Skip to main content
Home

Information Retrieval

Information Retrieval is the process of finding and extracting relevant information from large collections of data

Resources

  • Introduction to Digital Humanities in Galaxy

    EN
    Galaxy is an open source data analysis platform that focuses on reproducible research and supports High-performance computing. You need no programming skills or installations. Open your browser and join us for an introduction to Galaxy to see how you can use our 4000+ tools and chain them into reproducible workflows.
    Authors
    • Daniela Schneider
    • Rand Zoabi

  • Introduction to Europeana APIs

    EN
    This course provides a comprehensive understanding of Europeana as a digital platform through a walkthrough of the Application Programming Interfaces (APIs) it offers. It provides the knowledge and skills to understand the purpose they serve and the functionality they have, to exploit them by formulating efficient queries for cultural heritage information retrieval. Building on use cases, it delves into the APIs required to achieve research goals, exploring their features and providing familiarisation with supported data formats.
    Authors
    • Vicky Dritsou

  • Introduction to Computer Science: Cloud Computing and Beyond

    EN
    This resource introduces the fundamental concepts of cloud computing. Particular emphasis is placed on how cloud technologies support scalable, secure, and flexible environments for data storage, analysis, and application deployment.
    Authors
    • Alessandro Costantini

  • Designing the Backbone of the RHDT using AI and Network Science

    EN
    This resource discusses how the ARTEMIS project combines artificial intelligence, network science, knowledge graphs, and semantic ontologies to transform unstructured cultural heritage data into structured, machine-readable knowledge supporting Reactive Heritage Digital Twins (RHDTs).
    Authors
    • Miriana Somenzi

  • Data Mining

    EN
    This video introduces data mining as a collection of computational methods for extracting knowledge from large digital datasets. It outlines the main stages of a data mining workflow - preprocessing and analysis - and explains key techniques including clustering, outlier detection, classification, and association pattern mining, with examples from humanities research.
    Authors
    • Maciej Janicki