Mandated vs. meaningful openness

Researchers must often respond to funder or institutional requirements or requests to make their data public. Without conversations around frontline community privacy or security needs, and without adequate training, researchers may inadvertently share data in ways that harm frontline communities’ interests. These interests may include common concerns with sensitive or personally identifiable information, but may also relate to community members’ agency and ownership over information about them or collected by them. For example, community members may simply want to know how their data is being used outside of initial research projects. Or they may want to impose restrictions on different users or types of use, or even reject the possibility of data being publicized due to potentially negative economic effects on their subsistence activities or ownership rights.

Solutions

1.

Blur data

Aggregate or otherwise de-identify published or shared versions of datasets to make it difficult to identify individuals.

2.

Agree on data licensing and sharing agreements

Co-develop, discuss, and use data licensing and sharing agreements to delineate how data can be used and under what conditions. These can help identify safeguards, establish reporting procedures, and provide a mechanism for accountability in case of harm.

Know of another resource or solution?

Resources

J-PAL's Guide to De-identifying Data

J-PAL's Guide to De-identifying Data includes various processes for de-identifying data to reduce the risk of harm to individuals.

J-PAL's Guide to De-identifying Data
Related solution
Blur data

OEDP's Tools and Templates for Community Data ()

OEDP's Tools and Templates for Community Data support community data stewards in being able to safely and equitably use and share community environmental data. They include tools like a data values statement template and data use and sharing agreement questions.

OEDP's Tools and Templates for Community Data ()

Local Contexts’ Data Labels

Local Contexts’ Data Labels identify and clarify indigenous communities’ rules, expectations, and responsibilities for Traditional Knowledge and Biocultural information.

Local Contexts’ Data Labels
Related solutions
Secure consent before sharing
Let communities own their data
Agree on data licensing and sharing agreements