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September 13-16, 2022
Dublin, Ireland + Virtual
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Note: The schedule is subject to change.

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IMPORTANT NOTE: Timing of sessions and room locations are subject to change.

Thursday, September 15 • 11:00 - 11:40
Community Data: What to Measure and Why? - Cali Dolfi, Red Hat

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Community metrics give you a lot of data about an open source community -- but do they give you insight? By understanding the nuances of open source communities, community managers can capitalize on data science workflow to move from metrics just being a checkbox of raw data to obtaining information that pushes the boundaries on what can be discovered. With this holistic information, community members can make path changes and more powerfully advocate for themselves in the language of the new age: data analysis. This will look at visualizations and metrics from the perspective of the individual community and from the ever-connected open source ecosystem. Through the Augur OSS project, Project Sandiego is able to take on the task of giving insight, not just data points. From here, we can begin to question what assumptions we make, from the data and community perspective. From this presentation you will leave with a better understanding of how to give actionable insights for people within and outside a particular community that builds on the historical knowledge around open source. This will also serve as a specific use-case examination on the care and nuance that should be considered for any data science analysis performed. *This will include new findings from NA presentation

Speakers
avatar for Cali Dolfi

Cali Dolfi

Senior Data Scientist, Red Hat
Cali Dolfi is a Senior Data Scientist in the Open Source Program Office at Red Hat. Her work focuses on changing the way we look at open source communities through the lens of data science and machine learning. Outside of data science, her passion lies in making careers in technology... Read More →



Thursday September 15, 2022 11:00 - 11:40 IST
Wicklow Meeting Room 2 (Level 2)
  Open AI & Data Forum, Community data analysis