Kurt, E., Devlin, G., Asokan, A., & Segura, T. (2024). Data from: Gene delivery from granular scaffolds for tunable biologics manufacturing. Duke Research Data Repository. https://doi.org/10.7924/r4280h775
Kastenholz, H. V., Topper, M. I. , Warren, W. S. , Fischer, M. C., & Grass, D. (2024). Data and scripts from: Noninvasive identification of carbon-based black pigments with pump-probe microscopy. Duke Research Data Repository. V2 https://doi.org/10.7924/r48059m9k
Martin, A., Liu, G., Joo, B., Wu, R., Kabir, M. S., Draeger, E., Randles, A. (2024). Data and Scripts from: Designing a GPU-accelerated communication layer for efficient fluid-structure interaction computations on heterogenous systems. Duke Research Data Repository. https://doi.org/10.7924/r43n2cd83
D’Agostino, V. W., Deutsch, R. J., Kwan, M., Sunassee, E. D., Madonna, M. C., Palmer, G. M., Crouch, B. T., & Ramanujam, N. (2024). Data from: In vivo spectroscopy to concurrently characterize five metabolic and vascular endpoints relevant to aggressive breast cancer. Duke Research Data Repository. https://doi.org/10.7924/r4vx0k33z
Hussain, M., Grill, W. M., & Pelot, N. A. (2024). Data and scripts from: Highly efficient modeling and optimization of neural fiber responses to electrical stimulation. Duke Research Data Repository. https://doi.org/10.7924/r48g8tf24
Kastenholz, H. V., Topper, M. I. , Warren, W. S. , Fischer, M. C., & Grass, D. (2024). Data and scripts from: Noninvasive identification of carbon-based black pigments with pump-probe microscopy. Duke Research Data Repository. https://doi.org/10.7924/r43j3nx2k
Wang, S., Hu, Y., Kouznetsova, T. B., Sapir, L., Chen, D., Herzog-Arbeitman, A., Johnson, J. A., Rubinstein, M., & Craig, S. L. (2023). Data from: Facile Mechanochemical Cycloreversion of Polymer Cross-linkers Enhances Tear Resistance. Duke Research Data Repository. https://doi.org/10.7924/r43r1215n
Martin, A., Liu, G., Ladd, W., Lee, S., Gounley, J., Vetter, J., Patel, S., Rizzi, S., Mateevitsi, V., Insley, J., Randles, A. (2023). Data and scripts from: Performance evaluation of heterogenous GPU programming frameworks for hemodynamic simulations. Duke Research Data Repository. https://doi.org/10.7924/r45t3t64k
Tanade, C., Rakestraw, E., Ladd, W., Draeger, E., Randles, A. (2023). Data from: Cloud computing to enable wearable-driven longitudinal hemodynamic maps. Duke Research Data Repository. https://doi.org/10.7924/r4f76jd8n
Wang, B., Zhang, J., Li, Z., Grill, W. M., Peterchev, A. V., & Goetz, S. M. (2022). Data from: Optimized Monophasic Pulses with Equivalent Electric Field for Rapid-Rate Transcranial Magnetic Stimulation. Duke Research Data Repository. https://doi.org/10.7924/r4wd41f7d