To study the effect of whistler mode waves on the superthermal electron velocity space at Mars, a numerical model was built to solve the bounce-averaged quasi-linear diffusion equation on a crustal field. This dataset includes the input and output variables to this model for the simulations performed in Shane and Liemohn, 2022. The studies using this dataset were conducted by Alex Shane in the Climate and Space Sciences and Engineering Department at the University of Michigan. This research was supported by the National Aeronautics and Space Administration (NASA) Grant NNX16AQ04G to the University of Michigan and the Rackham Predoctoral Fellowship.
Shane, A. D., & Liemohn, M. W. (2022). Modeling wave-particle interactions with photoelectrons on the dayside crustal fields of Mars. Geophysical Research Letters, 49, e2021GL096941. https://doi.org/10.1029/2021GL096941
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
High-resolution, low-angle XRD analysis of oriented clay samples (.txt files) and TC/EA, Mass Spectronometric analysis of oxygen and hydrogen isotopes (.xslx files)
This data and scripts are meant to test and show that seizure onset dynamics can be modulated using anti-epileptic drugs. A zip file is included that contains all waveform data, MATLAB processing scripts, and metadata. The MATLAB scripts allow for visual review validation and objective feature analysis. The file includes various README files explaining the scripts and their relationships in greater detail.
This data set includes four zipped files each containing unprocessed cell images from a single cell line collected as raw data, the scripts used to process these images and tabular files with the processed data outputs. This data set supports the PLOS ONE publication, "Cell-morphodynamic phenotype classification with application to cancer metastasis using cell magnetorotation and machine-learning."
Mathematica Diffusion Simulation: Programmed by Coburn, Caleb. Simulation of diffusion in organic heterostructures, including least square fits and statistical goodness of fit analysis. Used to calculate fits to transient data in Fig 1, 3 and Extended Data Fig.2. Example data file included for download
Matlab Montecarlo simulation: Programmed by Coburn, Caleb. Montecarlo simulation of charge diffusion on a cubic lattice to determine lateral diffusion length as a function of barrier height, assuming thermionic emission over the barrier.
Matlab 2D Diffusion Simulation:Programmed by Coburn, Caleb. Modified from BYU Physics 430 Course Manual. Simulates diffusion around a film discontinuity, such a cut. Used to generate fits to Extended Data Fig. 1
Burlingame, Q., Coburn, C., Che, X., Panda, A., Qu, Y., & Forrest, S. R. (2018). Centimetre-scale electron diffusion in photoactive organic heterostructures. Nature, 554(7690), 77-80. https://doi.org/10.1038/nature25148
These codes were produced as part of the Army Research Office Multi-University Research Initiative ARO MURI W911NF-17-1-0306 "From Data-Driven Operator Theoretic Schemes to Prediction, Inference, and Control of Systems"
The code can be run using the runAll.sh shell script (in Linux and OS-X); code should work similarly under windows.
The Brune source model is widely used in studies of complex earthquakes with multiple episodes of high moment release (i.e., multiple subevents). In this study, we investigate how corner frequency estimates of earthquakes with multiple subevents are biased if they are based on the Brune source model. By assuming complex sources as a sum of multiple Brune sources, we analyze 1,640 source time functions (STFs) of Mw 5.5-8.0 earthquakes in the SCARDEC catalog to estimate the corner frequencies, onset times, and seismic moments of subevents. We identify more subevents for strike-slip earthquakes than dip-slip earthquakes, and the number of resolvable subevents increases with magnitude. We find that earthquake corner frequency correlates best with the corner frequency of the subevent with the highest moment release (i.e., the largest subevent). This suggests that, when the Brune model is used, the estimated corner frequency and therefore the stress drop of a complex earthquake is determined primarily by the largest subevent rather than the total rupture area. and Our results imply that the stress variation of asperities, rather than the average stress change of the whole fault, contributes to the large variance of stress drop estimates.
Citation to related publication:
Meichen Liu, Yihe Huang, Jeroen Ritsema. 2021. Characterizing Multi-Subevent Earthquakes Using the Brune Source Model [Preprint]. https://essoar.org (2021) DOI: doi.org/10.1002/essoar.10507564.1
The dataset includes 51 children (age range = 6-12 years) who listened to the first chapter of Alice’s Adventures in Wonderland during fNIRS neuroimaging. We also provide the text of the story with several word-by-word predictors motivated by research in Theory of Mind development and language. These annotated, naturalistic datasets can be used to replicate prior work and test new hypotheses about everyday social cognition and natural language comprehension in the developing brain.
We created various files, including GIS files and data files for both the UM Hydrologic Modeling Team and for our own Escherichia coli sampling project. The UM Hydrologic Team used the files we created to make their models more accurate. For example, we edited Clinton River subwatershed files to better reflect below and above-ground infrastructure, and provided them to the modeling team. For our own E. coli subproject we created time series, GIS files, and R code to better understand the influence of precipitation and streamflow on E. coli dynamics. Our time-series data is based on baseline and storm sampling we conducted in the summer of 2021. We used GIS files to explore the subwatersheds of our E. coli sampling locations. Finally, we created R code to help us visualize and analyze the data.