There are many worthy purposes for sharing data widely. In research, data sharing enables replication and validation of scientific findings and maximizes return on research investment, so it is not surprising that sponsors and publishers expect or mandate the sharing of data where possible. In organizations, data sharing leads to insights on operations and opportunities to improve goods and services. However, data containing sensitive information about individuals or personal data collected under various agreements cannot be shared openly without appropriate safeguards. An extensive body of statutes, regulations, institutional policies, consent forms, data sharing agreements, and common practices govern how sensitive data should be used and disclosed in different contexts. Researchers, institutions and companies that manage and share data must interpret how the various legal requirements and other data privacy and security standards apply to their handling of a given dataset.
DataTags helps data holders navigate these complex issues.
Learn more about research related to DataTags, and about the Privacy Tools for Sharing Research Data project, at: http://privacytools.seas.harvard.edu/datatags.
Our sample data-tagging implementation tool is called PolicyModels (read more).
Potentially harmful personal information
Sensitive personal information
Very sensitive personal information
Maximum sensitive personal information