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FIGURE 5 in Rediscovery and redescription of the Andean earth-snake Atractus wagleri (Reptilia: Serpentes: Colubridae)
FIGURE 5. Geographical distribution of Atractus sanguineus and Atractus wagleri. Open symbols correspond to respective type localities of the species.
FIGURE 4 in Rediscovery and redescription of the Andean earth-snake Atractus wagleri (Reptilia: Serpentes: Colubridae)
FIGURE 4. Sulcate (left) and asulcate (right) views of the hemipenis of Atractus wagleri (MHUA 14504). Scale = 10mm.
Data of the publication: Dispersive heterodyne probing method for laser frequency stabilization based on spectral hole burning in rare-earth doped crystals by O. Gobron et al.
<p>Data corresponding to the figures of the publication "Dispersive heterodyne probing method for laser frequency stabilization based on spectral hole burning in rare-earth doped crystals" by O. Gobron et al. (https://doi.org/10.1364/OE.25.015539). A text file describes data in each compressed folder, please refer to the caption in the publication for more details. </p>
Ionosphere-Thermosphere Data Published in "Challenges to Understanding the Earth's Ionosphere and Thermosphere"
<p>These data sets accompany an AGU Centennial publication "Challenges to Understanding the Earth's Ionosphere and Thermosphere" by R.A. Heelis and A. Maute. The data file tiegcm_totTgcm.s_181_12UT_GCpaper.nc is output from a thermosphere-ionosphere-electrodynamics general circulation model (TIEGCM) simulation described in the publication (DOI:10.1007/s11214-017-0330-3). The simulation is for 2009 solar minimum conditions. The ionospheric current and neutral wind in file "heelis_empirical_f130_12UT_extracted.nc" is based on a stand-alone dynamo model (described in doi:10.1002/2017JA024841) driven by the Horizontal Wind Model and ionospheric conductivities based on empirical models of the ionosphere (IRI) and the neutral atmosphere (MSIS). The calculation is for solar medium condition (F10.7=130) and DOY 181 under geomagnetic quiescent times.</p>
Input Data for A Fast Surrogate Model for 3D-Earth Glacial Isostatic Adjustment using Tensorflow (v2.8.0) Artificial Neural Networks
<p>Training datasets for the manuscript A Fast Surrogate Model for 3D-Earth Glacial Isostatic Adjustment using Tensorflow (v2.8.0) Artificial Neural Networks. Two separate datasets are contained for training the ANNs: the 3D-spherically-symmetric (SS) rate-of-change of relative sea level (ROCRSL) and the 3D-SS rate of change of radial displacement (ROCRAD) as a function of SS profiles. Two other datasets contain RSL projections from the explicit (i.e. Seakon 3D - Seakon SS + NMSS ) model and the NMSS model, labelled Seakon_plus_NMSS_RSL and NMSS respectively.</p> <p>Filenames denote the structure of the SS profile: </p> <p>???_?.??_??.*.csv = LT_UMV_LMV.*.{csv,nc}<br> </p> <p>LT = elastic lithosphere thickness (km)</p> <p>UMV = upper mantle viscosity (1E21 Pa s)</p> <p>LMV = lower mantle viscosity (1E21 Pa s)</p> <p>i.e. 96_0.5_10.seakon_S40RTS_lr18-SS.rrad.roc.r360x180.P5.density_wSSRRADROC.csv.bz2 has the SS profile</p> <p>96km elastic lithosphere, 0.5E21 Pa s upper mantle viscosity, 10E21 Pa s lower mantle viscosity</p> <p> </p> <p>The columns of the input files are as follows:</p> <p>LT, UMV, LMV, longitude, latitude, time(t=0), ice(t=0), SS_ROC_RSL (t=0), time(t=-1), ice(t=-1), time(t=-2), ice(t=-2), time(t=-3), ice(t=-3), time(t=-4), ice(t=-4), 3D-SS_ROC_RSL(t=0)</p> <p>units for the above are as follows:</p> <p>km, 1E21 Pas, 1E2 Pas, degrees east (0->360), degrees (-180->180), days since 2000, m, mm/year, days since 2000, m, days since 2000, m, days since 2000, m, days since 2000, m, mm/year</p> <p>where 'days since 2000' assumes exactly 365.25 days per year.</p>
Melting and Defect Transitions in FeO up to Pressures of Earth's Core-Mantle Boundary
<p>The tables here represent the Source Data for Figure 6 in the manuscript titled "Melting and defect transitions in FeO up to pressures of Earth's core-mantle boundary.</p>
Table 8 for the Study: "Correlation Study: Triggering and Magnitude of Earthquakes in Italy (≥M4.3) in Relation to the Positions and Gravitational Forces of the Sun, Moon, and Planets Relative to Earth."
<p>Summary distribution and probability graphs for the study: <i>"Correlation Study: Triggering and Magnitude of Earthquakes in Italy (≥M4.3) in Relation to the Positions and Gravitational Forces of the Sun, Moon, and Planets Relative to Earth". </i>Summary distribution and probability of the three σFR indices (parameters A, B and % Alert Time) of the Hypothesis 2 for all 200 earthquakes, for Section 3.2.2.</p>
Table 6 for the Study: "Correlation Study: Triggering and Magnitude of Earthquakes in Italy (≥M4.3) in Relation to the Positions and Gravitational Forces of the Sun, Moon, and Planets Relative to Earth."
<p>Summary Excel tables for the study: <i>"Correlation Study: Triggering and Magnitude of Earthquakes in Italy (≥M4.3) in Relation to the Positions and Gravitational Forces of the Sun, Moon, and Planets Relative to Earth". </i>Results of the first hypothesis, for both the sample of 200 and the 63 earthquakes that occurred after 1988, for Section 3.1.</p>
Table 7 for the Study: "Correlation Study: Triggering and Magnitude of Earthquakes in Italy (≥M4.3) in Relation to the Positions and Gravitational Forces of the Sun, Moon, and Planets Relative to Earth."
<p>Summary graphs for the study: <i>"Correlation Study: Triggering and Magnitude of Earthquakes in Italy (≥M4.3) in Relation to the Positions and Gravitational Forces of the Sun, Moon, and Planets Relative to Earth". </i>Data distributions of the Hypothesis 2, with the R² regression coefficients of the three σFR values and indices (parameters A, B and % Alert Time) of both the 200 earthquakes and the 63 earthquakes after 1988, for Section 3.2.1.</p><p>The R² regression coefficients of each curve are calculated in the Excel files on the 2 Drives: </p><p>https://docs.google.com/spreadsheets/d/17UGHZlvZ2N-g6TOzgqpIe248_IitBmka8DbGWsv-lKQ/edit?usp=sharing, for the 200 earthquakes.</p><p>https://docs.google.com/spreadsheets/d/1Pyd4wZxZrq6G76nM2SwHYBBjbPZz7uM8GeO7PXG7obU/edit?usp=sharing, for the 63 earthquakes after 1988.</p>
Table 9 for the Study: "Correlation Study: Triggering and Magnitude of Earthquakes in Italy (≥M4.3) in Relation to the Positions and Gravitational Forces of the Sun, Moon, and Planets Relative to Earth."
<p><strong>Overview of the 200 analyzed earthquakes encompassing values and indices for σFR Parameters A, B, and the percentile of Alert Time across the three Analysis Lines, for Section 3.2.6.</strong></p><p>It contains 1 Excel calculation file, consisting of 3 excel sheet for each of the 3 lines of analysis, for both the 200 earthquakes since 1600 and for the 63 earthquakes that occurred after 1988.</p>
Global patterns and drivers of dissolved organic matter across Earth systems: Insights from H/C and O/C ratios
<p>Dissolved organic matter (DOM) is ubiquitous and contains a complex pool of thousands of distinct molecules, and their chemical characteristics help us inform the fate of global carbon. Yet, a more holistic perspective of molecular characteristics of DOM and underlying mechanisms across Earth systems and climates remain understudied. Here, we present a comprehensive analysis of the molecular characteristics of DOM using two abundance-weighted average indices, i.e., H/C and O/C ratios by compiling 2,995 samples from 317 studies covering the waters, land, plant, petroleum, and atmosphere systems, and the climatic regions from tropics to tundra. H/C ratios are lower on average in waters (H/C = 1.15 ± 0.005) and land (H/C = 1.20 ± 0.010) than the other systems, while their O/C ratios rank between plant and atmosphere. In the waters and land systems, the H/C ratios of DOM vary from the highest to the lowest in the habitats of land-to-ocean continuum generally as snow > glacier > marine ≥ freshwater/soil > groundwater. The H/C ratios show predictably U-shaped patterns along latitudinal gradients indicating the lowest abundance of more hydrogen saturated molecules at around mid-latitudes of 40°-50° in river water, lake water, and forest soil. The two ratios are primarily controlled by the environmental factors such as pH, dissolved oxygen, and carbon and nitrogen contents. We further unveil additional and considerable links between the ratios and the extremes of climatic factors such as precipitation of warmest quarter and maximum temperature of warmest month. Our synthesis provides molecular-level perspectives to characterize the global distribution and underlying drivers of DOM, which is complementary for our understanding global carbon cycle's processes under future global change.</p>
Determination of borehole tiltmeter orientation using earth tides
<p>These data are used in the article Determination of borehole tiltmeter orientation using earth tides. LSBB 2012, LSBB 2021 and ETNA 2022 datasets are measured by optical borehole tiltmeters with a sampling frequency of 1 Hz. They contain four columns: number, channel d1, channel d2, and channel d3. ETNA 2023 dataset is measured by LILY tiltmeter, comprising three columns: time, ux, and uy.</p>
FIGURE 3 in Predictive-like distribution mapping using Google Earth: Reassessment of the distribution of the bromeligenous frog, Scinax v-signatus (Anura: Hylidae)
FIGURE 3. (A) Specimen of the bromeliad Alcantarea imperialis with inflorescence photographed about 1 km from the type locality of Scinax v-signatus in the Municipality of Teresópolis, State of Rio de Janeiro. (B) A granitic outcrop with several individuals of A. imperialis, photographed in the Municipality of Miguel Pereira, State of Rio de Janeiro. (C) The same outcrop as it appears on Google Earth images. Yellow arrows indicate small whitish dots that represent individual bromeliads.
FIGURE 4 in Predictive-like distribution mapping using Google Earth: Reassessment of the distribution of the bromeligenous frog, Scinax v-signatus (Anura: Hylidae)
FIGURE 4. Relief map of the State of Rio de Janeiro (Modified from http://www.agritempo.gov.br/altimetria/RJ.html) with the potential distribution of Alcantarea imperialis produced based on images of Google Earth (yellow circles), occurrence of Scinax v-signatus based on voucher specimens (blue stars), localities for Scinax insperatus (gray triangle), localities we identified A. imperialis on the field but were unable to detect using Google Earth (white square), and localities Based on A. imperialis from the Herbarium on the Jardim Botânico do Rio de Janeiro (Square) – http://www.jbrj.gov.br/.
FIGURE 2 in Predictive-like distribution mapping using Google Earth: Reassessment of the distribution of the bromeligenous frog, Scinax v-signatus (Anura: Hylidae)
FIGURE 2. Map from GAA depicting the distribution of Scinax v-signatus. Note that the type locality (Black circle) was not included in distribution area.
FIGURE 1 in Predictive-like distribution mapping using Google Earth: Reassessment of the distribution of the bromeligenous frog, Scinax v-signatus (Anura: Hylidae)
FIGURE 1. Map of southeastern Brazil highlighting the area of the States of Minas Gerais, Rio de Janeiro, and Espírito Santo. Symbols indicate the type localities for Scinax v-signatus (white circle), the type locality for S. arduous (white square) and S. belloni (black square). The white hexagons indicate three new localities for S. arduous. The star indicates two localities where an undescribed species was collected in the south of the State of the Espírito Santo, and the black circle indicates the locality for another undescribed species collected in the north of the State of Rio de Janeiro.
Model data for "The GERB Obs4MIPs Radiative Flux Dataset: A new tool for climate model evaluation", submitted to Earth System Science Data
<p>© Crown Copyright, Met Office</p><p>The E1hrClimMon files contain the monthly mean diurnal cycles of TOA radiative fluxes (all-sky and clear-sky) for amip experiment of two configurations of HadGEM3: GC3.1 and GC5.0. The monthly mean diurnal cycle is constructed by averaging each UTC hourly mean over the entire month. The HadGEM3 OLR diagnostics used in this study differ from those submitted to CFMIP3. The OLR diagnostics submitted to CFMIP3 contain a correction that accounts for the surface temperature adjustment by the boundary layer scheme in model time steps between radiation time steps. This OLR diagnostic adjustment is introduced to conserve energy, but it significantly distorts the diurnal cycle of OLR. For comparison with the GERB obs4MIPs products, the OLR without this correction is recommended.</p><p>The COSP file contains the average monthly climatologies for the variables cfadLidarsr532 and clisccp for the amip simulations of GC3.1 and GC5.0.</p>
Model codes, data, and plot scripts for the paper "A numerical modeling study on the Earth's surface brightening effect of cirrus thinning"
<p>The model codes, data, and plot scripts used in the paper "A numerical modeling study on the Earth's surface brightening effect of cirrus thinning".</p><ul><li>Mods: the modified CAM model code used in these three experiments.</li><li>Results: post-processing NCL scripts and the results used for making plots.</li><li>figs: the NCL scripts and figures used in the paper.</li><li>Parcel: parcel model that represents the ice nucleation process.</li></ul><p> </p>
Quantum critical phase of FeO spans conditions of Earth's lower mantle
<p>The tables here represent the Source Data for figures in the manuscript titled "Quantum critical phase of FeO spans conditions of Earth's mantle base".</p><p> </p>
fogo de chão / earth fire
earth fire construction in backyard at home place Source: Objaverse 1.0 / Sketchfab
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.