Data science and reproducibility

This catalogue respects all FAIR guidelines and best practices. It is based on the IEEE Standard for Learning Object Metadata (IEEE 2002) that has been customised in order to be compliant with the EOSC Training Resource Profile - Data Model.

Description

Slides on data science and reproducibility issues from lectures and workshops given by Michel Dumontier, professor in Data Science at Maastricht University.

1 - General
1.1 - Identifier
12
1.2 - URL type
URI
1.3 - URL
https://training.incf.org/course/data-science-and-reproducibility
1.4 - Title
Data science and reproducibility
1.5 - Language
en
1.6 - Description
("en_US","2 lessons: slides on data science and reproducibility issues from lectures given by Michel Dumontier, professor in Data Science at Maastricht University.")
1.7 - Keywords
data science
scientific reproducibility
fair principles
fair metrics
1.8 - Geographical availability
("en_US","2017")
2 - Life Cycle
2.1 - Version
("en_US","Not available")
2.2 - Status
Final
2.3 - Contribute
2.3.1 - Role
Author
2.3.2 - Entity
Michel Dumontier, professor in Data Science at Maastricht University
2.4 - Date
2017-01-26
3 - Educational
3.1 - Interactivity type
Expositive
3.2 - Learning resource type
Slide
3.3 - Interactivity level
Very low
3.4 - Semantic density
Low
3.5 - Target group
Learner
3.6 - Context
Training
3.7 - Expertise level
Easy
3.8 - Typical learning time
3H
3.9 - Learning outcome(s)
3.10 - Access rights
© Copyright INCF.org 2020. All Rights Reserved.
3.11 - Cost
No
3.12 - Copyright and other restrictions
Yes
3.13 - Conditions of use
("en_US","© Copyright INCF.org 2020. All Rights Reserved.")
4 - Technical
4.1 - Size
Not available
4.2 - Scientific domain and subdomain
4.3 - Topic codes
G1: Introduction to FAIR principles
G3: Performing a FAIRness self-assessment
G5: Basic Research Data Management (RDM)
5 - Relation
5.1 - Kind
5.2 - Entry
Access the resource

Details

Code12
Uploaded byLucia Vaira
Available since24/01/20 11:37

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