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back up to How to plan data ingest for VIVO

  • Structure of the University
    • Departments, Divisions, Groups
    • Different organizations within the campus might have conflicting views of this data
      • For example, a research group that reports to one unit but is funded by another.
  • Names and network IDs from an LDAP directory
  • Personnel records from Human Resources
    • Must be filtered to remove sensitive data.
  • Grants information from the Office of Sponsored Funds
    • Because of data challenges, this data is only harvested a few times each year.
  • List of classes from the Registrar's Office
  • Publications from a faculty reporting system
    • This also provides many challenges.
      • For example, two co-authors may provide slightly different names for the same journal article, or for the journal itself.
    • If the data comes from a well-curated source (e.g. PubMed), the challenges of data cleaning are greatly reduced, but the challenges of disambiguation are increased.
      • In the on-campus faculty reporting system, it is common practice to use an ID that definitively associates the data with the faculty member.
      • When reading from a public source, are these the same authors or different authors?
        • James L. Fox
        • J. L. Fox
        • J. Leroy Fox
        • James L. Foxx

next topic: Public vs. private data

 

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