![Number of entries from unaltered (opaque) and total (transparent) material, separated by proxy type and binned by geologic stage. The diagenetic determinations in panels a-c are based on the expert-assigned DiagenesisFlag field. This flag applies to all proxies barring TEX86 and U37K′\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\rm{U}}}_{37}^{K{\prime} }$$\end{document}; here, TEX86 fidelity is based on entries with BIT, MI, and ΔRI values all below 0.5, while U37K′\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\rm{U}}}_{37}^{K{\prime} }$$\end{document} data whose paleolatitude is greater than 70°N are considered compromised by alkenone production in sea ice. Due to a large increase in the number of entries approaching the Holocene, the y-axis has been scaled in panels a and d, cropping the upper limits of the Quaternary data from these plots.](https://www.researchgate.net/profile/Matthew-Staitis/publication/366063784/figure/fig5/AS:11431281251508658@1718251224609/Number-of-entries-from-unaltered-opaque-and-total-transparent-material-separated-by_Q320.jpg)
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The PhanSST global database
of Phanerozoic sea surface
temperature proxy data
Emily J. Judd
1 ✉ , Jessica E. Tierney2, Brian T. Huber
1, Scott L. Wing1, Daniel J. Lunt
3,
Heather L. Ford
4, Gordon N. Inglis5, Erin L. McClymont
6, Charlotte L. O’Brien7,
Ronnakrit Rattanasriampaipong
8, Weimin Si9, Matthew L. Staitis10, Kaustubh Thirumalai
2,
Eleni Anagnostou
11, Marlow Julius Cramwinckel
5,12, Robin R. Dawson13, David Evans
14,
William R. Gray
15, Ethan L. Grossman16, Michael J. Henehan
17, Brittany N. Hupp18,
Kenneth G. MacLeod
19, Lauren K. O’Connor20, Maria Luisa Sánchez Montes21, Haijun Song22
& Yi Ge Zhang
8
Paleotemperature proxy data form the cornerstone of paleoclimate research and are integral to
understanding the evolution of the Earth system across the Phanerozoic Eon. Here, we present
PhanSST, a database containing over 150,000 data points from ve proxy systems that can be used to
estimate past sea surface temperature. The geochemical data have a near-global spatial distribution
and temporally span most of the Phanerozoic. Each proxy value is associated with consistent and
queryable metadata elds, including information about the location, age, and taxonomy of the
organism from which the data derive. To promote transparency and reproducibility, we include all
available published data, regardless of interpreted preservation state or vital eects. However, we
also provide expert-assigned diagenetic assessments, ecological and environmental ags, and other
proxy-specic elds, which facilitate informed and responsible reuse of the database. The data are
quality control checked and the foraminiferal taxonomy has been updated. PhanSST will serve as a
valuable resource to the paleoclimate community and has myriad applications, including evolutionary,
geochemical, diagenetic, and proxy calibration studies.
1Smithsonian National Museum of Natural History, Department of Paleobiology, Washington, DC, 20560, USA.
2University of Arizona, Department of Geosciences, Tuscon, AZ, 85721, USA. 3University of Bristol, School
of Geographical Sciences, Bristol, BS8 1SS, UK. 4Queen Mary University of London, School of Geography,
London, E1 4NS, UK. 5University of Southampton, School of Ocean and Earth Science, National Oceanography
Centre Southampton, Southampton, SO14 3ZH, UK. 6Durham University, Department of Geography, Durham,
DH1 3LE, UK. 7University College London, Department of Geography, London, WC1E 6BT, UK. 8Texas A&M
University, Department of Oceanography, College Station, TX, 77843, USA. 9Brown University, Department of
Earth, Environmental and Planetary Sciences, Providence, RI, 02912, USA. 10University of Edinburgh, School
of Geosciences, Edinburgh, EH8 9XP, UK. 11GEOMAR Helmholtz Centre for Ocean Research Kiel, 24148, Kiel,
Germany. 12Utrecht University, Department of Earth Sciences, Utrecht, 3584 CB, The Netherlands. 13University of
Massachusetts Amherst, Department of Geosciences, Amherst, MA, 01003, USA. 14Goethe University Frankfurt,
Institute of Geosciences, 60438, Frankfurt am Main, Germany. 15Université Paris-Saclay, Laboratoire des Sciences
du Climat et de l’Environnement, Gif-sur-Yvette, France. 16Texas A&M University, Department of Geology and
Geophysics, College Station, TX, 77843, USA. 17GFZ German Research Centre for Geosciences, Section 3.3 Earth
Surface Geochemistry, 14473, Potsdam, Germany. 18Oregon State University, College of Earth, Ocean and
Atmospheric Sciences, Corvallis, OR, 97331, USA. 19University of Missouri, Department of Geological Sciences,
Columbia, MO, 65211, USA. 20University of Manchester, Department of Earth and Environmental Sciences,
Manchester, M13 9PL, UK. 21University of East Anglia, School of Environmental Sciences, Norwich, NR4 7TJ, UK.
22China University of Geosciences, State Key Laboratory of Biogeology and Environmental Geology, School of Earth
Sciences, Wuhan, 430074, China. ✉e-mail: ejjudd@syr.edu
DATA DESCRIPTOR
OPEN
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Background & Summary
Geochemical proxy temperature data from ancient oceans are a key component of paleoclimate research1–3.
High-resolution paleotemperature data from a single site can identify both long-term4,5 and orbital-scale6,7 cli-
mate variability, while multi-site comparisons from a given time slice provide insight into the spatial patterns
of past climate change8–12. Additionally, such data are essential for validating Earth system models (ESMs)13–16
and provide critical context for rst-order temporal trends in other aspects of the Earth system, including evolu-
tion17,18, geochemical cycling19,20, and tectonics21,22. While single-proxy data sets that span the Phanerozoic23–25
and multi-proxy compilations for select time slices8–10,15 exist, a comprehensive, multi-proxy database of temper-
ature data spanning the Phanerozoic has yet to be published.
PhanSST, a database of sea surface temperature (SST) proxy data spanning the Phanerozoic Eon, seeks to ll
this gap. e compilation currently contains 150,691 discrete proxy values, which can be used to estimate past
SST. ese data come from ve dierent proxies, amassed from 660 references, and represent more than 1,600
unique sampling locations. Each proxy value is associated with a suite of consistent and queryable metadata
elds, and the database is available in a machine-readable format. To the best of our knowledge, this is the largest
compilation of Phanerozoic paleotemperature proxy data to date. Our intention is to make PhanSST a living
database, growing, improving, and evolving through time. Accordingly, we encourage the paleoclimate commu-
nity to contribute their published data to the compilation going forward.
In addition to the initial purpose of the compilation outlined below, PhanSST is an invaluable resource to
the paleoclimate community and can be used for a wide range of additional applications, including evolution-
ary and geochemical studies. e spatial and temporal reach of PhanSST facilitates paleoclimatic syntheses,
including studies investigating climate sensitivity, the biotic and geochemical impacts of climate change, and the
mechanisms driving large-scale global climate. e data are presented in their native proxy units, which permits
exibility in the choice of calibration model and additionally enables the compilation to be used in proxy cali-
bration studies. Further, PhanSST includes data from known and suspected diagenetically altered material, with
both expert-assigned binary diagenesis ags and additional metadata elds, allowing the database to be used in
research investigating spatial and temporal trends in preservation.
Version 0.0.1 of PhanSST is presented in a tabular (csv) format. A static copy of Version 0.0.1 is archived in
the NOAA-NCEI Paleoclimatology Database (https://www.ncei.noaa.gov/access/paleo-search/study/36813)26,
and dynamic versions of the most recent release can be found on Zenodo (https://doi.org/10.5281/
zenodo.7049233)27 and at the PhanSST website (https://www.paleo-temperature.org). In the following sec-
tions, we provide information regarding the data sources and selection criteria, review the denitions and
decision-making behind the metadata elds associated with each proxy measurement, and outline the quality
control process. We explore the broad spatial and temporal trends of the compilation, discuss future usage and
limitations of the data set, and address the ongoing goals for the database to ensure that it remains a valuable
asset to the paleoclimate community.
Methods
What is the primary purpose of the compilation? The PhanSST database is a part of the broader
PhanTASTIC (Phanerozoic Technique Averaged Surface Temperature Integrated Curve) initiative28, which aims
to produce an internally consistent and statistically robust record of Earth’s global mean surface temperature over
the last 539 million years. Ultimately, these data will be integrated with ESMs29 using data assimilation10,30 to recon-
struct relevant global climate elds and calculate the global mean surface temperature at the geochronologic age
(i.e., stage) level across the Phanerozoic. Requisite to this goal is a compendium of paleotemperature proxy data.
PhanSST is a community-wide, collaborative eort. Each of the authors of this data descriptor contributed
their time and expertise, entering data and quality control checking the records. Despite the more focused pri-
mary purpose of the compilation, we want to ensure that PhanSST will serve as a community-wide resource. e
curated metadata elds, therefore, reect a desire to maximize potential reuse of the database.
Why geochemical SST proxy data? We focused on compiling geochemical SST proxy data for several
reasons. First, the ocean comprises approximately 70% of the Earth’s surface, and SST proxies provide better
long-term temporal coverage and generally more precise age control than terrestrial records due to more continu-
ous deposition in marine environments. Second, the proxy types in PhanSST can be readily converted from proxy
values into temperature units using established calibration models that propagate errors and account for seasonal
biases31–36. ird, terrestrial air temperature proxies are a function of elevation and lapse rate, making inter-
pretations of terrestrial data dependent upon paleogeographic and paleoaltimetric assumptions that are poorly
constrained in deep time. SST proxies are still subject to assumptions, such as seawater chemistry, seasonality
and depth of production, and temporal uniformity of proxy systems34,35,37, and interpretations can be sensitive to
the inuence of plate movement or shis in surface current positions38,39. However, the comparative richness of
the marine record generally makes identifying anomalous sites or entries more straightforward. While beyond
the scope of our current eorts, we hope that in the near future a parallel compilation of terrestrial temperature
proxies will be developed.
Which SST proxy data? PhanSST included data from ve SST proxies:oxygen isotopes of macro- and
microfossil carbonate (δ18Ocarbonate), oxygen isotopes of conodont phosphate (δ18Ophosphate), magnesium to calcium
ratios (Mg/Ca) of planktonic foraminifera, the tetraether index of 86 carbons (TEX86), and the alkenone unsatu-
ration ratio (
UK
37
′
). While we did also collect marine carbonate clumped isotope data (Δ47), those data are not
included in the current release (Version 0.0.1) of PhanSST. Although laboratory-specic Δ47-temperature calibra-
tions are robust, interlaboratory dierences in the way Δ47 data have been corrected raise concerns over the
compatibility of the isotope values themselves40. Since PhanSST presents proxy values, rather than
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paleotemperature estimates, we opted to omit the Δ47 data for now. With that said, a newly proposed reference
frame40 oers a promising way forward and we hope to include these data in the near future.
All of the proxy data included in the database have been previously published in peer-reviewed journal
articles, theses and dissertations, book chapters, or public repositories (e.g., PANGAEA)4–7,24,41–695; the data-
base does not include any unpublished data. e proxy values themselves, dominantly derive from either data
tables contained within the original journal article, supplements, or public repositories. In general, data locked
in PDF tables were extracted using Tabula (https://tabula.technology). Some dark data and missing metadata
were obtained through personal communication with the original authors and, in very rare instances, data were
digitized from gures. Details regarding the source of dark data can be found in the Quality Control logs (see
Compilation quality-control for log availability).
PhanSST expands upon several existing compilations. e majority of oxygen isotope values from carbonate
macrofossils come from Grossman and Joachimski23 and the initial references of phosphate oxygen isotope
values mostly derive from Grossman and Joachimski23 and Song et al.25. Likewise, many of the Cretaceous data
were originally collated by O’Brien et al.696, Paleogene data by Hollis et al.8 and Evans et al.697, and Pliocene data
by McClymont et al.9. To the extent possible, data sourced from these compilations were cross-checked with
their initial publications to ensure completeness and avoid propagating any unintentional errors, and missing
data or applicable metadata elds were lled in. e remaining data in PhanSST were scoured from the liter-
ature by the authors, who worked in teams based on their expertise with specic proxies and geologic time
intervals. We used keyword queries in Google Scholar to identify missing references, and eorts were made to
target literature from data-poor regions (e.g., South America) and time intervals (e.g., Silurian). While (quasi-)
automated data discovery and entry methods show promise as a means of maximizing database completion and
minimizing bias698, given the broad nature of data sources and formats, such an approach was not tenable here.
Selection criteria varied slightly by proxy and time interval. anks to the initiatives of the International
Ocean Discovery Program (IODP) and its predecessors, late Mesozoic and Cenozoic SST data are plentiful and
data derived from cores oen consist of high-resolution time series. In this case, we focused on compiling time
series spanning at least one million years, recognizing that Quaternary data, for example, are compiled in more
detail in other works699,700. Exceptions to this rule were made for data from undersampled regions (e.g., the
Southern Ocean) or time periods (e.g., the Paleocene). In contrast to the high data density of the Cenozoic, data
fromthe early Mesozoic and Paleozoic are comparatively scarce. Several proxies, such as
′
UK
37
, TEX86, and
foraminiferal-based records are limited to more recent times, and almost all ocean crust older than ~180 Ma has
been recycled through subduction701, removing a key sampling environment. As such, data availability is
restricted by outcrop exposure, subject to more frequent and larger unconformities, and limited to fossiliferous
horizons. Compared to core data, these records generally contain fewer measurements per site, lack a continu-
ous time series, and have less precise age control or report relative rather than numeric ages. erefore, we
incorporated all available Paleozoic and Mesozoic proxy data, regardless of record duration. We did, however,
require that all sites provide some level of age control; data that could not be assigned a relative age at the stage
level were excluded.
Which metadata elds are included and why? e metadata elds included in PhanSST reect a bal-
ance between the scope of the PhanTASTIC project and our intention to maximize the reuse potential of the
database. Metadata elds were carefully curated to facilitate future updates to age models and proxy-, species- or
methodological-specic corrections. For example, core information, sampling depth, and biostratigraphic infor-
mation are included, permitting age model updates. On the other hand, we do not list age uncertainties because
(1) age uncertainties are not consistently reported in the source publications, (2) it was not tractable to update all
age models to a consistent timescale, and (3) the PhanTASTIC project focuses on temperature evolution at the
stage level. Since we strive to make PhanSST a living database, we anticipate that updated age models, with uncer-
tainties, may be added at a later date with assistance from the paleoclimate community.
To ensure a comprehensive and methodologically traceable compilation, we also opted to include diagenet-
ically altered samples. Some samples can condently be characterized as altered; however, diagenetic processes
are gradual and criteria for what constitutes diagenetically altered material can be subjective and may change as
new insights into diagenetic processes are gained. Consequently, rather than excluding such data, we have: (1)
applied an expert-assigned binary diagenesis ag to make it clear which samples have been previously suggested
or interpreted as altered, and (2) included relevant supplementary elds, such as elemental concentrations702
and conodont color alteration index (CAI) values703, allowing users to impose their own informed diagenetic
assessments. is approach ensures that all data are available for future studies that may wish to adopt dierent
alteration criteria or explore the spatial and temporal patterns of diagenesis.
Which metadata elds are excluded and why? e metadata elds currently provided have been care-
fully selected to help end users make informed decisions of which data to include in analyses and how best to
correct them. However, two key metadata elds are omitted from PhanSST: paleotemperature estimates and
paleocoordinates. ough we acknowledge that these elds are of particular interest, this was done deliberately to
promote responsible and intentional reuse of the database.
Estimating SSTs from the proxy values involves applying and inverting a statistical model of how each proxy
responds to environmental parameters; it is therefore a derived or modeled quantity subject to user-based deci-
sions about how to treat each proxy system. In order to estimate SST for each of the more than 150,000 data
points, we would need to make executive decisions regarding (1) which calibration to use for each proxy system,
(2) what assumptions to use for non-thermal predictor variables (such as seawater chemistry), and (3) which (if
any) taxon-specic or analytical corrections to apply. For any given proxy, there are a variety of proxy system
models (PSMs) with which to estimate SST. For example, within the TEX86 proxy system, SSTs can be calculated
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using the
TEXH
86
704, TEX86-linear696, or BAYSPAR31 calibrations, among others, and exploration into region- and
time-specic modications to these calibrations is ongoing (e.g., Steinig et al.574). Holding all else constant, the
dierence in inferred temperatures across these calibrations can exceed 5 °C, and there is no consensus among
the paleoclimate community on the most appropriate calibration(s) to use8,705. In fact, the choice of which cali-
bration is most appropriate can vary based on the time interval and temporal resolution of the study, the taxon
from which the data derive, the geographic breadth of the data, and the types of questions being investigated.
Similarly, in order to calculate SST, many of these proxy systems require assumptions about non-thermal predic-
tor variables (e.g., δ18Oseawater). Relating the proxy values to SST would therefore require decision making about
these seawater chemistry values in the geologic past, which are largely unconstrained. Given the volume of data
contained within PhanSST, these assumptions would need to be based on broad, rst-order principles (e.g., a
standardized δ18Oseawater of −1‰ for ice-free times and 0‰ value during times of glaciation), but such simpli-
cations are incorrect (e.g., we observe a >5‰ spatial spread in values in the modern ocean706, a nuance that is
wholly overlooked by assuming a single “ice-free” or “glaciated” value). ough there are better ways of estimat-
ing these values (e.g., extracting the local δ18Oseawater values from an isotope-enabled ESM), such methods cannot
be consistently applied across the entire database. And, in fact, the necessary resolution of these assumptions
changes based on the scope of the study. Studies, for example, investigating Phanerozoic trends in oxygen iso-
topes may wish to apply a single calibration to all data points and use simplied assumptions for consistency,
whereas studies investigating a specic time slice (e.g., the Cretaceous), may benet from taking a more intricate
approach (e.g., using taxa-specic PSMs and drawing δ18Oseawater values from ESMs). It is therefore not possible
for us to calculate SSTs for each data point while applying a traceable, scientically sound, and ubiquitously
appropriate methodology. Moreover, it is important that those who wish to calculate SSTs from the proxy data
within PhanSST be fully aware of the decision making (and uncertainty) associated with the various PSMs and
assumptions.
Likewise, we do not include paleogeographic information, which is also a derived quantity based on geophys-
ical models. Here, we plot data using the plate rotation model of Scotese and Wright (2018)707 for illustrative
purposes, since it is one of the few rotations that extends back through the Phanerozoic. However, there are a
variety of dierent plate rotation models available, particularly in more recent time intervals, and the projected
paleocoordinates can vary signicantly based on the rotation model used38. As with the SST estimates, the choice
of which paleorotation is most appropriate to use is dependent upon the application of the data and the temporal
resolution of the study. If, for example, a user is plotting data on existing ESM output, then they will want to use
the same rotation that was used in the simulation so that the paleolongitudes (which are largely unconstrained)
align. Alternatively, if they are testing a hypothesis about the timing or inuence of a specic gateway opening,
they may wish to use several dierent rotations and compare the results. Further, when aligning data in deep
time, the time scale of the study matters, and the choice of time interval with which to align the data will again
be dependent on the scope of the investigation. Time slice studies may wish to rotate all data to the same time
frame (e.g., 56 Ma) despite the fact that the data may span a few million years in either direction. Other studies
looking at changes in ocean dynamics from the same site across a 5-million-year window may wish to rotate
each discrete data point to their precise numeric age. Given the variety of methods for estimating both SST and
paleocoordinates, and the inherent complexity and uncertainty behind those singular discrete values, we believe
that it is important for end users to go through the decision making process themselves to ensure that the values
are best tailored toward their respective application. We do, however, recognize that users who wish to calculate
SSTs or paleocoordinates may benet from guidance. We oer some broad recommendations on how to select
and apply temperature calibrations and rotation models below in the Applying the database in deep time section.
Data Records
In addition to the proxy value itself, each entry is associated with an array of relevant metadata elds, which vary
depending on the nature of the record and the proxy. In total, there are more than 40 metadata elds that can
broadly be grouped into six categories: (1) sample site and other identifying information, (2) age information,
(3) proxy information, (4) taxonomic and environmental information, (5) proxy-specic information, and (6)
reference metadata. Basic descriptions of these elds can be found in Table1 and specics on how and why each
eld is assigned are provided below.
For clarity and consistency while reading this data descriptor, entries refer to each discrete proxy value and
its associated metadata (i.e., a row), elds refer to the metadata collected for each entry (i.e., a column), and
sampling sites refer to the unique geographic coordinates of entries, independent of the number entries from
that location. Unique sampling sites can be parsed temporally (e.g., by stage, period, 2.5 myr bins, etc.), by proxy
type, or a combination of the two.
Sample ID and location elds. e rst eld is the SampleID, which reects the unique (oen alphanu-
meric) identication code associated with each entry, as originally published. Currently, the SampleID eld only
applies to samples collected in outcrop; however, in the future, we plan to also include the unique drill core sample
IDs. e remaining sample site elds provide information on the geographic and stratigraphic position of each
entry. Data that come from drill cores, such as those from IODP, list the SiteName, referencing the expedition
site, and also include sampling depth information (i.e., SiteHole, MBSF, and/or MCD) to allow age models to be
updated in the future. Data collected in outcrop similarly include a SiteName and SampleDepth, as well as the
geologic Formation from which the data were collected, when available.
Each entry is also associated with the modern coordinates of the sampling site (ModLat and ModLon) and
tags indicating the ContinentOcean and Country of collection for easy ltering. For consistency, the coordi-
nates of sample sites were rounded to two decimal degrees. In some cases (predominantly Paleozoic data col-
lected in outcrop and published in older journal articles) precise coordinates were not provided423,483,501. In
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Field name When applicable Description of eld (units)
Sample ID and location elds
SampleID All data Unique sample identication code, as originally published
SiteName All data Name of the drill core site or section
SiteHole Drill core data e alphabetic hole specier
MBSF Drill core data Depth below the sea oor (m)
MCD Drill core data e mean composite depth (m)
SampleDepth Outcrop data Stratigraphic height or depth (m)
Formation Outcrop data Geologic formation name
Country All data e country or ocean of the data collection site
ContinentOcean All data e continent or ocean basin of the data collection site
ModLat All data Modern latitude of collection site rounded to two decimals; negative values indicate
the Southern Hemisphere (decimal degrees)
ModLon All data Modern longitude of the collection site rounded to two decimals; negative values
indicate the Western Hemisphere (decimal degrees)
Age elds
Age All data Age, in reference to GTS2020 [710] (Ma)
Period All data e geologic period
Stage All data e geologic stage (i.e., geochronologic age)
StagePosition All data (barring those only
assigned to a stage) Further specication of relative age (Early, Middle, or Late)
Biozone Outcrop data Conodont, graptolite, and/or ammonite biozone
AgeFlag All data Flag indicating if relative age elds were autolled from numeric age values (0), age
values were autolled from relative age elds (1), or both relative and numeric age
values were provided (2)
Proxy elds
ProxyValue All data Reported proxy value (native proxy units)
ProxyType All data Reference to the proxy type; see Table2
ValueType All data Reference to the averaging of the data (see text)
DiagenesisFlag δ18O and Mg/Ca data Binary expert-assigned ag indicating good (0) or questionable (1) preservation
Taxonomic, environmental, and ecological elds
Taxon1 All data First-order taxonomic classication (see Table3)
Taxon2 δ18O data Second-order (class) classication of mollusks (see Table3)
Taxon3 All δ18O and Mg/Ca data ird-order (genus or species) classication (see Table3)
Environment Outcrop data Depositional environment (e.g., mid-shelf, epeiric)
Ecology All δ18O and Mg/Ca data Ecological preference of the sampled taxon (e.g., surface, benthic)
Proxy-specic elds
AnalyticalTechnique All δ18O data e analytical technique used to obtain the data (IRMS vs SIMS)
CL Macrofossil δ18Ocarbonate data Cathodoluminescence microscopy assessment
Elemental Suite Macrofossil δ18Ocarbonate data All reported elemental concentrations (i.e., Fe, Mn, Mg, Sr, Ca) or ratios (i.e., Sr/Ca,
Mg/Ca, Mn/Sr)
NBS120c δ18Ophosphate data e NBS120c standard value used to correct the data (‰)
Durango δ18Ophosphate data e Durango standard value used to correct SIMS data (‰)
MaximumCAI δ18Ophosphate data e maximum reported conodont color alteration index value for that sample or
horizon
ModWaterDepth Mg/Ca data e modern water depth of the sampling site
CleaningMethod Mg/Ca data A binary ag to indicate either oxidative-only cleaning (0) or inclusion of a reductive
cleaning step (1)
Fractional abundances TEX86 d ata Fields to indicate the fractional abundances of the GDGTs
Index values TEX86 data Branched and isoprenoid tetraether (BIT), methane (MI), and delta ring (dRI) index
values
Reference metadata
LeadAuthor All data e last name of the rst author of the original publication
Year All data e year of the original publication
PublicationDOI All data e DOI of the original publication
DataDOI All data e DOI of the online repository hosting the data
Tab le 1. Field names and descriptions.
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these instances, locations were estimated using context from the original publication, outcrop information from
Macrostrat708, and occurrence data from the Paleobiology Database709.
Age elds. Six age elds provide information related to the age of each sample, including numeric Age (in
Ma) as well as the Period and Stage, which provide relative age information. We use the chronostratigraphic term
“stage” as a synonym for the geochronologic “age” to mitigate confusion between numeric ages and geochron-
ologic ages. Biostratigraphic information (e.g., conodont zone), when provided, is retained in the Biozone eld,
permitting future age model updates for data collected in outcrop with each new iteration of the Geologic Time
Scale (GTS). We did not divide data from the Holocene Epoch into their respective stages; all data younger than
0.0117 Ma were assigned to the “Holocene” in the Stage eld. Likewise, all data from the unnamed stage of the
Pleistocene were assigned to the “Upper Pleistocene” and all data from the Pridoli Epoch are assigned to the
“Pridoli”.
Given the broad nature of this compilation and the variety of time scales and proxies incorporated, con-
straining entry ages in a consistent and traceable way required a multifaceted approach. Data coming from
IODP cores, for example, oen have higher precision age models tied to sampling depth. In contrast, data from
outcrops are frequently more challenging to date precisely and quantitatively. us, ages were assigned in one
of three ways: by entering a numeric age and auto-lling the relative age information, by entering relative age
information and auto-lling the numeric age information, or by retaining both the manually entered numeric
and relative age information. e AgeFlag eld identies which approach was taken(indicated by a0, 1, or 2,
respectively).
Numeric age assignments with auto-lled relative ages. If the original paper provided a precise numeric age, the
period and stage elds were automatically lled in using age boundaries from GTS 2020710. We made eorts to
use recent drill core age models, but given the size of the data compilation, updating all of the age models was
not feasible. When available, we compiled sampling depth information (i.e., SiteHole, MBSF, and/or MCD) so
that age models may be updated in the future. If the data came from an existing compilation, we deferred to their
reported age unless a more recent age model was readily available. Data with precise numeric ages are denoted
by a zero (0) in the AgeFlag eld.
Relative age assignments with auto-lled numeric age. Some data can only be relatively dated. In the absence
of precise numeric ages, the Stage eld was entered manually. Stage duration is highly variable, with a median
length of 4.65 myr. In general, stage duration scales with availability of material and scientic interest in the time
interval, with a maximum duration of 21.56 myr (Norian) and a minimum of 11.7 kyr (Holocene, though not a
dened stage as discussed above).
e stage assignments are largely based on the divisions reported in the original publication, though some
data points have been updated based on the biostratigraphic information contained in the Biozone eld based
on the divisions in GTS 2020710. Relative ages were further qualied using the StagePosition eld (i.e., early,
middle, or late), if such information was available or could be realistically constrained using the biozonations. A
numeric age was then estimated based on the stage boundaries of GTS 2020710. Entries constrained only at the
stage level were assigned numeric ages based on the arithmetic mean of the upper and lower stage boundaries.
Numeric ages of entries tied to a specic stage position (early, middle, or late) were estimated by dividing the
stage into three equal time slices and assigning a numeric age based on the midpoint of that entry’s respective
third. Data with numeric age assignments extrapolated from relative age information are denoted by a one (1)
in the AgeFlag eld.
Note that assigning ages in this manner means that high(er)-resolution relative time series information is
not retained in the auto-lled numeric ages. For example, in some original publications, authors constructed
their own relative age model and assigned sequential relative ages to stratigraphically successive data points.
However, it was not feasible to convert these relative time series into numeric ages using a consistent and trace-
able methodology. For the purposes of the PhanTASTIC project, stage-level temporal resolution is sucient.
Regardless, stratigraphic position, when available, was recorded in the MBSF, MCD, and/or SampleDepth elds,
which allows users to construct relative time series if desired.
Proxy elds. e four proxy elds consist of the proxy value, the proxy type, value type, and preservation
state. Table2 provides a list of all ProxyType options. ValueType options include: (1) ‘im’, indicating the proxy
value represents an individual measurement from an individual specimen, (2) ‘ia’, indicating the proxy value
reects the average of replicate measurements from a single individual specimen, and (3) ‘pa’, indicating the meas-
urement reects a population average from multiple specimens. All TEX86 and
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entries, by denition, are listed
a s ‘ pa’.
e DiagenesisFlag eld is a binary ag specifying preservation state. We generally deferred to the expert
opinion of the authors responsible for entering and/or quality-control checking the data and were as conserv-
ative as possible, agging both known and suspected alteration. Both δ18O and Mg/Ca foraminiferal data were
agged based on the preservation of the tests themselves (e.g., glassy vs. frosty)466 rather than an assessment of
the delity of the proxy value. Unlike foraminifera, there is currently no method for assessing the preservational
state of the macrofossil δ18Ocarbonate and δ18Ophosphate that is consistently applied across the literature. e diage-
netic ags provided here, therefore, generally reect the interpretation of the original authors. We also agged
any δ18Ocarbonate entries lower than −10‰ as these values would yield unrealistically warm SSTs, suggesting that
either diagenesis or non-marine seawater compositions aected the measurement. We appreciate that diagen-
esis is a spectrum and applying a subjective binary ag both overlooks the nuance of the processes involved
and is inherently not reproducible. To that end, we have also provided additional diagenetic elds specic to
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δ18Ocarbonate and δ18Ophosphate data, such as trace and major element ratios and concentrations of macrofossils,
cathodoluminescence assessments, and maximum CAI of conodonts. End users can dene their own diagenetic
thresholds (e.g., Mn/Sr <0.5) and use these elds to consistently lter or ag suspect data.
We have not indicated the preservation state of TEX86 nor
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entries because although methods for assess-
ing preservation and thermal maturity exist8,711, they are not consistently reported in the literature. Nevertheless,
as with the oxygen isotope data, we have included some proxy-specific fields to help filter TEX86 data for
non-thermal inuences (see below).
Taxonomic and environmental elds. e taxonomic and environmental elds provide information
about the organism from which the proxy data derive (Table3), as well as the depositional environment and ecol-
ogy of the sampled taxon. e Taxon1 eld refers to the rst-order taxonomic aliation of the organism, primar-
ily classied at the phylum level. e Taxon2 eld, applicable only to mollusk δ18Ocarbonate data, further species
the class, and the Taxon3 eld species the binomial species name of the sampled organism, when available. ese
elds help characterize the paleoenvironment from which the data come, permit the compilation to be ltered by
taxon, and contain information pertinent to species-specic calibrations34,35.
While most of the entries in PhanSST are reported as representing SST, in reality very few of the taxa from
which the data derive genuinely lived at the sea surface. Some PSMs, such as BAYSPAR31 for TEX86 and
BAYSPLINE33 for
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, are based on calibrations between core tops and modern SST, partly (but not completely,
since organisms might migrate vertically through time) alleviating this limitation. However, it can be dicult to
interpret data that come from (1) proxies that do not have modern core top calibrations, (2) extinct taxa, or (3)
intervals with substantially dierent environmental conditions than today that may have promoted vertical
migration within the water column. When available and applicable, we have therefore included metadata per-
taining to the depositional environment of the source material (e.g., marginal, mid-shelf, slope) in the
Environment eld and the lifestyle or depth habitat of the organism in the Ecology eld. e ecology of carbonate
macrofossils and conodonts are classied as either benthic, nektic, planktic, or some combination therein, while
planktic foraminifera are classied as either surface/mixed layer or (sub-)thermocline. ese elds allow users
to easily lter or index entries within the compilation to t their needs. For example, depending on the scope of
the study, it may be germane to lter out benthic brachiopods deposited in slope environments while retaining
brachiopods from inner shelf environments, as they are more likely to record temperatures closer to sea surface
values. Alternatively, comparing data from surface- and thermocline-dwelling species of foraminifera with ben-
thic bivalve data from the same site could elucidate vertical temperature gradients of ancient oceans.
Proxy-specic elds. Certain proxy-specic elds were added to the compilation to permit data correc-
tions, facilitate the implementation of PSMs, aid in diagenetic assessments, and improve the overall utility of
the database. For traceability purposes (i.e., to ensure that our records reect the original data tables) we have
not corrected any of the proxy values, but we do provide the information required to enable a correction. For
example, the δ18O data from both carbonates and phosphates include information about the AnalyticalTechnique
(i.e., IRMS or SIMS), as evidence suggests there may be methodological osets in the isotope values148,712. e
NBS-120c value used to standardize the δ18Ophosphate data is reported in the NBS120c eld. e consensus value of
21.7‰ is now generally applied, but the value of the standard varies signicantly in older literature. Additionally,
in the case of SIMS data, we also report the Durango standard value. Reporting the “uncorrected” published
values, while also specifying the method, permits end-user autonomy of how to treat data derived by dierent
means.
To assist in assessments of sample preservation, we have included available trace, minor, and major element
concentrations and ratios associated with δ18Ocarbonate macrofossil data (e.g., Fe, Mn, Mg, Sr, Sr/Ca, Mg/Ca).
Diagenetic processes yield predictable directional changes to these concentrations and ratios; by comparing fos-
sil values to modern taxon- or site-specic ranges, informed threshold values can thus be used to further assess
the preservation state of samples713,714. For the same reason, when reported, we have also included categorical
diagenetic assessments using cathodoluminescence microscopy702 (CL; L, luminescent; SL, slightly luminescent;
NL, non-luminescent).
When available, δ18Ophosphate entries are associated with the maximum reported conodont color alteration
index (MaximumCAI) value703 for that sample or horizon. Phosphate is generally considered to be more resist-
ant to diagenetic alteration than carbonate, but there remains some debate as to when data from conodont ele-
ments should be considered altered148,312. Inclusion of this eld allows users to impose their own CAI threshold
criteria for assessing preservation or to analyze relationships between isotope values and CAI.
Field value Proxy type Units
d18a δ18O of aragonite ‰; VPDB
d18c δ18O of calcite ‰; VPDB
d18p δ18O of phosphate ‰; VSMOW
mg Mg/Ca ratio mmol/mol
tex TEX86 unitless ratio
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unitless ratio
Tab le 2. Proxy types.
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Additional elds are included for foraminiferal Mg/Ca data to provide information needed for using the
Bayesian Mg/Ca forward model, BAYMAG35. Previous studies have demonstrated that Mg/Ca values are
dependent upon both the bottom water calcite saturation state715 () and the cleaning method used prior to
analysis716, and, as such, are included as predictor variables in BAYMAG. All Mg/Ca entries, therefore, include
the water depth of the modern sampling site (ModWaterDepth), which can be used in tandem with the geo-
graphic information to inform paleodepth estimates (and thus constrain ), and a binary CleaningMethod eld
to indicate whether the cleaning omitted (0) or included (1) a reductive step.
TEX86 entries include the fractional abundances of the isoprenoidal glycerol dialkyl glycerol tetraethers
(GDGTs), if available, following the recommendation of Hollis et al.8. Additionally, we have included the
branched and isoprenoid tetraether index717 (BIT), the methane index718 (MI), and the delta ring index719 (ΔRI),
which can all be used to assess the extent to which TEX86 values may be aected by non-thermal factors. ese
elds allow the end-user to screen data by the indices and threshold values of their choice.
Reference metadata. e reference elds contain the publication metadata, including the lead (rst)
author, publication year, and the DOI to the publication where the data were originally reported (PublicationDOI).
We also recognize that many of these data are available in online repositories (e.g., PANGAEA). While Version
0.0.1 of PhanSST only provides the PublicationDOI, in future releases we plan to also utilize the DataDOI to
direct users to any online repository hosting the data. To aid in ease of machine readability, accents and special
characters in the LeadAuthor and other applicable elds were removed. However, the full and unaltered database
citations, including the article title and journal name, can be found in a supplementary Excel (.xlsx) le available
on Zenodo and the PhanSST website.
Technical Validation
Compilation quality-control. Data were quality-control (QC) checked by the authors of this data descrip-
tor. For data deriving from drill cores, we generated a PDF le for each unique sampling site, parsed by the
SiteName and ProxyType elds (Fig.1). e QC PDFs included relevant metadata, such as the reference elds
(LeadAuthor, Year, DOI) and the sample site coordinates. All data for a given site were plotted versus both time
and core depth and colored by reference, with dierent symbols to indicate the DiagenesisFlag and AgeFlag assign-
ments of each data point. QC PDFs of data derived from planktic foraminifera (δ18Ocarbonate and Mg/Ca) contained
plots colored by species. Using shared Google spreadsheets for each proxy, we veried that the information in
each QC PDF matched the publication values, working in teams based on expertise (Fig.2a). Each unique site,
proxy, and reference was evaluated based on a standardized suite of criteria to ensure consistency throughout the
process (Fig.3). ose assisting in the QC process checked boxes to conrm the delity of the journal metadata,
modern coordinates, age model, depth information, proxy value, preservation state, taxa, cleaning method (for
Mg/Ca), and fractional abundances (for TEX86). We corrected any issues identied and maintained a detailed log
of comments, providing a record of our QC process. Links to read-only versions of these QC logs are available on
the PhanSST website and in the “Read Me” documentation on Zenodo.
Given the large number of foraminiferal species in the database and the potential for outdated taxonomy, we
conducted a systematic QC of the listed species. Species names were checked for outdated taxonomic assign-
ments and misspellings (Fig.2b), using databases like Mikrotax. We added an expert-assigned Ecology eld to
help distinguish surface or mixed layer dwelling species from (sub-)thermocline species.
e macrofossil δ18Ocarbonate and δ18Ophosphate data were QC checked separately. ese data generally come
from outcrops, with many studies reporting multiple sampling sites but only a few measurements at each site.
ese dierences inhibited straightforward parsing of the carbonate macrofossil and phosphate records into
site-level QC PDFs, so instead these data were inspected manually. Records were checked by comparing proxy
values and metadata between existing compilations23 and original publications. As with the core data, a detailed
log of the changes made was retained.
In addition to the manual quality-control checks described above, we also performed automated analyses for
each eld to identify any missing or erroneous metadata (available on GitHub; see Code Availability). For exam-
ple, the ModLat and ModLon elds were screened to ensure that latitude and longitude values were in decimal
degrees and fall between −90 to 90 and −180 to 180, respectively. Country names and sample coordinates were
further crosschecked against publicly available country shapeles data, to ensure accuracy and consistency in
Taxon1 Taxon2 Taxon3 Description
br Binomial species name Brachiopod
mMollusk
bi Binomial species name Bivalve
ce Binomial species name Cephalopod
ga Binomial species name Gastropod
ot Binomial species name Other
co Binomial species name Conodonts
ha Haptophyte
pf Binomial species name Planktonic foraminifera
th aumarchaeota
Tab le 3. Taxonomic speciers.
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the assignment. ContinentOcean assignments were veried by indexing all categorical responses (e.g., NA, North
America; SO, Southern Ocean) and mapping each data point tagged to each respective geographic region. Stage
and period names were compared to the list of accepted names from GTS 2020710, and checked for agreement
between numeric age, period, and stage. We also printed lists of the unique period, stage, proxy type, value type,
and taxonomic names to ensure they each respectively conformed to our accepted convention. Lists of all unique
values for each categorical eld (e.g., ProxyType, ValueType, AnalyticalMethod) were printed to identify any
inconsistencies, and every eld was queried to identify missing values.
General database statistics. Version 0.0.1 of PhanSST contains 150,691 entries, drawn from 660 dierent
references, representing 1,643 unique sampling sites and 93 of the 100 Phanerozoic stages. Of these, 25,782 have
been agged as diagenetically altered, though this ag does not apply to the TEX86 or
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entries; there remain
nearly 120,000 unagged data points aer applying a conservative screening of these latter proxies to ensure
delity of their values (i.e., excluding TEX86 data whose BIT, MI, or ΔRI values are greater than 0.5 and
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data
whose paleolatitude is greater than 70°N, since these data are considered compromised by alkenone production
in sea ice33,720,721). Across the database, δ18Ocarbonate data are the most common, constituting over half of all entries
(Table4). is prevalence reects the fact that carbonate oxygen isotope data (1) are the oldest quantitative pale-
otemperature proxy, with its origins dating back to 1947722, (2) represent one of only two SST proxies available
across the entirety of the Phanerozoic, and (3) are commonly measured on foraminifera from drill cores.
However, the δ18Ocarbonate data are commonly aected by diagenesis, with ~30% of the 81,633 entries agged as
altered. Despite their limited temporal availability,
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data account for a quarter of all entries, and all data, bar-
ring those from regions prone to sea ice, are considered to reect a primary SST signal. e high volume of
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data is likely a result of the ease of analysis and straightforward relationship between temperature and proxy
value. e remaining three proxies collectively contribute to the remaining quarter of the entries in PhanSST, with
conodont δ18Ophosphate being the least common.
Below, we highlight some of the rst-order spatial and temporal trends in data density, sampling locations,
and proxy values of PhanSST. e syntheses presented illustrate just a small fraction of the spatio-temporal pat-
terns uniquely elucidated by a compilation of this size and demonstrate the potential of the database to facilitate
paleoclimatic, geochemical, and paleoecological research.
Temporal trends in data density. e Cenozoic accounts for just 12% of Phanerozoic time, while the Mesozoic
accounts for 35% and the Paleozoic, 53%. e number of PhanSST entries and sampling sites per era, however,
follow a dierent distribution (Table5), reecting fundamental dierences in the availability, density, and nature
of paleo-SST archives across geologic time. Cenozoic entries account for 79% of the data in PhanSST, while
Fig. 1 Example QC PDF for the foraminiferal δ18Ocarbonate data from ODP Site 761.
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Cenozoic sampling sites account for a quarter of the unique sites. e over-representation of Cenozoic entries
reects the fact that the majority of these data come from drill cores, with long and oen high-resolution
records, and many of these cores have SST data from multiple proxies. Additionally, several of the proxies, such
as
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723, are restricted to more recent times. Collectively, these factors inuence both the number of entries and
unique sampling sites from each proxy type through time (Figs.4,5). While carbonate oxygen isotope data,
largely from planktonic foraminifera, dominate the Cenozoic record, many of these data have been agged as
altered due to pervasive secondary bottom water calcite precipitation overprinting the SST signal466,724. TEX86
data are common throughout the Cenozoic, while both the number of entries and sampling sites of the
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data
grow rapidly across the late Neogene through Holocene. Overall, despite rising numbers of entries across the era,
the number of unique sampling sites at the stage level remains fairly consistent within each proxy, with only the
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sites showing a large increase towards the present (Fig.4). e abundance of multi-proxy records from the
same cores further moderates the total number of unique sites across all proxies.
Mesozoic entries account for just 12% of the compilation but contribute over a third of all sampling sites
(Table5). e Mesozoic marks a transition in both depositional environment and proxy availability. In the
Cretaceous, IODP data are still available, as are foraminiferal-based and TEX86 records. However, the the lack
of pelagic calciers and preserved biomarkers in sediments predating the mid to late Mesozoic limits both the
Fig. 3 e QC criteria under which each site and reference was evaluated. Checking the boxes of the same color
and heading in Fig.2a conrms that all of the criteria for a given category have been met.
Fig. 2 (a) Example QC Google spreadsheet for foraminiferal Mg/Ca data, parsed by site and publication. e
delity of the entered data was checked based on eight suites of standardized criteria (see Fig.3). (b) Example of
the foraminifera taxonomy QC Google spreadsheet. Foraminiferal species were checked separately for outdated
or misspelled names and agged by environment.
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materials and proxies available. Further, nearly all seaoor older than the late Jurassic has been subducted701,
which fundamentally restricts the temporal reach of ocean drill cores. erefore, records from the early portion
of the Mesozoic dominantly come from shelf deposits or epeiric seas. ese data are generally collected from
outcrops and thus lack the high-resolution, high sampling density of core data. Additionally, the early Mesozoic
marks the initial breakup of Pangaea; the supercontinent reduced continental margin area and prohibited wide-
spread continental ooding725,726 and so compared to other intervals in Earth’s history, there are very few marine
environments preserved727. e reduction in early Mesozoic marine sedimentary occurrences is mirrored in
PhanSST by the dearth of Triassic and early Jurassic sampling sites (Fig.4b). Overall, the era is dominated by
carbonate isotope data. Phosphate isotope data are common in the Triassic, but disappear at the close of the
period, reecting the extinction of conodonts728. Conversely, TEX86 data make an appearance in the latter half
of the Mesozoic, and foraminiferal Mg/Ca data, though present in the Cretaceous, are infrequent, both in terms
of entries and sampling sites.
Despite comprising more than half of the Phanerozoic Eon, Paleozoic entries account for just 9% of all data
but constitute 40% of sampling sites (Table5). All Paleozoic entries come from marine sediments deposited on
continental margins or interiors. Paleozoic studies oen report multiple sample sites, with only a small number
of measurements from each site67,252,616. us, the number of sampling sites remains high, but the data density at
each site is drastically reduced (Fig.5). Only carbonate and phosphate oxygen isotope data are available for the
Paleozoic (Fig.4). Notable declines in data density in the Cambrian, Silurian, and Permian (Figs.4 and 5) can
be ascribed to a combination of preservation and eustasy, limiting the number of sites and material available for
analysis.
Spatial trends in data density. e spatial distribution of PhanSST sampling sites are inherently uneven, both
with respect to their modern (Fig.6) and paleo-locations (Fig.7). Paleo-latitude and -longitude of each entry
was estimated using the plate model of Scotese and Wright707, implemented in G-Plates (Version 2.2.0)729. In
terms of their modern distributions, Cenozoic data are the most spatially widespread and entry numbers at
each site are high, owing to the availability of ocean core data and the high-resolution, multi-proxy studies
they permit. Mesozoic sampling sites reect the mix of high sample densities at the few ocean cores that extend
back into the Cretaceous and lower sample density associated with outcrop data. By the Paleozoic, all samples
are situated on land and heavily weighted toward the Northern Hemisphere, with most data coming from a
few key regions (i.e., North America, Europe, Australia, and China). e Southern Hemisphere is consistently
underrepresented across all three eras and ve proxies and spanning both marine and continental deposits. e
paucity of data from South America and Africa, as well as the southern sectors of the Indian and Pacic oceans,
mirrors patterns in paleontological data709,730 and highlights the tectonic, depositional, and colonial biases in
paleoclimate data.
Viewing the paleogeographic distribution of the data by period (Fig.7) further accentuates depositional and
latitudinal biases. e Cambrian through Jurassic data overwhelmingly come from epeiric sea settings, while the
Cretaceous through Quaternary records are biased toward continental margins. Likewise, the Paleozoic records,
which dominantly come from modern-day North America and Europe, are weighted toward the tropics; as
these continents migrate northward through the Mesozoic and into the Cenozoic, the records begin to favor the
Northern Hemisphere mid latitudes. e Cenozoic data are more evenly distributed across latitudes due to the
availability of core data; however, the Southern Hemisphere remains underrepresented.
Proxy
All entries Unaltered entries
NProportion NProportion
δ18Ocarbonate 81,633 54% 56,691 48%
δ18Ophosphate 6,358 4% 6,014 5%
Mg/Ca 13,249 9% 9,846 8%
TEX86 14,091 9% 11,176 9%
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35,360 24% 35,324 30%
Tot a l 150,691 119,051
Tab le 4. Summary of entries by proxy. See text and caption of Fig.5 for details regarding how preservation was
assessed.
Era
Proportion of.. .
Phanerozoic Entries Sites
Cenozoic 12% 79% 24%
Mesozoic 35% 12% 35%
Paleozoic 53% 9% 41%
Tab le 5. Summary of the proportion of entries and sites by geologic era, with the proportion of Phanerozoic
time encompassed within each era for comparison.
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Fig. 4 Summary of the (a) number of unique sampling locations through geologic time, (b) proportion of
entries, and (c) proportion of unique sampling sites separated by proxy type and binned by geologic stage.
Colors in panels b and c follow the convention of panel a.
Fig. 5 Number of entries from unaltered (opaque) and total (transparent) material, separated by proxy type and
binned by geologic stage. e diagenetic determinations in panels a-c are based on the expert-assigned
DiagenesisFlag eld. is ag applies to all proxies barring TEX86 and
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; here, TEX86 delity is based on
entries with BIT, MI, and ΔRI values all below 0.5, while
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data whose paleolatitude is greater than 70°N are
considered compromised by alkenone production in sea ice. Due to a large increase in the number of entries
approaching the Holocene, the y-axis has been scaled in panels a and d, cropping the upper limits of the
Quaternary data from these plots.
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Fig. 6 Summary of the modern spatial distribution of sampling sites by (a–c) era and (d–h) proxy type, with the
size of each point scaled to the number of entries at each site. All panels are plotted on the same scale.
Fig. 7 Summary of the paleogeographic spatial distribution of sampling sites, colored by proxy type and
separated by geologic period. Histograms to the right of each map show the relative latitudinal distribution of all
unique sampling sites within 5° bins.
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Spatio-temporal trends in proxy values. e volume of data contained within PhanSST and the consistent and
queryable metadata elds permit a large-scale view of the evolution of proxy information through geological
time. Figure8 shows heat maps of proxy values from unaltered material across the temporal range of each proxy,
with data temporally averaged by stage and spatially averaged into 15° paleolatitudinal bins. Vertical trends show
the latitudinal proxy gradient at any given stage, while the horizontal trends show temporal evolution of proxy
values within a latitudinal bin.
e δ18Ocarbonate data (Fig.8a) exhibit a clear trend toward higher values towards modern day. is trend has
long been recognized and interpreted to reect either a secular evolution in the oxygen isotopic evolution of
seawater through geologic time, a true temperature signal from a much warmer ancient world, or an increased
inuence of diagenetic alteration with age24,312,731,732. Viewing the data as a function of both space and time
importantly highlights that very few early Paleozoic records extend beyond the tropics, while tropical Mesozoic
and (unaltered) Cenozoic data are less common. us, to some extent the observed shi toward higher values
should logically follow based on the dierences in the latitudinal extent of the data, regardless of the prevail-
ing climate regime. e end of the Paleozoic also marks a transition from data sourced from warmer epeiric
environments to continental margins, where temperatures are more likely to resemble zonal mean values39.
Dierences in the geographic and environmental spread of data between eras may therefore partly explain the
long-term temporal trend.
e δ18Ophosphate data are restricted mainly to the low latitudes in the Paleozoic, boreal mid latitudes in the
Mesozoic, and absent in the Cenozoic (Fig.8b). While there is a long-term temporal trend toward higher values
across the Paleozoic, the enrichment observed in the tropics during the Carboniferous and Permian is consistent
with cooling during the Late Paleozoic Icehouse733.
e Mg/Ca data have a sporadic temporal and spatial distribution (Fig.8c). Even during more data dense
intervals, such as the late Neogene, latitudinal gradients deviate from a temperature-driven expectation. e
Mg/Ca proxy is sensitive to several non-thermal factors, such as the magnesium to calcium ratio of the seawater
at the time of precipitation, the bottom water calcite saturation state at the time of burial, surface ocean pH and
salinity, and the cleaning method used during analysis35–37. Cumulatively, these factors inhibit straightforward
interpretation of the raw proxy values.
Compared to the other data types,
UK
37
′
data have a short temporal span but a wide spatial distribution. Nearly
all latitudinal bins from the mid-Neogene through modern are represented (Fig.8e). Unencumbered by
non-thermal inuences, the alkenone data exhibit well developed latitudinal gradients. However, because
UK
37
′
is
a ratio bounded on a 0 (cold) to 1 (hot) scale, tropical locations consistently approach the limit of the proxy,
making it challenging to discern temporal trends in these regions. ough lacking the spatial coverage of the
UK
37
′
data, the TEX86 data exhibit spatial and temporal trends broadly consistent with the current understanding of
Cenozoic climate3 and have the most consistent high latitude southern hemisphere coverage (Fig.8d).
Fig. 8 Summary of the spatio-temporal trends in proxy values from unaltered materials, separated by proxy
type and binned temporally by stage and spatially by 15° paleolatitudinal bins. e scale of each color bar is
unique to each proxy type, but for all panels, cooler colors correspond with proxy values associated with cooler
temperatures and vice versa.
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e paleogeographic maps (Fig.7) and heatmaps (Fig.8) demonstrate that all proxies have discontinuous
spatial and temporal coverage, but that collectively the Cenozoic is well represented. Geographic coverage
diminishes in the Mesozoic and is extremely limited in the Paleozoic. Dierences in the distribution of data,
both between eras and proxies, likely bias our collective understanding of Phanerozoic climate. Identifying and
acknowledge these patterns can aid in the interpretation of global climate trends and inform decisions regarding
where to target future data acquisition eorts, with respect to both geography and proxy type.
Usage Notes
Informed user notice. Although PhanSST has many applications beyond its initial intended purpose, sev-
eral sources of uncertainty are not constrained in the current version of the compilation. As noted above, the
database does not include age uncertainty. Additionally, while specic screening procedures (e.g., XRD, SEM)
can help identify altered material, the assessment of preservation is subjective, particularly in a binary sense. We
have taken a conservative approach, agging everything that was either (1) interpreted as altered in the original
publication, (2) was deemed suspect based on the expert opinion of the co-authors or (3) was beyond the reason-
able range of values for any given proxy. As discussed above, since there are a wide range of metrics and thresh-
olds used to evaluate the delity of dierent proxy values, we have chosen to include proxy-specic elds that
can aid in diagenetic assessments. We recommend that end-users utilize these elds, in tandem with the binary
DiagenesisFlag, to make their own informed diagenetic determinations.
All entries in PhanSST contain raw proxy values. is decision was intentional to ensure consistency between
PhanSST and the original data tables from which the data were drawn and to ensure traceability. However, where
appropriate, the database includes relevant information to assist in species- or methodological-specic correc-
tions (e.g., AnalyticalTechnique, NBS120c, Durango, CleaningMethod). We encourage users to make use of these
elds to guarantee appropriate reuse.
Finally, we would like to recognize that the data contained within PhanSST represent the collective work of
countless researchers and required an enormous amount of time, eort, and resources to generate. PhanSST has
compiled these disparate data sets, but can stake no claim in their generation. Upon reuse (and when realisti-
cally feasible), we recommend users to cite both PhanSST and the original data references to ensure appropriate
attribution.
Applying the database in deep time. When applying these data in the deep time, it will oen be neces-
sary to (1) convert the proxy values to temperature estimates and (2) estimate their paleogeographic position. In
addition to the considerations outline above in the Informed user notice, it is important to make deliberate and
justiable decisions regarding the choice of temperature calibration and plate rotation model.
For any given proxy system, there are a variety of dierent PSMs available with which to estimate SST. ere
is justication to use dierent calibrations under dierent circumstances (e.g., based on the location, age, or
taxon from which the data derive) and in many instances it may also be useful to report estimates of SSTs
using multiple calibrations (see the Which metadata elds are excluded and why? section for specic examples).
Similarly, in order to calculate SST, many of these proxies require assumptions about the seawater chemistry of
Proxy system Non-thermal assumptions Calibrations
δ18Ocarbonate δ18O of the seawater Bemis et al.734
pH of the seawater Grossman and Ku, 1986735
Kim and O’Niel, 1997736
Malevich et al.34
See also Grossman, 2012737 for more
δ18Ophosphate δ18O of the seawater Lécuyer et al.366
Pucéat et al.738
Mg/Ca Mg/Ca of the seawater Anand et al.739
pH of the seawater Gray and Evans, 201936
Bottom water saturation state () Tierney et al.35
Salinity
Cleaning method (oxidative or reductive)
TEX86 Schouten et al.740
Kim et al.704
O’Brien et al.696
Tierney and Tingley, 201431
′
UK
37
Müller et al.741
Conte et al.742
Tierney and Tingley, 201833
Tab le 6. Non-exhaustive list of non-thermal predictor variables associated with each proxy system in PhanSST,
and some commonly-used calibrations. Note that in addition to the listed non-thermal assumptions, some
calibrations may also take into account other factors, such as the paleolatitude or the ocean basin from which
the data derive.
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ancient oceans and other non-thermal variables. In Table6 we have provided a non-exhaustive list of calibration
references and potential non-thermal predictor variables for each proxy system. Some of the PSMs are straight-
forward transfer functions366,704,734, allowing SSTs to be calculated by hand or, e.g., in Excel, while others involve
a system of equations31,34,35 that can be implemented in various coding languages (e.g., Python, R, or Matlab). It
is important to be aware that dierent PSMs also handle uncertainty dierently, with some providing 1σ calibra-
tion uncertainty, some providing 95% condence intervals, and others providing no means of estimating error.
ere are, similarly, myriad plate rotation models to choose from and the choice of which model is most
appropriate depends upon several factors. e most important consideration is how the paleogeographic infor-
mation will be used. If the data will be plotted on top of an ESM output, then to ensure the data are placed
correctly the user will want to use the same rotation model as the simulation. is is particularly true in deeper
time, when paleolongitudes are unconstrained38. Once the user has determined the appropriate plate model,
rotation of the data can be implemented using G-Plates729, either through the command line in Python or via a
graphical user interface. Conversely, if the data are being used to investigate latitudinal temperature gradients
during a specic time slice, then it may be prudent to try several rotation models to get a sense of the uncertainty
in the values. If only paleolatitude is needed, we recommend using the Paleolatitude Calculator38, which pro-
vides estimates–and uncertainties–from several dierent plate rotation models.
Data availability and nature of a living database. A static copy of PhanSST Version 0.0.1 is archived
in the NOAA-NCEI Paleoclimatology Database (https://www.ncei.noaa.gov/access/paleo-search/study/36813)26.
Version controlled releases of the database and additional reference and database metadata can be found on Zenodo
(https://doi.org/10.5281/zenodo.7049233)27 and at the PhanSST website (https://www.paleo-temperature.org).
We chose to host the data on Zenodo because it (1) permits version control and (2) interfaces directly with
GitHub, which will allow us to release new versions of the database under the same DOI. Despite our best eorts
to identify data from the literature and QC each entry, given the sheer volume of data contained within PhanSST,
there are undoubtedly errors or data sets that we have overlooked. Any issues or omissions identied by end-users
can be reported on the PhanSST website and the erroneous information will be updated in future releases of the
database. Likewise, the website contains a blank data entry template and instructions for entering and submit-
ting missing or newly published data. Completed data entry forms can be submitted via the PhanSST website or
emailed directly to PhanSST@outlook.com. We encourage the community to contribute newly published data so
that the database can continue to grow. rough continued crowd-sourcing of data entry and QC, PhanSST will
remain a useful resource for the paleoclimate community for years to come.
Code availability
Figures4–8 were produced in Matlab. Example code and auxiliary functions to (1) reproduce Figs.4–6 and
(2) run the automated QC checks on the database are available on GitHub (https://github.com/EJJudd/
SciDataSupplement). e paleocoordinates used to produce Figs.7,8 were estimated using the plate model of
Scotese and Wright707, implemented in G-Plates (Version 2.2.0)729.
Received: 1 March 2022; Accepted: 8 November 2022;
Published: 6 December 2022
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SCIENTIFIC DATA | (2022) 9:753 | https://doi.org/10.1038/s41597-022-01826-0
www.nature.com/scientificdata
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Acknowledgements
e authors would like to acknowledge the numerous researchers who generated the original data, many of
whom kindly provided additional metadata upon request. EJJ was supported by the PhanTASTIC Postdoctoral
Fellowship, funded by Roland and Debra Sauermann. JET was supported by grant #2016-015 from the Heising-
Simons Foundation. Additional funding support was provided by: NERC NE/N015045/1 to HLF, GCRF Royal
Society Dorothy Hodgkin Fellowship DHF\ R1\191178 to GNI, the Schlanger Ocean Drilling Fellowship to
RR, NSF OCE-2202760 to WS, the Student Awards Agency Scotland to MLS, and the University of Arizona
Technology and Research Initiative Fund to KT.
Author contributions
Authorship is subdivided into three tiers. e rst tier (E.J.J., J.E.T., B.T.H., S.L.W. & D.J.L.) reects the core
PhanTASTIC team; i.e., those who conceived of, supervised, and/or carried out the execution of the broader
PhanSST project. B.T.H. and S.L.W. conceived the project and acquired funding. E.J.J. and J.E.T. spearheaded the
data collection and developed the metadata structure. E.J.J. performed the database statistics analyses and draed
the manuscript. All rst tier authors contributed edits to the initial dra of the manuscript. e second tier (listed
alphabetically: H.L.F., G.N.I., E.L.M., C.L.O., R.R., W.S., M.L.S., K.T.) are those who contributed substantially to
the database by: entering >15 of records into the template and/or quality control checking >25 records. e third
tier (listed alphabetically: E.A., M.J.C., R.R.D., D.E., W.R.G., E.L.G., M.J.H., B.N.H., K.G.M., L.K.O., M.L.S.M.,
H.S., Y.G.Z.) includes everyone else who, at a minimum, entered at least one record into the database template.
All authors reviewed the manuscript.
Competing interests
e authors declare no competing interests.
Additional information
Correspondence and requests for materials should be addressed to E.J.J.
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