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- Creator:
- Hegedus, Alexander M
- Description:
- This is the README for the LunarSynchrotronArray package, maintained by Dr. Alex Hegedus alexhege@umich.edu This code repository corresponds to the Hegedus et al. 2020 (accepted) Radio Science paper, "Measuring the Earth's Synchrotron Emission from Radiation Belts with a Lunar Near Side Radio Array". The arxiv link for the paper is https://arxiv.org/abs/1912.04482. The DOI link is https://doi.org/10.1029/2019RS006891 , The Earth's Ionosphere is home to a large population of energetic electrons that live in the balance of many factors including input from the Solar wind, and the influence of the Earth's magnetic field. These energetic electrons emit radio waves as they traverse Earth's magnetosphere, leading to short‐lived, strong radio emissions from local regions, as well as persistent weaker emissions that act as a global signature of the population breakdown of all the energetic electrons. Characterizing this weaker emission (Synchrotron Emission) would lead to a greater understanding of the energetic electron populations on a day to day level. A radio array on the near side of the Moon would always be facing the Earth, and would well suited for measuring its low frequency radio emissions. In this work we simulate such a radio array on the lunar near side, to image this weaker synchrotron emission. The specific geometry and location of the test array were made using the most recent lunar maps made by the Lunar Reconnaissance Orbiter. This array would give us unprecedented day to day knowledge of the electron environment around our planet, providing reports of Earth's strong and weak radio emissions, giving both local and global information. , This set of codes should guide you through making the figures in the paper, as well as hopefully being accessible enough for changing the code for your own array. I would encourage you to please reach out to collaborate if that is the case! Requirements: , and CASA 4.7.1 (or greater?) built on python 2.7 Example link for Red Hat 7 https://casa.nrao.edu/download/distro/casa/release/el7/casa-release-4.7.1-el7.tar.gz Users may follow this guide to download and install the correct version of CASA for their system https://casa.nrao.edu/casadocs/casa-5.5.0/introduction/obtaining-and-installing CASA executables should be fairly straightforward to extract from the untarred files. gcc 4.8.5 or above (or below?) GCC installation instructions can be found here: https://gcc.gnu.org/install/ SPICE (I use cspice here) https://naif.jpl.nasa.gov/naif/toolkit_C.html As seen in lunar_furnsh.txt which loads the SPICE kernels, you also must download KERNELS_TO_LOAD = ( '/home/alexhege/SPICE/LunarEph/moon_pa_de421_1900-2050.bpc' '/home/alexhege/SPICE/LunarEph/moon_080317.tf' '/home/alexhege/SPICE/LunarEph/moon_assoc_me.tf' '/home/alexhege/SPICE/LunarEph/pck00010.tpc' '/home/alexhege/SPICE/LunarEph/naif0008.tls' '/home/alexhege/SPICE/LunarEph/de430.bsp' ) All of which can be found at https://naif.jpl.nasa.gov/pub/naif/generic_kernels/ SLDEM2015_128_60S_60N_000_360_FLOAT.IMG for the lunar surface data by LRO LOLA found at http://imbrium.mit.edu/DATA/SLDEM2015/GLOBAL/FLOAT_IMG/
- Citation to related publication:
- Hegedus, A., Nenon, Q., Brunet, A., Kasper, J., Sicard, A., Cecconi, B., MacDowall, R., & Baker, D. (2019). Measuring the Earth's Synchrotron Emission from Radiation Belts with a Lunar Near Side Radio Array. https://arxiv.org/abs/1912.04482 and Hegedus, A., Nenon, Q., Brunet, A., Kasper, J., Sicard, A., Cecconi, B., MacDowall, R., & Baker, D. (2020). Radio Science. https://doi.org/10.1029/2019RS006891
- Discipline:
- Engineering and Science
- Title:
- Data and Code Set for "Measuring the Earth's Synchrotron Emission from Radiation Belts with a Lunar Near Side Radio Array"
-
- Creator:
- Nelson, Arin D.
- Description:
- These MATLAB data files contain all the observations and model output used in the article Improved Internal Gravity Wave Spectral Continuum in a Regional Ocean Model by Nelson et al., recently submitted to Journal of Geophysical Research: Oceans.
- Keyword:
- Ocean, Ocean Mooring, Ocean Modeling, and Internal Waves
- Citation to related publication:
- Nelson et al., Improved Internal Gravity Wave Spectral Continuumin a Regional Ocean Model, submitted to Journal of Geophysical Research: Oceans
- Discipline:
- Science
- Title:
- Data for article Improved Internal Gravity Wave Spectral Continuum in a Regional Ocean Model
-
- Creator:
- Smith, Joeseph P., Gronewold, Andrew D., Read, Laura, Crooks, James L., School for Environment and Sustainability, University of Michigan, Department of Civil and Environmental Engineering, University of Michigan, and Cooperative Institute for Great Lakes Research
- Description:
- Using the statistical programming package R ( https://cran.r-project.org/), and JAGS (Just Another Gibbs Sampler, http://mcmc-jags.sourceforge.net/), we processed multiple estimates of the Laurentian Great Lakes water balance components -- over-lake precipitation, evaporation, lateral tributary runoff, connecting channel flows, and diversions -- feeding them into prior distributions (using data from 1950 through 1979), and likelihood functions. The Bayesian Network is coded in the BUGS language. Water balance computations assume that monthly change in storage for a given lake is the difference between beginning of month water levels surrounding each month. For example, the change in storage for June 2015 is the difference between the beginning of month water level for July 2015 and that for June 2015., More details on the model can be found in the following summary report for the International Watersheds Initiative of the International Joint Commission, where the model was used to generate a new water balance historical record from 1950 through 2015: https://www.glerl.noaa.gov/pubs/fulltext/2018/20180021.pdf. Large Lake Statistical Water Balance Model (L2SWBM): https://www.glerl.noaa.gov/data/WaterBalanceModel/, and This data set has a shorter timespan to accommodate a prior which uses data not used in the likelihood functions.
- Keyword:
- Water, Balance, Great Lakes, Laurentian, Machine, Learning, Lakes, Bayesian, and Network
- Citation to related publication:
- Discipline:
- Engineering and Science
- Title:
- Large Lake Statistical Water Balance Model - Laurentian Great Lakes - 1 month time window - 1980 through 2015 monthly summary data and model output
-
- Creator:
- Smith, Joeseph P., Gronewold, Andrew D., Read, Laura, Crooks, James L., School for Environment and Sustainability, University of Michigan, Department of Civil and Environmental Engineering, University of Michigan, and Cooperative Institute for Great Lakes Research
- Description:
- Using the statistical programming package R ( https://cran.r-project.org/), and JAGS (Just Another Gibbs Sampler, http://mcmc-jags.sourceforge.net/), we processed multiple estimates of the Laurentian Great Lakes water balance components -- over-lake precipitation, evaporation, lateral tributary runoff, connecting channel flows, and diversions -- feeding them into prior distributions (using data from 1950 through 1979), and likelihood functions. The Bayesian Network is coded in the BUGS language. Water balance computations assume that monthly change in storage for a given lake is the difference between beginning of month water levels surrounding each month. For example, the change in storage for June 2015 is the difference between the beginning of month water level for July 2015 and that for June 2015., More details on the model can be found in the following summary report for the International Watersheds Initiative of the International Joint Commission, where the model was used to generate a new water balance historical record from 1950 through 2015: https://www.glerl.noaa.gov/pubs/fulltext/2018/20180021.pdf. Large Lake Statistical Water Balance Model (L2SWBM): https://www.glerl.noaa.gov/data/WaterBalanceModel/ , and This data set has a shorter timespan to accommodate a prior which uses data not used in the likelihood functions.
- Keyword:
- Water, Balance, Great Lakes, Laurentian, Machine, Learning, Lakes, Bayesian, and Network
- Citation to related publication:
- Discipline:
- Engineering and Science
- Title:
- Large Lake Statistical Water Balance Model - Laurentian Great Lakes - 6 month time window - 1980 through 2015 monthly summary data and model output
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- Creator:
- Bustamante, Angela C., Opron, Kristopher, Ehlenbach, William J., Crane, Paul K., Keene, Dirk, Standiford, Theodore J., and Singer, Benjamin H.
- Description:
- This study was conducted to detect and analyze modules, or clusters of genes, associated with sepsis, using RNAseq data obtained from 12 participants who died of sepsis and 12 participants who died of non-infectious critical illness while hospitalized. This deposit contains the input data and parameters needed to reproduce the weighted gene co-expression network analysis (WGCNA) and gene enrichment analysis performed on this data. This analysis requires the R packages "WGCNA" version 1.68 and "DESeq2" version 1.22.2 available for download from bioconductor ( http://bioconductor.org). The external bioinformatics tool DAVID version 6.8 ( https://david.ncifcrf.gov/) was used as an additional gene enrichment analysis. Please see the supplemental methods document within this deposit and published research letter for more detailed information.
- Keyword:
- Sepsis, RNAseq, Transcriptomics, Human, and Brain
- Citation to related publication:
- Discipline:
- Science
- Title:
- Transcriptomic Profiles of Sepsis in the Human Brain
-
- Creator:
- Niemi, Nathan A. and Abbey, Alyssa L.
- Description:
- These data were produced in the scope of research into the timing, rate, and magnitude of extensional exhumation along the length of the Rio Grande Rift in Colorado and New Mexico. 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 absolute timing, exhumation rate and total magnitude of exhumation on the normal faults that bound the Rio Grande Rift. and The QTQt program mentioned (Version QTQt64R5.6.2a was used for the data presented in this deposit) is not openly available for download, but is described in the Gallagher 2012 publication referenced, and can be requested from its author. For more information on the request process and a user guide, see http://www.iearth.org.au/codes/QTQt/
- Keyword:
- thermochronology, helium dating, (U-Th)/He, Rio Grande Rift, New Mexico, Colorado, and extensional tectonics
- Citation to related publication:
- Abbey, A. L. and Niemi, N. A., in press, Perspectives on continental rifting processes from spatiotemporal patterns of faulting and magmatism in the Rio Grande rift, USA: Tectonics
- Discipline:
- Science
- Title:
- Dataset of apatite and zircon (U-Th)/He thermochronologic analyses from the Rio Grande Rift, Colorado and New Mexico, USA to accompany Abbey and Niemi, 2019, Tectonics.
-
- Creator:
- University of Michigan Museum of Zoology
- Description:
- Scan of specimen ummz:mammals:124092 (Phyllops falcatus) - WholeBody. Raw - Dataset includes 1601 TIF images (each 1085 x 1386 x 1 voxel at 0.0328326086085239 mm resolution, derived from 1601 scan projections), xtek and vgi files for volume reconstruction. and Scan of specimen ummz:mammals:124092 (Phyllops falcatus) - WholeBody. Reconstructed - Dataset includes 2000 TIF images (each 1085 x 1386 x 1 voxel at 0.032833 mm resolution, derived from 1601 scan projections), xtek and vgi files for volume reconstruction.
- Keyword:
- Animalia, Chordata, Mammalia, Chiroptera, Phyllostomidae, Phyllops falcatus, 1987339099, computed tomography, X-ray, and 3D
- Citation to related publication:
- For more information on the original UMMZ specimen, see: https://www.gbif.org/occurrence/1987339099
- Discipline:
- Science
- Title:
- Computed tomography voxel dataset for ummz:mammals:124092-Phyllops falcatus-WholeBody
-
- Creator:
- University of Michigan Museum of Zoology
- Description:
- Scan of specimen ummz:herps:246849 (THAMNODYNASTES PALLIDUS) - Skull. Raw - Dataset includes 1601 TIF images (each 1416 x 914 x 1 voxel at 0.0150099083890765 mm resolution, derived from 1601 scan projections), xtek and vgi files for volume reconstruction. and Scan of specimen ummz:herps:246849 (THAMNODYNASTES PALLIDUS) - Skull. Reconstructed - Dataset includes 654 TIF images (each 1416 x 914 x 1 voxel at 0.015010 mm resolution, derived from 1601 scan projections), xtek and vgi files for volume reconstruction.
- Keyword:
- Animalia, Chordata, Reptilia, OPHIDIA, COLUBRIDAE, THAMNODYNASTES PALLIDUS, 1987213257, computed tomography, X-ray, and 3D
- Citation to related publication:
- For more information on the original UMMZ specimen, see: https://www.gbif.org/occurrence/1987213257
- Discipline:
- Science
- Title:
- Computed tomography voxel dataset for ummz:herps:246849-THAMNODYNASTES PALLIDUS-Skull
-
- Creator:
- University of Michigan Museum of Zoology
- Description:
- Scan of specimen ummz:herps:246849 (THAMNODYNASTES PALLIDUS) - WholeBody. Raw - Dataset includes 1601 TIF images (each 2000 x 2000 x 1 voxel at 0.0327439804971583 mm resolution, derived from 1601 scan projections), xtek and vgi files for volume reconstruction. and Scan of specimen ummz:herps:246849 (THAMNODYNASTES PALLIDUS) - WholeBody. Reconstructed - Dataset includes 1240 TIF images (each 2000 x 2000 x 1 voxel at 0.032744 mm resolution, derived from 1601 scan projections), xtek and vgi files for volume reconstruction.
- Keyword:
- Animalia, Chordata, Reptilia, OPHIDIA, COLUBRIDAE, THAMNODYNASTES PALLIDUS, 1987213257, computed tomography, X-ray, and 3D
- Citation to related publication:
- For more information on the original UMMZ specimen, see: https://www.gbif.org/occurrence/1987213257
- Discipline:
- Science
- Title:
- Computed tomography voxel dataset for ummz:herps:246849-THAMNODYNASTES PALLIDUS-WholeBody
-
- Creator:
- Smith, Joeseph P., Gronewold, Andrew D., Read, Laura, Crooks, James L., School for Environment and Sustainability, University of Michigan, Department of Civil and Environmental Engineering, University of Michigan, and Cooperative Institute for Great Lakes Research, University of Michigan
- Description:
- Using the statistical programming package R ( https://cran.r-project.org/), and JAGS (Just Another Gibbs Sampler, http://mcmc-jags.sourceforge.net/), we processed multiple estimates of the Laurentian Great Lakes water balance components -- over-lake precipitation, evaporation, lateral tributary runoff, connecting channel flows, and diversions -- feeding them into prior distributions (using data from 1950 through 1979), and likelihood functions. The Bayesian Network is coded in the BUGS language. Water balance computations assume that monthly change in storage for a given lake is the difference between beginning of month water levels surrounding each month. For example, the change in storage for June 2015 is the difference between the beginning of month water level for July 2015 and that for June 2015., More details on the model can be found in the following summary report for the International Watersheds Initiative of the International Joint Commission, where the model was used to generate a new water balance historical record from 1950 through 2015: https://www.glerl.noaa.gov/pubs/fulltext/2018/20180021.pdf. Large Lake Statistical Water Balance Model (L2SWBM): https://www.glerl.noaa.gov/data/WaterBalanceModel/, and This data set has a shorter timespan to accommodate a prior which uses data not used in the likelihood functions.
- Keyword:
- Water, Balance, Great Lakes, Laurentian, Machine Learning, Machine, Learning, Lakes, Bayesian, and Network
- Citation to related publication:
- Discipline:
- Engineering and Science
- Title:
- Large Lake Statistical Water Balance Model - 12 month time window - 1980 through 2015 monthly summary data and model output