Open Research on three aspects:
- Global Scope
- Systemic Reach
- Local Implementation
FAIR data improves your research at many level. BUT their are requirement to make data FAIR, such as :
- coordination of data infrastructures
- making data accessible on many platforms
- etc.
Awareness of Open Science and its tools is still very low in the scientific community (EU Working Group on Educaiton and Skills under Open Science, 2017)
It is important for researcher to have a bit of knowledge of the tools/methods to make their data FAIR. The most important thing, is that some people - us - can share with them an expertise about these tools/methods, and help ease the confusion that the researchers might feel while putting in practice FAIR.
Focus on qualitative data:
- Databases, example of plant science
- Data Re-use cases
- preparing specimens
- preparin gand performing imaging
- data storage dissemination
- ....
- ...
- Analysis
Epistemic troubles :
- RD collected represent highly selected data types
- selection basesd on political-economic conditions of sharing
- peer reviews structure unclear
- misalignement between it and research need
- no sustainable plans for maintenance
- ....
Lessons Learnt on a general field
- Context specific data curaiton is key to data re-use
- Long-term maintenance is key to trustowrthiness (update, LT Policy)
- Which data and why?
- data & materials (connect digital data with data in the physical world)
- Role of ethics, humanities & social sciences in data management (increase quality and reusability)
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PLENARY : RESEARCH PAPERS
1. Measuring FAIR Principles to Inform Fitness for Use
Carolyn Hank from University of Tennessee
"Fitness for use" > focus on the "reusable" aspect of FAIR
Method : interview
Job-related demographics with questions such as, 'what is your current job title?', 'how many years have you work in this instition?, 'how many have you been work in the discipline?' etc.
Findability >'how did you find the data?', 'DOI', 'metadata?'
Accessibility > 'How did you access the data?' 'Open format?' 'was the data free?' 'was the metadata accessible?'
Interoperability>'was the data in a useable format' 'encoded?' 'machine-actionnable?'
Reusability>'were the metatadata sufficient ?' etc.
Potential implications : data can be FAI, but R requires more research
=> create ne knowledge of how scientists access and use data
=> producing a framework to enable re-use
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2. Giving datasets context : a comparison study of institutional repositories that apply varying degrees of curaiton
(Amy Koshoffer, Cincinnati, USA)
1. How do the metadata vary for each insittution?
2. completeness of metadata
3. curated datasets do have more documentation
4. DOIs more with curated datasets
5. keywords
What is curation?
- appraisal/selection
- check/run files : include clode review, review sensitive information, merde!
4 universities : Cincinnati (rep :
20 datasets