Wednesday, May 09, 2018

World Taxonomists and Systematists via ORCID

Taxonomist mapDavid Shorthouse (@dpsspiders) makes some very cool things, and his latest project World Taxonomists & Systematists is a great example of using automation to assemble a list of the world's taxonomists and systematists. The project uses ORCID. As many researchers will know, ORCID's goal is to have every researcher uniquely identified by an ORCID id (mine is that is linked to all a researcher's academic output, including papers, datasets, and more. So David has been querying ORCID for keywords such as taxonomist, taxonomy, nomenclature, or systematics to locate taxonomists and add them to his list. For more detail see his post on the ORCID blog.

Using ORCIDs to help taxonomists gain visibility is an idea that's been a round for a little while. I blogged about it in Possible project: #itaxonomist, combining taxonomic names, DOIs, and ORCID to measure taxonomic impact, at which time David was already doing another cool piece of work linking collectors to ORCIDs and their collecting effort, see e.g. data for Terry A. Wheeler.

There are, of course, a bunch of obstacles to this approach. Many taxonomists lack ORCIDs, and I keep coming across "private" ORCIDs where taxonomists have an ORCID id but don't make their profile public, which makes it hard to identify them as taxonomists. Typically I discover these profiles via metadata in CrossRef, which will list the ORCID id for any authors that have them and have made them know to the publisher of their paper.

ORCID ids are only available for people who are alive (or alive recently enough to have registered), so there will be many taxonomists who will never have an ORCID id. In this case, it may be Wikidata to the rescue:

Many taxonomists have Wikidata entries because they are either notable enough to be in Wikipedia, or they have an entry in Wikispecies, and people like Andy Mabbett (@pigsonthewing) have been diligently ensuring these people have Wikidata entries. There's huge scope for making use of these links.

Meanwhile, if you are a taxonomist or a systematist and you don't have an ORCID, get yourself one at ORCID, claim your papers, and you should appear shortly in the World Taxonomists & Systematists list.

2018 GBIF Ebbe Nielsen Challenge now open

Http images ctfassets net uo17ejk9rkwj L6lRFOvdQG4M4yY0k0Cei ad53f85a57368b017fecb8907393d32a ebbe 2018Last year I finished my four-year stint as Chair of the GBIF Science Committee. During that time, partly as a result of my urging, GBIF launched an annual "GBIF Ebbe Nielsen Challenge", and I'm please that this year GBIF is continuing to run the challenge. In 2015 and 2016 the challenge received some great entries.

Last year's challenge (GBIF Challenge 2017: Liberating species records from open data repositories for scientific discovery and reuse didn't attract quite the same degree of attention, and GBIF quietly didn't make an award. I think part of the problem was that there's a fine balance between having a wide open challenge which attracts all sorts of interesting entries, some a little off the wall (my favourite was GBIF data converted to 3D plastic prints for physical data visualisation) versus a specific topic which might yield one or more tools that could, say, be integrated into the GBIF portal. But if you make it too narrow then you run the risk of getting fewer entries, which is what happened in 2017. Ironically, since the 2017 challenge I've come across work that would have made a great entry, such as a thesis by Ivelize Rocha Bernardo Promoting Interoperability of Biodiversity Spreadsheets via. Purpose Recognition, see also Bernardo, I. R., Borges, M., Baranauskas, M. C. C., & Santanchè, A. (2015). Interpretation of Construction Patterns for Biodiversity Spreadsheets. Lecture Notes in Business Information Processing, 397–414. doi:10.1007/978-3-319-22348-3_22.

This year the topic is pretty open:

The 2018 Challenge will award €34,000 for advancements in open science that feature tools and techniques that improve the access, utility or quality of GBIF-mediated data. Under this open-ended call, challenge submissions may build on existing tools and features, such as the GBIF API, Integrated Publishing Toolkit, data validator, relative species occurrence tool, among others—or develop new applications, methods, workflows or analyses.

Lots of scope, and since I'm not longer part of the GBIF Science Committee it's tempting to think about taking part. The judging criteria are pretty tough and results-oriented:

Winning entries will demonstrably extend and increase the usefulness, openness and visibility of GBIF-mediated data for identified stakeholder groups. Each submission is expected to demonstrate advantages for at least three of the following groups: researchers, policymakers, educators, students and citizen scientists.

So, maybe less scope for off-the-wall stuff, but an incentive to clearly articulate why a submission matters.

The actual submission process is, sadly, rather more opaque than in previous years where it was run in the open on Devpost where you can still see previous submissions (e.g., those for 2015). Devpost has lots of great features but isn't cheap, so the decision is understandable. Maybe some participants will keep the rest of the community informed via, say, Twitter, or perhaps people will keep things close to their chest. In any event, I hope the 2018 challenge inspires people to think about doing something both cool and useful with biodiversity data. Oh, and did I mention that a total of €34,000 in prizes is up for grabs? Deadline for submission is 5 September 2018.

iSpecies meets Lifemap

It's been a little quiet on this blog as I've been teaching, and spending a lot of time data wrangling and trying to get my head around "data lakes" and "triple stores". So there are a few things to catch up on, and a few side projects to report on.

I continue to play with iSpecies, which is a simple mashup off biodiversity data sources. When I last blogged about iSpecies I'd added TreeBASE as a source (iSpecies meets TreeBASE). iSpecies also queries Open Tree of Life, and I've always wanted a better way of displaying the phylogenetic context of a species or genus. TreeBASE is great for a detailed, data-driven view, but doesn't put the taxon in a larger context, nor does the simple visualisation I developed for Open Tree of Life.

A nice large-scale tree visualisation is Lifemap (see De Vienne, D. M. (2016). Lifemap: Exploring the Entire Tree of Life. PLOS Biology, 14(12), e2001624. doi:10.1371/journal.pbio.2001624), and it dawned on me that since Lifemap uses the same toolkit (leaflet.js) that I use to display a map of GBIF records, I could easily add it to iSpecies. After looking at the Lifemap HTML I figured out the API call I need to pan the map to given taxon using Open Tree of Life taxon identifiers, and violà, I now have a global tree of life that shows where the query taxon fits in that tree.

Here's a screenshot of a search for Podocarpus showing the first 300 records from GBIF, and the position of Podocarpus in the tree of life. The tree is interactive so you can zoom and pan just like the GBIF map.

Screenshot 2018 05 09 16 58 00

Here's another one for the genus Timonius:

Screenshot 2018 05 09 17 58 32

Very much still at the "quick and dirty" stage, but I continue to marvel at how much information can be assembled "on the fly" from a few sources, and how much richer this seems than what biodiversity informatics projects offer. There's a huge amount of information that is simpy being missed or under-utilised in this area.

Wednesday, January 24, 2018

Guest post: The Not problem

Bob mesibovThe following is a guest post by Bob Mesibov.

Nico Franz and Beckett Sterner created a stir last year with a preprint in bioRxiv about expert validation (or the lack of it) in the "backbone" classifications used by aggregators. The final version of the paper was published this month in the OUP journal Database (doi:10.1093/database/bax100).

To see what effect "backbone" taxonomies are having on aggregated occurrence records, I've recently been auditing datasets from GBIF and the Atlas of Living Australia. The results are remarkable, and I'll be submitting a write-up of the audits for formal publication shortly. Here I'd like to share the fascinating case of the genus Not Chan, 2016.

I found this genus in GBIF. A Darwin Core record uploaded by the New Zealand Arthropod Collection (NZAC02015964) had the string "not identified on slide" in the scientificName field, and no other taxonomic information.

GBIF processed this record and matched it to the genus Not Chan, 2016, which is noted as "doubtful" and "incertae sedis".

There are 949 other records of this genus around the world, carefully mapped by GBIF. The occurrences come from NZAC and nine other datasets. The full scientific names and their numbers of GBIF records are:

2Not argostemma
14not Buellia
1not found, check spelling
1Not given (see specimen note) bucculenta
1Not given (see specimen note) ortoni
1Not given (see specimen note) ptychophora
1Not given (see specimen note) subpalliata
1not identified on slide
1not indentified
1Not known not known
1Not known sp.
1not Lecania
4Not listed
873Not naturalised in SA sp.
18Not payena
5not Punctelia
18not used
6Not used capricornia Pleijel & Rouse, 2000

GBIF cites this article on barnacles as the source of the genus, although the name should really be Not Chan et al., 2016. A careful reading of this article left me baffled, since the authors nowhere use "not" as a scientific name.

Next I checked the Catalogue of Life. Did CoL list this genus, and did CoL attribute it to Chan? No, but "Not assigned" appears 479 times among the names of suprageneric taxa, and the December 2018 CoL checklist includes the infraspecies "Not diogenes rectmanus Lanchester,1902" as a synonym.

The Encyclopedia of Life also has "Not" pages, but these have in turn been aggregated on the "EOL pages that don't represent real taxa" page, and under the listing for the "Not assigned36" page someone has written:

This page contains a bunch of nodes from the EOL staff Scratchpad. NB someone should go in and clean up that classification.

"Someone should go in and clean up that classification" is also the GBIF approach to its "backbone" taxonomy, although they think of that as "we would like the biodiversity informatics community and expert taxonomists to point out where we've messed up". Franz and Sterner (2018) have also called for collaboration, but in the direction of allowing for multiple taxonomic schemes and differing identications in aggregated biodiversity data. Technically, that would be tricky. Maybe the challenge of setting up taxonomic concept graphs will attract brilliant developers to GBIF and other aggregators.

Meanwhile, Not Chan, 2016 will endure and aggregated biodiversity records will retain their vast assortment of invalid data items, character encoding failures, incorrect formatting, duplications and truncated data items. In a post last November on the GitHub CoL+ pages I wrote:

Being old and cynical, I can speculate that in the time spent arguing the "politics" of aggregation in recent years, a competent digital librarian or data scientist would have fixed all the CoL issues and would be halfway through GBIF's. But neither of those aggregators employ digital librarians or data scientists, and I'm guessing that CoL+ won't employ one, either.

Monday, December 11, 2017

Towards a digital natural history museum


These notes are the result of a few events I've been involved in the last couple of months, including TDWG 2017 in Ottawa, a thesis defence in Paris, and a meeting of the Science Advisory Board of the Natural History Museum in London. For my own benefit if no one else's, I want to sketch out some (less than coherent) ideas for how a natural history museum becomes truly digital.


The digital world poses several challenges for a museum. In terms of volume of biodiversity data, museums are already well behind two major trends, observations from citizen science and genomics. The majority of records in GBIF are observations, and genomics databases are growing exponentially, through older initiatives such as barcoding, and newer methods such as environmental genomics. While natural history collections contain an estimated 109 specimens or "lots" [1], less than a few percent of that has been digitised, and it is not obvious that massive progress in increasing this percentage will be made any time soon.

Furthermore, for citizen science and genomics it is not only the amount of data but the network effects that are possible with that data that make it so powerful. Network effects arise when the value of something increases as more people use it (the classic example is the telephone network). In the case of citizen science, apart from the obvious social network that can form around a particular taxon (e.g., birds), there are network effects from having a large number of identified observations. iNaturalist is using machine learning to suggest identifications of photos taken by members. The more members join and add photos and identifications, the more reliable the machine identifications become, which in turn makes it more desirable to join the network. Genomics data also shows network effects. In effect, a DNA sequence is useless without other sequences to compare it with (it is no accident that the paper describing BLAST is one of the most highly cited in biology). The more sequences a genomics database has the more useful it is.

For museums the explosion of citizen science and genomics begs the question "is there any museum data that can show similar network effects"? We should also ask whether there will be an order of magnitude increase in digitisation of specimens in the near future. If not, then one could argue that museums are going to struggle to remain digitally relevant if they remain minority biodiversity data providers. Being part of organisations such as GBIF certainly helps, but GBIF doesn't (yet) offer much in the way of network effects.


We could divide the users of museums into three distinct (but overlapping) communities. These are:

  1. Scientists
  2. Visitors
  3. Staff

Scientists make use of research and data generated by the museum. If the museum doesn't support science (both inside and outside the museum) then the rationale for the collections (and associated staff) evaporates. Hence, digitisation must support scientific research.

Visitors in this sense means both physical and online visitors. Online visitors will have a purely digital experience, but in person visitors can have both physical and digital experiences.

In many ways the most neglected category is the museum staff. Perhaps best way to make progress towards a digital museum is having the staff committed to that vision, and this means digitisation should wherever possible make their work easier. In many organisations going digital means a difficult transition period of digitising material, dealing with crappy software that makes their lives worse, and a lack of obvious tangible benefits (digitisation for digitisation's sake). Hence outcomes that deliver benefits to people doing actual work should be prioritised. This is another way of saying that museums need to operate as "platforms", the best way to ensure that external scientists will use the museums digital services is if the research of the museum's own staff depends on those services.

Some things to do

For each idea I sketch a "vision", some ways to get there, what I think the current reality is (and, let's be honest, what I expect it to still be like in 10 years time).

Vision: Anyone with an image of an organism can get a answer to the question "what is this?"

Task: Image the collection in 2D and 3D. Computers can now "see", and can accomplish tasks such as identify species and traits (such as the presence of disease [2]) from images. This ability is based on machine learning from large numbers of images. The museum could contribute to this by imaging as many specimens as possible. For example, a library of butterfly photos could significantly increase the accuracy of identifications by tools such as iNaturalist. Creating 3D models of specimens could generate vast numbers of training images [3] to further improve the accuracy of identifications. The museum could aim to provide identifications for the majority of species likely to be encountered/photographed by its users and other citizen scientists.

Reality: Imaging is unlikely to be driven by identification and machine learning, beiggest use is to provide eye-catching images for museum publicity.

Who can help: iNaturalist has experience with machine learning. More and more of research is appearing on image recognition, deep learning, and species identification.

Vision: Anyone with a DNA sequence can get a answer to the question "what is this?"

Task: DNA sequence the collection, focussing first on specimens that (a) have been identified and (b) represent taxonomic groups that are dominated by "dark taxa" in GenBank. Many sequences being added to GenBank are unidentified and hence unnamed. These will only become named (and hence potentially connected to more information) if we have sequences from identified material of those species (or close relatives). Often discussions of sequences focus on doing the type specimens. While this satisfies the desire to pin a name to a sequence in the most rigorous way, it doesn't focus on what users need - an answer to "what is this?" The number of identified specimens will far exceed the number of type specimens, and many types will not be easily sequenced. Sequencing identified specimens puts the greatest amount of museum-based information into sequence space. This will become even more relevant as citizen science starts to expand to include DNA sequences (e.g., using tools like MinION).

Reality: Lack of clarity over what taxa to prioritise, emphasis on type specimens, concerns over whether DNA barcoding is out of date compared to other techniques (ignoring importance of global standardisation as a way to make data maximally useful) will all contribute to a piecemeal approach.

Who can help: Explore initiatives such as the Planetary Biodiversity Mission.

Vision: A physical visitor to the museum has a digital experience deeply informed by the museum's knowledge

Task: The physical walls of the museum are not barriers separating displays from science but rather interfaces to that knowledge. Any specimen on display is linked to what we know about it. If there is a fossil on a wall, we can instantly see the drawings made of that specimen in various publications, 3D scans to interact with, information about the species, the people who did the work (whether historical figures or current staff), and external media (e.g., BBC programs).

Reality: Piecemeal, short-lived gimmicky experiments (such as virtual reality), no clear attempt to link to knowledge that visitors can learn from or create themselves. Augmented reality is arguably more interesting, but without connections to knowledge it is a gimmick.

Who could help: Many of the links between specimens, species, and people full into the domain of Wikipedia and Wikidata, hence lots of opportunities for working with GLAM Wiki community.

Vision: A museum researcher can access all published information about a species, specimen, or locality via a single web site.

Task: All books and journals in the museum library that are not available online should be digitised. This should focus on materials post 1923 as pre-1923 is being done by BHL. The initial goal is to provide its researchers with the best possible access to knowledge, the secondary goal is to open that up to the rest of the world. All digitised content should be available to researchers within the museum using a model similar to the Haithi Trust which manages content scanned by Google Books. The museum aggressively pursues permission to open as much of the digitised content up as it can, starting with its own books and journals. But it scans first, sorts out permissions later. For many uses, full access isn't necessarily needed, at least for discovery. For example, by indexing text for scientific names, specimen codes, and localities, researchers could quickly discover if a text is relevant, even if ultimately direct physically access is the only possibility for reading it.

Reality: Piecemeal digitisation hampered by the chilling effects of copyright, combined with limited resources means the bulk of our scientific knowledge is hard to access. A lack of ambition means incremental digitisation, with most taxonomic research remaining inaccessible, and new research constrained by needing access to legacy works in physical form.

Who could help: Consider models such as Hathi, work with BHL and publishers to open up more content, and text mining researchers to help maximise use even for content that can't be opened up straight away.

Vision: The museum as a "connection machine" to augment knowledge

Task: While a museum can't compete in terms of digital volume, it can compete for richness and depth of linking. Given a user with a specimen, an image, a name, a place, how can the museum use its extensive knowledge base to augment that user's experience? By placing the thing in a broader context (based on links derived from image -> identity tools, sequence -> identity tools, names to entities e.g., species, people and places, and links between those entites) the museum can enhance our experience of that thing.

Reality: The goal of having everything linked together into a knowledge graph is often talked about, but generally fails to happen, partly because things rapidly descend into discussions about technology (most of which sucks), and squabbling over identifiers and vocabularies. There is also a lack of clear drivers, other than "wouldn't it be cool?". Hence expect regular calls to link things together (e.g., Let’s rise up to unite taxonomy and technology), demos and proof of concept tools, but little concrete progress.

Who can help: The Wikidata community, initiatives such as (some of these are no longer alive but useful to investigate) Big Data Europe, BBC Things. The BBC's defunct Wildlife Finder is an example of what can be achieved with fairly simple technology.


The fundamental challenge the museum faces is that it is analogue in an increasingly digital world. It cannot be, nor should it be, completely digital. For one thing it can't compete, for another its physical collection, physical space, and human expertise are all aspects that make a museum unique. But it needs to engage with visitors that are digitally literate, it needs to integrate with the burgeoning digital knowledge being generated by both citizens and scientists, and it needs to provide its own researchers with the best possible access to the museum's knowledge. Above all, it needs to have a clear vision of what "being digital means".


1. Ariño, A. H. (2010). Approaches to estimating the universe of natural history collections data. Biodiversity Informatics, 7(2).

2. Ramcharan, A., Baranowski, K., McCloskey, P., Ahmed, B., Legg, J., & Hughes, D. P. (2017). Deep Learning for Image-Based Cassava Disease Detection. Frontiers in Plant Science, 8.

3. Xingchao Peng, Baochen Sun, Karim Ali, Kate Saenko (2014) Learning Deep Object Detectors from 3D Models.

Tuesday, December 05, 2017

Blue Planet II, the BBC, and the Semantic Web: a tale of lessons forgotten and opportunities lost

David Attenborough’s latest homage to biodiversity, Blue Planet II is, as always, visually magnificent. Much of its impact derives from the new views of life afforded by technological advances in cameras, drones, diving gear, and submersibles. One might hope that the supporting information online reflected the equivalent technological advances made in describing and sharing information. Sadly, this is not the case. Instead the BBC offers a web site with a video clips and a poster... a $%@£ poster.

Oceans poster feat

This is a huge missed opportunity. Where do people go to learn more about the organisms featured in an episode? How do we discover related content on the BBC and elsewhere? How do we discover the science underpinning each episode that has been so exquisitely filmed and edited?

Perhaps the lack of an online resource reflects a lack of resources, or expertise? Yet one look at the series (and the "Into the blue" epilogues) tells us that resources are hardly limiting. Furthermore, the BBC has previously constructed rich, informative web sites to support natural history programming. The now deprecated BBC Nature Wildlife site had an extensive series of web pages for the organisms featured in BBC programmes, with links to individual clips. For each organism the corresponding web page listed key traits such as behaviours, habitats, and geographic distribution, and each of these traits had its own web page list all organisms with those traits (see, for example the page for Steller's Sea Eagle).

Screenshot 2017 12 05 13 12 02

Underlying all this information was a simple vocabulary (the Wildlife Ontology), and the entire corpus is also available in RDF: in other words, the BBC used Semantic Web technologies to structure this information. To get this data you simply append ".rdf" to the URL for a web page. For example, below is the RDF for Steller's Sea Eagle. It is not pretty, but it is a great example of machine-readable data which enables all sorts of interesting things to be built.

<?xml version="1.0" encoding="utf-8"?>
<rdf:Description rdf:about="/nature/species/Steller's_Sea_Eagle">
<foaf:primaryTopic rdf:resource="/nature/species/Steller's_Sea_Eagle#species"/>
<rdfs:seeAlso rdf:resource="/nature/species"/>
<wo:Species rdf:about="/nature/life/Steller's_Sea_Eagle#species">
<rdfs:label>Steller's sea eagle</rdfs:label>
<wo:name rdf:resource="'s_Sea_Eagle#name"/>
<foaf:depiction rdf:resource=""/>
<dc:description>Steller’s sea eagles are native to eastern Russia, inhabiting coastal cliffs and estuaries where they can easily access good fishing territories. They feed primarily on salmon, which they catch by swooping from perches located by the water's edge. Pairs are monogamous and hatch an average of two chicks each season, although crows and martens commonly take both eggs and young birds from the nest. During winter a small number of birds remain in Russia to tough it out, but the majority fly south to Japan.</dc:description>
<owl:sameAs rdf:resource="'s_Sea_Eagle"/>
<wo:adaptation rdf:resource="/nature/adaptations/Altricial#adaptation"/>
<wo:adaptation rdf:resource="/nature/adaptations/Animal_migration#adaptation"/>
<wo:adaptation rdf:resource="/nature/adaptations/Carnivore#adaptation"/>
<wo:adaptation rdf:resource="/nature/adaptations/Flight#adaptation"/>
<wo:adaptation rdf:resource="/nature/adaptations/Hearing_(sense)#adaptation"/>
<wo:adaptation rdf:resource="/nature/adaptations/Monogamous_pairing_in_animals#adaptation"/>
<wo:adaptation rdf:resource="/nature/adaptations/Oviparity#adaptation"/>
<wo:adaptation rdf:resource="/nature/adaptations/Parental_investment#adaptation"/>
<wo:livesIn rdf:resource="/nature/habitats/Coast#habitat"/>
<wo:livesIn rdf:resource="/nature/habitats/Estuary#habitat"/>
<wo:livesIn rdf:resource="/nature/habitats/Marsh#habitat"/>
<wo:livesIn rdf:resource="/nature/habitats/River#habitat"/>
<wo:livesIn rdf:resource="/nature/habitats/Swamp#habitat"/>
<wo:genus rdf:resource="/nature/life/Sea_eagle#genus"/>
<wo:family rdf:resource="/nature/life/Accipitridae#family"/>
<wo:order rdf:resource="/nature/life/Falconiformes#order"/>
<wo:class rdf:resource="/nature/life/Bird#class"/>
<wo:phylum rdf:resource="/nature/life/Chordate#phylum"/>
<wo:kingdom rdf:resource="/nature/life/Animal#kingdom"/>
<wo:TaxonName rdf:about="/nature/species/Steller's_Sea_Eagle#name">
<rdfs:label>Haliaeetus pelagicus</rdfs:label>
<wo:commonName>Steller's sea eagle</wo:commonName>
<foaf:Image rdf:about="">
<foaf:depicts rdf:resource="/nature/species/Steller's_Sea_Eagle#species"/>
<foaf:thumbnail rdf:resource=""/>
<po:Clip rdf:about="">
<dc:title>Lunch on the wing</dc:title>
<po:subject rdf:resource="/nature/species/Steller's_Sea_Eagle#species"/>
<po:Clip rdf:about="">
<dc:title>Steller's sea eagle</dc:title>
<po:subject rdf:resource="/nature/species/Steller's_Sea_Eagle#species"/>
<dctypes:Sound rdf:about="">
<dc:title>Calls from Steller's and white-tailed sea eagles</dc:title>
<dc:subject rdf:resource="/nature/species/Steller's_Sea_Eagle#species"/>
<foaf:Document rdf:about="'s_Sea_Eagle">
<foaf:primaryTopic rdf:resource="/nature/species/Steller's_Sea_Eagle#species"/>
<foaf:Document rdf:about="">
<foaf:primaryTopic rdf:resource="/nature/species/Steller's_Sea_Eagle#species"/>
<foaf:Document rdf:about="">
<foaf:primaryTopic rdf:resource="/nature/species/Steller's_Sea_Eagle#species"/>
<foaf:Document rdf:about="">
<foaf:primaryTopic rdf:resource="/nature/species/Steller's_Sea_Eagle#species"/>
<foaf:Document rdf:about="">
<foaf:primaryTopic rdf:resource="/nature/species/Steller's_Sea_Eagle#species"/>
<foaf:Document rdf:about="">
<foaf:primaryTopic rdf:resource="/nature/species/Steller's_Sea_Eagle#species"/>
<foaf:Document rdf:about="">
<foaf:primaryTopic rdf:resource="/nature/species/Steller's_Sea_Eagle#species"/>
<wo:ReproductionStrategy rdf:about="/nature/adaptations/Altricial#adaptation">
<rdfs:label>Helpless young</rdfs:label>
<wo:SurvivalStrategy rdf:about="/nature/adaptations/Animal_migration#adaptation">
<wo:FeedingHabit rdf:about="/nature/adaptations/Carnivore#adaptation">
<wo:LocomotionAdaptation rdf:about="/nature/adaptations/Flight#adaptation">
<rdfs:label>Adapted to flying</rdfs:label>
<wo:CommunicationAdaptation rdf:about="/nature/adaptations/Hearing_(sense)#adaptation">
<rdfs:label>Acoustic communication</rdfs:label>
<wo:ReproductionStrategy rdf:about="/nature/adaptations/Monogamous_pairing_in_animals#adaptation">
<wo:ReproductionStrategy rdf:about="/nature/adaptations/Oviparity#adaptation">
<rdfs:label>Egg layer</rdfs:label>
<wo:LifeCycle rdf:about="/nature/adaptations/Parental_investment#adaptation">
<rdfs:label>Parental investment</rdfs:label>
<wo:TerrestrialHabitat rdf:about="/nature/habitats/Coast#habitat">
<wo:MarineHabitat rdf:about="/nature/habitats/Estuary#habitat">
<wo:FreshwaterHabitat rdf:about="/nature/habitats/Marsh#habitat">
<wo:FreshwaterHabitat rdf:about="/nature/habitats/River#habitat">
<rdfs:label>Rivers and streams</rdfs:label>
<wo:FreshwaterHabitat rdf:about="/nature/habitats/Swamp#habitat">
<wo:Genus rdf:about="/nature/genus/Sea_eagle#genus">
<wo:species rdf:resource="/nature/life/Steller's_Sea_Eagle#species"/>
<wo:species rdf:resource="/nature/life/African_Fish_Eagle#species"/>
<wo:species rdf:resource="/nature/life/White-tailed_Eagle#species"/>
<wo:Family rdf:about="/nature/family/Accipitridae#family">
<wo:Order rdf:about="/nature/order/Falconiformes#order">
<wo:Class rdf:about="/nature/class/Bird#class">
<wo:Phylum rdf:about="/nature/phylum/Chordate#phylum">
<wo:Kingdom rdf:about="/nature/kingdom/Animal#kingdom">

For some reason, this web site is now deprecated. As an exercise I grabbed the RDF from the web site, did a little cleaning, and merged it together resulting in a set of around 94,500 triples (statements of the form “subject”, “predicate”, “object”). For example, this triple says that Steller's Sea Eagle is monogamous.


One reason the Semantic Web has struggled to gain widespread adoption is the long list of things you need to get to the point where it is usable. You need data consistently structured using the same vocabulary. You need identifiers that everyone agrees on (or at least can map their own identifiers too). And you need a triple store, which is essentially a graph database, a technology that is still unfamiliar to many. But in this case the BBC has done a lot of the hard work by cleverly minting identifiers based on Wikipedia URLs (”slugs”), and developing a vocabulary to express relationships between organisms, traits, and habitats. All that’s needed is a way to query this data. Rather than use a triple store (most of which are not much fun to install or maintain) I’ve used the delightfully simple approach of employing a Hexastore. Hexastores provide fast querying of graphs by indexing all six permutations of the subject, predicates, object triple (hence “hexa”). The approach is sufficiently simple that for moderately sized databases we can implement it in Javascript and run it in a web browser.

As a demonstration, I created a very crude hexastore-based version of the BBC pages (

Screenshot 2017 12 05 13 13 51

Once you load the page there are no further server requests, other than fetching images. Every query is “live” but takes place in the browser. You can click on the image for a species and get some textural information, as well as images representing traits of that organism. Click on a trait and you discover what organisms share those traits. This example is trivial, but surprisingly rich. I’ve found it fascinating to simply bounce around the images discovering unexpected facts about different species. There’s lots of potential for serendipitous discovery, as well as an enhanced appreciation for just how rich the BBC’s content is. If the Encyclopedia of Life were this engaging I’d be it’s biggest fan.

The question then, is why a similar approach was not taken for Blue Planet II? It can’t be a lack of resources, this series has amazing production values. And yet a wonderful opportunity has been missed. Why not build on the existing work and create an interactive resource that encourages people to explore more deeply and learn more? Much of the existing data could be used, as well as adding all the new species and behaviours we see on our TV screens. Blue Planet also highlights the impacts humans are having on the marine environment, these could be added as categories as well to show wat organisms are susceptible to different impacted (e.g., plastics).

That the BBC thinks a poster is an adequate for of engagement in the digital age speaks of a corporation that, in spite of many triumphs in the digital sphere (e.g., iPlayer) has not fully grasped the role the web can play in making its content more widely useful and relevant, beyond enthralling viewers on a Sunday evening. It also seems oblivious to the fact that it already knows how to deliver rich, informative online content (as evidenced by the now deprecated Wildlife application). So please, BBC, can we have a resource that enables us to learn more about the organisms and habitats that are the subjects of the grandeur and beauty we see on our TV screens?

Follow up

Below is some of the discussion this post generated on Twitter.

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:

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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).