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Analysis of institutional authors

Ramos-Carreno, CarlosCorresponding AuthorTorrecilla, Jose LuisAuthorSuarez, AlbertoAuthor

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May 26, 2024
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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-Carreño, C; Carbajo-Berrocal, M; Torrecilla, JL; Marcos, P; Suárez, A
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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
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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.
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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 3/169, thus managing to position itself as a Q1 (Primer Cuartil), in the category Statistics & Probability. Notably, the journal is positioned above the 90th percentile.

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 2026-04-01:

  • WoS: 21
  • Scopus: 18
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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 2026-04-01:

  • 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: 36.
  • 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: 35 (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: 7.
  • 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: http://hdl.handle.net/10486/713696
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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 CARREÑO, CARLOS) and Last Author (SUAREZ GONZALEZ, ALBERTO).

the author responsible for correspondence tasks has been RAMOS CARREÑO, CARLOS.

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Project objectives

El presente trabajo tiene como objetivos principales: proporcionar un conjunto integral de herramientas para la representación, preprocesamiento y análisis exploratorio de datos funcionales; integrar la biblioteca scikit-fda en el ecosistema científico de Python, asegurando conformidad con la interfaz de programación de aplicaciones de scikit-learn; facilitar el aprovechamiento de funcionalidades avanzadas de aprendizaje automático como pipelines, selección de modelos y ajuste de hiperparámetros; promover la distribución del paquete como software libre y de código abierto bajo licencia BSD de 3 cláusulas; y fomentar la colaboración de la comunidad de análisis de datos funcionales mediante documentación extensa, tutoriales paso a paso y ejemplos detallados de uso.
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Most relevant results

La aportación presenta el paquete scikit-fda, una herramienta en Python para el análisis de datos funcionales. Entre los resultados más relevantes se destacan: proporciona un conjunto integral de herramientas para la representación, preprocesamiento y análisis exploratorio de datos funcionales; está integrado en el ecosistema científico de Python, facilitando su uso conjunto con otras librerías; cumple con la interfaz de programación de aplicaciones de scikit-learn, permitiendo aprovechar funcionalidades avanzadas como pipelines, selección de modelos y ajuste de hiperparámetros; se distribuye como software libre bajo licencia BSD de 3 cláusulas; y cuenta con documentación extensa que incluye tutoriales paso a paso y ejemplos detallados de uso.
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Awards linked to the item

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.
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