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[journal article]

dc.contributor.authorSmirnova, Ninade
dc.contributor.authorMayr, Philippde
dc.date.accessioned2022-11-30T15:18:50Z
dc.date.available2022-11-30T15:18:50Z
dc.date.issued2022de
dc.identifier.issn1588-2861de
dc.identifier.urihttps://www.ssoar.info/ssoar/handle/document/83218
dc.description.abstractAnalysis of acknowledgments is particularly interesting as acknowledgments may give information not only about funding, but they are also able to reveal hidden contributions to authorship and the researcher’s collaboration patterns, context in which research was conducted, and specific aspects of the academic work. The focus of the present research is the analysis of a large sample of acknowledgement texts indexed in the Web of Science (WoS) Core Collection. Record types "article" and "review" from four different scientific domains, namely social sciences, economics, oceanography and computer science, published from 2014 to 2019 in a scientific journal in English were considered. Six types of acknowledged entities, i.e., funding agency, grant number, individuals, university, corporation and miscellaneous, were extracted from the acknowledgement texts using a named entity recognition tagger and subsequently examined. A general analysis of the acknowledgement texts showed that indexing of funding information in WoS is incomplete. The analysis of the automatically extracted entities revealed differences and distinct patterns in the distribution of acknowledged entities of different types between different scientific domains. A strong association was found between acknowledged entity and scientific domain, and acknowledged entity and entity type. Only negligible correlation was found between the number of citations and the number of acknowledged entities. Generally, the number of words in the acknowledgement texts positively correlates with the number of acknowledged funding organizations, universities, individuals and miscellaneous entities. At the same time, acknowledgement texts with the larger number of sentences have more acknowledged individuals and miscellaneous categories.de
dc.description.abstractDie Analyse von Danksagungstexten in wissenschaftlichen Veröffentlichungen ist besonders interessant, da sie nicht nur Aufschluss über die Finanzierung geben, sondern auch verborgene Beiträge zur Autorenschaft und zu den Kooperationsmustern der Forschenden, zum Kontext, in dem die Forschung durchgeführt wurde, sowie zu bestimmten Aspekten der wissenschaftlichen Arbeit offenlegen können. Der Schwerpunkt dieser Publikation liegt auf der Analyse einer großen Stichprobe von Danksagungstexten, die in der Web of Science (WoS) Core Collection indexiert sind.de
dc.languageende
dc.subject.ddcNews media, journalism, publishingen
dc.subject.ddcPublizistische Medien, Journalismus,Verlagswesende
dc.subject.otherAcknowledgements; Web of Science; Acknowledged entities; Named entity recognitionde
dc.titleA comprehensive analysis of acknowledgement texts in Web of Science: a case study on four scientific domainsde
dc.description.reviewbegutachtet (peer reviewed)de
dc.description.reviewpeer revieweden
dc.source.journalScientometrics
dc.publisher.countryNLDde
dc.subject.classozSzientometrie, Bibliometrie, Informetriede
dc.subject.classozScientometrics, Bibliometrics, Informetricsen
dc.identifier.urnurn:nbn:de:0168-ssoar-83218-3
dc.rights.licenceCreative Commons - Attribution 4.0en
dc.rights.licenceCreative Commons - Namensnennung 4.0de
ssoar.contributor.institutionGESISde
internal.statusformal und inhaltlich fertig erschlossende
dc.type.stockarticlede
dc.type.documentjournal articleen
dc.type.documentZeitschriftenartikelde
internal.identifier.classoz1080503
internal.identifier.journal763
internal.identifier.document32
internal.identifier.ddc070
dc.identifier.doihttps://doi.org/10.1007/s11192-022-04554-9de
dc.description.pubstatusPublished Versionen
dc.description.pubstatusVeröffentlichungsversionde
internal.identifier.licence16
internal.identifier.pubstatus1
internal.identifier.review1
ssoar.wgl.collectiontruede
internal.pdf.validtrue
internal.pdf.wellformedtrue
internal.pdf.encryptedfalse
ssoar.licence.fundGefördert durch die Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 491156185 / Funded by the German Research Foundation (DFG) - Project number 491156185


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