Survey respondents were cancer-affected patients seen at an academic medical center, and self-reported experiences with genetic testing and counseling. This is raw dataset is saved in comma separated value (.csv) format.
Genome-wide predictions of all transcription factor binding sites on the D. melanogaster genome were developed for use in predicting the locations of Polycomb response elements, as described in https://doi.org/10.1101/516500
Khabiri, M., & Freddolino, P. L. (2019). Genome-wide Prediction of Potential Polycomb Response Elements and their Functions. Preprint. BioRxiv, 516500. https://doi.org/10.1101/516500
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.
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.
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.
