Unlocking Potential with the Dataverse Data Platform

TY - GEN

T1 - Unlocking Potential with the Dataverse Data Platform

AU - Tykhonov, Vyacheslav

PY - 2024/11/18

Y1 - 2024/11/18

N2 - DANS (Data Archiving and Networked Services) actively fosters collaboration across academic and research communities, promoting Open Science and Data Sharing to enhance interdisciplinary work. Through platforms like DataverseNL and DANS Data Stations, DANS provides a national infrastructure for secure, accessible, and long-term data preservation. Additionally, the ODISSEI portal aggregates metadata from multiple sources, creating unified access to diverse datasets and streamlining the discovery process. By publishing DANS metadata as Open Linked Data, the organization promotes transparency and interoperability, making it easier to connect datasets across various disciplines. Shared controlled vocabularies contribute to semantic interoperability, allowing for consistent integration of data from different fields. The Croissant ML standard enables AI-ready metadata by providing machine-readable format that support advanced analysis and research. This integrated infrastructure opens new possibilities, such as automated metadata translation, enrichment, taxonomy creation, and knowledge graph building, all of which are critical to advancements in artificial intelligence and machine learning.

AB - DANS (Data Archiving and Networked Services) actively fosters collaboration across academic and research communities, promoting Open Science and Data Sharing to enhance interdisciplinary work. Through platforms like DataverseNL and DANS Data Stations, DANS provides a national infrastructure for secure, accessible, and long-term data preservation. Additionally, the ODISSEI portal aggregates metadata from multiple sources, creating unified access to diverse datasets and streamlining the discovery process. By publishing DANS metadata as Open Linked Data, the organization promotes transparency and interoperability, making it easier to connect datasets across various disciplines. Shared controlled vocabularies contribute to semantic interoperability, allowing for consistent integration of data from different fields. The Croissant ML standard enables AI-ready metadata by providing machine-readable format that support advanced analysis and research. This integrated infrastructure opens new possibilities, such as automated metadata translation, enrichment, taxonomy creation, and knowledge graph building, all of which are critical to advancements in artificial intelligence and machine learning.

U2 - 10.5281/zenodo.14169625

DO - 10.5281/zenodo.14169625

M3 - Other contribution

ER -