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.

Anyway, just before they shutdown because of, I understood, lack of commercial interest, they archived all data, including the ChemPedia datasets. I was happy to be reminded about that, because I am not sure I had archived that data.

(Yes, it is indeed an Elsevier journal…)

Last week, ChEMBL 13 was released, with even more data, data fixes, etc. Since my RDF for ChEMBL 09 my workflow has become more solid and uses more common ontologies, started using more common ontologies and ontologies I just like, such as CHEMINF and CiTO. Below is an overview of the resource types present in the RDF: activities (almost 7M now), chemical entities, assays, targets, and documents.

The data on Kasabi will be updated soon, and the SPARQL end point hosted by Uppsala University was updated yesterday, including the SNORQL frontend:

The new data is not fully backwards compatible. The changes to the RDF include the use of cito:citesAsDataSource, more typing using existing ontologies, e.g. with cheminf:CHEMINF_000000 and pro:PR_000000001 from the PRotein Ontology.

A paper dedicated to the ChEMBL-RDF is in preparation. Existing use cases can be found here.

The new triples I wanted to upload, link the new public CHEMBL identifiers (like CHEMBL25 for aspirin) to the internal ChEMBL database identifier I used for ChEMBL 09 for the URIs. So, I am adding a lot of triples like:

<http://data.kasabi.com/dataset/chembl-rdf/09/molecule/m517180> <http://www.w3.org/2002/07/owl#sameAs>
<http://data.kasabi.com/dataset/chembl-rdf/09/chemblid/CHEMBL1>

And the pytassium code I use to upload this to Kasabi looks like:

import pytassium
import time

dataset = pytassium.Dataset('chembl-rdf','XXX')

# Store the contents of a turtle file
dataset.store_file('chemblids.nt', media_type='text/plain')

So, that omission in my log book has been corrected now.

Kasabi makes the RDF data available via a few APIs, depending on the APIs selected by the uploader. I picked all five of them, just to see how things work. Of direct interest are the SPARQL end point, but also the option to host the data as dereferencable resources. Cool! That was just what was missing for me.

Now, using the API requires you to get an account. This will allow Kasabi to control the traffic, and as such creates a business model around providing services around Open Data. I think this approach will work. But just to make clear, this does mean you need to get an account first, if you like to play with this data. Once you got an account, you get an API key, and you can append that to any URI with ?apikey=XXXX to authenticate yourself. I think this does mean Kasabi will have to go to a https connection, which is not yet the case. Moreover, you will need to subscribe to the data set too. That, in fact, with #altmetrics in mind, sounds really interesting :)

The ChemPedia RDF data is available at: http://beta.kasabi.com/dataset/chempedia-rdf

This web page will give the five APIs, of which the augmentation one is really interesting, but I have not played with that yet to say much about it. The idea of that API is to augment RDF you post with data from the data set. Like in a augmented reality. That should be cool for mashups.

Now, the APIs I do understand include this SPARQL end point (remember to add your API key!):

http://labs.kasabi.com/explorer/sparql/sparql-endpoint-chempedia-rdf

And the Linked Data feature. In the next post, I will explain how I tweaked the original data, how I uploaded it, and how this resulted in the dereferencable resources, like:

http://data.kasabi.com/dataset/chempedia-rdf/substances/2-2595-7562-8125.html

Note the links for RDF/XML, RDF/JSON, and Turtle, directly accessible by replacing the .html extension with .rdf, .json, and .ttl respectively. An API key does not seem required for this, which makes perfect sense.

It took me some chatting with the people from Talis, who have been very helpful, as the whole platform was a bit overwhelming. But, for the first time ever, I actually got Linked Open Data online, in a Linked Data manner.

Now, the first principle was about the need for access to data (McPrinciple #1). This principle goes without saying, one would think, but is not widely accepted yet. This is why Open Data promotion is still needed. For example, data in papers still is not freely redistributable, as Peter points out once again.

Anyway, this post is not about McPrinciple #1, but about the second principle.

Molecular Chemometrics Principles #2: In order to reproduce cheminformatics studies you need to be able to understand the input data.

Readers of my blog will surely recognize this theme. Clearly this theme explains my past fetish for the Chemical Markup Language, and my more recent work on the Resource Description Framework.

And it is so easy to jump to conclusions. Easy to make mistakes. And this is not just at the received side; the sending person may have accidentally made a mistake, or left something accidentally unclear, causing incorrect assumptions, and therefore errors in the cheminformatics computation. Now, if the data was semantically (clearly) annotated, and the meaning was clear, it was also trivial to see when a mistake had sneaked in. Think of it as a check bit.

“Well, isn’t this a bit exaggerated,” you might say. Perhaps, perhaps not. An simple, recent example. We all know SMILES, right? And we all know that lower case element symbols indicate aromaticity, right? That is, c1ccccc1 is aromatic, right? So, what’s the problem then?

Now, consider the SMILES string c1ccc1. Lower case carbon element symbols, so aromatic, right? Oh, wait…

Therefore, be clear in what you mean. It saves us from a lot of trouble.

Further reading:

• The Molecular Chemometrics Principles #1: access to data
• Molecular Chemometrics, 2006 (doi:10.1080/10408340600969601)

More on the meeting later.