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

Deep-Time Data Infrastructure: A DCO Legacy Program

Robert M. Hazen—Geophysical Lab, Carnegie InstitutionDCO Data Science Day—RPI—June 5, 2014

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.

Deep-Time Data CollaboratorsCarnegie Institution Robert Hazen Xiaoming Liu Anat ShaharRutgers Paul FalkowskiRPI Peter FoxUniv. of Arizona Robert Downs Mihei Ducea Grethe Hystad Barbara Lafuente Hexiong Yang Alex Pires Joaquin Ruiz Joshua Golden Melissa McMillan Shaunna Morrison

CalTech Ralph MillikenUniv. of Maine Edward GrewSmithsonian Inst. Timothy McCoyUniv. of Manitoba Andrey BekkerMINDAT.ORG Jolyon RalphColorado State Holly Stein Aaron ZimmermanUniv. of Tennessee Linda KahUniv College London Dominic PapineauGeorge Mason Univ. Stephen Elmore

Johns Hopkins Univ. Dimitri Sverjensky Charlene Estrada John Ferry Namhey LeeHarvard University Andrew KnollIndiana University David BishUniv. of Michigan Rodney EwingUniv. of Maryland James Farquhar John NanceUniv. of Wisconsin John ValleyGeol. Survey Canada Wouter Bleeker

Deep-Time Data ResourcesMineralogy 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

Mineralogy and petrology data

Paleobiology data

Fossil species and assemblages

Age

Biominerals; isotopic composition

Molecular biomarkers

Host lithology

Geological/tectonic context

Deep-Time Data Resources


Paleobiology data

Proteomics data

Enzyme structure and function

Age (from phylogenetics)

Active site composition

Microbial context



Paleobiology data

Proteomics data

Geochemistry data and modeling

Thermochemical data

Equilibrium and reaction path models



Paleobiology data

Proteomics data

Geochemistry data and modeling

Paleotectonic & Paleomagnetic Data

Age


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

Evolution Database.

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

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.

The Potential of Deep-Time Data

The Rise of Atmospheric Oxygen

Lyons et al. (2014) Nature 506, 307-314.

D.E.Canfield (2014) Oxygen. Princeton Univ. Press


Kump (2008) Nature 451, 277-278.

?


D.E.Canfield (2014) Oxygen. Princeton Univ. Press.

Lyons et al. (2014) Nature 506, 307-314.

= Major metal element

= Major non-metal element

= Trace element

The Rise of Oxygen: Evidence from redox-sensitive elements

log fO2 ~ -72

Geochemical modeling is key.

The Rise of Subsurface Oxygen

SideriteFeCO3

log fO2 < -68


Azurite&

Malachite

log fO2 > -43


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)

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)

The Rise of Subsurface Oxygen:Basalt weathering before/after the GOE

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

Piemontite

Garnierite

Xanthoxenite

Chrysocolla

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.

Enzymes reveal Earth’s geochemical history.

Ferredoxin (before the GOE)

Nitrogenase (after the GOE)



Golden et al. (2013), EPSL

GOE HERE

SE HERE


Kump (2008) Nature 451, 277-278.


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.


Most of what scientists do most of the time is start with a known phenomenon, and then collect

relevant data and develop explanatory hypotheses.

Data-Driven Discovery Data-Driven Discovery

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.

DeductionDeduction

RESULTS: Molybdenite (MoS2) through Time

GOE HERE

SE HERE

Golden et al. (2013) EPSL 366:1-5.

RESULTS: Cu/Ni in carbonates vs. time

SE HERE GOE HERE

Xiaoming Liu et al. (2013)

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.

InductionInduction

The Supercontinent Cycle

The Supercontinent CycleSUPERCONTINENT STAGE INTERVAL DURATION

Kenorland (Superia) Assembly 2.8-2.5 300Stable 2.5-2.4 100Breakup 2.4-2.0 400

Columbia (Nuna) Assembly 2.0-1.8 200Stable 1.8-1.6 200Breakup 1.6-1.2 400

Rodinia Assembly 1.2-1.0 200Stable 1.0-0.75 250Breakup 0.75-0.6 150

Pannotia Assembly 0.6-0.56 40Stable 0.56-0.54 20Breakup 0.54-0.43 110

Pangaea Assembly 0.43-0.25 180Stable 0.25-0.175 75Breakup 0.175-present 175

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)

RESULTS: Mo Mineral Evolution

Temporal distribution of molybdenite (MoS2)Golden et al. (2013) EPSL 366:1-5.

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 Rodinia asembly.

Hazen et al. (2012) Amer. Mineral. 97:1013.

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)

AbductionAbduction

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.

AbductionAbduction

THE CHALLENGE: Recognizing statistically meaningful patterns in

large data resources:

1. Correlations among many variables

Data-Driven DiscoveryData-Driven Discovery

Large integrated data resources can be explored with multivariate techniques (i.e., principal component analysis).

DATA-DRIVEN DISCOVERYDATA-DRIVEN DISCOVERY

Search for highly correlated patterns

among linear combinations of many different

variables.

THE CHALLENGE: Recognizing statistically meaningful patterns in

large data resources:

2. Meaningful trends in data vs. time


RESULTS: Molybdenite (MoS2) through Time

Golden et al. (2013) EPSL 366:1-5.

432 molybdenite samples

• Analyze equal sized bins.

• Apply statistical tests: linear regression of log Re

content vs. time. (Montgomery et al. 2006)

Are these trends statistically significant?


THE CHALLENGE: Recognizing statistically meaningful patterns

in large data resources:

3. Peak-to-noise problem


Peaks in ages of ~40,000 zircon crystalsPeaks in ages of ~40,000 zircon crystals

Condie & Aster (2010) Precambrian Research 180:227-236.

Condie & Aster (2010) Precambrian Research 180:227-236.

Monte Carlo Mean Kernal Density AnalysisMonte Carlo Mean Kernal Density Analysis

THE CHALLENGE: Recognizing statistically meaningful patterns

in large data resources:

4. Visualization opportunities


Element abundances versus numbers of mineral species (Hazen, Grew, Downs et al.)

Why Do We See the Minerals We See?

Too few species:Ga, Rb, Hf

Too many species:As, Hg, Sb, U

Island area versus numbers of biological species (MacArthur and Wilson, 1967)


What percentage of minerals incorporating element X, also incorporates element Y? (Hazen, Fox, Downs et al.)

Cobalt minerals that also incorporate arsenic


Frequency distributions of 4933 mineral species: 22% of mineral species are known from only one locality.


Frequency distributions of 4933 mineral species: 22% of mineral species are known from only one locality.

Therefore:

(1) Numerous additional minerals exist on Earthbut as yet remain undescribed.

(2) Numerous other plausible minerals do not now exist on Earth, but might have in the past,

or might occur on other Earth-like planets.

(3) If we “played the tape over again,” then the first 4933 minerals to be found would likely

differ by ~1000 mineral species.


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.

CONCLUSIONS

We are poised to make fundamental discoveries about our planetary home through development, integration, and

exploration of deep-time data resources.

Data-Driven Discovery

Please join this effort:• Archive your data

• Release “dark data”• Help us build this resource

Statistical tests: linear regression of log Re content vs. time

(Montgomery et al. 2006):

Log(Re) = β0+β1t+β2x2+β3x3+β4x4+β5x5+β6x6

[t = time; βi = regression parameters; xi = indicator variables]

β0=0; β1=0.0059(8); β2=4.6(7); β3=12(2); β4=15(2); β5=18(2); β6=19(2)




David & Alm (2011) “Rapid evolutionary innovation during an Archean genetic expansion.” Nature 469,93-96.

Documents

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