COLLNET - a success story about worldwide collaboration in science and technology. - Leuven : ISSI Society. - 1 Online-Ressource (Seite 26-28)Online-Ausg.: ISSI Newsletter. - Leuven : ISSY Society, ISSN 1998-5460, Volume 18 (2022), 2
https://dx.doi.org/10.22032/dbt.55568
Metrics, indicators, mapping and data visualizations in webometrics, informetrics and scientometrics. - Delhi : B.K. Books International, 2022. - xi, 649 Seiten ISBN 978-81-932517-7-5
COLLNET - a success story about worldwide collaboration in science. - In: The 'Social Gestalt' of scientific communication: Festschrift in honour of Hildrun Kretschmer, (2022), S. 247-297
The 'Social Gestalt' of scientific communication: Festschrift in honour of Hildrun Kretschmer. - New Delhi : Taru Publications, 2022. - x, 454 Seiten ISBN 81-901493-5-0
Announcement & call for papers : 16th International Conference on Webometrics, Informetrics and Scientometrics (WIS) & 21st COLLNET meeting. - In: Collnet journal of scientometrics and information management, ISSN 2168-930X, Bd. 16 (2022), 1, S. 3-6
https://doi.org/10.1080/09737766.2022.2086837
From the editor's desk. - In: Collnet journal of scientometrics and information management, ISSN 2168-930X, Bd. 16 (2022), 1, S. 1-2
https://doi.org/10.1080/09737766.2022.2103907
A comprehensive comparison of arXiv and the Web of Science (WoS). - SLA-Asia, Special Libraries Association. - 1 Online-Ressource (Seite 55-69)Online-Ausgabe: ICoASL 2021 : 7th International Conference of Asian Special Libraries, November 24, 2021, National Library of Korea, Seoul, South Korea. - SLA-Asia, Special Libraries Association, [2021]. - ISBN 978-89-965885-3-5
Scientific exchange is increasingly shifting to the Internet. Today, online literature and citation databases are important tools for scientific work, e.g. for exchanging information or investigating the current state of the art on a topic. Due to the large number of literature and citation databases that exist, and the limited amount of time available for a search, it is necessary to choose few or even only one database. In this paper, we carry out a comprehensive comparison between Web of Science and arXiv. We compile a list of criteria for the comparison of these resources based on a literature analysis. Finally, 62 documents were found that dealt with comparisons between literature databases. Based on these comparisons, a concept matrix was created according to Webster & Watson (2002), in which the criteria for the comparison were summarized. These criteria were then integrated into an adapted version of the criteria catalogue for the comparison of software packages from Jadhav &Sonar (2009) in order to provide a comprehensive picture, not only of content aspects, but also of functionality and usability issues. Based on these criteria, the Web of Science and arXiv databases were compared. The main results can be summarized as follows: arXiv covers only a limited number of disciplines and has a strong focus on physics, mathematics and computer science. Web of Science covers significantly more subject areas and generally includes significantly more papers, which in contrast to arXiv all come from peer-reviewed journals. arXiv's biggest advantage is the topicality of the articles, since preprints are also accepted and thus the peer-review process can be bridged. Both databases are intuitive to use and have a similarly good simple search, but Web of Science’s advanced search gives an experienced user much more possibilities to refine searches and to formulate distinctive queries. In general, Web of Science offers significantly more possibilities to conduct comprehensive literature searches due to the additionally stored citation data and corresponding analysis functions. arXiv, on the other hand, is particularly well suited to learn about the latest state of the offered disciplines.
https://dx.doi.org/10.22032/dbt.55570
Comparison of two science mapping tools based on software technical evaluation and bibliometric case studies. - In: Collnet journal of scientometrics and information management, ISSN 2168-930X, Bd. 15 (2021), 2, S. 365-396
Bibliometrics is used to apply statistical methods to books articles, and other publication. One research topic of bibliometrics is science mapping, which examines scientific objects to determine the cognitive structure, development, and acting persons. With CiteSpace and VOSviewer two of the most popular visualization tools are compared. The evaluation of the software solutions is carried out in two steps, the first step is a purely software technical evaluation based on the framework of Jadhav and Sonar (2011). In addition to functional similarities and differences between the tools, qualitative and technical aspects are examined. Both CiteSpace and VOSviewer, share a large number of bibliometric functionalities, which are each extended by additional functions. They use different algorithms for normalization, mapping and clustering. In the second part, on the basis of own case studies, in which selected bibliometric analyses (Co-Occurrence-, Co-Citation- and Co-Authorship- analyses) are carried out, the workflow of solving a given task with these tools is analyzed and the results are evaluated. Both tools support the steps of a science mapping process, which consists of the phases of data retrieval, preprocessing, network extraction, normalization, mapping, analysis, visualization and interpretation. As a result, can be noted that visualizations created with VOSviewer have better clarity and user-friendliness. CiteSpace, on the other hand, offers advantages in the evaluative analysis of network visualizations, e.g. by enabling analysis of the cluster nodes using a Cluster Explorer.
https://doi.org/10.1080/09737766.2021.1960220
Towards augmenting metadata management by machine learning. - In: Informatik 2021, (2021), S. 1467-1476
Managing metadata is an important section of master data management. It is a complex, comprehensive and labor-intensive task. This paper explores whether and how metadata management can be augmented by machine learning. We deduce requirements for managing metadata from the literature and from expert interviews. We also identify features of machine learning algorithms. We assess 15 machine learning algorithms to determine their contribution to meeting the requirements and the extent to which they can support metadata management. Supervised and unsupervised learning algorithms as well as neural networks have the greatest potential to support metadata management effectively. Reinforcement learning, however, does not seem to be well suited to augment metadata management. Using Support Vector Machines and identification of metadata as an example, we show how machine learning algorithms can support metadata management.
https://dx.doi.org/10.18420/informatik2021-121
From threat data to actionable intelligence: an exploratory analysis of the intelligence cycle implementation in cyber threat intelligence sharing platforms. - In: ARES 2021, (2021), 85, insges. 9 S.
In the last couple of years, organizations have demonstrated an increasing willingness to share data, information and intelligence regarding emerging threats to collectively protect against todays sophisticated cyber attacks. Accordingly, several vendors started to implement software solutions that facilitate this exchange and appear under the name cyber threat intelligence sharing platforms. However, recent investigations have shown that these platforms differ significantly in their functional scope and often only provide threat data instead of the promised actionable intelligence. Moreover, it is unclear to what extent the platforms implement the expected intelligence cycle processes. In order to close this gap, we investigate the state-of-the-art in scientific literature and analyze the functional scope of nine threat intelligence sharing platforms with respect to the intelligence cycle. Our study provides a comprehensive list of software functions that should be implemented by cyber threat intelligence sharing platforms in order to support the intelligence cycle to generate actionable threat intelligence.
https://doi.org/10.1145/3465481.3470048