In the dataset, "_T" means temperature effects only, without "_T" means temperature and precipitation effects are both considered, "_co2" means CO2 effects are considered on the based of temperature and precipitation effects.
The contained data comprises what was collected during the characterization of the quad-magnetometer as described in 'Quad-Mag Board for CubeSat Applications'. There are approximately 38 hours of data that compromise a stability test, 10 hours of noise floor testing data, and 10 minutes of sensitivity testing data. Each data file has three-axis measurements from four individual magnetometers over the specified time period at a 65 Hz sampling rate.
Strabel, B. P., Regoli, L. H., Moldwin, M. B., Ojeda, L. V., Shi, Y., Thoma, J. D., Narrett, I. S., Bronner, B., and Pellioni, M.: Quad-Mag board for CubeSat applications, Geosci. Instrum. Method. Data Syst., 11, 375–388, https://doi.org/10.5194/gi-11-375-2022, 2022.
Reconstructed CT slices for tooth-apical of Physeter (University of Michigan Museum of Paleontology catalog number UMMP_R_102) as a series of TIFF images. Raw projections are not included in this dataset. The reconstructed slice data from the scan are offered here as a series of unsigned 16-bit integer TIFF images. The upper left corner of the first image (*_0000.tif) is the XYZ origin.
The data included are those that were used in the creation of a model described in the manuscript titled "Predictions of Electron Flux in the near-Earth Plasma Sheet from Solar Wind Driving" by Swiger et al., 2022,
published in the Space Weather Journal.
doi: pending, TBD and The manuscript describes the development and assessment of a model that predicts electron flux (from 83 eV to 93 keV energies) in a region of Earth's magnetosphere called the plasma sheet. The model uses inputs of solar wind parameters including, but not limited, to solar wind speed and the interplanetary magnetic field.
Swiger, B. M., Liemohn, M. W., Ganushkina, N. Y., & Dubyagin, S. V. (2022). Energetic electron flux predictions in the near-Earth plasma sheet from solar wind driving. Space Weather, 20, e2022SW003150. https://doi.org/10.1029/2022SW003150
The data were used to study the high-frequency geomagnetic disturbances within the magnetic field data. Included in this repository are the python scripts that perform an identification and classification of high-frequency signals within the magnetometer data that is downloaded from the databases listed in the Methodology section. All analysis and plots were created using subsequent Python libraries. The machine learning study implemented libraries from the sci-kit learn software. All of the specific methodology can be accessed in the readme.txt script.
These data were produced in the scope of research into understanding the application of zircon (U-Th)/He thermochronometric data derived from rocks with complex radiation damage distributions to the extraction of long-term (>1 Gyr) thermal histories of the Earth's upper crust. The samples used in this study were collected from the Front Range in Colorado, USA. The low-temperature (apatite and zircon (U-Th)/He) thermochronometric ages presented in this data set are sensitive to near-surface temperatures (~80C and 180C, respectively) and record the progressive exhumation of the rock mass from which the samples were collected towards the Earth's surface. These thermochronometric ages, and the differences between them, provide insight into the deep-time (~1000 Ma - 100 Ma) thermal history of the Colorado Front Range.
These are the data required to implement the authentic research experience (ARE) that we created and describe in our paper "The Batrachian Barf Bowl: An authentic research experience using ecological data from frog diets." We created an open-source “bowl game” competition that challenges students to identify, measure, and compare diet items across vouchered frog specimens.
Larson, J. G., Crowell, H. L., Walsh, L. L., & Davis Rabosky, A. R. (2022). The Batrachian Barf Bowl: An authentic research experience using ecological data from frog diets. Ecology and Evolution, 12, e9095. https://doi.org/10.1002/ece3.9095 and The above article is also available in Deep Blue Documents at https://hdl.handle.net/2027.42/174122
Understanding the state and composition of an exoplanetary atmosphere depends upon several parameters such as heating, cooling, mixing and reactions between constituent chemical species. Only a few types of atmospheric species can be detected remotely spectroscopically and only if their abundance is large enough to be detectable. In this initial study, we model the atmosphere of a Venus-like planet orbiting the M-type star GJ 436 to determine the global neutral temperature structure, winds, and energy balance as the radial distance of the planet from the star decreases.
C. D. Parkinson, S. W. Bougher, F. P. Mills, R. Hu, G. Gronoff, J. Li, A. Brecht, D. Adams, and Y. L. Yung. Venus as an Exoplanet: I. An Initial Exploration of the 3-D Energy Balance for a CO2 Exoplanetary Atmosphere Around an M-Dwarf Star, J. Geophysical Research, X, (2022). doi:....
Here we present an investigation of the variability of Venus' extended oxygen corona. For that, we employ a combination of a fluid model VTGCM for simulating Venus' ionosphere and thermosphere and kinetic model AMPS. We have found excellent agreement of the model results with PVO observations of the corona when the modeling is done assuming the solar maximum conditions, which corresponds to the solar conditions during the observations. We also found that the oxygen density strongly depends on the solar conditions and varies by order of magnitude over a solar cycle. That explains why the extended oxygen corona was observed only at the solar maximum. The result presented in this paper will be used in a later study of the planet's interaction with the ambient solar wind, where the corona model defines the mass loading coefficient.
The raw seismic records are downloaded from Incorporated Research Institutions for Seismology. The facilities of IRIS Data Services, and specifically the IRIS Data Management Center, were used for access to waveforms, related metadata, and/or derived products used in this study. The synthetic seismograms are generated by SPECMFEM3D_Globe software which was downloaded from the Computational Infrastructure for Geodynamics ( https://geodynamics.org/).
