Have a look around our new website for the discovery and sharing of research data and let us know what you think. See How to Submit for instructions on how to publish your research data and code.
This dataset is created for the paper titled 'Co-benefits of Transport Demand Reductions from Compact Urban Development in Chinese Cities' and published on Nature Sustainability. We construct 6 scenarios of compact urban development, alternative energy vehicle deployment, and power decarbonization to explore the co-benefits of transport demand reductions via compact urban development for carbon emissions, energy use, air quality, and human health in China in 2050. This dataset provides the following gridded information for the scenarios: (1) monthly mean surface PM2.5 concentrations from the WRF-Chem model; (2) annual PM2.5-related premature deaths calculated by the GEMM model; (3) 2015 population in China; (4) mask for provinces in China; (5) longitude and latitude of each grid center.
Khanna, Jaya; Medvigy, David; Fueglistaler, Stephan; Walko, Robert
Abstract:
More than 20% Amazon rainforest has been cleared in the past three decades triggering important hydroclimatic changes. Small-scale (~few kilometers) deforestation in the 1980s has caused thermally-triggered atmospheric circulations that increase regional cloudiness and precipitation frequency. However, these circulations are predicted to diminish as deforestation increases. Here we use multi-decadal satellite records and numerical model simulations to show a regime shift in the regional hydroclimate accompanying increasing deforestation in Rondônia, Brazil. Compared to the 1980s, present-day deforested areas in downwind western Rondônia are found to be wetter than upwind eastern deforested areas during the local dry season. The resultant precipitation change in the two regions is approximately ±25% of the deforested area mean. Meso-resolution simulations robustly reproduce this transition when forced with increasing deforestation alone, showing a negligible role of large-scale climate variability. Furthermore, deforestation-induced surface roughness reduction is found to play an essential role in the present-day dry season hydroclimate. Our study illustrates the strong scale-sensitivity of the climatic response to Amazonian deforestation and suggests that deforestation is sufficiently advanced to have caused a shift from a thermally- to a dynamically-driven hydroclimatic regime.
Kim, Donghoon; Duffy, Thomas S.; Smith, Raymond F.; Ocampo, Ian K.; Coppari, Federica; Marshall, Michelle C.; Ginnane, Mary Kate; Wicks, June; Tracy, Sally J.; Millot, Marius; Lazicki, Amy; Rygg, Jame R.; Eggert, Jon H.
This dataset contains all data relevant to a forthcoming publication in which we used molecular simulation methods to study the phase behavior of supercooled water. The dataset contains simulation input and output files, processed data files, and image files used to create all plots in the manuscript. Python analysis scripts are also included, including instructions for how to re-generate all plots in the manuscript.
Gartner, Thomas III; Zhang, Linfeng; Piaggi, Pablo; Car, Roberto; Panagiotopoulos, Athanassios; Debenedetti, Pablo
Abstract:
This dataset contains all data related to the publication "Signatures of a liquid-liquid transition in an ab initio deep neural network model for water", by Gartner et al., 2020. In this work, we used neural networks to generate a computational model for water using high-accuracy quantum chemistry calculations. Then, we used advanced molecular simulations to demonstrate evidence that suggests this model exhibits a liquid-liquid transition, a phenomenon that can explain many of water's anomalous properties. This dataset contains links to all software used, all data generated as part of this work, as well as scripts to generate and analyze all data and generate the plots reported in the publication.
This dataset is a sequence of laser-induced fluorescence images of a dye injected in a channel flow with canopy-like stainless steel rods simulating a vegetation canopy stand. The data is acquired close to the channel bottom at z/h=0.2, where z is the height referenced to the channel bed and h is the canopy height. The dataset provides spatial distribution of scalar concentration in a plane parallel to the channel bed. The data has been used (but the data itself has not been published or available to the public) in previous work. The references are: Ghannam, K., Poggi, D., Porporato, A., & Katul, G. (2015). The spatio-temporal statistical structure and ergodic behaviour of scalar turbulence within a rod canopy. Boundary-Layer Meteorology,157(3), 447–460. Ghannam, K, Poggi, D., Bou-Zeid, E., Katul, G. (2020). Inverse cascade evidenced by information entropy of passive scalars in submerged canopy flows. Geophysical Research Letters (accepted).
Data set corresponding to "NAPS: Integrating pose estimation and tag-based tracking." This dataset contains the corresponding videos, tracking scripts, and SLEAP models along with SLEAP, NAPS, and ArUco tracking results.
Physical and biogeochemical variables from the NOAA-GFDL Earth System Model 2M experiments, and previously published observation-based datasets, used for the study 'Hydrological cycle amplification reshapes warming-driven oxygen loss in Atlantic Ocean'.
Does the default mode network (DMN) reconfigure to encode information about the changing environment? This question has proven difficult, because patterns of functional connectivity reflect a mixture of stimulus-induced neural processes, intrinsic neural processes and non-neuronal noise. Here we introduce inter-subject functional correlation (ISFC), which isolates stimulus-dependent inter-regional correlations between brains exposed to the same stimulus. During fMRI, we had subjects listen to a real-life auditory narrative and to temporally scrambled versions of the narrative. We used ISFC to isolate correlation patterns within the DMN that were locked to the processing of each narrative segment and specific to its meaning within the narrative context. The momentary configurations of DMN ISFC were highly replicable across groups. Moreover, DMN coupling strength predicted memory of narrative segments. Thus, ISFC opens new avenues for linking brain network dynamics to stimulus features and behaviour.
Maingi, R.; Hu, J. S.; Sun, Z.; Tritz, K.; Zuo, G. Z.; Xu, W.; Huang, M.; Meng, X. C.; Canik, J. M.; Diallo, A.; Lunsford, R.; Mansfield, D. K.; Osborne, T. H.; Gong, X. Z.; Wang, Y. F.; Li, Y. Y.
Gan, K.; Ahn, J. -W.; Gray, T. K.; Zweben, S. J.; Fredrickson, E. D.; Scotti, F.; Maingi, R.; Park, J. -K.; Canal, G. P.; Soukhanovskii, V. A.; McLean, A. G.; Wirth, B. D.
Maingi, R.; Canik, J. M.; Bell, R. E.; Boyle, D. P.; Diallo, A.; Kaita, R.; Kaye, S. M.; LeBlanc, B. P.; Sabbagh, S. A.; Scotti, F.; Soukhanovskii, V. A.