Skills development for Open Science Sharing (open)
Description: Skills development for Open Science Sharing (open) Data Open Science webinars Open Access week Veerle Van den Eynden 20 October 2020 Skills for data sharing Skills for researchers Skills for research support staff Data sharing open data
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slide1. Skills development for Open Science
Sharing (open) Data
Open Science webinars
Open Access week
Veerle Van den Eynden
20 October 2020<br>
slide2. Skills for data sharing Skills for researchers
Skills for research support staff
Data sharing / open data only makes sense if data are fit for reuse (examples)
FAIR data a better standard than open data<br>
slide3. Example Only research paper explains what data mean, their provenance.
Ù§ Open
Ù§ Evidence
X Reuse<br>
slide4. 6-page data descriptor; machine-readable metadata; usage info, ...
Ù§ Open
Ù§ Reuse<br>
slide5. Skills to share FAIR data Documentation and annotation: explain what data mean
Metadata creation (and taxonomies and ontologies)
Pseudonymisation / anonymisation
Open / standard file formats
Legal: data protection, IP, copyright
Licensing of data
Responsible reuse:
Citation and attribution
Handling sensitive data
Proper data mining
Repositories and how to use them<br>
slide6. Skills for researchers University students get a good grounding in research methods
Studies rarely cover the lifecycle of data and the practicalities of making data shareable for the longer term
So researchers focus on the immediate activities needed to collect data, interpret and report on them<br>
slide7. How to teach skills? Effective learning of data skills:
active learning by making processes visible
directly experiencing methods
critical reflection on practice
Hands-on
Practical examples
Templates
Tools<br>
slide8. Examples Active learning:
practical tasks, (lab) exercises, quizzes
Learning by doing / experiencing:
write a data management plan
create a metadata record
Critical reflection:
group discussions of real-case data challenges
discussions on individual data needs to explore shared challenges<br>
slide9. Online self-learning courses<br>
slide10. For research support staff Learn about those same topics
Understand the research practices
Try out the various courses
To providing training to researchers, reuse existing training courses / materials, e.g.:
CESSDA training package (with exercises)
Open Science Training Handbook (with exercises)
Digital Open Science course for reuse: 60 hours with 15–20 hours of lectures and tutorials
UKDS exercises
SSHOC Training Toolkit<br>
slide11. Beyond skills For Open Science to progress, skills are important, but also:
Attitudes to data sharing
Open Science culture within organisations
Support services and infrastructure<br>
slide12. Thank you !<br>
Sharing (open) Data
Open Science webinars
Open Access week
Veerle Van den Eynden
20 October 2020<br>
slide2. Skills for data sharing Skills for researchers
Skills for research support staff
Data sharing / open data only makes sense if data are fit for reuse (examples)
FAIR data a better standard than open data<br>
slide3. Example Only research paper explains what data mean, their provenance.
Ù§ Open
Ù§ Evidence
X Reuse<br>
slide4. 6-page data descriptor; machine-readable metadata; usage info, ...
Ù§ Open
Ù§ Reuse<br>
slide5. Skills to share FAIR data Documentation and annotation: explain what data mean
Metadata creation (and taxonomies and ontologies)
Pseudonymisation / anonymisation
Open / standard file formats
Legal: data protection, IP, copyright
Licensing of data
Responsible reuse:
Citation and attribution
Handling sensitive data
Proper data mining
Repositories and how to use them<br>
slide6. Skills for researchers University students get a good grounding in research methods
Studies rarely cover the lifecycle of data and the practicalities of making data shareable for the longer term
So researchers focus on the immediate activities needed to collect data, interpret and report on them<br>
slide7. How to teach skills? Effective learning of data skills:
active learning by making processes visible
directly experiencing methods
critical reflection on practice
Hands-on
Practical examples
Templates
Tools<br>
slide8. Examples Active learning:
practical tasks, (lab) exercises, quizzes
Learning by doing / experiencing:
write a data management plan
create a metadata record
Critical reflection:
group discussions of real-case data challenges
discussions on individual data needs to explore shared challenges<br>
slide9. Online self-learning courses<br>
slide10. For research support staff Learn about those same topics
Understand the research practices
Try out the various courses
To providing training to researchers, reuse existing training courses / materials, e.g.:
CESSDA training package (with exercises)
Open Science Training Handbook (with exercises)
Digital Open Science course for reuse: 60 hours with 15–20 hours of lectures and tutorials
UKDS exercises
SSHOC Training Toolkit<br>
slide11. Beyond skills For Open Science to progress, skills are important, but also:
Attitudes to data sharing
Open Science culture within organisations
Support services and infrastructure<br>
slide12. Thank you !<br>