FAIR Enough: Develop and Assess a FAIR-Compliant Dataset for Large Language Model Training?

S Raza, S Ghuge, C Ding, E Dolatabadi, D Pandya - Data Intelligence, 2024 - direct.mit.edu
The rapid evolution of Large Language Models (LLMs) highlights the necessity for ethical
considerations and data integrity in AI development, particularly emphasizing the role of …

[HTML][HTML] A FAIR catalog of ontology-driven conceptual models

TP Sales, PPF Barcelos, CM Fonseca, IV Souza… - Data & Knowledge …, 2023 - Elsevier
Multi-domain model catalogs serve as empirical sources of knowledge and insights about
specific domains, about the use of a modeling language's constructs, as well as about the …

Building expertise on FAIR through evolving Bring Your Own Data (BYOD) workshops: describing the data, software, and management-focused approaches and their …

CH Bernabé, L Thielemans, R Kaliyaperumal… - Data …, 2024 - direct.mit.edu
ABSTRACT Since 2014,“Bring Your Own Data” workshops (BYODs) have been organised to
inform people about the process and benefits of making resources Findable, Accessible …

The extended EA ModelSet—a FAIR dataset for researching and reasoning enterprise architecture modeling practices

PL Glaser, E Sallinger, D Bork - Software and Systems Modeling, 2025 - Springer
Conceptual modeling research is increasingly investigating the application of artificial
intelligence (AI) and machine learning (ML) to automate tasks like model creation …