dataesr/scientific-paragraphs-categorization huggingface dataset:
gsfc. nasa. gov/atbd/chlor_a/) similarly based on maximum band ratios, providing confidence in the quality of the compiled data. The inherent optical properties (aph, adg and bbp) are available at 550 unique wavelengths between 300 and 850 nm. There is a total of 3293, 1654 and 792 observations, for aph, adg and bbp, respectively. For aph the total number of observations is distributed among NOMAD (1190), TPSS (966), COASTCOLOUR (593), AWI (458), SeaBASS (  14 ) and MERMAID (72). For adg the contributions are as follows: NOMAD (1079), COASTCOLOUR (531), SeaBASS (11) and MERMAID (33). The bbp ob-servations come from NOMAD (371), COASTCOLOUR (154), SeaBASS (32) and MERMAID (235). The data distribution of aph, adg and bbp at 44X nm and 55X nm for each dataset is provided in Fig. 12a-f. Median values of aph, adg and bbp at 44X and 55X nm for each dataset are summarized in Table  3.
For additional analysis, the following band ratios for the absorption coefficients were calculated: aph(490) / aph(443), aph(412) / aph(443), adg(443) / adg(490) and adg(412) / adg(443). Data within 2 nm of the wavelengths were used to maximize the number of points. The distribution of the ratios is shown in Fig. 13. Several observations were found to be outside the thresholds used in the IOCCG report 5 (2006) for quality control (see dotted vertical black lines in Fig. 13 ). These points are highlighted here for information but retained in the database, as these were mostly from NOMAD and there was an inter-   443 ). Data within 2 nm of the wavelengths were used. The graphical convention is identical to Fig. 2. The vertical dashed lines show the lower and upper thresholds used for quality control in the IOCCG report 5.
The total number of points for adg ratios are divided between NOMAD (89 %), COASTCOLOUR (7 %), MERMAID (3 %) and SeaBASS (1 %). The total number of points for aph ratios are divided between NOMAD (36 %), TPSS (29 %), COASTCOLOUR (18 %), AWI (14 %), MERMAID (2 %) and SeaBASS (1 %). est to preserve this dataset as a whole. Also, not discarding these data allows further manipulation with different quality control criteria. On the annual scale, the observations of the inherent optical properties are strongly underrepresented in the Southern Hemisphere where there is a complete absence of data in several months of the year (Fig. 3 ). Overall, the geographic coverage for observations of aph, adg and bbp (Fig. 14 ) is poor, with most open ocean regions not being sampled, except for the Atlantic Ocean.
Small clusters of data are located in particular coastal regions. Finally, for the diffuse attenuation coefficient for downward irradiance (kd) there are 25 unique wavelengths between 405 and 709 nm. There is a total of 2454 observations from NOMAD (2266), SeaBASS (118) and MER-MAID (70). Data distribution of kd at 44X and 55X nm for each dataset is shown in Fig. 12g  and h. No kd data at these wavelengths were available for the SeaBASS dataset (only at 490 nm). Median values of kd at 44X nm span between 0. 08 m -1 (NOMAD) and 0. 1 m -1 (MERMAID), whereas at 55X nm the kd values are approximately 0. 1 m -1 (NO-MAD and MERMAID). NOMAD provides the best geographical coverage (Fig. 15 ), with a higher coverage in the Atlantic, compared with other oceans. With the exception of the coastal regions of North America and the Sea of Japan, most coastal regions are not sampled.
In the Northern Hemisphere, kd is distributed roughly evenly across all months of the year, but in the Southern Hemisphere there are few data points during the austral winter and none at all in September (Fig. 3 ). For total suspended matter (tsm) there is a total of 1546 observations divided between COASTCOLOUR (1199) and MERMAID (347). The observations of tsm are available in a greater number in the Northern Hemisphere (Fig. 3 ) and are distributed across several coastal regions around Europe, the Mediterranean Sea, the South China Sea, Indonesia and Australia (Fig. 15 ). Although most of the stations with concurrent variables are from the NOMAD dataset, for completeness, an examination of bio-optical relationships is provided (Fig. 16 ). The relation between aph at 443 nm and chlorophyll a (Fig.
16  a) agrees with  Bricaud et al. (2004). A total of 2953 points exist with these two variables available (34 % from NO-MAD, 32 % from TPSS, 11 % from AWI, 11% from COAST-COLOUR, and the remaining 12 % from MERMAID and SeaBASS). The relation between the sum of aph and adg at 443 nm and rrs at 443 nm (Fig. 16 b ) shows a similar dispersion, with the exception of some scattered points, to an equivalent analysis on the IOCCG report 5 (see their Fig. 2. 3). Again, the scattered data were retained in the final table to preserve the NOMAD dataset. A total of 1112 points exist for which these three variables are available (97 % from NO-MAD). The relation between the ratio rrs(490) / rrs(555) and kd(490) (Fig. 16c ) shows a good agreement with the NASA KD2S standard algorithm (https://oceancolor.
Note on former version A former version of this article was published on 3 June 2016 and is available at https://doi. org/10. 5194/essd-8-235-2016. ences de l'Univers (INSU), the Sorbonne Université (SU) and the Institut de la Mer de Villefranche (IMEV). We thank ACRI-ST, ARGANS and ESA for access to the MERMAID database (http: //hermes. acri. fr/mermaid, last access: 10 July 2019). We thank Annelies Hommersom, Pierre Yves Deschamps, Gavin Tilstone and David Siegel for allowing the use of MERMAID data for which they are principal investigators. We thank the British Oceanographic Data Centre (BODC) for access to AMT data and in particular to Polly Hadziabdic and Rob Thomas for their help in questions about the AMT dataset. We thank Victoria Hill, Patrick Holligan, Gerald Moore and Emilio Suarez for the use of AMT data for which they are principal investigators.
We thank Sam Ahmed, Hui Feng, Alex Gilerson and Brent Holben for allowing the use of the AERONET-OC data for which they are principal investigators. We thank also the AERONET staff and site support people. The Australian Integrated Marine Observing System (IMOS) and CSIRO are acknowledged for funding the Lucinda AERONET-OC site. We thank Bob Bidigare, Matthew Church, Ricardo Letelier and Jasmine Nahorniak for making the HOT data available, as well as the National Science Foundation for support of the HOT research (grant OCE 09-26766). We thank Yves Dandonneau for allowing the use of GeP&CO data. We thank the ICES database on the marine environment  (Copenhagen, Denmark, 2014)  for allowing the use of their archived data and Marilynn Sørensen for the help with questions about the ICES dataset.
• W). Data were obtained from the AERONET-OC website. Compiled standard variable: rrs. Robert Arnone (WaveCIS), Sam Ahmed (LISCO), Vittorio Brando (Lucinda), Dick Crout (Wave-CIS), Hui Feng (MVCO), Alex Gilerson (LISCO), Rick Gould (WaveCIS), Brent Holben (COVEin situ marine data from multiple contributors. Bio-optical global data between 1997 and 2012 were extracted from the SeaBASS website. Pigment data were mostly extracted using the Pigment search tool, which provides data directly from the archives. Radiometric data were extracted using the Validation tool, which only provides in situ data with matchups for oceancolour sensors. Compiled standard variables: rrs, chla_hplc, chl_fluor, aph, adg, bbp and kd. MERIS Match-up In situ Database (MERMAID) Global database of in situ bio-optical data matched with concurrent MERIS Level 2 satellite oceancolour products.
Data were provided by the British Oceanographic Data Centre (BODC). Compiled standard variables: chla_hplc and chl_fluor. Ray Barlow, Stuart Gibb, Victoria Hill, Patrick Holligan, Gerald Moore, Leonie O'Dowd, Alex Poulton, Emilio Suarez International Council for the Exploration of the Sea (ICES) Database of several collections of data related to the marine environment. It has compiled observations of chlorophyll a concentration in the northern European seas, between 1997 and 2012. Data were provided by the ICES database on the marine environment (2014, Copenhagen, Denmark). Compiled standard variables: chla_hplc and chl_fluor. Not available Hawaii Ocean Time-series (HOT) Multidisciplinary programme that makes repeated biological, chemical and physical oceanographic observations near Oahu, Hawaii.
Measurements of chlorophyll a concentration between 1997 and 2012 were extracted from the project website. Compiled standard variables: chla_hplc and chl_fluor. Bob Bidigare, Matthew Church, Ricardo Letelier, situ data collection aboard a merchant ship from France to New Caledonia, between 1999 and 2002. Measurements of chlorophyll a concentration were obtained from the project website. Compiled standard variables: chla_hplc and chla_fluor. Yves Dandonneau ARCSSPP Arctic System Science Primary Production database. Available from the NODC FTP site. Compiled standard variable: chla_fluor. Patricia Matrai.
able and discussed below (seeFigs. 11 and 16). The geographic distribution of remote-sensing reflectance observations (Fig. 5) shows a higher number of observations in some coastal regions, such as those of North America and northern Europe. The central regions of the ocean show a lower number of observations, with the Atlantic Ocean having the highest density in relation to the other oceans. The best geographic coverage is provided by the NOMAD database. Data from SeaBASS are fewer in number but are still important. Data from MERMAID are mainly located along the coasts of Europe, North America and the central region of the North Atlantic Ocean. The observations from COASTCOLOUR are concentrated in 17 coastal sites around the world, while AWI data are available for the Atlantic, Pacific and Southern Ocean.
Acknowledgements. This paper is a contribution to the  ESA OC-CCI  project. This work is also a contribution to project  PEst-OE/MAR/UI0199/2014. We would like to thank the efforts of the teams responsible for collection of the data in the field and of the teams responsible for processing and storing the data in archives, without which this work would not be possible. We thank  Tamoghna Acharyya  and  Robert Brewin  at  Plymouth Marine Laboratory  for their initial contribution to this work. We thank the  NOAA (US)  for making available the MOBY data and  Yong Sung Kim  for the help in questions about MOBY data. BOUSSOLE is supported and funded by the  European Space Agency (ESA) , the  Centre National d'Etudes Spatiales (CNES) , the  Centre National de la Recherche Scientifique (CNRS) , the  Institut National des Sci -We thank  NASA ,  SeaBASS  and the  Ocean Biology Processing Group (OBPG)  for access to SeaBASS and NOMAD data.
For the BIOCHEM data we thank the  Fisheries and Oceans Canada  and the following data contributors:  Diane Archambault ,  Hughes Benoit ,  Esther Bonneau ,  Eugene Colbourne ,  Alain Gagne ,  Yves Gagnon ,  Tom Hurlbut ,  Catherine Johnson ,  Pierre Joly ,  Maurice Levasseur ,  Jean-Francois Lussier ,  Sonia Michaud ,  Patrick Ouellet ,  Jacques Plourde ,  Stephane Plourde ,  Luc Savoie ,  Michael Scarratt ,  Philippe Schwab ,  Michel Starr  and  François Villeneuve. We also thank  Laure Devine  for the help in processing the BIOCHEM dataset. We thank  Ralph Goericke  for allowing the use of the CalCOFI and CCELTER data. CalCOFI research is supported by contributions from the participating agencies: the  California State Department of Fish and Wildlife, NOAA, National Marine Fisheries Service, Southwest Fisheries Science Center , and the  University of California, Integrative Oceanography Division at the Scripps Institution of Oceanography, UCSD.
The authors would like to thank the  Oceanic Platform of the Canary Islands (PLOCAN)  and its staff for making freely available the use of this ESTOC dataset. We thank the following MARE-DAT data providers:  Robert Bidigare ,  Denise Cummings ,  Giacomo DiTullio ,  Chris Gallienne ,  Ralf Goericke ,  Patrick Holligan ,  David Karl ,  Michael Landry ,  Michael Lomas ,  Michael Lucas ,  Jean-Claude Marty ,  Walker Smith ,  Rick Stumpf ,  Emilio Suarez ,  Koji Suzuki ,  Maria Vernet  and  Simon Wright. We thank  Oscar Schofield ,  Raymond Smith  and  Maria Vernet  for allowing the use of the PALMER data. Data from the Palmer LTER data repository were supported by the  Office of Polar Programs ,  NSF  grants  OPP-9011927 ,  OPP-9632763  and  OPP-0217282. We thank the Sea-DataNet Pan-European infrastructure for ocean and marine data management (http://www.
seadatanet. org,last access: 10 July 2019). We thank  Emmanuel Boss  for the TARA data. Funding for the collection and processing of the TARA dataset was provided by the  NASA   Ocean Biology and Biogeochemistry programme unwww. earth-syst-sci-data. net/11/1037/2019/ Earth Syst. Sci. Data,  11 ,  1037-1068 , 2019.
All data were available through the PANGAEA repository. Observations of concentration of chlorophyll a as well as 1 nm spectrally resolved remote-sensing reflectances and the algal pigment absorption coefficient were considered. The methods for these observations are described by Taylor et  al. (2011a). For chlorophyll, data from the following cruises were used: ANT-XXIV/1, ANT-XXIV/4, ANT-XXVI/4 and MSM18/3 (https://doi. org/10. 1594/PANGAEA. 847820; Bracher et al. , 2015); SO202/2 (https://doi. org/10. 1594/PANGAEA. 820607; Zindler et al. , 2013); ANT-XXVII/2 (https://doi. org/10. 1594/PANGAEA. 848590; Bracher, 2015); ANT-XXV/1 (https://doi. org/10. 1594/PANGAEA. 819099; Taylor et al. , 2011b); and ANT-XXVIII/3 and SO218 (https://doi. org/10. 1594/PANGAEA. 848591; Soppa et al. , 2014). Concerning remote-sensing reflectances, the observations taken during cruises ANT-XXIV/4 and ANT-XXVI/4 (https://doi.
Mainly European seas. Provided by BODC. Compiled standard variables: chla_fluor and chla_hplc. Not available CALCOFI Cruise data from the California Cooperative Oceanic Fisheries Investigations programme. Available from CalCOFI website. Compiled standard variable: chla_fluor. Ralf Goericke CIMT Sampling from the Center for Integrated Marine Technology (California). Available from CIMT website. Compiled standard variable: chla_fluor. Raphael Kudela COASTCOLOUR Quality-controlled compilation of bio-optical data in several coastal sites. Available from PANGAEA. Compiled standard variables: chla_fluor, chla_hplc, rrs, aph, adg, bbp and tsm. HPLC. Available from PANGAEA. Compiled standard variable: chla_hplc.
and were mainly from the Gulf of St Lawrence (western North Atlantic). The compiled variable was chla_fluor. 2. 2. 16 British Oceanographic Data Centre (BODC) BODC is the designated marine science data centre for the United Kingdom. The data used in this work derive from a specific request for discrete observations of chlorophyll a concentration since 1997. Initially, this request was used to compile AMT data (see Sect. 2. 2. 9). The remaining data comprising observations of chlorophyll a concentration from fluorometric and HPLC methods, mostly sampled in the North Atlantic, were analysed and added (the dataset string for this data source is bodc). Data not flagged with highest quality or without method of measurement were discarded. The compiled variables were chla_hplc and chla_fluor. 2.
2. 17 California Cooperative Oceanic Fisheries Investigations (CALCOFI) CalCOFI is a partnership of the California Department of Fish and Wildlife, National Oceanic and Atmospheric Ad- ministration Fisheries Service, and Scripps Institution of Oceanography. CalCOFI has conducted quarterly cruises off southern and central California since 1949. Data col- lected in the upper 500 m include temperature, salinity, oxygen, nutrients, chlorophyll, primary productivity, plank- ton biodiversity and biomass. For this work, only observa- tions of chlorophyll a concentration derived from fluoromet- ric methods flagged with highest quality were used. Data were acquired from the file CalCOFI_Database_194903- 201701_csv_20Sept2017. zip available at http://www. calcofi. org (last access: 10 July 2019) The compiled variable was chla_fluor.
org/ 10. 6073/pasta/7feb632dabb30f0e79683017721a83c7; Goer-icke, 2017) were compiled. The compiled variable was chla_fluor. phyll measurements from in vitro fluorometric methods were extracted (from http://www. dfo-mpo. gc. ca/science/ data-donnees/biochem/index-eng. html, last access: 10 July 2019) with close guidance by the BioChem help desk, confirming quality and methods. The used data span from 1997 to 2014.
Mass spectrometry-based proteomic analyses Proteins were stacked in the top of a SDS-PAGE gel (4-12% NuPAGE, Life Technologies) and stained with Coomassie blue R-250 before in-gel digestion using modified trypsin (Promega, sequencing grade) as previously described  (48). Resulting peptides were analyzed by online nanoLC-MS/MS (UltiMate 3000 and LTQ-Orbitrap Velos Pro, Thermo Scientific). For this, peptides were sampled on a 300 m × 5 mm PepMap C18 precolumn and separated on a 75 m × 250 mm C18 column (PepMap, Thermo Scientific). MS and MS/MS data were acquired using Xcalibur (Thermo Scientific). Peptides and proteins were identified using Mascot (version 2. 5. 1) through concomitant searches against Uniprot (C. elegans taxonomy), classical contaminants database (homemade) and the corresponding reversed databases.
The Proline software (http://proline. profiproteomics. fr) was used to filter the results (conservation of rank 1 peptides, peptide identification FDR < 1% as calculated on peptide-spectrum match scores by employing the reverse database strategy, minimum peptide score of 25, and minimum of 1 specific peptide per identified protein group) before performing a compilation, grouping and comparison of the protein groups from the different samples. In WDR-5. 1 mass spectrometry, 242 proteins were found with a spectral count (SC) WDR-5. 1 ≥ 3 and a SC control (without WDR-5::HA transgene) = 0 or SC WDR-5. 1/SC control ≥5 for at least one replicate; in CFP-1 mass spectrometry 178 proteins were found with a SC CFP-1 ≥3 and a SC control (without CFP-1::GFP transgene) = 0.
Plasmids construction for Y2H Plasmids used for expression of BD and AD fusions were derived from pEG202 (Clontech; Genbank Accession Number U89960) and pJG4-5 plasmids (Clontech; Genbank Accession Number U89961, respectively  (49). Constructions were generated by cloning the cDNA of the gene of interest in the XhoI restriction site of the pEG202 and pJG4-5 plasmids using the Gibson method  (50). CFP-1 truncations were obtained by the same reaction using the cfp-1 cDNA sequence as template. PCR reactions were carried out using pHusion polymerase and primers listed in Supplementary Table  S1. All products were verified by sequencing. pSH18-34, bearing a ␤-galactosidase gene under the control of four overlapping LexA operators was used as reporter vector  (51).
Comparison of gene expression changes in cfp-1, set-2 and sin-3 embryos RNAs were extracted from wild-type, cfp-1, set-2 and sin-3 frozen early stage embryos prepared by hypochlorite treatment of young adults (>95% <200 cell stage). Two to three independent biological replicates were performed for each strain. RNAs were extracted with NucleoZol [Macherey-Nagel] according to manufacturer's instructions and treated with DNAse [Turbo-free DNAse, Ambion]. Integrity of RNA was assessed on Tape Station 4200 [Agilent]. RNA-seq librairies were generated at the GenomEast Platform [IGBMC, Strasbourg, France] using the directional mRNA-Seq SamplePrep [Illumina] and sequenced using the Illumina Hiseq 4000 technology. All RNA-seq data were mapped to the C. elegans reference genome (WS254) by RNA-STAR (Version 2.
4. 1d). Reads below a mapping score of 10 were filtered using SAMtools (Version 0. 1. 19). Of the 46 771 annotated genes, 20 183 were selected as protein coding genes and among them, 11 630 had sufficient read representation (baseMean > 10) for further analysis. The gene expression level in each sample was calculated by htseq-count (Version 0. 7. 2) and differential expression between the different strains was calculated with DESeq2  (54). Gene expression data are available at GEO with the accession GSE110072.
SIN-3, HDA-1 and CFP-1::GFP ChIP-seq data processing CFP-1::GFP (GEO GSE49870), SIN-3 and HDA-1 ChIP-seq reads were aligned to the ce11 assembly of the C. elegans genome using BWA v. 0. 7. 7  (57)  with default settings (BWA-backtrack algorithm). The SAMtools v. 0. 1. 19 'view' utility was used to convert the alignments to BAM format. Normalized ChIP-seq coverage tracks was generated using the BEADS algorithm  (58). ChIP-seq peaks were called for SIN-3, HDA-1 and CFP-1::GFP in wild-type embryos using MACS2 v. 2. 1. 1 (  59 ) with a q-value cut-off of 0. 05 and fragment size of 150bp against summed ChIP-seq input (GEO GSE87524). Peaks overlapping non-mappable (GEM-mappability < 25%;  (60)  or blacklisted regions (https://gist. githubusercontent. com/Przemol/ef62ac7ed41d3a84ad6c478132417770/ raw/56e98b99e6188c8fb3dfb806ff6f382fe91c27fb/ CombinedBlacklists.
bed) were discarded. Peak summits were extended 150 bp upstream and downstream, creating 300 bp peak regions. Intersecting regions from the two replicates were kept and extended to 300 bp to obtain confident peak calls. SIN-3 peak calls are the intersection of peaks obtained using the Q5986 and Q6013 antibodies. To determine factor overlaps, the 300 bp peak call regions from CFP-1, SIN-3 and HDA-1 were intersected, also keeping regions with only one factor. Regions from this 'superset' were rescaled to 300bp and each region annotated for overlap with a CFP-1, SIN-3, HDA-1 or MRG-1 peak (MRG-1 peaks were obtained from  (61) ; Supplementary Table  S2  gives these regions and their annotations. We used SeqPlots (62) for k-means clustering of CFP-1::GFP and H3K4me3 signals in wild-type, cfp-1 mutants, and set-2 mutants to separate CFP-1::GFP peaks into strong and weak COMPASS sites, and to visualise CFP-1::GFP, HDA-1 SIN-3, MRG-1 (GEO GSE50333) and H3K4me3 ChIP-seq tracks as heatmaps.
ChIP-seq signal quantifications To compare SIN-3 and HDA-1 binding between wildtype and cfp-1 mutant embryos, we quantified average BEADS normalized, z-scored signal on different peak sets. The signal was obtained using the bigWigSummary utility from Kent library (65) implemented in rtracklayer package in R. These signals were represented as overlaid violin plots (showing signal distribution) and Tukey box plots (showing estimation of statistical significance of difference between medians as notches)  (66). The comparison of H3K4me3 levels in wt, cfp-1, and set-2 mutants was done in the same way, using spike-in normalized signal tracks for quantification.
ACKNOWLEDGEMENTS We are grateful to  G. Benoit  for help in bioinformatics analysis,  JL. Bessereau  and  J. Govin  for scientific discussion, and  R. Margueron  for critical reading of the manuscript. We acknowledge the discovery platform and informatics group at EDyP, the  PSMN (P ôle Scientifique de Modélisation Numérique)  of the  ENS de Lyon , and the  SFR Biosciences (PLATIM and PSF). Thanks to  B. Meyer  for ASH2 antibodies and the  Caenorhabditis Genetic Center , which is supported by the  National Center for Research , for strains.
FUNDING Agence Nationale de la Recherche (ANR)  [ 15-CE12-0018-01 ]; CNRS; Fondation ARC 155265; J. A. acknowledges support by a  Wellcome Trust   Senior Research Fellowship  [101863]; core support from the  Wellcome Trust  [ 092096 ];  Cancer Research UK  [ C6946/A14492 ]; Proteomic experiments were partly supported by the  Proteomics French Infrastructure  [ ANR-10-INBS-08-01 ];  Labex GRAL  [ ANR-10-LABX-49-01 ]. Funding for open access charge: [ ANR-15-CE12-0018-01 ].