Table 2: Population Data Science characteristics and implications for collective action.

Focus:	Implication(s)	Response to implication(s)
People and population-level insights	Required knowledge and skills are not likely to exist in a single discipline	Population Data Science is a multi-disciplinary field that encourages collective action, including the public’s voice.
Linking and interpreting multiple data sources / types	Technical approach to linkage - bringing together disparate data without common identifiers, preserving privacy	Population Data Science will include advancing the science of linkage technology.
	Analysis of complex data	Population Data Science will develop new tools for data analysis and will promote the training of practitioners.
		Population data science will develop methods for data analysis that do not require movement or (in some cases) direct viewing of sensitive data
	Interpreting data in a secondary context	Population Data Science will develop the assessment and reporting frameworks needed to document data with sufficient detail to inform accurate assessments
The use of data for public good, for positive impact on citizens and society	Understanding the values and expectations of the public and other key stakeholders, and then building systems to meet those.	Population Data Science is committed to public and stakeholder involvement and engagement in its many forms.
		Population Data Scientists will advance the science of public engagement to promote public understanding of data usage.
Technical and policy infrastructure built around legal, ethical, and privacy norms	Required knowledge and skills are not likely to exist in a single discipline	Population Data Science is a multi-disciplinary field that has a strong focus on stakeholder engagement and commitment to capacity building.
	Data are complex and sensitive.	Population Data Scientists commit to ethical and rigorous science within the legal framework of their jurisdictions.