Another occasional conference report (day 1). So why is one about “persistent identifiers” important, and particularly to the chemistry domain?
The PID most familiar to most chemists is the DOI (digital object identifier). In fact there are many; some 60 types have been collected by ORCID (themselves purveyors of researcher identifiers). They sometimes even have different names; in life sciences they tend to be known instead as accession numbers. One theme common to many (probably not all) is that they represent sources of metadata about the object being identified. Further information if which allows you (or a machine) to decide if acquiring the full object is worthwhile. So in no particular order, here are some of the things I learnt today.
You can try this example: https://app.dimensions.ai/discover/publication?search_text=10.6084&search_type=kws&full_search=true which retrieves articles in which the data repository with prefix 10.6084 (Figshare) is cited. Try also the prefix 10.14469 which is the Imperial College repository.
Apart from the presentations themselves, PIDapalooza is unusual for some other activities. Thus you could go get your PIDnails done, with a selection of 8 or so tasteful logos to choose from. There will be tattoos tomorrow (this is a conference for younger people after all). I may grab a photo or two to provide evidence!
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The datacite-allusers forum has been discussing the topic of "Tracking citations to datasets". Three examples have been quoted:
https://scholar.google.com/scholar?q=10.14469 returns 72 matches. Although relatively few are false positives, there is clearly something amiss if eg http://search.datacite.org/ui?q=prefix:10.14469 returns 195,434 matches! That is a success rate of 0.04% Google!!
http://europepmc.org/search?query=(REF%3A%2710.14469%27) gives 540 matches, of which visual inspection suggests a high proportion are false positives.
https://app.dimensions.ai/discover/publication?search_text=10.14469 returns 24 hits, which are to publications citing the prefix 10.14469 (a data repository). In fact again I have gone through these 24, and 7 are false positives. We are also aware of around 10 false negatives.
So some ways to go yet before connecting data with articles becomes reliable.