Data/Repository discovery

Topic, definition and scope

• “Everyone has the right to share in scientific advancement and its benefits”
  Article 27, Universal Declaration of Human Rights
• Data discovery is a process of understanding data and extracting valuable insight from multiple data streams according to data uses and purposes.

Image: https://phaidra.univie.ac.at/download/o:1201054

FAIR element(s)

• Findable: Data should be available in a discoverable resource (i.e. repository), have appropriate description (i.e. metadata) and have a persistent identifier (PID)
• Accessible: Data should be retrievable and understandable for both humans and machines
• Interoperable: Machines and humans can interpret and use the data in different settings and will be able to distinguish the metadata from the data file
• Reusable: The ultimate goal of FAIR is to advance the reuse of data in the future research and allow integration with other compatible data sources.

Summary of Tasks / Actions

• Discussing reproducibility: why FAIR principles are important for data discovery?
• How do you search for data? See also the FAIRsharing educational factsheet for databases
  • Speaking about the process of data discovery, from developing a clear picture of the data to evaluating data quality.
  • Use lesson plan in (Unit 1: Topic 3: Data Life Cycle approach to FAIR/FAIR right from the start) to go through the data life cycle in the following scenario.

Research data cycle

• Present a researcher’s story in any life science field and set up a search strategy. The story can be something like:

“A Bio-Chemistry researcher needs some enzymology data for a research question: how enzymes are key factors to increase the rate of metabolism in the human body?”

• How did the researcher discover and access such data?
• Did the researcher list the characteristics of the data you want to discover
• Evaluate the quality of data

• Check the terms and conditions of access and use

• Let’s take the scenario above and look for any type of data you are interested about (e.g.‘mitochondrial beta-oxidation”) in different data sources:
  • OpenAIRE - Research Graph

  • [OpenAIRE

    Open Access](https://explore.openaire.eu/search/find?resultbestaccessright=%22Open%2520Access%22&fv0=miksa&f0=q&active=result)

  • DataCite
  • Re3data.org
  • Dataset Search (google.com)
  • FAIRsharing

• Of these resources, * Which one provided the most relevant data for your search terms? Which one provides facilities to refine your search ( i.e. filters)? * Try to search for more detailed search terms. How did the search results improve? * Is there a citation clarification for your selected data?Are there any differences in citation clarification between these data sources? * Can you find a licence for selected data? Is there any clarification how the data can be reused?

• How can data resources make data more discoverable by linking data to publications?
  • Cross-linking between journal publications and data repositories: a selection of examples
  • Service for data resources: Europe PMC external links service
• Identifying innovative search tools for data discovery: demo on how to find the data behind a publication using Europe PMC, a literature database.
  • Finding the data behind the publication with Europe PMC
  • Discovering data using Europe PMC SciLite annotations
• Citation, licences and copyrights help to clarify the “R” in the FAIR principles.
  • How to understand database conditions and attributes when choosing a repository (FAIRsharing documentation)
  • How to licence data (openaire.eu)

  • [How to Cite Datasets and Link to Publications

    DCC](https://www.dcc.ac.uk/guidance/how-guides/cite-datasets)

Materials / Equipment

• Internet and browser
• https://europepmc.org/

Take home tasks/preparation

• Hands-on exercise: Find the data behind a publication of your interest using Europe PMC and answer the questions:
  • Could you find the data citation on the publication?
  • Is the data linked to the data repository?
  • Could you access the data? Is the data format machine-readable?
  • Could you easily find the licensing for the data of interest?
  • How do you believe the use of FAIR principles contributed for your data discovery?

Additional resources