last week I was at WikiCite 2017, a fascinating three day event in Vienna. Wikicite is "a proposal to build a bibliographic database in Wikidata to serve all Wikimedia projects", and is attracting increasing attention from academics, librarians, publishers, data geeks, and others. You can get a sense of the project by following @WikiCite on Twitter.

I went to the meeting in part to learn more about WikiCite, and also to spend some time hacking on Wikispecies. I'd been to only one Wiki event before (a Wiki Science Conference) so I'm still finding my way around this community. I spent the first two days listening to talks while coding away (more on this below), but on Wednesday put my own coding aside to join a bunch of people hacking the CrossRef event API in a great session led by Joe Wass. I've put some notes and code in GitHub. The event API tracks what people do with DOIs, including adding them to Wikipedia pages when citing a source in support of an assertion. A significant fraction of DOI resolutions are from Wikipedia pages, which is one reason why CrossRef was present at WikiCite.


In practice WikiCite's goal of building a bibliographic database to serve all Wikimedia projects means that articles, books, and other bibliographic items that are cited by Wikimedia projects will each be added to Wikidata. For example, the ZooKeys paper "Diversity of manota williston (Diptera, mycetophilidae) in ulu temburong national park, brunei" is item Q21188431 in Wikidata. Wikidata stores the key bibliographic metadata, including identifiers such as the DOI (which many at the WikiCite meeting pronounced "doy" much to my initial confusion). Screenshot 2017 05 31 12 46 43

This article was published in ZooKeys, which itself has a Wikidata item (Q219980), so in Wikidata the article is linked to the journal (i.e., "ZooKeys" isn't just a dumb string but a link to another Wikidata item). The article is also linked to two articles that it cites, and each of these is also a Wikidata item.

These citation links are one reason people are interested in WikiCite - it could be the basis of a free and open citation graph (for the benefits of such a graph see this piece by David Shotton doi:10.1038/502295a, a participant at the meeting in Vienna). Already some cool tools are being built on top of citation data in Wikidata, such as Scholia by Finn Årup Nielsen, Daniel Mietchen and Egon Willighagen. Here, for example, is my academic profile based on information in Wikidata. It's woefully incomplete, but intriguing. For a more complete example view Egon Willighagen's profile.

To some extent the utility of tools like Scholia will depend on how complete Wikidata's coverage is of the academic literature, which in turn raises the inevitable question of scope. Does Wikicite want to include just the literature cited in the various Wikimedia projects, or does it want to expand to include the total sum of academic literature?

Wikispecies, Wikidata and the bibliography of life

Wikispecies is one of the Wikimedia projects, and the only one that is topic-specific (the others are typically global in scope but have content in different languages, or host different data types such as images, scanned books, or structured data). As I've sketched out in an earlier post (Thoughts on Wikipedia, Wikidata, and the Biodiversity Heritage Library) I think Wikicite and Wikidata are potentially very important to projects such as BHL and the "bibliography of life". Much of our knowledge about the world's biodiversity is contained in the academic literature, and much of this is poorly known with no central database where we can find it, and much of it is still not digitised. It is tempting to think that Wikidata might be a platform around which the biodiversity community could focus its efforts on assembling a global database of biodiversity literature. Already major taxonomic journals such as ZooKeys are being fed into Wikidata, so it has a significant corpus of biodiversity literature already.

One way to grow this corpus is to focus on Wikispecies. In a post before the Wikicite meeting (Notes for WikiCite 2017: Wikispecies reference parsing) I elaborated on this idea. There are two stumbing blocks, one specific to Wikispecies, one a more general Wikidata issue.

The first issue is that Wikispecies bibliographic data is relatively unstructured, which makes converting it into structured data something of a challenge. I spent much of Wikicite hacking some code to do this on Glitch (more on Glitch later), you can see the results here: This web site takes a Wikispecies reference and tries to convert it into CSL-JSON. Still very much a work in progress, but I've started building tools that use this web site as a service and process larger numbers of Wikispecies citations.

The second issue is how you get data into Wikidata, and this is something that's never been entirely clear to me. There are tools for adding an article using its DOI (sourcemd) but this isn't scalable, and doesn't handle the case of articles that don't have DOIs. This is still a "How do you Snapchat? You just Snapchat" moment. Wikidata desparately needs tools and a clear procedure whereby people like me with lots of bibliographic data can contribute.


Another reason for my interest in Wikispecies (and other sources of bibliographic data such as the listed of cited literature being made available by CrossRef, see The Initiative for Open Citations) is that this data can be fed into BHL to locate more articles in that archive. Once these articles have been located they are stored in BioStor and BHL itself, but it makes sense to have them more accessible, and Wikidata looks to be an obvious candidate. Given that Wikispecies is essentially a crowd-source taxonomic database there is considerable overlap in content between Wikispecies and BHL. The Wikidata data model also allows for some of things that taxonomists care about, such as linking dates of publication to evidence relative to those dates (in older publications determining the publication date often requires quite extensive research).


Leaving aside the specific issues about how to get bibliographic data into Wikidata, I guess the question to ask is whether it makes sense to be developing large databases of bibliographic data without either using Wikidata as the platform to hold that data, or at least linking to Wikidata. Projects such as Gene Wiki are migrating from Wikipedia to Wikidata (see "Wikidata as a semantic framework for the Gene Wiki initiative" doi:10.1093/database/baw015), perhaps those of us interested in biodiversity literature could use projects like Gene Wiki as role models for how we could both contribute and benefit from Wikidata and Wikicite.

I've barely scratched the surface of what was discussed at Wikicite, for more details see the program. It is a very different sort of meeting in that the participants come from pretty diverse backgrounds, which helps shake up your own assumptions about what matters and how things should be done. It's also great that it's a meeting at which people write code or otherwise hack stuff together, so things actually get done. I've come away with lots to think about, and renewed enthusiasm about the role Wikimedia is playing in structuring our knowledge about the world.

Friday, March 24, 2017

This is what phylodiversity looks like

Following on from earlier posts exploring how to map DNA barcodes and putting barcodes into GBIF it's time to think about taking advantage of what makes barcodes different from typical occurrence data. At present GBIF displays data as dots on a map (as do I in But barcodes come with a lot more information than that. I'm interested in exploring how we might measure and visualise biodiversity using just sequences.

Based on a talk by Zachary Tong (Going Organic - Genomic sequencing in Elasticsearch) I've started to play with n-gram searches on DNA barcodes using Elasticsearch, an open source search engine. The idea is that we break the DNA sequence into every possible "word" of length n (also called a k-mer or k-tuple, where k = n).

For example, for n = 5, the sequence GTATCGGTAACGAACTT would look like this:



The sequence GTATCGGTAACGAACTT comes from Hajibabaei and Singer (2009) who discussed "Googling" DNA sequences using search engines (see also Kuksa and Pavlovic, 2009). If we index sequences in this way then we can do BLAST-like searches very quickly using Elasticsearch. This means it's feasible to take a DNA barcode and ask "what sequences look like this?" and return an answer qucikly enoigh for a user not to get bored waiting.

Another nice feature of Elasticsearch is that it supports geospatial queries, so we can ask for, say, all the barcodes in a particular region. Having got such a list, what we really want is not a list of sequences but a phylogenetic tree. Traditionally this can be a time consuming operation, we have to take the sequences, align them, then input that alignment into a tree building algorithm. Or do we?

There's growing interest in "alignment-free" phylogenetics, a phrase I'd heard but not really followed up. Yang and Zhang (2008) described an approach where every sequences is encoded as a vector of all possible k-tuples. For DNA sequences k = 5 there are 45 = 1024 possible combinations of the bases A, C, G, and T, so a sequence is represented as a vector with 1024 elements, each one is the frequency of the corresponding 5-tuple. The "distance" between two sequences is the mathematical distance between these vectors for the two sequences. Hence we no longer need to align the sequences being comapred, we simply chunk them into all "words" of 5 bases in length, and compare the frequencies of the 1024 different possible "words".

In their study Yang and Zhang (2008) found that:

We compared tuples of different sizes and found that tuple size 5 combines both performance speed and accuracy; tuples of shorter lengths contain less information and include more randomness; tuples of longer lengths contain more information and less random- ness, but the vector size expands exponentially and gets too large and computationally inefficient.

So we can use the same word size for both Elasticsearch indexing and for computing the distance matrix. We still need to create a tree, for which we could use something quick like neighbour-joining (NJ). This method is sufficiently quick to be available in Javascript and hence can be computed by a web browser (e.g., biosustain/neighbor-joining).

Putting this all together, I've built a rough-and-ready demo that takes some DNA barcodes, puts them on a map, then enables you to draw a box on a map and the demo will retrieve the DNA barcodes in that area, compute a distance matrix using 5-tuples, then build a NJ tree, all on the fly in your web browser.

This is all very crude, and I need to explore scalability (at the moment I limit the results to the first 200 DNA sequences found), but it's encouraging. I like the idea that, in principle, we could go to any part of the globe, ask "what's there?" and get back a phylogenetic tree for the DNA barcodes in that area.

This also means that we could start exploring phylogenetic diversity using DNA barcodes, as Faith & Baker (2006) wanted a decade ago:

...PD has been advocated as a way to make the best-possible use of the wealth of new data expected from large-scale DNA “barcoding” programs. This prospect raises interesting bio-informatics issues (discussed below), including how to link multiple sources of evidence for phylogenetic inference, and how to create a web-based linking of PD assessments to the barcode–of-life database (BoLD).

The phylogenetic diversity of an area is essentially the length of the tree of DNA barcodes, so if we build a tree we have a measure of diversity. Note that this contrasts with other approaches, such as Miraldo et al.'s "An Anthropocene map of genetic diversity" which measured genetic diversity within species but not between (!).

Practical issues

There are a bunch of practical issues to work through, such as how scalable it is to compute phylogenies using Javascript on the fly. For example, could we do something like generate a one degree by one degree grid of the Earth, take all the barcodes in each cell and compute a phylogeny for each cell? Could we do this in CouchDB? What about sampling, should we be taking a finite, random sample of sequences so that we try and avoid sampling bias?

There are also data management issues. I'm exploring downloading DNA barcodes, creating a Darwin Core Archive file using the Global Genome Biodiversity Network (GGBN) data standard, then converting the Darwin Core Archive into JSON and sending that to Elasticsearch. The reason for the intermediate step of creating the archive is so that we can edit the data, add missing geospatial informations, etc. I envisage having a set of archives, hosted say on GitHub. These archives could also be directly imported into GBIF, ready for the time that GBIF can handle genomic data.


  • Faith, D. P., & Baker, A. M. (2006). Phylogenetic diversity (PD) and biodiversity conservation: some bioinformatics challenges. Evol Bioinform Online. 2006; 2: 121–128. PMC2674678
  • Hajibabaei, M., & Singer, G. A. (2009). Googling DNA sequences on the World Wide Web. BMC Bioinformatics. Springer Nature.
  • Kuksa, P., & Pavlovic, V. (2009). Efficient alignment-free DNA barcode analytics. BMC Bioinformatics. Springer Nature.
  • Miraldo, A., Li, S., Borregaard, M. K., Florez-Rodriguez, A., Gopalakrishnan, S., Rizvanovic, M., … Nogues-Bravo, D. (2016, September 29). An Anthropocene map of genetic diversity. Science. American Association for the Advancement of Science (AAAS).
  • Yang, K., & Zhang, L. (2008, January 10). Performance comparison between k-tuple distance and four model-based distances in phylogenetic tree reconstruction. Nucleic Acids Research. Oxford University Press (OUP).

Notes for WikiCite 2017: Wikispecies reference parsing

Wikispecies logo svg In preparation for WikiCite 2017 I'm looking more closely at extracting bibliographic information from Wikispecies. The WikiCite project "is a proposal to build a bibliographic database in Wikidata to serve all Wikimedia projects". One reason for doing this is so that each factual statement in WikiData can be linked to evidence for that statement. Practical efforts towards this goal include tools to add details of articles from CrossRef and PubMed straight into Wikidata, and tools to extract citations from Wikipedia (as these are likely to be sources of evidence for statements made in Wikipedia articles).

Wikispecies occupies a rather isoldated spot in the Wiikipedia landscape. Unlike other sites which are essentially comprehensive encyclopedias in different languages, Wikispecies focusses on one domain - taxonomy. In a sense, it's a prototype of Wikidata in that it provides basic facts (who described what species when, and what is the classification of those species) that in principle can be reused by any of the other wikis. However, in practice this doesn't seem to have happened much.

What Wikispecies has become, however, is a crowd-sourced database of the taxonomic literture. For someone like me who is desparately gathering up bibliographic data so that I can extract articles from the Biodiversity Heritage Library (BHL), this is a potential goldmine. But, there's a catch. Unlike, say, the English language Wikipedia which has a single widely-used template for describing a publication, Wikispecies has it's own method of representing articles. It uses a somewhat confusing mix of templates for author names, and then uses barely standardised formatting rules to mark out parts of a publication (such as journal, volume, issue, etc.). Instead of a single template to describe a publication, in Wikispecies a publication my itself be described by a unique template. This has some advantages, in that the same reference can be transcluded into multiple articles (in other words, you enter the bibliographic details once). But this leaves us with many individual templates with multiple, idiosyncratic styles of representing bibliographic data. Some have tried to get the Wikispecies community to adopt the same template as Wikipedia (see e.g., this discussion) but this proposal has met with a lot of resistance. From my perspective as a potential consumer of data, the current situation in Wikispecies is frustrating, but the reality is that the people who create the content get to decide how they structure that content. And understandably, they are less than impressed by requests that might help others (such as data miners) at the expense of making their own work more difficult.

In summary, if I want to make use of Wikispecies I am going to need to develop a set of parsers than can make a reasonable fist of parsing all the myriad citation formats used in Wikispecies (my first attempts are on GitHub). I'm looking at parsing the references and converting them to a more standard format in JSON (I've made some notes on various bibliographic formats in JSON such as BibJSON and CSL-JSON). One outcome of this work will be, I hope, more articles discovered in BHL and hence added to BioStor), and more links to identifiers, which could be fed back into Wikispecies. I also want to explore linking the authors of these papers to identifiers, as already sketched out in The Biodiversity Heritage Library meets Wikidata via Wikispecies: adding author identifiers to BioStor.

Wednesday, February 15, 2017

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:

Screenshot 2017 02 15 19 28 17

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.

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.

Saturday, January 14, 2017

Displaying taxonomic classifications from Wikidata using d3js and SPARQL

Sahelanthropus tchadensis TM 266 01 060 1 Following on from previous posts The Semantic Web made fun: d3sparql and The Biodiversity Heritage Library meets Wikidata via Wikispecies: adding author identifiers to BioStor I've put together an example query that can be used to extract a taxonomic classification from Wikidata. The query is inspired by the example, and uses the wikidata property P171 ("parent taxon") which is subproperty of rdfs:subClassOf (the property used in the d3sparql example which queries the Uniprot taxonomy).

The following SPARQL query generates a list of nodes in the tree representing the classification of Hominini (humans, chimps, and their extinct relatives):

PREFIX wdt: <>
PREFIX wd: <>
SELECT ?root_name ?parent_name ?child_name WHERE
 VALUES ?root_name {"Hominini"}
 ?root wdt:P225 ?root_name .
 ?child wdt:P171+ ?root .
 ?child wdt:P171 ?parent .
 ?child wdt:P225 ?child_name .
 ?parent wdt:P225 ?parent_name .

Using as the endpoint, in this generates the following diagram:

Screenshot 2017 01 14 11 41 55

There are some obvious issues with this classification, such as genera that lack descendant species (e.g., Cyphanthropus). Indeed, we could imagine developing SPARQL queries to flag up such errors (see A use case for RDF in taxonomy). But the availability and accessibility of Wikidata and its SPARQL interface makes it a great playground to explore the utility of SPARQL for exploring taxonomic data.

Wednesday, January 11, 2017

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):

Screenshot 2017 01 11 16 30 57

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).


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":

  ?item rdfs:label "George Albert Boulenger"@en .
  ?article schema:about ?item .
  ?article schema:isPartOf  .
   ?item wdt:P213 ?isni .
   ?item wdt:P214 ?viaf .
   ?item wdt:P18 ?image .
   ?item wdt:P496 ?orcid .
   ?item wdt:P586 ?ipni .
   ?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: Screenshot 2017 01 11 17 04 16 Here is the result for noted carcinologist Jocelyn Crane who currently lacks identifiers: Screenshot 2017 01 11 17 05 32 A nice surprise was Bernard Landry: Screenshot 2017 01 11 17 07 14 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.