Search Constraints
Filtering by:
Language
English
Remove constraint Language: English
Resource type
Dataset
Remove constraint Resource type: Dataset
Discipline
Science
Remove constraint Discipline: Science
Number of results to display per page
View results as:
Search Results
-
- Creator:
- Tye, Alexander R, Niemi, Nathan A, Safarov, Rafig T, Kadirov, Fakhraddin A, Babayev, Gulam R
- Description:
- The Eastern Greater Caucasus is a mountain belt in western Asia that formed as an accretionary prism above an active subduction zone. Because of the bedrock exposure in the range, it offers a unique opportunity to research deformation processes in accretionary prisms, which are ubiquitous above the Earth's many subduction zones but are typically submarine and difficult to investigate. The data presented here result from field geologic mapping in several swaths roughly perpendicular to the mountain range that together span the entire range across strike. The data serve will serve as the basis for inference of the deep structural architecture of the range and characterization of the styles of deformation present in the range.
- Keyword:
- structural geology, Greater Caucasus, tectonics, geologic mapping, and accretionary prism
- Citation to related publication:
- Tye, A. R., Niemi, N. A., Safarov, R. T., Kadirov, F. A., & Babayev, G. R. (2021). Sedimentary response to a collision orogeny recorded in detrital zircon provenance of Greater Caucasus foreland basin sediments. Basin Research, 33(2), 933–967. https://doi.org/10.1111/bre.12499
- Discipline:
- Science
-
- Creator:
- Liu, Meichen
- Description:
- We intend to figure out the difference of stress drops, which is a characteristic source parameter, between shallow and deep-focus earthquakes. Significant stress drop difference may shed light on the difference of physical mechanisms of shallow and deep-focus earthquakes, which has been a elusive question. We select from deep-focus earthquakes (> 400 km) in 2000-2018 and obtain their stress drops using P and S waves. We find that stress drops of deep-focus earthquakes are about one order of magnitude higher than that of shallow earthquakes, indicating about one order of magnitude higher shear strength of shallow faults than faults in the mantle. The wide range of stress drops further suggests coexistence of phase transformation and shear-induced melting mechanisms of deep-focus earthquakes.
- Citation to related publication:
- Liu, M., Huang, Y., & Ritsema, J. (2020, March 4). Stress drop variation of deep-focus earthquakes based on empirical Green's function [preprint]. Submitted to Geophysical Research Letters. https://doi.org/10.31223/osf.io/8jx6p and Liu, M., Huang, Y., & Ritsema, J. (2020). Stress Drop Variation of Deep-Focus Earthquakes Based on Empirical Green’s Functions. Geophysical Research Letters, 47(9), e2019GL086055. https://doi.org/10.1029/2019GL086055
- Discipline:
- Science
-
- Creator:
- Tye, Alexander R, Niemi, Nathan A, Safarov, Rafig T, Kadirov, Fakhraddin A, Babayev, Gulam R
- Description:
- Apatite fission track thermochronometry data were collected from the Eastern Greater Caucasus orogen, Azerbaijan. Thermochronometry data constrain the history of exhumation and deformation of rocks within the orogen, which is an active accretionary prism. Thermochronometry data record the timing of cooling of a rock sample beneath a given closure temperature. Given an assumed or inferred geothermal gradient, thermochronometric ages can be used to infer exhumation rates and make interpretations about rates of deformation in orogens. The apatite fission track data presented here are analyzed in concert with apatite (U-Th)/He and zircon (U-Th)/He ages reported in Tye et al., in prep., to characterize the exhumation history of the Eastern Greater Caucasus.
- Keyword:
- thermochronometry, apatite fission track, Caucasus
- Citation to related publication:
- Tye, A. R., Niemi, N. A., Safarov, R. T., Kadirov, F. A., & Babayev, G. R. (2021). Sedimentary response to a collision orogeny recorded in detrital zircon provenance of Greater Caucasus foreland basin sediments. Basin Research, 33(2), 933–967. https://doi.org/10.1111/bre.12499
- Discipline:
- Science
-
- 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:
- Smith, J., Gronewald, A. et al. Summary Report: Development of the Large Lake Statistical Water Balance Model for Constructing a New Historical Record of the Great Lakes Water Balance. Submitted to: The International Watersheds Initiative of the International Joint Commission. Accessible at 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 Gronewold, A.D., Smith, J.P., Read, L. and Crooks, J.L., 2020. Reconciling the water balance of large lake systems. Advances in Water Resources, p.103505.
- Discipline:
- Science and Engineering
-
- Creator:
- Davis Rabosky, Alison R., Moore, Talia Y., Sánchez-Paredes, Ciara M., Westeen, Erin P., Larson, Joanna G., Sealey, Briana A., and Balinski, Bailey A.
- Description:
- Animals in nature use diverse strategies to evade or deter their predators, including many vivid behavioural displays only qualitatively described from field encounters with natural predators or humans. Within venomous snake mimicry, stereotyped anti-predator displays are suggested to be a critical component of the warning signal given by toxic models and thus under strong selection for independent convergence in mimetic species. However, no studies have systematically quantified variation in snake anti-predator displays across taxonomically broad clades to test how these behaviours evolve across species within a phylogenetic comparative methods framework. Here we describe a new, high-throughput approach for collecting and scoring snake anti-predator displays in the field that demonstrates both low observer bias and infinite extension across any species. Then, we show our method's utility in quantitatively comparing the behaviour of 20 highly-divergent snake species from the Amazonian lowlands of Peru. We found that a simple experimental setup varying simulated predator cues was very successful in eliciting anti-predator displays across species and that high-speed videography captured a greater diversity of behavioural responses than described in the literature. We also found that although different display components evolve at different rates with complicated patterns of covariance, there is clear evidence of evolutionary convergence in anti-predator displays among distantly related elapid coral snakes and their colubrid mimics. We conclude that our approach significantly advances opportunity for future analyses of snake behaviour, kinematics, and the evolution of anti-predator signals more generally, especially macroevolutionary analyses across clades with similarly intractable behavioural diversity.
- Keyword:
- Batesian mimicry, phylogenetic comparative methods, signal evolution, aposematism, simulated predator cues, coral snakes, and Peruvian Amazon
- Citation to related publication:
- Alison R. Davis Rabosky, Talia Y. Moore, Ciara M. Sanchez-Paredes, Erin P. Westeen, Joanna G. Larson, Briana A. Sealey, Bailey A. Balinski (2020) Convergence and divergence in anti-predator displays: A novel approach to quantitative behavioural comparison in snakes. Biological Journal of the Linnean Society http://dx.doi.org/10.1093/biolinnean/blaa222
- Discipline:
- Science
-
- 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., & Niemi, N. A. (2020). Perspectives on Continental Rifting Processes From Spatiotemporal Patterns of Faulting and Magmatism in the Rio Grande Rift, USA. Tectonics, 39(1), e2019TC005635. https://doi.org/10.1029/2019TC005635
- Discipline:
- Science
-
- 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:
- Bustamante, A.C., Opron, K., Ehlenbach, W.J., Larson, E.B., Crane, P.K., Keene, C.D., Standiford, T.J., Singer, B.H., 2020. Transcriptomic Profiles of Sepsis in the Human Brain. Am J Respir Crit Care Med. https://doi.org/10.1164/rccm.201909-1713LE
- Discipline:
- Science
-
- 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:
- Smith, J., Gronewald, A. et al. Summary Report: Development of the Large Lake Statistical Water Balance Model for Constructing a New Historical Record of the Great Lakes Water Balance. Submitted to: The International Watersheds Initiative of the International Joint Commission. Accessible at 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 Gronewold, A.D., Smith, J.P., Read, L. and Crooks, J.L., 2020. Reconciling the water balance of large lake systems. Advances in Water Resources, p.103505.
- Discipline:
- Science and Engineering
-
- 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:
- Smith, J., Gronewald, A. et al. Summary Report: Development of the Large Lake Statistical Water Balance Model for Constructing a New Historical Record of the Great Lakes Water Balance. Submitted to: The International Watersheds Initiative of the International Joint Commission. Accessible at 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 Gronewold, A.D., Smith, J.P., Read, L. and Crooks, J.L., 2020. Reconciling the water balance of large lake systems. Advances in Water Resources, p.103505.
- Discipline:
- Science and Engineering
-
- 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