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Date

22. 7.2006 15:22:28

Source

Information Research. 6(2001), no.2

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Abstract

The use of research impact metrics and analytics has become an integral component to many aspects of institutional assessment. Many platforms currently exist to provide such analytics, both proprietary and open source; however, the functionality of these systems may not always overlap to serve uniquely specific needs. In this paper, I describe a novel web-based platform, named Manifold, that I built to serve custom research impact assessment needs in the University of Minnesota Medical School. Built on a standard LAMP architecture, Manifold automatically pulls publication data for faculty from Scopus through APIs, calculates impact metrics through automated analytics, and dynamically generates report-like profiles that visualize those metrics. Work on this project has resulted in many lessons learned about challenges to sustainability and scalability in developing a system of such magnitude.

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Content

Der Text ist eine überarbeitete Version des von Herb, U. (2018): Zwangsehen und Bastarde : Wohin steuert Big Data die Wissenschaft? In: Information - Wissenschaft & Praxis, 69(2-3), S. 81-88. DOI:10.1515/iwp-2018-0021.

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Abstract

Diese Note legt dar, dass der sogenannte h-Index (Hirschs bibliometrischer Index) im Wesentlichen dieselbe Information wiedergibt wie die Gesamtanzahl von Zitationen von Publikationen einer Autorin oder eines Autors, also ein nutzloser bibliometrischer Index ist. Dies basiert auf einem faszinierenden Satz der Wahrscheinlichkeitstheorie, der hier ebenfalls erläutert wird.

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Abstract

Using citation data to assess research ultimately means using citation-based statistics to rank things.journals, papers, people, programs, and disciplines. The statistical tools used to rank these things are often misunderstood and misused. - For journals, the impact factor is most often used for ranking. This is a simple average derived from the distribution of citations for a collection of articles in the journal. The average captures only a small amount of information about that distribution, and it is a rather crude statistic. In addition, there are many confounding factors when judging journals by citations, and any comparison of journals requires caution when using impact factors. Using the impact factor alone to judge a journal is like using weight alone to judge a person's health. - For papers, instead of relying on the actual count of citations to compare individual papers, people frequently substitute the impact factor of the journals in which the papers appear. They believe that higher impact factors must mean higher citation counts. But this is often not the case! This is a pervasive misuse of statistics that needs to be challenged whenever and wherever it occurs. -For individual scientists, complete citation records can be difficult to compare. As a consequence, there have been attempts to find simple statistics that capture the full complexity of a scientist's citation record with a single number. The most notable of these is the h-index, which seems to be gaining in popularity. But even a casual inspection of the h-index and its variants shows that these are naive attempts to understand complicated citation records. While they capture a small amount of information about the distribution of a scientist's citations, they lose crucial information that is essential for the assessment of research.
The validity of statistics such as the impact factor and h-index is neither well understood nor well studied. The connection of these statistics with research quality is sometimes established on the basis of "experience." The justification for relying on them is that they are "readily available." The few studies of these statistics that were done focused narrowly on showing a correlation with some other measure of quality rather than on determining how one can best derive useful information from citation data. We do not dismiss citation statistics as a tool for assessing the quality of research.citation data and statistics can provide some valuable information. We recognize that assessment must be practical, and for this reason easily-derived citation statistics almost surely will be part of the process. But citation data provide only a limited and incomplete view of research quality, and the statistics derived from citation data are sometimes poorly understood and misused. Research is too important to measure its value with only a single coarse tool. We hope those involved in assessment will read both the commentary and the details of this report in order to understand not only the limitations of citation statistics but also how better to use them. If we set high standards for the conduct of science, surely we should set equally high standards for assessing its quality.

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Source

Open information science. 1(2017) no.1, S.41-55

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Content

Vgl. auch Open Password, Nr.167 vom 01.03.2017 :"Eugene Garfield, Begründer und Pionier der Zitationsindexierung und der Ziationsanalyse, ohne den die Informationswissenschaft heute anders aussähe, ist im Alter von 91 Jahren gestorben. Er hinterlässt Frau, drei Söhne, eine Tochter, eine Stieftochter, zwei Enkeltöchter und zwei Großelternkinder. Garfield machte seinen ersten Abschluss als Bachelor in Chemie an der Columbia University in New York City im Jahre 1949. 1954 sattelte er einen Abschluss in Bibliothekswissenschaft drauf. 1961 sollte er im Fach strukturelle Linguistik promovieren. Als Chemie-Student war er nach eigenen Angaben weder besonders gut noch besonders glücklich. Sein "Erweckungserlebnis" hatte er auf einer Tagung der American Chemical Society, als er entdeckte, dass sich mit der Suche nach Literatur womöglich ein Lebensunterhalt bestreiten lasse. "So I went to the Chairman of the meeting and said: "How do you get a job in this racket?" Ab 1955 war Garfield zunächst als Berater für pharmazeutische Unternehmen tätig. Dort spezialisierte er sich auf Fachinformationen, indem er Inhalte relevanter Fachzeitschriften erarbeitete. 1955 schlug er in "Science" seine bahnbrechende Idee vor, Zitationen wissenschaftlicher Veröffentlichungen systematisch zu erfassen und Zusammenhänge zwischen Zitaten deutlich zu machen. 1960 gründete Garfield das Institute für Scientific Informationen, dessen CEO er bis 1992 blieb. 1964 brachte er den Scientific Information Index heraus. Weitere Maßgrößen wie der Social Science Index (ab 1973), der Arts and Humanities Citation Index (ab 1978) und der Journal Citation Index folgten. Diese Verzeichnisse wurden in dem "Web of Science" zusammengefasst und als Datenbank elektronisch zugänglich gemacht. Damit wurde es den Forschern ermöglich, die für sie relevante Literatur "at their fingertips" zu finden und sich in ihr zurechtzufinden. Darüber hinaus wurde es mit Hilfe der Rankings von Garfields Messgrößen möglich, die relative wissenschaftliche Bedeutung wissenschaftlicher Beiträge, Autoren, wissenschaftlicher Einrichtungen, Regionen und Länder zu messen.