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opencc-by-4.0Jun 2022View details →

A Review of Global Bedrock (234U/238U) Disequilibrium and its Controlling factors on Earth's Surface

The 234U-238U disequilibrium system offers a broad spectrum of applications to study various Earth-surface processes, such as the evolution of weathering profile and erosion through time and sediment transport processes across the river catchments. Many of these applications require a critical assumption regarding the initial bedrock (234U/238U) activity ratios ( ), typically assumed to be in secular equilibrium. However, it was until recently that this assumption was gradually challenged. To address this uncertainty and the controlling factors responsible for  disequilibrium, we measured  of the uncomminuted bedrock retrieved from Qinghai, China, in which one of the samples (ZC2F) exhibits severe 234U deficiency, with  measured to be 0.691 ± 0.004 (2σ; n = 3). To complement our new data, we compiled the published global  values ranging from 0.700 to 1.280, with an average of 0.981 ± 0.070 (2σ; n = 160), indicating that visibly unweathered rocks worldwide could have activity ratios higher or lower than 1. This widely observed 234U depletion among global bedrock (especially in the near-surface region, < 10 m) cannot result from the direct α-recoil of 234U alone but is attributed to the preferential release of 234U from microfissures created during rock diagenesis and probably from hydrothermal alteration processes. Moreover, the bedrock 234U deficiency corroborates the local lithology, primarily defined by the mineral crystal sizes and abundance of specific minerals (biotite and quartz in granites) and the local precipitation amount where the bedrocks were recovered. In addition, bedrock samples can likewise be enriched with 234U, as evidenced by the nine chemically treated bedrocks’  compiled in this study, ranging from 1.002 to 1.020 (n = 9), attributed to alpha recoil gain from neighboring grains or surface deposits enriched with uranium. As bedrock  can be modified by geologic factors, such as tectonism, lithology, and climate, notably in the near-surface region,  should be constrained with an optimal sampling strategy to provide a holistic perspective of evaluating catchment-scale Earth-surface processes.

opencc-by-4.0Sep 2022View details →

Code and data for results and figures of the manuscript "Multi-million year cycles in modelled δ13C as a response to astronomical forcing of organic matter fluxes." submitted to Earth System Dynamics

This dataset contains the code of the model used in the manuscript submitted to Earth System Dynamics "Multi-million year cycles in modelled δ13C as a response to astronomical forcing of organic matter fluxes.". It also contains some model outputs and code to draw the figures.

opencc-by-4.0Dec 2021View details →

Supplementary Data for "The phosphorus budget of the silicate Earth based on an updated estimate of P/Nd ratio".

Supplementary data tables for Ma et al. (2022) associated with the paper entitled "The phosphorus budget of the silicate Earth based on an updated estimate of P/Nd ratio".

opennotspecifiedDec 2017View details →

FIGURE 1 in Inordinate Fondness Multiplied And Redistributed: The Number Of Species On Earth And The New Pie Of Life

FIGURE 1. TRADITIONAL AND NEW ESTIMATES OF THE PIE OF LIFE The pie on the left shows a traditional estimate of the relative richness of different groups of organisms based on numbers of described species (Wilson 1992), the middle shows an estimate based on projected richness of different groups (Mora et al. 2011), and the pie on the right shows estimates based on the projected richness of different groups in the present study. The pie on the right is estimated based on Scenario 1 (Table 1), but other scenarios and assumptions give very similar estimates of the relative richness of different groups (Tables 1 – 4), even as total species richness changes. See the online edition for a color version of this figure. Figure from the online version (the print version is black and white)

opencc-by-4.0Aug 2016View details →

Supplementary material 1 from: Canseco-Márquez L, Pavón-Vázquez CJ, López-Luna MA, Nieto-Montes de Oca A (2016) A new species of earth snake (Dipsadidae, Geophis) from Mexico. ZooKeys 610: 131-145. https://doi.org/10.3897/zookeys.610.8605

Table S1. Specimens examined. : Explanation note: All of the specimens are from Mexico. The table is arranged alphabetically by species name and specimen voucher number, in that order.

opencc-by-4.0Apr 2024View details →

West Heslerton Anglo-Saxon Settlement -Primary Excavation Archive for interactive viewing in Google Earth Pro

The Landscape Research Centre pioneered digital recording in field archaeology using hand-held computers in the field from the mid 1980s onwards. The excavation of an Early Anglo-Saxon settlement covering nearlly 25Ha, funded by English Heritage from the rescue archaeology commissions budget was one of the largest excavations in Europe conducted between 1986 and 1996 with an analytical program that contuniued into th 2020s. The digital plans provide an interactive interface to the primary excavation archives when this file id loaded inot Google Earth Pro.

Dataset related to the paper submitted in Journal of Geophysical Research : Solid Earth, named : A Controlled-Source Physical Model for Long Period Events

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Changes Monitoring in Hongjiannao Lake from 1987-2023 using Google Earth Engine and Analysis of Climatic and Anthropogenic Forces

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Model configuration and input files for: "CARIB12: A Regional Community Earth System Model / Modular Ocean Model 6 Configuration of the Caribbean Sea"

This collection hosts the configuration and input files to reproduce the regional CESM/MOM6 simulation in: "CARIB12: A Regional Community Earth System Model / Modular Ocean Model 6 Configuration of the Caribbean Sea".  The initial, open boundary conditions and monthly means for the sponge layers were generated from the GLORYS12V1 reanalysis: <a href="https://doi.org/10.3389/feart.2021.698876">https://doi.org/10.3389/feart.2021.698876</a> The tidal amplitudes and phases were generated from the TPXO model: <a href="https://doi.org/10.1175/1520-0426(2002)019<0183:EIMOBO>2.0.CO;2">https://doi.org/10.1175/1520-0426(2002)019<0183:EIMOBO>2.0.CO;2 </a> The monthly Chlorophyll-a climatology was generated from the SeaWifs mission dataset: <a href="10.5067/ORBVIEW-2/SEAWIFS/L3M/CHL/2018">10.5067/ORBVIEW-2/SEAWIFS/L3M/CHL/2018</a> The river runoff to ocean was generated from the GloFAS dataset: <a href="https://doi.org/10.24381/cds.a4fdd6b9">https://doi.org/10.24381/cds.a4fdd6b9 </a> The topography was generated from the Shuttle Radar Topography Mission: <a href="https://doi.org/10.1029/2019EA000658">https://doi.org/10.1029/2019EA000658</a> and smoothed with a Cressman weighted interpolation scheme. This collection does not include the JRA55-do atmospheric forcing dataset: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ocemod.2018.07.002" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.ocemod.2018.07.002</a>  Steps to setup a regional configuration of CESM/MOM6 can be found here: <a href="https://github.com/NCAR/regional_cesm_mom6.git">https://github.com/NCAR/regional_cesm_mom6.git</a>

Model output for: "CARIB12: A Regional Community Earth System Model / Modular Ocean Model 6 Configuration of the Caribbean Sea"

This collection hosts the model output fields used in : "CARIB12: A Regional Community Earth System Model / Modular Ocean Model 6 Configuration of the Caribbean Sea".  daily_surface_fields.tar: contains daily output of sea surface salinity, surface u and v velocity components, sea-surface height and mixed layer depths. Files are split in two: 2000-01-01:2009-12-31 and 2010-01-01:2019-12-31 as specified by each filename. monthly_3D_tracers.tar: contains monthly mean output of temperature and salinity for the full 3D field (lat,lon,depth). Files are split in two: 2000-01-01:2009-12-31 and 2010-01-01:2019-12-31 as specified by each filename.

Mobility of magmas within the Earth: Insights from the elasticity and transport properties of hydrous albitic melts

Results from first-principles molecular dynamics simulations of anhydrous and hydrous albitic melts.

opencc-by-4.0Jun 2024View details →

Data for "The Source of Hydrogen in Earth's Building Blocks"

Sulfur X-ray absorption near edge structure spectroscopy data from LAR 12252, collected at beamline I18 at Diamond Light Source.

Optimized Ensemble Algorithm of SVMD-SVR+BiLSTM for Short-Term Forecasting of Earth Rotation Parameters

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Melting of Hydrated Subducted Lithospheric Mantle for the Origin of Ultra-Low Velocity Zones in the Cold Regions of Earth's Core-Mantle Boundary

These are the datasets used in the figures. We submitted our manuscript to a peer-reviewed journal (Date: 2024-JUL-05).

Accompanying Files for "M/K-Dwarf Exoplanets With Similar Sizes and Instellations to Earth Typically Follow Near-Circular Orbits"

=== gammaposteriors.tar.gz === Each file (in the tarball) is named after the corresponding planetary object. The file contains samples drawn from the a-posteriori distribution of rho_{*,transit}/rho_{*,independent}. This ratio is referred to as gamma in the paper. === stellarposteriors.tar.gz === Each file contains the joint posterior distribution for the fundamental stellar parameters. The columns are: Mass (Solar masses), Radius (Solar radii), Density (kg/m^3) === transittimes.tar.gz === A pair of files are produced for each KOI/TOI, named appropriately. The pdf figure is a summary figure showing the TTV data with errors (black), along with the best fitting sinusoidal model in brown. We also report the FAP (false alarm probability) of that signal, the associated TTV period and amplitude as annotations on the figure. We also provide the actual transit times for each epoch, in the files *times.dat, using the columns... Epoch, Transit Time (BJD_UTC - 2,457,000), Error, TTV (mins), Error (mins) === lightcurves.tar.gz === Each file contains a detrended light curve, detrended using the method marginalisation process described in the paper. The columns are: Time (BJD_UTC - 2,400,000*) Relative Intensity Uncertainty For the TOIs, we include a fourth column which is the CROWDSAP value. * For TOIs, this becomes 2,457,000 === hbms -> *.tar.gz === Each tarball contains samples drawn from the a-posteriori joint distribution of the Hierarchical Bayesian Model (HBM) fits. Each tarball is a distinct model, and thus HBM: exp.tar.gz - exponential distribution beta.tar.gz - Beta distribution rayleigh.tar.gz - Rayleigh distribution tremaine.tar.gz - Tremaine distribution exp_without4087.tar.gz - exponential distribution excluding KOI-4087.01 beta_without4087.tar.gz - Beta distribution excluding KOI-4087.01 exp_without4087_fake_0p0.tar.gz - exponential distribution excluding KOI-4087.01, but applied to fake data where e=0.0 There are two types of files in each container. popchain_*.dat contains posterior samples of the population/hyper-parameters. hkplate_*.dat contains posterior samples of the population/hyper-parameters. There are 20 walkers for each file type, labelled 1-20 at the suffix of the file. The columns in each file are organised as follows... popchain_exp*.dat mu_e, var_e, loglike popchain_rayl*.dat mu_e, var_e, loglike popchain_trem*.dat mu_e, var_e, loglike popchain_beta*.dat mu_e, var_e, alpha, beta, loglike hkplatechain_*.dat h'_1, k'_1, h'_2, k'_2,...

Raw Data for Publication "Earth observations reveal impacts of climate variability on maize cropping systems in Sub-Saharan Africa"

Phenological metrics extracted for all agricultural fields used in the study. Data also includes the coordinates of the fields.

Dataset of two-impulse Earth–Moon transfers in a four-body model

The Dataset.mat file contains data of all two-impulse Earth–Moon transfers in a four-body model generated in Topputo, F., "On optimal two-impulse Earth–Moon transfers in a four-body model". Celestial Mechanics & Dynamical Astronomy, 117, 279–313 (2013). https://doi.org/10.1007/s10569-013-9513-8 The journal article and its supplementary material file are also provided. In Dataset.mat, each row represents a solution entry, with the elements: Solution achieved after MS optimization 1) Transfer cost [adimensional] 2) Transfer time [adimensional] 3) n (multiple shooting nodes) 4) ExitFlag (output of the optimization, see Matlab's fmincon) 5) Boundary Condion Error (constraints violation, see Matlab's fmincon) 6) t0 (initial time) [adimensional] 7) tf (final time) [adimensional] 8-11) Initial state (x0, y0, xv0, yv0) [adimensional] 12-15) Final state (xf, yf, xvf, yvf) [adimensional] Initial guess prior MS optimization 16) alpha (angle on departure circular orbit) [adimensional] 17) vcoeff (initial velocity coefficient) [adimensional] 18) t0 (initial time) [adimensional] 19) dt (time of flight) [adimensional] To reproduce the solutions in the dataset it is enough to integrate the initial conditon (x0, y0, xv0, yv0) from the intilal time (t0) to the final time (tf) with the equations of motion given in the paper. Note: the instantaneous Sun angle is ωs * t. Refer to the paper for the detailed description of all parameters and constants adopted to generate the dataset of solutions.

Data from: Contextualizing the 2019–2020 Kangaroo Island Bushfires: Quantifying Landscape-Level Influences on Past Severity and Recovery with Landsat and Google Earth Engine

Paper Abstract: The 2019–2020 Kangaroo Island bushfires in South Australia burned almost half of the island. To understand how to avoid future severe ‘mega-fires’ and how vegetation may recover from 2019–2020, we can utilize information from the bulk of historical fires in an area. Landsat time-series of vegetation change provide this opportunity, but there has been little analysis of large numbers of fires to build a landscape-level understanding and quantify drivers in an Australian context. In this study, we built a yearly cloud-free surface reflectance normalized burn ratio (NBR) time-series (1988–2020) using all available summer Landsat images over Kangaroo Island. Data were collected in Google Earth Engine and fitted with LandTrendr. Burn severity and post-fire recovery were quantified for 47 fires, with a new recovery metric facilitating comparison where fire frequency is high. Variables representing the current burn, fire history, vegetation structure, and topography were related to severity and yearly recovery with random forest and bivariate analysis. Results show that the 2019–2020 bushfires were the most widespread and severe, followed by 2007–2008. Vegetation recovers quickly, with NBR stabilizing ten years post-fire on average. Severity is most influenced by fire frequency, vegetation capacity and land use with more severe burns in nature conservation areas with dense vegetation and a history of frequent fires. Influence on recovery varied with time since fire, with initial (year 1–3) faster recovery observed in areas with less surviving vegetation. Later (year 6–10) recovery was most influenced by a variable representing burn year and further investigation indicates that precipitation increases in later post-fire years likely facilitated faster recovery. The relative abundance of eucalypt woodlands also has a positive influence on recovery in middle and later years. These results provide valuable information to land managers on Kangaroo Island and in similar environments, who should consider adjusting practices to limit future mega-fire risk and potential ecosystem shifts if severe fires become more frequent with climate change.   Data details: See paper: <a href="https://www.mdpi.com/2072-4292/12/23/3942">Remote Sensing | Free Full-Text | Contextualizing the 2019–2020 Kangaroo Island Bushfires: Quantifying Landscape-Level Influences on Past Severity and Recovery with Landsat and Google Earth Engine (mdpi.com)</a> See code on GitHub: <a href="https://github.com/ZZMitch/KangarooIslandFireHistory_1988to2020">ZZMitch/KangarooIslandFireHistory_1988to2020: Code from "Contextualizing the 2019–2020 Kangaroo Island Bushfires: Quantifying Landscape-Level Influences on Past Severity and Recovery with Landsat and Google Earth Engine" (RS, 2020) (github.com)</a>   If you use these data, please reference:  Bonney, M.T., He, Y., Myint, S.W., 2020. Contextualizing the 2019–2020 Kangaroo Island bushfires: Quantifying landscape-level influences on past severity and recovery with Landsat and Google Earth Engine. Remote Sensing 12(23), <a href="https://doi.org/10.3390/rs12233942" rel="nofollow">https://doi.org/10.3390/rs12233942</a>.

opencc-by-sa-4.0Oct 2017View details →

Model Spectra for Morley et al. 2017 (Observing the Atmospheres of Known Temperate Earth-sized Planets with JWST)

README This file contains the model spectra presented in Morley et al. 2017, Observing the Atmospheres of Known Temperate Earth-sized Planets with JWST.  The models are organized as follows:  ########################## ## Transmission spectra ## ########################## transmission_spectra contains 4 folders: alb0.0_massradiusrel includes models with Bond albedo=0.0, and planet masses assuming the Weiss & Marcy 2014 mass-radius relationship alb0.3_massradiusrel includes models with Bond albedo=0.3, and planet masses assuming the Weiss & Marcy 2014 mass-radius relationship alb0.0_measured_masses includes models with Bond albedo=0.0, and observed planet masses, as described in Morley et al. 2017 alb0.3_measured_masses includes models with Bond albedo=0.3, and observed planet masses, as described in Morley et al. 2017 each file contains the wavelength and model transit depth from 0.3 to 250 microns, calculated at 1 cm-1 wavenumber resolution.  The file name indicates the planet name, model surface pressure (in bar), Bond albedo, and the assumed composition (Earth-, Venus-, or Titan-based compositions, calculated in chemical equilibrium at each layer of the model, as described in Morley et al. 2017).  (e.g. trans_spect_massradiusrel_gj1132b_psurf0.1_alb0.0_chem_earth.p.txt is a model of GJ 1132b, assuming the Weiss/Marcy mass-radius relationship, with a surface pressure of 0.1 bar, Bond albedo of 0.0, and an Earth-based composition).  ########################## ##   Emission spectra   ## ########################## emission_spectra contains 4 folders: alb0.0 includes models with Bond albedo=0.0 alb0.3 includes models with Bond albedo=0.3 alb0.7 includes models with Bond albedo=0.7 emission_spectra_dividestar includes models that have already been divided by a model stellar spectrum, for convenience.  each file contains the wavelength and model thermal emission flux in W/m2/m from 0.3 to 250 microns, calculated at 1 cm-1 wavenumber resolution.  The file name indicates the planet name, model surface pressure (in bar), Bond albedo, and the assumed composition (Earth-, Venus-, or Titan-based compositions, calculated in chemical equilibrium at each layer of the model, as described in Morley et al. 2017).  (e.g. gj1132b_psurf0.01_alb0.0_chem_earth.spec is a model of GJ 1132b with a surface pressure of 0.01 bar, Bond albedo of 0.0, and an Earth-based composition).  The models in emission_spectra_dividestar instead contain the flux of the planet divided by the flux of the star.    ########################## ##    Eclipse Depths    ## ########################## eclipse_depths contains a file for each planet with calculated eclipse depths for each of the wide-band JWST MIRI filters (http://ircamera.as.arizona.edu/MIRI/pces.htm).  (new version from Jan 29 2018 has fixed a bug in MIRI eclipse depths that affected the longest wavelength filter for GJ 1132b and LHS 1140b only).