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The BSC Dataverse is the institutional research data repository of the Barcelona Supercomputing Center - Centro Nacional de Supercomputación (BSC-CNS). It seeks to enable the storage, sharing, and search of research data coming from the BSC researchers, collaborators, and affiliated projects.
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31 to 40 of 82 Results
CA-FR_Parallel_Corpus
Nov 3, 2025 - Language Technologies Laboratory
De Luca Fornaciari, Francesca; Mash, Audrey; Melero, Maite; Villegas, Marta, 2025, "CA-FR_Parallel_Corpus", https://dataverse.bsc.es/dataset.xhtml?persistentId=perma:BSC/V7AZKB, BSC Dataverse, V2
The CA-FR Parallel Corpus is a Catalan-French textual dataset created to support Catalan in NLP tasks, specifically Machine Translation. The dataset is structured at the sentence level and can be used to train Bilingual Machine Translation models between French and Catalan in any direction, as well as Multilingual Machine Translation models.
CA-DE_Parallel_Corpus
Nov 3, 2025 - Language Technologies Laboratory
De Luca Fornaciari, Francesca; Mash, Audrey; Melero, Maite; Villegas, Marta, 2025, "CA-DE_Parallel_Corpus", https://dataverse.bsc.es/dataset.xhtml?persistentId=perma:BSC/DYAZII, BSC Dataverse, V2
The CA-DE Parallel Corpus is a Catalan-German textual dataset created to support Catalan in NLP tasks, specifically Machine Translation. The dataset is structured at the sentence level and can be used to train Bilingual Machine Translation models between German and Catalan in any direction, as well as Multilingual Machine Translation models.
CA-PT_Parallel_Corpus
Nov 3, 2025 - Language Technologies Laboratory
De Luca Fornaciari, Francesca; Mash, Audrey; Melero, Maite; Villegas, Marta, 2025, "CA-PT_Parallel_Corpus", https://dataverse.bsc.es/dataset.xhtml?persistentId=perma:BSC/XYBQ8Q, BSC Dataverse, V2
The CA-PT Parallel Corpus is a Catalan-Portuguese textual dataset created to support Catalan in NLP tasks, specifically Machine Translation. The dataset is structured at the sentence level and can be used to train Bilingual Machine Translation models between Portuguese and Catalan in any direction, as well as Multilingual Machine Translation models...
CA-EN_Parallel_Corpus
Nov 3, 2025 - Language Technologies Laboratory
De Luca Fornaciari, Francesca; Villegas, Marta; Melero, Maite; Mash, Audrey, 2025, "CA-EN_Parallel_Corpus", https://dataverse.bsc.es/dataset.xhtml?persistentId=perma:BSC/ERUHKY, BSC Dataverse, V2
The CA-EN Parallel Corpus is a Catalan-English textual dataset of parallel sentences created to support Catalan in NLP tasks, specifically Machine Translation. The dataset can be used to train Bilingual Machine Translation models between English and Catalan in any direction, as well as Multilingual Machine Translation models.
Replication Data for: Beyond the Link: Assessing LLMs’ Ability to Classify Political Content Across Global Media
Oct 30, 2025 - COMPASS - COMplex Political And Social Simulations
DE LA FUENTE CUESTA, ALEJANDRO; Alberto Martínez Serra; Nienke Visscher; Cardenal, Ana S., 2025, "Replication Data for: Beyond the Link: Assessing LLMs’ Ability to Classify Political Content Across Global Media", https://doi.org/10.82201/8UPPY6, BSC Dataverse, V2, UNF:6:j4VEdzl/g0wp+AX0/Pik1w== [fileUNF]
This dataset and replication package accompany the paper: “Beyond the Link: Assessing LLMs’ Ability to Classify Political Content Across Global Media.” by Alejandro De La Fuente-Cuesta, Alberto Martínez-Serra, R. Nienke Visscher, and Ana S. Cardenal (2025). The materials include the data and code necessary to reproduce all analyses presented in the...
BSC Dataverse - Introduction and organization
Oct 27, 2025 - Research Data Management
Jené Cortada, Aina; Saiz Antón, José Javier; Checchia Adell, Paula; Gómez-Cortés, Felipe; Carrascosa, Adrian, 2025, "BSC Dataverse - Introduction and organization", https://doi.org/10.82201/FHX4RX, BSC Dataverse, V1
This dataset provides the support material for the BSC Dataverse training session. The session introduces participants to the BSC Dataverse platform, explains who can use it, and guides them through its structure, workflows, and best practices. It also presents real-world use cases from current BSC Dataverse users. The material helps both users and...
Machine learning models for 3D complex shape analysis and classification on the NEBD+ dataset
Oct 17, 2025 - Computational Archaeology
Berganzo-Besga, Iban, 2025, "Machine learning models for 3D complex shape analysis and classification on the NEBD+ dataset", https://dataverse.bsc.es/dataset.xhtml?persistentId=perma:BSC/J0HVTR, BSC Dataverse, V1, UNF:6:GSgdAZ4nevEX2NwRGqdJfQ== [fileUNF]
This dataset presents diverse models trained using machine learning (ML) like traditional architectures for geometric morphometrics (GMM) such as scikit-learn GBM, KNN, LDA, RF and SVM, more advance ones like XGBoost GBM, and deep learning architectures such as MLP, TabPFN and MeshCNN. The models were trained on the NEBD+ dataset.
NEBD+: Enhanced Northern European Barley Dataset for 3D complex shape analysis and classification.
Oct 17, 2025 - Computational Archaeology
Berganzo-Besga, Iban; Livarda, Alexandra; Aliende Garcia, Paloma; Wallace, Michael; Orengo, Hector A., 2025, "NEBD+: Enhanced Northern European Barley Dataset for 3D complex shape analysis and classification.", https://dataverse.bsc.es/dataset.xhtml?persistentId=perma:BSC/ETUHIO, BSC Dataverse, V1, UNF:6:tg5jUfTY6pD8q18Yc/9EDA== [fileUNF]
This datasheet describes a new dataset for the tasks of 3D complex shape analysis and classification. The dataset consists of 697 barley grains, grouped into the following categories: six-row Bere (Bere-R6), six-row Scandinavian (Scand-R6), two-row non-Scottish British (Brit-R2), and two-row (non-Bere) Scottish (Scot-R2), all of Orkney and Western...
Ensemble of historical simulations at 10km resolution with IFS-NEMO [Cycle 2 of the Climate DT] - member r4
Oct 3, 2025 - Earth Sciences
Marc Batlle Martín, 2025, "Ensemble of historical simulations at 10km resolution with IFS-NEMO [Cycle 2 of the Climate DT] - member r4", https://doi.org/10.82201/OUCRUQ, BSC Dataverse, V1
This ensemble of 3 historical simulations for the 1990-2014 period has been produced with a 10-km global configuration of IFS-NEMO, the Climate Digital Twin for climate adaptation developed under the Destination Earth initiative of the European Union. This configuration corresponds to the second cycle of the Climate Digital Twin.
Ensemble of historical simulations at 10km resolution with IFS-NEMO [Cycle 2 of the Climate DT] - member r3
Oct 2, 2025 - Earth Sciences
Marc Batlle Martín, 2025, "Ensemble of historical simulations at 10km resolution with IFS-NEMO [Cycle 2 of the Climate DT] - member r3", https://doi.org/10.82201/TE7JWA, BSC Dataverse, V1
This ensemble of 3 historical simulations for the 1990-2014 period has been produced with a 10-km global configuration of IFS-NEMO, the Climate Digital Twin for climate adaptation developed under the Destination Earth initiative of the European Union. This configuration corresponds to the second cycle of the Climate Digital Twin.
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