Physical Sample identifiers – the future?

I have variously talked about persistent identifiers on this blog. These largely take the form of DOIs (Digital object identifiers), and here they relate to either journal articles or datasets associated with either the article or the blog post or both. Other disciplines, particularly the earth sciences, have long used persistent identifiers (PIDs) to identify physical objects rather than digital ones. One of my ambitions is to assign such identifiers to a small but highly historical collection of physical objects in my possession, as described at this post. As a prelude to this project, here I describe some ways of searching for physical objects that have been assigned a PID. Thanks Rorie for providing these! 

  1. Here is a general search for physical objects with associated metadata describing them as registered with DataCite. https://commons.datacite.org/doi.org?query=types.resourceTypeGeneral:PhysicalObject (11,269,090 items)
  2. The search can be slightly constrained to find only identifiers that originate from the earlier IGSN ID (International generic sample number) see here for details and https://www.igsn.org/about/ for the organisation set up) using the syntax query=client.client_type:igsnCatalog types.resourceTypeGeneral:PhysicalObject (9,642,030 items)

The exciting prospect is that in due time, such searches could be constrained by adding specifically chemical properties, most obviously eg an InChI identifier. At the moment, it is unlikely any existing samples have even been registered with such a term.

  1. Thus combining two queries would give the following:
    query=client.client_type:igsnCatalog types.resourceTypeGeneral:PhysicalObject+AND+subjects.subjectScheme:inchikey+AND+subjects.subject:*
  2. Removing the PhysicalObject constrain gives a different response:
    query=(subjects.subjectScheme:inchikey+AND+subjects.subject:*+OR+subjects.subjectScheme:inchi+AND+subjects.subject:*)

 When this becomes possible, (see project above!), it would enable for example journal articles (or the FAIR data associated with them) to reference information about a physical sample associated with eg the preparation of a molecule new to science.

Henry Rzepa

Henry Rzepa is Emeritus Professor of Computational Chemistry at Imperial College London.

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