A5101919803- Scientific Computing and Data Management
- Research Data Management Practices
- Semantic Web and Ontologies
- Image Processing and 3D Reconstruction
- Big Data and Business Intelligence
- Data Quality and Management
- Digital Humanities and Scholarship
- Distributed and Parallel Computing Systems
- Machine Learning and Data Classification
- Library Science and Information Systems
- Advanced Data Storage Technologies
- Digital Transformation in Industry
- Manufacturing Process and Optimization
- Robotics and Automated Systems
- Computational and Text Analysis Methods
- Biomedical Text Mining and Ontologies
- Financial Crisis of the 21st Century
- Russia and Soviet political economy
- Historical Geography and Cartography
- Business Process Modeling and Analysis
- Big Data Technologies and Applications
- Flexible and Reconfigurable Manufacturing Systems
- Digital and Traditional Archives Management
- Geographic Information Systems Studies
- 3D Modeling in Geospatial Applications
Data Archiving Networked Services (DANS)
2018-2024
Data is a critical resource for Machine Learning (ML), yet working with data remains key friction point. This paper introduces Croissant, metadata format datasets that simplifies how used by ML tools and frameworks. Croissant makes more discoverable, portable interoperable, thereby addressing significant challenges in management responsible AI. already supported several popular dataset repositories, spanning hundreds of thousands datasets, ready to be loaded into the most
Data is a critical resource for Machine Learning (ML), yet working with data remains key friction point. This paper introduces Croissant, metadata format datasets that simplifies how used by ML tools and frameworks. Croissant makes more discoverable, portable interoperable, thereby addressing significant challenges in management responsible AI. already supported several popular dataset repositories, spanning hundreds of thousands datasets, ready to be loaded into the most
This deliverable introduces the provisional recommendations for assessing data FAIRness, and ‘FAIR enabling’ repository features, coming from FAIRsFAIR. It also provides a roadmap implementation of FAIR requirements, details about architecture, other technical considerations related to Software- Service-Quality Assurance in line with focus EOSC-SYNERGY project.
ABSTRACT We observe a growing universe of machine‐readable knowledge organisation systems (KOS) or even wider ‘semantic artifacts. see at the same time, various attempts to bring semantic artifacts together via registries, catalogues and cross‐walks among ontologies. This poster reflects how newest research on interoperability informs current practice for data repositories registry service providers. focus domain humanities cultural heritage, using different examples from Europe Netherlands:...