Measuring Scholarly Impact: Methods and Practice
Editat de Ying Ding, Ronald Rousseau, Dietmar Wolframen Limba Engleză Paperback – 23 aug 2016
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Specificații
ISBN-13: 9783319348636
ISBN-10: 3319348639
Pagini: 360
Ilustrații: XIV, 346 p. 89 illus., 68 illus. in color.
Dimensiuni: 155 x 235 x 19 mm
Greutate: 0.51 kg
Ediția:Softcover reprint of the original 1st ed. 2014
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland
ISBN-10: 3319348639
Pagini: 360
Ilustrații: XIV, 346 p. 89 illus., 68 illus. in color.
Dimensiuni: 155 x 235 x 19 mm
Greutate: 0.51 kg
Ediția:Softcover reprint of the original 1st ed. 2014
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland
Cuprins
Community detection and visualization of networks with the map equation framework.- Link Prediction.- Network analysis and indicators.- PageRank-related methods for analyzing citation networks.- Systems Life Cycle and its relation with the Triple Helix.- Spatial scientometrics and scholarly impact: A review of recent studies, tools and methods.- Researchers’ publication patterns and their use for author disambiguation.- Knowledge integration and diffusion: Measures and mapping of diversity and coherence.- Limited dependent variables models and probabilistic prediction in informetrics.- Text mining with the Stanford CoreNLP.- Topic modeling: Measuring scholarly impact using a topical lens.- The substantive and practical significance of citation impact differences between institutions: Guidelines for the analysis of percentiles using effect sizes and confidence intervals.- Visualizing bibliometric networks.- Replicable science of science studies.
Caracteristici
Provides a complete guide to analyzing scholarly communication and the informetrics used for the assessment of scholarly impact Consolidates techniques and technologies for measuring scholarly impact from the fields of statistical science, scientific visualization, network analysis, text mining and information retrieval Equips data scientists with the ability to apply these techniques and technologies to other social network analyses and metrics-related research