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I'm familiar with DOIs been allocated to historic time series, to give academics a unique, citable identifier for datasets. Similar to how a DOI is allocated to a specific journal article.

As the International DOI Foundation says:

A DOI name provides a means of persistently identifying a piece of intellectual property on a digital network and associating it with related current data in a structured extensible way.

What's the procedure for DOIs for datasets that continue to grow: for example, a time-series of external temperature data for an airport? Is one DOI allocated to the ever-growing time-series. Is there a DOI for each year's worth of archived data? Something else?

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  • Maybe you want to give an example of a dataset that has a DOI? I was not aware that this is even a thing.
    – xLeitix
    Commented Feb 26, 2015 at 15:57
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    @xLeitix See, for example, the Data cite FAQ. There are also many Q&As on the topic on Open Data Stack Exchange.
    – gerrit
    Commented Feb 26, 2015 at 16:41
  • That being said, I think this question might get better answers on Open Data.
    – gerrit
    Commented Feb 26, 2015 at 16:42
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    If it is off topic here, I think we should let it get closed first before considering migration; see this meta discussion
    – ff524
    Commented Feb 26, 2015 at 21:50
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    @xLeitix All data sets at the Dryad Data Repository receive a DOI, and this is the case for most other data repositories as well.
    – fileunderwater
    Commented Mar 4, 2015 at 10:44

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There does not seem to be an absolute rule, though "new DOI for a new version" seems most common. Speak to whoever's hosting your data in case they have a different preference.

The California Digital Library summarises various approaches here. Some require registering a new DOI for each new tranche of the data; some will reuse the DOI but use a new version number or date to help you identify the relevant bit; some distinguish between major and minor additions.

It's worth noting that the "generate a new DOI for a new version" approach assumes that you're adding, say, a new month's data to the set with each version. If it's being updated on a daily or hourly basis, this approach breaks down; you don't want to generate dozens of DOIs for only marginally different versions! Here, the "snapshot" approach recommended by the Digital Curation Centre is much more efficient - produce a static copy of the dataset as it currently stands, on an as-needed basis or at standard intervals, and cite that.

Update: the new STM Report on scholarly publishing, out today, notes that the "The RDA Data Citation Working Group is investigating possible technical solutions [for dynamic data]" without giving much more detail (p. 140); the most recent material to come out of that group seems to be this workshop report from last year.

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  • That's a great collection of links. All of these approaches and variations on them are used in the wild. Worth noting that the DataCite metadata schema (for dataset DOIs) allows for various types of link between datasets, including "is new version of" and "is previous version of".
    – Jez
    Commented Mar 19, 2015 at 13:29

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