281 to 290 of 413 Results
Jan 29, 2026 - David Mayhew
Mayhew, David, 2026, "America’s Congress: Actions in the Public Sphere, James Madison Through Newt Gingrich", https://doi.org/10.60600/YU/AXN2RP, Yale Dataverse, V1
Dataset and materials for David R. Mayhew, America’s Congress: Actions in the Public Sphere, James Madison Through Newt Gingrich (New Haven, CT: Yale University Press, 2000) |
Jan 29, 2026
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Jan 22, 2026
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Jan 13, 2026
This dataverse contains associated data with published projects. |
Jan 9, 2026Environmental Performance Index Dataverse
This collection lists Environmental Performance Index reports and supporting datasets. |
Jan 9, 2026
This dataverse contains an archive of Environmental Performance Index (EPI) reports and data sets. The Environmental Performance Index provides a data-driven summary of the state of sustainability around the world. The EPI utilizes a proximity-to-target methodology focused on a core set of environmental outcomes linked to policy goals. |
Jan 9, 2026 - Houston Claure Dataverse
Claure, Houston; Narcomey, Austin, 2026, "HRI_2026_Dynamic_Fairness_Dataset", https://doi.org/10.60600/YU/O5J291, Yale Dataverse, V1, UNF:6:Y7ymEdfMjR+RzZ1xxmLrKg== [fileUNF]
Dataset for the paper, "The Dynamics of Human Fairness Judgments Towards a Robot", published in The ACM/IEEE International Conference on Human-Robot Interaction (HRI). |
Jan 9, 2026
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Jan 7, 2026 - ISPS Data Archive
Paige Vaughn; Gregory Huber, 2025, "Seeing the State in Action: Public Preferences About and Judgments of Common Police–Civilian Interactions", https://doi.org/10.60600/YU/OCEBTW, Yale Dataverse, V1
New technologies allow unprecedented public visibility of routine police–civilian interactions, but we know little about how the public wants the police to behave during them. We examine public evaluations about preferred punishment and fair treatment using vignette experiments that randomize multiple features of police–civilian interactions. These... |
Jan 7, 2026 - ISPS Data Archive
Christopher Kenny; Cory McCartan; Shiro Kuriwaki; Tyler Simko; Kosuke Imai, 2024, "Evaluating Bias and Noise Induced by the U.S. Census Bureau’s Privacy Protection Methods", https://doi.org/10.60600/YU/RFDUQU, Yale Dataverse, V1
The U.S. Census Bureau faces a difficult trade-off between the accuracy of Census statistics and the protection of individual information. We conduct an independent evaluation of bias and noise induced by the Bureau’s two main disclosure avoidance systems: the TopDown algorithm used for the 2020 Census and the swapping algorithm implemented for the... |
