Teti, Z., & Blumsack, S. (2021). RTOGov: regional transmission organization member voting data. Duke Research Data Repository. https://doi.org/10.7924/r40k2dx36
Zhang, Y., Wang, Z., Kouznetsova, T., Sha, Y., Xu, E., Shannahan, L., … Craig, S. (2020). Data from: Distal conformational locks guide reaction pathways for enhanced metallocene mechanochemistry. Duke Research Data Repository. https://doi.org/10.7924/r4kw5cx8q
Lozier, M.S., F. Li, S. Bacon, F. Bahr, A.S. Bower, S.A. Cunningham, M.F. de Jong, L. de Steur, B. deYoung, J. Fischer, S.F. Gary, B.J.W. Greenan, N.P. Holliday, A. Houk, L. Houpert, M.E. Inall, W.E. Johns, C. Johnson, J. Karstensen, G. Koman, I.A. Le Bras, X. Lin, N. Mackay, M. Oltmanns, R.S. Pickart, A.L. Ramsey, D. Rayner, F. Straneo, D.J. Torres, I. Yashayaev, J. Zhao (2019). Meridional overturning circulation and the associated heat and freshwater transports observed by the OSNAP (Overturning in the Subpolar North Atlantic Program) Array from 2014 to 2016. Duke Digital Repository. https://doi.org/10.7924/r4z60gf0f
Charbonneau, P. and Yaida, S. (2016). Data and scripts from: Nontrivial critical fixed point for replica-symmetry-breaking transitions. Duke Digital Repository. https://doi.org/doi:10.7924/G86Q1V5C
Charbonneau, P. and Yaida, S. (2018). Data and scripts from: Nontrivial critical fixed point for replica-symmetry-breaking transitions. Duke Digital Repository. V2 https://doi.org/10.7924/r4cf9ns11
Wilson, J. W., Degan, S., Gainey, C. S., Mitropoulos, T., Simpson, M. J., Zhang, J. Y., & Warren, W. S. (2019). Data from: In vivo pump-probe and multiphoton fluorescence microscopy of melanoma and pigmented lesions in a mouse model. Duke Digital Repository. https://doi.org/10.7924/r4cc0zp95
Morse, P. K., Roy, S., Agoritsas, E., Stanifer, E., Corwin, E. I., & Manning, M. L. (2021). Data from: A direct link between active matter and sheared granular systems. Duke Research Data Repository. https://doi.org/10.7924/r4cv4kb23
Hayes, M. C., Gray, P. C., Harris, G., Sedgwick, W. C., Crawford, V. D., Chazal, N., Crofts, S., & Johnston, D. W. (2020). Data from: Drones and deep learning produce accurate and efficient monitoring of large-scale seabird colonies. Duke Research Data Repository. https://doi.org/10.7924/r4dn45v9g
