dataesr/scientific-paragraphs-categorization huggingface dataset:
4 (data not shown). The dried extracts were resuspended in 6 mL of H 2 O. 1 mL of 50 mM phosphate buffer adjusted to pH 7. 4 was added, and the solutions were then freezedried. The dried extracts containing phosphate buffer were resuspended in 1 mL of D 2 O containing 3 % of 3-(trimethylsilyl)propionic-2,2,3,3-d4 acid sodium salt (TMSP), vortexed and then centrifuged at 14,000 rpm for 20 min. A volume of 700 μL of the supernatant, the particulate free extract, was transferred into a 5 mm Norell 500-7 NMR tube and analyzed. 1 H NMR spectra were acquired for all samples using a 500 MHz ECZR JEOL (Tokyo, Japan) spectrometer equipped with a double resonance 5 mm multinuclear FG/RO Digital Autotune Probe S including a z axis gradient coil, and an auto sample changer 30 used at room temperature.
Automation routines were carried using jaf scripts available upon request to A. Botana (JEOL UK). 1 H NMR spectra were obtained using the Delta 5. 3 software. Prior to measurement, samples were stored for 5 min inside the probehead for temperature equilibration at 25 °C. Automatic tuning and matching of the probe were carried out for each sample. A 1 H NMR spectrum was acquired with water peak suppression using a standard proton single pulse experiment consisting of 2 dummy scans and 32 scans, for 65,536 data points, spectral width of 15 ppm, an acquisition time of 8. 72 s, a relaxation delay of 10. 64 s. Spectral processing and post-processing steps: FID was multiplied by an exponential function equivalent to a 57 mHz line-broadening factor before applying a Fourier transformation. Transformed spectra were automatically corrected for phase and baseline distortions and calibrated to the reference signal of TMSP at 0.
00 ppm. Each spectrum in the region 10. 00 -0. 2 ppm was segmented into intelligent bins, and the corresponding spectra areas were integrated using NMRProcFlow software. Statistical analysis was performed with MetaboAnalyst. Signals corresponding to the solvents were suppressed and a CSN normalization together with Pareto Scaling were used. The annotation of metabolites in the hydroalcoholic extracts was performed by comparison of the major signals with those of the reference compounds (alanine, valine, leucine, isoleucine, threonine, glycine, lactate, formate, acetate, acrylic acid, trigonelline, glycine betaine, proline betaine, choline, phosphocholine, glycerophosphocholine, glucose, glycerol) assumed to be present (diagnostic peaks) or known to occur in reef-building corals. Betaine standards that were not commercially available (alanine betaine, hydroxyproline betaine, taurine betaine) were prepared following procedures already described  41.
The annotation was confirmed by spiking these reference compounds into one sample per genus. 2D NMR experiments (COSY, TOCSY, HSQC and HMBC) were used to characterize homarine. Sarcosine (N-methylglycine) was characterized by comparison with data bases and the Chenomx identification software. Reporting summary. Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. l'Environnement Mondial (FFEM), Etienne Bourgois, the Tara Ocean Foundation teams and crew. Tara Pacific would not exist without the continuous support of the participating institutes. University of Galway is acknowledged for financial support to OPT through a start-up grant. Structural analyses (LC-MS and NMR) were performed using the facilities of the MSXM ("Métabolites Secondaires, Xénobiotiques et Métabolomique") technical platform of the Bio2Mar ("Biodiversité et Biotechnologies Marines") platform at the University of Perpignan.
Acknowledgements The Tara Pacific expedition would not have been possible without the participation and commitment of over 200 scientists, the  Tara Ocean Foundation  and its sailors, artists, staff and partners, the  R/V Tara crew , and the  Tara Pacific Expedition Participants  (https://doi. org/10. 5281/zenodo. 3777760). We are thankful for the commitment of the people and the following institutions, for their financial and scientific support that made this expedition possible: CNRS, PSL,  CSM ,  EPHE, Genoscope/CEA ,  Inserm ,  Université Cote d'Azur, ANR , the  Tara Ocean Foundation  and its partners Agnès B. ,  UNESCO-IOC , the  Veolia Environment Foundation ,  Région Bretagne ,  Serge Ferrari ,  Billerudkorsnas ,  Amerisource Bergen Company, Altran, Lorient Agglomeration, Oceans by Disney , the  Prince Albert II de Monaco Foundation ,  L'Oréal, Biotherm ,  France Collectivités ,  Fonds Français  pour.
Data availability The LC-MS and NMR raw data are stored in the platform Metabolights under the reference MTBLS7951. The molecular networks were built on the platform GNPS and the corresponding link can be found here: Millepora pos: https://gnps. ucsd. edu/ProteoSAFe/status. jsp?task=6dede5cf7154454 caecf65460cd49925 Pocillopora pos: https://gnps. ucsd. edu/ProteoSAFe/status. jsp?task= 245c071ae17b4b9c9c1776538e7a5a6e Porites pos: https://gnps. ucsd. edu/ProteoSAFe/ status. jsp?task=7cd3f7743a7542fcba0ba2d9accf4a13 Millepora neg: https://gnps. ucsd. edu/ProteoSAFe/status. jsp?task=3715a73f8c3e421094b14c5104e083dc Pocillopora neg: https://gnps. ucsd. edu/ProteoSAFe/status. jsp?task=712c15dc69d149b19b80e0cd0a3c74b7 Porites neg: https://gnps. ucsd. edu/ProteoSAFe/status. jsp?task=68cca8c5d372401c9b09 f64255be9d69.
Patient selection A retrospective patient record review was performed on Schistosoma infected migrants and returned travellers from miscellaneous geographical areas, as seen by seven European travel clinics located in Amsterdam, Barcelona, Berlin, Bordeaux, Madrid, Munich, and Negrar-Verona. Patient records were individually screened by the travel clinics applying a standardised data collection form. Cases were included if Schistosoma eggs of any species were detected in urine and/or faeces and if an FBC was performed. The following data items were collected: age; sex; migrant/traveller status; Hb level; erythrocytes; haematocrit; MCV; thrombocytes; total leukocytes; eosinophils; basophils; neutrophils; lymphocytes; monocytes; schistosomiasis serology; co-infection with other helminths; and concomitant diagnoses.
Outcomes and data analysis Continuous data were presented in means and standard deviation (SD) and, if appropriate, in median and ranges; qualitative data were presented as proportions. The normality of the distribution of the data was determined by visual inspection. Not normally distributed data was log-transformed. If after log-transformation of not normally distributed data, a non-normal distribution remained, non-parametric tests were used for analysis. As a Schistosoma-negative control group was lacking, we decided to compare our results with FBC reference intervals used in the Central Diagnostic Laboratory of the Amsterdam UMClocation University of Amsterdam (CDL-AUMC). A one-sample t-test was used to calculate the difference in means of FBC parameters of Schistosoma infected returned travellers and migrants, and the means of FBC reference values from the CDL-AUMC; a onesample Wilcoxon signed rank test was used to compare medians for not-normally distributed data.
Contrary to most earlier studies  [3, 5, 7, 9] , we did not find higher, but rather significantly lower thrombocytes among individuals older than 16 years. In the sub-analyses, thrombocytes were significantly lower in migrants than in returned travellers. This could be due to the fact that in migrants the moment of exposure often dates back longer, thus they are more like to have schistosomiasis that may have progressed to the hepatosplenic stage, which is associated with low platelet counts, as a result of hypersplenism and portal hypertension (this was also observed in our dataset)  [10, [16] [17] [18]. However, although the decline in platelets was more pronounced in intestinal schistosomiasis, lower counts were also observed among individuals with urogenital schistosomiasis, in which hepatosplenic schistosomiasis did not play a role.
Planktonic Foraminifera are unique paleo-environmental indicators through their excellent fossil record in ocean sediments. Their distribution and diversity are affected by different environmental factors including anthropogenically forced ocean and climate change. Until now, historical changes in their distribution have not been fully assessed at the global scale. Here we present the FORCIS (Foraminifera Response to Climatic Stress) database on foraminiferal species diversity and distribution in the global ocean from 1910 until 2018 including published and unpublished data. the FORCIS database includes data collected using plankton tows, continuous plankton recorder, sediment traps and plankton pump, and contains ~22,000, ~157,000, ~9,000, ~400 subsamples, respectively (one single plankton aliquot collected within a depth range, time interval, size fraction range, at a single location) from each category.
plankton pumps and CPRs take "snapshots" of the distributions of both living and dead Foraminifera species in the water column, while data obtained from sediment traps are "time integrators" of mostly fluxes of dead individuals (tests) settling from the surface ocean. The CPR and plankton pump collecting techniques mainly sample surface waters to about 10 m depth  30, 31  , while oblique or vertical towed plankton nets, such as the commonly used Multinet (e. g. ,  Schiebel 12  ), sample the productive water column to the export zone (hundreds of meters) over single or multiple depth levels (Fig. 1 ). The data presented in the FORCIS database aim to improve our understanding about (1) potential spatial and vertical migrations, (2) phenology, and (3) the various of effects global climate change on the planktonic Foraminifera biogeography, as well as their vertical and seasonal distribution over the past decades.
Methods Data collection. The database currently includes 188,000 planktonic Foraminifera subsamples. Each subsample represents one aliquot of planktonic foraminifera specimens collected within a specific depth range, time interval, size fraction range or identified as living or dead, at a single location sampled via plankton pumps and nets, CPR and sediment traps (more details in the Data Records section). The compiled data were gathered from published scientific literature, PhD or master's theses, books, databases, unpublished datasets, and reports. Some data were directly provided by contributors from the FORCIS working group and their personal networks. The dataset includes contributions from around 140 published and unpublished references  1, 23,  reporting on the diversity and distribution of planktonic Foraminifera, spanning a time interval of more than a century (1910-2018).
Most o  the datasets published before 1960 were digitized from tables or plots from dissertations (some available as a hardcopy only) and scientific papers, or sourced from the contributor's digital data files. Data on planktonic Foraminifera were extracted manually or automatically using WebPlotDigitizer-4. 3 software 169 , including number concentrations of tests (ind/m 3 ), relative numbers (%), and fluxes (ind/m 2 /day) collected from the global ocean using plankton nets, CPR, plankton pumps, and sediment traps (Table  1  and Fig. 2A, B ). Moreover, data indicating only the presence or the absence of species were also retrieved. Binned data of species (i. e. , estimated number concentrations or percentage concentrations reported for a minimum and maximum depth range like for the CPR data in the North Atlantic) were also collected and included in the FORCIS database.
Foraminifera abundance data are divided into four categories, i. e. , raw values (numbers of individuals), number concentrations (ind/m 3 ), percentage concentrations (%), or fluxes (ind/m 2 /day). Database design and architecture. The FORCIS database is composed of ten tables with the counts of modern planktonic Foraminifera and metadata (Table  2 ), built and designed using PostgreSQL, which allows filtering and quality checking of the data during the importing and extraction steps. In the data tables, sites, profiles, casts, samples, subsamples, and counts are interconnected by five unique identifiers (primary keys) (Fig. 3 )  labeled as 'site_id' , 'profile_id' , 'cast_id' , 'sample_id' , and 'subsample_id'. They present hierarchical levels and are automatically generated according to naming rules if not provided in the original dataset, during data import.
Each line in the database is assigned to a reference ('ref_id'), for example, a published paper, manuscript, book, or unpublished study with information on the data contributor. Moreover, the FORCIS database comprises information regarding the sampling methodology (i. e. , sampling device, mesh size), and nature of the subsamples where applicable, for example, distinction between individuals with filled or empty tests (often referred to as "dead specimens"). Eight major ocean basins are distinguished, the Arctic, Antarctic, South Pacific, North Pacific, South Atlantic, North Atlantic, Indian Ocean, and the Mediterranean Sea using QGIS software (3. 16 Hannover; available online https://www. qgis. org/es/site/), whose boundaries were defined by adapting the shapefile published by the International Hydrographic Organization (IHO) database map  170  (Fig.
4 ). Each sampling site ('site_id') in the FORCIS database is associated with its respective oceanic basin. Importing the datasets. The metadata and count data were collected using a spreadsheet template (available at Zenodo 171 ). Several variables in this template are mandatory fields to make sure that related tables of the database can be filled and linked together, i. e. , for each hierarchical level: site coordinates ('site_lon_start_ decimal' , 'site_lat_start_decimal'); profile date ('profile_date_time'); cast information ('cast_min_depth' , 'cast_ max_depth' , 'cast_sampling_device_name'); sample information ('sampling_device_type' , 'sample_min_depth' , 'sample_max_depth' , 'sample_date_time_start'); subsample information ('subsample_count_type' , 'subsample_ size_fraction_min'), and bibliographic information (either full reference or 'doi' for recent datasets).
Profiles Primary key: 'profile_id' Foreign key (s): 'site_id' Number of variables: 15 For the net data, profiles are distinguished by their time of collection and location as well. Overall, profile_id's have the same coordinates, and different times of sampling were incremented. For the sediment trap data, the profile_id is incremented when the deployment has changed. The date, depth range of each profile, availability of the environmental data including ambient seawater chemistry, and profile season are also included in the table profiles.
Casts Primary key: 'cast_id' Foreign key (s): 'site_id' and 'profile_id' Number of variables: 8 Provides information regarding the casts, the sampling device name, depth range of a cast, mesh size, and net opening of the plankton tow. For data safety, updates of the FORCIS database were routinely saved under different versions and stored in a SQL server. In parallel, data quality control and curation were done during the database development to ensure maximum quality consistency.
Samples Database harmonization, curation, and quality control. All dataset entries underwent a series of quality control, curation, harmonization, and standardization steps, during and after inclusion in the database, in close collaboration between database managers and data contributors. For example, 'site_lon_start_decimal' and 'site_lat_start_decimal' were quality controlled and checked, for example, for redundancies and inconsistencies to avoid replicating datasets. Imported data were first screened by the database managers to check for inconsistencies such as negative depth range (i. e. , minimum depth larger than maximum depth), and second by the members of FORCIS working group, to apply quality control and minimize the errors. Different maps were generated to validate the geographical data distribution, check and correct our entries for outliers.
For example, maps of species distributions were produced to check for regional and ecological plausibility that helped quality check the dataset for mistyping while assembling the data and for the taxonomy harmonization. However, none of the data retrieved from the original publication was corrected or excluded. Species counting information distinguishes the absence of information (NA, Not Available) from the absence of the specimens (value of zero). Harmonization of the taxonomy. Species names were initially kept in the database as given by the data contributor or by the original publication, with minor corrections being made for spelling errors. Genus attributions had to be harmonized to a common standard  5, 7, 172  , to ensure that each taxon is labeled in the database with a unique binomen or trinomen.
Since 1960, new species have been described that were not recognised before or lumped with others (e. g. , Neogloboquadrina incompta  173  , Globorotalia eastropacia  174  , Berggrenia pumilio  175  , Globigerinella calida  176  , Globorotalia cavernula  176  , Globorotalia ungulata  177  , Orcadia riedeli  178  , Tenuitellita fleisheri  179  ). As such additional information is not always provided, the validated taxonomy has been subsequently mapped onto a "lumped taxonomy" comprising 46 species and categories that could be recognized in all datasets. The names used for all formally described taxa follow Schiebel and Hemleben  5  and references therein, as expanded by Morard et al. 172  , and revised by Brummer and Kucera 7 and references therein. In most cases, the mapping of synonyms onto the validated taxonomy and the contraction of the validated taxonomy onto the lumped taxonomy was straightforward and the procedure can be understood directly from the synonym lists provided (available at Zenodo 171 ).
The nomenclature of G. ruber in the FORCIS database follows the concept of Morard et al. 172  , with pink-pigmented specimens, when counted separately, being named G. ruber ruber, and non-pigmented specimens being attributed either to G. ruber albus with inflated chambers, or G. elongatus with compressed chambers. Where the distinction between G. ruber albus and G. elongatus has not been made, the counts are reported as the sum of both species (g_ruber_albus_or_elongatus). In cases where not even the shell pigmentation has been considered, we only report the count for all three categories together (g_ruber_any). extracting data. The hierarchical structure of the database, split into different related tables, facilitates swift extraction of large merged data volumes. It is possible to retrieve count data and/or metadata separately and to apply filters to extract specific sub-datasets.
As the SQL was only used to develop and quality check the FORCIS database, the finalized version of the database was extracted from the SQL and converted to ". csv" files and made available on Zenodo to facilitate the handling of the data for the users. To facilitate the handling of the database in the Zenodo ". csv" files, an R-package was compiled (https://frbcesab. github. io/forcis/), providing basic functions to extract the data from the different files based on different taxonomy levels and harmonize the species counts into a unique count type. In the final published database, all data coming from different sampling devices were put into separate ". csv" files. Only the data of the CPR from the Southern Hemisphere have been separated from those CPR data collected from the Northern Hemisphere as the data structure is different (species-level resolved counts vs.
Data Records The FORCIS database is published as five ". csv" files composed of data from four types of sampling devices, i. e. , plankton tows, plankton pump, CPR (". csv" file for each data from the Southern and from the Northern Hemispheres), and moored sediment traps, and the associated dataset is uploaded on the Zenodo repository  171  (Fig. 2 ). These files encompass more than 188,000 subsamples including ~157,000 CPR (since 1991), ~22,000 net (since 1910), ~9,000 sediment trap (since 1978), and 400 pump (since 1985) subsamples (Table  1 ). The data in FORCIS are presented as follows: each row in the database is a subsample (i. e. , one single plankton aliquot collected within a water depth range, time interval, size fraction, at a single location) associated to 1) "block 1": the metadata (i.
e. , location, date, depth, cast, environmental data of this record), and 2) "block 2": the original data as reported in the data sources (abundance and/or diversity). The FORCIS database metadata has a hierarchical structure (Fig. 3 ): first, all sites are assigned to a site_id associated with the coordinates (site_ lon_start_decimal and site_lon_end_decimal) and site_ocean_basin. Then, for each profile collected at the different site, a profile_id is attributed, based on the profile_date_time (time of the collection) and coordinates (Table  2 ). The depth range (profile_depth_min and profile_depth_max) of each profile, and environmental data including ambient seawater chemistry (profile_env_data_availability and profile_chemical_data_availability), and profile_season are given. Information regarding the different cast_id used for each profile_id is provided in the metadata block, such as: cast_sampling_device_name, cast_min_depth, cast_max_depth, cast_mesh_size of the plankton tow.
technical Validation The compilation of ~188,000 subsamples resulted in a high number of counts in the FORCIS database (more than 1,300,000 species counts and ~1,200,000 non-zero counts), compared to fossil planktonic Foraminifera databases such as ForCenS (~4,000 subsamples, and ~ 60,000 counts) that reports data of the planktonic Foraminifera found in the surface sediment samples  181. The Triton database 182 holds ~500,000 non-zero counts of planktonic Foraminifera occurrences during the Cenozoic. However, the FORCIS database holds a lower number of samples compared to the COPEPOD database (~400,000) which is a global-coverage database of zooplankton abundance, phytoplankton abundance, and zooplankton biomass data  183. Temporal data coverage varies temporally and spatially, but is highest after 1990 and in the Northern Hemisphere (Fig.
5 ). The plankton net dataset presents the widest temporal (from 1910 until 2017), and spatial ranges (from 61° S to 86° N, and 180° W to 180° E, Table  1 ). The sediment trap dataset includes data from 1978 to 2018, from 65° S to 77° N, and 177° W to 179° E. The CPR dataset covers the subtropical to polar oceans, from 30° N to 79° N, and 79° W to 20° E in the Northern Hemisphere, and from 77° S to 40° S, and 180° W to 180° E in the Southern Hemisphere. All CPR samples included here  30, 31  were collected during a time period from 1991 to 2018 (Fig. 5A ). The pump dataset has the smallest regional coverage ranging from 22° S to 53° N, and 39° W to 143° E. Despite more than a century of work, large parts of the ocean have remained unsampled for planktonic Foraminifera, e. g. , the Southern Pacific Ocean (Fig.
2 ). The temporal coverage of the FORCIS database exposes a low sampling effort especially during the time period before 1960, with only ~1,000 subsamples collected between 1910 and 1960 (Fig. 5B ). In addition, few datasets are available from certain seasons, such as winter data from high latitudes due to the lack of sampling campaigns. The FORCIS database comprises an extensive coverage of the Northern Hemisphere (Fig. 2A ), especially of the North Atlantic Ocean. In contrast, plankton tows and sediment traps from the Southern Ocean are sparse due to difficulties associated with sampling in remote and stormy regions. However, despite these temporal and spatial gaps, the amount of data in FORCIS covers broad swaths of the global ocean and facilitates comparison of changes in distribution and diversity within and between different provinces over time (Fig.