Thursday, October 02, 2014

BioStor and JournalMap: a geographic interface to articles from the Biodiversity Heritage Library

The recent jump from ~11000 to ~17000 articles in JournalMap is mostly due to JournalMap ingesting content from my BioStor database. BioStor extracts articles from the Biodiversity Heritage Library (BHL), and in turn these get fed back into BHL as "parts" (you can see these in the "Table of Contents" tab when viewing a scanned volume in BHL).

In addition to extracting articles, BioStor pulls out latitude and longitude pairs mentioned in the OCR text and creates little Google Maps for articles that have geotagged content. Working with Jason Karl (@jwkarl), JournalMap now talks to BioStor and grabs all its geotagged articles so that you can browse them in JournalMap. As a consequence, journals such as Proceedings of The Biological Society of Washington now appear on their map (this journal is third most geotagged journal in JournalMap).

As an example of what you can do in JournalMap, here's a screenshot showing localities in Tanzania, and an article from BioStor being displayed:

JournalMap is an elegant interface to the biodiversity literature, and adding BioStor as a source is a nice example of how the Biodiversity Heritage Library's content is becoming more widely used. BioStor would only be possible if BHL made its content and metadata available for easy downloading. This is a lesson I wish other projects would learn. Instead of focussing on building flash-looking portals, make sure (a) you have lots of content, and (b) make it easy for developers to get that content so they can do cool things with it. BHL does well in this regard — other projects, such as BHL-Europe, not so much.

Tuesday, September 23, 2014

Exploring the chameleon dataset: broken GBIF links and lack of georeferencing

Following on from the discussion of the African chameleon data, I've started to explore Angelique Hjarding's data in more detail. The data is available from figshare (doi:10.6084/m9.figshare.1141858), so I've grabbed a copy and put it in github. Several things are immediately apparent.

  1. There is a lot of ungeoreferenced data. With a little work this could be geotagged and hence placed on a map.
  2. There are some errors with the georeferenced data (chameleons in Soutb America or off the coast, a locality in Tanzania that is now in Ethiopia, etc.).
  3. Rather alarmingly, most of the URLs to GBIF records that Angelique gives in the dataset no longer resolve.

The last point is worrying, and reflects the fact that at present you can't trust GBIF occurrence URLs to be stable over time. Most of the specimens in Angelique's data are probably still in GBIF, but the GBIF occurrenceID (and hence URL) will have changed. This pretty much kills any notion of reproducibility, and it will require some fussing to be able to find the new URLs for these records.

That the GBIF occurrenceIDs are no longer valid also makes it very difficult to make use of any data cleaning I or anyone else attempts with this data. If I georeference some of the specimens, I can't simply tell GBIF that I've got improved data. Nor is it obvious how I would give this information to the original providers using, say VertNet's github repositories. All in all a mess, and a sad reflection on our inability to have persistent identifiers for occurrences.

To help explore the data I've created some GeoJSON files to get a sense of the distribution of the data. Here are the point localities, a few have clearly got issues.

I also drew some polygons around points for the same taxon, to get a sense of their distributions.

Taxa represent by less than three distinct localities are presented by place marker, the rest by polygons.

I'll keep playing with this data as time allows, and try to get a sense of how hard it would be to go from what GBIF provides to what is actually going to be useful.

Monday, September 22, 2014

GBIF Science Committee Report slides #gb21

FullSizeRenderJust back from GB21, the GBIF Governing Board meeting (the first such meeting I've attended). It was in New Delhi, and this was also my first time in india, which is an amazing place. At some point I may blog about the experience: the heat, the sheer number of people, the juxtaposition of wealth and poverty, the traffic (chaotic in a wonderfully self-organising sort of way), seeing birds of prey wheel overhead around a hotel in a major city, followed by fruit bats skimming the trees in the evening, the joys of haggling with tuk-tuk drivers, and the wonder that is the Taj Mahal.

Lots to also think about regarding the meeting. A somewhat unsatisfactory conversation about licensing started on Twitter, so at some point I need to revisit this.

But for now, here are the slides from my summary of the GBIF Science Committee's activities. It discusses the forthcoming Ebbe Nielsen Challenge (details still be worked on so the slides are not the final word), the challenges of adding sequence data to GBIF, and the much-discussed case of the chamaeleons.

Thursday, August 28, 2014

BioNames database can be downloaded

B8e253dc3be3d84f2c69c51b0af86c03 400x400My 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.

Monday, August 25, 2014

Geotagging stats for BioStor

PlaceMarkNote 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.

Tuesday, August 19, 2014

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

AHjardingThis 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 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
Krystal Tolley
Neil Burgess

Friday, August 15, 2014

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).



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)).



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 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.