Fig. 3 The Q-model. The Q-model. (A) Distribution of the paper impact c10 across all publications in the data set. The gray line corresponds to a log-normal.

Slides:



Advertisements
Similar presentations
Date of download: 7/10/2016 Copyright © ASME. All rights reserved. From: Investigation of Anisotropic Thermal Conductivity in Polymers Using Infrared Thermography.
Advertisements

and Statistics, 2016, Vol. 4, No. 1, 1-8. doi: /ajams-4-1-1
Section 8.2: The Sampling Distribution of a Sample Mean
Fig. 4 Careers and their Q parameter.
Review of Power of a Test
Fitting to a Normal Distribution
Date of download: 11/4/2017 Copyright © ASME. All rights reserved.
Chapter Six Normal Curves and Sampling Probability Distributions
About the Two Different Standard Normal (Z) Tables
Random-impact rule. Random-impact rule. The publication history of two Nobel laureates, Frank A. Wilczek (Nobel Prize in Physics, 2004) and John B. Fenn.
Chapter 5 Normal Distribution
CI for μ When σ is Unknown
PLGEM fits equally well on NSAF and GeneChip datasets
Underlying mechanisms.
Arikta Biswas, Amal Alex, Bidisha Sinha  Biophysical Journal 
Fractal atomic-level percolation in metallic glasses
Modulation of Neuronal Interactions Through Neuronal Synchronization
by Chuan-Chao Wang, Qi-Liang Ding, Huan Tao, and Hui Li
Combining satellite imagery and machine learning to predict poverty
Fig. 1 Characterizing citation dynamics
Integration of omic networks in a developmental atlas of maize
Use the graph of the given normal distribution to identify μ and σ.
Command of active matter by topological defects and patterns
by Christopher R. Ruehl, James F. Davies, and Kevin R. Wilson
Fitting to a Normal Distribution
Chapter 5 Normal Probability Distributions.
Dependence of DesII steady-state parameters V (●) and (○) on pH.
Bottom-up proteomic characterization of MALDI IMS samples.
Comment on “Cortical folding scales universally with surface area and thickness, not number of neurons” by Marc H. E. de Lussanet Science Volume 351(6275):
Fig. 3. H3N2 incidence forecasts based on the cluster model for the Unites States. H3N2 incidence forecasts based on the cluster model for the Unites States.
by Asaf Inbal, Jean Paul Ampuero, and Robert W. Clayton
ECOM method recovers time correlation with 2-ms precision from 219-ms imaging frames. ECOM method recovers time correlation with 2-ms precision from 219-ms.
Fig. 6 Relation between Q and other impact indicators.
Fig. 1 Patterns of productivity during a scientific career.
Quantifying Long-Term Scientific Impact
Fig. 2 Patterns of impact during a scientific career.
by Katelyn M. Gostic, Monique Ambrose, Michael Worobey, and James O
Fig. 6. CXM correlates with age and growth velocity.
by Khaled Nasr, Pooja Viswanathan, and Andreas Nieder
Robust Driving Forces for Transmembrane Helix Packing
by Justin G. Bohnet, Brian C. Sawyer, Joseph W. Britton, Michael L
Chapter 5 Normal Probability Distributions.
Fig. 3 Cultural turnover is accelerating.
Correlation between Ti→jS(r) and Ti→jM(r) for pair of locations (gray dots) separated by a distance of (A) r=1 km, (B) r=5 km, (C) r=20 km, (D) r=50 km,
Flux distributions for different rank and distance groups.
Chapter 5 Normal Probability Distributions.
Volume 66, Issue 4, Pages (May 2010)
Results for random clusters.
Top: probability distribution of the mass-to-light ratios observed when nebular emission is included in the fitting, stacked across all of the MC samples.
Reward associations do not explain transitive inference performance in monkeys by Greg Jensen, Yelda Alkan, Vincent P. Ferrera, and Herbert S. Terrace.
Fig. 5 Stability of the Q parameter.
Fig. 4 Model of the average SSE.
Fig. 2 Spatial distribution of earthquake density derived from a catalog spanning 93 nights of the LB Array data set. Spatial distribution of earthquake.
Diagnostic plots of the TGI PKPD model fitted to the A677 TGI data.
Yuri G. Strukov, A.S. Belmont  Biophysical Journal 
Fig. 4 Control analyses ensured that the relation between rotational acceleration and changes in FA does not depend on thresholds. Control analyses ensured.
Fig. 3 ET dynamics on the control and treatment watersheds during the pretreatment and treatment periods. ET dynamics on the control and treatment watersheds.
by Jacqueline Austermann, Jerry X
Fig. 4 Evolution of fraction of sickled RBCs under hypoxia.
Fig. 4 Demonstration of dynamic scale invariance at long times.
Master Summaries for Selected Identified Genes.
Fig. 5 Predictions of the efficacy of sickling inhibitors with the kinetic model. Predictions of the efficacy of sickling inhibitors with the kinetic model.
Free interstitial drug as a function of the initial load of soluble drug, ci0 and the intracellular binding capacity, bc,max. Free interstitial drug as.
Fig. 3 Maximal energy intake.
Fig. 2 In situ extraction of miRNAs using the nanowire-anchored microfluidic device. In situ extraction of miRNAs using the nanowire-anchored microfluidic.
Fig. 3 Depth-resolved structural characterization of perovskite nanocrystals in npSi films. Depth-resolved structural characterization of perovskite nanocrystals.
Fig. 5 Distributions of cell nuclear area values and internuclear distances in the breast tumor specimens (Figs. 3 and 4), where bin interval = 8 and n.
Fig. 2 Relaxation to a fitness maximum does not generate a logarithmic fitness trajectory. Relaxation to a fitness maximum does not generate a logarithmic.
Logarithmic fitness trajectory emerges from hopping between MSs
Correlation between journal impact factor and percentage of papers with image duplication. Correlation between journal impact factor and percentage of.
Presentation transcript:

Fig. 3 The Q-model. The Q-model. (A) Distribution of the paper impact c10 across all publications in the data set. The gray line corresponds to a log-normal function with average μ = 1.93 and SD σ2 = 1.05 (R2 = 0.98). (B) Distribution of the total number of papers published by a scientist (productivity). The gray line is a log-normal with μ = 3.6 and σ2 = 0.57 [weighted Kolmogorov-Smirnov (KS) test, P = 0.70]. (C) Citations of the highest-impact paper, , versus the number of publications N during a scientist’s career. Each gray point of the scatterplot corresponds to a scientist. The circles are the logarithmic binning of the scattered data. The cyan curve represents the prediction of the R-model, assuming that the impact of each paper is extracted randomly from the distribution P(c10) of Fig. 2A. The red curve corresponds to the analytical prediction (see eq. S35) of the Q-model (R2 = 0.97; see section S4.6 and fig. S29 for goodness of the fit). (D) versus . Each gray point in the scatterplot corresponds to a scientist, where is the average logarithm of her paper impact, excluding the most-cited paper . We report in cyan the R-model prediction and in red the analytical prediction (see eq. S36) of the Q-model (R2 = 0.99; see section S4.6 and fig. S29 for goodness of the fit). (E) Cumulative impact distribution of all papers published by three scientists with the same productivity, N ≃ 100, but different Q. (F) Distribution across all publications. For each paper α of scientist i, we have log pα = log c10,iα − log Qi, where . Therefore, the distribution of , except for a common translational factor μp, corresponds to the distribution of log c10,iα − 〈 log c10,i〉, which is a normal with μ = 0 and σ2 = 0.95 (KS test, p = 0.48). (G) Distribution of parameter Q, P(Q), for all scientists. The gray line corresponds to a log-normal function with μ = 0.93 and σ2 = 0.46 (weighted KS test, p = 0.59). (H) Cumulative distribution of the rescaled impact c10,iα/Qi for the three scientists in (E). The black line corresponds to the universal distribution P(p).The collapse is predicted by Eq. 1. Roberta Sinatra et al. Science 2016;354:aaf5239 Published by AAAS