recordings made in Barato village. Referred to as "text 2021-02" and "text 2021-03." Text 2021-03 is transcribed and annotated at the end of the reference grammar (see link to Deep Blue Documents). Text 2021-02 covers a subset of the same content and has not been transcribed as of late 2022. See also "notes" file inside the work.
A subset of the Kelenga recordings are being transcribed and will serve as data for the Kelenga reference grammar which, when finished, will be included in the collection "Bozo languages of Mali (documents)" in Deep Blue Documents (see link).
Data format: netcdf4
, Time series duration: 2016-06-01 to 2020-10-31, Temporal resolution: Daily, and Spatial resolution: The model output was regridded to a 0.05 degree rectilinear (lat/lon) grid using the conservative remapping method ("cdo remapcon" tool).
Minallah, S. (2022). A Study on the Atmospheric, Cryospheric, and Hydrologic Processes Governing the Evolution of Regional Hydroclimates (Doctoral dissertation, University of Michigan Ann Arbor). https://dx.doi.org/10.7302/6223
Reconstructed CT slices for tooth, occlusal part of sectioned tooth 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.
University of Michigan Museum of Paleontology, CTEES. CT Data of UMMP R 102, Physeter tooth (apical part of sectioned tooth) [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/msdh-gc24
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.
