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Category: Data visualisations

Data visualisations are graphical representations of scientific data, using tools like axes, scales, and colour gradients to quantify and display information. Through charts, graphs, and plots, they help users spot patterns, trends, and insights, making complex data easier to interpret, understand, and communicate effectively.

Crust elements

The most common elements inside the Earth’s crust.

The most common elements inside the Earth’s crust. The Scientific colour map ‘batlow‘ is used to represent individual elements to all readers.

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Surface areas on the Earth

A direct comparison of the relative surface area covered by individual land masses and oceans on the Earth.

A direct comparison of the relative surface area covered by individual land masses („continents“) and oceans on the Earth.

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Atmospheric CO2 concentration

Globally averaged concentration of carbon dioxide (CO2) in the atmosphere for the time period 803’719 BCE – 2018.

Globally averaged concentration of carbon dioxide (CO2) in the atmosphere for the time period 803’719 BCE – 2018. Shown is data from Bereiter et al. (2015) and the concentration is measured in parts per million (ppm). The long-term global average atmospheric concentrations of CO2 have been combined using several sources, all available at the NOAA/ESRL Global Monitoring Division. Not only the level of CO2 in the atmosphere matters, but also the rate at which it has changed. It took us a matter of decades to achieve larger changes than previous ones, which occurred over centuries or even thousands of years. This gives species, planetary systems, and ecosystems much less time to adapt. The Scientific colour map ‘bilbao‘ is used to represent data accurately and to all readers.

  • Creator: Fabio Crameri
  • This version: 02.11.2021
  • License: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
  • Specific citation: This graphic by Fabio Crameri based on data compiled by Bereiter et al. (2015) is available via the open-access s-ink.org repository.
  • Related reference: Bereiter, B., Eggleston, S., Schmitt, J., Nehrbass‐Ahles, C., Stocker, T. F., Fischer, H., … & Chappellaz, J. (2015). Revision of the EPICA Dome C CO2 record from 800 to 600 kyr before present. Geophysical Research Letters, 42(2), 542-549.
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World countries CO2 emissions

The World’s countries’ CO2 emissions per capita and year, for the year 2018.

The World’s countries’ carbon dioxide (CO2) emissions per capita and year, for the year 2018, driving climate change. Shown is the CAIT data, which is available on climatewatchdata.org/ghg-emissions. The Scientific colour map ‘bilbao‘ is used to represent data accurately and to all readers.

  • Creator: Fabio Crameri
  • This version: 01.11.2021
  • License: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
  • Specific citation: This graphic by Fabio Crameri based on CAIT data (Climate Watch, 2020) is available via the open-access s-Ink repository.
  • Related reference: Climate Watch, 2020, GHG Emissions, Washington, DC: World Resources Institute
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Seismic mantle tomography maps

Surface projected global horizontal seismic S-wave velocity anomaly maps for different mantle depths revealing the two large low shear-wave velocity provinces (LLSVPs).

Surface projected global horizontal seismic S-wave velocity anomaly maps for different mantle depths revealing the two large low shear-wave velocity provinces (LLSVPs) below the Pacific (named Jason) and Africa (named Tuzo). Shown is the S10MEAN model based on Doubrovine et al. (2016) averaging 10 tomography models allowing to compare relative variations in S-wave velocity. The Scientific colour map ‘batlow‘ is used to represent data accurately and to all readers.

  • Creator: Fabio Crameri
  • This version: 31.10.2021
  • License: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
  • Specific citation: This graphic by Fabio Crameri based on data compiled by Doubrovine et al. (2016) is available via the open-access s-Ink repository.
  • Related references: Doubrovine, P. V., Steinberger, B., and Torsvik, T. H. (2016), A failure to reject: Testing the correlation between large igneous provinces and deep mantle structures with EDF statistics, Geochem. Geophys. Geosyst., 17, 1130– 1163, doi:10.1002/2015GC006044.
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S-wave velocity maps

Global horizontal S-wave seismic velocity anomaly maps for different upper-mantle depths.

Global horizontal S-wave seismic velocity anomaly maps for different upper-mantle depths highlighting the variable base topography of the surface plates with seismically fast, deep continental roots and cratons reaching far down into the mantle. Shown is the average of two upper-mantle seismic tomography models, SL2013sv (Schaeffer & Lebedev, 2013) and 3D2016_09Sv (Debayle et al., 2016). The Scientific colour map ‘batlow‘ is used to represent data accurately and to all readers.

  • Creator: Fabio Crameri
  • This version: 30.10.2021
  • License: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
  • Specific citation: This graphic by Fabio Crameri based on data compiled on SubMachine (Hosseini et al., 2018) is available via the open-access s-Ink repository.
  • Related references:
    · Hosseini, K. , Matthews, K. J., Sigloch, K. , Shephard, G. E., Domeier, M. and Tsekhmistrenko, M. (2018), SubMachine: Web-Based tools for exploring seismic tomography and other models of Earth’s deep interior. Geochemistry, Geophysics, Geosystems, 19. doi:10.1029/2018GC007431
    · Debayle, E., Dubuffet, F., and Durand, S. (2016), An automatically updated S-wave model of the upper mantle and the depth extent of azimuthal anisotropy, Geophys. Res. Lett., 43, 674– 682, doi:10.1002/2015GL067329.
    · A. J. Schaeffer, S. Lebedev, Global shear speed structure of the upper mantle and transition zone, Geophysical Journal International, Volume 194, Issue 1, July 2013, Pages 417–449, https://doi.org/10.1093/gji/ggt095
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Lithosphere thickness map

Global maps displaying lateral variations in lithosphere thickness across the surface of the Earth.

Global maps displaying lateral variations in lithosphere thickness across the surface of the Earth. Oceanic lithosphere is assigned a thickness proportional to the square root of its age (ages are taken from Müller et al., 1997). For continental areas, characteristic thickness is determined following the method of Gung et al. (2003), who employ the maximum depth for which the seismic velocity anomaly (as determined using the seismic tomography model S20RTSb of Ritsema et al., 2004) is consistently greater than +2%. Moreover, a 100-km thickness is imposed as the minimum continental and maximum oceanic characteristic thickness. It should be kept in mind that material properties such as viscosity vary continuously throughout the depth of the lithosphere, so the definition of thickness may vary. The presented model does not assume any particular definition, but instead characterises lateral variations in layer thickness (see Conrad and Lithogow-Bertelloni, 2006). The Scientific colour map ‘acton‘ is used to represent data accurately and to all readers.

  • Creator: Fabio Crameri
  • This version: 25.10.2021
  • License: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
  • Specific citation: This graphic by Fabio Crameri based on data by Conrad & Lithgow-Bertelloni (2006) is available via the open-access s-ink.org repository.
  • Related reference: Conrad, C.P., and C. Lithgow-Bertelloni (2006), Influence of continental roots and asthenosphere on plate-mantle coupling, Geophysical Research Letters, 33, L05312, doi:10.1029/2005GL025621.
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Heat flow map

Global maps of the solid Earth’s surface heat flow based on Davies (2013).

Global maps of the solid Earth’s surface heat flow based on Davies (2013). Relying on over 38,000 measurements, the map is a combination of three components. First, in regions of young ocean crust (<67.7 Ma), the model estimate uses a half-space conduction model based on the age of the oceanic crust, since it is well known that raw data measurements are frequently influenced by significant hydrothermal circulation. Second, in other regions of data coverage, the estimate is based on data measurements. At the map resolution, these two categories (young ocean & data covered) cover 65% of Earth’s surface. Third, for all other regions the estimate is based on the assumption that there is a correlation between heat flow and geology. This assumption is assessed and the correlation is found to provide a minor improvement over assuming that heat flow would be represented by the global average.

The Scientific colour map ‘lipari‘ is used to represent data accurately and to all readers.

  • Creator: Fabio Crameri
  • Original version: 25.10.2021
  • This version: 10.05.2023
  • License: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
  • Specific citation: This graphic by Fabio Crameri based on Davies (2013) is available via the open-access s-ink.org repository.
  • Related reference: Davies, J. H. (2013), Global map of solid Earth surface heat flow, Geochem. Geophys. Geosyst., 14, 4608– 4622, doi:10.1002/ggge.20271.
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Cenozoic paleogeography (animation)

Global paleogeography with zoomed in figures showing the evolution of oceanic gateways active during the Cenozoic time.

Global paleogeography of Straume et al. (2020) with zoomed in figures showing the evolution of oceanic gateways active during the Cenozoic time (66 – 0 Ma).

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