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InteractiveResource . 2025
License: CC BY
Data sources: Datacite
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AI4ED WP4 Training Module: Data Management, FAIR Principles, and Erasmus+ Requirements

Authors: Thöricht, Heike; Steinmann, Lena; Drechsler, Rolf;

AI4ED WP4 Training Module: Data Management, FAIR Principles, and Erasmus+ Requirements

Abstract

This self-learning training material was developed as part of the project AI4Ed funded by the Erasmus+ Programme under Grant Agreement number 101087543, and is one module of a broader training course on key topics related to the project, including Ethics in AI, Machine Learning, and Digital Tutoring. The module focuses on data management planning (DMP) and is structured into three interconnected parts: Open Science, Data Management and a Data Horror Story (10 minutes)A broad introduction to the concepts of Open Science and Data Management, illustrating the importance of good data practices through an engaging narrative. RDM, DMP, FAIR,... wait what?! (15 minutes)An explanation of key terms and frameworks in the world of research data management, including RDM, DMP, FAIR principles, and more. Data Requirements in the Erasmus+ Project (35 minutes)A reflection on the specific data-related requirements in the AI4Ed project, the challenges faced by the consortium, and how these were addressed. The material is provided in HTML format, archived in a ZIP folder. Please note the following points for reuse: This is a self-paced learning resource, designed for individual use. The ZIP folder must be extracted before opening the content. Otherwise, the material will not display correctly. The entire folder structure must be preserved for the material to work properly. Do not extract or use individual files outside of the provided folder. The material includes embedded videos that are reused under Creative Commons BY (CC BY) licenses. An internet connection is required for viewing these videos, as they are not stored locally in the package. A Markdown (.md) file containing quiz questions for all three training parts is included to support adaptation and reuse. A selection of slides in PDF format is also included, to give an impression of the layout, style, and depth of content of the module. For more information about the AI4Ed project, visit the official website: https://ai4ed-project.eu/ This training resource was created by the Data Science Center of the University of Bremen (https://www.dsc-ub.de/en/) and supports educators, researchers, and project managers in understanding and applying data management principles in European and international projects, especially within the context of Erasmus+.

Related Organizations
Keywords

FAIR Principles, Open Science, AI4Ed, Research Data Management, Training, Data Management Plan, Erasmus+, Data Management

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average