Datasets for article in CARBON: Spatial dependence of the growth of polycyclic aromatic compounds in an ethylene counterflow flame.
The experiment VUV-AMS measurements ("VUV_AMS_C2H4_Counterflow.txt") consists aerosol mass spectra data from an atmospheric-pressure ethylene/oxygen/argon counterflow diffusion flame described in Johansson et al., Proc. Combust. Inst. 36, 799-806 (2017) doi:10.1016/j.proci.2016.07.130., The experiment VUV-MBMS measurements ("VUV_MBMS_C2H4_Counterflow.txt") consists gas-phase data from an atmospheric-pressure ethylene/oxygen/argon counterflow diffusion flame described in Johansson et al., Proc. Combust. Inst. 36, 799-806 (2017) doi:10.1016/j.proci.2016.07.130., 2D CFD simulation results by KAUST mechanism II ("CFD_KM2_results.xlsx") consists stabilized CFD gas-phase species profiles along different x,y,z coordinates. Species are given by mole fractions., The SNapS2 simulation results ("SNapS2_results.zip") consist streamline I (from fuel side), i (from oxidizer side), and middle (DFFO = 5.0mm) for producing results in Fig. 5, Fig. 6, and Table 1. Three folders under each streamline ("C5H6", "C6H5CH3", and "C6H6") represent simulations by using different seeds (cyclopentadiene, toluene, and benzene respectively). The text files inside each folder are a single trace (time-history) for one SNapS2 simulation. Text file name consists "starting time"+"."+"simulation number"+".txt". For example 0.041.25.txt meaning the 25th simulation starting at 0.041s. Four columns inside the text files represent time, molecular mass, reaction index, and SMILES (Simplified molecular-input line-entry system) of the molecule., and Data citation: Wang, Q., Elvati, P., Kim, D., Johansson, K.O., Schrader, P.E., Michelsen, H.A., Violi, A. (2019). Spatial dependence of the growth of polycyclic aromatic compounds in an ethylene counterflow flame: experimental measurements and simulation results [Data set]. University of Michigan Deep Blue Data Repository. https://doi.org/10.7302/69e6-cd20
Citation to related publication:
Wang, Q., Elvati, P., Kim, D., Johansson, K.O., Schrader, P.E., Michelsen, H.A., Violi, A., 2019. Spatial dependence of the growth of polycyclic aromatic compounds in an ethylene counterflow flame. Carbon 149, 328–335. https://doi.org/10.1016/j.carbon.2019.03.017
Johnson, J. E., & Molnar, P. H. ( 2019). Widespread and persistent deposition of iron formations for two billion years. Geophysical Research Letters, 46, 3327– 3339. https://doi.org/10.1029/2019GL081970
WRF-Chem simulation with 1.33 km resolution using the MYJ PBL scheme over the Baltimore-Washington region and WRF-Chem simulation with 1.33 km resolution using the YSU PBL scheme over the Baltimore-Washington region
Li, Y., Barth, M. C., and Steiner, A. L.: Comparing turbulent mixing of atmospheric oxidants across model scales, Atmospheric Environment, 199, 88-101, https://doi.org/10.1016/j.atmosenv.2018.11.004, 2018.
The research that produced this data involves exploring the sensitivity of orographic precipitation to changes in microphysical parameters found in the Morrison microphysics scheme within CM1 model. These microphysical sensitivities are also tested within different environments. The tests can be described as "one-at-a-time" experiments, i.e., an individual parameter is perturbed while keeping the rest constant. Annareli Morales conducted this research for her PhD research while working at the Mesoscale and Microscale Meteorology lab at NCAR in Boulder, CO.
Morales, A., H. Morrison, and D. Posselt, 2018: Orographic precipitation response to microphysical parameter perturbations for idealized moist nearly neutral flow. Journal of Atmospheric Science, 75, 1933-1953, https://doi.org/10.1175/JAS-D-17-0389.1
Brightness from an all-sky imager has been used as a spatiotemporal constraint for auroral inputs selected from in situ rocket measurements which are used to drive the ionospheric model. This method allows for realistic ionospheric forcing that is not captured in traditional "on-off" methods of describing PMAFs. Transient forcing (simulated PMAFs) and steady forcing ("on-off") simulations have been generated for comparison.
Burleigh, M., Zettergren, M., Lynch, K., Lessard, M., Moen, J., Clausen, L., Kenward, D., Hysell, D., and Liemohn, M. (2019). Transient ionospheric upflow driven by poleward moving auroral forms observed during the Rocket Experiment for Neutral Upwelling 2 (RENU2) campaign. Geophysical Research Letters. (Submitted).
Genome-wide predictions of all transcription factor binding sites on the D. melanogaster genome were developed for use in predicting the locations of Polycomb response elements, as described in https://doi.org/10.1101/516500
Khabiri, M., & Freddolino, P. L. (2019). Genome-wide Prediction of Potential Polycomb Response Elements and their Functions. Preprint. BioRxiv, 516500. https://doi.org/10.1101/516500
The dataset contains the Fortran programs applied in the latest CESM/IMPACT model as well as the data created from this model, which are used in the referenced paper.
Zhu, J. et al. Decrease in radiative forcing by organic aerosol nucleation, climate, and land use change. Nature Communications 10, 423 (24 January 2019). https://doi.org/10.1038/s41467-019-08407-7
Estimated phylogenetic relationships based on more than 18,000 loci in 93 individuals (full data) or 21 individuals (subset data) representing 19 described species and two putative undescribed species. Nine files are part of this dataset, including all input files to infer the phylogenetic reconstructions and the outputs obtained, in addition to a pruned tree used to infer the ancestral state reconstructions.
Andréa T. Thomaz, Tiago P. Carvalho, Luiz R. Malabarba, L. Lacey Knowles, Geographic distributions, phenotypes, and phylogenetic relationships of Phalloceros (Cyprinodontiformes: Poeciliidae): insights about diversification among sympatric species pools, Molecular Phylogenetics and Evolution, 2018, ISSN 1055-7903, https://doi.org/10.1016/j.ympev.2018.12.008
The modeling research conducted to produce this dataset focuses on the solar wind dynamic pressure drop events and how they affect the Earth's intrinsically coupled Magnetosphere, Ionosphere and Thermosphere systems. This study specifically focuses on the 11 June 2017 event, where the solar wind dynamic pressure dropped significantly following a period of higher pressure. We model the response to this pressure drop using University of Michigan Space Weather Modeling Framework ( http://csem.engin.umich.edu/tools/swmf/). The simulation results were created using BATS-R-US and GITM models. The observational data required for model comparisons were taken from OMNI ( https://omniweb.gsfc.nasa.gov) and CDAWeb ( https://cdaweb.gsfc.nasa.gov/sp_phys/) Databases.
Ozturk, D. S., Zou, S., Slavin, J. A., & Ridley, A. J. ( 2019). Response of the geospace system to the solar wind dynamic pressure decrease on 11 June 2017: Numerical models and observations. Journal of Geophysical Research: Space Physics, 124, 2613– 2627. https://doi.org/10.1029/2018JA026315