River, M. (2019). Data from: Suspended sediment mineralogy in stormflow of the Southern Piedmont. Duke Digital Repository. https://doi.org/10.7924/r4jq0xh5k
Altan, I., & Charbonneau, P. (2019). Data and scripts from: Obtaining soft matter models of proteins and their phase behavior. Duke Digital Repository. https://doi.org/10.7924/r4ww7bs1p
Birolo, G., Charbonneau, P., & Hu, Y. (2019). Data and scripts from: Dynamics around the Site Percolation Threshold on High-Dimensional Hypercubic Lattices. Duke Digital Repository. https://doi.org/10.7924/r4571cf37
Charbonneau, P., Corwin, E. I., Fu, L., Tsekenis, G., & van der Naald, M. (2019). Data and scripts from: Glassy, Gardner-like phenomenology in minimally polydisperse crystalline systems. Duke Digital Repository. https://doi.org/10.7924/r4k93500n
Charbonneau, P., Hu, Y., Raju, A., Sethna, J., & Yaida, S. (2019). Data and scripts from: Morphology of renormalization-group flow for the de Almeida–Thouless–Gardner universality class. Duke Digital Repository. https://doi.org/10.7924/r4zc7wm7d
Berthier, L., Charbonneau, P., Kundu, J. (2019). Data and scripts from: Bypassing sluggishness: SWAP algorithm and glassiness in high dimensions. Duke Digital Repository. https://doi.org/10.7924/r49w0dr6j
Berthier, L., Charbonneau, P., Ninarello, A., Ozawa, M., & Yaida, S. (2019). Data and scripts from: Zero-temperature glass transition in two dimensions. Duke Digital Repository. https://doi.org/10.7924/r46w9b248
Duke University Libraries' Multispectral Imaging Team. (2019). [Acts of the Apostles]. (multispectral file stack). Duke Digital Repository. https://doi.org/10.7924/r4c53jx21
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
Khan, A., James, S., Quinn, M., Altan, I., Charbonneau, P., & McManus, J. (2019). Data and scripts from: temperature-dependent interactions explain normal and inverted solubility in a γD-crystallin mutant. Duke Digital Repository. https://doi.org/10.7924/r49w0dx6s
Keating, S., Rountree, W., Grebe, E., Pappas, A. L., Stone, M., Hampton, D.,...Busch, M. P. (2019). Data from: Development of an international external quality assurance program for HIV-1 incidence using the Limiting Antigen Avidity assay. Duke Digital Repository. https://doi.org/10.7924/r4ff3r13q
Op 't Eynde, J., Yu, A., Eckersley, C., & Bass, C. (2019). Data from: Primary blast wave protection in combat helmet design: a historical comparison between present data and World War I. Duke Digital Repository. https://doi.org/10.7924/r4r49m981
Eghdami, M., & Barros, A. (2019). Namelists and scripts from: Vertical Dependence of Horizontal Scaling Behavior of Orographic Wind and Moisture Fields in Atmospheric Models. Duke Digital Repository. https://doi.org/10.7924/r4154jq8h
Huang, O., Long, W., Bottenus, N., Trahey, G., Farsiu, S. Palmeri, M. L. (2019). UltraDuke Version 1. Duke Digital Repository. https://doi.org/10.7924/r49z94h1s
Altan, I., James, S., Khan, A., Quinn, M., Charbonneau, P., & McManus, J. (2019). Data and scripts from: Using Schematic Models to Understand the Microscopic Basis for Inverted Solubility in gammaD-crystallin. Duke Digital Repository. https://doi.org/10.7924/r4fq9v942
Mremi, A., Broadwater, G., Jackson, K. Amsi, P., Mbulwa, C., Hyslop, T., ... Hall, A. (2019). Data from: Breast cancer in Tanzanian, black American, and white American women: An assessment of prognostic and predictive features, including tumor infiltrating lymphocytes. Duke Digital Repository. https://doi.org/10.7924/r4bv7g09k
Liao, M., & Barros, A. P. (2019). The Integrated Precipitation and Hydrology Experiment - Hydrologic Applications for the Southeast US (IPHEX-H4SE) Part IV: High-Resolution Enhanced Stage IV-Raingauge Combined Precipitation Product. Duke Digital Repository. https://idn.duke.edu/ark:/87924/r4pc2zd75
Flenner, E., Berthier, L., Charbonneau, P., & Fullerton, C. (2019). Data from: Front-mediated melting of isotropic ultrastable glasses. Duke Digital Repository. https://doi.org/10.7924/r4542pd2c
Ridge, J. T., Gray, P. C., Windle, A. E., & Johnston, D. W. (2020), Deep learning for coastal resource conservation: automating detection of shellfish reefs. Remote Sens Ecol Conserv. doi:10.1002/rse2.134
Ikegami, K., de March, C. A., Nagai, M. H., Ghosh, S., Do, M., Sharma, R., ... Matsunami, H. (2019). Data from: Structural instability and divergence from conserved residues underlie intracellular retention of mammalian odorant receptors. Duke Digital Repository. https://doi.org/10.7924/r40867k2k
Cummer, S. A. (2020). Data from: Indirectly measured ambient electric fields for lightning initiation in fast breakdown regions. Duke Digital Repository. https://doi.org/10.7924/r4g44p43t
Mitra, S., Zhong, J., MacAlpine, D., Hartemink, A., MacAlpine, H. (2020). Data from: RoboCOP: Multivariate state space model integrating epigenomic accessibility data to elucidate genome-wide chromatin occupancy. Duke Research Data Repository. https://doi.org/10.7924/r4hx1b43s
How can an amorphous material be rigid? Glass – the prototypical and ubiquitous amorphous solid – inhabits an incredibly ramified and complex energy landscape, which presumably underlies its rigidity. But how? Dealing with so many relevant energy minima and the ensuing far-from-equilibrium dynamics has emerged as one of the central problems in statistical physics. Tackling it requires new tools and concepts. The Simons Collaboration on Cracking the Glass Problem, addressing such fundamental issues as disorder, nonlinear response and far-from-equilibrium dynamics, builds upon three powerful approaches: the study of marginal stability at jamming, the mean-field theory of glasses in infinite dimension, and the dynamics of systems in complex landscapes. The convergence of recent breakthroughs in these areas generates a unique opportunity to come to grips with these three outstanding and intimately related challenges. This collection of datasets is associated with publications from the Charbonneau group and their collaborators as part of the Simons collaboration.
The Integrated Precipitation and Hydrology EXperiment (IPHEX) seeks to characterize warm season orographic precipitation regimes, and the relationship between precipitation regimes and hydrologic processes in regions of complex terrain. IPHEX includes two major activities:
1. The development, evaluation and improvement of remote-sensing precipitation algorithms in support of the Global Precipitation Measurement Mission (GPM) through a NASA GPM ground validation field campaign: IPHEX-GVFC (https://iphex.pratt.duke.edu/node/64)
2. The evaluation of Quantitative Precipitation Estimation (QPE) products for hydrologic forecasting and water resource applications in the Upper Tennessee, Catawba-Santee, Yadkin-Pee Dee and Savannah river basins: IPHEX- HAP (H4SE) (https://iphex.pratt.duke.edu/node/65). NOAA HMT has synergy with this project.
OSNAP is an international program designed to provide a continuous record of the full-water column, trans-basin fluxes of heat, mass and freshwater in the subpolar North Atlantic. The OSNAP observing system consists of two legs: one extending from southern Labrador to the southwestern tip of Greenland across the mouth of the Labrador Sea (OSNAP West), and the second from the southeastern tip of Greenland to Scotland (OSNAP East).
The observing system also includes subsurface floats (OSNAP Floats) in order to trace the pathways of overflow waters in the basin and to assess the connectivity of currents crossing the OSNAP line.
OSNAP is a partner in the North Atlantic Virtual Institute (NAVIS), which connects science teams around the world studying climate variability and change in the North Atlantic. http://navinstitute.org/... [Read More]
Kundu, J., & Charbonneau, P. (2020). Data From: Postponing the dynamical transition density using competing interactions. Duke Research Data Repository. https://doi.org/10.7924/r4xd0wb95
Warnell, K., & Olander, L. (2020). Data from: Opportunity assessment for carbon and resilience benefits on natural and working lands in North. Carolina. Duke Research Data Repository. https://doi.org/10.7924/r4ww7cd91
Gunsch, C. (2020). Data from: Evaluation of the mycobiome of ballast water and implications for fungal pathogen distribution. Duke Research Data Repository. https://doi.org/10.7924/r4t72cv5v
Flanagan, N., Wang, H., Winton, S., Richardson, C. (2020). Data from: Low-severity fire as a mechanism of organic matter protection in global peatlands: thermal alteration slows decomposition. Duke Research Data Repository. https://doi.org/10.7924/r4s46nm6p
Yi, G. & Grill, W. M. (2020). Data and code from: Waveforms optimized to produce closed-state Na+ inactivation eliminate onset response in nerve conduction block. Duke Research Data Repository. https://doi.org/10.7924/r4z31t79k
Kaleem, S. & Swisher, C. B. (2020). Data from: Electrographic Seizure Detection by Neuro ICU Nurses via Bedside Real-Time Quantitative EEG. Duke Research Data Repository. https://doi.org/10.7924/r4mp51700
Jin, Y., Ru, X., Su, N., Beratan, D., Zhang, P., & Yang, W. (2020). Data from: Revisiting the Hole Size in Double Helical DNA with Localized Orbital Scaling Corrections. Duke Research Data Repository. https://doi.org/10.7924/r4k072k9s
Hall, III, R. P., Bhatia, S. M., Streilein, R. D. (2020). Data from: Correlation of IgG autoantibodies against acetylcholine receptors and desmogleins in patients with pemphigus treated with steroid sparing agents or rituximab. Duke Research Data Repository. https://doi.org/10.7924/r4rf5r157
DiGiacomo, A. E., Bird, C. N., Pan, V. G., Dobroski, K., Atkins-Davis, C., Johnston, D. W., Ridge, J. T. (2020). Data from: Modeling salt marsh vegetation height using Unoccupied Aircraft Systems and Structure from Motion. Duke Research Data Repository. https://doi.org/10.7924/r4w956k1q
Caves, E., Schweikert, L. E., Green, P. A., Zipple, M. N., Taboada, C., Peters, S., Nowicki, S., & Johnsen, S. (2020). Data and scripts from: Variation in carotenoid-containing retinal oil droplets correlates with variation in perception of carotenoid coloration. Duke Research Data Repository. https://doi.org/10.7924/r4jw8dj9h
Cardones, A. R., Hall, III, R. P., Sullivan, K., Hooten, J., Lee, S. Y., Liu, B. L., Green, C., Chao, N., Rowe Nichols, K., Bañez, L., Shah, A., Leung, N., & Palmeri, M. L. (2020). Data from: Quantifying skin stiffness in graft-versus-host disease, morphea and systemic sclerosis using acoustic radiation force impulse imaging and shear wave elastography. Duke Research Data Repository. https://doi.org/10.7924/r4h995b4q
Jeuland, M., Ohlendorf, N., Saparapa, R., & Steckel, J. (2020). Data from: Climate implications of electrification projects in the developing world: a systematic review. Duke Research Data Repository. https://doi.org/10.7924/r42n55g1z
Chavez, S. P., Silva, Y., & Barros, A. P. (2020). Data from: High-elevation monsoon precipitation processes in the Central Andes of Peru. Duke Research Data Repository. V2 https://doi.org/10.7924/r41n84j94
Lin, Y., Kouznetsova, T., Chang, C., & Craig, S. (2020). Data from: Enhanced polymer mechanical degradation through mechanochemically unveiled lactonization. Duke Research Data Repository. V2 https://doi.org/10.7924/r4fq9x365
Fischer, E., Fischer, M., Grass, D., Henrion, I., Warren, W., Westman, E. (2020). Video data files from: Low-cost measurement of facemask efficacy for filtering expelled droplets during speech. Duke Research Data Repository. V2 https://doi.org/10.7924/r4ww7dx6q
Berthier, L., Charbonneau, P., & Kundu, J. (2020). Data from: Finite-dimensional vestige of spinodal criticality above the dynamical glass transition. Duke Research Data Repository. https://doi.org/10.7924/r4jh3m094
Campbell, L. M., Gray, N., & Gruby, R. (2020). Data from: Q-Sort Concourse and Data for the Human Dimensions of Large MPAs project. Duke Research Data Repository. https://doi.org/10.7924/r4j38sg3b
Zhang, Y. (2020). Data from: Contributions of World Regions to the Global Tropospheric Ozone Burden Change from 1980 to 2010. Duke Research Data Repository. https://doi.org/10.7924/r40p13p11
Kotarba, M., Burgin, A., & Cottrell, K. (2020). Blue Devil Breakdown: enrollment data from 1970 to 2020. Duke Research Data Repository. https://doi.org/10.7924/r4db82p1j
Peña, E., Pelot, N. A., & Grill, W. M. (2020). Data from: Quantitative comparisons of block thresholds and onset responses for charge-balanced kilohertz frequency waveforms. Duke Research Data Repository. https://doi.org/10.7924/r4b27wq4m
Murphy, S. K., Itchon-Ramos, N., Visco, Z., Huang, Z., Grenier, C., Schrott, R., Acharya, K., Boudreau, M.-H., Price, T. M., Raburn, D. J., Corcoran, D. L., Lucas, J. E., Mitchell, J. T., McClernon, J., Cauley, M., Hall, B. J., Levin, E. D., & Kollins, S. H. (2020). Data from: Cannabinoid exposure and altered DNA methylation in rat and human sperm. Duke Research Data Repository. https://doi.org/10.7924/r4v122j79
Bird, C. N., Duprey, A. H., Dale, J., & Johnston, D. W. (2020). Data and scripts from: A semi-automated method for estimating Adelie penguin colony abundance from a fusion of multispectral and thermal imagery collected with Unoccupied Aircraft Systems. Duke Research Data Repository. https://doi.org/10.7924/r4cv4jq6j
Li, S. & Katul, G. (2020). Data from: Contaminant removal efficiency of floating treatment wetlands. Duke Research Data Repository. https://doi.org/10.7924/r48k7bv5t
Shindell, D., Zhang, Y., Seltzer, K., Faluvegi, G., Naik, V., Horowitz, L., He, J., Lamarque, J.-F., Sudo, K., & Collins, B. (2020). Data from modeling in support of the Global Methane Assessment, UN Environment, 2020. Duke Research Data Repository. https://doi.org/10.7924/r4qn65b0z