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  • × author_ss:"Schreiber, M."
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  1. Schreiber, M.: ¬A variant of the h-index to measure recent performance (2015) 0.00 
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    Abstract
    The predictive power of the h-index has been shown to depend on citations to rather old publications. This has raised doubts about its usefulness for predicting future scientific achievements. Here, I investigate a variant that considers only recent publications and is therefore more useful in academic hiring processes and for the allocation of research resources. It is simply defined in analogy to the usual h-index, but takes into account only publications from recent years, and it can easily be determined from the ISI Web of Knowledge.
    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.11, S.2373-2380
  2. Schreiber, M.: Uncertainties and ambiguities in percentiles and how to avoid them (2013) 0.00 
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    Date
    22. 3.2013 19:52:05
    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.3, S.640-643
  3. Schreiber, M.: Das Web ist eine Wolke (2009) 0.00 
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    Content
    "Auf einem Uralt-PC HD-Videos schneiden, Handys mit 500 GByte Speicherplatz ausstatten, Software und Daten auf jedem beliebigen Computer abrufen - das soll Cloud Computing ermöglichen. Und das alles nur über einen Browser sowie einen schnellen Internetanschluss. Was genau verbirgt sich aber hinter diesem Begriff, der seit Monaten durch die Medien wandert?. CHIP erklärt das Prinzip und verrät, ob Cloud Computing nur ein Hype oder die Zukunft ist. Hardware ade: Die Software läuft in jedem Browser Der Grundgedanke beim Cloud Computing ist, dass alle Anwendungen im Web laufen - von einfacher Software bis hin zu kompletten Betriebssystemen. Der User muss sich keine teure Hardware anschaffen, sich keine Gedanken um die Aktualisierung des Systems machen und auch keine Software mehr kaufen. Das klingt nach Zukunftsmusik, aber die Ansätze sind bereits vorhanden. Google zeigt, wie's geht: Office-Tools, E-Mail-Konten, RSS-Reader, ein Kalender und weitere Programme laufen plattformunabhängig im Webbrowser. Alle Programme und Daten lagern auf den Google-Servern und werden je nach Bedarf geladen. Möglich wird das durch riesige Serverparks von Unternehmen wie Microsoft, Google, Amazon oder IBM: Die Anlagen stellen viel mehr Leistung bereit, als sie verbrauchen können.
    Ein Grid dient hauptsächlich zur Bewältigung rechenintensiver Aufgaben. Der Unterschied zu Clustern: Grids bestehen aus einer losen Verkettung weltweit verstreuter Server, denen sich diverse Institutionen anschließen können. Standardisierte Bibliotheken und Middleware erleichtern die Zusammenarbeit: Die dritte Voraussetzung für das Cloud Computing ist das Utility Computing. Hier bieten Unternehmen Leistungen wie Onlinespeicher, virtuelle Server und Software als gebündelten Service an und rechnen nach verbrauchter Leistung ab. Neue Möglichkeiten: Software nach dem Baukastenprinzip Auf diesen Grundlagen aufbauend entsteht das Cloud Computing. Es verbindet die Komponenten und eröffnet dadurch diverse Möglichkeiten, etwa die "Infrastructure as a Service" (IaaS): Die Betreiber übernehmen die komplette Infrastruktur, etwa virtualisierte Hardware. Diese ist wie bei der"Amazon Elastic Compute Cloud" (EC2) je nach Anforderung skalierbar. Die "Platform as a Service" (PaaS) richtet sich hauptsächlich an Entwickler: Hier stellt der Betreiber kein Enduser-Programm, sondern eine komplette Arbeitsumgebung bereit. So können Software-Anbieter eigene Webapplikationen schreiben und vertreiben. Das wohl bekannteste Beispiel ist die "Google App Engine' die Python als Programmiersprache sowie das Python-Web-Framework "Django" einsetzt. Die fertige Software liegt auf den Servern des Betreibers und benötigt weder eine lokale Installation noch eigene Hardware. PaaS wird daher auch als "Cloudware" bezeichnet.
  4. Schreiber, M.: Revisiting the g-index : the average number of citations in the g-core (2009) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.1, S.169-174
  5. Schreiber, M.: Inconsistencies in the highly cited publications indicator (2013) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.6, S.1298-1302
  6. Schreiber, M.: Do we need the g-index? (2013) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.11, S.2396-2399
  7. Schreiber, M.: ¬A case study of the modified Hirsch index hm accounting for multiple coauthors (2009) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 60(2009) no.6, S.1274-1282
  8. Schreiber, M.: Empirical evidence for the relevance of fractional scoring in the calculation of percentile rank scores (2013) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.4, S.861-867
  9. Schreiber, M.: Fractionalized counting of publications for the g-Index (2009) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 60(2009) no.10, S.2145-2150
  10. Waltman, L.; Schreiber, M.: On the calculation of percentile-based bibliometric indicators (2013) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.2, S.372-379
  11. Schreiber, M.: ¬An empirical investigation of the g-index for 26 physicists in comparison with the h-index, the A-index, and the R-index (2008) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 59(2008) no.9, S.1513-1522
  12. Schreiber, M.: Inconsistencies of recently proposed citation impact indicators and how to avoid them (2012) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 63(2012) no.10, S.2062-2073

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