dataesr/scientific-paragraphs-categorization huggingface dataset:
Regional risk index of drought vulnerability. A risk index of vulnerability to drought was calculated per wine producing region or country by coupling winegrape varietal coverage data with summer stem Ψ 50 values of Vitis vinifera varieties. First, information on Vitis vinifera varietal coverage were retrieved from the online database of regional and national winegrape bearing areas by variety  72  (https:// econo mics. adela ide. edu. au/ wineecono mics/ datab ases# datab ase-of-regio nal-natio nal-and-global-wineg rape-beari ng-areas-by-varie ty-1960-to-2016). The dataset we compiled provided varietal coverage of 22 Vitis varieties (all the Vitis vinifera studied but Müsküle, for which online data were not available, as well as Regent), for a total of 653 wine regions across 48 countries (Supplementary Data S1, Supplementary Fig.
S6a, b ). Second, this dataset was used to calculate, for each wine region or country, the bearing area of the four clusters of vulnerability to xylem embolism (low, lowto-medium, medium-to-high, high) that were previously identified from the hierarchical clustering analysis (see the Statistical analyses section; Supplementary Fig. S6c, d ). Third, each cluster was assigned a specific weight (or score) on a 0-1 scale of vulnerability to xylem embolism: (i) low vulnerability: range 0-0. 25 (median = 0. 125); (ii) low-to-medium vulnerability: range 0. 25-0. 50 (median = 0. 375); (iii) medium-to-high vulnerability: range 0. 50-0. 75 (median = 0. 625); (iv) high vulnerability: range 0. 75-1 (median = 0. 875). The risk index (RI), accounting for both the bearing area and the vulnerability to xylem embolism of each cluster, was then calculated as follows (Supplementary Data S2): where each bearing area is represented as a percent of the regional area.
This risk index was mapped for 329 regions including 13 countries for which our varietal coverage dataset represented at least 40% of the regional or national winegrape bearing area. Thus, we provided a risk index for 52% of the global wine producing regions. Differences in summer stem vulnerability to xylem embolism and xylem anatomical traits among types of varieties (Vitis vinifera vs hybrids vs rootstocks) as well as differences in summer stem vulnerability to xylem embolism among Vitis vinifera subspecies (proles occidentalis vs proles orientalis vs proles pontica), geographical origins (Balkans vs Eastern Mediterranean & Caucasus vs Iberian Peninsula vs Western & Central Europe) and between berry skin colors (black vs white) were studied with generalized linear models (GLM procedure in SAS), where type of varieties/geographical origin/berry skin color was treated as a fixed factor and variety nested within type of varieties/geographical origin/berry skin color as a random factor.
Acknowledgements The authors thank the experimental teams of UMR EGFV ( Nicolas Hocquard ,  Cyril Hevin ,  Maria-Das-Dores Lafargue ,  Jean-Pierre Petit ,  Laure Morgadinho ) and  UMR SAVE  ( Jérôme Jolivet ,  Sébastien Gambier ,  Nathalie Ferrer ) for providing plant material,  Marie Harel  for her assistance with the in situ flow-centrifuge measurements, as well as  Patrick Léger  ( UMR BIOGECO ) and  Andréa Bayens  for their assistance with optical measurements and analyses. The authors also thank the  Experimental Viticultural Unit of Bordeaux 1442 ,  INRAE ,  F-33883 Villenave d'Ornon , for its contribution to the maintenance of the VitAdapt experiment and grapevine collections, where plant material has been collected. This work was supported by the  French Ministry of Agriculture, Agrifood and Forestry (FranceAgriMer and CNIV)  via the ' Plan National de Déperissement du Vignoble' program  ( 22001150-1506 ,  PHYSIOPATH  project), the  French National Agency for Research via the Cluster of Excellence COTE.
In this effort, the strategy adopted by Tara Pacific is to provide open access data and early and full releases of the datasets once validated or published. Such an approach ensures a long-lasting preservation, discovery, and exploration of data by the scientific community, which will lead to new hypotheses and emerging concepts. Here, we present an overview of the sampling strategy used to collect coral holobiont specimens in connection with its local, large scale, or historical environment. We also provide a critical assessment of the environmental context. We provide the full registries describing the geospatial, temporal, and methodological information for every sample and connect it to the various sampling events or stations. Extensive environmental context is also provided at the level of samples or stations.
5 m under the waterline using a membrane pump (10 LPM; Shurflo), circulated through a vortex debubbler, a flow meter, and distributed to a number of flow-through instruments. A thermosalinograph [TSG] (SeaBird Electronics SBE45/SBE38), measured temperature, conductivity, and thus salinity. Salinity measurements where intercalibrated against unfiltered seawater samples [SAL] taken every week from the surface ocean, and corrected for any observed bias. Moreover, temperature and salinity measurements were validated against Argo floats data collocated with Tara. A CDOM fluorometer [WSCD] (WETLabs), measured the fluorescence of coloured dissolved organic matter [fdom]. An [ACS] spectrophotometer (WETLabs) measured hyperspectral (4 nm resolution) attenuation and absorption in the visible and near infrared except between Panama and Tahiti where an AC-9 multispectral spectrophotometer (WETLabs) was used instead.
between measured [poc] and measured [cp], or applying an empirical relation between measured [poc] and [bbp]  29. Phytoplankton organic carbon [cphyto] was estimated by an empirical relationship with [bbp]  30. An indicator for size distribution of particles between 0. 2 and ~20 µm [gamma] was calculated from [cp]  31. A brief description of the methods to analyse, calibrate, correct, and estimate bio-optical proxies are detailed in the section Technical Validation and more extensively explained in each processing report attached with the dataset. An Equilibrator Inlet Mass Spectrometer [EIMS] (Pfeiffer Vacuum Quadrupole 1-100 amu) measured the Oxygen to Argon ratio in percent [o2ar], coupled with an optode (Aanderaa optode 4835) measuring oxygen concentration in the seawater [O2]. Concurrently with samples collected through the [MAST-PUMP], two instruments were installed aboard Tara to measure the size distribution and abundance of atmospheric aerosol particles: a scanning mobility particle sizer ([SMPS], SMPS-C GRIMM Aerosol Technik Ainring GmbH & Co.
KG, Ainring, Germany) measuring particles in the size range 0. 025-0. 70 µm, and an optical particle counter ([EDM]; EDM180 GRIMM Aerosol Technik Ainring GmbH & Co. KG, Ainring, Germany) measuring all particles in the size range 0. 25-32 µm. The SMPS was set to perform a full scan of particle distribution every 5 min and the EDM produced a particle size distribution every 60 s. Data provided from [EDM] includes both the total particle concentration (nb cm -3 ) in the size range 0. 25-32 µm every 60 seconds, and through a second dataset averaged every 30 minutes, both the particle concentration (nb cm -3 ) together with its normalized size distribution (dN/dlogDp (nb cm -3 ), i. e. , the concentration divided by the log of the size width of the bin),while data from [SMPS] are averaged at the hour scale and provided both at the scale of particle concentration (nb cm -3 ) together with its normalized size distribution (dN/dlogDp (nb cm -3 )).
Together with navigation data such as speed over ground [sog] and course over ground [cog] meteorological station (BATOS-II, Météo France) measured air temperature, relative humidity, and atmospheric pressure at 7 m above sea level. True and apparent wind speed and direction was measured at about 27 m above sea level. In October 2016 a Photosynthetically Active Radiation [par] sensor (Biospherical Instruments Inc. QCR-2150) was mounted at the stern of the boat (~5 m altitude).
Data records The full collection of datasets has been deposited either at Pangaea or at Zenodo depending on their nature, but also on the likelihood to be updated. Provenance metadata. Tara Pacific datasets are articulated around a consistent set of provenance metadata that provide temporal (UTC date and time) and spatial (latitude, longitude, depth or altitude) references as well as annotations about environmental features and place names, using controlled vocabulary from the environmental ontology (https://www. ebi. ac. uk/ols/ontologies/envo) and the marine regions gazetteers (https://www. marineregions. org/). These metadata are available at three granular levels: sampling stations and sites, sampling events, and samples collected at a specific depth. A [sampling-design-label] is provided to facilitate the identification and integration of data that originate from the same open ocean station (OA###), island (I##), site (S##) or coral colony (C###), and hence share provenance and environmental context.
For example, data originating from coral colony number twelve on the second site of the fourth island visited by Tara will bear the sampling design label OA000-I04-S02-C012. Similarly, data collected at station number 99 in the middle of the Pacific Ocean will bear the sampling design label OA099-I00-S00-C000, and data collected at open ocean station number 41 within 200 nautical miles of island number four will bear the sampling design label OA041-I04-S00-C000. Each sample is also characterized by its sampling event which have several properties such as its date and time (UTC) of sampling ([sampling-event_date_time-utc]), the type of event from which the sample originates ([sampling-event_device_label]), the material sampled ([sample-material_label]; see Table  3 ), the protocol used ([sampling-protocol_label]; see Table  3 ) and finally the barcode attributed to the final sample obtained and replicated on the logsheets ([sample-storage_container-label]).
Finally, each sample, in addition to its original barcode was characterized by an event label and a sample label composed of that previous information such as: The provenance context of all samples collected during the Tara Pacific Expedition is available as a single UTF-8 encoded tab-separated-values file, in open access at Zenodo and replicated in part at BioSamples (XYZ). In addition to georeferences and place names, the provenance metadata includes sample unique identifiers, taxonomic annotation from NCBI, and links to sampling logsheets and campaign summary reports. Additionally, the full repository containing the campaign summary reports, sampling authorisations, logsheets and the full record of coral images could be consulted on Pangaea (https://store. pangaea. de/Projects/ TARA-PACIFIC/).
The full list of sampling events is consultable on the following dataset  32  : https://doi. org/10. 1594/PANGAEA. 944548. Environmental context for data analysis. Rich collection of environmental parameters collected from either samples, on-board measurements, satellite imagery, operational models or even calculated from astronomical atlas were compiled and made available for further analysis. These environmental measurements were provided in a multi-layered way in open access to either Pangaea or Zenodo (Tables  4  and 5 ), depending on the potentiality to require updates, with (1) raw measurements at the measure level for both physical samples or for on-board continuous measurements, accompanied with their quality check flags (2) a combined version regrouping all measurement at the sampling event level and adding satellite imaging and results obtained from operational models.
analysis ([NUT], [MTE-USC], [CARB], [FCM], [HPLC], and [CTD]). For [CARB] , additional parameters of the carbonate system were calculated with CO2SYS. m v3. 1. 1  33  using in situ temperature, total alkalinity, total dissolved inorganic carbon, salinity, phosphate and silicate concentrations as inputs together with recommended parameters  [34] [35] [36] [37]  (K1K2 = 10; KSO4 = 3; KF = 2; BOR = 2). Data sets are available in open access at the Data Publisher for Earth & Environmental Science PANGAEA.
Combined version at the event level. A compilation of all environmental measures obtained during a given sampling event was produced by compiling the boat's sensor data available during the time-lapse of the station and measurements originating from satellite imagery (MODIS-AQUA satellite -Level 3 mapped product, 8-day average, 4 km resolution) recovered using OpenDAB protocols at https://oceandata. sci. gsfc. nasa. gov. The zone corresponding to the station position and date was recovered either by taking a two-pixel buffer around the given location (total zone being a 5 by 5 pixels square of 20 km side) and in order to propose an alternative measure in the inevitable case where clouds were present an alternative 12-pixels buffer was taken (total zone being a 25 by 25 pixels square of 100 km side).
This compilation of environmental data at the scale of the event was further enriched using data from reanalyzed (ie. forced with observations) operational models obtained from Copernicus Marine Services (GLOBAL_ REANALYSIS_PHY_001_030  46  , daily mean for sea surface height, salinity, temperature, current speeds, mixed layer depth; GLOBAL_REANALYSIS_BIO_001_029  47  daily mean for Chl a, phytoplankton carbon, O 2 , NO 3 , PO 4 , SIOH, Fe concentrations, Primary production, pH and CO2 partial pressure and GLOBAL_REANALYSIS_ WAV_001_032-TDS  48  for sea surface waves) but also using almanach  49, 50  to calculate essential sun and moon parameters (position, rises and sets, phase, etc). Environmental context at the granularity of samples. The environmental context of all samples collected during the Tara Pacific Expedition is available together with the provenance file in open access at Zenodo.
In some cases, certain parameters were not available at specific sampling sites due to technical issues or sensor availability, however, various basin scale studies and statistical tests require a complete dataset for all sampled sites. During the Tara Pacific expedition, many parameters were concurrently measured in-situ, estimated from remote sensing and/or modelled. For instance, sea surface temperature was measured on the boat using the thermosalinograph included in the underway system, but also with satellite and estimated from a model. Each of these three modes of acquisition have their caveat and accuracy, however, within a certain confidence interval, missing in-situ data can be replaced by its remotely sensed or modelled equivalent. We provide here a simplified version at the sampling site level by replacing missing in-situ data by their closest and most accurate satellite or modelled equivalent.
In each case, in-situ data was considered as the most accurate source of data, with a preference to HPLC pigments analysis followed by measurements done by the ACS, while satellite and modelled data were used only if in-situ data was not available. We evaluated the accuracy of ACS and of each satellite and modelled datasets by linear regressions with their in-situ counterparts. A bias of the modelled or satellite data was identified when the slope of the regression was different to 1 and/or an intercept was different to 0. The satellite and modelled data were forced to match the in-situ data by dividing by the slope and subtracting the intercept. This is the case for SST. When large bias persisted between matchups with observations, the corrected data was not used to replace missing in-situ data.
Historical climate data and indices for climate variability for coral collection sites. It's becoming increasingly clear that stress resilience, in particular thermal tolerance, is shaped not only by maximum monthly mean temperatures (MMMs), but also by long-term and short-term climate variability, even at the scale of reefs  [58] [59] [60]. In order to provide an overview of past climate variability and marine heatwaves experienced by corals sampled at each site, we built a high-resolution historical dataset that spans from 2002 to each sites' sampling date. Ocean skin temperature (11 and 12 µm spectral bands longwave algorithm) was extracted from 1 km resolution level-2 MODIS-Aqua and MODIS-Terra from 2002 to the sampling date and from level-2 VIIRS-SNPP from 2012 to the sampling date. Day and night overpasses were used to maximize data recovery.
Following recommendations from NASA Ocean Color (OB. DAAC), only SST products of quality 0 and 1 were used. The 9 closest pixels to the sampling sites of each scene were extracted. All the extracted pixels from the 3 platforms were then averaged daily to obtain daily SST averages and standard deviations time series for each sampling site, from 2002 to the sampling date. Each time series was first averaged on a Julian day basis to provide a seasonal average. This yearly seasonal average was triplicated and concatenated into a 3-year seasonal cycle to apply a digital low pass filter on the middle year without generating artefacts. A digital low pass filter (filter order 3, pass band ripple 0. 1; "filfilt" function in matlab) with 36 Julian days windows was applied to the concatenated time series to remove high frequency noise.
The middle year was then extracted from the concatenated time series to recover the seasonal cycle. The sea surface temperature anomaly was calculated as the SST minus the seasonal cycle over the full time series. Considering the short periods of missing data (mean of the 95th percentile of the duration of consecutive days with missing data: 9. 8 ± 4. 1 days), the missing values in the SST and SST anomaly time series were linearly interpolated in order to calculate thermal stress indices. The SST anomaly frequency was calculated as the number of days over the past 52 weeks when the SST anomaly is greater than or equal to 1 °C. Thermal stress indices relevant to coral reef health were then calculated using methodology developed for the Coral Reef Temperature Anomaly Database (CoRTAD)  60  (Table  6 ).
Events of cold temperature accumulation were also reported to cause bleaching and mortality  61, 62  , therefore, the same set of indices were calculated for cold stress adapting the CoRTAD method, but using the minimum weekly climatologies (Table  6 ). Further to that, we checked for previous occurrences of bleaching events at sampled reef sites by matching island coordinates to the Reef Check dataset (reefcheck. org) obtained from Sully et al. 58, 63. For each Tara Pacific island, coordinate we determined that Reef Check site that was closest (in terms of distance in km) and considered only Reef Check data that was within a 10 km circumference. A condensed table containing single values associated with each sampling site was created extracting the minimum, maximum, sum, averages, standard deviations, and value recorded at the sampling day of each of these indices (detailed in the readme file provided with the dataset).
Additional metrics of the last heating and cooling events as well as the time of recovery is also provided to represent the state of thermal stress at the day of sampling. Coral photographic resources and annotations. The [PHOTO] resource consists of two datasets. The first, obtained from the [SCUBA-3X10] protocol, was annotated for genus validation, gross morphological characteristics of the colony, algal contact, presence of boring organisms, sediment contact, predation, and health factors (such as presence of disease and coloration). The acquisition protocol of these annotations is described below. This dataset is also used for the description of morphotypes within each genus for taxonomic annotation in combination with genetic data. The second dataset, obtained following [SCUBA-SURVEY] protocol was used for the taxonomic annotation (as close to genus level as possible) of the coral host of the [CDIV] samples.
Of a total of 2,470 CDIV samples, 1711 samples had one or more pictures associated (3,085 total pictures), 759 samples had no photos. Overall, 11,460 coral photographs were generated and annotated allowing for a permanent record of all colonies sampled. All [PHOTO] were transferred to EcoTaxa  64. (1) Manual Annotations of in situ colony (CO) photos: Photo analysis for the genus validation and environmental context was conducted using Matlab with code developed and written specifically for the Tara Pacific Expedition  65. Photos were annotated individually, and annotations were conducted from January to April 2020. To prevent observer bias, photos were randomized, and the annotator was blind to any information regarding the location or the sampling site. The analysis included 1) identification to the genus level, 2) algal contact with types of algal genus if identifiable (Halimeda, Turbinaria, Dictyota, Lobophora, Crustose Coraline Algae (CCA), Sargassum, Galaxaura, other), 3) presence of boring organisms with types if identifiable (Bivalve, Spirobranchus, Tridacna, Urchin, Other Polychaete, Sponge, and Other), 5) contact with sediment (sand), 6) presence of predation marks.
Analysis of the 18 S marker aimed to generate coral host taxonomic annotations to the level of genus for every sample. The annotation was generated based on each sample's most abundant 18 S sequence by aligning to the NCBI 'nt' database with taxonomic labels. A 'lowest common ancestor' approach was used when there were multiple best hits. These alignment-based annotations were verified phylogenetically (i. e. taxonomic similarity agreed with sequence similarity). More than half of the samples were not annotated at genus or better level using this approach, due to the lack of resolution of the 18 S V9 marker. Where available, host taxonomic assignments were based on photo annotations. Otherwise, 18S-based annotations were used.
Technical Validation Numerous steps of quality control were operated at different levels of acquisition to ensure good quality of the different datasets and may vary depending on the type of measurement operated and if it originates from sensors on-board or from samples. inline measurements, models, and satellite data validity. [PAR] measurement validity was checked by first removing physically wrong data (ie. values greater than 0. 45 μE cm -2 sec -1 or lower than 0 μE cm -2 sec -1 ) and compared with clear sky matchup measurements from MODIS-Aqua & Terra. Comparison confirmed the good agreement between datasets but also the absence of sensor drift. Temperature and salinity were acquired by the [TSG]. The quality of the whole time series was manually checked, and the temperature validity was assessed by comparing the temperature reading of the two sensors placed at two different places along the inline system.
Potential drifts of the temperature sensor was investigated by comparing the temperature time series with satellites' sea surface temperature. Salinity measurements where intercalibrated against unfiltered seawater samples [SAL] taken every week from the surface ocean, and corrected for any observed bias. Moreover, temperature and salinity measurements were validated against Argo floats data collocated with Tara. The [ACS] absorption and attenuation signal due to dissolved matter, drift, and biofouling were estimated between two filter events by interpolating filtered water absorption and attenuation following the shape of the [fdom] from the [WSCD], when available. This method improves data quality in case of strong variation of dissolved matter absorption that the frequency of filter event would not capture properly (e.