Predicting the Dynamics of Research Impact

Häftad, Engelska, 2022

AvYannis Manolopoulos,Thanasis Vergoulis

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This book provides its readers with an introduction to interesting prediction and science dynamics problems in the field of Science of Science.

Produktinformation

Utgivningsdatum2022-09-24
Mått155 x 235 x 17 mm
Vikt476 g
FormatHäftad
SpråkEngelska
Antal sidor290
FörlagSpringer Nature Switzerland AG
ISBN9783030866709

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Systemvetenskap och AI inom Data och IT
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Yannis Manolopoulos is a Professor and Vice-Rector at the Open University of Cyprus as well as a Professor Emeritus at Aristotle University of Thessaloniki, Greece. Through his research interests in Data Management, he contributed to scientometrics with a number of indices, such as, the contemporary h-index, the trend h-index, the perfectionism index and the fractal dimension of a citation curve. Thanasis Vergoulis is a Scientific Associate at the Information Management Systems Institute of "Athena" Research Center. He has been involved in EU and national ICT projects related to big data, scientific data management, open science, and linked data. His research interests also span bioinformatics, text mining and information retrieval for scientific publications, scientometrics, and research analytics.

1. On Complete Evaluation Systems.- 2. Extrinsic Factors Affecting Citation Frequencies of Research Articles.- 3. Remarks on Dynamics of Research Production of Researchers and Research Organizations.- 4. Does Publicity in the Science Press Drive Citations? A Vindication of Peer Review.- 5. Ranking Papers by Expected Short-Term Impact.- 6. Properties of an Indicator of Citation Durability of Research Articles.- 7. Wider, or Deeper! On Predicting Future of Scientific Articles by Influence Dispersion Tree.- 8. Can Author Collaboration Reveal Impact? The Case of h-index.- 9. Identification of Promising Researchers through Fast-and-frugal Heuristics.- 10. Scientific Impact Vitality: The Citation Currency Ratio and Citation Currency Exergy Indicators.- 11. Detection, Analysis, and Prediction of Research Topics with Scientific Knowledge Graphs.- 12. Early Detection of Emerging Technologies using Temporal Features.- 13. Link Prediction in Bibliographic Networks.