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Data curation and assessment

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Carlo Minotti

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SciCat F2F 2022

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Outline

  • Kpi: how do we want to track usage, downloads, users clicks…
  • Data assessment: can we assess if our metadata/data is correct and how much variability does it contains?

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Advantages

  • Useful to get an idea of UI/search use
  • Performance optimisation
  • FAIR compliance
  • Searchability

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Kpi: how do we want to track usage, downloads, users clicks

Adding fields to collections

Pros:

  • Implemented at the SciCat level
  • No extra requirement to each facility
  • Fast to query

Cons:

  • Need to create extra fields upfront
  • Maintain
  • Not very flexible

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Kpi: how do we want to track usage, downloads, users clicks

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Improve the logging/monitoring (e.g. graylog)

Pros:

  • Richer logging, an advantage also outside the context of KPIs
  • More flexibility

Cons:

  • Aggregation at facility level
  • External monitoring system
  • Some work to enrich the logs

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Kpi: how do we want to track usage, downloads, users clicks

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Plots/Jupyter notebooks on existing data (e.g. number of triggered retrieve jobs)

Pros:

  • Some statistics are already available

Cons:

  • Not really flexible
  • Queries hit directly the DB, performance?

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Data assessment: can we assess if our metadata/data is correct and how much variability does it contains?

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Simple scripts that check formatting in DB (e.g. number of empty fields/values)

Pros:

  • Flexible

Cons:

  • Difficult to define what to check
  • Too flexible?
  • No consistent scoring across facilities

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Data assessment: can we assess if our metadata/data is correct and how much variability does it contains?

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Scripts that check based on well defined interfaces (e.g. OAI-PMH, search-API, Tubingen’s schemas)

Pros:

  • Easy to define what to check
  • Known and agreed standards
  • Facility independent
  • Easy to provide a consistent score across facilities
  • Could be a central service

Cons:

  • Not flexible
  • Need to agree on interface upfront

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