The results of bibliometric studies provided by bibliometric research groups, for example, the Centre for Science and Technology Studies (CWTS) and the Institute for Research Information and Quality Assurance (iFQ), are often used in the process of research assessment. Their databases use Web of Science (WoS) citation data, which they match according to their own matching algorithms-in the case of CWTS for standard usage in their studies and in the case of iFQ on an experimental basis. Because the problem of nonmatched citations in the WoS persists due to inaccuracies in the references or inaccuracies introduced in the data extraction process, it is important to ascertain how well these inaccuracies are rectified in these citation matching algorithms. This article evaluates the algorithms of CWTS and iFQ in comparison to the WoS in a quantitative and a qualitative analysis. The analysis builds upon the method and the manually verified corpus of a previous study. The algorithm of CWTS performs best, closely followed by that of iFQ. The WoS algorithm still performs quite well (F1 score: 96.41%), but shows deficits in matching references containing inaccuracies. An additional problem is posed by incorrectly provided cited reference information in source articles by the WoS.
This study presents the first analysis of h-index sequences on a larger scale. Exemplarily, we investigated researchers from three different fields within Computer Science. We use Google Scholar citation profiles as data source to construct the h-index sequences of individual researchers. Our ultimate goal is to develop a self-evaluation tool, to assess one's own development of the h-index in comparison to other researchers in the same field, maybe identify career role models in the field and assess career development with future chances of success. The results of this study show that the average h-index sequences behave differently for the datasets, which is partly due to the different sample sizes. Hence, further research will be needed to confirm if every research field behaves differently. In addition, we applied the algorithm developed by Wu et al. [22] to our data to classify the h-index sequences of individual authors according to five different shape categories. The majority of researchers has an S-shaped h-index sequence, followed by IS-shaped and linear sequences. Purely concave or convex sequences hardly ever occur. The researchers with the highest h-indices after 10 career years respectively belong to the S-shaped and IS-shaped categories with a few linear category occurrences. Hence, having a linear h-index is not only very hard to achieve, it is also not a guaranty to be the researcher with the highest h-index in a field.
Erfahrungen aus drei Projekten im Umfeld von Europeana und des DFG-Exzellenzclusters Bild Wissen Gestaltung an der Humboldt-Universität zu Berlin Im Artikel werden laufende Arbeiten und Ergebnisse der Forschergruppe Wissensmanagement beschrieben. Diese entstanden vor allem durch die am Lehrstuhl Wissensmanagement angesiedelten Projekte Europeana v2.0, Digitised Manuscripts to Europeana (DM2E) sowie von Teilprojekten des vor kurzem gestarteten DFG-Exzellenzclusters Bild Wissen Gestaltung. Die Projekte befassen sich mit Spezialisierungen des Europeana Data Model, der Umwandlung von Metadaten in RDF und der automatisierten und nutzerbasierten semantischen Anreicherung dieser Daten auf Basis eigens entwickelter oder modifizierter Anwendungen sowie der Modellierung von Forschungsaktivitäten, welche derzeit auf die digitale Geisteswissenschaft zugeschnitten ist. Allen Projekten gemeinsam ist die konzeptionelle oder technische Modellierung von Informationsentitäten oder Nutzeraktivitäten, welche am Ende im Linked Data Web repräsentiert werden. Schlagwörter: Wissensmanagement, Digitale Geisteswissenschaft, Ontologie, Linked (Open) Data, Modellierung, Datentransformation, Scholarly Domain Model, Europeana Data Model Modeling and Ontologies in Knowledge Management. Lessons learned in three research projects in the context of Europeana and the Excellence Cluster at Humboldt-Universität zu Berlin.The contribution reports on the work of the research unit Knowledge Management as well as on the results obtained so far as part of the projects affiliated with the chair Knowledge Management such as Europeana v2.0, Digitised Manuscripts to Europeana (DM2E) and of base projects as part of the recently started DFG excellence cluster Knowledge Image Gestaltung. The projects deal with specialising the Europeana Data Model, the transformation of metadata to RDF as well as the automated and user generated semantic enrichment of such data using newly developed or existing and modified applications as well as the modeling of research activity currently focusing on the Digital Humanities domain. All projects share the technical and conceptual modeling of information entities and user activities with the ultimate goal to be represented on the Linked Data Web.Modélisation et ontologies dans la gestion des connaissances. Expériences dans trois projets de recherche dans le contexte d'Europeana et du Cluster d'excellence á la « Humboldt-Universität zu Berlin » Cette contribution décrit les travaux du groupe de recherche « gestion des connaissances » ainsi que les résultats obtenus jusqu'alors dans des projets tels que Europeana v2.0, Digitised Manuscripts to Europeana (DM2E) ou encore les débuts des projets de base faisant partie du Cluster d'excellence (DFG) Bild Wissen Gestaltung. Ces projets produisent et étudient des spécialisations du Europeana Data Model, c'est à dire la transformation des métadonnées en RDF et l'enrichissement sémantique automatisé / par l'utilisateur des dites métadonnées à l'aide des applications développées ou encore e...
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