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Journal Article

Teaching Research Data Management with DataLad: A Multi-year, Multi-domain Effort

Michał Szczepanik; Adina S. Wagner; Stephan Heunis; Laura K. Waite; Simon B. Eickhoff; Michael Hanke
Neuroinformatics · Vol. 22, Issue 4 · pp. 635-645 · 2024

Abstract

Research data management has become an indispensable skill in modern neuroscience. Researchers can benefit from following good practices as well as from having proficiency in using particular software solutions. But as these domain-agnostic skills are commonly not included in domain-specific graduate education, community efforts increasingly provide early career scientists with opportunities for organised training and materials for self-study. Investing effort in user documentation and interacting with the user base can, in turn, help developers improve quality of their software. In this work, we detail and evaluate our multi-modal teaching approach to research data management in the DataLad ecosystem, both in general and with concrete software use. Spanning an online and printed handbook, a modular course suitable for in-person and virtual teaching, and a flexible collection of research data management tips in a knowledge base, our free and open source collection of training material has made research data management and software training available to various different stakeholders over the past five years.

Bibliographic Information

JournalNeuroinformatics
PublisherSpringer
Publication Date2024-05-07
Publication Year2024
Volume22
Issue4
Pages635-645
Document TypeJournal Article
eISSN1559-0089
DOI10.1007/s12021-024-09665-7

Access Information

NARA Access Coverage2003-01-01~Current
Journal Homepagehttps://www.springer.com/journal/12021
Publisher PageOpen Publisher Page
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