dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2021)  changes their phosphorylation status after 20 min of light. The bioinformatic tool MAPanalyzer allows the analysis of known MAPs (Microtubule Associated Proteins) or predicted ones  (Zhou et al. , 2015). According to MAPanalyzer database, 5% of the genes in the Arabidopsis genome are predicted to have MT binding motifs with a very high (>99%) or high (>95%) specificity threshold (Fig. S2 ). The 11 frequency of predicted MAPs increased significantly (14%) in the etiolated WT phosphoproteome (Fig. S2 ). Additionally, this frequency even significantly increased (45%) in the pool of phosphoproteins that changed their phosphorylation pattern after 20 min of light compared to darkness (Fig. S2 , Table  1 ). Association to MTs of WDL1, WDL4 and IQD32 is supported experimentally  (Yuen et al.
Database organization Coral records in the database are organized into seven groups based on the availability of paired proxy time series, temporal coverage, and record resolution (Table  1 ). Groups 1-3 contain records with paired Sr/Ca-δ 18 O time series. Group 1 records have monthly to bimonthly temporal resolution and cover at least 80% of the 20th century. Records in Group 2 are similar in resolution to records in Group 1, but cover less than 80% of the 20th century. Group 3 records contain any paired Sr/Ca-δ 18 O time series that have lower than bimonthly resolution. Group 4 records are δ 18 O-only time series with monthly to bimonthly resolution, while Group 5 records are δ 18 O-only time series with lower than bimonthly resolution. Groups 6 and 7 mirror Groups 4 and 5 respectively, but for Sr/Ca-only records.
Following the Iso2k database protocols  (Konecky et al. , 2020) , each record in the CoralHydro2k database is assigned a unique nine-digit alphanumeric identifier. These unique identifiers are generated using the first two letters of the lead author surname (AN), the last two digits of publication year (01), a three-letter code indicating the location of the record (ABC), and a twodigit core-ID number (02). The two-digit core-ID number begins at '01' by default and increases with each successive record from the same site and publication. Identifiers have the final format "AN01ABC02". For example, record AB08MEN01 was published by  Abram et al. , in 2008 , is a record from the Mentawai Islands, and is the first core from that study.
Metadata The CoralHydro2k database contains 55 metadata fields that inform the use of each coral record: 32 metadata fields are standardized and quality controlled, while 23 fields are unstructured. Standardized metadata fields use controlled vocabulary or numeric information with uniform units, making them easily searchable by database users. Unstructured metadata are free-form text entries that are less rigorously quality controlled but are included to aid the interpretation of the coral records. Metadata included in the CoralHydro2k database is organized into four categories (Entity, Publication, Analysis, and Calibration) based on standards recommended by MARPA  (Dassié et al. , 2017)  and PaCTS1. 0  (Khider et al. , 2019). Entity metadata provides identifying information for each coral record (Table  2 ), including geographic coordinates, location names, 165 water depth of the coral colony, coral species, and any core names included in the original publications.
For records featured in multiple publications, bibliographical information for publications is stored in the order established by the source data repository. First citations are found in the pub1 metadata fields, and subsequent citations are found in pub2 and pub3. Analysis metadata (Table  5 ) provides information about the laboratory analysis of the samples, including (when available) information related to subsampling the cores, coral extension rate and tissue thickness, the units of reported variables, and analytical precision for geochemical time series. When available, information on the measurement of the international coral reference material JCp-1  (Okai et al. , 2002; Hathorne et al. , 2013)  is included for Sr/Ca records. Calibration metadata (Table  6 ) includes any proxy-SST slopes, intercepts, correlations, and information about regression methods used, as reported in the original publications.
Quality control and validation As records included in the CoralHydro2k database are published in peer-reviewed scientific journals, our quality control efforts were focused on the consistency of metadata and the accurate integration of records into the database. More specifically, the quality control team worked to ensure that (i) metadata and proxy time series were entered correctly into the database, (ii) metadata followed a standardized vocabulary or format, and (iii) records were sorted into the correct group based on the types of proxies available, length, and resolution. For sites where coral records were either extended or revised in subsequent studies, we include the most recent version of the record in the database and include citation information and other metadata from previous studies.
A quality control checklist was used to ensure each field was in a standard format and contained information consistent with that in original publications and other online repositories. When information was unavailable, the corresponding fields were left blank. Users of the database should not view the inclusion of a record as an endorsement of its fidelity by CoralHydro2k for reconstructing a climate parameter, as non-climatic factors (e. g. , coral skeletal structure or growth rate) can complicate the extraction of climate signals from geochemical records (see  Reed et al. , 2021; DeLong et al. , 2013 DeLong et al. , , 2016)). We strongly suggest users further assess records and original publications, or consult the original author or a coral paleoclimate expert if they have questions or concerns.
Relation to other PAGES2k products CoralHydro2k was inspired by PAGES (2k) compilations of marine and hydrological proxy records such as Ocean2k  (Tierney et al. , 2015; McGregor et al. , 2015) , SISAL  (Atsawawaranunt et al. , 2018; Comas-Bru et al. , 2020) , and Iso2k  (Konecky et al. , 2020) , but was created to address a different set of research questions. As the database is designed specifically for coral-based proxy records, we employ more inclusive record selection criteria that allow us to include records that do not meet the length requirements of previous PAGES 2k data compilations but are important to contextualize ongoing climate change during the Common Era. The CoralHydro2k database also contains new, updated, or extended records that were published after previous PAGES efforts, and will continue to be actively curated and updated annually.
Spatial and temporal coverage The CoralHydro2k database includes 233 proxy time series from 124 unique locations sorted into seven groups (Fig. 1a ). The proxy time series are stored as "records", with 54 records containing paired Sr/Ca and δ Atlantic, where cooler and/or more variable ocean conditions are generally unfavorable for coral growth. The majority of records in the database fall between 1800 and 2010 CE (Fig. 1b ). Approximately 28% of records in the database cover time intervals earlier than the 1800s, with most of these records coming from corals that are dead when collected (often referred to as "fossil" corals), which provide short, discrete time series often spanning several decades. The oldest such record in the database is a coral from Hainan Island in the South China Sea that covers 167-309 CE  (Xiao et al.
Sr/Ca and paired records in the database that have core top dates prior to the late 1990s typically represent updates or extensions 255 to previously published δ 18 O records (e. g. ,  Felis et al. , 2000 Felis et al. , , 2018) )  or fossil records. A majority of the records included in the CoralHydro2k database offer seasonal or sub-seasonal resolution: 76% of 260 the records in the CoralHydro2k database have monthly or bimonthly resolution, 6% have quarterly to biannual resolution, and 18% have annual or lower resolution (Fig. 3 ). Proxy records in the CoralHydro2k database capture SST variability on seasonal and longer timescales. To highlight relationships between temperature and proxy records in CoralHydro2k, we calculate Pearson correlation coefficients between the records and local SST (2º grid area) from the NOAA ERSSTv5 data set  (Huang et al.
g. ,  Deser et al. , 2010; Freeman et al. , 2017; Kennedy et al. , 2019). Thus, the proxy-SST correlations presented here may deviate from those stated in each record's original publication. Patterns observed in proxy-SST correlations are also mirrored in the dominant mode of variability displayed by each record. To examine the relative contributions of seasonal, interannual, and decadal variability in coral records, we apply a 13month highpass (seasonal), a 2-7 year bandpass (interannual), and a 10-year lowpass filter to all monthly and bimonthly records that are at least 30 years long. Filtering was performed using a 6th-order Butterworth filter in MATLAB, with the filter order used to optimize filtering in the decadal band. Variance for each filtered series is normalized by the proxy record variance determined for the entire record length to enable comparison between δ 18 O and Sr/Ca (Fig.
5 ). The seasonal variance in both proxies increases with latitude (Fig. 5a-b ), with records in the subtropics exhibiting greater seasonal variance than records close to the equator. Conversely, records close to the equator contain higher proportions of interannual variance (Fig. 5c-d ). This pattern is more apparent among longer coral δ 18 O records, as several Sr/Ca records in the database do not meet the 30year length requirement for bandpass filtering. For global or regional climate reconstructions, it is useful to consider the relationship of gridded SST data products to the proxy network as opposed to the relationship of individual records to the nearest grid point in those data products. To assess the reconstruction potential of the CoralHydro2k proxy network, we calculate the median absolute correlation between each ERSSTv5 grid box and all available records in the database within a 3,000 km radius (Fig.
6 ). We find significant (assuming a 90% confidence interval) annual Sr/Ca-SST correlations across 56% of the tropical and subtropical oceans (Fig. 6b ), and significant annual δ 18 O-SST correlations across 60% (Fig. 6d ). Consistent with previous results, bimonthly correlations between SST and both proxies are higher (Fig. 6a, c ) due to the seasonal cycle. Whereas non-climatic factors, such as age-model errors  (Comboul et al. , 2014; Lawman et al. , 2020b; Loope et al. , 2020) , may lower the correlation between coral proxies and SST in some regions, significant correlations observed here highlight the fact the CoralHydro2k database captures regional to global patterns of climate variability, and thus, is suitable for reconstructing SST variability across much of the tropical and subtropical oceans.
Many δ 18 O-SSS correlations at annual resolution are not significant; however, this may be more reflective of the SSS data set used here rather than the integrity of records in the database. Historical SSS observational records are much shorter and sparser than SST before the satellite era  (Good et al. , 2013; Boyer et al. , 2018; Friedman et al. , 2017) , especially in the tropical and subtropical oceans. Consequently, much larger discrepancies exist among gridded SSS data sets than those found between gridded SST products  (Carton et al. , 2018 (Carton et al. , , 2019;; Zweng et al. , 2019). New and emerging salinity products such as NASA's Soil Moisture Active Passive (SMAP) Sea Surface Salinity (Vazquez-Cuervo and Gomez-Valdes, 2018), ESA's Soil Moisture and Ocean Salinity Mission (SMOS;  Boutin et al.
, 2016) , Aquarius  (Drucker and Riser, 2014) , and Argo  (Schmid et al. , 2007)  will be important calibration data sets for future coral studies or reconstructions that cover the years since 2011 CE. Nonetheless, the lack of long, historical SSS records highlights the need for independent coral-based constraints on long-term hydrological trends across the tropical and subtropical oceans. We assess the "local" reproducibility of coral records in the database by comparing each proxy record to records of the same type within a 50 km radius with at least 20 years in common (Fig. 7 ). As the CoralHydro2k database represents the most comprehensive coral-based proxy compilation effort to date, approximately 36% of the records are within a 50 km radius of one to five contemporaneous records. Bimonthly absolute correlations for Sr/Ca records within 50 km of each other range from 0.
General applications The CoralHydro2k database is the most comprehensive compilation of coral δ 18 O and Sr/Ca records to date. The database offers extensive coverage of monthly to annually resolved marine proxy records that can be used to investigate nearsurface hydrology and temperature variability across the global tropics and subtropics. Comparable information at similarly high resolution is rarely available with other marine paleo-archives. Paired coral Sr/Ca-δ 18 O records allow for independent reconstruction and investigation of pre-industrial temperature and hydrologic changes at seasonal, interannual, and decadal time scales. The inclusion of both unpaired and short proxy records, many of which did not meet the selection criteria of previous PAGES 2k data compilations, allows the CoralHydro2k database to be used for applications beyond large-scale temperature and hydrology reconstructions.
This includes, and is certainly not limited to, proxy calibration studies, proxysystem model development, and paleo-data assimilation efforts. Records in the CoralHydro2k database can also be compared to model outputs, either by converting coral Sr/Ca into temperature for direct comparison or by using proxy system modeling to estimate proxy composition from climate model output. Coral δ 18 O records and coral-derived δ 18 Osw records can also be directly compared with new simulations from isotope-enabled models. A brief overview of how to access, query, and cite the database is provided in the sections below.
Searching the CoralHydro2k database Each LiPD serialization of the CoralHydro2k database contains the variables 'D' and 'TS', where 'D' is a site-centric storage of data and 'TS' is an expanded data structure holding the same information stored in a 'per time series' format. MATLAB and R serializations also contain an 'sTS' variable to be consistent with other PAGES2k data sets, which contains the same information as variable 'TS'. Proxy records stored in the CoralHydro2k database can be searched for using a variety of keywords or parameters. We suggest users initially narrow their search by filtering for groups of interest using the field paleoData_coralHydro2kGroup, which sorts records based on proxy type, record resolution, and temporal coverage. A detailed summary of group definitions is in Table  1.
The database can also be queried by: • Proxy type, using the field paleoData_variableName. • Temporal coverage, using minYear and maxYear to search for proxy record start and end years, respectively. • Record resolution, which can be searched by nominal resolution (Table  3 ) using hasResolution_nominal or numerically (minimum, mean, median, maximum) using fields beginning with 'hasResolution_'. See Table  2  for more information. • Location, using geographic coordinates (geo_latitude and geo_longitude), site name (geo_siteName), or ocean basin (geo_ocean). geo_ocean is the level one ocean basin listed in the World Ocean Atlas  (Boyer et al. , 2018)  for the geographic coordinates of the record. • Coral species, using paleoData_archiveSpecies.
Data availability, updates, and versioning The development of the CoralHydro2k database was guided by FAIR data principles  (Wilkinson et al. , 2016) , which strive to make scholarly data Findable, Accessible, Interoperable, and Reusable. Thus, the CoralHydro2k database employs the LiPD framework (McKay and Emile-Geay, 2016), a standardized, machine-readable format for archiving and describing paleoclimate data, with serializations for MATLAB, R, and Python available at https://doi. org/10. 25921/yp94-v135  (Walter et al. , 2022). Also available on the database website is a MATLAB example script to help new users search the database. One of CoralHydro2k's core goals is to create an actively curated coral database. We encourage the community to submit newly published coral δ 18 O and Sr/Ca records using the data submission form located on the repository website linked above.
Newly published records will be compiled and added to the database on an annual basis. Updates to the database will follow the versioning scheme used by the PAGES2k database (PAGES2k Consortium, 2017). The first release of the CoralHydro2k database is version 1. 0. 0. The version number has three counters in the following form C1. C2. C3. The first counter, C1, is updated with each publication of a formal update of the data set. The second counter, C2, is updated when a record is added or removed. The third counter, C3, is updated when a modification is made to the data or metadata. It is anticipated that future versions and a change log describing updates with each new version will be made available at the same location as the original data release.
Citation Researchers utilizing the whole CoralHydro2k database or a significant portion of the database should cite this paper and the paper describing the most recent version of the database. If only a small subset of the records is being used, researchers should also cite the papers that originally describe each coral record used. Citation information associated with each record in the database as well as a link to the original public archive of each data set is included in the metadata to facilitate users in crediting the original data generators in their use of the coral data.
Conclusion Shallow-water corals provide monthly to annually resolved climate records from data-scarce locations across the tropical and subtropical oceans and are incredibly useful for extending modern-day observations back into the preindustrial era, contextualizing anthropogenic climate trends, and improving the skill of future climate projections. The PAGES CoralHydro2k project was formed to facilitate the use of coral paleoclimate records by the broader scientific community. Our first effort on this front is the CoralHydro2k database: a mostly unfunded endeavor representing the collective efforts of 40+ researchers across different career stages, institutes, and time zones, meeting monthly to bi-weekly and working asynchronously over the past four years. Subsequent publications from the CoralHydro2k project will use this database to evaluate proxy-SST calibrations and methodological differences used in coral-based climate reconstructions as well as investigate past tropical ocean hydroclimate trends using data assimilation and comparison to isotope enabled-models.
Furthermore, the CoralHydro2k team has also been collecting instrumental seawater δ 18 O data as part of our database compilation efforts. That database will be released in the near future -also following the FAIR standards -and will also be maintained with active curation (see  DeLong et al. , in press). While the fruits of the CoralHydro2k database are likely to come over the next 5-10 years, continuing to invest as a community in compiling standardized data sets will inevitably elevate the utility of each record. The CoralHydro2k database is a comprehensive, machine-readable, standardized, and actively curated database of coral δ 18 O and Sr/Ca records. Records in the CoralHydro2k database track large-scale regional SST and hydrology signals across seasonal, interannual, and decadal timescales with a high degree of reproducibility.
As such, the records in the database can be used for investigating tropical and subtropical SST and hydrology variability on societally relevant time scales and can be combined with large networks of terrestrial paleo-archives of climate variability such as tree rings, ice cores, or speleothems to investigate past and present ocean-atmosphere-land interactions. Moreover, the database enables global-scale comparisons of coral-based paleoclimate reconstructions with state-of-the-art climate models, either through the use of forward models  (Thompson et al. , 2011; Dee et al. , 2015 Dee et al. , , 2017;; Tardif et al. , 2019) , or directly in the case of isotope-enabled models  (Konecky et al. , 2020). The comprehensive and high-resolution nature of the CoralHydro2k database also makes it ideally suited as an input database for paleoclimate data assimilation efforts such as the Last Millennium Reanalysis  (Hakim et al.
Acknowledgements CoralHydro2k is a contribution to Phases 3 and 4 of the PAGES 2k Network. We would like to thank PAGES IPO for providing logistical, technical, and financial support for community-driven projects such as CoralHydro2k. More specifically, we'd like to extend our gratitude to Sarah Eggleston, Belen Martrat, Angela Wade, and the PAGES2k coordinators for helping facilitate various aspects of this project over the last four years. Most importantly, we would like to thank the original data generators of each coral-based proxy record for making their data publicly available via the World Data Center PANGAEA, the NOAA NCEI World Data Service for Paleoclimatology, or other means, without whom this effort would not be possible. We would like to thank all the researchers whose publicly archived data was included in the CoralHydro2k database (Appendix A).
We would also like to thank the following researchers who provided data that were not previously archived or were archived in places other than NOAA or PANGAEA and are now included in CoralHydro2k and NOAA: Tianran Chen, Wenfeng Deng, Juan P. D  'Olivo, Heitor Evangelista, Jennifer A. Flannery, Eberhard Gischler, Nathalie F. Goodkin, Y. Kawakubo, K. Halimeda Kilbourne, Braddock K. Linsley, Christopher R. Maupin, Hussein R. Sayani, Sujata A. Murty, David Storz, Takaaki K. Watanabe, Henry C. Wu, Hangfang Xiao. We would also like to acknowledge software and data sources that made this project possible. Sea surface temperature data (ERSSTv5) was provided by the NOAA/OAR/ESRL Physical Sciences Laboratory in Boulder, Colorado, USA, from their website at https://psl. noaa. gov/data/gridded/data. noaa.
18 O time series, 79 records containing only δ 18 O time series, and 46 records containing only Sr/Ca time series. For 19 of the paired δ 18 O and Sr/Ca records, we also include in the database the coral-derived δ 18 Osw time series calculated by the authors of the original publication. Records in the CoralHydro2k database extend from 33° N to 28° S and across all tropical oceans. The majority of these records are concentrated in the Indo-Pacific Warm Pool and the western tropical Atlantic, as conditions there are favorable for coral growth and reefs are more accessible to researchers. Record density is low in the eastern tropical Pacific and eastern tropical.
https://doi. org/10. 5194/essd-2022-172 Preprint. Discussion started: 17 August 2022 c Author(s) 2022. CC BY 4. 0 License. MATLAB package found at www. eoas. ubc. ca/~rich/map. html. Lastly, we would like to thank NOAA NCEI and the LiPD 505 team for facilitating archival and distribution of the CoralHydro2k database. This work was partially funded by Georgia Tech's President's Undergraduate Research Award (PURA) and the Rutt Bridges Undergraduate Research Award to R. Walter. KLD partially funded by NSF Award #2102931 and Department of the Interior South Central Climate Adaptation Science Center Cooperative Agreement G19AC00086. NJA, BE and JAH gratefully acknowledge funding from the Australian Research Council (FT160100029 and CE170100023). 510 https://doi. org/10. 5194/essd-2022-172 Preprint.