dataesr/scientific-paragraphs-categorization huggingface dataset:
The number of aerosols actually activated in a time step is the difference between the number of potentially activated aerosols and the number of aerosols previously activated during the simulation. The condensation/evaporation rate is derived using the Langlois (1973) saturation adjustment scheme. Cloud droplet sedimentation is computed by assuming Stokes law for the cloud droplet sedimentation velocity and assuming that the cloud droplet size distribution n c (D) fits a generalized gamma law: n c (D) = N c α (ν) λ αν D αν-1 exp -(λD) α , ( 5 ) where λ is the slope parameter, depending on the prognostic variables r c and N c : λ = π 6 ρ w (ν + 3/α) (ν) N c ρ a r c 1/3 , (6) α and ν are the parameters of the gamma law, and ρ a is the density of dry air. They were adjusted using droplet spectra measurements from the FM-100 database of our case study and were set at α = 1 and ν = 8.
The simulation begins at 23:20 UTC on 14 November 2011 before any fog has formed and it lasted for 12 h. Temperature, humidity and wind speed vertical profiles were initialized with data from the radiosonde launched from Trappes. Meteorological conditions at Trappes can differ slightly from those at the SIRTA site. Therefore wind, temperature and humidity were modified in the nocturnal boundary layer up to 400 m a. g. l. to fit the data recorded at the 30 m meteorological mast at the SIRTA site, as illustrated in Fig. A1. The soil temperature and moisture were given by the soil measurements, corresponding to a surface temperature of 276 K and a soil moisture of 70 %. Following the profiles from soundings, a geostrophic wind of 8 m s -1 was prescribed, without any other forcing. To generate turbulence, a white noise of 0.
Acknowledgements. The authors are very grateful to all SIRTA operators and database managers. This research was partially funded by the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) under the  SESAR WP 11. 2. 2  project (grant agreement  11-120809-C ). Edited by: Patrick Chuang Reviewed by:  Sian Lane  and two anonymous referees.
Capacity Building Building national and regional resilience to increasing climate variability, climate change and extreme weather events in the Caribbean includes increasing national and regional weather and climate data, related knowledge platforms and human capacity. By involving university students and other young scientists of the region in the field campaign and the long-term research activities related to it, EUREC 4 A will help train the next generation of regional climate scientists and operational forecasters, develop databases that will facilitate the dissemination and use of the data collected in the area during field studies, and will promote international partnership and collaboration networks. (Bony et al. 2015). Through its alignment with two flagship missions of the European Space Agency and the cutting-edge of modelling, EUREC 4 A should also provide a new reference data set which can be used to assess the modelling and the remote sensing for the years to come.
Acknowledgements The authors acknowledge  Aurelien Bourdon , the  CNRS-Meteo-France-CNES SAFIRE facility  for the scientific airborne operations (http://www. safire. fr), Didier Bruneau  and  Jacques Pelon  for technical discussions, and the professional  ULA pilot Franck Toussaint  and the  Air Creation company  for having made the ULA-borne lidar tests possible. The paper benefited from stimulating discussions at the  International Space Science Institute (ISSI)  workshop on '' Shallow clouds and water vapour, circulation and climate sensitivity ''. The EUREC 4 A project is supported by the  European Research Council (ERC) , under the European Union's  Horizon 2020 research and innovation programme  (Grant Agreement No. 694768 ), by the  Max Planck Society  and by  DFG (Deutsche Forschungsgemeinschaft ,  German Research Foundation )  Priority Program   SPP 1294.
cacao improvement and highlighting a genome structure with only slight rearrangements to that of the putative ancestor of the dicotyledon taxon  [1]. This draft genome sequence was performed using a whole genome shotgun (WGS) strategy comprising Roche/454 reads for contig assembly, Sanger BAC end sequences for the scaffolding step and a genetic map of 1259 markers to anchor the scaffolds to chromosomes. The final assembly covered 76% of the estimated size of B97-61/B2 and comprised 4792 scaffolds. In addition, 97. 8% of the unigenes assembled from the transcriptome data were recovered in the assembly. Despite the fact that this standard quality draft genome assembly  [5]  was used as a useful reference, some improvements are still possible, including identifying misassemblies, and reducing the number of scaffolds, gaps, and sequences not anchored to the 10 chromosomes.
Sequence data For this work, we reused some of the data in the dataset produced in the Theobroma cacao B97-61/B2 first draft genome project: Errors in the raw LR dataset were corrected using a hybrid approach by the cleaned SR with LoRDEC  [15]. We use a k-mer length of 23 and a solidity threshold of 3. Finally, uncorrected regions at the extremities of LR were trimmed. This yielded 3 million LR with an average length of 2573 bp, representing 21× genome coverage.
Genetic markers A total of 434 individuals from the cross between UF676 and ICS95 were sequenced by Diversity Arrays Technology using Illumina HiSeq2000 after DNA restriction with enzymes PstI and MseI. Sequencing fragments were analyzed using Tassel 5 GBS v2. 2. 24 pipeline  [19] , and parameter (-mnQS 20). Reads were aligned to scaffolds using Bowtie2 (end-to-end algorithm) and in -very-sensitive mode. Reads that aligned at different locations of the genome were discarded. SNPs were called and variant call data were filtered out with VCFtools  [20]. First, indels and non-biallelic sites were excluded. Then, genotyped data with less than 10 reads were recorded as missing data and SNPs with more than 50% of missing data were excluded. Finally, SNPs with a minor allele frequency > 0. 01 and P-value >1e-6 (Χ 2 test) were selected for further analysis.
Functional annotation was performed with Blastp for each predicted coding sequence against the UniProtKB/ Swiss-Prot and UniProtKB/TrEMBL databases  [22]. Based on three parameters: (i) Qcov (Query coverage = length high-scoring segment pair (HSP)/length query), (ii) Scov (Subject coverage = length HSP/ length subject) and (iii) identity, we kept only the best results to assign a putative function to a polypeptide. InterProScan  [23]  was used to compare sequences with the InterPro database  [24]  to obtain additional protein signature information. KEGG pathways  [25]  have been reconstructed with the BlastKOALA annotation server  [26]. The KEEG Orthology assignments were done against the KEGG plant gene database at the genus level. Additionally, we provided a consensus annotation that combined the NCBI Refseq annotation and the transferred genes from the previously annotated reference genome.
Assembly Since the original Theobroma cacao Criollo draft genome was released in 2011  [1] , reductions in the cost of sequencing, improved technologies and the publication of the complete Theobroma cacao chloroplastic genome  [27]  as well as release of the complete mitochondrial genome of the closely related species Gossypium hirsutum  [28]  made it possible to update the quality of the Theobroma cacao Criollo genome assembly. To reach this goal, we re-scaffolded the original 25,912 contigs of the first version of the Theobroma cacao Criollo assembly as described in material and methods. We first checked the consistency of the original contig dataset by searching for the absence of overlap of read pairs in a region that may result in initial contig assembly errors with the Illumina large insert size libraries (Fig.
1 ). We discovered that 53 contigs presented a non-overlapped read pair region. These misassembled regions were used to split the corresponding contigs and the new contig dataset created was analyzed to detect organelle sequences. A total of 37 contigs were detected as chloroplastic and 21 were similar to mitochondrion. These contigs were discarded for the scaffolding step as well as short contigs (<1000 bp). The final clean contigs dataset comprised 25,527 sequences.
A total of 2 × 10 9 single end Illumina reads were generated and each individual of the progenies produced a mean of 129. 5 Mb of high quality sequence. Reads were then aligned to the 554 scaffolds dataset with Bowtie2 and SNP markers were called with the Tassel 5 GBS pipeline. After non-diallelic and indel markers were filtered out, the 434 individuals were genotyped with 39,408 SNP markers. Genotype data with less than 10 reads per data point were recorded as "missing data". From this raw dataset, we selected a subset of 4857 SNP  markers with a percentage of missing data <50% and with a segregation distortion ratio (P > 1e-6). The molecular markers were then grouped using JoinMap4. 1 software and a linkage group was assigned to each marker. The number of markers per linkage group varied from 694 for linkage group 1 to 298 for linkage group 10 (Table  2 ).
Chromosome reconstruction and annotation The pairwise recombination frequencies between each SNP markers were exported from JoinMap4. 1 software and used to anchor and orientate the scaffolds to the chromosomes using the method described by  [10]. Figure  2  illustrates the process of chromosome reconstruction for chromosome 1. First (Fig. 2a ), blocks of already ordered markers were created based on their position on the scaffolds. Then, the recombination frequencies were used to calculate the mean divergence between scaffolds. Finally, the scaffolds were grouped using an UPGMA-like approach and oriented and positioned on a chromosome with a round of optimization (Fig. 2b ). The process was repeated for the 10 Theobroma cacao chromosomes giving a total of 134 scaffolds anchored and oriented to the chromosomes (Fig.
Annotation The structural annotations of the protein-coding genes computed during the first draft genome project were transferred to the new assembly version. Out of the 28,798 predicted genes, 28,391 (98. 6%) were relocated to the assembly version 2. A total of 92% of genes previously located  We observed in several manually curated genes, that the structural NCBI RefSeq genome annotation was generally of lower quality than the first published annotation in particular because the NCBI RefSeq tended to merge adjacent gene models. Otherwise the NCBI RefSeq annotation integrated RNAseq data and predicted alternative transcripts and could be a useful resource in gap closed regions where no genes were predicted by the first annotation system. Therefore, we made a consensus annotation to select the best structural predictions between both datasets as described in material and methods.
In the final assembly, we closed almost half the gaps (34. 6 Mb vs. 18. 5 Mb) with a combination of long read sequences and large insert size mate paired libraries. After error correction, gaps closed by Pacific Bioscience reads represent 4. 4 Mb of sequence. The LR data would have had a bigger impact if they were generated with the P6-C4 chemistry, which yields longer average read lengths than C2 chemistry (average length of 2573 nucleotides of the corrected LR dataset used in this study). In addition, the absence of non-overlapped read pair regions in the gap filled scaffolds confirmed the efficiency of the gap closure step. The cumulative size of the new assembly (324. 7 Mb) is reduced by 2. 2 Mb in comparison with the first genome assembly. This reduction is mainly due to the better efficiency of large size mate pair templates to evaluate gap size during scaffolding process.
Funding We thank  The French  agricultural research and international cooperation organization ( CIRAD ) for funding the sequencing libraries. ER is supported by the  Institut de Biologie Computationnelle  ( ANR-11-BINF-0002 ), the  MASTODONS C3G  projet, the  ATGC bioinformatics platform , and  France Génomique. In all cases the funders were not involved in any aspect of the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
The Santa Maria di Leuca (SML) cold-water coral province (northern Ionian Sea) has the largest occurrence of a living white coral community currently known in the Mediterranean Sea. Madrepora oculata and Lophelia pertusa, identified as marking sensitive habitats of relevance by the General Fisheries Commission for the Mediterranean, have been observed heterogeneously distributed on the summits of several mounds. This particularly patchy and uneven distribution in addition to their importance for regional biodiversity highlights the need to better understand their environmental preferences and predict their distribution. Bathymetric data (40 m resolution) was used to derive seafloor characteristics. A fine scale index quantifying the landscape elevation (Bathymetric Position Index at 120 m resolution) was used to select all the elevated features considered as candidate morphologies for potential coral mounds.
These methods have the advantage of extrapolating observations in space and time to areas where no information on species occurrence is available, provided that the same predictive variable dataset is used. Habitat Suitability Models (HSMs) have been used frequently over the last few decades. This increasingly popular technique, which is still recent for deep-sea species and ecosystems, is an efficient and cost-effective means of extending the coverage of existing information to the global scale  (Vierod et al. , 2014). HSMs compare the particular conditions of sites where the species has been observed to the conditions of the entire area studied, in order to identify where suitable environments are distributed in space  (Pearson, 2008). At the global scale, these models have already identified environmental parameters like temperature, depth, slope, aragonite saturation, and oxygen as the main factors influencing CWC distributions  (Tittensor et al.
Species Data The two scleractinian species L. pertusa and M. oculata were considered in this study, with no distinction between the two species as they were observed frequently interlaced one within the other  (Mastrototaro et al. , 2010; Arnaud-Haond et al. , in press). They were considered globally as CWC to study their regional distribution in the SML province. Presence data points were collected during different oceanographic cruises. They were obtained from video films recorded during underwater inspections with Remotely Operated Vehicles (ROVs) and specimens sampled using box-cores or grabs in the framework of a number of national and European research projects (Table  1 ). All the coordinates of the physical samples were directly implemented in a GIS. They were collected in the eight main areas such as MS01, MS03, MS03 bis, MS04 (or Atlantis mound), MS04 bis, MS06, MS08, and Reef A (Figure  1C ), which have already been described in the literature  (Carlier et al.
2 software suite (©ESRI) depending on the format of data source. A total of 22 mounds located in eight main areas are known coral mounds. Most of them were located in the center or in the eastern part of the SML province, within the Mass Transport Deposit (MTD) areas described by  Savini et al. (2016). Some colonies were also observed in the western part of the central area next to a fault (MS08) and in the southern part (MS01; Figure  1C ). The position of the presence points was finally altered to meet the DTM resolution: CWC presence points were reduced to only one presence point per raster cell (40 × 40 m) or pixel to fit the resolution of the bathymetric raster in order to limit repetition and spatial autocorrelation in further analyses. The working dataset was composed of 228 pixels with CWC presence.
Seafloor Characteristics Derived from Bathymetry Acoustic data were collected using a RESON SEABAT 8160 Multi Beam Echo Sounder (MBES) during the two main oceanographic cruises (the 2004 APLABES cruise and the 2010 CoralFISH cruise; Table  1 ). Post-processing of MBES data was carried out using dedicated software (Caris Hips and Sips 6. 1) to produce a DTM. A total survey area of 2,000 km 2 was covered by multibeam data with a cell size of 40 m resolution  (Savini et al. , 2014). The DTM was then considered only from -400 to -1,000 m depth (except inside the canyon where the maximum depth is 1,398 m) for further analysis. These values correspond to the shallower limit of the DTM that corresponds to the upper limit of the Adriatic Deep Water  (Budillon et al. , 2010)  and to the maximum depth of CWC distribution in the Mediterranean Sea  (Freiwald et al.
, 2009). This bathymetric water depth range corresponds to the CWC depth range previously observed in the SML Province  (Budillon et al. , 2010; Savini and Corselli, 2010; Savini et al. , 2014). Moreover, the resolution of the bathymetric data decreases with depth and could not be considered below 1,000 m depth in our map. A set of 13 variables was computed from the DTM, using the Benthic Terrain Modeler (BTM) tool (V. 3. 0) implemented with ArcGIS 10. 2 software  (Wright et al. , 2005). These variables express different terrain attributes. The slope and the orientation, converted to Northness and Eastness (the cosine and sine of the angle of the slope direction, respectively), were selected for the study. These parameters are often used to approximate the physical processes that can interact between current and topography, sediment stability, seabed structural features, and seabed structural complexity, depending on the scale of analysis  (Frederiksen et al.
Extraction of Candidate Morphologies In order to extract elevated features that could be representative of coral mounds, we used a fine scale BPI with a window size of 1 pixel for the inner radius and 3 pixels for the outer radius (BPI 3), corresponding to 120 m. Cells with a BPI3value > 1 were selected as possible candidate morphologies for further analysis. Cells with a BPI3-value between 1 and 0 were considered as too flat or having over-gentle slope morphologies to be a mound, whereas those with negative BPI3-values were considered as having concave (holes) morphologies  (Verfaillie et al. , 2007). Each group of a minimum of four adjacent pixels with a BPI3 > 1 was converted into a polygonal feature. The dataset was composed of 7,447 polygonal features called candidate morphologies.
Maxent Model Method A HSM was built with the free Maxent software package, version 3. 33 (https://www. cs. princeton. edu/~schapire/maxent/, Elith et al. , 2006). The Maxent model, a general-purpose machinelearning method aimed at estimating a target probability distribution, by finding the probability distribution of maximum entropy (i. e. , that which is the most spread out, or closest to uniform), has been found to surpass other statistical models in assessing species/habitat distribution with presence-only data  (Elith et al. , 2006; Tittensor et al. , 2009). The Maxent model gives good results even with small sample sizes  (Pearson et al. , 2007; Wisz et al. , 2008). It has been frequently used for predictive CWC habitat mapping  (Tittensor et al. , 2009; O'Hara and Tittensor, 2010; Davies and Guinotte, 2011; Howell et al.
The permutation importance values were also considered. These measures assess the contribution for each variable by randomly permuting its values among the training points and measuring the resulting decrease of the training Area Under the receive operator Curve (AUC) (normalized to give a percentage). The AUC-value, ranging from 0 to 1, indicates how well the model fits the data: a test AUC below 0. 5 means that the model is no better than random, an AUC of 1 means the model is ideal, whereas an AUC higher than 0. 7 can be considered as appropriate  (Elith et al. , 2011). Cross-validation was also used to assess the model. Presence data were randomly partitioned into 10 replicates to compare the training datasets and testing datasets for model validation. Thus, nine of the subsamples were used to calibrate the model, whereas the last one was used for validation (around 205 points to run and 23 points for testing).
Validations of Geomorphometric Proxies and the Maxent Method In this study, the AUC-values used for the Maxent method were very high (0. 95 ± s. d. 0. 01), which indicates that the model predicts suitability areas in the SML Province with good reliability. However, the AUC-value has already been criticized  (Austin, 2007; Lobo et al. , 2008). As the calibration and the tested data were chosen randomly by the software, they may have been too close to each other and not truly independent, possibly over-estimating the AUC-value  (Radosavljevic and Anderson, 2014). However, to avoid possible autocorrelation, the models were run using only one randomly chosen occurrence point for each candidate morphology (22 occurrence points). The results decreased the AUC-value to 0. 92, which was still a high value.
On the basis of hydroacoustic data and gravity core investigations,  Fusi et al. (2006)  hypothesized that the lower limit of probable coral mound occurrences could be extended downwards to about 1,600-m water depth (in  Freiwald et al. , 2009). An extended analysis deeper in the area may lead to the identification of new suitable areas for CWC, but to do this, the dataset must be completed by a deep exploration. Our analysis provides valuable information on CWC preferences. The results show that VRM5 (200 m resolution) was the most important explanatory variable (53%) for mapping probable suitable areas for CWC in the SML Province. Roughness indices, frequently used as a proxy for hard bottom substrates  (Dunn and Halpin, 2009) , emphasize the fact that the CWC studied needed hard bottoms or firm grounds to settle, as mentioned by  Taviani et al.
5° × 2. 5° for atmospheric fields and 1° × 1° for oceanic variables. The self-describing network Common Data Form (netCDF) format is adopted, and three types of metadata are specified in each case: dimensions, variables, and global attributes. Work is ongoing to ensure that metadata and dimension conventions are compliant to allow an impending migration to the Earth System Grid Federation (ESGF) to take place. The database is accessed by scientists around the world. Since its inception the database has seen a steady increase in active users, who originate from more than 30 countries. Figure  1  shows the evolution of hindcast systems included in CHFP and the total download, per year, from 2012 to 2016. A gradual increase in the number of models is visible.
POTENTIAL RESEARCH USE OF CHFP. There is obviously a wide range of research questions that can be addressed with such a hindcast dataset. Many uses of seasonal prediction hinge on the prediction or determining the limits of predictability of near surface temperatures and of rainfall in the tropics  (Rajeevan et al. 2012). Seasonal forecasting has also shown recent application in midlatitudes, with current modeling systems now showing skill at predicting the North Atlantic Oscillation (NAO) for the winter ahead  (Scaife et al. 2014). Yuan et al. (2015)  demonstrated the use of CHFP precipitation hindcasts in combination with those of the North American Multimodel Ensemble (NMME;  Kirtman et al. 2014)  for hydrological applications both in tropical and midlatitude basins. hindcasts. Comparing these hindcasts with the actual measured evolution of the atmosphere and surface measurements of temperature and precipitation allows the biases to be characterized and accounted for  (Di Giuseppe et al.