SWMF is used to study the segmentation of SED plume into polar cap patches during the geomagnetic storm on Sep 7, 2017. The database includes the 3D output in the upper atmosphere from GITM, the 2D output from Ionospheric Electrodynamics (IE) and 3D output from BATSRUS. The output from GITM can be read with thermo_batch_new.pro. The output from IE can be opened with Spacepy at https://pythonhosted.org/SpacePy/. The output from BATSRUS can be opened with tecplot.
More details can be found in Readme.txt.
Wang, Z., Zou, S., Coppeans, T., Ren, J., Ridley, A., & Gombosi, T. (2019). Segmentation of SED by Boundary Flows Associated With Westward Drifting Partial Ring current. Geophysical Research Letters, 46(14), 7920–7928. https://doi.org/10.1029/2019GL084041
This is data is a large assortment of over 50 1,4-polybutadiene star-linear blends that can be used for assessing and developing predictive models. The data are presented in CSV files.
Hall, R., Desai, P. S., Kang, B.-G., Huang, Q., Lee, S., Chang, T., Venerus, D. C., Mays, J., Ntetsikas, K., Polymeropoulos, G., Hadjichristidis, N., & Larson, R. G. (2019). Assessing the Range of Validity of Current Tube Models through Analysis of a Comprehensive Set of Star–Linear 1,4-Polybutadiene Polymer Blends. Macromolecules, 52(20), 7831–7846. https://doi.org/10.1021/acs.macromol.9b00642
The outer epithelial layer of zebrafish retinae contains a crystalline array of cone photoreceptors, called the cone mosaic. As this mosaic grows by mitotic addition of new photoreceptors at the rim of the hemispheric retina, topological defects, called “Y-Junctions”, form to maintain approximately constant cell spacing. The generation of topological defects due to growth on a curved surface is a distinct feature of the cone mosaic not seen in other well-studied biological patterns like the R8 photoreceptor array in the _ Drosophila compound eye. Since defects can provide insight into cell-cell interactions responsible for pattern formation, here we characterize the arrangement of cones in individual Y-Junction cores (see Set of images for Figures 1 and 2 and 6 and Supplementary Figure 7) as well as the spatial distribution of Y-junctions across entire retinae (see Dataset for analyzing spatial distribution of Y-junctions in flat-mounted retinae). We find that for individual Y-junctions, the distribution of cones near the core corresponds closely to structures observed in physical crystals (see Set of images for Figures 1 and 2 and 6 and Supplementary Figure 7). In addition, Y-Junctions are organized into lines, called grain boundaries, from the retinal center to the periphery (see Dataset for analyzing spatial distribution of Y-junctions in flat-mounted retinae and Dataset for measuring tendency of Y-junctions to line up into grain boundaries during incorporation into retinae). In physical crystals, regardless of the initial distribution of defects, defects can coalesce into grain boundaries via the mobility of individual particles. By imaging in live fish, we demonstrate that grain boundaries in the cone mosaic instead appear during initial mosaic formation, without requiring defect motion (see Dataset for measuring tendency of Y-junctions to line up into grain boundaries during incorporation into retinae and Dataset for analyzing Y-junction motion in live fish retinae). Motivated by this observation, we show that a computational model of repulsive cell-cell interactions generates a mosaic with grain boundaries (see Code and example simulations of phase-field crystal model (for cone mosaic formation)). In contrast to paradigmatic models of fate specification in mostly motionless cell packings (see Code and accompanying input data for simulating lateral inhibition on motionless cell packing), this finding emphasizes the role of cell motion, guided by cell-cell interactions during differentiation, in forming biological crystals. Such a route to the formation of regular patterns may be especially valuable in situations, like growth on a curved surface, where the resulting long-ranged, elastic, effective interactions between defects can help to group them into grain boundaries.
The dataset contains bulk sedimentary d15N, TOC, and TN data measured every 2 mm on the core SPR0901-03KC. Flood and turbidite layers are shaded with blue and orange in the files. and This work is supported by NSF OCE-1304327.
Wang, Y. , Hendy, I. L. and Thunell, R. (2019), Local and remote forcing of denitrification in the Northeast Pacific for the last 2000 years. Paleoceanography and Paleoclimatology. Volume 34, issue 8, pages 1517-1533. https://doi.org/10.1029/2019PA003577
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 and Penner, J. E.: Indirect effects of secondary organic aerosol on cirrus clouds, (2019), Journal of Geophysical Research: Atmospheres, https://doi.org/10.1029/2019JD032233
Numerous small and moderate injection-induced earthquakes have been recorded in North America, Europe and Asia. Here we present a detailed analysis about microearthquakes in an in-situ injection-induced earthquake experiment, which provides an unprecedented opportunity to investigate the mechanisms of induced earthquakes. Our analysis illuminates meter-scale earthquake sources distributed in a network of preexisting rock fractures. The majority of induced earthquakes in our analysis happened when injection pressure reached a peak, indicating a direct response of rock fractures to fluid pressure perturbation. But the relatively low ratio of stress drop to crustal strength reveals that a very small fraction of the crustal shear strength is released by earthquakes, supporting the previous notion that fluid injection induces large aseismic deformation during the experiment. and Citation for dataset: Huang, Y., De Barros, L. (2019). Seismograms of earthquake pairs in the injection experiment [Data set]. University of Michigan - Deep Blue.
Huang, Y., De Barros, L., Cappa, F. (2019). Illuminating the Rupturing of Microseismic Sources in an Injection‐Induced Earthquake Experiment. Geophysical Research Letters, 46(16), 9563-9572. https://doi.org/10.1029/2019GL083856
Files contain the atmospheric CO2 mole fraction responses to land flux type (HRcasa, HRcorpse, HRmimics) and land flux region (latband variable). Land flux regions are categorized as: Northern Hemisphere high latitudes (NHL; 61 to 90°N), midlatitudes (NML; 24 to 60°N), tropics (NT; 1 to 23°N), Southern Hemisphere tropics (ST; 0 to 23°S), and extratropics (SE; 24 to 90°S). See the README file for how these land flux region definitions relate to the file's latband variable. and To cite dataset: Basile, S., Lin, X., Keppel-Aleks, G. (2019). Simulated CO2 dataset using the atmospheric transport model GEOSChem v12.0.0: Response to regional land carbon fluxes [Data set]. University of Michigan - Deep Blue. https://doi.org/10.7302/xjzc-xy05
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. and Penner, J. E.: Global modelling of secondary organic aerosol (SOA) with organic nucleation, (2019), Journal of Geophysical Research: Atmospheres, 124, 8260– 8286, https://doi.org/10.1029/2019JD030414