Authors : Jordan D. Dworkin, Kristin A. Linn, Erin G. Teich, Perry Zurn, Russell T. Shinohara, Danielle S. Bassett
Like many scientific disciplines, neuroscience has increasingly attempted to confront pervasive gender imbalances within the field. While much of the conversation has centered around publishing and conference participation, recent research in other fields has called attention to the prevalence of gender bias in citation practices.
Because of the downstream effects that citations can have on visibility and career advancement, understanding and eliminating gender bias in citation practices is vital for addressing inequity in a scientific community. In this study, we sought to determine whether there is evidence of gender bias in the citation practices of neuroscientists.
Utilizing data from five top neuroscience journals, we indeed find that reference lists tend to include more papers with men as first and last author than would be expected if gender was not a factor in referencing. Importantly, we show that this overcitation of men and undercitation of women is driven largely by the citation practices of men, and is increasing with time despite greater diversity in the academy.
We develop a co-authorship network to determine the degree to which homophily in researchers’ social networks explains gendered citation practices and we find that men tend to overcite other men even when their social networks are representative of the field.
We discuss possible mechanisms and consider how individual researchers might incorporate these findings into their own referencing practices.
DOI : https://doi.org/10.1101/2020.01.03.894378
Authors : Bo-Christer Björk, Sari Kanto-Karvonen, J. Tuomas Harviainen
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.
URL : https://arxiv.org/abs/1912.10228
Authors : Garret Christensen, Allan Dafoe, Edward Miguel, Don A. Moore, Andrew K. Rose
This study estimates the effect of data sharing on the citations of academic articles, using journal policies as a natural experiment. We begin by examining 17 high-impact journals that have adopted the requirement that data from published articles be publicly posted.
We match these 17 journals to 13 journals without policy changes and find that empirical articles published just before their change in editorial policy have citation rates with no statistically significant difference from those published shortly after the shift.
We then ask whether this null result stems from poor compliance with data sharing policies, and use the data sharing policy changes as instrumental variables to examine more closely two leading journals in economics and political science with relatively strong enforcement of new data policies.
We find that articles that make their data available receive 97 additional citations (estimate standard error of 34).
We conclude that: a) authors who share data may be rewarded eventually with additional scholarly citations, and b) data-posting policies alone do not increase the impact of articles published in a journal unless those policies are enforced.
URL : A study of the impact of data sharing on article citations using journal policies as a natural experiment
DOI : https://doi.org/10.1371/journal.pone.0225883
Authors : James Bradley, Sitaram Devarakonda, Avon Davey, Dmitriy Korobskiy, Siyu Liu, Djamil Lakhdar-Hamina, Tandy Warnow, George Chacko
Citation analysis of the scientific literature has been used to study and define disciplinary boundaries, to trace the dissemination of knowledge, and to estimate impact. Co-citation, the frequency with which pairs of publications are cited, provides insight into how documents relate to each other and across fields.
Co-citation analysis has been used to characterize combinations of prior work as conventional or innovative and to derive features of highly cited publications. Given the organization of science into disciplines, a key question is the sensitivity of such analyses to frame of reference.
Our study examines this question using semantically themed citation networks. We observe that trends reported to be true across the scientific literature do not hold for focused citation networks, and we conclude that inferring novelty using co-citation analysis and random graph models benefits from disciplinary context.
DOI : https://doi.org/10.1162/qss_a_00007
Authors : Akira Inokuchi, Yusuf Sulistyo Nugroho, Fumiaki Konishi, Hideaki Hata, Akito Monden, Kenichi Matsumoto
Academic publications have been evaluated with the impact on research communities based on the number of citations. On the other hand, the impact of academic publications on industry has been rarely studied.
This paper investigates how academic publications contribute to software development by analyzing publication citations in source code comments in open source software repositories.
We propose an automated approach of detecting academic publications based on Named Entity Recognition, and achieve 0.90 in F1 as detection accuracy. We conduct a large-scale study of publication citations with 319,438,977 comments collected from active 25,925 repositories written in seven programming languages.
Our findings indicate that academic publications can be knowledge sources of software development, and there can be potential issues of obsoleting knowledge.
URL : https://arxiv.org/abs/1910.06932
Authors : Vicente P. Gerrero-Bote, Rodrigo Sánchez-Jiménez, Félix De-Moya-Anegón
Patents include citations, both to other patents and to documents that are not patents (NPL, Non-patent literature). Among the latter include citations to articles published in scientific journals.
Just as the scientific impact is studied through the citation of articles and other scientific works, the technological impact of scientific works can also be studied through the citation they receive from patents.
The NPL references included in the patents are far from being standardized, so determining which scientific article they refer to is not trivial. This paper presents a procedure for linking the NPL references of the patents collected in the Patstat database and the scientific works indexed in the Scopus bibliographic database.
This procedure consists of two phases: a broad generation of candidate couples and another phase of validation of couples. It has been implemented with reasonable good results and affordable costs.
URL : https://recyt.fecyt.es/index.php/EPI/article/view/epi.2019.jul.01
Authors : Giovanni Colavizza, Iain Hrynaszkiewicz, Isla Staden, Kirstie Whitaker, Barbara McGillivray
Efforts to make research results open and reproducible are increasingly reflected by journal policies encouraging or mandating authors to provide data availability statements.
As a consequence of this, there has been a strong uptake of data availability statements in recent literature. Nevertheless, it is still unclear what proportion of these statements actually contain well-formed links to data, for example via a URL or permanent identifier, and if there is an added value in providing them.
We consider 531,889 journal articles published by PLOS and BMC which are part of the PubMed Open Access collection, categorize their data availability statements according to their content and analyze the citation advantage of different statement categories via regression.
We find that, following mandated publisher policies, data availability statements have become common by now, yet statements containing a link to a repository are still just a fraction of the total.
We also find that articles with these statements, in particular, can have up to 25.36% higher citation impact on average: an encouraging result for all publishers and authors who make the effort of sharing their data. All our data and code are made available in order to reproduce and extend our results.
URL : https://arxiv.org/abs/1907.02565