Confronta i metodi

	Sleeping Beauties and Delayed Recognition	Usage Bibliometrics (Downloads and COUNTER)
Campo	Bibliometria	Bibliometria
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	2004	2009
Ideatore	Anthony F. J. van Raan; Qing Ke, Emilio Ferrara, Filippo Radicchi & Alessandro Flammini	Johan Bollen, Herbert Van de Sompel & colleagues (MESUR project)
Tipo	Citation-trajectory pipeline for detecting delayed recognition	Usage-log pipeline for impact metrics from downloads and views
Fonte seminale	van Raan, A. F. J. (2004). Sleeping Beauties in science. Scientometrics, 59(3), 467-472.	Bollen, J., Van de Sompel, H., Hagberg, A., & Chute, R. (2009). A Principal Component Analysis of 39 Scientific Impact Measures. PLoS ONE, 4(6), e6022.
Alias	Sleeping Beauty Detection, Delayed Recognition Analysis, Beauty Coefficient, Premature Discovery Detection	Download Metrics, Usage Factor Analysis, Usage-Based Impact Metrics, COUNTER Usage Analysis
Correlati	3	3
Sintesi	A Sleeping Beauty is a publication that goes almost unnoticed for many years and then, sometimes decades later, suddenly attracts intense citation attention. Anthony van Raan introduced the metaphor to scientometrics in 2004, reporting the first systematic measurement of how often such delayed-recognition papers occur and deriving an awakening-probability function. Qing Ke and colleagues made the concept operational at scale in 2015 with a parameter-free beauty coefficient that, unlike earlier fixed thresholds, lets any citation trajectory be scored on a continuum of how deeply and how long it slept before awakening. Detecting Sleeping Beauties matters because they show that immediate citation impact is an imperfect proxy for scientific value: some of the most consequential ideas, including foundational work later recognized with prizes, were premature for their time and lay dormant until the field caught up.	Usage bibliometrics measures the impact of scholarly works from how often they are downloaded and viewed rather than how often they are cited. Drawing on server and publisher logs standardized through the COUNTER code of practice, it turns raw access events into impact indicators such as the usage factor. The MESUR project led by Johan Bollen and Herbert Van de Sompel was pivotal: their 2008 work demonstrated usage-based impact metrics built from large-scale usage logs, and their 2009 principal component analysis of thirty-nine impact measures showed that scientific impact is multidimensional, with usage metrics occupying a distinct region of the space from citation metrics. Usage signals accrue almost immediately and reflect a far larger readership than the subset of authors who eventually cite, making them an early and broad complement to citation analysis, provided the logs are carefully standardized.