This presentation was provided by Leslie McIntosh of Digital Science and Cynthia Hudson-Vitale of the Association for Research Libraries (ARL), during the fourth session of our Spring 2023 NISO Training Series "Quality Assurance of Data Sets." The class focused on Provenance for Data Cleaning, and was held June 13, 2023.

1. Provenance for Data Cleaning
Assurance of Data Sets: A 2023 NISO Training Series
Leslie McIntosh, PhD
VP, Research Integrity
Digital Science
Guest Lecturer: Cynthia Hudson Vitale
Director, Science Policy
Association of

2. Agenda
● Review OpenRefine activity

3. Review - What is Provenance?
NIH
https://www.nnlm.gov/guides/data-glossary/data-provenance
The term “data provenance”, sometimes called “data lineage,” refers to a documented trail that
accounts for the origin of a piece of data and where it has moved from to where it is presently.
ARDC Australia Research Data Commons
https://ardc.edu.au/resource/data-provenance/
Data provenance is the documentation of where a piece of data comes from and the processes
and methodology by which it was produced.

4. What are some examples of what metadata elements are
captured in a data provenance record?

5. Data cleaning
Data cleansing or data cleaning is the process of detecting and correcting (or
removing) corrupt or inaccurate records from a record set, table, or database and
refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data
and then replacing, modifying, or deleting the dirty or coarse data.
Chat prompt - What are some of the most popular data cleaning tools you use or the
individuals you work with use?

6. Activity: Provenance for data cleaning tools
Break into 3 groups:
- Review the activity you just completed in OpenRefine
- What provenance metadata elements are available in OpenRefine?
- What elements of provenance metadata are missing or are inadequate?
- Other observations
- Report out

7. Share Around: Provenance for data cleaning in OpenRefine

8. Activity: Other data cleaning tools
Identify your favorite (haha) data cleaning tool
Review the tool for the following:
- What provenance metadata elements are available?
- What elements of provenance metadata are missing or are inadequate?

9. Thank you!

10. Thank you!