Measuring Time-Dynamics and Time-Stability of Journal Rankings in Mathematics and Physics by Means of Fractional p-Variations

[EN] Journal rankings of specific research fields are often used for evaluation purposes, both of authors and institutions. These rankings can be defined by means of several methods, as expert assessment, scholarly-based agreements, or by the ordering induced by a numeric index associated to the prestige of the journals. In order to be efficient and accepted by the research community, it must preserve the ordering over time, at least up to a point. Otherwise, the procedure for defining the ranking must be revised to assure that it reflects the presumably stable characteristic prestige that it claims to be quantifying. A mathematical model based on fractional p-variations of the values of the order number of each journal in a time series of journal rankings is explained, and its main properties are shown. As an example, we study the evolution of two given ordered lists of journals through an eleven-year series. These journal ranks are defined by using the 2-year Impact Factor of Thomson-Reuters (nowadays Clarivate Analytics) lists for MATHEMATICS and PHYSICS, APPLIED from 2002 to 2013. As an application of our model, we define an index that precludes the use of journal ranks for evaluation purposes when some minimal requirements on the associated fractional p-variations are not satisfied. The final conclusion is that the list of mathematics does not satisfy the requirements on the p-variations, while the list of applied physics does.The work of the first author was supported by Ministerio de Economi, Industria y Competitividad, Spain, under Research Grant CSO2015-65594-C2-1R Y 2R (MINECO/FEDER, UE). The work of the third author was supported by Ministerio de Economi, Industria y Competitividad, Spain, under Research Grant MTM2016-77054-C2-1-P. We did not receive any funds for covering the costs to publish in open access.Ferrer Sapena, A.; Díaz Novillo, S.; Sánchez Pérez, EA. (2017). Measuring Time-Dynamics and Time-Stability of Journal Rankings in Mathematics and Physics by Means of Fractional p-Variations. Publications. 5(3):1-14. https://doi.org/10.3390/publications5030021S1145

Where Should I Submit My Work for Publication? An Asymmetrical Classification Model to Optimize Choice

[EN] Choosing a journal to publish a work is a task that involves many variables. Usually, the authors' experience allows them to classify journals into categories, according to their suitability and the characteristics of the article. However, there are certain aspects in the choice that are probabilistic in nature, whose modelling may provide some help. Suppose an author has to choose a journal from a preference list to publish an article. The researcher is interested in publishing the paper in a journal with a rank number less than or equal to k. For this purpose, a simple classification model is presented in order to choose the best journal from the list, from which some fundamental consequences can be deduced and simple rules derived. For example, if the list contains 100 journals and is ordered using 2-year impact factor, the rule "send to the journal at the k - 10 position" is adequate.Ferrer Sapena, A.; Calabuig, JM.; García-Raffi, LM.; Sánchez Pérez, EA. (2020). Where Should I Submit My Work for Publication? An Asymmetrical Classification Model to Optimize Choice. Journal of Classification. 37:490-508. https://doi.org/10.1007/s00357-019-09331-7S49050837Althouse, B.M., West, J.D., Bergstrom, C.T., Bergstrom, T. (2009). Differences in impact factor across fields and over time. Journal of the Association for Information Science and Technology, 60(1), 27–34.Black, S. (2012). How much do core journals change over a decade? Library Resources and Technical Services, 56, 80–93.Bradshaw, C.J., & Brook, B.W. (2016). How to rank journals. PloS One, 11 (3), e0149852.Ferrer-Sapena, A., Diaz-Novillo, S., Sánchez-Pérez, E.A. (2017). Measuring time-dynamics and time-stability of Journal Rankings in Mathematics and Physics by Means of Fractional p-Variations. Publications, 5(3), 21.Ferrer-Sapena, A., Sánchez-Pérez, E.A., González, L.M., Peset, F., Aleixandre-Benavent, R. (2015). Mathematical properties of weighted impact factors based on measures of prestige of the citing journals. Scientometrics, 105(3), 2089–2108.Ferrer-Sapena, A., Sánchez-Pérez, E.A., González, L.M., Peset, F., Aleixandre-Benavent, R. (2016). The impact factor as a measuring tool of the prestige of the journals in research assessment in mathematics. Research Evaluation, 25(3), 306–314.Gastel, B., & Day, R.A. (2016). How to write and publish a scientific paper. Santa Barbara: ABC-CLIO.Gibbs, A. (2016). Improving publication: advice for busy higher education academics. International Journal for Academic Development, 21(3), 255–258.Grabisch, M.M., Marichal, J.L., Mesiar, R., Pap, E. (2009). Aggregation functions. Cambridge: Cambridge University Press.Haghdoost, A., Zare, M., Bazrafshan, A. (2014). How variable are the journal impact measures?. Online Information Review, 38, 723–737.Hol, E.M. (2013). Empirical studies on volatility in international stock markets. Berlin: Springer.Kelly, J.L. Jr. (2011). A new interpretation of information rate. Manuscript. Reprinted in: The Kelly capital growth investment criterion: theory and practice (pp. 25–34).Klement, E.P., Mesiar, R., Pap, E. (2000). Triangular norms. Dordrecht/Boston/London: Kluwer Academic Publishers.Klement, E.P., Mesiar, R., Pap, E. (2001). Uniform approximation of associative copulas by strict and non-strict copulas. Illinois Journal of Mathematics, 45 (4), 1393–1400.Lawrence, P.A. (2003). The politics of publication. Nature, 422(6929), 259–261.Lu, J., Wu, D., Mao, M., Wang, W., Zhang, G. (2015). Recommender system application developments: a survey. Decision Support Systems, 74, 12–32.Mansilla, R., Köppen, E., Cocho, G., Miramontes, P. (2007). On the behavior of journal impact factor rank-order distribution. Journal of Informetrics, 1, 155–160.Murtagh, F., Orlov, M., Mirkin, B. (2018). Qualitative judgement of research impact: Domain taxonomy as a fundamental framework for judgement of the quality of research. Journal of Classification, 35, 5–28.Neff, B.D., & Olden, J.D. (2010). Not so fast: inflation in impact factors contributes to apparent improvements in journal quality. BioScience, 60(6), 455–459.Nelsen, R.B. (1999). An introduction to copulas. New York: Springer.Pajić, D. (2015). On the stability of citation-based journal rankings. Journal of Informetric, 9, 990–1006.Shannon, C.E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27, 379–423. Reprinted in: ACM SIGMOBILE Mobile Computing and Communications Review, 5(1)(2001), 3-55.Xu, H., Martin, E., Mahidadia, A. (2014). Contents and time sensitive document ranking of scientific literature. Journal of Informetrics, 8, 546–561

TimeRank: A dynamic approach to rate scholars using citations

Rating has become a common practice of modern science. No rating system can be considered as final, but instead several approaches can be taken, which magnify different aspects of the fabric of science. We introduce an approach for rating scholars which uses citations in a dynamic fashion, allocating ratings by considering the relative position of two authors at the time of the citation among them. Our main goal is to introduce the notion of citation timing as a complement to the usual suspects of popularity and prestige. We aim to produce a rating able to account for a variety of interesting phenomena, such as positioning raising stars on a more even footing with established researchers. We apply our method on the bibliometrics community using data from the Web of Science from 2000 to 2016, showing how the dynamic method is more effective than alternatives in this respect

Scaling symmetry, renormalization, and time series modeling

We present and discuss a stochastic model of financial assets dynamics based on the idea of an inverse renormalization group strategy. With this strategy we construct the multivariate distributions of elementary returns based on the scaling with time of the probability density of their aggregates. In its simplest version the model is the product of an endogenous auto-regressive component and a random rescaling factor designed to embody also exogenous influences. Mathematical properties like increments' stationarity and ergodicity can be proven. Thanks to the relatively low number of parameters, model calibration can be conveniently based on a method of moments, as exemplified in the case of historical data of the S&P500 index. The calibrated model accounts very well for many stylized facts, like volatility clustering, power law decay of the volatility autocorrelation function, and multiscaling with time of the aggregated return distribution. In agreement with empirical evidence in finance, the dynamics is not invariant under time reversal and, with suitable generalizations, skewness of the return distribution and leverage effects can be included. The analytical tractability of the model opens interesting perspectives for applications, for instance in terms of obtaining closed formulas for derivative pricing. Further important features are: The possibility of making contact, in certain limits, with auto-regressive models widely used in finance; The possibility of partially resolving the long-memory and short-memory components of the volatility, with consistent results when applied to historical series.Comment: Main text (17 pages, 13 figures) plus Supplementary Material (16 pages, 5 figures

Study on open science: The general state of the play in Open Science principles and practices at European life sciences institutes

Nowadays, open science is a hot topic on all levels and also is one of the priorities of the European Research Area. Components that are commonly associated with open science are open access, open data, open methodology, open source, open peer review, open science policies and citizen science. Open science may a great potential to connect and influence the practices of researchers, funding institutions and the public. In this paper, we evaluate the level of openness based on public surveys at four European life sciences institute

Mathematical Methods, Modelling and Applications

This volume deals with novel high-quality research results of a wide class of mathematical models with applications in engineering, nature, and social sciences. Analytical and numeric, deterministic and uncertain dimensions are treated. Complex and multidisciplinary models are treated, including novel techniques of obtaining observation data and pattern recognition. Among the examples of treated problems, we encounter problems in engineering, social sciences, physics, biology, and health sciences. The novelty arises with respect to the mathematical treatment of the problem. Mathematical models are built, some of them under a deterministic approach, and other ones taking into account the uncertainty of the data, deriving random models. Several resulting mathematical representations of the models are shown as equations and systems of equations of different types: difference equations, ordinary differential equations, partial differential equations, integral equations, and algebraic equations. Across the chapters of the book, a wide class of approaches can be found to solve the displayed mathematical models, from analytical to numeric techniques, such as finite difference schemes, finite volume methods, iteration schemes, and numerical integration methods

Developing Efficient Strategies For Global Sensitivity Analysis Of Complex Environmental Systems Models

Complex Environmental Systems Models (CESMs) have been developed and applied as vital tools to tackle the ecological, water, food, and energy crises that humanity faces, and have been used widely to support decision-making about management of the quality and quantity of Earth’s resources. CESMs are often controlled by many interacting and uncertain parameters, and typically integrate data from multiple sources at different spatio-temporal scales, which make them highly complex. Global Sensitivity Analysis (GSA) techniques have proven to be promising for deepening our understanding of the model complexity and interactions between various parameters and providing helpful recommendations for further model development and data acquisition. Aside from the complexity issue, the computationally expensive nature of the CESMs precludes effective application of the existing GSA techniques in quantifying the global influence of each parameter on variability of the CESMs’ outputs. This is because a comprehensive sensitivity analysis often requires performing a very large number of model runs. Therefore, there is a need to break down this barrier by the development of more efficient strategies for sensitivity analysis. The research undertaken in this dissertation is mainly focused on alleviating the computational burden associated with GSA of the computationally expensive CESMs through developing efficiency-increasing strategies for robust sensitivity analysis. This is accomplished by: (1) proposing an efficient sequential sampling strategy for robust sampling-based analysis of CESMs; (2) developing an automated parameter grouping strategy of high-dimensional CESMs, (3) introducing a new robustness measure for convergence assessment of the GSA methods; and (4) investigating time-saving strategies for handling simulation failures/crashes during the sensitivity analysis of computationally expensive CESMs. This dissertation provides a set of innovative numerical techniques that can be used in conjunction with any GSA algorithm and be integrated in model building and systems analysis procedures in any field where models are used. A range of analytical test functions and environmental models with varying complexity and dimensionality are utilized across this research to test the performance of the proposed methods. These methods, which are embedded in the VARS–TOOL software package, can also provide information useful for diagnostic testing, parameter identifiability analysis, model simplification, model calibration, and experimental design. They can be further applied to address a range of decision making-related problems such as characterizing the main causes of risk in the context of probabilistic risk assessment and exploring the CESMs’ sensitivity to a wide range of plausible future changes (e.g., hydrometeorological conditions) in the context of scenario analysis