Dugald Stuart Page 1936-2022

My dad died last weekend. Below is a notice in today's New Zealand Herald. I'm in New Zealand for his funeral. Don't really have the words for this right now.

David Remsen

I heard yesterday from Martin Kalfatovic (BHL) that David Remsen has died. Very sad news. It's starting to feel like iPhylo might end up being a list of obituaries of people working on biodiversity informatics (e.g., Scott Federhen).

I spent several happy visits at MBL at Woods Hole talking to Dave at the height of the uBio project, which really kickstarted large scale indexing of taxonomic names, and the use of taxonomic name finding tools to index the literature. His work on uBio with David ("Paddy") Patterson led to the Encyclopedia of Life (EOL).

A number of the things I'm currently working on are things Dave started. For example, I recently uploaded a version of his dataset for Nomenclator Zoologicus[1] to ChecklistBank where I'm working on augmenting that original dataset by adding links to the taxonomic literature. My BioRSS project is essentially an attempt to revive uBioRSS[2] (see Revisiting RSS to monitor the latest taxonomic research).

I have fond memories of those visits to Woods Hole. A very sad day indeed.

Update: The David Remsen Memorial Fund has been set up on GoFundMe.

1. Remsen, D. P., Norton, C., & Patterson, D. J. (2006). Taxonomic Informatics Tools for the Electronic Nomenclator Zoologicus. The Biological Bulletin, 210(1), 18–24. https://doi.org/10.2307/4134533

2. Patrick R. Leary, David P. Remsen, Catherine N. Norton, David J. Patterson, Indra Neil Sarkar, uBioRSS: Tracking taxonomic literature using RSS, Bioinformatics, Volume 23, Issue 11, June 2007, Pages 1434–1436, https://doi.org/10.1093/bioinformatics/btm109

The ideal taxonomic journal

This is just some random notes on an “ideal” taxonomic journal, inspired in part by some recent discussions on “turbo-taxonomy” (e.g., https://doi.org/10.3897/zookeys.1087.76720 and https://doi.org/10.1186/1742-9994-10-15), and also examples such as the Australian Journal of Taxonomy https://doi.org/10.54102/ajt.qxi3r which seems well-intentioned but limited.

XML

One approach is to have highly structured text that embeds detailed markup, and ideally a tool that generates markup in XML. This is the approach taken by Pensoft. There is an inevitable trade-off between the burden on authors of marking up text versus making the paper machine readable. In some ways this seems misplaced effort given that there is little evidence that publications by themselves have much value (see The Business of Extracting Knowledge from Academic Publications). “Value” in this case means as a source of data or factual statements that we can compute over. Human-readable text is not a good way to convey this sort of information.

It’s also interesting that many editing tools are going in the opposite direction, for example there are minimalist tools using Markdown where the goal is to get out of the author’s way, rather than impose a way of writing. Text is written by humans for humans, so the tools should be human-friendly.

The idea of publishing using XML is attractive in that it gives you XML that can be archived by, say, PubMed Central, but other than that the value seems limited. A cursory glance at download stats for journals that provide PDF and XML downloads, such as PLoS One and ZooKeys, PDF is by far the more popular format. So arguably there is little value in providing XML. Those who have tried to use JATS-XML as an authoring tool have not had a happy time: How we tried to JATS XML. However, there are various tools to help with the process, such as docxToJats,
texture, and jats-xml-to-pdf if this is the route one wants to take.

Automating writing manuscripts

The dream, of course, is to have a tool where you store all your taxonomic data (literature, specimens, characters, images, sequences, media files, etc.) and at the click of a button generate a paper. Certainly some of this can be automated, much nomenclatural and specimen information could be converted to human-readable text. Ideally this computer-generated text would not be edited (otherwise it could get out of sync with the underlying data). The text should be transcluded. As an aside, one way to do this would be to include things such as lists of material examined as images rather than text while the manuscript is being edited. In the same way that you (probably) wouldn’t edit a photograph within your text editor, you shouldn’t be editing data. When the manuscript is published the data-generated portions can then be output as text.

Of course all of this assumes that we have taxonomic data in a database (or some other storage format, including plain text and Mark-down, e.g. Obsidian, markdown, and taxonomic trees) that can generate outputs in the various formats that we need.

Archiving data and images

One of the really nice things that Plazi do is have a pipeline that sends taxonomic descriptions and images to Zenodo, and similar data to GBIF. Any taxonomic journal should be able to do this. Indeed, arguably each taxonomic treatment within the paper should be linked to the Zenodo DOI at the time of publication. Indeed, we could imagine ultimately having treatments as transclusions within the larger manuscript. Alternatively we could store the treatments as parts of the larger article (rather like chapters in a book), each with a CrossRef DOI. I’m still sceptical about whether these treatments are as important as we make out, see Does anyone cite taxonomic treatments?. But having machine-readable taxonomic data archived and accessible is a good thing. Uploading the same data to GBIF makes much of that data immediately accessible. Now that GBIF offers hosted portals there is the possibility of having custom interfaces to data from a particular journal.

Name and identifier registration

We would also want automatic registration of new taxonomic names, for which there are pipelines (see “A common registration-to-publication automated pipeline for nomenclatural acts for higher plants (International Plant Names Index, IPNI), fungi (Index Fungorum, MycoBank) and animals (ZooBank)” https://doi.org/10.3897/zookeys.550.9551). These pipelines do not seem to be documented in much detail, and the data formats differ across registration agencies (e.g., IPNI and ZooBank). For example, ZooBank seems to require TaxPub XML.

Registration of names and identifiers, especially across multiple registration agencies (ZooBank, CrossRef, DataCite, etc.) requires some coordination, especially when one registration agency requires identifiers from another.

Summary

If data is key, then the taxonomic paper itself becomes something of a wrapper around that data. It still serves the function of being human-readable, providing broader context for the work, and as an archive that conforms to currently accepted ways to publish taxonomic names. But in some ways it is the last interesting part of the process.

Written with StackEdit.

DNA barcoding as intergenerational transfer of taxonomic knowledge

I tweeted about this but want to bookmark it for later as well. The paper “A molecular-based identification resource for the arthropods of Finland” doi:10.1111/1755-0998.13510 contains the following:

…the annotated barcode records assembled by FinBOL participants represent a tremendous intergenerational transfer of taxonomic knowledge … the time contributed by current taxonomists in identifying and contributing voucher specimens represents a great gift to future generations who will benefit from their expertise when they are no longer able to process new material.

I think this is a very clever way to characterise the project. In an age of machine learning this may be commonest way to share knowledge , namely as expert-labelled training data used to build tools for others. Of course, this means the expertise itself may be lost, which has implications for updating the models if the data isn’t complete. But it speaks to Charles Godfrey’s theme of “Taxonomy as information science”.

Note that the knowledge is also transformed in the sense that the underlying expertise of interpreting morphology, ecology, behaviour, genomics, and the past literature is not what is being passed on. Instead it is probabilities that a DNA sequence belongs to a particular taxon.

This feels is different to, say iNaturalist, where there is a machine learning model to identify images. In that case, the model is built on something the community itself has created, and continues to create. Yes, the underlying idea is that same: “experts” have labelled the data, a model is trained, the model is used. But the benefits of the iNaturalist model are immediately applicable to the people whose data built the model. In the case of barcoding, because the technology itself is still not in the hands of many (relative to, say, digital imaging), the benefits are perhaps less tangible. Obviously researchers working with environmental DNA will find it very useful, but broader impact may await the arrival of citizen science DNA barcoding.

The other consideration is whether the barcoding helps taxonomists. Is it to be used to help prioritise future work (“we are getting lots of unknown sequences in these taxa, lets do some taxonomy there”), or is it simply capturing the knowledge of a generation that won’t be replaced:

The need to capture such knowledge is essential because there are, for example, no young Finnish taxonomists who can critically identify species in many key groups of ar- thropods (e.g., aphids, chewing lice, chalcid wasps, gall midges, most mite lineages).

The cycle of collect data, test and refine model, collect more data, rinse and repeat that happens with iNaturalist creates a feedback loop. It’s not clear that a similar cycle exists for DNA barcoding.

Written with StackEdit.

Local global identifiers for decentralised wikis

I've been thinking a bit about how one could use a Markdown wiki-like tool such as Obsidian to work with taxonomic data (see earlier posts Obsidian, markdown, and taxonomic trees and Personal knowledge graphs: Obsidian, Roam, Wikidata, and Xanadu).

One "gotcha" would be how to name pages. If we treat the database as entirely local, then the page names don't matter, but what if we envisage sharing the database, or merging it with others (for example, if we divided a taxon up into chunks, and different people worked on those different chunks)?

This is the attraction of globally unique identifiers. You and I can independently work on the same thing, such as data linked to scientific paper, safe in the knowledge that if we both use the DOI for that paper we can easily combine what we've done. But global identifiers can also be a pain, especially if we need to use a service to look them up ("is there a DOI for this paper?", "what is the LSID for this taxonomic name?").

Life would be easier if we could generate identifiers "locally", but had some assurance that they would be globally unique, and that anyone else generating an identifier for the same thing would arrive at the same identifier (this eliminates things such as UUIDs which are intentionally designed to prvent people genrrating the same identifier). One approach is "content addressing" (see, e.g. Principles of Content Addressing - dead link but in the Wayabck Machine, see also btrask/stronglink). For example, we can generate a cryptographic hash of a file (such as a PDF) and use that as the identifier.

Now the problem is that we have globally unique, but ugly and unfriendly identifiers (such as "6c98136eba9084ea9a5fc0b7693fed8648014505"). What we need are nice, easy to use identifiers we can use as page names. Wikispecies serves as a possible role model, where taxon names serve as page names, as do simplified citations (e.g., authors and years). This model runs into the problem that taxon names aren't unique, nor are author + year combinations. In Wikispecies this is resolved by having a centralised database where it's first come, first served. If there is a name clash you have to create a new name for your page. This works, but what if you have multiple databases un by different people? How do we ensure the identifiers are the same?

Then I remembered Roger Hyam's flight of fantasy over a decade ago: SpeciesIndex.org – an impractical, practical solution. He proposed the following rules to generate a unique URI for a taxonomic name:

• The URI must start with "http://speciesindex.org" followed by one or more of the following separated by slashes.
• First word of name. Must only contain letters. Must not be the same as one of the names of the nomenclatural codes (icbn or iczn). Optional but highly recommended.
• Second word of name. Must only contain letters and not be a nomenclatural code name. Optional.
• Third word of name. Must only contain letters and not be a nomenclatural code name. Optional.
• Year of publication. Must be an integer greater than 1650 and equal to or less than the current year. If this is an ICZN name then this should be the year the species (epithet) was published as is commonly cited after the name. If this is an ICBN name at species or below then it is the date of the combination. Optional. Recommended for zoological names if known. Not recommended for botanical names unless there is a known problem with homonyms in use by non-taxonomists.
• Nomenclatural code governing the name of the taxon. Currently this must be either 'icbn' or 'iczn'. This may be omitted if the code is unknown or not relevant. Other codes may be added to this list.
• Qualifier This must be a Version 4 RFC-4122 UUID. Optional. Used to generate a new independent identifier for a taxon for which the conventional name is unknown or does not exist or to indicate a particular taxon concept that bears the embedded name.
• The whole speciesindex.org URI string should be considered case sensitive. Everything should be lower case apart from the first letter of words that are specified as having upper case in their relevant codes e.g. names at and above the rank of genus.

Roger is basically arging that while names aren't unique (i.e., we have homonyms such as Abronia) they are pretty close to being so, and with a few tweaks we can come up with a unique representation. Another way to think about this if we had a database of all taxonomics, we could construct a trie and for each name find the shortest set of name parts (genus, species, etc), year, and code that gave us a unique string for that name. In many cases the species name may be all we need, in other cases we may need to add year and/or nomenclatural code to arrive at a unique string.

What about bibliographic references? Well many of us will have databases (e.g., Endnote, Mendeley, Zotero, etc.) which generate "cite keys". These are typically short, memorable identifiers for a reference that are unique within that database. There is an interesting discussion on the JabRef forum regarding a "Universal Citekey Generator", and source code is available cparnot/universal-citekey-js. I've yet to explore this in detail, but it looks a promising way to generate unique identifiers from basic metadata (echos of more elaborate schemes such as SICIs). For example,

Senna AR, Guedes UN, Andrade LF, Pereira-Filho GH. 2021. A new species of amphipod Pariphinotus Kunkel, 1910 (Amphipoda: Phliantidae) from Southwestern Atlantic. Zool Stud 60:57. doi:10.6620/ZS.2021.60-57.

becomes "Senna:2021ck". So if two people have the same, core, metadata for a paper they can generate the same key.

Hence it seems with a few conventions (and maybe some simple tools to support them) we could have decentralised wiki-like tools that used the same identifiers for the same things, and yet those identfiiers were short and human-friendly.

Does anyone cite taxonomic treatments?

Taxonomic treatments have come up in various discussions I'm involved in, and I'm curious as to whether they are actually being used, in particular, whether they are actually being cited. Consider the following quote:

The taxa are described in taxonomic treatments, well defined sections of scientific publications (Catapano 2019). They include a nomenclatural section and one or more sections including descriptions, material citations referring to studied specimens, or notes ecology and behavior. In case the treatment does not describe a new discovered taxon, previous treatments are cited in the form of treatment citations. This citation can refer to a previous treatment and add additional data, or it can be a statement synonymizing the taxon with another taxon. This allows building a citation network, and ultimately is a constituent part of the catalogue of life. - Taxonomic Treatments as Open FAIR Digital Objects https://doi.org/10.3897/rio.8.e93709

"Traditional" academic citation is from article to article. For example, consider these two papers:

Li Y, Li S, Lin Y (2021) Taxonomic study on fourteen symphytognathid species from Asia (Araneae, Symphytognathidae). ZooKeys 1072: 1-47. https://doi.org/10.3897/zookeys.1072.67935

Miller J, Griswold C, Yin C (2009) The symphytognathoid spiders of the Gaoligongshan, Yunnan, China (Araneae: Araneoidea): Systematics and diversity of micro-orbweavers. ZooKeys 11: 9-195. https://doi.org/10.3897/zookeys.11.160

Li et al. 2021 cites Miller et al. 2009 (although Pensoft seems to have broken the citation such that it does appear correctly either on their web page or in CrossRef).

So, we have this link: [article]10.3897/zookeys.1072.67935 --cites--> [article]10.3897/zookeys.11.160. One article cites another.

In their 2021 paper Li et al. discuss Patu jidanweishi Miller, Griswold & Yin, 2009:

There is a treatment for the original description of Patu jidanweishi at https://doi.org/10.5281/zenodo.3792232, which was created by Plazi with a time stamp "2020-05-06T04:59:53.278684+00:00". The original publication date was 2009, the treatments are being added retrospectively.

In an ideal world my expectation would be that Li et al. 2021 would have cited the treatment, instead of just providing the text string "Patu jidanweishi Miller, Griswold & Yin, 2009: 64, figs 65A–E, 66A, B, 67A–D, 68A–F, 69A–F, 70A–F and 71A–F (♂♀)." Isn't the expectation under the treatment model that we would have seen this relationship:

[article]10.3897/zookeys.1072.67935 --cites--> [treatment]https://doi.org/10.5281/zenodo.3792232

Furthermore, if it is the case that "[i]n case the treatment does not describe a new discovered taxon, previous treatments are cited in the form of treatment citations" then we should also see a citation between treatments, in other words Li et al.'s 2021 treatment of Patu jidanweishi (which doesn't seem to have a DOI but is available on Plazi' web site as https://tb.plazi.org/GgServer/html/1CD9FEC313A35240938EC58ABB858E74) should also cite the original treatment? It doesn't - but it does cite the Miller et al. paper.

So in this example we don't see articles citing treatments, nor do we see treatments citing treatments. Playing Devil's advocate, why then do we have treatments? Does't the lack of citations suggest that - despite some taxonomists saying this is the unit that matters - they actually don't. If we pay attention to what people do rather than what they say they do, they cite articles.

Now, there are all sorts of reasons why we don't see [article] -> [treatment] citations, or [treatment] -> [treatment] citations. Treatments are being added after the fact by Plazi, not by the authors of the original work. And in many cases the treatments that could be cited haven't appeared until after that potentially citing work was published. In the example above the Miller et al. paper dates from 2009, but the treatment extracted only went online in 2020. And while there is a long standing culture of citing publications (ideally using DOIs) there isn't an equivalent culture of citing treatments (beyond the simple text strings).

Obviously this is but one example. I'd need to do some exploration of the citation graph to get a better sense of citations patterns, perhaps using CrossRef's event data. But my sense is that taxonomists don't cite treatments.

I'm guessing Plazi would respond by saying treatments are cited, for example (indirectly) in GBIF downloads. This is true, although arguably people aren't citing the treatment, they're citing specimen data in those treatments, and that specimen data could be extracted at the level of articles rather than treatments. In other words, it's not the treatments themselves that people are citing.

To be clear, I think there is value in being able to identify those "well defined sections" of a publication that deal with a given taxon (i.e., treatments), but it's not clear to me that these are actually the citable units people might hope them to be. Likewise, journals such as ZooKeys have DOIs for individual figures. Does anyone actually cite those?

Can we use the citation graph to measure the quality of a taxonomic database?

More arm-waving notes on taxonomic databases. I've started to add data to ChecklistBank and this has got me thinking about the issue of data quality. When you add data to ChecklistBank you are asked to give a measure of confidence based on the Catalogue of Life Checklist Confidence system of one - five stars: ★ - ★★★★★. I'm scepetical about the notion of confidence or "trust" when it is reduced to a star system (see also Can you trust EOL?). I could literally pick any number of stars, there's no way to measure what number of stars is appropriate. This feeds into my biggest reservation about the Catalogue of Life, it's almost entirely authority based, not evidence based. That is, rather than give us evidence for why a particular taxon is valid, we are (mostly) just given a list of taxa are asked to accept those as gospel, based on assertions by one or more authorities. I'm not necessarly doubting the knowledge of those making these lists, it's just that I think we need to do better than "these are the accepted taxa because I say so" implict in the Catalogue of Life.

So, is there any way we could objectively measure the quality of a particular taxonomic checklist? Since I have a long standing interest in link the primary taxonomic litertaure to names in databases (since that's where the evidence is), I keep wondering whether measures based on that literture could be developed.

I recently revisited the fascinating (and quite old) literature on rates of synonymy:

Gaston Kevin J. and Mound Laurence A. 1993 Taxonomy, hypothesis testing and the biodiversity crisisProc. R. Soc. Lond. B.251139–142 http://doi.org/10.1098/rspb.1993.0020

Andrew R. Solow, Laurence A. Mound, Kevin J. Gaston, Estimating the Rate of Synonymy, Systematic Biology, Volume 44, Issue 1, March 1995, Pages 93–96, https://doi.org/10.1093/sysbio/44.1.93

A key point these papers make is that the observed rate of synonymy is quite high (that is, many "new species" end up being merged with already known species), and that because it can take time to discover that a species is a synonym the actual rate may be even higher. In other words, in diagrams like the one reproduced below, the reason the proportion of synonyms declines the nearer we get to the present day (this paper came out in 1995) is not because are are creating fewer synonyms but because we've not yet had time to do the work to uncover the remaining synonyms.

Put another way, these papers are arguing that real work of taxonomy is revision, not species discovery, especially since it's not uncommon for > 50% of species in a taxon to end up being synonymised. Indeed, if a taxoomic group has few synonyms then these authors would argue that's a sign of neglect. More revisionary work would likely uncover additional synonyms. So, what we need is a way to measure the amount of research on a taxonomic group. It occurs to me that we could use the citation graph as a way to tackle this. Lets imagine we have a set of taxa (say a family) and we have all the papers that described new species or undertook revisions (or both). The extensiveness of that work could be measured by the citation graph. For example, build the citation graph for those papers. How many original species decsriptions are not cited? Those species have been potentially neglected. How many large-scale revisions have there been (as measured by the numbers of taxonomic papers those revisions cite)? There are some interesting approaches to quantifying this, such as using hubs and authorities.

I'm aware that taxonomists have not had the happiest relationship with citations:

Pinto ÂP, Mejdalani G, Mounce R, Silveira LF, Marinoni L, Rafael JA. Are publications on zoological taxonomy under attack? R Soc Open Sci. 2021 Feb 10;8(2):201617. doi: 10.1098/rsos.201617. PMID: 33972859; PMCID: PMC8074659.

Still, I think there is an intriguing possibility here. For this approach to work, we need to have linked taxonomic names to publications, and have citation data for those publications. This is happening on various platforms. Wikidata, for example, is becoming a repository of the taxonomic literature, some of it with citation links.

Page RDM. 2022. Wikidata and the bibliography of life. PeerJ 10:e13712 https://doi.org/10.7717/peerj.13712

Time for some experiments.