Home > Research > Publications & Outputs > Comment on "Quantifying long-term scientific im...

Research

Links

Text available via DOI:

View graph of relations

Comment on "Quantifying long-term scientific impact"

Research output: Contribution to Journal/Magazine › Comment/debate › peer-review

Published

Standard

Comment on "Quantifying long-term scientific impact". / Wang, Jian; Mei, Yajun; Hicks, Diana.
In: Science, Vol. 345, No. 6193, 11.07.2014, p. 149b.

Research output: Contribution to Journal/Magazine › Comment/debate › peer-review

Harvard

Wang, J, Mei, Y & Hicks, D 2014, 'Comment on "Quantifying long-term scientific impact"', Science, vol. 345, no. 6193, pp. 149b. https://doi.org/10.1126/science.1248770

APA

Wang, J., Mei, Y., & Hicks, D. (2014). Comment on "Quantifying long-term scientific impact". Science, 345(6193), 149b. https://doi.org/10.1126/science.1248770

Vancouver

Wang J, Mei Y, Hicks D. Comment on "Quantifying long-term scientific impact". Science. 2014 Jul 11;345(6193):149b. doi: 10.1126/science.1248770

Author

Wang, Jian ; Mei, Yajun ; Hicks, Diana. / Comment on "Quantifying long-term scientific impact". In: Science. 2014 ; Vol. 345, No. 6193. pp. 149b.

Bibtex

@article{e4f5412f8bc24ca2b98493a3b5158b45,
title = "Comment on {"}Quantifying long-term scientific impact{"}",
abstract = "Wang et al. (Reports, 4 October 2013, p. 127) claimed high prediction power for their model of citation dynamics. We replicate their analysis but find discouraging results: 14.75% papers are estimated with unreasonably large μ (>5) and λ (>10) and correspondingly enormous prediction errors. The prediction power is even worse than simply using short-term citations to approximate long-term citations.",
author = "Jian Wang and Yajun Mei and Diana Hicks",
year = "2014",
month = jul,
day = "11",
doi = "10.1126/science.1248770",
language = "English",
volume = "345",
pages = "149b",
journal = "Science",
issn = "0036-8075",
publisher = "American Association for the Advancement of Science",
number = "6193",

}

RIS

TY - JOUR

T1 - Comment on "Quantifying long-term scientific impact"

AU - Wang, Jian

AU - Mei, Yajun

AU - Hicks, Diana

PY - 2014/7/11

Y1 - 2014/7/11

N2 - Wang et al. (Reports, 4 October 2013, p. 127) claimed high prediction power for their model of citation dynamics. We replicate their analysis but find discouraging results: 14.75% papers are estimated with unreasonably large μ (>5) and λ (>10) and correspondingly enormous prediction errors. The prediction power is even worse than simply using short-term citations to approximate long-term citations.

AB - Wang et al. (Reports, 4 October 2013, p. 127) claimed high prediction power for their model of citation dynamics. We replicate their analysis but find discouraging results: 14.75% papers are estimated with unreasonably large μ (>5) and λ (>10) and correspondingly enormous prediction errors. The prediction power is even worse than simply using short-term citations to approximate long-term citations.

U2 - 10.1126/science.1248770

DO - 10.1126/science.1248770

M3 - Comment/debate

C2 - 25013056

AN - SCOPUS:84904120494

VL - 345

SP - 149b

JO - Science

JF - Science

SN - 0036-8075

IS - 6193

ER -