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- Creator:
- Zhang, Yan, Fujian Normal University, Yang, Ping, Fujian Normal University, Tong, Chuan, Fujian Normal University, Zhang, Xinyan, Chinese Academy of Sciences, Changchun, Liu, Xingtu, Chinese Academy of Sciences, Changchun, Zhang, Shaoqing, Chinese Academy of Sciences, Changchun, Meyers, Philip. University of Michigan, and Gao, Chuanyu , Chinese Academy of Sciences, Changchun
- Description:
- A high-resolution study of bulk properties in a peat sequence from the Xinjiang Altai Mountains of northwestern China, has allowed reconstruction of local variations in peat properties and peat C and N accumulation rates (CAR and NAR) during the Holocene. Analyses of peat bulk density, loss on ignition, and concentrations of TOC and TN and their elemental ratios and stable isotopic values suggest that changes in peat-forming vegetation types during different parts of this epoch are the major factors responsible for the variations of peat properties in this sequence.
- Keyword:
- peat properties, stable carbon and nitrogen isotopes, carbon and nitrogen accumulation rates, and Altai Mountains of northwestern China
- Citation to related publication:
- Zhang, Y., Yang, P., Gao, C., Tong, C., Zhang, X., Liu, X., Zhang, S., & Meyers, P. A. (2020). Peat Properties and Holocene Carbon and Nitrogen Accumulation Rates in a Peatland in the Xinjiang Altai Mountains, Northwestern China. Journal of Geophysical Research: Biogeosciences, 125(12), e2019JG005615. https://doi.org/10.1029/2019JG005615
- Discipline:
- Science
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- Creator:
- Arbic, B.K., Elipot, S., Menemenlis, D., and Shriver, J.F.
- Description:
- The datafiles and Matlab code files in this repository contain the information needed to produce the figures in the paper. We also include the code used to process the raw model output files into spectra.
- Citation to related publication:
- B.K. Arbic, S. Elipot, J.M. Brasch, D. Menemenlis, A.L. Ponte, J.F. Shriver, X. Yu, E.D. Zaron, M.H. Alford, M.C. Buijsman, R. Abernathey, D. Garcia, L. Guan, P.E. Martin, and A.D. Nelson (2022), Near-surface oceanic kinetic energy distributions from drifter observations and numerical models. In review.
- Discipline:
- Science
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- Creator:
- Light, Charles X, Arbic, Brian K, Martin, Paige E, Brodeau, Laurent, Farrar, J Thomas, Griffies, Stephen M, Kirtman, Ben P, Laurindo, Lucas, Menemenlis, Dimitris, Molod, Andrea, Nelson, Arin D, Nyadjro, Ebenezer, O'Rourke, Amanda K, Shriver, Jay, Siqueira, Leo, Small, R Justin, and Strobach, Udi
- Description:
- The precipitation data itself is the output of the models/datasets that we analyze in our paper. Most of it is in .nc or .nc4 format, although we provide code to extract the data into time series .mat files. We used MATLAB to perform our analysis.
- Keyword:
- precipitation and power spectra
- Citation to related publication:
- Light, C.X., Arbic, B.K., Martin, P.E., Brodeau, L., Farrar, J.T., Griffies, S.M., Kirtman, B.P., Laurindo, L.C., Menemenlis, D., Molod, A., Nelson, A.D., Nyadjro, E., O'Rourke, A.K., Shriver, J.F., Siqueira, L., Small, R.J., Strobach, E. (2022). Effects of grid spacing on high-frequency precipitation variance in coupled high-resolution global ocean-atmosphere models. Climate Dynamics, https://doi.org/10.1007/s00382-022-06257-6
- Discipline:
- Science
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- Creator:
- Jiao, Zhenbang, Chen, Yang, and Manchester, Ward
- Description:
- GOES_flare_list: contains a list of more than 12,013 flare events. The list has 6 columns, flare classification, active region number, date, start time end time, emission peak time. SHARP_data.hdf5 files contain time series of 20 physical variables derived from the SDO/HMI SHARP data files. These data are saved at a 12 minute cadence and are used to train the LSTM model.
- Keyword:
- Solar Flare Prediction and Machine Learning
- Citation to related publication:
- Jiao, Z., Sun, H., Wang, X., Manchester, W., Gombosi, T., Hero, A., & Chen, Y. (2020). Solar Flare Intensity Prediction With Machine Learning Models. Space Weather, 18(7), e2020SW002440. https://doi.org/10.1029/2020SW002440 and Chen, Y., & Manchester, W. (2019). Data and Data products for machine learning applied to solar flares [Data set], University of Michigan - Deep Blue. https://doi.org/10.7302/qnsq-cs38
- Discipline:
- Engineering and Science
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- Creator:
- BIRDS Lab U. Michigan
- Description:
- These data were produced for ARO W911NF-14-1-0573 "Morphologically Modulated Dynamics" and ARO MURI W911NF-17-1-0306 "From Data-Driven Operator Theoretic Schemes to Prediction, Inference, and Control of Systems" to explore the trade-offs between various oscillator coupling models in modeling multilegged locomotion. The data were also used extensively in examining multi-contact slipping, in the studying the influence of number of legs on otherwise identical locomotion patterns, and in the use of geometric mechanics models for multilegged locomotion. Folder and file names encode the meta-data, with names following an informative naming convention documented in the README.
- Keyword:
- phase, multilegged, robot, and locomotion
- Citation to related publication:
- Zhao, D. & Revzen, S. Multi-legged steering and slipping with low DoF hexapod robots Bioinspiration & biomimetics, 2020, 15, 045001 https://doi.org/10.1088/1748-3190/ab84c0 and Zhao, D. Ph.D. Thesis "Locomotion of low-DOF multi-legged robots" University of Michigan 2021 https://deepblue.lib.umich.edu/handle/2027.42/169985
- Discipline:
- Science and Engineering
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Geotechnical observations of weathered rock across a tectonic and climatic gradient in Central Nepal
- Creator:
- Medwedeff, William, G (University of Michigan Earth & Environmental Science), Clark, Marin, K (University of Michigan Earth & Environmental Science), Zekkos, Dimitrios (University of California, Berkeley), West, A., Joshua (University of Southern California), and Chamlagain, Deepak (Tribhuvan University, Kathmandu Nepal)
- Description:
- These datasets support the findings of Medwedeff et al. (2021) in JGR: Earth Surface. In this article, we present seismic and geotechnical characterizations of the shallow subsurface across a 200 km by 50 km swath of the central Himalayan Range, in Nepal. By pairing widely-distributed 1D shear wave velocity surveys and engineering outcrop descriptions per the Geological Strength Index classification system, we evaluate landscape-scale patterns in near-surface mechanical characteristics and their relation to environmental factors known to affect rock strength. We find that near-surface strength is more dependent on the degree of weathering, rather than the mineral and textural differences between the metamorphic lithologies found in the central Himalaya. Furthermore, weathering varies systematically with topography. Bedrock ridge top sites are highly weathered and have S-wave seismic velocities and shear strength characteristics that are more typical of engineering soils, whereas sites near the bedrock channel bottom tend to be less weathered and characterized by high S-wave velocities and shear strength estimates typical of hard rock. Weathering of bedrock on hillslopes is significantly more variable, resulting in S-wave velocities that range between the ridge and channel endmembers. We hypothesize variability in the hillslope environment may be partly explained by the stochastic nature of mass wasting, which clears away weathered material where landslide scars are recent. These results underscore the mechanical heterogeneity in the shallow subsurface and highlight the need to account for bedrock weathering when estimating strength parameters for regional landslide hazard analysis.
- Keyword:
- rock strength, critical zone, shallow seismic, and chemical weathering
- Discipline:
- Science
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- Creator:
- Gorchov Negron, Alan M., Kort, Eric A., Conley, Stephen A., and Smith, Mackenzie L.
- Description:
- This data-set contains data used in the publication "Airborne Assessment of Methane Emissions from Offshore Platforms in the U.S. Gulf of Mexico" by Gorchov Negron et al. (2020). There are 46,032 rows and 45 columns in the data. and The aircraft sampled offshore facilities with two unique sampling strategies: facility-level samples and regional box samples. Gorchov Negron et al. used facility-level samples to calculate facility-level fluxes and regional box samples, in conjunction with vertical profiles, to calculate regional-level fluxes. Meteorological parameters in the data were evaluated to discern when assumptions for each method were met. The facility-level fluxes were used to generate a facility-level aerial measurement-based inventory that was scaled up for comparison with regional-level fluxes.
- Keyword:
- Methane Emissions, Offshore Oil and Gas Platforms, Airborne Measurements, Greenhouse Gas Mitigation, and Gulf of Mexico
- Citation to related publication:
- Alan M. Gorchov Negron, Eric A. Kort, Stephen A. Conley, Mackenzie L. Smith. "Airborne Assessment of Methane Emissions from Offshore Platforms in the U.S. Gulf of Mexico". Environ. Sci. Technol. 2020. http://dx.doi.org/10.1021/acs.est.0c00179
- Discipline:
- Science
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- Creator:
- Yiwen, Mei
- Description:
- The datasets of this archive are produced for a research project on the development of an advanced hydrologic modeling system for the St. Lawrence river basin. The outputted datasets from model simulations are in Netcdf 4 format. The author recommend using the netCDF Operators (NCO) program for data processing. For visualization and plotting, the author recommend using software like MATLAB, Python or R.
- Keyword:
- Hydrologic modeling, reanalysis product, St. Lawrence river, water balance, WRF-Hydro
- Discipline:
- Science
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- Creator:
- McCuen, Brett A.
- Description:
- These data are TLA events identified in MACCS magnetometer data throughout 2015. These events are short-timescale (< 60 s), large -amplitude (> 6 nT/s) magnetic disturbances measured at Earth's surface that are analyzed for space weather research purposes. and The events were identified in a year's worth of magnetic field data using an algorithm developed in the MATLAB platform. The algorithm dBdt_main.m can be run using the associated scripts (clean_maccs.m, simple_dbdt.m, extremes1.m, newdbdt.m) to return the events in the 2015_AllEvents.csv file. The substorm onset delays of each event are determined with the onset_delays.m script and the substorm event list 20191127-15-56-substorms.csv (both included).
- Keyword:
- space weather impacts, geomagnetically induced currents, GIC, transient induced currents, transient large amplitude, dB/dt search algorithm, and TLA
- Citation to related publication:
- Engebretson, M. J., Pilipenko, V. A., Ahmed, L. Y., Posch, J. L., Steinmetz, E. S., Moldwin, M. B., … Vorobev, A. V. (2019). Nighttime Magnetic Perturbation Events Observed in Arctic Canada: 1. Survey and Statistical Analysis. Journal of Geophysical Research: Space Physics, 124(9), 7442–7458. https://doi.org/10.1029/2019JA026794
- Discipline:
- Science
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- Creator:
- Li, Jieming, Zhang, Leyou, Johnson-Buck, Alexander, and Walter, Nils G.
- Description:
- Traces from single-molecule fluorescence microscopy (SMFM) experiments exhibit photophysical artifacts that typically necessitate human expert screening, which is time-consuming and introduces potential for user-dependent expectation bias. Here, we have used deep learning to develop a rapid, automatic SMFM trace selector, termed AutoSiM, that improves the sensitivity and specificity of an assay for a DNA point mutation based on single-molecule recognition through equilibrium Poisson sampling (SiMREPS). The improved performance of AutoSiM is based on accepting both more true positives and fewer false positives than the conventional approach of hidden Markov modeling (HMM) followed by thresholding. As a second application, the selector was used for automated screening of single-molecule Förster resonance energy transfer (smFRET) data to identify high-quality traces for further analysis, and achieves ~90% concordance with manual selection while requiring less processing time. AutoSiM can be adapted readily to novel datasets, requiring only modest Transfer Learning.
- Keyword:
- deep learning, single-molecule fluorescence, total internal reflection microscopy, SiMREPS, smFRET, and Forster resonance energy transfer
- Citation to related publication:
- Li, J., Zhang, L., Johnson-Buck, A., & Walter, N. G. (2020). Automatic classification and segmentation of single-molecule fluorescence time traces with deep learning. Nature Communications, 11(1), 5833. https://doi.org/10.1038/s41467-020-19673-1 and Hayward, S., Lund, P., Kang, Q., Johnson-Buck, A., Tewari, M., Walter, N. (2018). Single-molecule microscopy image data and analysis files for "Ultra-specific and Amplification-free Quantification of Mutant DNA by Single-molecule Kinetic Fingerprinting" [Data set]. University of Michigan - Deep Blue. https://doi.org/10.7302/Z2CZ35DF
- Discipline:
- Science