This presentation was provided by Courtney R. Butler of The Federal Reserve Bank - Kansas City, during part two of the NISO two-part webinar "Building Data Science Skills: Strategic Support for the Work, Part Two," which was held on March 18, 2020.

1. FEDERAL RESERVE BANK OF KANSAS CITY
The views expressed here are those of the author
and do not necessarily reflect the official views of
the Federal Reserve Bank of Kansas City or the
Federal Reserve System
Courtney R. Butler
Senior Data Librarian
Enhancing Core Capabilities and Understanding Unique Value
Building Data Science Skills

2. FEDERAL RESERVE BANK OF KANSAS CITY
My Background
• MLS with specialization in Archives and Records Management
• Work history: government archives, public library special collections,
academic library reference desk, hospital clinical librarian
• Current position:
– Support both research and operational staff
– Responsibilities have included data acquisition, research reproducibility, data
curation, data preservation, and data management
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Expectations
• Increasing overlap between data science and data librarianship
• Highly valuable
– Expert coder
– Advanced degree in related subject area
• Actual requirements should depend on the role and needs of the organization
– Be realistic
– Place value on librarian skillset
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Consider…
• You do not have to “do” the thing to support the thing
• Librarians are lifelong learners
• Data science is not a whole different animal
• Each organization’s patrons have unique needs
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Illustrations
• Speak the Language
• Understand Needs
• Provide Resources
• Foster Connections
• Advocate for Good Practices
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Speak the Language
• Essential concepts and terminology
– Basic coding and troubleshooting
– Common coding languages and packages
– Common file formats
– Common methodologies
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Learn the Language
• Papers
• Google
• Trainings and conferences
• Books and standards
• Researchers!
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• Trainings
– Internal curriculum
– MOOCs
– Vendor visits
Provide Resources
• Finding data
– Subject guides
– Data catalogs
– Reference interviews
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Understand Needs
• Data Licensing
– Usage
• Research vs operational
• Methods
– Machine learning
– Web scraping
– NLP
• Publication/sharing
– Storage and Access
• Viewing vs downloading
• HPC
• API vs large scale data feeds
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Foster Connections
• Support communication across stakeholders:
– IT
– Legal
– Procurement
– Other researchers
– Information security
– Records/Archives
– Audit
– Data providers
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Advocate for Good Practices
• Reproducibility and Data Management
– Documentation
– Metadata
– Code commenting
– Open source formats
– Versioning
– Storage and backups
– File naming and organization
– Containerization
– Preservation
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Takeaways
• Maintain a growth mindset
• Emphasize strengths and unique value
• Build partnerships
• Connect with library community
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13. FEDERAL RESERVE BANK OF KANSAS CITY
Thank you!
courtney.butler@kc.frb.org