Data and the FAIR Principles

This catalogue respects all FAIR guidelines and best practices and use the IEEE Standard for Learning Object Metadata (IEEE 2002).

Description

This module provides five lessons focused on best practices aimed at ensuring that a researcher’s data is properly managed and published to ensure it enables reproducible research.

1 - General
1.1 - Identifier
4
1.2 - Catalog
URL
1.3 - Entry
http://www.repronim.org/module-FAIR-data/
1.4 - Title
Data and the FAIR Principles
1.5 - Language
en
1.6 - Description
This module provides five lessons to ensure that a researcher’s data is properly managed and published to ensure it enables reproducible research.
1.7 - Keywords
fair data
fair principles
1.9 - Coverage
2016
2 - Life Cycle
2.1 - Version
Not available
2.2 - Status
Final
2.3 - Contribute
2.3.1 - Role
Author
2.3.2 - Entity
ReproNim
2.4 - Date
2016
3 - Educational
3.1 - Interactivity type
Expositive
3.2 - Learning resource type
Lecture
3.3 - Interactivity level
Low
3.4 - Semantic density
Medium
3.5 - Intended end user role
Learner
3.6 - Context
Training
3.7 - Difficulty
Knowledge-dependent
3.8 - Typical learning time
Knowledge-dependent
3.9 - Rights
Copyright © 2016 Neurohackweek and ReproNim
3.10 - Cost
No
3.11 - Copyright and other restrictions
Yes
3.12 - Conditions of use
This module provides five lessons to ensure that a researcher’s data is properly managed and published to ensure it enables reproducible research.
4 - Technical
4.1 - Location
http://www.repronim.org/module-FAIR-data/
4.2 - Size
Not available
4.3 - Topic codes
G1: Introduction to FAIR principles
G4: GDPR (General Data Protection Regulation) issues related to data sharing
G5: Basic Research Data Management (RDM)
R9: Linked Data and ontologies
R16: Workflow engines for automated data processing
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Uploaded byLucia Vaira
Available since10/12/19 12:14

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