Archives par mot-clé : citations

How Frequently are Articles in Predatory Open Access Journals Cited

Björk, Bo-Christer, Sari Kanto-Karvonen, et J. Tuomas Harviainen. « How Frequently are Articles in Predatory Open Access Journals Cited ». arXiv:1912.10228 [cs], 21 décembre 2019.
Predatory journals are Open Access journals of highly questionable scientific quality. Such journals pretend to use peer review for quality assurance, and spam academics with requests for submissions, in order to collect author payments. In recent years predatory journals have received a lot of negative media. While much has been said about the harm that such journals cause to academic publishing in general, an overlooked aspect is how much articles in such journals are actually read and in particular cited, that is if they have any significant impact on the research in their fields. Other studies have already demonstrated that only some of the articles in predatory journals contain faulty and directly harmful results, while a lot of the articles present mediocre and poorly reported studies. We studied citation statistics over a five-year period in Google Scholar for 250 random articles published in such journals in 2014, and found an average of 2,6 citations per article and that 60 % of the articles had no citations at all. For comparison a random sample of articles published in the approximately 25,000 peer reviewed journals included in the Scopus index had an average of 18,1 citations in the same period with only 9 % receiving no citations. We conclude that articles published in predatory journals have little scientific impact.

FAIR Data Reuse : the Path through Data Citation

Groth, Paul, Helena Cousijn, Tim Clark, et Carole Goble. « FAIR Data Reuse – the Path through Data Citation ». Data Intelligence, 1 novembre 2019, 78‑86.

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.

The Open Citations Movement : The Story So Far

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.

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.

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Challenges in Matching Dataset Citation Strings to Datasets in Social Science

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:

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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