dataesr/scientific-paragraphs-categorization huggingface dataset:
WTC was executed using the R package 'biwavelet' version 0. 20. 11 (Gouhier et al 2018). For the purposes of the correlation and WTC analyses, the paleoclimate data were resampled to a lower resolution of 50 yeartime steps that was compatible with the CHAR records, using spline estimation; for the correlation with ethane levels in Greenland, biomass burning data were resampled to the time resolution of the ethane data (Software Autosignal). In our next step in the analysis, spatial correlation maps (Von Storch and Zwiers 1999) were created by analysing the relationship between biomass burning, as computed from modern fire statistics, and gridded temperature data from the Climate Research Unit (CRU TS 4. 2 data, resolution of 0. 5°latitude × 0. 5°l ongitude). Temperature data were averaged at the grid level over the April-September season.
Results and discussion Multi-millennial spatiotemporal variability in fire activity Modern fire rotations (i. e. the time required to burn the equivalent of a specified area; period 1959-1999) range from 25 years to over 500 years across the study area (figure 1(a); Boulanger et al 2014). Of note is that the area has seen an increase in the proportion (p) of its forests that have been affected by extreme biomass burning (i. e. area burned exceeded an empirical extreme detection threshold, see Data and Methods) during the AD 1980s, peaking in the early AD 1990s and then declining (figure 1(b); proportions were computed using the Canadian National Fire Database, hereafter pĈ NFD ; see Data and Methods). This phenomenon was observed in analyses of both human-ignited and lightning-ignited fires (figure  S7 ).
During this period, biomass burning decreased. After 250 BP, we observed an increase in biomass burning and a decrease in FRI krig (figure  4 ). A positive trend in biomass burning during 1750-1950 AD, roughly coinciding with the transition from the Pre-Industrial period to the Anthropocene (~1950 AD), was found across 49% of the area, but mostly limited to the western half (figure  S9 ). Notable fluctuations in biomass burning since 1000 BP are broadly consistent with independent estimates of biomass burning emissions that were recently obtained from paleoatmospheric ethane in Greenland polar ice cores (figures 4(c) and S10; data from Nicewonger et al 2018). Biomass burning is one of two important sources of ethane in the preindustrial atmosphere  (Nicewonger et al 2018). This demonstrates the relevance of the information that can be deduced from lake-sediment data for large-scale environmental issues: the correlation between the two independent records implies that fire activity in the study area is an important source for fire emissions transport into Greenland  (Kehrwald et al 2012).
Chlorella vulgaris nuclear genome annotation and phylogenetic analysis Identification of genes present in the assembled C. vulgaris 211/11P genome was performed by integration of directional RNA-seq data obtained from C. vulgaris 211/11P cells cultivated in LL and HL into the gene annotation pipeline. Genome annotation identified 10 724 genes, coding for 11 082 transcripts with an average length of 3062 bp and 8. 12 exons per gene on average (Table  1 ). The gene models predicted for C. vulgaris 211/11P were used to determine codon usage (Table  S5 ), which is found similar to the codon usage of C. reinhardtii  (Merchant et al. , 2007). The number of protein-coding genes is significantly higher compared with the previous genome presented for C. vulgaris UTEX395 where only 7100 transcripts were predicted  (Guarnieri et al.
PacBio sequencing Genomic DNA (16 lg) was used for the preparation of two independent single-molecule real-time (SMRT) bell libraries according to the manufacturer's protocol (Pacific Biosciences; 20-kb template preparation using BluePippin, SageScience, Beverly, MA, USA, size selection system with a 15-kb cut-off). Sequencing was performed on a PacBio RS-II platform (Pacific Biosciences, CA, USA) generating 6. 4 Gb of SMRT data using PacBio P6-C4 chemistry.
Genome assembly The C. vulgaris 211/11P genome was assembled using FALCON  (Chin et al. , 2016)  v1. 8. 7. A second assembly run was performed using the 12% of PacBio subreads that did not align on the first assembly, applying more relaxed parameters. The two assemblies were merged. PacBio subreads were aligned to the assembly using pbalign (v0. 2. 0. 138342) and then the GenomicConsensus package (v0. 9. 2) with the Quiver algorithm was used to remove errors present in the consensus sequences. To further improve the genome quality, a second polishing iteration was performed using Illumina data: reads were aligned using BWA-MEM software (0. 7. 15-r1140) and the Pilon (v1. 22) tool was used to correct errors. A hybrid assembly combining the polished PacBio assembly with the optical map was performed with the Bionano Solve Pipeline (v5678.
Gene annotation Gene annotation of the nuclear genome was performed using the unsupervised RNA-seq-based BRAKER1 pipeline, which takes advantage of two gene predictors: GeneMark-ET 4. 32 and AUGUS-TUS 3. 0. 3 (Specht et al. , 2011). Briefly, both RNA-seq data from the two different growth conditions, LL and HL, were used for the annotation. Quality of reads obtained from each sample was assessed using FastQC software (http://www. bioinformatics. bab © 2019 The Authors. The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd, The Plant Journal, (2019), 100, 1289-1305 raham. ac. uk/projects/fastqc/) and reads with more than 10% of undetermined bases or more than 50 bases with a quality score <7 were discarded. Reads were then clipped from the adapter sequences using Scythe software version 0.
980 (https://github. com/vsbuffalo/scythe), and low-quality ends (Q score <20 on a 10nt window) were trimmed with Sickle version 0. 940 (https:// github. com/vsbuffalo/sickle). The two RNA-seq data were merged and alignment of reads to the assembled genome was performed using HISAT2 (https://ccb. jhu. edu/software/hisat2/index. shtml) v2. 0. 1. Finally, the aligned RNA-seq reads were used as input for the BRAKER1 pipeline. The quality and completeness of the transcriptome were evaluated using BUSCO, http://busco. ezlab. org/) (Simão et al. , 2015). The web application GeSeq was used to annotate the organelle genomes with default parameters plus the tRNAscan-SE activated and selecting C. reinhardtii in the NCBI RefSeq database  (Tillich et al. , 2017). Some genes were also manually curated based on RNA-seq mapped reads.
Transcriptome functional annotation Transcriptome functional annotation was performed by the Blas-t2Go platform based on NCBI's RefSeq database  (Conesa et al. , 2005). Annotated sequences were analyzed using the KAAS (KEGG Automatic Annotation Server) platform to obtain KO annotation  (Kanehisa and Goto, 2000; Kanehisa et al. , 2016 Kanehisa et al. , , 2017)). Transcripts differently expressed with KO annotations were visualized using the KEGG Mapper platform, while the remaining transcripts functionally annotated were manually inspected by retrieving the function of the closest homolog gene.
Phylogenetic analysis Phylogenetic analysis was performed by BUSCO analysis as previously reported  (Waterhouse et al. , 2017). In particular, 111 singlecopy genes shared with other species with an available genome were used for protein alignment and phylogenetic tree construction. BUSCO 3. 0. 2 software with the eukaryota_odb9 database and the genome of each species Chlorella vulgaris, Chlorella protothecoides sp0710, Chlorella variabilis NC64A, Coccomyxa subellipsoidae, Chlamydomonas reinhardtii, Volvox carteri, Chromochloris zofingiensis, Arabidopsis thaliana, Micromonas pusilla CCMP1545 and Ostreococcus tauri were used to identify the single-copy orthologous genes. Of these, only those shared between the 10 species were selected. For each protein a multiple alignment was performed among the species using MUSCLE 3.
ACKNOWLEDGEMENTS The research was supported by the  ERC   Starting Grant SOLENAL-GAE  ( 679814 ) to MB. We also acknowledge the  European Union Regional Developing Fund (ERDF) , the  R egion Provence Alpes Côte d'Azur , the  French Ministry of Research and the CEA  for funding the H elioBiotec platform. We thank the  Centro Piattaforme Tecnologiche  for providing access to the core facilities of the  University of Verona, Italy.
In addition, ocean color data provide limited information on the morphology and size distribution of marine particles. Major advances in our understanding of global ocean ecosystems will require measurements from new technologies, specifically lidar and polarimetry. These new techniques have been widely used for atmospheric applications but have not had as much as interest from the ocean color community. This is due to many factors including limited access to in-situ instruments and/or space-borne sensors and lack of attention in university courses and ocean science summer schools curricula. However, lidar and polarimetry technology.
INTRODUCTION Since the inception of ocean color satellite observation systems, remote sensing has been based on measurements of the radiance emerging at the top of the Ocean color remote sensing utilizes the intensity and spectral variation of visible light scattered upward from beneath the ocean surface to derive concentrations of biogeochemical constituents and inherent optical properties within the ocean surface layer. Passive ocean color space-borne observations began in the late 1970s with the launch of the CZCS space mission. An uninterrupted record of global ocean color data has been sustained since 1997 (thanks to SeaWiFS, MODIS-AQUA, MERIS, VIIRS and OLCI sensors) and will continue at least until 2035 with the NASA/PACE and ESA/Sentinel-3 space missions. These passive observations have enabled a global view of the distribution of marine particles [phytoplankton, total suspended matter (TSM) and colored dissolved organic matter (CDOM),  McClain (2009) ].
Light Induced Fluorescence Lidar The applications of Light Induced Fluorescence (LIF) through shipborne lidar systems date back to a few decades ago. LIF has an extensive history of providing data for oceanographic research and monitoring, as the detection of oil spills  (Pisano et al. , 2015; Babichenko et al. , 2016)  and other pollutants  (Barbini et al. , 1999) , quantification and characterization of phytoplankton and CDOM  (Palmer et al. , 2013) , as well as the estimation of TSM  (Aibulatov et al. , 2008). LIF Lidar datasets have also successfully served as validation for satellite-derived oceanographic measurements  (Fiorani et al. , 2004 (Fiorani et al. , , 2006)). The fluorescence echoes from UV excitation assume a direct correlation with the concentration of the chromophore molecules contained in the excited target  (Hoge and Swift, 1981).
Based on that experience, improvements were made to the lidar and it has since acquired data on several airborne deployments, including the Ship-Aircraft Bio-Optical Research (SABOR) mission in 2014 and three deployments for the North Atlantic and Marine Ecosystems Study (NAAMES) in 2015, 2016, and 2017. The HSRL retrievals of b bp and K d show excellent agreement with ship-based in-situ estimates made on SABOR  (Schulien et al. , 2017; Hostetler et al. , 2018)  and satellite ocean color retrievals  (Hair et al. , 2016). Data from the HSRL-1 are also used to retrieve estimates of both total and particulate depolarization, and those data along with the b bp and K d profiles are currently being assessed for information on community composition. Figure  3  shows aerosol backscatter, aerosol optical depth, b bp , K d , and particulate depolarization retrievals from HSRL-1 on 13 May 2016 NAAMES flight in the Western North Atlantic.
Satellite While aircraft-flown lidars have provided ocean measurements for decades (see above), the power of satellite-based lidar for ocean biology measurements has been demonstrated only recently  (Behrenfeld et al. , 2013 (Behrenfeld et al. , , 2017;; Churnside et al. , 2013; Lu et al. , 2014; Hostetler et al. , 2018). These studies involved analysis of data from the CALIOP instrument on the CALIPSO platform which was designed for atmospheric measurements. It turned out, however, that its 532-nm channels are also sensitive to ocean backscatter. The relatively coarse vertical resolution of CALIOP (30 m in the atmosphere and 23 m in the ocean) and poor detector transient response make vertically resolved ocean retrievals impractical. However, significant scientific impacts have been realized using vertically integrated CALIOP subsurface ocean data.
Simultaneous retrieval of aerosol and water-leaving radiance Several simultaneous retrieval algorithms have been developed using multi-angle polarization measurements, where the aerosol properties and the water-leaving radiance are retrieved simultaneously. Chowdhary et al. (2005)  developed a joint retrieval algorithm using the RSP data that retrieved the aerosol properties and water optical properties with a bio-optical model parameterized by chlorophyll concentration. Hasekamp et al. (2011)  developed a retrieval algorithm using measurements from PARASOL with a bimodal aerosol model and an ocean model parameterized by chlorophyll concentration, wind speed and direction, and foam coverage. Xu et al. (2016)  developed a retrieval algorithm using the AirMSPI dataset with a multi-pixel smoothing constraint and a simplified bio-optical model.
ACKNOWLEDGMENTS Portions of this work were performed at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration. The JPL work was supported by the  PACE  science team grant, under  Paula Bontempi. NASA ,  CNES , and  ESA  are acknowledgments for the access to the CALIPSO and Sentinel-1 images. CNES through the  TOSCA program  is acknowledged for funding the publication.
Results A total of 251 observations from September 2016 to April 2018 reported by the Mediterranean scientific network were combined with the 170 observations by citizens (June 2017-April 2018; ~26 monthly observations) to yield a total of 421 observations (40. 38 and 59. 62% reported by citizens vs. scientists respectively; Fig. 1  and Table A  in Supplementary Material). Initially 289 observations were reported from the sea-watchers' platform, which were scrutinized by expert scientists to filter out possible confusions between congeneric species P. nobilis and P. rudis as well as other kind of errors in the reports by sea-watchers. The spatio-temporal representation of this combined data set (Fig. 2 ) points to the southern part of the Iberian Peninsula as the first region where the MME was detected (Fig.
After uploading an observation, the scientific team of the module curated each record based on sea-watcher experience, availability of complementary information and/ or un uploaded photo, and frequently, through a dialogue with the sea-watcher. From these sources, essential information like recent abnormal mortality, date, GPS coordinates, temperature, salinity, and depth were extracted for each observation to fit predictive models (see section Data analyses below). A trustworthy curated observation of one individual of a population reported as dead or ill under suspicious (not by predators, anchoring etc. ) conditions was included in the model. Temperature and salinity data were extracted from the nearest available point of the database 'EN4. 2. 1: quality controlled subsurface ocean temperature and salinity profiles and objective analyses' (downloaded from https://www.
metoffice. gov. uk/hadobs/en4/ on April 30 th 2018  69  ) except for sites where hydrographic values were carefully measured in situ by scientists (e. g. , the Mar Menor coastal lagoon, the Alfacs Bay affected by River Ebro discharges). The EN4. 2. 1 dataset contains monthly objective analysis of temperature and salinity with the corrections  69  in a 1-degree grid and 42 different depths (from 5 m to 5000 m), spanning a period from 1900 to present.
Data analyses. The potential role of the surface currents on the expansion of the disease was investigated simulating the drift of 1000 virtual particles (Par) in a high-resolution regional model of the western Mediterranean Sea, the WMOP model  70, 71. This model is developed and operated by SOCIB, the Balearic Islands Coastal Observing and Forecasting System. The WMOP model provides daily predictions of hydrodynamic conditions with a 2-km resolution from the Strait of Gibraltar to Corsica and Sardinia. It is a regional implementation of the Regional Ocean Modelling System (ROMS  72  ), downscaling simulations from the Copernicus Marine Service Mediterranean model, and forced by the high resolution HIRLAM model from the Spanish Meteorological Agency. Details of the operational system can be found in  70, 71.
More concretely, simulations of currents were conducted in areas and within time-intervals (t 0 , … t n ; time in days) where the arrival of the disease (final time; t f ) at a given point (p i ) was detected by observations of first absence of mortality (pen shell alive at p i t 0 ) and then indications of mortality by probable infection (pen shell dead or ill at p i t f ). Given the available dataset, one backward simulation (from 2016/10/24 to 2016/09/09 in the Balearic Islands) and two forward simulations (from 2017/06/09 to 2017/07/24 in the L' Ametlla de Mar area and from 2017/10/27 to 2017/12/11 in North Catalonia) were produced in order to analyse the time for a particle to drift: (i) from p 1 (visible mortality at p 1 t 0 ) to p 2 (no infection observed at p 2 t 0 and mortality at p 2 t f ) in the case of forward simulations (tracking particles destination), and inversely, (ii) from p 2 t f to p 1 in the case of backward simulation (tracking particles provenance).
To predict the beginning of a local mortality event at a given point (p 1 or p 2 in the case of backward or forward simulation, respectively), the subset of simulated particles drifting within a buffer zone of 650 km 2 around selected point (as center of such buffer zone) were selected before extracting the final time (arrival time; t f ) of each of these particles. These predicted times were compared checking the time delay between empirical dates of P. nobilis infected in the starting (p 1 ) and ending locations (p 2 ). The relationship with establishment success of the disease after arrival and environmental variables was addressed using a generalized additive model (GAM) with the gam function of the mgcv library within R software  74. GAMs extend general linear models allowing for complex correlations between response and explanatory variables  75.
In this sense, the potential effect of temperature (Temp) and salinity (Sal) on the presence/absence of the disease (Disease) was tested fitting a binomial GAM [Disease~ s(Temp) + s(Sal)]. It is important to highlight that a subset of data was selected to fit the GAM (n = 271 observations; 232 presence and 39 absence of disease). Only data with a probable spatio-temporal prevalence of the disease were selected, avoiding the bias caused by the spatio-temporal absence of the disease (e. g. , data from Eastern Mediterranean where the disease has not yet been detected and the P. nobilis populations are still healthy).
Acknowledgements The authors are very grateful to all citizens (sea-watchers) who reported their observations and improved data collection. This study was financed by the special action project " NACRAS -Evaluación de la magnitud de un evento de mortalidad combinando censos científicos ampliados con observaciones de ciencia ciudadana " cofunded by the  Regional Government and Direcció General d'Innovació i Recerca of the Balearic Islands  and the  European Social Fund   2014-2020. Salinity and temperature data at Alfacs Bay was provided from the  "Programa de Seguimiento de la calidad de las aguas , moluscos y fitoplancton tóxico en las zonas de producción de marisco del litoral catalán (PSQAM) de la DGPAM. "The authors acknowledge the  MEDCLIC  project, funded by  "La Caixa" Foundation , contributing to the development of the WMOP hydrodynamic model.
Maite Vázquez-Luis  and  Miguel Cabanellas-Reboredo  were supported by a postdoctoral contract co-funded by the  Regional Government of the Balearic Islands  and the  European Social Fund   2014-2020. Also,  Vázquez-Luis  was also supported by a postdoctoral contract  Juan de la Cierva-Incorporación  ( IJCI-2016-29329 ) of  Ministerio de Ciencia, Innovación y Universidades. Elvira Álvarez  was supported by a  Personal Técnico de Apoyo  contract ( PTA2015-10829-I ) funded by the  Spanish Ministry of Economy and Competiveness. Iris E. Hendriks was supported by  Ramon y Cajal Fellowship   RYC-2014-14970 , cofunded by the  Conselleria d'Innovació, Recerca i Turisme of the Balearic Government (Pla de ciència , tecnologia, innovació i emprenedoria  2013-2017 ) and the  Spanish Ministry of Economy, Industry and Competitiveness.
The future international Surface Water and Ocean Topography (SWOT) Mission, planned for launch in 2021, will make high-resolution 2D observations of sea-surface height using SAR radar interferometric techniques. SWOT will map the global and coastal oceans up to 77. 6 • latitude every 21 days over a swath of 120 km (20 km nadir gap). Today's 2D mapped altimeter data can resolve ocean scales of 150 km wavelength whereas the SWOT measurement will extend our 2D observations down to 15-30 km, depending on sea state. SWOT will offer new opportunities to observe the oceanic dynamic processes at scales that are important in the generation and dissipation of kinetic energy in the ocean, and that facilitate the exchange of energy between the ocean interior and the upper layer. The active vertical exchanges linked to these scales have impacts on the local and global budgets of heat and carbon, and on nutrients for biogeochemical cycles.
This review paper highlights the issues being addressed by the SWOT science community to understand SWOT's very precise sea surface height (SSH)/surface pressure observations, and it explores how SWOT data will be combined with other satellite and in situ data and models to better understand the upper ocean 4D circulation (x, y, z, t) over the next decade. SWOT will provide unprecedented 2D ocean SSH observations down to 15-30 km in wavelength, which encompasses the scales of "balanced" geostrophic eddy motions, high-frequency internal tides and internal waves.
Smaller-scale horizontal gradients can also be linked to energetic vertical velocities and tracer transports  (Lévy et al. , 2012a) , and understanding the ocean stirring and convergence at these finer scales is important for global tracer budgets, biogeochemical applications, and climate. As the name suggests, the Surface Water and Ocean Topography (SWOT) mission will bring together two scientific communities -oceanographers and hydrologists. For the hydrology community, SWOT SAR interferometric data will enable the observation of the surface elevation of lakes, rivers, and floodplains, and will provide a global estimate of discharge for rivers >100 m wide, and water storage for lakes >250 m 2. SWOT will also provide unprecedented observations in coastal and estuarine regions, of interest to both communities.