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Johannes Stegmann

Publications and source records attributed to Johannes Stegmann.

6 recordsLinked to original sources

MeSH descriptors indicate the knowledge growth in the SARS-CoV-2/COVID-19 pandemic

The scientific papers dealing with the novel betacoronavirus SARS-CoV-2 and the coronavirus disease 2019 (COVID-19) caused by this virus, published in 2020 and recorded in the database PUBMED, were retrieved on April 27, 2020. About 20\% of the records contain Medical Subject Headings (MeSH), keywords assigned to records in the course of the indexing process in order to summarise the articles' contents. The temporal sequence of the first occurrences of the keywords was determined, thus giving insight into the growth of the knowledge base of the pandemic.

cs.DL

Research performance of UNU - A bibliometric analysis of the United Nations University

The scientific paper output of the United Nations University (UNU) was bibliometrically analysed. It was found that (i) a noticeable continous paper output starts in 1995, (ii) about 65% of the research papers have been published as international cooperations and 18% as single-authored papers, (iv) the research papers rank above world average according to Pudovkin-Garfield Percentile Rank Index, and (v) paper content indicate the wide variety of scientific topics UNU has been and is working on.

cs.DL

Research at UNIS - The University Centre in Svalbard. A bibliometric study

The scientific output 1994-2014 of the University Centre in Svalbard (UNIS) was bibliometrically analysed. It was found that the majority of the papers have been published as international cooperations and rank above world average. Analysis of the content of the papers reveals that UNIS works and publishes in a wide variety of scientific topics.

cs.DL

Transitive Text Mining for Information Extraction and Hypothesis Generation

Transitive text mining - also named Swanson Linking (SL) after its primary and principal researcher - tries to establish meaningful links between literature sets which are virtually disjoint in the sense that each does not mention the main concept of the other. If successful, SL may give rise to the development of new hypotheses. In this communication we describe our approach to transitive text mining which employs co-occurrence analysis of the medical subject headings (MeSH), the descriptors assigned to papers indexed in PubMed. In addition, we will outline the current state of our web-based information system which will enable our users to perform literature-driven hypothesis building on their own.

cs.IR