BioNames database can be downloaded

My BioNames project has been going for over a year now, but I hadn't gotten around to providing bulk access to the data I've been collecting and cleaning. I've gone some way towards fixing this. You can now grab a snapshot of the BioNames database as a Darwin Core Archive here. This snapshot was generated on the 22nd August, so it is already a little out of date (BioNames is edited almost daily as I clean and annotate it when I should be doing other things).

The data dump doesn't capture all the information in the BioNames as I've tried to keep it simple, and Darwin Core is a bit of a pain to deal with. The actual database is in CouchDB which is (mostly) an absolute joy to work with. I replicate the database to Cloudant, which means there's a copy "in the cloud". A number of my other CouchDB projects are also in Cloudant, in the case of Australian Faunal Directory and BOL DNA Barcode Map the data is also served directly from Cloudant.

Geotagging stats for BioStor

Note to self for upcoming discussion with JournalMap.

As of Monday August 25th, BioStor has 106,617 articles comprising 1,484,050 BHL pages. From the full text for these articles, I have extracted 45,452 distinct localities (i.e., geotagged with latitude and longitude). 15,860 BHL pages in BioStor pages have at least one geotag, these pages belong to 5,675 BioStor articles.

In summary, BioStor has 5,675 full-text articles that are geotagged. The largest number of geotags for an article is 2,421, for Distribución geográfica de la fauna de anfibios del Uruguay (doi:10.5479/si.23317515.134.1).

The SQL for the queries is here.

Guest post: Response to the discussion on Red List assessments of East African chameleons

This is guest post by Angelique Hjarding in response to discussion on this blog about the paper below.

Hjarding, A., Tolley, K. A., & Burgess, N. D. (2014, July 10). Red List assessments of East African chameleons: a case study of why we need experts. Oryx. Cambridge University Press (CUP). doi:10.1017/s0030605313001427

Thank you for highlighting our recent publication and for the very interesting comments. We wanted to take the opportunity to address some of the issues brought up in both your review and from reader comments.

One of the most important issues that has been raised is the sharing of cleaned and vetted datasets. It has been suggested that the datasets used in our study be uploaded to a repository that can be cited and shared. This is possible for data that was downloaded from GBIF as they have already done the legwork to obtain data sharing agreements with the contributing organizations. So as long as credit is properly given to the source of the data, publicly sharing data accessed through GBIF should be acceptable. At the time the manuscript was submitted for publication, we were unaware of sites such as http://figshare.com where the data could be stored and shared with no additional cost to the contributor. The dataset used in the study that used GBIF data has now been made available in this way.

Angelique Hjarding. (2014). Endemic Chameleons of Kenya and Tanzania. Figshare. doi:10.6084/m9.figshare.1141858

It starts to get tricky with doing the same for the expert vetted data. This dataset consists primarily of data gather by the expert from museum records and published literature. So in this case it is not a question of why the expert doesn’t share. The question is why the museum data and any additional literature records are not on GBIF already. As has been pointed out in our analysis (and confirmed by Rod) most of these museums do not currently have data sharing agreements with GBIF. Therefore, the expert who compiled the data does not have the permission of the museums to share their data second hand. Bottom line, all of the data used in this study that was not accessed through GBIF is currently available from the sources directly. That is, for anyone who wants to take the time contact the museums for permission to use their data for research and to compile it. We also do not believe there is blame on museums that have not yet shared their data with forums such as GBIF. Mobilisation of data is an enormous task, and near impossible if funds and staff are not available. With regards to the particular comment regarding the lack of data sharing by NHML and other museums, we need to recognise what the task at hand would mean, and rather address ways such a monumental, and valuable, collection could be mobilised. A further issue should be raised around literature records that are not necessarily encapsulated in museum collections, but are buried in old and obscure manuscripts. To our knowledge, there is no way to mobilise those records either, because they are not attached to a specimen. Further, because there are no specimens, extreme care must be taken if such records were to be mobilised in order to ensure quality control. Again, assistance of expert knowledge would be highly beneficial, yet these things take time and require funds.

Another issue that was raised is why didn’t we go directly to GBIF to fix the records? The point of our research was not to clean and update GBIF/museum data but to evaluate the effect of expert vetting and museum data mobilization in an applied conservation setting. As it has been pointed out, the lead author was working at GBIF during the course of the research. An effort was made to provide a checklist of the updated taxonomy to GBIF at the time, but there was no GBIF mechanism for providing updates. This appears to still be the case. In addition, two GBIF staff provided comments on the paper and were acknowledged for their input. We are happy to provide an updated taxonomy to help improve the data quality, should some submission tool for updates be made available.

Finally we would like to address the question, why use GBIF data if we know it needs some work before it can be used? We believe this is a very important debate for at least two reasons. First, when data is made public, we believe there are many researchers who work under the assumption that the data is ready for use with minimal further work. We believe they assume that the taxonomy is up to date; that the records are in the right place; and that the records provided relate to the name that is attached to those records. Many of the papers that have used GBIF data have undertaken broad scale macroecological analyses where, perhaps, the errors we have shown matter little. But some of these synthetic studies have also proposed that their results can be used for decision making by companies, which starts to raise concerns especially if the company wants to know the exact species that its activities could impact. As we have shown, for chameleons at least, such advice would be hard to provide using the raw GBIF data.

Second, we are aware that there is another group of researchers using GBIF data who "know that to use GBIF's data you need to do a certain amount of previous work and run some tests, and if the data does not pass the tests, you don't use it." We are not sure of the tests that are run, and it would be useful to have these spelled out for broader debate and potentially the development of some agreed protocols for data cleaning for various uses.

Our underlying reason for writing the paper was not to enter into debate of which data are best between GBIF and an expert compiled dataset. We are extremely pleased that GBIF data exist, and are freely available for the use of all. This certainly has to be part of the future of 'better data for better decisions', but we are concerned that we should not just accept that the data is the best we can get, but should instead look for ways to improve it, for all kinds of purposes. As such, we would like to suggest that the discussion focuses some energy on ways to address the shortcomings of the present system, but also that the community who would benefit from the data address ways to assist the dataholders to mobilise their information in terms of accessing the resources required to digitise and make data available, and maintain updated taxonomy for their holdings. In an era of declining funding for Museum-based taxonomy in many parts of the world this is certainly a challenge that needs to be addressed.

We welcome further discussion as this is a very important topic, not only for conservation but also in terms of improved access to biodiversity knowledge, which is critical for many reasons.

Angelique Hjarding http://orcid.org/0000-0002-9279-4893
Krystal Tolley
Neil Burgess

Some design notes on modelling links between specimens and other kinds of data

If we view biodiversity data as part of the "biodiversity knowledge graph" then specimens are a fairly central feature of that graph. I'm looking at ways to link specimens to sequences, taxa, publications, etc., and doing this across multiple data providers. Here are some rough notes on trying to model this in a simple way.

For simplicity let's suppose that we have this basic model:



A specimen comes from a locality (ideally we have the latitude and longitude of that locality), it is assigned to a taxon, we have data derived from that specimen (e.g., one or more DNA sequences), and we have one or more publications about that specimen (e.g., a paper that publishes a taxon name for which the specimen is a type, or a paper that publishes a sequence for which the specimen is a voucher).

NCBI

In GenBank we have sequences that have accession numbers, and these are linked to taxa (identified by NCBI tax ids). A nice feature of sequence databases is that taxa are explicitly defined by extension, that is, a taxon is the set of sequences assigned to a given taxon. Most (but not all, see Miller et al. doi:10.1186/1756-0500-2-101) sequences are also linked to a publication, which will usually have a PubMed id (PMID), and sometimes a DOI. Many sequences are also georeferenced (see Guest post: response to "Putting GenBank Data on the Map"). Most sequences aren't linked to a voucher specimen, but there is the implict notion of a source (in RDF-speak, many specimens are "blank nodes" Blank nodes for specimens without URI). Some sequences are associated with a specimen that has a museum code, and some are explicitly linked to the specimen by a URL.

DNA barcodes

Barcodes, as represented in BOLD are similar to sequences in GenBank. We have explicit taxa ("BINs") each of which has a URL, some also having DOIs. Most barcodes are georeferenced. There's some ambiguity about whether the URL for a barcode record identifies the barcode sequence, the specimen, or both. There may be a voucher code for the specimen. Some barcodes are linked to publications, but not (as far as I can see) in the data obtained from the API. Some barcodes are linked to the corresponding record in GenBank (which may or may not be supressed, see Dark taxa even darker: NCBI pulls (some) DNA barcodes from GenBank (updated)).

GBIF

At it's core GBIF has occurrence records (many of these are specimen-based, but the majority of data in GBIF is actually observation-based), each of which has a unique id, and which is linked to a taxon, also with a unique id. As with the sequence databases, a taxon is a set of occurrences that have been assigned to that taxon. Many records in GBIF are georeferenced. There are limited cross links to other database - some occurrences list associated GenBank sequences. Some GBIF occurrences actually are sequences (e.g., the European Molecular Biology Laboratory Australian Mirror and the soon to be indexed Geographically tagged INSDC sequences), and barcodes are also making their way into GBIF (e.g., Zoologische Staatssammlung Muenchen - International Barcode of Life (iBOL) - Barcode of Life Project Specimen Data). Links to publications are limited.

Museums and herbaria

Some individual natural history collections which are online provide specimen-level web pages and URLs (some even have DOIs, see DOIs for specimens are here, but we're not quite there yet), and some museums list associated GenBank sequences. In the diagram I've not linked the specimens to a taxon, because most specimens are tagged by a name, not an explicit taxon concept (unlike NCBI, BOLD, or GBIF).

Literature

Literature databases (represented here by BioStor, but could be other sources such as ZooKeys, for example) may contain articles that mention specimen codes. These articles may also mention taxon names, and geographic localities (including coordinates) (see, for example, Linking GBIF and the Biodiversity Heritage Library. Mining text for names, specimens, and localities is fairly easy, but linking these together is harder (i.e., this specimen is of this taxon, and was found at this locality).

Linking together

If we have these separate sources and this trivial model, then we can imagine trying to tie information about the same specimen together across the different databases. Why might we want to do this. Here are three reasons:

1. Augmentation Combining information can enhance our understanding of a specimen. Perhaps a specimen in GBIF is a geographic outlier. A publication that mentions the specimen includes it in a new taxon, perhaps discovered by sequencing DNA extarcted from that specimen. Linking this information together resolves the problematic distribution.
2. Provenance What is the evidence that a particular specimen belongs to a particualr taxon, or was collected at a particular locality? If we connect specimens to the literature we we can review the evidence for ourselves. If we have sequences we can run BLAST, build a tree, and see if we should rethink our classification of that sequence. Imagine being able to browse GBIF and see the evidence for each dot on the map?
3. Citation Mentions in the literature, use as vouchers for DNA barcoding or other forms of sequencing can be thought of a "citation" of that specimen. Museums hosting that material could use metrics base don this to demonstrate the value of their collection (see also The impact of museum collections: one collection ≈ one Nobel Prize).

Making the links

All this is well and good, the trick is to actually make the links. Here things get horribly messy very quickly. Museum specimens are cited in inconsistent ways, we don't have widely used unique, resolvable specimen identifiers, and even if we did have these identifiers we don't have a global discovery mechanism for matching voucher codes to identifiers. GBIF would be an obvious part of a "global discovery mechanism" (bit like CrossRef but for specimens), GBIF can have multiple records for the same specimen. Sometimes this is because GBIF not only aggregates data from primary sources (such as museums) but also other aggregations which may themselves already include specimens harvested from primary sources. GBIF can also have multiple records because museums keep messing with their databases, try new variants of the Darwin Core triple, etc., resulting in records that look "new" to GBIF. Whole collections can be duplicate din this way.

One way to tackle this multiplicity of specimen records is to think in terms of "clusters" of specimens that are, in some sense, the same thing across multiple databases. For example, clustering a set of duplicated GBIF records together with the sequences derived from those specimens, perhaps including a DNA barcode, and a list of papers that mention that specimen. This is represented by the yellow bar through the diagram, it connects all the different pieces of information about a specimen into a single cluster. More *cough* later.

Seven percent of GBIF data is usable - quick thoughts on Hjarding et al. 2014

Update: Angelique Hjarding and her co-authors have responded in a guest post on iPhylo.

The quality and fitness for use of GBIF-mobilised data is a topic of interest to anyone that uses GBIF data. As an example, a recent paper on African chameleons comes to some rather alarming conclusions concerning the utility of GBIF data:

Hjarding, A., Tolley, K. A., & Burgess, N. D. (2014, July 10). Red List assessments of East African chameleons: a case study of why we need experts. Oryx. Cambridge University Press (CUP). doi:10.1017/s0030605313001427

Here's the abstract (unfortunately the paper is behind a paywall):

The IUCN Red List of Threatened Species uses geographical distribution as a key criterion in assessing the conservation status of species. Accurate knowledge of a species’ distribution is therefore essential to ensure the correct categorization is applied. Here we compare the geographical distribution of 35 species of chameleons endemic to East Africa, using data from the Global Biodiversity Information Facility (GBIF) and data compiled by a taxonomic expert. Data screening showed 99.9%of GBIF records used outdated taxonomy and 20% had no locality coordinates. Conversely the expert dataset used 100%up-to-date taxonomy and only seven records (3%) had no coordinates. Both datasets were used to generate range maps for each species, which were then used in preliminary Red List categorization. There was disparity in the categories of 10 species, with eight being assigned a lower threat category based on GBIF data compared with expert data, and the other two assigned a higher category. Our results suggest that before conducting desktop assessments of the threatened status of species, aggregated museum locality data should be vetted against current taxonomy and localities should be verified. We conclude that available online databases are not an adequate substitute for taxonomic experts in assessing the threatened status of species and that Red List assessments may be compromised unless this extra step of verification is carried out.

The authors used two data sets, one from GBIF, the other provided by an expert to compute the conservation status for each chameleon species endemic to Kenya and/or Tanzania. After screening the GBIF data for taxonomic and geographic issues, a mere 7% of the data remained - 93% of the 2304 records downloaded from GBIF were discarded.

This study raises a number of questions, some of which I will touch on here. Before doing so, it's worth noting that it's unfortunate that neither of the two data sets used in this study (the data downloaded from GBIF, and the expert data set assembled by Colin Tilbury) are provided by the authors, so our ability to further explore the results is limited. This is a pity, especially now that citable data repositories such as Dryad and Figshare are available. The value of this paper would have been enhanced if both datasets were archived.

Below is Table 1 from the paper, "Museums from which locality records for East African chameleons were obtained for the expert and GBIF datasets":

Museum	Expert dataset	GBIF
Afrika Museum, The Netherlands	x
American Museum of Natural History, USA	x
Bishop Museum, USA		x
British Museum of Natural History, UK	x
Brussels Museum of Natural Sciences, Belgium	x
California Academy of Sciences, USA		x
Ditsong Museum, South Africa	x	x
Los Angeles County Museum of Natural History, USA		x
Museum für Naturkunde, Germany	x
Museum of Comparative Zoology (Harvard University), USA		x
Naturhistorisches Museum Wien, Austria	x
Smithsonian Institution, USA		x
South African Museum, South Africa	x
Trento Museum of Natural Sciences, Italy	x
University of Dar es Salaam, Tanzania	x
Zoological Research Museum Alexander Koenig, Germany	x

It is striking that there is virtually no overlap in data sources available to GBIF and the sources used by the expert. Some of the museums have no presence in GBIF, including some major collections (I'm looking at you, The Natural History Museum), but some museums do contribute to GBIF, but not their herpetology specimens. So, GBIF has some work to do in mobilising more data (Why is this data not in GBIF? What are the impediments to that happening?). Then there are museums that have data in GBIF, but not in a form useful for this study. For example, the American Museum of Natural History has 327,622 herpetology specimens in GBIF, but not one of these is georeferenced! Given that there are records in GenBank for AMNH specimens that are georeferenced, I suspect that the AMNH collection has deliberately not made geographic coordinates available, which raises the obvious question - why?

GBIF coverage

I had a quick look at GBIF to get some idea of the geographic coverage of the relevant herpetology collections (or animal collections if herps weren't separated out). Below are maps for some of these collections. The AMNH is empty, as is the smaller Zoological Research Museum Alexander Koenig collection (which supplied some of the expert data).

American Museum of Natural History, USA

Bishop Museum, USA

California Academy of Sciences, USA

Ditsong Museum, South Africa

Los Angeles County Museum of Natural History, USA

Museum für Naturkunde, Germany

Museum of Comparative Zoology (Harvard University), USA

Smithsonian Institution, USA

Zoological Research Museum Alexander Koenig, Germany

Some collections are relevant, such as the California Academy of Sciences, but a number of the collections in GBIF simply don't have georeferenced data on chameleons. Then there are several museums that are listed as sources for the expert database and which contribute to GBIF, but haven't digitised their herp collections, or haven't made these available to GBIF.

Taxonomy

The other issue encountered by Hjarding et al. 2014 is that the GBIF taxonomy for chameleons is out of date (2302 of 2304 GBIF-sourced records needed to be updated). Chameleons are a fairly small group, and it's not like there are hundreds of new species being discovered each year (see timeline in BioNames), 2006 was a bumper year with 12 new taxonomic names added. But there has been a lot of recent phylogenetic work which has clarified relationships, and as a result species get shuffled around different genera, resulting in a plethora of synonyms. GBIF's taxonomy has lagged behind current research, and also manages to horribly mangle the chameleon taxonomy is does have. For example, the genus Trioceros is not even placed within the chameleon family Chamaeleonidae but is simply listed as a reptile, which means anyone searching for data on the family Chamaeleonidae will all the Trioceros species.

Summary

The use case for this study seems one of the most basic that GBIF should be able to meet - given some distributions of organisms, compute an assessment of their conservation status. That GBIF-mobilised data is so patently not up to the task in this case is cause for concern.

However, I don't see this is simply a case of expert data set versus GBIF data, I think it's more complicated than that. A big issue here is data availability, and also the extent of data release (assuming that the AMNH is actively withholding geographic coordinates for some, if not most of its specimens). GBIF should be asking those museums that provide data why they've not made georeferenced data available, and if its because the museums simply haven't been able to do this, then how can it help this process? It should also be asking why museums which are part of GBIF haven't mobilised their herpetology data, and again, what can it do to help? Lastly, in an age of rapid taxonomic change driven by phylogenetic analysis, GBIF needs to overhaul the glacial pace at which it incorporates new taxonomic information.

Realizing Lessons of the Last 20 Years: A Manifesto for Data Provisioning & Aggregation Services for the Digital Humanities (A Position Paper)

I stumbled across this paper (found on the GBIF Public Library):

Oldman, D., de Doerr, M., de Jong, G., Norton, B., & Wikman, T. (2014, July). Realizing Lessons of the Last 20 Years: A Manifesto for Data Provisioning and Aggregation Services for the Digital Humanities (A Position Paper) System. D-Lib Magazine. CNRI Acct. doi:10.1045/july2014-oldman

The first sentence of the abstract makes the paper sound a bit of a slog to read, but actually it's a great fun, full of pithy comments on the state of digital humanities. Almost all of this is highly relevant to mobilising natural history data. Here are the paper's main points (emphasis added):

1. Cultural heritage data provided by different organisations cannot be properly integrated using data models based wholly or partly on a fixed set of data fields and values, and even less so on 'core metadata'. Additionally, integration based on artificial and/or overly generalised relationships (divorced from local practice and knowledge) simply create superficial aggregations of data that remain effectively siloed since all useful meaning is available only from the primary source. This approach creates highly limited resources unable to reveal the significance of the source information, support meaningful harmonisation of data or support more sophisticated use cases. It is restricted to simple query and retrieval by 'finding aids' criteria.
2. The same level of quality in data representation is required for public engagement as it is for research and education. The proposition that general audiences do not need the same level of quality and the ability to travel through different datasets using semantic relationships is a fiction and is damaging to the establishment of new and enduring audiences.
3. Thirdly, data provisioning for integrated systems must be based on a distributed system of processes in which data providers are an integral part, and not on a simple and mechanical view of information system aggregation, regardless of the complexity of the chosen data models. This more distributed approach requires a new reference model for the sector. This position contrasts with many past and existing systems that are largely centralised and where the expertise and practice of providers is divorced.

Recommended reading.