This title appears in the Scientific Report :
2023
Please use the identifier:
http://dx.doi.org/10.34734/FZJ-2023-05331 in citations.
Please use the identifier: http://dx.doi.org/10.5281/ZENODO.10010615 in citations.
Lightweight data publishing on Jülich Data with DataLad
Lightweight data publishing on Jülich Data with DataLad
Jülich DATA is the central institutional repository for research data of the Research Center Jülich and supports sharing, preserving, citing, exploring, and analyzing research data with descriptive metadata, without hosting large files. In this tutorial, participants will discover how DataLad can in...
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Personal Name(s): | Szczepanik, Michał (Corresponding author) |
---|---|
Contributing Institute: |
Gehirn & Verhalten; INM-7 |
Imprint: |
2023
|
DOI: |
10.34734/FZJ-2023-05331 |
DOI: |
10.5281/ZENODO.10010615 |
Conference: | INM Retreat 2023, Jülich (Germany), 2023-10-17 - 2023-10-18 |
Document Type: |
Talk (non-conference) |
Research Program: |
Datenmanagement für computergestützte Modellierung (INF) Neuroscientific Data Analytics and AI |
Link: |
OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://dx.doi.org/10.5281/ZENODO.10010615 in citations.
Jülich DATA is the central institutional repository for research data of the Research Center Jülich and supports sharing, preserving, citing, exploring, and analyzing research data with descriptive metadata, without hosting large files. In this tutorial, participants will discover how DataLad can integrate with Dataverse and have the best of both worlds: Discoverability and metadata with Jülich DATA, and actionable data tracking with DataLad. With conceptual and hands-on elements, we will learn how to publish or clone lightweight DataLad datasets to and from Jülich DATA. |