Characteristic Features of Rank-Probability Word Distribution in Scientific and Belletristic Literature

Voloshynovska, Iryna A.

doi:10.1080/09296174.2011.583405

Cited by 16 publications

(17 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Practically, this is supplemented by trial and errors of local fits in moving windows of different sizes; the groups are deduced and confirmed, choosing them from the best R 2 among various realistic attempts. More complicated functions (Popescu et al 2010, Voloshynovska 2011) could be used, but a detailed parameter interpretation for such function combinations is a challenging task and requests further study, much outside this report. Nevertheless, the CAs can be seen as forming "circles" around the PI, as in e.g.…”

Section: Modelmentioning

confidence: 99%

Zipf–Mandelbrot–Pareto model for co-authorship popularity

Ausloos

2014

Scientometrics

View full text Add to dashboard Cite

Each co-author (CA) of any scientist can be given a rank (r) of importance according to the number (J) of joint publications which the authors have together. In this paper, the Zipf-Mandelbrot-Pareto law, i.e. J ∝ 1/(ν + r) ζ is shown to reproduce the empirical relationship between J and r and shown to be preferable to a mere power law, J ∝ 1/r α . The CA core value, i.e. the core number of CAs, is unaffected, of course. The demonstration is made on data for two authors, with a high number of joint publications, recently considered by Bougrine (2014) and for 7 authors, distinguishing between their "journal" and "proceedings" publications as suggested by Miskiewicz (2013). The rank-size statistics is discussed and the α and ζ exponents are compared. The correlation coefficient is much improved (∼ 0.99, instead of 0.92). There are marked deviations of such a co-authorship popularity law depending on sub-fields. On one hand, this suggests an interpretation of the parameter ν. On the other hand, it suggests a novel model on the (likely time dependent) structural and publishing properties of research teams.Thus, one can propose a scenario for how a research team is formed and grows. This is based on a hierarchy utility concept, justifying the empirical Zipf-Mandelbrot-Pareto law, assuming a simple form for the CA publication/cost ratio, cr = c0 log2(ν + r). In conclusion, such a law and model can suggest practical applications on measures of research teams.In Appendices, the frequency-size cumulative distribution function is discussed for two sub-fields, with other technicalities * previously at GRAPES, ULG, Liège, Belgium;

show abstract

Section: Modelmentioning

confidence: 99%

Zipf–Mandelbrot–Pareto model for co-authorship popularity

Ausloos

2014

Scientometrics

View full text Add to dashboard Cite

show abstract

“…in other related contexts [21][22][23][24][25][26]. It can be shown that it is related to a power law with exponential cut-off, the so called the Yule-Simon distribution [27] …”

Section: Discussionmentioning

confidence: 99%

New Region Planning in France? Better Order or More Disorder?

Ausloos

2015

Entropy

View full text Add to dashboard Cite

This paper grounds the critique of the reduction of regions in a country , not only in its geographical and social context but also in its entropic space. The various recent plans leading to the reduction of the number of regions in metropolitan France are discussed, based on the mere distribution in the number of municipalities in the plans and analyzed according to various distribution laws. Each case, except the present distribution with 22 regions, on the mainland, does not seem to fit presently used theoretical models. In addition, the number of inhabitants is examined in each plan. The same conclusion holds. Therefore, a theoretical argument based on entropy considerations is proposed, thereby pointing to whether more order or less disorder is the key question-discounting political considerations.

show abstract

“…Poor fit accuracy and lack of meaningful parameters' interpretation limit the application of power law functions for approximation of experimental data. In the present study, the expression of the Zipfian function Equation (25), previously suggested by Voloshynovska for the analysis of word frequencies in texts (Voloshynovska, 2011), was adopted for the approximation of the rank-activity distribution of our experimental data (Figure 4):…”

Section: Quantitation Of Differentiation Tendency In Anabolic Activitymentioning

confidence: 99%

“…Since neither the CV nor the HC accounts for the outcome of subpopulations, the challenge of quantitating heterogeneity in multimodal single-cell activity had to be tackled. With this purpose, a modification of power Zipf 's (1935) law describing the rank-frequency distribution of words in literary texts (Voloshynovska, 2011) was applied. The slope in rank-activity distribution of single cells, interpreted as differentiation tendency index (DTI), was suggested for heterogeneity quantitation.…”

Section: Introductionmentioning

confidence: 99%

Quantitation and Comparison of Phenotypic Heterogeneity Among Single Cells of Monoclonal Microbial Populations

Calabrese

Voloshynovska²,

Musat

et al. 2019

Front. Microbiol.

View full text Add to dashboard Cite

Phenotypic heterogeneity within microbial populations arises even when the cells are exposed to putatively constant and homogeneous conditions. The outcome of this phenomenon can affect the whole function of the population, resulting in, for example, new "adapted" metabolic strategies and impacting its fitness at given environmental conditions. Accounting for phenotypic heterogeneity becomes thus necessary, due to its relevance in medical and applied microbiology as well as in environmental processes. Still, a comprehensive evaluation of this phenomenon requires a common and unique method of quantitation, which allows for the comparison between different studies carried out with different approaches. Consequently, in this study, two widely applicable indices for quantitation of heterogeneity were developed. The heterogeneity coefficient (HC) is valid when the population follows unimodal activity, while the differentiation tendency index (DTI) accounts for heterogeneity implying outbreak of subpopulations and multimodal activity. We demonstrated the applicability of HC and DTI for heterogeneity quantitation on stable isotope probing with nanoscale secondary ion mass spectrometry (SIP-nanoSIMS), flow cytometry, and optical microscopy datasets. The HC was found to provide a more accurate and precise measure of heterogeneity, being at the same time consistent with the coefficient of variation (CV) applied so far. The DTI is able to describe the differentiation in single-cell activity within monoclonal populations resolving subpopulations with low cell abundance, individual cells with similar phenotypic features (e.g., isotopic content close to natural abundance, as detected with nanoSIMS). The developed quantitation approach allows for a better understanding on the impact and the implications of phenotypic heterogeneity in environmental, medical and applied microbiology, microbial ecology, cell biology, and biotechnology.

show abstract

Characteristic Features of Rank-Probability Word Distribution in Scientific and Belletristic Literature

Cited by 16 publications

References 7 publications

Zipf–Mandelbrot–Pareto model for co-authorship popularity

Zipf–Mandelbrot–Pareto model for co-authorship popularity

New Region Planning in France? Better Order or More Disorder?

Quantitation and Comparison of Phenotypic Heterogeneity Among Single Cells of Monoclonal Microbial Populations

Contact Info

Product

Resources

About