dataesr/scientific-paragraphs-categorization huggingface dataset:
Gene clustering and gene ontology analysis Cluster analysis was performed for genes that were differentially expressed during the cell cycle, defined as a more than two-fold change in exon read counts at any time during the cell cycle, in both steady-state mRNA and polysomal mRNA datasets. For both datasets, genes were subsequently clustered based on scaled expression levels (z-score) using the k-means clustering algorithm with a maximum of 1,000 iterations in R v2. 14. 2. Several independent clustering runs were performed with increasing numbers of clusters. Determination of the optimal number of clusters was guided by the percentage of variance that was captured by the clusters. We selected the smallest number of clusters that captured more than 90% of the variance (expressed as within group sum of squares) and for which an increase in clusters did not yield a cluster with a novel expression profile (as determined by eye).
For both steady-state mRNA and polysomal mRNA, more than 90% of variance could be explained by five or more clusters. Adding a sixth cluster to the polysomal mRNA dataset resulted in a novel cluster (B. 6; genes expressed at ring and schizont stage) that was not observed with five or less clusters. The optimal number of clusters was therefore determined to be five clusters for the steady-state mRNA dataset and six clusters for polysomal mRNA dataset. GO analysis was performed for each cluster using the Bioconductor R package goseq  [79]. Enriched GO terms were identified using a false discovery rate cutoff of 0. 05.
observed a high correlation for gene expression values of biological replicates (Pearson R = 0. 90; Figure S1A in Additional file 2), confirming the reproducibility of our methodology. Genome-wide gene expression levels also correlated well between our steady-state mRNA-Seq dataset and previously published microarray (R > 0. 54) and RNA-Seq datasets (R > 0. 70) [3,4,18] (Figure S1B,C in Additional file 2).
Acknowledgements We thank  Piyada Juntawong ,  Maureen Hummel , and  Julia Bailey-Serres  for their help in polysome profiling experiments, and  John Weger ,  Rebecca Sun , and  Glenn Hicks  ( Institute for Integrative Genome Biology, University of California Riverside ) for their assistance in the library preparation and sequencing process. We thank  Julie Bailey-Serres ,  Frances Sladek  and  Fedor Karginov  for critical reading of the manuscript. The 3D7 parasite strain was obtained through the  MR4  ( MRA-102 ) deposited by  DJ Carucci. This study was financially supported by the  Human Frontier Science Program  (grant  LT000507/2011-L  to EMB) and the  National Institutes of Health  (grant  R01 AI85077-01A1  to KLR).
Coccolithophores are calcifying marine phytoplankton of the class Prymnesiophyceae. They are considered to play an import role in the global carbon cycle through the production and export of organic carbon and calcite. We have compiled observations of global coccolithophore abundance from several existing databases as well as individual contributions of published and unpublished datasets. We make conservative estimates of carbon biomass using standardised conversion methods and provide estimates of uncertainty associated with these values. The quality-controlled database contains 57 321 individual observations at various taxonomic levels. This corresponds to 11 503 observations of total coccolithophore abundance and biomass. The data span a time period of 1929-2008, with observations from all ocean basins and all seasons, and at depths ranging from the surface to 500 m.
, 2010; Vogt et al. , 2010; Manizza et al. , 2010) , but has also highlighted a need for extensive observational datasets for model parameterisation and validation  (Hood et al. , 2006; Le Quéré et al. , 2005; Anderson, 2005). The MARine Ecosystem DATa (MAREDAT) project (as part of the MARine Ecosystem Model Intercomparison Project -MAREMIP) seeks to compile global biomass data for PFTs commonly represented in marine ecosystem models: silicifiers, calcifiers (including coccolithophores, pteropods and foraminifera), DMS-producers, pico-phytoplankton, diazotrophs, bacteria, and three zooplankton sizeclasses (micro-, meso-and macrozooplankton). A summary of the findings for all groups is presented in  Buitenhuis et al. (2013). This paper presents a database of global coccolithophore biomass distributions compiled as part of the MAREDAT effort.
There is, therefore, a continuing need for in situ observations of coccolithophores in order to better understand their distribution, ecology and contribution to global plankton biomass. This database compiles existing published and unpublished coccolithophore abundance data and provides standardised biomass estimates using species-specific conversion factors. We also provide a detailed discussion of our conversion methods and quality control procedures and discuss the uncertainties associated with the biomass values. Although this dataset was born from the needs of the modelling community, we anticipate that it will be of use to scientists from a range of fields including biological oceanography, marine ecology, biogeochemistry and remote sensing.
Origin of data Our data consists of abundance measurements obtained from several existing databases (NMFS-COPEPOD, BODC, OBIS, OCB DMO, Pangaea, WOD09, OOV) 1 , as well as published and unpublished data from a number of contributing authors (P. Ajani, H. Andruleit, J. Arístegui, L. Beaufort, M. Estrada, D. Karentz, E. Kopczyńska, R. Lee, T. Pritchard and C. Widdicombe). Table  1  summarises the origin of all  datasets, sorted in temporal order. The database contains 58 384 data points when all counts of individual taxa are considered separately, which equates to 11 503 samples of total coccolithophore abundance collected from 6741 depthresolved stations. Abundance data were standardised to units of cells per litre, and ancillary data such as temperature, salinity, chlorophyll and nutrients were retained where available.
Biomass conversion To convert the abundance data (cell counts per unit volume) to biomass estimates (expressed as the concentration of organic carbon per unit water volume), we first needed to multiply the abundance data by the average biovolume for each species, and then multiplied the resulting biovolume concentration with the average organic carbon content per biovolume. We determined cell biovolumes for each of the taxonomic groups reported in the database based on an extensive literature survey. Coccolithophore taxonomy has been subject to numerous revisions over the time span of the dataset, making it challenging to match historical data to current species names and descriptions. For consistency, data entries were matched to currently accepted species names following the taxonomic scheme of  Jordan et al.
(2004)  wherever possible. Where full taxonomic information was not provided, data were matched to the lowest taxonomic group possible. Data that could not be assigned to a particular taxonomic group were categorised as unidentified coccolithophores. We identified a total of 195 taxonomic groups for this dataset (Table  A3 ), ranging from identifications at the sub-species to the family level. Morphotype information is reported for Emiliania huxleyi in only one dataset, and we have therefore chosen to use a single biomass conversion factor for all occurrences of this species. Additionally, 2258 samples consisted of combined counts of coccolithophores without further size or taxonomic information, and 1988 samples contained at least some counts of unidentified or partially identified coccolithophores.
, 2012). While these 16 species represent only a small fraction (10 %) of the species represented in the database, they include some of the more dominant coccolithophores in terms of both abundance and frequency of observation: these 16 species together account for an average of 75 ± 32 % of coccolithophore abundance per sample (median = 92 %), and we therefore consider them to be reasonably representative for the purposes of estimating coccolithophore biomass. Given the lack of data and the lack of consistency among the few available cytoplasm measurements, we chose to estimate coccolithophore biovolumes by assuming cytoplasm dimensions to be 60 % of the mean coccosphere dimensions for all species -this value represents the midpoint of observed ratios of cytoplasm to total coccosphere diameter.
These calculations can be expected to overestimate organic biomass for species with a higher ratio of coccosphere to cytoplasm volume, and underestimate biomass for species with a lower ratio. Biovolumes are calculated based on the mid-point of coccosphere dimensions. Uncertainty ranges are provided using biovolumes and biomasses calculated from 0. 6 × minimum coccosphere dimensions and 0. 6 × maximum coccosphere dimensions. The range of coccosphere dimensions (e. g. diameter, length, width) for each species or sub-species in the database was determined based on a literature survey (Table  A3 ). For some datapoints, coccosphere dimensions were provided alongside abundance data. In these cases the provided measurements were used in preference to our literature-based values. Biovolume estimates were then further converted to carbon biomass (units: µg C L -1 ) using the prymnesiophytespecific conversion factor developed by  Menden-Deuer and Lessard (2000).
Biovolume and biomass values based on the mid-point are hereafter referred to as "mean" biovolume and biomass. We assess the likely over-or under-estimation of our mean biomass estimates for different species of coccolithophore through a comparison with direct biomass measurements as well as biomass values calculated from measured cytoplasm dimensions for 16 species (Table  2 ). For 23 species only a single set of dimensions or a single biovolume value was reported in the literature. In these cases, we have assumed the reported values to be the mean estimates. Minimum and maximum biovolume values were estimated for these species based on the ratios of minimum and maximum biovolume to mean biovolume observed for all other species in the database. These ratios were found to be 0. 5 (± standard deviation of 0.
2) for minimum biovolume/mean biovolume, and 2. 1 (±0. 8) for maximum biovolume/mean biovolume. For cell counts with identifications only to the level of genus or family, or for combined counts of multiple species, we calculate minimum and maximum biomass values per cell based on the absolute minimum and maximum of all species reported for that taxonomic group. Mean biomass values per cell were calculated by taking the Table  2. Comparison of coccolithophore biomass estimates from coccosphere dimensions (assuming cytoplasm diameter = 0. 6 × coccosphere diameter) with biomass estimates from observed cytoplasm diameters for 16 species. Cytoplasm dimensions are from  Stoll et al. (2002)  as well as previously unpublished measurements from the datasets presented in  Franklin et al. (2009)  and  Poulton et al.
(2010)  mean of all reported biomass values for species within the taxonomic group. Taking the mean of the biomass values avoided weighting mean biomass values towards a single large species. For some genera, however, insufficient specieslevel data were available to calculate biomass using this approach. In these cases we were able to obtain a range of coccosphere dimensions from the literature, and calculated biovolumes and biomasses based on the mid-point of these values as detailed above for the species-specific cell counts. For cell counts of unidentified coccolithophores, we have chosen to use a spherical coccosphere with diameter of 10 µm (cell diameter of 6 µm) to calculate our mean biovolume and biomass estimates. This value was selected based on the diameters of species most commonly occurring in the database.
The large uncertainty associated with this value is taken into account by providing minimum and maximum biovolume and biomass estimates based on the absolute minimum and maximum values across all species in the database. Following the biomass conversions, data were compiled to total coccolithophore biomass per sample for the purposes of further analyses. Further taxonomic information is reported in the attached dataset (doi:10. 1594/PANGAEA. 785092) and coccolithophore biodiversity patterns will be discussed in O  'Brien et al. (2013).
heimii. For the remaining 9194 non-zero samples, we used Chauvenet's criterion to identify statistical outliers in the log-normalized biomass data  (Buitenhuis et al. , 2013; Glover et al. , 2011). Based on this analysis, we identified one sample with a biomass value with probability of deviation from the mean greater than 1/2n, with n = 8997 being the number of non-zero samples (two-sided z score: |zc| = 4. 03). This sample is denoted by a flag value of 4. An additional flag column denotes the quantification method used for determining coccolithophore abundance. Of the 9193 non-zero samples included in the database, 4209 are known to have been analysed using light microscopy, 500 using SEM and 197 with flow cytometry. For the remaining 4287 the method is unknown. Coccolithophore counts from SEM are consistently higher than those obtained using light microscopy due to the better identification of smaller and more fragile species.
For example, Bollmann et al. (2002)  found that species such as syracosphaerids, small reticulofenestrids, small gephyrocapsids and holococcolithophores are likely to be missed in light microscopy analyses. Cell density has been shown to differ up to 23 % between the two methods when analysing samples with large numbers of small species such as E. huxleyi, Gephyrocapsa ericsonii and G. protohuxleyi. We have made a statistical comparison of abundance and biomass values to determine whether a systematic bias can be associated with the enumeration method for samples in our database (Table  3 , Fig. 1 ). Our comparison of coccolithophore abundance and biomass shows greater differences between methods than would be expected from previous comparisons of enumeration methods, but we suggest that these differences are likely to be at least partially explained by real differences in coccolithophore abundance and community composition.
For example, we expect that SEM is more likely to be used for samples with a known portion of small coccolithophores which are difficult to identify or enumerate using light microscopy alone. Although median biomass from SEM studies is higher by a factor of four than the median for light microscopy studies, the highest values reported in the dataset are from light microscopy studies. Since the quantification method is unknown for nearly 50 % of samples, we have chosen to retain SEM data in the gridded dataset and all analyses, though users may access a subset of this data from the raw file. In contrast, we have excluded 199 datapoints collected using flow cytometry from the gridded dataset. These values are significantly higher again than those collected using either SEM or light microscopy.
Based on our full quality control procedure we removed a total of 888 flagged samples for the purposes of our analyses, and a further 32 samples were corrected to remove the contribution of T. heimii to total coccolithophore biomass (note: one sample contained data for T. heimii only). All data are included in the published raw dataset in the event that a user has different requirements for the quality control procedure, while the gridded dataset contains the unflagged datapoints only. An additional column in the raw dataset denotes the taxonomic level to which coccolithophores are identified, as this has a major influence on the level of uncertainty associated with our biomass calculations. Coccolithophores identified to species level are denoted by the flag value 0, those identified to genus or family level as flag value 1, and unidentified coccolithophores as flag value 3.
If coccosphere dimensions are known, cells identified to genus or family level receive flag value 2, and unidentified coccolithophores receive flag value 4. All samples of unidentified or partially identified coccolithophores have been included in our analyses and in the gridded file. Several datasets report biomass values in addition to abundance data. While we have chosen to use our own conversion methods for consistency, it is likely that the original biomass values are based on more accurate estimates of cell size. All original biomass values are included in the submitted database and can be substituted for our estimates if desired.
Results Excluding flagged data, the database contains coccolithophore biomass observations for 11 503 samples, collected from 6741 depth-resolved stations (Fig. 2 ). Highest coccolithophore abundance is 9. 8 × 10 6 cells L -1. 2507, or 21. 8 % of samples, were found to be zero values. These data were retained in the dataset, since confirmed zero values hold valuable information for the study of plankton distributions. There is, however, inconsistency in the reporting of zero values in plankton datasets: often abundance data are reported only for a limited range of target groups that are expected to be present. There is also likely to be a bias due to sampling focusing on areas where coccolithophores are expected to occur. Values reported in the subsequent sections are therefore calculated based on non-zero data only.
Spatial and temporal coverage The database includes non-zero coccolithophore observations from the surface to a depth of 500 m (Fig. 3b , with 83. 9 % of observations (84. 1 % with zero values included) from the upper 50 m and 61. 5 % (63. 3 %) from the upper 10 m of the water column. Mean depth is 27. 0 (±40. 5) m and median depth is 10. 0 m. Data are reported from all ocean basins, with 54. 4 % of samples (58. 9 % with zero values included) from the Northern Hemisphere and 45. 4 % (40. 9 %) from the Southern Hemisphere (Table  4 ). 31. 6 % of nonzero data are from the Atlantic Ocean, 40. 2 % from the Pacific Ocean and 10. 4 % from the Indian Ocean. Despite the high number of observations reported from the Pacific compared to the Atlantic, the spatial coverage of this ocean basin is relatively poor, with many observations limited to intensively studied regions in Peruvian and Japanese coastal waters.
9. 9 % of non-zero observations are from the polar re-  gions, with 5. 1 % from the Southern Ocean and 4. 8 % from Arctic waters. Coccolithophores are reported to be present in only one sample below 60 • S (Table  5 , Fig. 4 ). In contrast, the database contains non-zero observations of coccolithophores in Arctic waters up to a maximum of 88. 92 • N. 46. 3 % of data are from tropical waters between 20 • S and 20 • N. Data are reported from the years 1929 to 2008 (Fig. 5 ). A total of 66 non-zero observations are reported for  1929-1930, with no further observations until 1954. 78. 7 % of observations were collected between 1980 and 2008, and 51. 8 % between 1990 and 2008. Data are reported from all months of the year in both hemispheres, although relatively few data were collected during the winter months (13.
Poulton, personal observation). The synthesis of datasets obtained from these different methods would be greatly improved by further comparative studies similar to those carried out by  Bollmann et al. (2002) , as it is currently unclear to what extent small and rare species are being overlooked in different ocean regions as a result of these methodological differences. Users of the gridded data file should also take into consideration the sparse nature of the original data. Often monthly mean gridded values have been derived from relatively few individual datapoints that do not represent the full range of values that occur in a given location. We expect to see a bias toward higher biomass values, given that studies are often conducted in locations and times of year when blooms are expected to occur.
We have not included estimates of inorganic carbon content in the database, as we do not feel that useful estimates of coccolithophore calcite can currently be provided from the abundance data. The ratio of inorganic : organic carbon has been shown to vary considerably with environmental and growth conditions  (Zondervan, 2007) , with ratios for the species E. huxleyi alone ranging from 0. 26 to 2. 3 (van  Bleijswijk et al. , 1994; Paasche, 2002). While some estimates have been made of the relationship between inorganic and organic carbon for E. huxleyi-dominated communities (e. g. Fernández et al. , 1993; Poulton et al. , 2010) , the relationship of calcite content to biomass for other coccolithophore communities remains less well understood. The biomass estimates presented here represent a first attempt to assess global coccolithophore biomass distributions.
While we recognise that the uncertainties associated with these biomass estimates are significant, we nevertheless feel that they provide a more informative dataset than would a compilation of abundance data alone given the large size variation among coccolithophore species. The coccolithophores present particular challenges for the compilation and synthesis of diverse datasets due to the wide range of methods used for their quantification as well as the limited understanding of cell dimensions. The strong biases associated with the different methods highlight the need for coccolithophore abundance data to be published alongside appropriate metadata to allow users to assess data quality.
Conclusions This database represents the largest effort to date to compile coccolithophore abundance observations and provide standardised biomass estimates to the scientific community. We report our biovolume and biomass conversion procedures in detail and discuss the associated uncertainties. We anticipate that this dataset, together with others from the MAREDAT special issue, will be a valuable resource for studies of plankton distributions and ecology and in particular for the evaluation and development of marine ecosystem models. While data are clearly lacking for certain regions, the dataset nevertheless represents the largest available compilation of global coccolithophore abundance and biomass. We hope to improve the spatial and temporal coverage of the dataset as well as the accuracy of biomass conversions as additional data become available in the future.
A2 Gridded netcdf biomass product Monthly mean biomass data have been gridded onto a 360 × 180 • grid, with a vertical resolution of 33 depth levels (equivalent to World Ocean Atlas depths) and a temporal resolution of 12 months (climatological monthly means). This dataset is provided in netcdf format for easy use in model evaluation exercises. The netcdf file can be downloaded from PAN-GAEA (doi:10. 1594/PANGAEA. 785092). This file contains total and non-zero abundance and biomass values. For all fields, the means, medians and standard deviations resulting from multiple observations in each of the 1 • pixels are given. The ranges in biomass values due to uncertainties in cell size are not included as variables in the netcdf product, but are given as ranges (minimum cell biomass, maximum cell biomass) in the data table.
A3 Biomass conversion details Table  A3. Biomass conversion details for coccolithophore taxa reported in the database: biovolume category (best available taxonomic description, species names corrected where possible); number of datapoints (n, flagged and unflagged data); coccosphere shape (S = sphere, PS = prolate spheroid, C = cone, CY = cylinder, DC = double cone, V = various shapes, L = species dimensions unknown, cell biovolume estimate from literature); minimum, maximum and mean coccosphere dimensions (µ m), cell biovolume (µ m 3 ) and cell biomass (pg C L -1 ).
NMFS-COPEPOD: National Marine and Fisheries Service -The Coastal & Oceanic Plankton Ecology, Production, & Observation Database; BODC: British Oceanographic Data Centre; OBIS: the Ocean Biogeographic Information System; OCB DMO: Ocean Carbon and Biogeochemistry Coordination and Data Management Office; Pangaea: Data Publisher for Earth and Environmental Science; WOD09: World Ocean Database 2009; OOV: Observatoire Océanologique de Villefrance-sur-Mer.