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Citations

7

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Grant support

The authors want to express their gratitude to the developers who made contributions to the scikit-fda package. In particular, we thank David Garcia Fernandez, Amanda Hernando Bernabe, Yujian Hong, Pedro Martin Rodriguez-Ponga Eyries, Pablo Perez Manso, Elena Petrunina, Luis Alberto Rodriguez Ramirez, and & Aacute;lvaro Sanchez Romero for their participation in the project. The authors acknowledge financial support from the Spanish Ministry of Education and Innovation, projects PID2019-106827GB-I00/AEI/10.13039/501100011033 and PID2019-109387GB-I00. This research was also supported by an FPU grant (Formacion de Profesorado Universitario) from the Spanish Ministry of Science, Innovation and Universities (MICIU) with reference FPU18/00047.

Analysis of institutional authors

Torrecilla, Jose LuisAuthorSuarez, AlbertoAuthor

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Article

scikit-fda: A Python Package for functional data analysis

Publicated to:Journal of Statistical Software. 109 (2): 1-37 - 2024-05-01 109(2), DOI: 10.18637/jss.v109.i02

Authors: Ramos-Carreno, Carlos; Carbajo-Berrocal, Miguel; Torrecilla, Jose Luis; Marcos, Pablo; Suarez, Alberto

Affiliations

Univ Autonoma Madrid, Escuela Politecn Super, Dept Comp Sci, Madrid 28049, Spain - Author
Univ Autonoma Madrid, Fac Ciencias, Dept Math, Madrid 28049, Spain - Author
Univ Autonoma Madrid, Madrid, Spain - Author

Abstract

The library scikit-fda is a Python package for functional data analysis (FDA). It provides a comprehensive set of tools for representation, preprocessing, and exploratory analysis of functional data. The library is built upon and integrated in Python's scientific ecosystem. In particular, it conforms to the scikit-learn application programming interface so as to take advantage of the functionality for machine learning provided by this package: Pipelines, model selection, and hyperparameter tuning, among others. The scikit-fda package has been released as free and open-source software under a 3-clause BSD license and is open to contributions from the FDA community. The library's extensive documentation includes step-by-step tutorials and detailed examples of use.

Keywords

ClassificationComputational statisticsDeptFeature-selectionFunctional data analysisInteractive data visualizationModelsPythonReductionRegressionScikit-learScikit-learn

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Journal of Statistical Software due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2024 there are still no calculated indicators, but in 2023, it was in position 30/170, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Interdisciplinary Applications.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2025-06-23:

  • Scopus: 7

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-06-23:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 27.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 26 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 6.1.
  • The number of mentions on the social network X (formerly Twitter): 14 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Additionally, the work has been submitted to a journal classified as Diamond in relation to this type of editorial policy.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://repositorio.uam.es/handle/10486/713696

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Ramos-Carreno, Carlos) and Last Author (SUAREZ GONZALEZ, ALBERTO).

the author responsible for correspondence tasks has been Ramos-Carreno, Carlos.