This page highlights resources from different organizations about data sharing and related topics.
Preparing a Successful Data Request
Legal Issues in Data Sharing
Data Aggregation
- Connecticut Data Aggregation Guidance, Connecticut Office of Policy and Management
- FERPA Data Suppression Guidelines, Connecticut State Department of Education, 2015
- Guidance Regarding Methods for De-identification of Protected Health Information in Accordance with the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule, U.S. Department of Health & Human Services, 2015
- Guidelines for Data De-Identification or Anonymization, Educause, 2015
- De-Identification of Personal Information, Simson L. Garfinkel, National Institute of Standards and Technology, U.S. Department of Commerce, 2015
- Guide to Basic Data Anonymization Techniques, Personal Data Protection Commission of Singapore, 2018
Linking Data
- Linkage Feasibility—To Link or Not To Link, Dusetzina SB, Tyree S, Meyer AM, et al., Agency for Healthcare Research and Quality, 2014
- Data Matching Software Tools, Jonathan de Bruin
- Improving deduplication of identities, Matthew J. Bauman, 2018
- Linking Administrative Data: Strategies and Methods, California Policy Lab, 2018
- Challenges in administrative data linkage for research, Katie Harron, Chris Dibben, James Boyd, Anders Hjern, Mahmoud Azimaee, Mauricio L Barreto, Harvey Goldstein, 2017
- Modernizing Person-Level Entity Resolution with Biometrically Linked Records, Matthew Gross, Michael Mueller-Smith, University of Michigan, 2020
- How Well Do Automated Linking Methods Perform? Lessons from U.S. Historical Data, Martha Bailey, Connor Cole, Morgan Henderson, Catherine Massey, University of Michigan & National Bureau of Economic Research, 2019
- Record Linkage Innovations for Human Services, Emily R. Wiegand, Robert M. Goerge, 2019
Bias in Data
- Algorithmic Justice League
- Confronting Structural Racism in Research and Policy Analysis, Urban Institute, 2019
- Data 4 Black Lives
- How I’m Fighting Bias in Algorithms, Joy Buolamwini, TED Talks, 2016
- Racial bias in a medical algorithm favors white patients over sicker black patients, Carolyn Y. Johnson, The Washington Post, 2019
- The era of blind faith in big data must end, Cathy O’Neil, Ted Talks, 2017
- Weapons of Math Destruction, Cathy O’Neil, 2017
- When computers make biased health decisions, black patients pay the price, study says, Amina Khan, Los Angeles Times, 2019
Safeguarding Data
