Still, Zotero 8 brought in prefix and suffix support, and I was wondering if this could be used for CiTO citation intent annotations. And it can. This was the result, and make of it what you want:

Compact identifiers find a balance between compactness in writing and being a persistent, unique, and global identifier. It “is a string constructed by concatenating a namespace prefix, a separating colon, and a locally unique identifier (LUI)” (doi:10.1038/sdata.2018.29). For example, for proteins it can represent the PDB structure 2gc4 as pdb:2gc4. There is a clear similarity with the SciCrunch Research Resource Identifiers (RRIDs) as used by several journals, like eLife (doi:10.1007/s12021-015-9284-3).

When the prefixes are defined by community standards, then a compact identifier can be resolved. There currently are multiple providers of prefix files (doi:10.1038/sdata.2018.29), including Identifiers.org (doi:10.1093/bioinformatics/btaa864) and Bioregistry (doi:10.1038/s41597-022-01807-3). The Bioregistry has an overview of more than twenty registries of prefixes and their metadata (doi:10.1038/s41597-022-01807-3). The metadata commonly includes information on the URL pattern for each identifier. Often this is more than one pattern, as there may more several databases with information for the same identifier.

It is the URL pattern in the database that allows services to resolve the compact identifier into a link to a database. The above registries correspond to three existing resolvers that will take a compact identifier as part of a resolver URL and redirect to the database with the record matching that identifier:

• Name-to-Thing (N2T): https://n2t.net/
• Identifiers.org: https://identifiers.org/
• The Bioregistry: https://bioregistry.io/

Each of these URLs can be extended with a compact identifier. For example, a taxon record from the NCBI databases or the PDB entry mentioned earlier:

• https://bioregistry.io/pdb:2gc4
• https://identifiers.org/col:6MB3T (col is the prefix for the Catalogue of Life)

Why use in reports?

Using persistent identifiers is generally accepted as a good practice that benefits science and has been part of the ideas of FAIR data (doi:10.1038/sdata.2016.18) and of Open Science. Compact identifiers make it easy to be precise in reports about what things the reports talk about: they are relatively short but very precise at the same time. also, that has the benefit that they are much easier to reuse than labels of things and concepts that intrinsically have a certain level of uncertainty; a database entry has commonly a very specific meaning.

Examples uses

The use of compact identifiers can be used in two ways. The simplest is to just put the compact identifier as plain text in the document, possibly in parentheses (with the compact identifier highlighted here in bold):

This report is only about the experimental data of the human (NCBITaxon:9606) cell lines.

Or:

We found that BRCA1 (ensembl:ENSG00000012048) played an important role.

Alternatively, you can add a hyperlink with one of the resolvers, for example, Identifiers.org:

We found that BRCA1 (ensembl:ENSG00000012048) played an important role.

Compact identifiers for material identifiers

The European Registry of Materials proposes to use the compact identifier for their ERM identifiers (doi:10.1186/s13321-022-00614-7):

For example, the NanoSolveIT project registered a material with the ERM00000001 identifier. The full Uniform Resource Identifier (URI) for this compound is https://nanocommons.github.io/identifiers/registry#ERM00000001 which is too long to be used in documentation. The corresponding compact identifier erm:ERM00000001 is easy to use in written material, analogous to the use of Protein Data Bank (PDB) identifiers for proteins in journals.

Compact identifiers for citation intent annotations

The compact identifier has also been used as the method to include citation intentions in journal articles (doi:10.1186/s13321-020-00448-1, compact identifier here highlighted in bold):

We take advantage here of the ability to add notes to full form [..] references in bibliographies. These are referred to as bibnotes. The content of the note will be strictly formatted: it will use the syntax [cito:usesMethodIn] and formatted in bold. That is, the bibnote starts with the [ character, followed by one of the CiTO types, and ends with the ] character. If you wish to provide more than one annotation, you can repeat this syntax, separated by one or more spaces, for example: [cito:usesMethodIn] [cito:citeAsAuthority].

Note that in this use, the square brackets and bold typeface are used to make them easier to be recognized. Also, note that this document uses this approach to indicate the intention of why the cited articles are cited.

Conclusion

This document described what the compact identifier is, how it helps linking to online databases, and how they can be used in written reports as plain text, optionally hyperlinked with one of the compact identifier resolvers.

Acknowledgments

I thank github:tabbassidaloii, github:cthoyt, and github:larsgw for their comment on this GitHub repo.

Things are coming together, and while commercial publishers (SpringerNature, Elsevier, MDPI, Frontiers, etc) are focused on profit (“shareholder value”) instead of the community they serve, Open Science is providing working, real-world, inexpensive, superior FAIR solutions for scientific dissemination. Maybe European universities are not convinced yet (see Björn’s post), but it is happening.

Two things that are happening are OpenAlex and OpenCitations. CiTO adoption not so much yet, but I am not giving up yet. Simply because Open Science doesn’t go away and everything can be picked up tomorrow. Each holiday I am picking up the Citation Typing Ontology and this holiday the use of nanopublications for CiTO intent annotation of April this year.

Yesterday, I played with the nanopublication templates used by NanoDash, got to the triples of it, and ended up using the web interface to create a derived template from Tobias’ template from April. What makes this nanopublication template special is that it uses the CiTO ontology.

The difference is that the original template used ScholarlyWork as type for the citing resource, while the derivative uses CreativeWork from the schema.org namespace, allowing things like this:

• article to software release: example nanopub
• article to blog: example nanopub
• blog to article: example nanopub
• blog to blog: example nanopub

The last three are possible because of the Rogue Scholar DOIs. Let’s continue with the fourth example, the blog to blog citation. While an URL is a unique, global identifier, the digital preservation depends on a lot of things. On the other hand, a DOI with the associated metadata is easier to preserve. For example, because it can be spread more easily than the digital object itself.

So, when Martin Fenner and I started archiving the Depth-First blog of Rich Apodaca to digitally preserve his blog, it also automatically gave the blog posts DOIs. This makes the blog more FAIR, just like it does for my blog. And being more FAIR, we can use the DOIs for other things too, like blog to blog citations with CiTO intent annotation, as nanopublications. (Technically, any Springer Nature journal can do this, but they found reasons to not do it.)

So, let’s take this blog post. I have today updated this to not use depth-first.com URLs but, following Martin’s example, use the DOIs for those posts instead.

And when I make a nanopublication out of this, I can add the citation intent, and then it looks like this:

For some reason, the DOIs do not show up as references as they do for this post for the DOIs of the posts of Martin Paul Eve, Björn Brembs, and Martin Fenner. It does for this post citing Depth-First.

So, from now on, I will use DOIs when citing other blog posts, and I hope many other blogs will start using Rogue Scholar or some other service to generate DOIs for single blog posts. I also have to figure out if I want to use DOIs to link to posts in my own blog. And hopefully, OpenCitations will soon accept citations provided by nanopublications. With or without CiTO intent annotations, whatever comes first. Oh, and I cannot wait to see the citations who up in Altmetric.com :)

Let’s see where this is going.

Traditional citation counts do not make this discussion obvious, but if we make our publishing sufficiently FAIR (it’s far from that, right now), then we can get a step closer. For example, with the Citation Typing Ontology we can show how the papers relate to each other:

This network is based on public knowledge in Wikidata and actually can be easily reproduced by anyone with this query:

#defaultView:Graph
SELECT DISTINCT ?focus1 ?focus1Label ?focus2 ?focus2Label ?edgeLabel WHERE {
  VALUES ?focus1 { wd:Q124174475 wd:Q124174776 wd:Q124174815 }
  VALUES ?focus2 { wd:Q124174475 wd:Q124174776 wd:Q124174815 }
  ?focus1 p:P2860 ?citation .
  ?citation ps:P2860 ?focus2 ; pq:P3712 ?edge .
  ?edge rdfs:label ?edgeLabel . FILTER(LANG(?edgeLabel) = "en")
  SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE],mul,en". }
}

The two “focus” values are an identical list of the articles I want to see. To make sure to get citations between all of them, I have to give them twice.

In the above example I have used VALUES for this, but I can also generate the controlled list of items between the citations I want to visualize with any SPARQL fragment too. This query does that (or here as GitHub Gist, but something else too: it uses a trick I learned from Finn Nielsen from this patch from the Scholia project (doi:10.1007/978-3-319-70407-4_36)).

Here, I select the articles by replacing the above VALUES lines with this fragment (P50 is ‘author’ and Q20895241 is me in Wikidata):

  ?focus1 wdt:P50 wd:Q20895241 .
  ?focus2 wdt:P50 wd:Q20895241 .

And, to be honest, then I get this network which is much richer than I expected:

I wonder how far we can push this. Can we also do this for the Journal of Cheminformatics? After all, this journal had a CiTO Pilot and, indeed, the results do not disappoint! All I had to do was replace the focus section:

  ?focus1 wdt:P1433 wd:Q6294930 .
  ?focus2 wdt:P1433 wd:Q6294930 .

First, the bad news. I still did not get around to the following to tasks I have. First, I need to write up a step-by-step guide how to create CiTO nanopublications and matching draft article. Second, I still need to work out how to update the JATS workflow for CiTO annotation in BioHackrXiv.

Wakefield

Let’s first start with a dataset. Peroni mentioned a study they did (10.1007/S11192-021-04097-5) into why the famous Wakefield paper (doi:10.1016/S0140-6736(97)11096-0) is cited. They published their data set on Zenodo (doi:10.5281/zenodo.13166142) with CCZero, so I imported it into Wikidata. Well, at least the citations of articles already in Wikidata. I used a Bacting (doi:10.21105/joss.02558) script and it actually was quite short. In the end, this added some 500 new citation intentions to Wikidata, now at almost 2000. This is also the third dataset with explicit CiTO intention annotations (see also this post).

This is what the CiTO section of the Wakefield paper in Scholia (doi:10.1007/978-3-319-70407-4_36) now looks like:

WikiPathways

A second thing I want to show is a potentional CiTO intention annotation dataset. Almost two years ago Alex Pico started a new WikiPathways feature as part of the new website (doi:10.1093/NAR/GKAD960)): a list of citations to specific pathways (in WikiPathways). Alex’ setup is fully automated and using PubMed Central and find mentions in figure captions:

Beyond citations to previous WikiPathways journal articles, we have identified 1228 mentions of a total of 582 unique WikiPathways pathway model identifiers, e.g. WP4846, in PubMedCentral articles over the past 13 years.

The file format is a pretty basic YAML file:

Additional mentions are found in the main text and tables in the article. These are not always picked up. These can be added manually. Over the past months and the past two weeks particularly, I have been adding additional mentions, not listed yet. We now passed 1500 mentions but I cannot easily give the other statistics.

BTW, anyone can add these citations with the ‘edit’ pencil and some Microsoft GitHub editing (but as far as I am concerned, please feel free to also just mention the paper on the WikiPathways Community Forum):

So, in the next few days I plan to do two things: 1. generate RDF for the YAML file and make that part of the monthly WikiPathways RDF release; 2. extract citations and offer this back to the OpenCitations project; and, 3. add the citations into Wikidata. Of course, all with cito:usesDataFrom :)

There is a fourth things that I am still thinking about. I can also use the above data the annotation citations to the WikiPathways papers if they also mention a WikiPathways identifier as cito:usesDataFrom, but I cannot fully oversee the implications of that. What do you think?

Now, in this post I will document an update of how we can use nanopublications for citation intention annotation, and compare this to existing solutions. I have been collecting and indexing the CiTO intention annotations in Wikidata and visualizing the corpus with Scholia at qlever.scholia.wiki/cito/ . There are currently 22 journal articles with explicit CiTO annoation, largely thanks to a Journal of Cheminformatics pilot (e.g. see doi:10.1186/s13321-023-00683-2). Recently, the preprint/report server BioHackrXiv started CiTO support too, also visible in the statistics on Scholia with another 17 papers. A third source is data sets from bibliometric-like studies, as explained in this post . Nanopublications would be a fourth solution.

So, why another solutions? Like the datasets, assuming DataCite approaches, have clear provenance, but the overhead of and needed time for creating a dataset with citation intent annotations can be limiting. And because nanopublications can be linked to ORCID identifiers, we can even discover which citation intent annotations are created by the original authors of articles. Another advantage is that nanopubs are basically RDF and we can query them easily, allowing the citation intentions to migrate to Wikidata. Scholia already saw an update to recognize nanopublications as a unique kind reference (see the new Wikidata property Nanopublication identifier (P12545)).

NanoDash template

So, if we can make it easy for people to define nanopublications with CiTO citation intent annotations, than we can start formalizing intent annotations from a much wider range of use cases. For example, we can annotate historically important discussions. Anyone can retrospectively annotate all their own articles, making them more FAIR. And if we use DOI links, then it no longer is limited to journal articles, but we can use of for software and data citations too. This is where a recent template comes in created by Tobias Kuhn, one of the main nanopub developers:

This nanopublication template defines the minimal needs of the assumptions, along with useful provenance and nanopub info. Basically, the assertion defines that one DOI is a ScholarlyWork and using the CiTO, defines that it cites one or more article works (with DOI). For each citations, one can select any of the known CiTO intent types, e.g. ‘extends’ or ‘uses method’ in, as in this nanopublication created with this template:

SPARQL-ing CiTO annotations

Besides the template, Tobias also started a SPARQL query to which I added restrictions that the citing and cited resources needs to have a DOI, giving us this query:

prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#>
prefix np: <http://www.nanopub.org/nschema#>
prefix npa: <http://purl.org/nanopub/admin/>
prefix npx: <http://purl.org/nanopub/x/>
prefix xsd: <http://www.w3.org/2001/XMLSchema#>
prefix dct: <http://purl.org/dc/terms/>

select ?np ?label ?subj ?citationrel ?obj ?date where {
  graph npa:graph {
    ?np npa:hasValidSignatureForPublicKey ?pubkey .
    ?np dct:created ?date .
    ?np np:hasAssertion ?assertion .
    optional { ?np rdfs:label ?label . }
    filter not exists { ?npx npx:invalidates ?np ; npa:hasValidSignatureForPublicKey ?pubkey . }
    filter not exists { ?np npx:hasNanopubType npx:ExampleNanopub . }
  }
  graph ?assertion {
    ?subj ?citationrel ?obj .
    filter(regex(str(?citationrel), "^http://purl.org/spar/cito/.*$"))
    filter(regex(str(?subj), "doi.org/10"))
    filter(regex(str(?obj), "doi.org/10"))
  }
}

This includes 6 citation intentions defined by 4 nanopublications added during the Open Science Retreat:

• RAUjZE1JMu by me for a paper by Marija Purgar
• RAXgI–5gc by Christian Meesters
• RATZNhd3l_j by Taichi Oichi
• RA6Q6wxSYy by Niklas Hohmann

From nanopublications to Wikidata

Now, this query also provides me with enough information to propagate the citation intent (a fact?) to Wikidata and cite the original nanopublication as reference. With a variation of the above SPARQL query, I can get the five most recent new nanopublications, convert them to QuickStatements, and then enjoy them in Wikidata. This is written up in this Bacting script.

The script needs to handle some situations. For example, it will not add items for DOIs not already in Wikidata. So, if neither of the two DOIs are known in Wikidata, then nothing gets added. If they both are, then it will add the citation intent. There are alternative solutions, but in practice that doesn’t matter and the QuickStatements is in all situations the same, and QuickStatements will only add the new information.

This is what it will look like in Wikidata:

And this is what it looks (yellow) when we compare the contributions from nanopublications now with the other sources:

Datasets as source of annotations

So, here’s a quick Wikidata update. I have been using Wikidata as infrastructure to collect and share CiTO annotations (see also the below “Scholia patch” posts). Some time ago I recovered my CiteULike CiTO annotations and made this available on Zenodo (doi:10.5281/zenodo.7368209).

And while thinking about datasets with CiTO annotations, I found two other datasets. One was from an article in Portuguese and one from an article by Peroni et al. with this data file. That data file is actually a zip, but inside the zip file is a CSV file with three interesting columns: cited_doi, citing_doi, and intext_citation.intent. There are many more columns and I can highly recommend browsing them. But these are the three I need to add data to Wikidata. The third column is free text, but using the CiTO for labels, making it relatively easy to convert to citation intentions from Wikidata (PS, thanks to Fvtvr3r for adding more!).

So, I had a cleaned file and started writing a Groovy Bioclipse script using Bacting. It basically does a few things: extract all DOIs, check which ones are in Wikidata, analyze the intext_citation.intent column content, and then generate QuickStatements (see this gist). Out of the 600 lines from the input, it creates some 200 new CiTO-annotated citations in Wikidata between some 150 article pairs:

The ability to include CiTO annotations from datasets is another welcome boost for the CiTO statistics in Wikidata. This SPARQL query shows an overview of sources that support the CiTO intention annotation, but note that a claim with a CiTO intention may also have CrossRef, PubMed, and COCI as reference. In those cases, they are primarily for the citations and not the intention.

There are now (the latest stats are here) 1202 citation intention annotations in Wikidata for 992 citations from 405 articles in 199 venues. Of these 27 articles have explicit annotations in the article itself and are found in 4 venues, two journals and two preprint servers). These annotated citations are to 510 articles in 190 different venues. This Scholia patch will add a new statistics, the number of datasets providing citation intentions, of which there are (as discussed) currently two in Wikidata. The latter two provide intentions for the majority of articles and are depicted in yellow in the below overview.

With an annotation in an 1938 article by Alan Turing! I ran into this article in November 2011 noting an apparent duplicate title in his article list. I turned out an earlier article had a correction with the same name. I added this clarification:

This is very trivial citation intention data that publishers could provide as open data.

Okay, that will do for today. There are actually some really interesting things in the pipeline, but I will have to write about that later. I have some deadlines I should start looking at. Below is some extra reading. Some more history

• 2021: BioHackathon Europe 2021 #1: CiTO annotations in BioHackrXiv
• 2021: Markdown template for the Journal of Cheminformatics with CiTO support
• 2020: CiTO updates #3: third paper in the collection and updated Scholia patch
• 2020: CiTO updates #2: annotation migration to Wikidata and first Scholia patch
• 2020: CiTO updates #1: first research paper in the Journal of Cheminformatics with CiTO annotation published
• July 2020: New Editorial: “Adoption of the Citation Typing Ontology by the Journal of Cheminformatics”
• 2015: “What You’re Doing Is Rather Desperate”
• 2012: CiTO / CiteULike: publishing innovation
• 2010: CiteULike CiTO Use Case #1: Wordles
• September 2010: A list of things I miss in CiteULike

Long story short: it worked! Thanks to the encouragements from Pjotr and Tazro and suggestions from Lars and some code on how to dump a Lua data structure to stdout.

In the Markdown/BibTeX combination you would normally write [@bibtexkey] to add the reference to the article with the given key in the .bib file. To type the citation (to state the intention why you cite that source), for example because you use a method in it, you write [@usesMethodIn:bibtexkey]. This is different from how it currently works for the Journal of Cheminformatics, where the intention cannot be given at citation level yet. You can even use more than one intention, e.g. [@usesMethodIn:extends:bibtexkey].

If you want to try it, just create a compatible Markdown file with BibTeX file in a new GitHub repository, and post the repository URL on this cool preview website.

Here’s what the created PDF could look like: