When each line of code is written it is surrounded by a sea of context: who in the community this is for, what problem we’re trying to solve, what technical assumptions we’re making, what we already tried but didn’t work, how much coffee we’ve had today. All of these have an effect on the software we write.
By the time the next person looks at that code, some of that context will have evaporated.
It turns out that one of the things that is really difficult at Crossref is checking whether a set of Crossref credentials has permission to act on a specific DOI prefix. This is the result of many legacy systems storing various mappings in various different software components, from our Content System through to our CRM. To this end, I wrote a basic application, credcheck, that will allow you to test a Crossref credential against an API.
Subject classifications have been available via the REST API for many years but have not been complete or reliable from the start and will soon be deprecated. dfdfd
The subject metadata element was born out of a Labs experiment intended to enrich the metadata returned via Crossref Metadata Search with All Subject Journal Classification codes from Scopus. This feature was developed when the REST API was still fairly new, and we now recognize that the initial implementation worked its way into the service prematurely.
Crossref and DOAJ share the aim to encourage the dissemination and use of scholarly research using online technologies and to work with and through regional and international networks, partners, and user communities for the achievement of their aims to build local institutional capacity and sustainability. Both organisations agreed to work together in 2021 in a variety of ways, but primarily to ‘encourage the dissemination and use of scholarly research using online technologies, and regional and international networks, partners and communities, helping to build local institutional capacity and sustainability around the world.
We’ve mentioned why data citation is important to the research community. Now it’s time to roll up our sleeves and get into the ‘how’. This part is important, as citing data in a standard way helps those citations be recognised, tracked, and used in a host of different services.
This week A Data Citation Roadmap for Scientific Publishers was published in Scientific Data. This roadmap is the outcome of a collaboration between different publishers that worked on identifying all steps you need to take as a publisher to implement data citation. If you want to know more about establishing a data policy, capturing data citations at the point of submission, or tagging data citations in your XML, we recommend you take a look at this article!
In this blog post, we’ll discuss the steps you need to take after you’ve implemented this roadmap. The steps in the roadmap describe how you can track & tag data citation yourself. Here we describe how Crossref can help you make these available to the rest of the community.
The ‘what’
Here’s the recap! From the Crossref perspective, there are two ways to add data citation links into the metadata that you register:
1. Metadata deposits using the references section of the schema
This is where ‘citations’ are normally recorded. Publishers include the data citation into the deposit of bibliographic references for each publication.
<citationkey="ref=3"><unstructured_citation>Morinha F, Dávila JA, Estela B, Cabral JA, Frías Ó, González JL, Travassos P, Carvalho D, Milá B, Blanco G (2017) Data from: Extreme genetic structure in a social bird species despite high dispersal capacity. Dryad Digital Repository. http://0-dx-doi-org.library.alliant.edu/10.5061/dryad.684v0</unstructured_citation\>
</citation></citation_list>
Or they can employ any number of reference tags currently accepted by Crossref.
We are exploring JATS4R recommendations to expand the current collection and better support these citations - more on this soon. We also encourage additional suggestions from the community.
2. Metadata deposits using the relations section of the schema
This is where other relationships can be recorded. Publishers assert the data link in the relationship section of the metadata deposit. Here, publishers can identify data which are direct outputs of the research results if this is known. This level of specificity is optional, but we’d recommend it as it can support scientific validation and research funding management.
Data and software citations via relation type enables precise tagging of the dataset and its specific relationship to the research results published. To tag the data & software citation in the metadata deposit, we ask for the description of the dataset & software (optional), dataset & software identifier and identifier type (DOI, PMID, PMCID, PURL, ARK, Handle, UUID, ECLI, and URI), and relationship type.
<programxmlns="http://0-www-crossref-org.library.alliant.edu/relations.xsd"><related_item><description>Data from: Extreme genetic structure in a social bird species despite high dispersal capacity</description><inter_work_relationrelationship-type="references"identifier-type="doi">10.5061/dryad.684v0</inter_work_relation></related_item></program></doi_relations>
In general, use the relation type references for data and software resources.
Publishers who wish to specify that the data or software resource was generated as part of the research results can use the isSupplementedBy relation type.
The ‘how’
I create my own XML and register it with Crossref
Add links to datasets into your reference lists, including their DOIs if available as shown above and deposit them with Crossref. We’ll do the rest. If you want to add references to existing metadata records, you don’t need to redeposit the full article metadata, you can send us a resource-only deposit that just contains the reference metadata to append that to the existing metadata for the article. You can also use this method if you prefer to deposit references in a separate workflow to registering your content (we know some members prefer to work this way).
I’ve started using Metadata Manager for journal article deposits
You can deposit data citations using either method using our new Metadata Manager tool. When entering journal article metadata, you can use the ‘Related Items’ section to enter the DOI (or other identifier) for the dataset, the type of identifier, a description of the relation type e.g. ‘Data from: Extreme genetic structure in a social bird species despite high dispersal capacity’, and the relation type - ‘references’ or ‘is supplemented by’ depending on the relationship between the data and the article as described above. When you make the deposit, this relationship information will be registered in Crossref along with the rest of the article metadata.
Metadata Manager also has a section where you can enter and match your references, and then deposit these with Crossref. If you choose this method, enter any data citations into the references section before depositing the article metadata with Crossref.
If you want to add this information to deposits you have already made using Metadata Manager, you can search for the journals and articles in the interface, bring up the existing metadata and add in the additional information before redepositing.
I use “simple text query” to search for and deposit references
Make sure you include any citations to data in the references you add into Simple Text Query. When you use simple text query to deposit these references, they will then be added into the article metadata in the Crossref database.
If you use OJS, they’re working on functionality (due for release soon) that will make it easier to deposit reference metadata with Crossref, so you can include citations to data in that.
All of this metadata—registered with Crossref—make it possible to build up pictures of data citations, linking, and relationships. Whether the citations come from the authors in the reference list or they are extracted by the publisher and then deposited, Crossref collects them across publishers. We then make the aggregate set freely available via Crossref’s APIs in multiple interfaces (REST, OAI-PMH, OpenURL) and formats (XML and JSON). DataCite does the same for data repositories and so this provides an easy way for publishers and data repositories to exchange information about data citations. As mentioned previously, this all feeds in Event Data. Data is made openly available to a wide host of parties across the extended research ecosystem including funders, research organisations, technology and service providers, indexers, research data frameworks such as Scholix, etc.
Do you have questions about how to add these links to your Crossref or DataCite metadata? We’ll be running a series of webinars in early 2019 to give you a chance to join us live and ask any questions you have. Eager to get started in the meantime? Let us know and we’ll start to coordinate.