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:

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.