Coauthorship networks: A directed network approach considering the order and number of coauthors

Kim, Jinseok; Diesner, Jana

doi:10.1002/asi.23361

Cited by 19 publications

(7 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While there are numerous proposals for proportioning differing levels of recognition to authors at different positions in the author list (e.g. Hagen, 2013 ; Kim and Diesner, 2015 ), the close link between the complete-normalized count and the count based on senior authorship indicates that senior authors are at least an accurate proxy for the overall number of individual authors, at the country level.…”

Section: Methodsmentioning

confidence: 99%

International authorship and collaboration across bioRxiv preprints

Abdill

Adamowicz

Blekhman

2020

eLife

View full text Add to dashboard Cite

Preprints are becoming well established in the life sciences, but relatively little is known about the demographics of the researchers who post preprints and those who do not, or about the collaborations between preprint authors. Here, based on an analysis of 67,885 preprints posted on bioRxiv, we find that some countries, notably the United States and the United Kingdom, are overrepresented on bioRxiv relative to their overall scientific output, while other countries (including China, Russia, and Turkey) show lower levels of bioRxiv adoption. We also describe a set of ‘contributor countries’ (including Uganda, Croatia and Thailand): researchers from these countries appear almost exclusively as non-senior authors on international collaborations. Lastly, we find multiple journals that publish a disproportionate number of preprints from some countries, a dynamic that almost always benefits manuscripts from the US.

show abstract

Section: Methodsmentioning

confidence: 99%

International authorship and collaboration across bioRxiv preprints

Abdill

Adamowicz

Blekhman

2020

eLife

View full text Add to dashboard Cite

show abstract

“…Several authors in bibliometrics have argued that the validity of bibliometric indicators should be tested by correlating them with judgements by peers (see e.g., Adams, Loach, & Szomszor, 2016;Kim & Diesner, 2015). Thelwall (2017) explains the background of those tests as follows: "If indicators tend to give scores that agree to a large extent with human judgements then it would be reasonable to replace human judgements with them when a decision is not important enough to justify the time necessary for experts to read the articles in question.…”

Section: Measuring Convergent Validitymentioning

confidence: 99%

Do we measure novelty when we analyze unusual combinations of cited references? A validation study of bibliometric novelty indicators based on F1000Prime data

Bornmann

Tekles

Zhang

et al. 2019

Journal of Informetrics

View full text Add to dashboard Cite

Lee, Walsh, and Wang (2015)based on Uzzi, Mukherjee, Stringer, andJones (2013)andWang, Veugelers, andStephan (2017) proposed scores based on cited references (cited journals) data which can be used to measure the novelty of papers (named as novelty scores U and W in this study). Although previous research has used novelty scores in various empirical analyses, no study has been published up to nowto the best of our knowledgewhich quantitatively tested the convergent validity of novelty scores: do these scores measure what they propose to measure? Using novelty assessments by faculty members (FMs) atF1000Prime for comparison, we tested the convergent validity of the two novelty scores (U and W). FMs' assessments not only refer to the quality of biomedical papers, but also to their characteristics (by assigning certain tags to the papers): for example, are the presented findings or formulated hypotheses novel (tags "new findings" and "hypothesis")? We used these and other tags to investigate the convergent validity of both novelty scores. Our study reveals different results for the novelty scores: the results for novelty score U are mostly in agreement with previously formulated expectations. We found, for instance, that for a standard deviation (one unit) increase in novelty score U, the expected number of assignments of the "new finding" tag increase by 7.47%. The results for novelty score W, however, do not reflect convergent validity with the FMs' assessments: only the results for some tags are in agreement with the expectations. Thus, we proposebased on our resultsthe use of novelty score U for measuring novelty quantitatively, but question the use of novelty score W.

show abstract

“…Last, we compare the detailed rank of institutions in terms of SpatialLeaderRank, other conventional indices, and academic impact indices. The conventional indices include PageRank, betweenness centrality, closeness centrality, indegree centrality, and publication number (Kim & Diesner, 2015;Wu, 2013). And the academic impact indices include citation count, citation-based h-index, altmetrics count, altmetrics-based h-index.…”

Section: Effectiveness Analysis Of Spatialleaderrankmentioning

confidence: 99%

Characterizing research leadership on geographically weighted collaboration network

Zhang

2021

Scientometrics

View full text Add to dashboard Cite

Research collaborations, especially long-distance and international collaborations, have become increasingly prevalent worldwide. Recent studies highlighted the significant role of research leadership in collaborations. However, existing measures of the research leadership do not take into account the intensity of leadership in the co-authorship network. More importantly, the spatial features, which influence the collaboration patterns and research outcomes, have not been incorporated in measuring the research leadership. To fill the gap, we construct an institution-level weighted co-authorship network that integrates two types of weight on the edges: the intensity of collaborations and the spatial score (the geographical distance adjusted by the cross-linguistic-border nature). Based on this network, we propose a novel metric, namely the spatial research leadership rank, to identify the leading institutions while considering both the collaboration intensity and the spatial features. The leadership of an institution is measured by the following three criteria: (a) the institution frequently plays the corresponding rule in papers with other institutions; (b) the institution frequently plays the corresponding rule in longer distance and even cross-linguistic-border collaborations; (c) the participating institutions led by the institution have high leadership status themselves. Harnessing a dataset of 323,146 journal publications in pharmaceutical sciences during 2010-2018, we perform a comprehensive analysis of the geographical distribution and dynamic patterns of research leadership flows at the institution level. The results demonstrate that the SpatialLeaderRank outperforms baseline metrics in predicting the scholarly impact of institutions. And the result remains robust in the field of Information Science and Library Science.

show abstract

Coauthorship networks: A directed network approach considering the order and number of coauthors

Cited by 19 publications

References 52 publications

International authorship and collaboration across bioRxiv preprints

International authorship and collaboration across bioRxiv preprints

Do we measure novelty when we analyze unusual combinations of cited references? A validation study of bibliometric novelty indicators based on F1000Prime data

Characterizing research leadership on geographically weighted collaboration network

Contact Info

Product

Resources

About