Session 1 - Plan and Design

The CARE Principles speak to how scientific data are used in ways that are purposeful and oriented towards enhancing wellbeing of people. The CARE Principles are likely to find expression across the data lifecycle from collection to curation, from access to application, with implications and responsibilities for a broad range of entities from funders to data users. (Carroll, Herczog, Hudson, et al.)

Description

In this section, we’ll enter the research data lifecycle at the plan and design stage, where you create your plan for your research data – the planning, storage, and sharing, among other things. Instead of the basics of creating a budget, creating a data management plan (DMP), and defining roles and responsibilities, we must consider how to weave or make actionable (“operationalize”) the CARE and FAIR principles. When your research data concerns people and/or communities, particularly those who are marginalized/minoritized, “research with people instead of about people” enhances the collective benefit and make you, the researcher/research group, responsible to those people.

Questions

• When crafting your DMP, how do/how can you plan to incorporate both CARE (the principles) and care (in terms of ethics)?
• What world will you make with your research data?¹

Resources

• Carroll, S.R., Herczog, E., Hudson, M. et al., Operationalizing the CARE and FAIR Principles for Indigenous data futures in Sci Data 8, 108 (April 2021)
• Belarde-Lewis, M., Littletree, S., Braine, I.S., Srader, K., Guerrero, N. and Palmer, C.L., Centering Relationality and CARE for Stewardship of Indigenous Research Data in Data Science Journal, 23: 32, pp. 1–16 (May 2024)
• de la Bellacasa, M. P., Matters of care in technoscience: Assembling neglected things in Social Studies of Science, 41(1), 85-106 (December 2010)
• A primer on an intersectional approach to data (2018) [PDF file]
• Boenig-Liptsin, M., Tanweer, A., & Edmundson, A., Data Science Ethos Lifecycle: Interplay of Ethical Thinking and Data Science Practice in Journal of Statistics and Data Science Education, 30(3), 228–240 (July 2022)
• Barrett, T., Okolo, C. T., Biira, B., Sherif, E., Zhang, A., & Battle, L., African Data Ethics: A Discursive Framework for Black Decolonial AI in Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency, 334–349 (June 2025)
• A Toolkit for Centering Racial Equity Within Data Integration (2020)

1. “If we accept that the very object of data science—data—are made through the web of interested relationships among researchers, communities, instruments and institutions, we can see how the practice of producing insights and tools based on these relational objects ripples through and reshapes these relationships” (Boenig-Liptsin, Tanweer, and Edmundson, 2022) ↩