Managing the Quality of Person Names in DBLP

Reuther, Patrick; Walter, Bernd; Ley, Michael; Weber, Alexander; Klink, Stefan

doi:10.1007/11863878_55

Cited by 24 publications

(11 citation statements)

References 1 publication

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“…On the other hand, the DBLP name disambiguation is based on a unique identifier assigned to authors when manuscripts are submitted to registered Computer Science conferences or journals. Thus, the DBLP database has arguably the most reliable name disambiguation available in a bibliometric database [60], and has also been used in several peer-reviewed studies to study scientific careers [23,44]. Critically, by replicating our study in three different databases, each with an independent method for name disambiguation, we argue that any possible errors resulting from misappropriated or missing publications are negligible.…”

Section: S21 Identifying Scientific Careersmentioning

confidence: 66%

Historical comparison of gender inequality in scientific careers across countries and disciplines

Huang

Gates

Sinatra

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

646

504

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There is extensive, yet fragmented, evidence of gender differences in academia suggesting that women are underrepresented in most scientific disciplines and publish fewer articles throughout a career, and their work acquires fewer citations. Here, we offer a comprehensive picture of longitudinal gender differences in performance through a bibliometric analysis of academic publishing careers by reconstructing the complete publication history of over 1.5 million gender-identified authors whose publishing career ended between 1955 and 2010, covering 83 countries and 13 disciplines. We find that, paradoxically, the increase of participation of women in science over the past 60 years was accompanied by an increase of gender differences in both productivity and impact. Most surprisingly, though, we uncover two gender invariants, finding that men and women publish at a comparable annual rate and have equivalent career-wise impact for the same size body of work. Finally, we demonstrate that differences in publishing career lengths and dropout rates explain a large portion of the reported career-wise differences in productivity and impact, although productivity differences still remain. This comprehensive picture of gender inequality in academia can help rephrase the conversation around the sustainability of women’s careers in academia, with important consequences for institutions and policy makers.

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Section: S21 Identifying Scientific Careersmentioning

confidence: 66%

Historical comparison of gender inequality in scientific careers across countries and disciplines

Huang

Gates

Sinatra

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

646

504

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“…This dataset records the bibliographies of 1,095,174 authors, totaling 1,919,594 documents. The quality of records is a key concern for DBLP (Reuther, Walter, Ley, Weber, & Klink, 2006). Non-western names are transliterated to the ISO-8859-1 (latin1) encoding.…”

Section: Demographics Of the Populationmentioning

confidence: 99%

Academic careers in Computer Science: continuance and transience of lifetime co-authorships

2014

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. Any correspondance concerning this service should be sent to the repository administrator: staff-oatao@listes-diff.inp-toulouse.fr Abstract Scholarly publications reify fruitful collaborations between co-authors. A branch of research in the Science Studies focuses on analyzing the co-authorship networks of established scientists. Such studies tell us about how their collaborations developed through their careers. This paper updates previous work by reporting a transversal and a longitudinal studies spanning the lifelong careers of a cohort of researchers from the DBLP bibliographic database. We mined 3,860 researchers' publication records to study the evolution patterns of their co-authorships. Two features of co-authors were considered: 1) their expertise, and 2) the history of their partnerships with the sampled researchers. Our findings reveal the ephemeral nature of most collaborations: 70% of the new co-authors were only one-shot partners since they did not appear to collaborate on any further publications. Overall, researchers consistently extended their co-authorships 1) by steadily enrolling beginning researchers (i.e., people who had never published before), and 2) by increasingly working with confirmed researchers with whom they already collaborated.

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“…In some cases the names can be identified thanks to email address or associated URL, but this is not a common feature. For the lion share of persons the first step is to use the co-author relationship graph, a bit like it was done in DBLP [19] and Arnetminer [20]. By comparing co-author graphs for two instances we can decide if they represent the same named entity (object) or not.…”

Section: B Matching Personsmentioning

confidence: 99%

How deep data becomes big data

Szczuka

Ślęzak²

2013

2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS)

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We present some problems and solutions for situations when compound and semantically rich nature of data records, such as scientific articles, creates challenges typical for big data processing. Using a case study of named entity matching in SONCA system we show how big data problems emerge and how they are solved by bringing together methods from database management and computational intelligence.

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Managing the Quality of Person Names in DBLP

Cited by 24 publications

References 1 publication

Historical comparison of gender inequality in scientific careers across countries and disciplines

Historical comparison of gender inequality in scientific careers across countries and disciplines

Academic careers in Computer Science: continuance and transience of lifetime co-authorships

How deep data becomes big data

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