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opencc-by-4.0Nov 2024View details →

103 anti-correlated structures identified in the solar wind

This is the entire table listing the 103 anti-correlated structures identified. Each row represents a structure, the listed information are: ['No.', 'Year', 'ST1', 'ET1', 'ST2', 'ET2','ST3', 'ET3', 'ST4', 'ET4'] ST1: start time for the Fe signal based on the weak solution, in DOY ET1: end time for the Fe signal based on the weak solution, in DOY ST2: start time for the O signal based on the weak solution, in DOY ET2: end time for the O signal based on the weak solution, in DOY ST3: start time for the Fe signal based on the strong solution, in DOY ET3: end time for the Fe signal based on the strong solution, in DOY ST4: start time for the O signal based on the strong solution, in DOY ET4: end time for the O signal based on the strong solution, in DOY

opencc-by-4.0Jan 2021View details →

Data and code for 'Influence of cross-correlation on the modelled uncertainty in stress–strain behavior of soft clays'

This dataset contains data and code used in the research work for the manuscript "Influence of cross-correlation on the modelled uncertainty in stress–strain behavior of soft clays". The study considered two case studies, Haarajoki clay and Suurpelto clay. Two settlement calculation methods were used: compression index method and Janbu (tangential stiffness) method. In addition, clay database FI-CLAY/14/856 was extended and used to study cross-correlations between compressibility paramaters at different clay sites. Version 2 of FI-CLAY/14/856 is provided, including some other updates and corrections also. The Monte Carlo simulation with Gaussian copula was implemented with Python in Jupyter Notebook environment. In addition to data and code, supplementary figures are also provided. The contents of the dataset-folder are briefly described below: <ul> <li>1_Data_Oedometer_test <ul> <li>Oedometer test data for Haarajoki clay and Suurpelto clay: <ul> <li>Data tables that include the clay specimen identifications, index properties, and oedometer test results (.xlsx)</li> <li>Oedometer raw data files that include all the available stress-strain measurements of both constant-rate-of-strain and incrementally loaded odometer tests (.xlsx)</li> </ul> </li> <li>Extended clay database FI-CLAY/14/856 (version 2) (.xlsx)</li> </ul> </li> <li>2_Code_Jupyter_Notebooks <ul> <li>Python code used to run the Monte Carlo simulations and to create the results figures (.ipynb)</li> <li>Readme-file (.txt)</li> </ul> </li> <li>3_Figures_Online_Supplement <ul> <li>Scatterplots with histograms that show the simulated compressibility parameters in each case (.pdf)</li> </ul> </li> </ul>   More information on database FI-CLAY/14/856 can be found from the original article (https://www.tandfonline.com/doi/full/10.1080/17499518.2020.1864410) and 304dB datbase compilation by TC304 (http://140.112.12.21/issmge/tc304.htm).

dryad40/100

Data from: Intraspecific correlations between growth and defense vary with resource availability and differ within- and among-populations

A paradigm in the plant defense literature is that defending against herbivores comes at a cost to growth, resulting in a growth-defense tradeoff. However, while there is strong evidence for growth-defense tradeoffs across species, evidence is mixed within species. Several mechanisms can account for this equivocal support within species, but teasing them apart requires examining growth-defense relationships both within and among populations, an approach seldom employed. We examined correlations between plant biomass (growth) and terpene production (defense) within and among populations of Monarda fistulosa, a perennial herb. We sampled populations from Montana and Wisconsin, regions that differ in resource availability characterized by different summer precipitation and associated abiotic conditions that influence plant productivity. We found negative, neutral, and positive growth-defense correlations, depending on the scale examined. Negative correlations occurred across populations originating from divergent regions, positive correlations occurred across populations originating from within the high-resource region, and neutral correlations were found within single populations. Collectively, these results challenge the general expectation of ubiquitous tradeoffs and support emerging views that resource availability (as it affects productivity) shapes the evolution of defense at different scales.

opencc-zeroJun 2021View details →

opencc-by-4.0Jul 2021View details →

Raw data for the manuscript "Enhanced Photonic Maxwell's Demon with Correlated Baths"

This folder contains the raw data and analysis coded need to reproduce all of the major results in the manuscript "Enhanced Photonic Maxwell’s Demon with Correlated Baths".  The folder "demonPower" contains the data and code related to the photon number difference that the demon can generate. There are four analysis scripts, written in MATLAB, one for each type of bath used.  Each script loads the data, and plots the final results in Fig. 4 of the paper.  There is one data file for each reflectivity.  The different columns in the data files correspond to different count rates measured with our tagger. Only the first two columns are relvant, they are the counts per second measured at detector A and B.  Each line is one second worth of data. The folder "g2data" contains the data and code used to characterize the thermal and split thermal sources.  The two txt data files contain lists of the the channel that registered an event together with the clock cycle of the time tagger at which the event was registerd. The data are described and analyzed in the MATLAB script "thermalLightG2_withSave.m".

opencc-by-4.0May 2021View details →

Data of "Impact of temporal correlations on high risk outbreaks of independent and cooperative SIR dynamics"

The data reported in the paper: Sajjadi et al. (2021) Impact of temporal correlations on high risk outbreaks of independent and cooperative SIR dynamics. PLoS ONE 16(7): e0253563. https://doi.org/10.1371/journal.pone.0253563 Each directory contains the data illustrated in one figure. The data structure and properties are described in .info files within each directory. All the simulations, analyses and illustrations have been conducted via the Epyc package (written in C++ and Python), developed by Sina Sajjadi. Epyc is available under GPLv3, at https://github.com/Sepante/Epyc.

Fig. 3 in Spatio-temporal correlations of large predators and their prey in western Thailand

Fig. 3. Kernel density estimates of daily predator activity patterns. A, tiger, leopard, and dhole; B, tiger and leopard; C, tiger and dhole; D, leopard and dhole. The shaded areas indicate the overlap coefficient; that is, the area under the minimum of the two density estimates.

Fig. 5 in Spatio-temporal correlations of large predators and their prey in western Thailand

Fig. 5. Kernel density estimates of daily activity patterns of leopards and prey species. A, leopard and gaur; B, leopard and sambar; C, leopard and barking deer; D, leopard and tapir; E, leopard and wild boar. The dashed lines are kernel density estimates for leopards and the solid lines are kernel density estimates for the prey species. The shaded areas indicate the overlap coefficient; that is, the area under the minimum of the two density estimates.

Fig. 4 in Spatio-temporal correlations of large predators and their prey in western Thailand

Fig. 4. Kernel density estimates of daily activity patterns of tigers and prey species. A, tiger and gaur; B, tiger and sambar; C, tiger and tapir; D, tiger and barking deer; E, tiger and wild boar. The dashed lines are kernel density estimates for tigers and the solid lines are kernel density estimates for the prey species. The shaded areas indicate the overlap coefficient; that is, the area under the minimum of the two density estimates.

Fig. 6 in Spatio-temporal correlations of large predators and their prey in western Thailand

Fig. 6. Kernel density estimates of daily activity patterns of dholes and prey species. A, dhole and gaur; B, dhole and sambar; C, dhole and tapir; D, dhole and barking deer; E, dhole and wild boar. The dashed lines are kernel density estimates for dholes and the solid lines are kernel density estimates for the prey species. The shaded areas indicate the overlap coefficient; that is, the area under the minimum of the two density estimates.

FIG. 10 in A new specimen of the ornithischian dinosaur Haya griva, cross-Gobi geologic correlation, and the age of the Zos Canyon beds

FIG. 10. Looking south from the base of the Zos Canyon section. The arrow indicates the Red Rum sublocality.

FIG. 9. B in A new specimen of the ornithischian dinosaur Haya griva, cross-Gobi geologic correlation, and the age of the Zos Canyon beds

FIG. 9. B. Shared diagnostic character (6) a shallow depression along the midline nasal suture anterior to the orbits on the dorsal surface of the skull.

FIG. 9. A in A new specimen of the ornithischian dinosaur Haya griva, cross-Gobi geologic correlation, and the age of the Zos Canyon beds

FIG. 9. A. Comparison of IGM 100/3181 (top) with the holotype of Haya griva (IGM 100/2017, bottom). Shared diagnostic characters (Makovicky et al., 2011) are: (1) homodont unserrated premaxillary teeth, (2) the lack of a rugose rhamphothecal pad on the anterior surface of the premaxilla, (3) the presence of a triangular maxillary fenestra, (4) a jugal with a bifurcated (forklike) posterior ramus where it abuts the quadratojugal, (5) the presence of a quadratojugal foramen.

FIG. 4. A in A new specimen of the ornithischian dinosaur Haya griva, cross-Gobi geologic correlation, and the age of the Zos Canyon beds

FIG. 4. A. Premaxillary tooth of IGM 100/3181 in mesial and distal views. Its precise orientation cannot be determined as it was found as float during preparation. B. Cheek tooth of IGM 100/3181. Its precise orientation cannot be determined as it was found as float during preparation, and is compatible with the morphology of both upper and lower teeth of referred Haya specimens.

FIG. 3 in A new specimen of the ornithischian dinosaur Haya griva, cross-Gobi geologic correlation, and the age of the Zos Canyon beds

FIG. 3. The left side of the cranium of IGM 100/3181, with interpretive drawing. Abbreviations are listed in appendix 1.

FIG. 1 in A new specimen of the ornithischian dinosaur Haya griva, cross-Gobi geologic correlation, and the age of the Zos Canyon beds

FIG. 1. Map of Mongolia showing the relative positions of the Javkhlant Formation exposures and Zos Canyon beds (near Ukhaa Tolgod). Zos Canyon is 7 km northwest of the Ukhaa Tolgod locality. See Pol and Norell (2004a: fig. 1) for detail.

opencc-by-4.0Nov 2020View details →

Text-fig. 4. Stratigraphic correlation of the five major fossil-bearing localities in the Mikhailovka quarry near Zheleznogorsk. 1 – loesses and soils, 2 – unlaminated loams, 3 – laminated loams, 4 – clays, 5 – sands, 6 – gravels, 7 – carbonate concretions, 8 – mollusk shells, 9 – small mammal remains, 10 – insect remains, 11 – plant macroremains. in Late Pleistocene (Eemian) Mollusk And Small Mammal Fauna From Mikhailovka-5 (Kursk Oblast, Central Russia)

Text-fig. 4. Stratigraphic correlation of the five major fossil-bearing localities in the Mikhailovka quarry near Zheleznogorsk. 1 – loesses and soils, 2 – unlaminated loams, 3 – laminated loams, 4 – clays, 5 – sands, 6 – gravels, 7 – carbonate concretions, 8 – mollusk shells, 9 – small mammal remains, 10 – insect remains, 11 – plant macroremains.

opencc-by-4.0Nov 2020View details →

Text-fig. 3. Correlation of geological sections with Pleistocene deposits of the Mikhailovka quarry near Zheleznogorsk. 1 – modern and fossil soils, 2 – loess-like loam, 3 – sandy clay, 4 – aleurites, 5 – horizontally laminated clays, 6 – brown loams, 7 – blue clays and loams, 8 – sands, 9 – gravels, 10 – mollusk shells, 11 – small mammal remains. in Late Pleistocene (Eemian) Mollusk And Small Mammal Fauna From Mikhailovka-5 (Kursk Oblast, Central Russia)

Text-fig. 3. Correlation of geological sections with Pleistocene deposits of the Mikhailovka quarry near Zheleznogorsk. 1 – modern and fossil soils, 2 – loess-like loam, 3 – sandy clay, 4 – aleurites, 5 – horizontally laminated clays, 6 – brown loams, 7 – blue clays and loams, 8 – sands, 9 – gravels, 10 – mollusk shells, 11 – small mammal remains.

opencc-by-4.0May 2007View details →

Fig. 6 in New Stratigraphic Data from the Erlian Basin: Implications for the Division, Correlation, and Definition of Paleogene Lithological Units in Nei Mongol (Inner Mongolia)

Fig. 6. Sketch profiles from (above) Camp Margetts and (below) 7 miles southwest (235°) of Camp Margetts (Granger, 1930).

opencc-by-4.0May 2007View details →

Fig. 4 in New Stratigraphic Data from the Erlian Basin: Implications for the Division, Correlation, and Definition of Paleogene Lithological Units in Nei Mongol (Inner Mongolia)

Fig. 4. Above, The Arshanto Formaiton (middle beds) and Irdin Manha Formation (upper beds) at the Nuhetingboerhe locality; the arrow points to the upper hiatus between the two formatitons. Below, Close-up view of the upper hiatus showing the lithology and erosional surface.