The resources used to inspire and execute this project are wide-ranging and interdisciplinary. We highlight in bold the work that has especially contributed to the development of FAIR2.
Abrams, J. A., Tabaac, A., Jung, S., & Else-Quest, N. M. (2020). Considerations for employing intersectionality in qualitative health research. Social Science & Medicine, 258, 113138. https://doi.org/10.1016/j.socscimed.2020.113138
American Economic Association. (2020, June 5). Statement from the AEA Executive Committee. Retrieved from https://www.aeaweb.org/news/member-announcements-june-5-2020
Canning, P., & Stacy, B. (2019). The Supplemental Nutrition Assistance Program (SNAP) and the Economy: New Estimates of the SNAP Multiplier (Economic Research Report No. 291963). United States Department of Agriculture, Economic Research Service. Retrieved from https://econpapers.repec.org/paper/agsuersrr/291963.htm
Cohen, M., Rohan, A., Pritchard, K., & Pettit, K. L. S. (2022). Guide to Data Chats: Convening Community Conversations about Data. Urban Institute.
Gray, C. (2019). Leaving benefits on the table: Evidence from SNAP. Journal of Public Economics, 179, 104054. https://doi.org/10.1016/j.jpubeco.2019.104054
Howe, C. J., Bailey, Z. D., Raifman, J. R., & Jackson, J. W. (2022). Recommendations for Using Causal Diagrams to Study Racial Health Disparities. American Journal of Epidemiology, 191(12), 1981–1989. https://doi.org/10.1093/aje/kwac140
Kenney, E. L., Soto, M. J., Fubini, M., Carleton, A., Lee, M., & Bleich, S. N. (2022). Simplification of Supplemental Nutrition Assistance Program Recertification Processes and Association with Uninterrupted Access to Benefits Among Participants with Young Children. JAMA, 5(9) https://doi.org/10.1001/jamanetworkopen.2022.30150
Kilbertus, N., Rojas Carulla, M., Parascandolo, G., Hardt, M., Janzing, D., & Schölkopf, B. (2017). Avoiding Discrimination through Causal Reasoning. Advances in Neural Information Processing Systems, 30. Curran Associates, Inc. Retrieved from https://proceedings.neurips.cc/paper/2017/hash/f5f8590cd58a54e94377e6ae2eded4d9-Abstract.html
Kleinberg, J., Ludwig, J., Mullainathan, S., & Sunstein, C. R. (2018). Discrimination in the Age of Algorithms. Journal of Legal Analysis, 10, 113–174. https://doi.org/10.1093/jla/laz001
Manski, C. F. (2013). Public Policy in an Uncertain World: Analysis and Decisions. Cambridge, MA: Harvard University Press.
Lane, S. R., McClendon, J., & Matthews, N. (2017). Finding, Serving, and Housing the Homeless: Using Collaborative Research to Prepare Social Work Students for Research and Practice. Journal of Teaching in Social Work, 37(3), 292–306. https://doi.org/10.1080/08841233.2017.1317689
Lundberg, I., Johnson, R., & Stewart, B. M. (2021). What Is Your Estimand? Defining the Target Quantity Connects Statistical Evidence to Theory. American Sociological Review, 86(3), 532–565. https://doi.org/10.1177/00031224211004187
Muhammad, K. G. (2019). The Condemnation of Blackness: Race, Crime, and the Making of Modern Urban America, With a New Preface. Cambridge, MA: Harvard University Press.
OHCHR. (2018). A Human Rights-Based Approach to Data. Geneva: Office of the United Nations High Commissioner for Human Rights. Retrieved from Office of the United Nations High Commissioner for Human Rights website: https://www.ohchr.org/sites/default/files/Documents/Issues/HRIndicators/GuidanceNoteonApproachtoData.pdf
Pearl, J. (1995). Causal diagrams for empirical research. Biometrika, 82(4), 669–688. https://doi.org/10.1093/biomet/82.4.669
Pearl, J. (2022). Causal Inference: History, Perspectives, Adventures, and Unification (An Interview with Judea Pearl). Observational Studies 8(2), 1-14. doi:10.1353/obs.2022.0007.
Pearl, J., & Mackenzie, D. (2018). The Book of Why: The New Science of Cause and Effect (1st ed.). USA: Basic Books, Inc.
Robinson, W. R., Renson, A., & Naimi, A. I. (2020). Teaching yourself about structural racism will improve your machine learning. Biostatistics, 21(2), 339–344. https://doi.org/10.1093/biostatistics/kxz040
Rocca-Serra, P., Sansone, S.-A., Gu, W., Welter, D., Abbassi Daloii, T., & Portell-Silva, L. (2022). Reflections on the Ethical values of FAIR. In D2.1 FAIR Cookbook. Zenodo. Retrieved from https://doi.org/10.5281/zenodo.6783564
Roewer-Despres, F., & Berscheid, J. (2020, November 29). Continuous Subject-in-the-Loop Integration: Centering AI on Marginalized Communities. arXiv. Retrieved from http://arxiv.org/abs/2012.01128
Salazar, Z. R., Vincent, L., Figgatt, M. C., Gilbert, M. K., & Dasgupta, N. (2021). Research led by people who use drugs: Centering the expertise of lived experience. Substance Abuse Treatment, Prevention, and Policy, 16(1), 70. https://doi.org/10.1186/s13011-021-00406-6
Schalet, A. T., Tropp, L. R., & Troy, L. M. (2020). Making Research Usable Beyond Academic Circles: A Relational Model of Public Engagement. Analyses of Social Issues and Public Policy, 20(1), 336–356. https://doi.org/10.1111/asap.12204
Wilkinson, M. D., Dumontier, M., Aalbersberg, Ij. J., Appleton, G., Axton, M., Baak, A., … Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3(1), 160018. https://doi.org/10.1038/sdata.2016.18