Making Data FAIR

FAIR Principles:

• Findable

• Accessible

• Interoperable

• Reusable

Goals

1. Identify a set of metadata that will make the datasets in the BDR (Brown Digital Repository) FAIR

2. Create a crosswalk between analogous properties in the MODS schema and the DataCite schema

3. Build a tool to collect metadata for a dataset and output it in an XML format in the DataCite schema

Challenges

• Cognitive load: Balancing the amount of metadata requested from researchers

• Technical: Converting tabular data into XML in the DataCite schema

• Time management: Planning and prioritizing a project with few tangible outputs

Accomplishments

• Developed a list of metadata that will make the datasets in the BDR FAIR

• Created a crosswalk between the MODS schema and the DataCite schema

• In progress: Tool to collect metadata for a dataset and output it in an XML format in the DataCite schema

• Additional Achievements:

  • Deeply researched data storage and the interaction between data science and libraries

  • Gained experience with OpenRefine for reformatting spreadsheets, saving editing scripts, and exporting XML files

  • Tackled the challenges of adopting data storage principles and new metadata schema

  • Learned to balance cognitive load with rigor in data collection

  • Gained experience in creating meeting agendas and a project management plan

  • Maintained frequent and valuable communication with client/mentor

  • Actively sought clarification and feedback