Note:

• CEDAR will not be used (discussed 24-10-2023)

• Data Stewardship Wizard (DSW) could maybe be used (Suggestion Rob Hooft).

• Some of Keep in mind that if the scenarios mention the DSW , but this could be another tool as well. No choice has been made.

Scenarios -

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Making data Findable

The first set of scenarios aim at a Researcher making an existing research dataset available Findable in the Health-RI catalogue.

In my opinion it would make sense to have two main solutions. To stick with the Metroline Analogy: a Basic and an Advanced Track:

1. Basic Track - A CEDAR / DSW type solutionfillable form-based solution, such as CEDAR or the DSW

   1. Most researchers will probably want to have a solution that takes little effort, but does meet the requirements imposed by employers, funders, etc.

   2. Health-RI provides easy to use forms with mandatory and optional fields as required by (initially) the core metadata schema and (later) extended metadata schemas

      1. Forms must have guidance to help researchers properly fill in the necessary data. Guidance could e.g. be (sections of) metroline pages

   3. Output should be something suitable for a FAIR Datapoint or Catalog (harvestable by Health-RI)

      1. Maybe even nicer if you could provide it provide the output directly to an FDP directly as an export option?

         1. E.g. You’re an Amsterdam UMC researcher it could suggest a specific FDPan Amsterdam UMC preferred FDP

         2. It would need some form of quality control / permission before the “ok, send it” button could be pressed?

2. Advanced Track - A manual solution [Dena: Advance track cannot be fully manual]] So I am wondering as we previously discussed we need to have a scenario where we define semi-automatic approach for extracting data, mapping and transformation and api creation]]

   1. The Basic Track does not meet your demands or you just prefer to do it manually

   2. Health-RI provides guidance (knowledge, references, recipes, perhaps scripts/software if it makes sense)

   3. The process should be able to start with nothing and end with having reached your goals

   where possible)

   1. Core can probably have a follow-along example; for extended maybe pick one extension for a follow-along example

   2. Custom stuff Metadata elements outside of the core and extensions should have clear guidance where possible with also recipes for domain specific problems.

      1. If custom things can be generalised to apply to e.g. more domains, that would be better

      2. Also, it may be useful to keep track of custom items? If lots of projects add the same custom item, it could be useful to add it to a new version of core.

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Scenario List

Basic Track

• BT1 - Make Simple Dataset Discoverable & Searchable Findable using DSW - Core

• BT2 - Make Simple Dataset Discoverable & Searchable Findable using DSW - Core + Extension

• BT3 - Make Simple Dataset Discoverable & Searchable Findable using DSW - Core (+ Extension) + Custom

  • Unfeasible: custom does not seem realistic in a DSW approach as HRI would need to create new forms for every project with custom items

Advanced Track

• AT1 - Make Simple Dataset Discoverable & Searchable Findable using Manual approach - Core

• AT2 - Make Simple Dataset Discoverable & Searchable Findable using Manual approach - Core + Extension

• AT3 - Make Simple Dataset Discoverable & Searchable Findable using Manual approach - Core (+ Extension) + Custom

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• AT2.1 - Clinical

  • AT2.1.1 - Cancer

• AT2.2 - Omics

  • AT2.2.1 - Proteomics

• AT2.3 - Funders

  • AT2.3.1 - ZonMW

• AT2.4 - Rare Diseases

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Scenario Ideas -

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Guidance on how to properly build something in a FAIR way from scratch. Lots of Metroline references here. Some examples

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Creating new FAIR Datasets and Transforming Existing Data to be more FAIR

• Clinical Data

  • Build Simple Clinical Dataset in Castor EDC and Publish it in Castor's FAIR Data Point

  • Transform an existing Simple Clinical Dataset in Castor EDC and Publish it in Castor's FAIR Data Point

  • (We could look into other EDCs as well if necessary. Design principles for FAIR should be the same)

• Omics Data

  • Build Simple Omics Dataset

    • Genomics, Proteomics, etc. Could all have a separate scenario if necessary and we could then link to metroline pages for e.g. recipes on how to tackle the specific problem.

  • Transform an existing Omics Dataset

• Imaging Data

  • Build Simple Imaging Dataset? No idea how this works

• … Data

  • Build Simple …

  • Transform …

Other types of Scenarios

• What does a researcher have to do if he/she wants to find data and then gain access to it?

  • Probably Architecture has this covered? TBD

Q: how does https://health-ri.atlassian.net/wiki/spaces/FSD/pages/107806721/Data+onboarding+on+the+national+catalogue#3.-How-to-onboard-your-information-to-the-catalogue%3F 3a, 3b and 3c affect the scenarios?

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