dataesr/scientific-paragraphs-categorization huggingface dataset:
Our data are deposited in PRIDE, UniProtKB and PeptideAtlas all provide evidence for each site using Universal Spectrum Identifiers  [36] , which can be rendered via https://proteomecentral. proteomexchange. org/usi/ (and shown in right panel on Figure  8B ). This allows a user to explore the evidence for a given site on a peptide, and test other possible explanations to see if the spectrum could support alternative explanations (peptides or sites within those peptides). Lastly, the phosphosite build is scheduled to appear in PlasmoDB in 2024, as this is the central database for Plasmodium researchers. We have reported a comprehensive meta-analysis of P. falciparum phosphoproteomics data sets. Data sets were re-analysed using a robust pipeline ensuring objective false localisation rate calculation.
Additional Gold, Silver, Bronze labelling was also provided as easy way to display confidence of phosphosites being correctly identified. Results from our meta-analysis have been deposited into UniProtKB, enabling sites to be used for further research with other bioinformatics tools. The data has also made available in PeptideAtlas and PRIDE, enabling detailed exploration of scores and visualization of source mass spectra, as a full evidence trail. We have also provided over-represented motifs found in these phosphoproteins as well as conservation data in relation to another 22 species of the genus Plasmodium, and from 115 different Plasmodium falciparum isolates, with respect to P. falciparum strain 3D7. We provided predictive disorder scores for all identified phosphosites and links to protein structures to visualise these proteins.
Methods Data selection The ProteomeXchange Consortium  [37]  was used to identify suitable P. falciparum phosphoproteomics datasets, via the PRIDE repository  [38]  and ProteomeXchange. Overall 11 were deemed suitable for phosphosite (localisation) reanalysis, based on inclusion criteria that data sets were generated by data dependent acquisition (DDA) methods, "high-quality" (i. e. likely to deliver >1,000 phosphosites), raw data were available and readable using open source tools: PXD000070  [39] , PXD001684  [4] , PXD002266  [40] , PXD005207  [41] , PXD009157  [42] , PXD009465  [43] , PXD012143  [44] , PXD015093  [45] , PXD015833  [30] , PXD020381  [46] , and PXD026474  [47]. These 11 data sets consist of both labelled (TMT or iTRAQ) and unlabelled MS data sets. Most of the studies focus on the blood stage in P.
falciparum life cycle, with only one study (PXD026474) including gametocytes. There was not data available for the liver stage of the parasite, possibly due to challenges with the development of liver models. A brief description of the studies' objectives extracted from the abstracts of their publications, P. falciparum stages included in the analysis, enrichment details and MS methods are shown in Table  2  and more details about the studies' objectives and biological samples in Supplementary Table  1. PXD026474 "understand -CDPKs -role in human parasite transmission from the host to the mosquito vector and thus investigated the role of the human-infective parasite Plasmodium falciparum CDPK4 in the parasite life cycle. P. falciparum cdpk42 parasites created by targeted gene deletion showed no effect in blood stage development or gametocyte development.
However, cdpk42 parasites showed a severe defect in male gametogenesis and the emergence of flagellated male gametes. " P. falciparum NF54 and Pfcdpk42 parasites were cultured as asexual blood stages according to standard procedures and received complete RPMI medium. Gametocytes were also cultured  [47]  Human Integrated Search Proteome  [48] , containing core isoforms from neXtProt  [49]  (2020 build). A fasta file was created combining these sequences with cRAP contaminant sequences (https://www. thegpm. org/crap/) plus decoy sequences. One decoy sequence was generated each protein and contaminant sequence using the de Bruijn method (with k=2)  [50]. Data analysis was performed using the Trans-Proteomic Pipeline (TPP)  [19] , including Comet  [51]  search engine for individual datasets, followed by PeptideProphet  [52] , iProphet  [53] , and PTMProphet  [54] ; these were grouped within each study according to the labelling and MS characteristics, as they determine search parameters.
Post-processing search results The data files obtained from searching with TPP were processed by custom Python scripts (https://github. com/PGB-LIV/mzidFLR) and analysed following a previously published pipeline  [22]. First, at the peptide-spectrum match (PSM) level, the FDR was calculated based on decoy sequences and the PSMs were filtered to 1% FDR. Data from most confident PSMs (1% FDR) were transformed to give a site localisation score for each phosphosite found on each PSM, removing matches to decoy PSMs and contaminant entries. A final site-based PSM score was obtained by multiplying the peptide identification probability by the site localisation probability and adjusted considering the number of occasions each site was observed, phosphorylated and not phosphorylated, in the dataset  [22].
Once the final probability at PSM-level was calculated, the data was collapsed to "peptidoform-site" level by taking the maximum score among all matches for each phosphosite on a given peptidoform within each analysis and study. FLR was calculated by ordering all peptidoform-sites by their final score. At this stage, matches to P. falciparum and human matches were separated according to their respective protein matches. A further categorisation at the protein-site level was achieved by using FLR calculations for each study. This Gold-Silver-Bronze (GSB) categorisation allows additional grading for our confidence in the findings among the most confident sites. A simple exclusive criterion was applied: Gold -sites observed at 2 or more independent studies at 1% FLR; Silver -sites observed in only 1 dataset at <1% FLR; Bronze: all other sites at 5% FLR.
2. 1) or above, via RStudio (2022. 02. 3 Build 492). Motif and pathway enrichment analysis All P. falciparum datasets were investigated using motif and pathway enrichment analysis. For this, we used all STY phosphosites at 5% FLR, combining all 11 studies and removing matches to the decoy amino acid Alanine. 15mer peptides centred on each phosphosite were generated to investigate motifs around these sites. They were compared against 15mer "background" sequences generated using any STY in these datasets, whether they were phosphorylated or not, at its centre (position 0). Statistically significant enriched motifs surrounding phosphosites were identified using the R package rmotifx  [55]. Results were thresholded via p-value < 1e-9 for pS and p< 1e-6 for pT and pY respectively; and a minimum of 20 sequences per motif.
The R package clusterProfiler  [56]  was used to carry out a pathway enrichment analysis considering those phosphoproteins containing specific enriched motifs. All other proteins in the search database were used as background for this analysis. A heatmap on the adjusted p-values of a subset of motifs with fold-change enrichment (versus the background) > 4 was produced representing motifs against GO (Gene Ontology) terms. Conservation analysis P. falciparum isolate 3D7 identified phosphosites were investigated regarding 22 species of the Plasmodium genus. The 22 species of Plasmodium were: P. gorilla clade G1(PPRFG01), P. reichenowi (PRCDC), P blacklocki G01 (PBLACG01), P. billcollinsi G01 (PBILCG01), P. adleri G01 (PADL01), P. gaboni (PGSY75), P. malariae (PmUG01), P. brasilianum strain Bolivian I (MKS88), P.
Conservation of phosphosites within Plasmodium falciparum was calculated using single nucleotide polymorphism (SNP) data stored in PlasmoDB (public search strategy: https://plasmodb. org/plasmo/app/workspace/strategies/import/b4d2489952494797), using variants from 115 aligned genomes from two unbiased SNP data set https://plasmodb. org/plasmo/app/record/dataset/DS_9a7f849906, and https://plasmodb. org/plasmo/app/record/dataset/DS_d1c8287de9  [58]. SNP sites were downloaded and non-synonymous variants, altering the amino acid sequence, were matched to the phosphosite positions in proteins (using Python 3. 9 code). Site conservation was estimated using the major allele frequency, calculated by PlasmoDB. We next explored the average disorder of amino acid positions within proteins using metapredict v2  [59]  running on Linux, comparing and correlating disorder scores with site classifications.
, 2014) , and around the globe there are only a handful of such timeseries stations. Although more have since been established, these time-series stations highlight how different locations experience different drivers and differing levels of variability  (Bates et al. , 2014). More recently, research communities have joined to form networks that increase data sharing, resulting in large collated datasets such as the Surface Ocean CO 2 Atlas (SOCAT; ∼ 28 million surface observations in version 2020,  Lauvset et al. , 2018; Bakker et al. , 2016) , the Global Ocean Data Analysis Project (GLODAP; ∼ 79 000 surface observations in version 2. 2020,  Lauvset et al. , 2021) , and most recently the Coastal Ocean Data Analysis Product in North America (CODAP-NA;  Jiang et al. , 2021). Several efforts have been made to develop interpolation products that can be used to make global assessments of how the marine carbonate chemistry is changing in both space and time (e.
g. Rödenbeck et al. , 2015). These include neural network  (Landschützer et al. , 2016; Denvil-Sommer et al. , 2019; Sasse et al. , 2013) , linear (e. g. Takahashi et al. , 2014)  and non-linear regression (e. g. Watson et al. , 2020)  approaches. Similarly, model and interpolated observation-based data are routinely assessed against global in situ datasets (e. g. a requirement for inclusion within the observational ocean carbon data presented in the Global Carbon Budget;  Friedlingstein et al. , 2022). Not only can in situ data and climatologies be used for assessing the marine carbonate system, but models, reanalyses and satellite Earth observations (EOs) are now frequently utilised either for their direct outputs or as inputs to algorithms. The ESA OceanSODA (Satellite Oceanographic Datasets for Acidification) project (https: //esa-oceansoda.
org, last access: 20 February 2023) utilises a range of data sources, including EO, to input into empirical and machine learning algorithms, generating synopticscale outputs of OA-relevant parameters. For example, the OceanSODA-ETHZ product  (Gregor and Gruber, 2021)  reproduces the global surface carbonate system from 1985 to 2020. At present there is no one dataset that matches up these various data in time, treating all data in a consistent manner to minimise biases caused by differences in space and time sampling of each dataset. Here we present a new matchup dataset that addresses this need, with a particular focus on the surface (less than 10 m) carbonate system. When attempting to collate large coincident datasets, data are often combined from diverse sources with different data densities, for example, combining daily station data from a cruise with high-frequency measurements from an underway system, or with daily satellite sea surface temperature (SST) at 1 km resolution.
The differences in data density between the collated datasets can create biases when the data are used, favouring datasets or regions with high data density. This problem is often overcome by binning the data in a map projection, most simply into cells of equal latitude and longitude extent, but this can also cause biases because, in the example of equal latitude-longitude bins, the bins become smaller and more elongated as the poles are approached. The bin boundaries are also generally unrelated to the data, which may result in the data being divided sub-optimally; for example, a bin boundary may pass through a large cluster of data, inappropriately dividing it. To overcome these problems and to provide a tool for matching in situ data with other datasets such as satellite, model or climatological data, which often have very different spatiotemporal scales both from the in situ data and from each other, a methodology has been developed to group in situ data into "regions of interest" (ROIs), spatiotemporal cylinders consisting of circles on the Earth's surface extending over a period of time.
These cylinders are positioned such that each contains as many in situ measurements as possible, with as little overlap between cylinders as possible. In this way, every in situ measurement is uniquely associated with one region of interest. All in situ measurements of the same parameter contained in a region are collated, including their estimated uncertainties, and regional summary statistics are calculated. After the ROIs have been defined using the in situ data, the other datasets are treated in the same way, collating measurements that lie within each in situ ROI, with their estimated uncertainties, and generating summary statistics. OceanSODA-MDB, a global matchup database (MDB), is presented here, consisting of ROIs with a maximum diameter of 100 km and duration of 10 d. An example application is shown, reparameterising the global total alkalinity (A T ) algorithm of  Takahashi et al.
(2014)  to give a new A T algorithm specific to the top 10 m. The MDB can be updated as and when in situ and other datasets are updated, and similar datasets at different spatiotemporal scales can be constructed, for example, for regional studies. This document describes the datasets that are present within the MDB, including some brief information about data collection and analysis, followed by a description of the methods used to create the MDB itself. We then provide some summary statistics for the in situ and other data held within the MDB and a discussion of the benefits of this method. Finally, we present the reparameterisation of the global A T algorithm of  Takahashi et al. (2014)  as an example application of the MDB.
Input datasets In situ carbonate system variables included in the MDB (with which all other datasets are matched) are pH, total dissolved inorganic carbon (C T ), total alkalinity (A T ) and partial pressure of CO 2 in water (pCO 2w , converted from fugacity and corrected for temperature differences if necessary). At least one of these four key variables must be measured for a sampling event to be included in the MDB. Measurement uncertainties and quality control (QC) flags, where available in the original datasets, are also included. The MDB only includes surface measurements, defined as depth less than 10 m. Other in situ measurements which are included if they are coincident with a measurement of one or more of the four primary variables (pH, C T , A T and pCO 2w ) include temperature (T ), salinity (S), sea-air difference in partial pressure of CO 2 , and other in situ measurements such as nutrients and chlorophyll a concentration (Chl a).
In addition, the following values not directly associated with the sampling event are included: water depth, distance from the nearest coast and monthly climatological temperature, salinity, dissolved oxygen, nitrate, phosphate, and silicate, all interpolated from global gridded climatologies to each data point. Where coincident C T and A T are available, pCO 2w , pH and the saturation states of aragonite ( A ) and calcite ( C ) are calculated from C T , A T , T , S and depth. All measurements of each of these variables in a ROI are collated and summary statistics generated. Other datasets not associated with individual data points (satellite, model and reanalysis data; see Table  1 ) are collated for each ROI and their summary statistics are added to the MDB. Standard measurement uncertainties are either taken from the source literature or given default values based on GLO-DAP and SOCAT default uncertainties.
Default uncertainties for C T and A T are 4 µmol kg -1  (Lauvset et al. , 2021). For pCO 2w , the default uncertainty is 5 µatm unless SOCAT data have been assigned a QC flag of A or B, in which case it is 2 µatm  (Lauvset et al. , 2018). The default uncertainty for pH is 0. 005  (Sulpis et al. , 2020). The standard deviation of the measurements used to calculate the climatological data is given as a proxy to uncertainty, and the assigned C T and A T measurement uncertainties are propagated through the calculations to produce uncertainty estimates for the calculated pCO 2w , pH, A and C. The input data for the MDB primarily come from publicly available online datasets, with exceptions noted below. Details for these input data are provided in Table  1. Below we briefly summarise the main methods for sample collection, analysis and quality control for each of the datasets, with the exception of data input from the World Ocean Atlas (WOA18;  Locarnini et al.
, 2018; Zweng et al. , 2019; Garcia et al. , 2019a, b; Boyer et al. , 2018) ; the Ocean Carbon and Acidification Data System (OCADS;  Jiang et al. , 2021) ; the Global Surface pCO 2 Database (LDEO v2018;  Takahashi et al. , 2020) ; the Global Ocean Data Analysis Project (GLO-DAPv2. 2020;  Olsen et al. , 2020 Olsen et al. , , 2016;; Key et al. , 2015) ; and the Surface Ocean CO 2 Atlas (SOCATv2020;  Lauvset et al. , 2018; Bakker et al. , 2016) , as these have significant detail about data collection and quality control already described in the associated project publications. Full details for all methods can be found in the associated references within this text and Table  1. Biogeochemical-ARGO (Bio-ARGO; Argo, 2021, Table 1, dataset no. 4) uses profiling floats and measures pH using the Deep-Sea DuraFET, a sensor comprising a Honeywell Ion Sensitive Field Effect Transistor (ISFET) and a chlorideion-selective electrode as the reference electrode, directly exposed to seawater  (Claustre et al.
, 2020). We used individual profiles, delayed mode data where available, otherwise realtime mode, averaging data from the top 10 m, and used the estimated pH uncertainty given in the original data files. Data from Plymouth Marine Laboratory (Table  1 , datasets no. 6 and 7) were produced using an Apollo SciTech AS-C3 DIC analyser for C T and using an Apollo SciTech AS-ALK2 analyser for A T , following the methods described in  Dickson et al. (2007). C T and A T measurements were calibrated using certified reference materials (CRMs) provided by Andrew G. Dickson from the Scripps Institute of Oceanography. The precision and accuracy of replicate CRM analyses were better than ±2 µmol kg -1. pH was determined spectrophotometrically on board the ship using m-cresol-purple dye  (Clayton and Byrne, 1993)  and again following best practice  (Dickson et al.
, 2007). The precision of triplicate pH samples was ±0. 001 units or better. Data from Woods Hole Oceanographic Institute (Table  1 , dataset no. 8) were produced using a single-operator multi-    Garcia et al. (2019a, b) ,  Boyer et al. (2018)  metabolic analyser coulometer system for C T and using an open cell titration method with 0. 1 N HCl for A T  (Dickson et al. , 2007) , both calibrated to Dickson CRMs (Scripps Institute of Oceanography). Pooled standard deviations for C T and A T were < 3. 04 and < 3. 87 µmol kg -1 , respectively. Data from Dalhousie University (Table  1 , dataset no. 9) were produced using a Marianda Versatile INstrument for the Determination of Total inorganic carbon and titration Alkalinity (VINDTA) 3C coupled with a coulometer (UIC, Inc. ) for A T and C T following standard methods  (Dickson et al.
, 2007). The instrument was calibrated against Dickson CRMs (Scripps Institute of Oceanography), and the reproducibility of the C T and A T measurements was < 2 and < 3 µmol kg -1 , respectively. Data from the Ocean Acidification Research Center at the University of Alaska Fairbanks (Table  1 , datasets no. 10-15) were produced using a VINDTA 3C coupled with a coulometer (UIC, Inc. ). Samples were standardised using Dickson CRMs (Scripps Institute of Oceanography). Uncertainty for cruises ranged from 1 to 4 µmol kg -1 for A T and 4 µmol kg -1 for C T. These data are now included in the Coastal Ocean Data Analysis Product in North America (CODAP-NA) and hence have been subjected to additional quality control  (Jiang et al. , 2021). CODAP-NA will be used in future versions of the MDB. Data from the Shirshov Institute of Oceanology (Table  1 , dataset no.
Under field conditions, the usual accuracy of the method for seawater analyses is equal to ±2. 5 µM kg -1. The method presented is easy and well applicable to the microanalysis of interstitial waters of marine sediments  (Pavlova et al. , 2008). Data from the V. I. Il'ichev Pacific Oceanological Institute (Table  1 , dataset no. 17, available on request from irina@poi. dvo. ru) were produced using an indicator titration method in which 25 mL of seawater was titrated for A T with 0. 02 M HCl in an open cell  (Bruevich, 1944; Pavlova et al. , 2008) , and a potentiometric method was applied to determine pH on the Pitzer pH scale  (Pitzer, 1991)  using a closed cell held at constant 20 • C temperature with a sodium and hydrogen glass electrode pair without liquid junctions  (Tishchenko et al. , 2001 (Tishchenko et al.
Data from the Climate Change Initiative Sea Surface Temperature (CCI SST) Level 4 Analysis Climate Data Record (Table  1 , dataset no. 18), produced by merging observations from satellite instruments NOAA Advanced Very High Resolution Radiometer (AVHRR) and ESA Along Track Scanning Radiometer (ATSR) using a data assimilation scheme, provide gap-free global daily fields of sea surface temperature (SST) at 0. 2 m depth on a global 0. 05 • grid  (Good et al. , 2019; Merchant et al. , 2019). We used the Version 2. 1 dataset produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project from 1981 to 2016 and the complementary Version 2. 0 dataset from the Copernicus Climate Change Service (C3S) from 2017 to 2020. Data from the Climate Change Initiative Sea Surface Salinity (CCI SSS) Level 4 Analysis (Table  1 , dataset no.
19), produced by merging observations from satellite instruments ESA Soil Moisture and Ocean Salinity (SMOS) (January 2010-November 2019), NASA Aquarius (August 2011-June 2015) and NASA Soil Moisture Active Passive (SMAP) (April 2015-November 2019) using optimal interpolation, provide gap-free weekly maps of sea surface salinity (SSS) on a global 25 km EASE grid  (Boutin et al. , 2020 (Boutin et al. , , 2021)). We used the Version 2. 31 dataset produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Salinity project from 2011 to 2019. Comparisons of the weekly Level 4 product against Argo floats over the whole period and at global scale show a satellite-in situ bias of 0. 0 and a root mean squared deviation (RMSD) of 0. 28, while comparisons against thermosalinograph (TSG) measurements show a bias of -0.
01 and an RMSD of 0. 49. Under optimal conditions (rain rate = 0 mm h -1 , 3 < 10 m wind speed < 12 m s -1 , SST > 5 • C, > 800 km from coast), the bias and RMSD are respectively 0. 0 and 0. 17 against Argo floats and 0. 0 and 0. 18 against TSG  (Boutin et al. , 2021). Data from the Arctic Sea Surface Salinity Level 3 composites (Table  1 , dataset no. 20), obtained from the Barcelona Expert Center (BEC; http://bec. icm. csic. es/, last access: 20 February 2023), provide a daily weighted average of SMOS SSS in all overpasses over a 9 d period on a 25 km EASE grid centred on the north pole  (BEC, 2021). We used the Version 3. 1 dataset from 2011 to 2019. Comparisons against Argo floats for the complete period show a bias of 0. 02 and a RMSD of 0. 39  (Olmedo et al. , 2018). Data from the SMAP Sea Surface Salinity Level 3 composites (   (Sathyendranath et al.