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minutes_2019_05_09
Purva Kulkarni edited this page May 13, 2019
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- Introduction of the objectives of the call and the tasks
- Introduction of the participants
- The inventory of tools for –omics data FAIRification: https://github.com/fairgenomes/information/wiki/Stream2
- Relation to the requirements of the FAIRgenomes, X-omics, UMD projects. See also: https://docs.google.com/spreadsheets/d/1rnLsmE62t15jCwJfx4mCL5USYSeiXNctCA0XPcgprds/edit#gid=0
- Next steps a. BYOD workshops for data FAIRification b. Communication internal and external (link to international initiatives such as GA4GH, 1MG, Phenomenal
- Any other business
- First we focus on genomics data/experiments before moving to fit in metabolomics information
- The initial focus would be on samples/experimental conditions/assays
- Suggestion by Morris - Add things to wiki and later repositories can be renamed as needed
- Suggestion by Andrew - Check PhenoMeNal (http://phenomenal-h2020.eu/home/) and take at a look at the existing use-cases within it.
- Suggestion by Peter-Bram - To work first on sample metadata and sample data
- Suggestion by Michael - Take public datasets and check if the ontologies/ data models fit into them. This can be compared at a workshop
- Another TC needed before the workshop where ontologies and data models will be comapred
- Each member brings a sample sheet relevant to individual projects/ research to see how comparable they are
- Katy will share a list of items that deal with GDPR and laws realting to FAIR data and will add links to the wiki/ google do (?)
- GA4GH discussion in the next TC before the workshop
- Mateusz would need input from everyone on this
- Plan for five workhops:
- Metadata template for samples for three themes (genomics, proteomics and metabolomics)
- Data integration
- Starting point for the workshop could be an output file from an -omics experiment and trying to make it FAIR
- Everyone should keep an eye on the progress in different projects on data FAIRification running within Europe and outside
[] Workshop preparation - Collect an example sample sheet, see which mapping can be done yourself and data models that can be built on it
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