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Deep-Time Data Infrastructure: A DCO Legacy Program

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  1. Deep-Time Data Infrastructure: A DCO Legacy Program Robert M. Hazen—Geophysical Lab, Carnegie Institution DCO Data Science Day—RPI—June 5, 2014

  2. Conclusions Vast, largely untapped, data resources inform our view of Earth’s dynamic history over 4.5 billion years. Combining those deep-time data resources into a single infrastructure represents an opportunity for accelerated “abductive” discovery.

  3. Carnegie Institution Robert Hazen Xiaoming Liu AnatShahar Rutgers Paul Falkowski RPI Peter Fox Univ. of Arizona Robert Downs MiheiDucea GretheHystad Barbara Lafuente Hexiong Yang Alex Pires Joaquin Ruiz Joshua Golden Melissa McMillan Shaunna Morrison Johns Hopkins Univ. DimitriSverjensky Charlene Estrada John Ferry Namhey Lee Harvard University Andrew Knoll Indiana University David Bish Univ. of Michigan Rodney Ewing Univ. of Maryland James Farquhar John Nance Univ. of Wisconsin John Valley Geol. Survey Canada WouterBleeker CalTech Ralph Milliken Univ. of Maine Edward Grew Smithsonian Inst. Timothy McCoy Univ. of Manitoba AndreyBekker MINDAT.ORG Jolyon Ralph Colorado State Holly Stein Aaron Zimmerman Univ. of Tennessee Linda Kah Univ College London Dominic Papineau George Mason Univ. Stephen Elmore Deep-Time Data Collaborators

  4. Deep-Time Data Resources Mineralogy and petrology data: Mineral species and assemblages Compositions (including isotopes) Age (ages) Geographic location; tectonic setting Crystal size; morphology; twinning Solid and fluid inclusions; defects; Magnetic domains; zoning; exsolution Surface properties; grain boundaries

  5. Deep-Time Data Resources Mineralogy and petrology data Paleobiology data Fossil species and assemblages Age Biominerals; isotopic composition Molecular biomarkers Host lithology Geological/tectonic context

  6. Deep-Time Data Resources Mineralogy and petrology data Paleobiology data Proteomics data Enzyme structure and function Age (from phylogenetics) Active site composition Microbial context

  7. Deep-Time Data Resources Mineralogy and petrology data Paleobiology data Proteomics data Geochemistry data and modeling Thermochemical data Equilibrium and reaction path models

  8. Deep-Time Data Resources Mineralogy and petrology data Paleobiology data Proteomics data Geochemistry data and modeling Paleotectonic & PaleomagneticData Age

  9. This is the IMA Mineral Database website, with a direct link to the Mineral Evolution Database.

  10. This map displays the localities. The popup demonstrates metadata for a given locality.

  11. The Potential of Deep-Time Data The Premise: Rocks, minerals, fossils, and life’s biochemistry hold clues to significant changes in Earth’s near-surface environment through 4.5 billion years of history.

  12. The Rise of Atmospheric Oxygen Lyons et al. (2014) Nature506, 307-314. D.E.Canfield(2014) Oxygen. Princeton Univ. Press

  13. The Rise of Atmospheric Oxygen ? Kump (2008) Nature 451, 277-278.

  14. The Rise of Atmospheric Oxygen D.E.Canfield(2014) Oxygen. Princeton Univ. Press. Lyons et al. (2014) Nature506, 307-314.

  15. The Rise of Oxygen: Evidence from redox-sensitive elements = Major metal element = Major non-metal element = Trace element

  16. The Rise of Subsurface Oxygen Geochemical modeling is key. log fO2 ~ -72

  17. The Rise of Subsurface Oxygen log fO2 < -68 Siderite FeCO3

  18. The Rise of Subsurface Oxygen Azurite & Malachite log fO2 > -43

  19. The Rise of Subsurface Oxygen:Basalt weathering before/after the GOE Reaction path calculations reveal changes in mineralogy as fluids and rocks not in equilibrium react with each other. Data from Sverjensky et al. (in prep)

  20. The Rise of Subsurface Oxygen:Basalt weathering before/after the GOE Reaction path calculations reveal changes in mineralogy as fluids and rocks not in equilibrium react with each other. Data from Sverjensky et al. (in prep)

  21. What minerals won’t form before the Great Oxidation Event? 598 of 643 Cu minerals 202 of 220 U minerals 319 of 451 Mn minerals 47 of 56 Ni minerals 582 of 790 Fe minerals Chrysocolla Piemontite Garnierite Xanthoxenite

  22. Co-evolution of the geosphere and biosphere Biologically mediated changes in Earth’s atmospheric composition at ~2.4 to 2.2 Ga represent the single most significant factor in Earth’s mineralogical diversity.

  23. Enzymes reveal Earth’s geochemical history. Ferredoxin (before the GOE)

  24. Enzymes reveal Earth’s geochemical history. Nitrogenase (after the GOE)

  25. The Rise of Subsurface Oxygen

  26. The Rise of Subsurface Oxygen Golden et al. (2013), EPSL SE HERE GOE HERE

  27. The Rise of Subsurface Oxygen Kump (2008) Nature 451, 277-278.

  28. The Rise of Subsurface Oxygen Hypothesis: There was a protracted “Great Subsurface Oxidation Interval” that postdated the GOE by a billion years. This interval was the single most significant factor in Earth’s mineralogical diversification.

  29. Data-Driven Discovery Most of what scientists do most of the time is start with a known phenomenon, and then collect relevant data and develop explanatory hypotheses.

  30. Deduction Earth’s atmospheric oxidation influenced the partitioning of redox-sensitive elements. Mo, Re, Ni, and Co are redox-sensitive elements. Therefore, we deduce that atmospheric oxidation influenced the partitioning of Mo, Re, Ni, and Co.

  31. RESULTS: Molybdenite (MoS2) through Time Golden et al. (2013) EPSL 366:1-5. SE HERE GOE HERE

  32. RESULTS: Cu/Ni in carbonates vs. time Xiaoming Liu et al. (2013) GOE HERE SE HERE

  33. Induction Each of the last 5 supercontinent cycles led to episodes of enhanced mineralization during intervals of continental convergence. Mo, Be, B, and Hg are mineral-forming elements. Therefore, we predict by induction that Mo, Be, B, and Hg minerals will display enhanced mineralization during intervals of continental convergence.

  34. The Supercontinent Cycle

  35. The Supercontinent Cycle SUPERCONTINENT STAGE INTERVAL DURATION Kenorland (Superia) Assembly 2.8-2.5 300 Stable 2.5-2.4 100 Breakup 2.4-2.0 400 Columbia (Nuna) Assembly 2.0-1.8 200 Stable 1.8-1.6 200 Breakup 1.6-1.2 400 RodiniaAssembly 1.2-1.0 200 Stable 1.0-0.75 250 Breakup 0.75-0.6 150 PannotiaAssembly 0.6-0.56 40 Stable 0.56-0.54 20 Breakup 0.54-0.43 110 Pangaea Assembly 0.43-0.25 180 Stable 0.25-0.175 75 Breakup 0.175-present 175

  36. RESULTS: The Supercontinent CYCLE The distribution of zircon crystals through time correlates with the supercontinent cycle over the past 3 billion years. (Condie & Aster 2010; Hawksworth et al. 2010)

  37. RESULTS: Mo Mineral Evolution Temporal distribution of molybdenite (MoS2) Golden et al. (2013) EPSL 366:1-5.

  38. Hg Mineral Evolution The distribution of mercury (Hg) minerals through time correlates with the SC cycle over the past 3 billion years, but there’s a gap during Rodiniaasembly. Hazen et al. (2012) Amer. Mineral.97:1013.

  39. Abduction Abduction is a form of logical inference that goes from reliable data (i.e., observations), to a hypothesis that seeks to explain those data. (Paraphrased from Wikipedia)

  40. Abduction Observations lead to new hypotheses. We have vast amounts of data on mineral species, compositions, isotopes, petrologic context, thermochemical parameters, tectonic settings, and the co-evolving biosphere through deep time. Previously unrecognized patterns and correlations will emerge from the integration and evaluation of those data.

  41. Data-Driven Discovery THE CHALLENGE: Recognizing statistically meaningful patterns in large data resources: 1. Correlations among many variables

  42. DATA-DRIVEN DISCOVERY Large integrated data resources can be explored with multivariate techniques (i.e., principal component analysis). Search for highly correlated patterns among linear combinations of many different variables.

  43. Data-Driven Discovery THE CHALLENGE: Recognizing statistically meaningful patterns in large data resources: 2. Meaningful trends in data vs. time

  44. RESULTS: Molybdenite (MoS2) through Time Golden et al. (2013) EPSL 366:1-5. 432 molybdenite samples

  45. Are these trends statistically significant? • Analyze equal sized bins. • Apply statistical tests: linear regression of log Re content vs. time. • (Montgomery et al. 2006)

  46. Data-Driven Discovery THE CHALLENGE: Recognizing statistically meaningful patterns in large data resources: 3. Peak-to-noise problem

  47. Peaks in ages of ~40,000 zircon crystals Condie & Aster (2010) Precambrian Research 180:227-236.

  48. Monte Carlo Mean Kernal Density Analysis Condie & Aster (2010) Precambrian Research180:227-236.

  49. Data-Driven Discovery THE CHALLENGE: Recognizing statistically meaningful patterns in large data resources: 4. Visualization opportunities

  50. Why Do We See the Minerals We See? Too many species: As, Hg, Sb, U Too few species: Ga, Rb, Hf Element abundances versus numbers of mineral species (Hazen, Grew, Downs et al.)