This project evaluated the binding of antibody fragments to membrane proteins fused to a short epitope sequence (“MPER”). This dataset includes atomic coordinates (.pdb files) for bioinformatic models of antibody fragment binding to an MPER epitope – membrane protein fusion.
These data are TLA events identified in MACCS magnetometer data throughout 2015. These events are short-timescale (< 60 s), large -amplitude (> 6 nT/s) magnetic disturbances measured at Earth's surface that are analyzed for space weather research purposes. and The events were identified in a year's worth of magnetic field data using an algorithm developed in the MATLAB platform. The algorithm dBdt_main.m can be run using the associated scripts (clean_maccs.m, simple_dbdt.m, extremes1.m, newdbdt.m) to return the events in the 2015_AllEvents.csv file. The substorm onset delays of each event are determined with the onset_delays.m script and the substorm event list 20191127-15-56-substorms.csv (both included).
Multi-satellite tracking of solar wind dynamic pressure pulse observations through the Earth's magnetosphere enables us to distinguish local changes with propagation signatures.
Vidal-Luengo, S. E., & Moldwin, M. B. (2021). Global magnetosphere response to solar wind dynamic pressure pulses during northward IMF using the heliophysics system observatory. Journal of Geophysical Research: Space Physics, 126, e2020JA028587. https://doi.org/10.1029/2020JA028587
This data set was created with the purpose to study the electron pitch angle distributions on dayside closed crustal fields at Mars and to compare with theoretical predictions made by numerical modeling. Analyzing the plasma environment of the crustal fields was another point of study to determine if whistler waves can interact with high energy superthermal electrons.
We use the term “performance summary display” (PSD) to mean a kind of visualization that relates performance levels to other types of information. In the context of healthcare organizations, PSDs are intended to be communicated to a healthcare professional, team, or organization. and Displays were identified, classified, and elements counted and coded. The performance summary display ontology provides a set of descriptions of components of displays that have been used to annotate performance feedback visualizations.
Lee, D., Panicker, V., Gross, C., Zhang, J., & Landis-Lewis, Z. (2020). What was visualized? A method for describing content of performance summary displays in feedback interventions. BMC medical research methodology, 20(1), 90. https://doi.org/10.1186/s12874-020-00951-x
Conducting quantitative metrics-based performance analysis of first-principles-based global magnetosphere models is an essential step in understanding their capabilities and limitations, and providing scope for improvements in order to enhance their space weather prediction capabilities for a range of solar conditions. In this study, a detailed comparison of the performance of three global magnetohydrodynamic (MHD) models in predicting the Earth’s magnetopause location and ionospheric cross polar cap potential (CPCP) has been presented. Using the Community Coordinated Modeling Center’s Run-on-Request system and extensive database on results from various magnetospheric scenarios simulated for a variety of solar wind conditions, the aforementioned model predictions have been compared for magnetopause standoff distance estimations obtained from six empirical models, and with cross polar cap potential estimations obtained from the Assimmilative Mapping of Ionospheric Electrodynamics (AMIE) Model and the Super Dual Auroral Radar Network (SuperDARN) observations. We have considered a range of events spanning different space weather activity to analyze the performance of these models. Using a fit performance metric analysis for each event, we have quantified the models’ reproducibility of magnetopause standoff distances and CPCP against empirically-predicted observations, and identified salient features that govern the performance characteristics of the modeled magnetospheric and ionospheric quantities.
Citation to related publication:
Mukhopadhyay, A., et al. (2020). Global Magnetohydrodynamic Simulations: Performance Quantification of Magnetopause Distances and Convection Potential Predictions. Forthcoming.
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
Please refer to the "README.txt" for more details., MATLAB R2018a (Mathworks, Natick, MA, USA) was used to process this data., and Excel (Microsoft Office) was used to store survey data on the comfort of both systems and also to provide absolute and relative intraobserver variablities for the DM device.
Comparison of anorectal function measured using wearable digital manometry and a high resolution manometry system Attari A, Chey WD, Baker JR, Ashton-Miller JA (2020) Comparison of anorectal function measured using wearable digital manometry and a high resolution manometry system. PLOS ONE 15(9): e0228761. https://doi.org/10.1371/journal.pone.0228761
Data for "Comparison of Anorectal Function Measured using Wearable Digital Manometry and a High Resolution Manometry System." article (PLOS ONE) PONE-D-20-01826R1
We collected hours of functional magnetic resonance imaging data from human subjects listening to natural stories. We developed a predictive model of the voxel-wise response and further applied it to thousands of new words to understand how the brain stores and connects different concepts. and This is a dataset for the paper:
Zhang, Y., Han, K., Worth, R., & Liu, Z. (2020). Connecting concepts in the brain by mapping cortical representations of semantic relations. Nature communications, 11(1), 1-13. https://doi.org/10.1038/s41467-020-15804-w. This project is also documented at https://osf.io/eq2ba/.
Zhang, Y., Han, K., Worth, R., & Liu, Z. (2020). Connecting concepts in the brain by mapping cortical representations of semantic relations. Nature communications, 11(1), 1-13. https://doi.org/10.1038/s41467-020-15804-w
The precipitation data itself is the output of the models/datasets that we analyze in our paper. Most of it is in .nc or .nc4 format, although we provide code to extract the data into time series .mat files.
We used MATLAB to perform our analysis.
