26 citations · 50 across the 10 of their papers we have counts for
11 papers
CroissantMiner: Automated Extraction and Validation of Croissant Metadata for ML Datasets
Berke Arda, Ahmetcan Yavuz, Paul Gerry +7
Croissant has emerged as a standard for machine-readable dataset metadata, yet populating its fields remains labor-intensive and requires careful reading of accompanying dataset do…
AI-Ready Research Workflows in Computational Social Science: Lessons on Building a Shared Language for Interdisciplinary Collaboration
Joan Giner-Miguelez, Alexandra Málaga, Felipe Gómez-Cortés +7
Artificial intelligence (AI) is gaining traction in the social sciences and humanities (SSH). However, adoption remains limited by technical barriers to high-performance computing…
Croissant Baker: Metadata Generation for Discoverable, Governable, and Reusable ML Datasets
Rafi Al Attrach, Rajna Fani, Sebastian Lobentanzer +17
Croissant has emerged as the metadata standard for machine learning datasets, providing a structured, JSON-LD-based format that makes dataset discovery, automated ingestion, and re…
When AI Meets Science: Research Diversity, Interdisciplinarity, Visibility, and Retractions across Disciplines in a Global Surge
Andrés F. Castro Torres, Joan Giner-Miguelez, Mercè Crosas
The extent to which Artificial Intelligence (AI) technologies can trigger generalized paradigm shifts in science is unclear. Although these technologies have revolutionized data co…
Enabling Content Management Systems as an Information Source in Model-driven Projects
Joan Giner-Miguelez, Abel Gómez, Jordi Cabot
Content Management Systems (CMSs) are the most popular tool when it comes to create and publish content across the web. Recently, CMSs have evolved, becoming \emph{headless}. Conte…
The Software Diversity Card: A Framework for Reporting Diversity in Software Projects
Joan Giner-Miguelez, Sergio Morales, Sergio Cobos +3
Context: Interest in diversity in software development has significantly increased in recent years. Reporting on diversity in software projects can enhance user trust and assist re…
A Standardized Machine-readable Dataset Documentation Format for Responsible AI
Nitisha Jain, Mubashara Akhtar, Joan Giner-Miguelez +15
Data is critical to advancing AI technologies, yet its quality and documentation remain significant challenges, leading to adverse downstream effects (e.g., potential biases) in AI…
Using Large Language Models to Enrich the Documentation of Datasets for Machine Learning
Joan Giner-Miguelez, Abel Gómez, Jordi Cabot
Recent regulatory initiatives like the European AI Act and relevant voices in the Machine Learning (ML) community stress the need to describe datasets along several key dimensions…
Croissant: A Metadata Format for ML-Ready Datasets
Mubashara Akhtar, Omar Benjelloun, Costanza Conforti +28
Data is a critical resource for machine learning (ML), yet working with data remains a key friction point. This paper introduces Croissant, a metadata format for datasets that crea…
On the Readiness of Scientific Data for a Fair and Transparent Use in Machine Learning
Joan Giner-Miguelez, Abel Gómez, Jordi Cabot
To ensure the fairness and trustworthiness of machine learning (ML) systems, recent legislative initiatives and relevant research in the ML community have pointed out the need to d…
A domain-specific language for describing machine learning datasets
Joan Giner-Miguelez, Abel Gómez, Jordi Cabot
Datasets play a central role in the training and evaluation of machine learning (ML) models. But they are also the root cause of many undesired model behaviors, such as biased pred…