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.