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December 3, 2025
Publications
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Article

The BRAINTEASER Datasets: Clinical, Wearable and Environmental Data for ALS & MS Progression Modeling

Publicated to: Scientific Data. 12 (1): 1854- - 2025-11-21 12(1), DOI: 10.1038/s41597-025-06095-1

Authors:

Faggioli, Guglielmo; Menotti, Laura; Marchesin, Stefano; Trescato, Isotta; Ahmad, Lara; Aidos, Helena; Alungulese, Anca Loredana; Bellazzi, Riccardo; Bergamaschi, Roberto; Birolo, Giovanni; Bosoni, Pietro; Cabrera-Umpierrez, Maria Fernanda; Cavalla, Paola; Chio, Adriano; Dagliati, Arianna; De Carvalho, Mamede; Fariselli, Piero; Garcia Dominguez, Jose Manuel; Gonzalez Martinez, Sergio; Gromicho, Marta; Guazzo, Alessandro; Jovanovic, Aleksandar; Kostic, Borko; Longato, Enrico; Madeira, Sara C; Manera, Umberto; Munoz Blanco, Jose Luis; Tavazzi, Eleonora; Tavazzi, Erica; Trasobares Iglesias, Elena; Urosevic, Vladimir; Vettoretti, Martina; Di Nunzio, Giorgio Maria; Silvello, Gianmaria; Di Camillo, Barbara; Ferro, Nicola
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Affiliations

Azienda Osped Unv Citta Salute & Sci, Turin, Italy - Author
Belit, Belgrade, Serbia - Author
CNR, Inst Cognit Sci & Technol, Rome, Italy - Author
Gregorio Maranon Hlth Res Inst, Madrid, Spain - Author
Gregorio Maranon Hosp Madrid, Madrid, Spain - Author
Gregorio Maranon Univ Hosp, ALS & Neuromuscular Disorders Dept, Madrid, Spain - Author
IRCCS Mondino Fdn, Pavia, Italy - Author
Univ Lisbon, Fac Ciencias, LASIGE, Lisbon, Portugal - Author
Univ Lisbon, Fac Med, Lisbon, Portugal - Author
Univ Padua, Dept Informat Engn, Padua, Italy - Author
Univ Pavia, Dept Elect Comp & Biomed Engn, Pavia, Italy - Author
Univ Politecn Madrid, Life Supporting Technol, Madrid, Spain - Author
Univ Turin, Rita Levi Montalcini Dept Neurosci, Turin, Italy - Author
Univ Turin, Turin, Italy - Author
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Abstract

Amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS) are debilitating diseases with unpredictable progression. Artificial Intelligence-based tools for modelling disease progression could significantly improve the quality of life for patients and caregivers while supporting clinicians in delivering more personalized and timely care. However, the limited availability of data hinders the development, testing, and reproducibility of such predictive tools. To address this challenge, we curated, in the context of the H2020 BRAINTEASER project, four datasets containing clinical data from a total of 2,290 ALS patients and 723 MS patients. These datasets also include environmental data and information collected through wearable devices. Unlike most existing resources, the BRAINTEASER datasets are gathered from clinical practice, offering a more accurate representation of the data that an AI progression prediction tool would encounter in real-world scenarios. In addition to manual and automated data quality checks, the research community has validated the datasets through three editions of the intelligent Disease Progression Prediction challenges held within the Conference and Labs of the Evaluation Forum (CLEF).
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Keywords

Amyotrophic lateral sclerosisArtificial intelligenceDisease progressionHumansMultiple sclerosisWearable electronic devices

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Scientific Data 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, 2025, it was in position 15/136, thus managing to position itself as a Q1 (Primer Cuartil), in the category Multidisciplinary Sciences.

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 2026-04-05:

  • WoS: 1
  • Scopus: 1
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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 2026-04-05:

  • 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: 4 (PlumX).

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.
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Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Italy; Portugal; Serbia.

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Awards linked to the item

This work was supported by the BRAINTEASER Project, as part of the European Union Horizon 2020 Research and Innovation Program under Grant Agreement no. GA101017598.
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