17 research outputs found
Investigating the contribution of author- and publication-specific features to scholars' h-index prediction
Evaluation of researchers' output is vital for hiring committees and funding
bodies, and it is usually measured via their scientific productivity,
citations, or a combined metric such as h-index. Assessing young researchers is
more critical because it takes a while to get citations and increment of
h-index. Hence, predicting the h-index can help to discover the researchers'
scientific impact. In addition, identifying the influential factors to predict
the scientific impact is helpful for researchers seeking solutions to improve
it. This study investigates the effect of author, paper and venue-specific
features on the future h-index. For this purpose, we used machine learning
methods to predict the h-index and feature analysis techniques to advance the
understanding of feature impact. Utilizing the bibliometric data in Scopus, we
defined and extracted two main groups of features. The first relates to prior
scientific impact, and we name it 'prior impact-based features' and includes
the number of publications, received citations, and h-index. The second group
is 'non-impact-based features' and contains the features related to author,
co-authorship, paper, and venue characteristics. We explored their importance
in predicting h-index for researchers in three different career phases. Also,
we examine the temporal dimension of predicting performance for different
feature categories to find out which features are more reliable for long- and
short-term prediction. We referred to the gender of the authors to examine the
role of this author's characteristics in the prediction task. Our findings
showed that gender has a very slight effect in predicting the h-index. We found
that non-impact-based features are more robust predictors for younger scholars
than seniors in the short term. Also, prior impact-based features lose their
power to predict more than other features in the long-term.Comment: 14 pages, 1 figur
Analyzing the network structure and gender differences among the members of the Networked Knowledge Organization Systems (NKOS) community
In this paper, we analyze a major part of the research output of the Networked Knowledge Organization Systems (NKOS) community in the period 2000-2016 from a network analytical perspective. We focus on the papers presented at the European and US NKOS workshops and in addition four special issues on NKOS in the last 16 years. For this purpose, we have generated an open dataset, the "NKOS bibliography" which covers the bibliographic information of the research output. We analyze the co-authorship network of this community which results in 123 papers with a sum of 256 distinct authors. We use standard network analytic measures such as degree, betweenness and closeness centrality to describe the co-authorship network of the NKOS dataset. First, we investigate global properties of the network over time. Second, we analyze the centrality of the authors in the NKOS network. Lastly, we investigate gender differences in collaboration behavior in this community. Our results show that apart from differences in centrality measures of the scholars, they have higher tendency to collaborate with those in the same institution or the same geographic proximity. We also find that homophily is higher among women in this community. Apart from small differences in closeness and clustering among men and women, we do not find any significant dissimilarities with respect to other centralities
What happens when a journal converts to open access? A bibliometric analysis
In recent years, increased stakeholder pressure to transition research to Open Access has led to many journals converting, or ‘flipping’, from a closed access (CA) to an open access (OA) publishing model. Changing the publishing model can influence the decision of authors to submit their papers to a journal, and increased article accessibility may influence citation behaviour. In this paper we aimed to understand how flipping a journal to an OA model influences the journal’s future publication volumes and citation impact. We analysed two independent sets of journals that had flipped to an OA model, one from the Directory of Open Access Journals (DOAJ) and one from the Open Access Directory (OAD), and compared their development with two respective control groups of similar journals. For bibliometric analyses, journals were matched to the Scopus database. We assessed changes in the number of articles published over time, as well as two citation metrics at the journal and article level: the normalised impact factor (IF) and the average relative citations (ARC), respectively. Our results show that overall, journals that flipped to an OA model increased their publication output compared to journals that remained closed. Mean normalised IF and ARC also generally increased following the flip to an OA model, at a greater rate than was observed in the control groups. However, the changes appear to vary largely by scientific discipline. Overall, these results indicate that flipping to an OA publishing model can bring positive changes to a journal
The relationship between bioRxiv preprints, citations and altmetrics
A potential motivation for scientists to deposit their scientific work as preprints is to enhance its citation or social impact. In this study we assessed the citation and altmetric advantage of bioRxiv, a preprint server for the biological sciences. We retrieved metadata of all bioRxiv preprints deposited between November 2013 and December 2017, and matched them to articles that were subsequently published in peer-reviewed journals. Citation data from Scopus and altmetric data from Altmetric.com were used to compare citation and online sharing behavior of bioRxiv preprints, their related journal articles, and nondeposited articles published in the same journals. We found that bioRxiv-deposited journal articles had sizably higher citation and altmetric counts compared to nondeposited articles. Regression analysis reveals that this advantage is not explained by multiple explanatory variables related to the articles' publication venues and authorship. Further research will be required to establish whether such an effect is causal in nature. bioRxiv preprints themselves are being directly cited in journal articles, regardless of whether the preprint has subsequently been published in a journal. bioRxiv preprints are also shared widely on Twitter and in blogs, but remain relatively scarce in mainstream media and Wikipedia articles, in comparison to peer-reviewed journal articles
Which Factors are associated with Open Access Publishing? A Springer Nature Case Study
Open Access (OA) facilitates access to articles. But, authors or funders
often must pay the publishing costs preventing authors who do not receive
financial support from participating in OA publishing and citation advantage
for OA articles. OA may exacerbate existing inequalities in the publication
system rather than overcome them. To investigate this, we studied 522,411
articles published by Springer Nature. Employing correlation and regression
analyses, we describe the relationship between authors affiliated with
countries from different income levels, their choice of publishing model, and
the citation impact of their papers. A machine learning classification method
helped us to explore the importance of different features in predicting the
publishing model. The results show that authors eligible for APC waivers
publish more in gold-OA journals than others. In contrast, authors eligible for
an APC discount have the lowest ratio of OA publications, leading to the
assumption that this discount insufficiently motivates authors to publish in
gold-OA journals. We found a strong correlation between the journal rank and
the publishing model in gold-OA journals, whereas the OA option is mostly
avoided in hybrid journals. Also, results show that the countries' income
level, seniority, and experience with OA publications are the most predictive
factors for OA publishing in hybrid journals
An Open Testbed for Author Name Disambiguation Evaluation
We implemented a method for author name disambiguation and categorized publications of authors with the same name. This testbed is applied to evaluate our implementation.We implemented a method for author name disambiguation and categorized publications of authors with the same name. This testbed is applied to evaluate our implementation