One of the key goals of the FAIR guiding principles is defined by its final principle – to optimize data sets for reuse by both humans and machines. To do so, data providers need to implement and support consistent machine readable metadata to describe their data sets. This can seem like a daunting task for data providers, whether it is determining what level of detail should be provided in the provenance metadata or figuring out what common shared vocabularies should be used. Additionally, for existing data sets it is often unclear what steps should be taken to enable maximal, appropriate reuse. Data citation already plays an important role in making data findable and accessible, providing persistent and unique identifiers plus metadata on over 16 million data sets. In this paper, we discuss how data citation and its underlying infrastructures, in particular associated metadata, provide an important pathway for enabling FAIR data reuse.
Peroni, Silvio. The Open Citations Movement : The Story So Far.
The 5th Conference on Scholarly Publishing in the
Context of Open Science – PUBMET 2018
20-21 September 2018, Zadar, Croatia, 20/09/2018. https://figshare.com/articles/The_Open_Citations_Movement_the_Story_So_Far/7110653
The Initiative for Open Citations (I4OC) was launched in April 2017 with the purpose of promoting the release of structured, separable, and open citation data. Thanks to the incredible support of a large (and still growing) number of publishers and stakeholders, in one year more than 500 million citations have been released to the public, and are currently used by third parties for building new services to serve the scholarly community.
Mathiak, Brigitte, Boland, Katarina. Challenges in Matching Dataset Citation Strings to Datasets in Social Science. D-Lib Magazine, January/February 2015, Volume 21, Number 1/2. DOI: http://www.dlib.org/dlib/january15/mathiak/01mathiak.html
Finding dataset citations in scientific publications to gain information on the usage of research data is an important step to increase visibility of data and to give datasets more weight in the scientific community. Unlike publication impact, which is readily measured by citation counts, dataset citation remains a great unknown. In recent work, we introduced an algorithm to find dataset citations in full text documents automatically, but, in fact, this is just half the road to travel. Once the citation string has been found, it has to be matched to the correct DOI. This is more complicated than it sounds. In social science, survey datasets are typically recorded in a much more fine-granular way than they are cited, differentiating between years, versions, samples, modes of the interview, countries, even questionnaire variants. At the same time, the actual citation strings typically ignore these details. This poses a number of challenges to the matching of citations strings to datasets. In this paper, we discuss these challenges in more detail and present our ideas on how to solve them using an ontology for research datasets.
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