dataesr/scientific-paragraphs-categorization huggingface dataset:
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 filtering. For consistency, the coordinates of sample sites were rounded to two decimal degrees. In some cases (predominantly Paleozoic data collected in outcrop and published in older journal articles) precise coordinates were not provided  423, 483, 501   these instances, locations were estimated using context from the original publication, outcrop information from Macrostrat  708  , and occurrence data from the Paleobiology Database 709. Age fields. Six age fields 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.
Data coming from IODP cores, for example, often have higher precision age models tied to sampling depth. In contrast, data from outcrops are frequently more challenging to date precisely and quantitatively. Thus, ages were assigned in one of three ways: by entering a numeric age and auto-filling the relative age information, by entering relative age information and auto-filling the numeric age information, or by retaining both the manually entered numeric and relative age information. The AgeFlag field identifies which approach was taken (indicated by a 0, 1, or 2, respectively). Numeric age assignments with auto-filled relative ages. If the original paper provided a precise numeric age, the period and stage fields were automatically filled in using age boundaries from GTS 2020  710. We made efforts 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 field. Relative age assignments with auto-filled numeric age. Some data can only be relatively dated. In the absence of precise numeric ages, the Stage field 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 scientific 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 defined stage as discussed above).
The 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 field based on the divisions in GTS 2020  710. Relative ages were further qualified using the StagePosition field (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 2020  710. 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 specific 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 field. Note that assigning ages in this manner means that high(er)-resolution relative time series information is not retained in the auto-filled 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 traceable methodology. For the purposes of the PhanTASTIC project, stage-level temporal resolution is sufficient. Regardless, stratigraphic position, when available, was recorded in the MBSF, MCD, and/or SampleDepth fields, which allows users to construct relative time series if desired.
To that end, we have also provided additional diagenetic fields specific to δ  18  O carbonate and δ  18  O phosphate data, such as trace and major element ratios and concentrations of macrofossils, cathodoluminescence assessments, and maximum CAI of conodonts. End users can define their own diagenetic thresholds (e. g. , Mn/Sr <0. 5) and use these fields to consistently filter or flag suspect data. We have not indicated the preservation state of TEX 86 nor ′ U K 37 entries because although methods for assessing preservation and thermal maturity exist  8, 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 TEX 86 data for non-thermal influences (see below). Taxonomic and environmental fields.
The taxonomic and environmental fields provide information about the organism from which the proxy data derive (Table  3 ), as well as the depositional environment and ecology of the sampled taxon. The Taxon1 field refers to the first-order taxonomic affiliation of the organism, primarily classified at the phylum level. The Taxon2 field, applicable only to mollusk δ  18  O carbonate data, further specifies the class, and the Taxon3 field specifies the binomial species name of the sampled organism, when available. These fields help characterize the paleoenvironment from which the data come, permit the compilation to be filtered by taxon, and contain information pertinent to species-specific calibrations  34, 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.
Proxy-specific fields. Certain proxy-specific fields were added to the compilation to permit data corrections, 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 reflect 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 δ 18 O data from both carbonates and phosphates include information about the AnalyticalTechnique (i. e. , IRMS or SIMS), as evidence suggests there may be methodological offsets in the isotope values  148, 712. The NBS-120c value used to standardize the δ  18  O phosphate data is reported in the NBS120c field. The consensus value of 21. 7‰ is now generally applied, but the value of the standard varies significantly in older literature.
Reference metadata. The reference fields contain the publication metadata, including the lead (first) 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 fields were removed. However, the full and unaltered database citations, including the article title and journal name, can be found in a supplementary Excel (. xlsx) file available on Zenodo and the PhanSST website.
2a ). Each unique site, proxy, and reference was evaluated based on a standardized suite of criteria to ensure consistency throughout the process (Fig. 3 ). Those assisting in the QC process checked boxes to confirm the fidelity of the journal metadata, modern coordinates, age model, depth information, proxy value, preservation state, taxa, cleaning method (for Mg/Ca), and fractional abundances (for TEX 86 ). We corrected any issues identified 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 assignments and misspellings (Fig. 2b ), using databases like Mikrotax. We added an expert-assigned Ecology field to help distinguish surface or mixed layer dwelling species from (sub-)thermocline species. The macrofossil δ  18  O carbonate and δ 18 O phosphate data were QC checked separately. These data generally come from outcrops, with many studies reporting multiple sampling sites but only a few measurements at each site. These differences 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 compilations  23  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 field to identify any missing or erroneous metadata (available on GitHub; see Code Availability). For example, the ModLat and ModLon fields 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 shapefiles data, to ensure accuracy and consistency in the assignment. ContinentOcean assignments were verified 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 2020  710  , 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 field (e. g. , ProxyType, ValueType, AnalyticalMethod) were printed to identify any inconsistencies, and every field was queried to identify missing values. General database statistics. Version 0. 0. 1 of PhanSST contains 150,691 entries, drawn from 660 different references, representing 1,643 unique sampling sites and 93 of the 100 Phanerozoic stages. Of these, 25,782 have been flagged as diagenetically altered, though this flag does not apply to the TEX 86 or ′ U K 37 entries; there remain nearly 120,000 unflagged data points after applying a conservative screening of these latter proxies to ensure fidelity of their values (i.
e. , excluding TEX 86 data whose BIT, MI, or ΔRI values are greater than 0. 5 and ′ U K 37 data whose paleolatitude is greater than 70°N, since these data are considered compromised by alkenone production in sea ice  33, 720, 721  ). Across the database, δ  18  O carbonate data are the most common, constituting over half of all entries (Table  4 ). This prevalence reflects the fact that carbonate oxygen isotope data (1) are the oldest quantitative paleotemperature proxy, with its origins dating back to 1947 722 , (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 δ 18 O carbonate data are commonly affected by diagenesis, with ~30% of the 81,633 entries flagged as altered. Despite their limited temporal availability, U K 37 ′ data account for a quarter of all entries, and all data, barring those from regions prone to sea ice, are considered to reflect a primary SST signal.
The high volume of ′ U K 37 data is likely a result of the ease of analysis and straightforward relationship between temperature and proxy value. The remaining three proxies collectively contribute to the remaining quarter of the entries in PhanSST, with conodont δ  18  O phosphate being the least common. Below, we highlight some of the first-order spatial and temporal trends in data density, sampling locations, and proxy values of PhanSST. The syntheses presented illustrate just a small fraction of the spatio-temporal patterns 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. The Cenozoic accounts for just 12% of Phanerozoic time, while the Mesozoic accounts for 35% and the Paleozoic, 53%.
The number of PhanSST entries and sampling sites per era, however, follow a different distribution (Table  5 ), reflecting fundamental differences in the availability, density, and nature of paleo-SST archives across geologic time. Cenozoic entries account for 79% of the data in PhanSST, while Cenozoic sampling sites account for a quarter of the unique sites. The over-representation of Cenozoic entries reflects the fact that the majority of these data come from drill cores, with long and often high-resolution records, and many of these cores have SST data from multiple proxies. Additionally, several of the proxies, such as ′ U K  , are restricted to more recent times. Collectively, these factors influence both the number of entries and unique sampling sites from each proxy type through time (Figs.
37 ′ sites showing a large increase towards the present (Fig. 4 ). The 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 (Table  5 ). The Mesozoic marks a transition in both depositional environment and proxy availability. In the Cretaceous, IODP data are still available, as are foraminiferal-based and TEX 86 records. However, the the lack of pelagic calcifiers and preserved biomarkers in sediments predating the mid to late Mesozoic limits both the Fig. 3  The QC criteria under which each site and reference was evaluated. Checking the boxes of the same color and heading in Fig. 2a  confirms that all of the criteria for a given category have been met.
materials and proxies available. Further, nearly all seafloor older than the late Jurassic has been subducted  701  , which fundamentally restricts the temporal reach of ocean drill cores. Therefore, records from the early portion of the Mesozoic dominantly come from shelf deposits or epeiric seas. These 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 widespread continental flooding  725, 726  and so compared to other intervals in Earth's history, there are very few marine environments preserved  727. The 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, reflecting the extinction of conodonts  728. Conversely, TEX 86 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 (Table  5 ). All Paleozoic entries come from marine sediments deposited on continental margins or interiors. Paleozoic studies often report multiple sample sites, with only a small number of measurements from each site  67, 252, 616. Thus, the number of sampling sites remains high, but the data density at each site is drastically reduced (Fig.
7 ) further accentuates depositional and latitudinal biases. The 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. The Cenozoic data are more evenly distributed across latitudes due to the availability of core data; however, the Southern Hemisphere remains underrepresented. Spatio-temporal trends in proxy values. The volume of data contained within PhanSST and the consistent and queryable metadata fields permit a large-scale view of the evolution of proxy information through geological time.
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. Though lacking the spatial coverage of the ′ U K 37 data, the TEX 86 data exhibit spatial and temporal trends broadly consistent with the current understanding of Cenozoic climate  3  and have the most consistent high latitude southern hemisphere coverage (Fig. 8d ). The 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. Differences in the distribution of data, both between eras and proxies, likely bias our collective understanding of Phanerozoic climate.
Usage Notes Informed user notice. Although PhanSST has many applications beyond its initial intended purpose, several 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 specific 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, flagging 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 reasonable range of values for any given proxy. As discussed above, since there are a wide range of metrics and thresholds used to evaluate the fidelity of different proxy values, we have chosen to include proxy-specific fields that can aid in diagenetic assessments.
We recommend that end-users utilize these fields, in tandem with the binary DiagenesisFlag, to make their own informed diagenetic determinations. All entries in PhanSST contain raw proxy values. This 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-specific corrections (e. g. , AnalyticalTechnique, NBS120c, Durango, CleaningMethod). We encourage users to make use of these fields 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, effort, and resources to generate.
PhanSST has compiled these disparate data sets, but can stake no claim in their generation. Upon reuse (and when realistically 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 often be necessary 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 justifiable decisions regarding the choice of temperature calibration and plate rotation model. For any given proxy system, there are a variety of different PSMs available with which to estimate SST. There is justification to use different calibrations under different circumstances (e.
Salinity Cleaning method (oxidative or reductive) TEX 86  Schouten et al. 740  Kim et al. 704  O'Brien et al. 696  Tierney and Tingley, 2014  31  U K 37 ′ Müller et al. 741  Conte et al. 742  Tierney and Tingley, 2018 33 Table  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. ancient oceans and other non-thermal variables. In Table  6  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 straightforward transfer functions  366, 704, 734  , allowing SSTs to be calculated by hand or, e.
Once the user has determined the appropriate plate model, rotation of the data can be implemented using G-Plates  729  , 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 specific 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 Calculator  38  , which provides estimates-and uncertainties-from several different 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 efforts 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 identified 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 submitting missing or newly published data.
acknowledgements The 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.