dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2008; Mougin et al. , 2014)  and forest  (Sea et al. , 2011)  canopies. The main outputs of the CAN-EYE software are the leaf area index, LAI, the aggregation factor or clumping index λo, the 15 vegetation cover, fCover, and the daily fraction of integrated absorbed photosynthetically active radiation, fAPAR (Table  1 ). Finally, the estimated mean vegetation variables at the 1 km scale, are computed by averaging all the 100 or 50 measurements acquired along the sampling transect for the herbaceous and forest canopy, respectively. The performance of the combined use of hemispherical photographs, and the CAN-EYE software for estimating LAI, λo, fAPAR and fCover at the 1 km transect 20 scale has been analysed by  Mougin et al. (2014)  for a herbaceous canopy. These variables are estimated at a spatial scale which is compatible with remote sensing observations performed by moderate resolution sensors.
Data base and data availability 5 The dataset presented in this paper, is part of the AMMA-CATCH observatory database (www. amma-catch. org), and is identified by the permanent identifier doi:10. 17178/AMMA-CATCH. CE. Veg_Gh, which describes its content with standardized metadata (DataCite 1 ), and gives access instructions. Table  3  shows the data availability included in this dataset: it covers the 2005-2017 period and is given for the 13 monitored transects. The dates of hemispherical photograph acquisitions are given in Table  S4. Over the 13-year period, a total of 658 sampling transects have been monitored, and approximately 52 10 000 hemispherical images have been acquired, processed and analysed. Associated data such as herbaceous aboveground masses, CO2, sensible and latent flux measurements, meteorological observations and soil moisture measurements collected at the Agoufou, Kelma and Eguerit sites can also be found in the AMMA-CATCH database.
The access to the data is available online, free of charge on the web AMMA-CATCH database portal: http://bd. amma-catch. org and without any barriers except a registration to get a login free-of-charge. Sign up is only required to download the data but 15 not to browse the content of the datasets (metadata include variables names, spatial extent and coordinates, temporal extent, contacts,. ), or to retrieve their content in a ISO 19115 2 standard file, or to localise on the map the acquisition sites. To access to data download, once connected to the portal, search for the dataset by identifying it by its doi suffix (CE. Veg_Gh) or title among all the datasets contained in the database, and unfold the tab describing it. The database architecture also allows to extract a subset of the data at the spatio-temporal or variable level.
Data Data were collected since 1993 along different container vessels operated or leased by EIMSKIP, mostly between Reykjavik (Iceland) and Argentia (southern Newfoundland). The ships most commonly crossed the NASPG in nearly 5 days -first close to the Reykjanes Ridge or to its west, reaching the Newfoundland slope southwest of 49 • N, 49 • W (AX02 transect on Fig. 1 ). However, weather (late autumn to early spring) and ice conditions (January-April) often influence the ship's route, so that it can also cross the central Irminger Sea or intersect meanders of the North Atlantic Current (NAC), further to the southeast. The sampling used here is largely based on surface samples collected every 3 months by a volunteer instructed at LOCEAN (or at LDEO before 1995). In addition, the ships have been equipped a large part of the time to measure continuously  T and S.
First, between April 1994 and May 2007 , the thermosalinograph (TSG) was a SBE21 thermosalinograph, and then between February 2011 and March 2016 it was a SBE45 micro TSG with an external temperature sensor SBE38. There was also a f CO 2 equilibration system by NOAA/AOML (Rick  Wanninkhof, Denis Pierrot) in 2003 -2007 and 2013 -2016 (data are in the SOCAT database, Bakker et al. , 2016 , and at http://www. aoml. noaa. gov/ocd/ocdweb/occ. html, last access: 30 January 2018). Both the TSG and f CO 2 equilibration system were installed on a water circuit pumping water at depths of 4-6 m. Expendable bathythermograph (XBT) probes have also regularly been deployed along the AX02 transect (Fig. 1 ) (profiles from approximately 1200 temperature probes collected between November 2008 and April 2016 at approximately 25 km resolution during these transects are available on the NOAA/AOML site http://www.
Inorganic nutrients (nitrate + nitrite, phosphate, silicic acid; later reported as NO 3 , PO 4 , Si) are analyzed since December 2001 in Reykjavik (MFRI). Water stable isotopes of seawater (δ 18 O and δD) have been analyzed since late 2011 mostly at LO-CEAN in Paris (some of these data are presented in  Benetti et al. , 2016, but  with emphasis on the subset on the Newfoundland shelf and slope). Finally, δ 13 C DIC has been analyzed in 2005-2006 at the University of Washington and since 2010 at LOCEAN  (Racapé et al. , 2014). The δ 13 C DIC data are expressed in ‰ relative to the reference V-PDB (Vienna PeeDee Belemnite)  (Craig, 1957). Conversion was done for nutrients from measurements in µmol L -1 into µmol kg -1 assuming measurements conducted at 25 • C. The water stable isotopes (δ 18 O and δD) are reported as concentration in ‰ in VSMOW (Vienna Standard Mean Ocean Water) scale  (Benetti et al.
, 2017). Salinity is expressed as a practical salinity without unit. Details on data collection, validation, and accuracy for all reported parameters are provided in Appendix A. An earlier version of part of the DIC / A t and nutrient data  had been made available through the PANGEA database  (Reverdin et al. , 2007 (Reverdin et al. , , 2015)) , whereas most of the water isotope data have been contributed to the GISS database  (Schmidt et al. , 1999). The seawater CO 2 chemistry can be fully described with the measured DIC and A t , using the dissociation constants of  Lueker et al. (2000)  as implemented in CO2SYS  (Lewis and Wallace, 1998; Pierrot et al. , 2006). f CO 2 or pH can thus be estimated from these equations, which also requires T and S data (also provided; see Appendix A), as well as nutrients.
When nutrient data are missing, we used in these equations the nutrient climatology derived from all the data at the cal-endar date of the sampling. The error in doing this has little impact on the computation for this region. When no A t was measured with DIC such as before 2001 and for some samples in 2005 and 2006, it is also possible to use parameterized A t based on salinity. The strong correlation between sea surface alkalinity and salinity in the open ocean can be described with an empirical linear relationship  (Millero et al. , 1998; Friis et al. , 2003). In our previous analysis we used a relation based on seasonal SURATLANT data but only for years  2001 -2002 (Corbière et al. , 2007;; Metzl et al. , 2010)  Our formula has a larger 0-crossing and explains a large part of the variance in A t , at least for S larger than 34 (Fig.
2 ). For the lower salinities found on the Newfoundland shelf, different sources of freshwater (from the Arctic or resulting from continental or sea ice melt inputs) contribute to deviations from the relation. The impact on f CO 2 or pH of using this relationship instead of others or measured A t is discussed in Appendix B. Trends in estimated seawater f CO 2 will be compared to other reported trends and to trends in atmospheric fugacity. Atmospheric CO 2 mean mole fraction data were provided by the Cooperative Global Atmospheric Data Integration Project  (Dlugokencky et al. , 2017). Here, the xCO 2 data collected at Mace Head, Ireland (53. 3 • N), were used, after some editing (mostly removing situations with a breeze from the land). The xCO 2 data were converted to fugacities at 100 % humidity following  Weiss and Price (1980).
Binning the time series T and S anomaly time series  (Reverdin et al. , 2018a)  are presented as Hovmöller diagrams in Appendix C. The sample data used here reproduce a good part of this long-term Earth Syst. Sci. Data, 10, 1901 Data, 10, -1924 Data, 10, , 2018  www. earth-syst-sci-data. net/10/1901/2018/ variability. However, sampling is insufficient to properly separate the interannual variability from longer-term changes. The discrete sampling also results in uncertainties in estimating a seasonal cycle, in particular for parameters such as δ 13 C DIC or δ 18 O which have been sampled for a longer period. We estimate an average seasonal cycle in 2001-2017. To reduce uncertainties in estimating an average seasonal cycle, we first remove from individual data an expected dependency in S estimated by linear regression for A t , DIC, δ 18 O, and δD (as done in  Friis et al.
Seasonal cycle In this section, we will present the average seasonal cycle portrayed in the 2001-2017 data (Fig. 3 ). For T (not presented), one finds a seasonal cycle range increasing from north (4 • C) to south (12. 3 • C in yellow box over the shelves and 6. 4 • C offshore in red box; corresponding boxes are on the top panel of Fig. 3 ). This is close to the ranges portrayed in SST (sea surface temperature) climatologies, such as HadSST3  (Kennedy et al. , 2011). The seasonal cycle in S (Fig. 3 ) is also found as described at the surface in the World Ocean Database 2013 (WOD13;  Boyer et al. , 2013)  with a maximum in February-May (February-April on shelf) followed by a gradual decline until a minimum in September (October, north of 62 • N). The amplitude also increases from north to south.
Conclusions The SURATLANT data set in 1993-2017 is mostly based on a discrete collection of surface samples (currently 2832 data points during 76 transects, but not always for all parameters). The collection methods and the parameters analyzed have not always been the same, due to funding as well as logistical and analytical issues. We have documented data issues and have thus edited the data set. The accuracy of the data is usually well documented (see Appendix A). We could not address whether there are remaining issues due for example to contamination of the water samples from pipes or water collection on-board a ship, or due to storage in bottles before analysis in a laboratory. We also derive a new A t -S relationship adapted to the data set that can be used to estimate f CO 2 and pH, as well as other parameters of the carbonate systems, for example when A t was not measured in 1993-1997.
For example, VOS Nuka Arctica data (Friederike Fröb and Are Olsen, personal communication, 2018) document a large spatial change in the recent decrease in SSS across the www. earth-syst-sci-data. net/10/1901/2018/ Earth Syst. Sci. Data, 10, 1901 Data, 10, -1924 Data, 10, , 2018  Reykjanes Ridge that implies (based on f CO 2 observations) different decadal trends in DIC between the two regions. However, part of the scatter we find in the discrete sample data set results from insufficient sampling of the seasonal variability. To provide a more complete analysis, it will be important to combine this set with other data, either from the same ships of opportunity (operated mostly by NOAA/AOML) or from other platforms. These include nearcontinuous underway surface temperature and salinity from TSGs, as well as pCO 2 measured with equilibrator systems.
Notice also that information on mixed layer depth and stratification was often provided by near-simultaneous XBT profiles. The investigation should also include the compilations of station data in GLODAP (Global Ocean Data Analysis Project;  Olsen et al. , 2016)  or the surface SOCAT  (Bakker et al. , 2016)  database. There is also a large array of complementary observations, such as from the Argo and the bio-Argo profiling platforms  (Organelli et al. , 2017). For example, these data suggest blooms in midwinter could be associated with net production and export of nutrient and carbon from the surface layer already in March  (Lacour et al. , 2017). Notice, however, that the bio-Argo floats have mostly sampled the rim of the subpolar gyre and provide only indirect evidence on near-surface carbon and nutrient that they did not measure.
A1 Salinity and temperature Most of the time salinity is obtained from water samples analyzed by a salinometer in a laboratory within 3 months of collection, and its accuracy is usually of 0. 01 or better. In a few instances, the salinity reported originates from the validated and adjusted TSG salinity data archived in the SSS repository at LEGOS (www. legos. obs-mip. fr/observations/sss, last access: 14 November 2017). The adjustment of the TSG salinity data results mostly from comparison with the water samples collected from a water faucet at the TSG as well as with upper-level data of Argo floats found nearby. The TSG salinity data usually have an accuracy better than 0. 03  (Alory et al. , 2015). Temperature data originate from different streams. In April 1994-April 1996, the reported T was measured at an intake in close contact to the outside seawater temperature (near 4-6 m).
This was also the case for reported data in July-August 2017 originating from the R/V Thalassa during the RREX2017 cruise  (Thierry, 2017). At other times, the reported T was measured by the TSG and adjusted to estimate a sea temperature, by correcting warming in pipes based on comparison with 5 m temperatures from XBTs deployed along the transects. We expect these T data from the TSG repository at LEGOS to have an accuracy of 0. 1 • C. In July 1993, January 1994, and in spring and autumn 2017 T was measured from a bucket rinsed and filled at the sea surface close to the aft end of the ship. These bucket measurements usually have an accuracy of 0. 1 • C, except in highwind conditions, when they are less accurate. During some cruises, SST was not directly measured and was provided from expendable bathythermographs launched close in time to the collection of the water samples, with an accuracy probably better than 0.
Furthermore, for the www. earth-syst-sci-data. net/10/1901/2018/ Earth Syst. Sci. Data, 10, 1901 Data, 10, -1924 Data, 10, , 2018   period June 1993-February 1997, the samples were stored in bottles with a ground glass stopper with the use of Apiezon grease, and DIC was determined by a coulometric method at LDEO  (Chipman et al. , 1993). For some samples in 2005-2006 associated with a δ 13 C DIC value, DIC was also estimated manometrically during the acid CO 2 extraction procedure with lesser accuracy (±5 µmol kg -1 ). We used these DIC values when there was no DIC estimated at LOCEAN. We first edited the data to remove suspicious values. In some instances, errors resulted from poor sample conservation in the bottles before analysis. For example, we often noticed erroneously large DIC and A t values from specific bottles in 2010-2015.
There are also a few transects when samples had to be discounted. In one case  (April 2007)  this happened because the samples were stored in a very hot space before shipment to France. For April 2015 (and to a lesser extent in June 2015), DIC values were too high for unknown reasons (it is possible the bottles had aged), and for January 2017, both DIC and A t were discounted as the origin of the water collected was suspicious (a problem also encountered for salinity samples and with the phosphate, with an anomalous contribution of particulate phosphate). In April 2015, we adjusted the DIC values by -10. 3 µmol kg -1 based on comparisons of estimated f CO 2 with those directly measured that are in the AOML and SOCAT database. When an adjustment is done, the data flag is changed from "good" to "probably good".
To provide further validation, we take advantage of the fact that, during some transects, sea surface f CO 2 underway measurements were also conducted using instrumentation as described by  Pierrot et al. (2009). These data are available at AOML (http: //www. aoml. noaa. gov/ocd/ocdweb/occ. html, last access: 30 January 2018) and regularly submitted and qualified in SO-CAT  (Bakker et al. , 2016). We have selected these data within 2 min time of discrete samples and compared them with f CO 2 calculated from DIC, A t pairs. For this comparison we only use samples when DIC and A t were measured (excluding A t derived from salinity when A t was not measured). A total of 172 points have been collocated for different years  (2004-2007 and 2014-2015) , for almost all seasons (months: January, February, April, June, July, and October- December).
Other isolated samples were dismissed either because not enough gas was collected after acidification or due to possible leaks on the mass spectrometer gas lines. These δ 13 C DIC values are expected to have a precision of ±0. 01 ‰  (Vangriesheim et al. , 2009)  and a reproducibility of ±0. 02 ‰. Issues on the accuracy of other δ 13 C DIC samples from LOCEAN have been raised, and LOCEAN participated in an interlaboratory comparison run by Claire Normandeau (Dalhousie University) with deep NASPG water samples conditioned by Dalhousie University. The results suggest that recent LOCEAN samples have a slightly poorer reproducibility (±0. 04 ‰ ) than earlier ones. These comparisons suggest an average bias of LOCEAN measurements of -0. 13 ‰. This is less than the 0. 25 ‰ (-0. 20 to -0. 30 ‰) bias corrected in the GLODAP database for LOCEAN samples collected during North Atlantic cruises in 2002 and 2006 and analyzed with the same method and standards as here.
A5 Water isotopologs Since December 2011, the oxygen isotopic composition of discrete seawater samples has been usually analyzed with a Picarro CRDS (cavity ring-down spectrometer; model L2130-I isotopic H 2 O) at LOCEAN-IPSL (Paris, France). The internal references, which have been used to calibrate the data in the VSMOW scale, have been previously calibrated using IAEA VSMOW reference water and vary from -6. 61 to 2. 24 ‰ for δ 18 O and from -44. 3 to 3. 31 ‰ for δD. All internal reference waters are stored in steel bottles with a slight overpressure of dry nitrogen to avoid evaporation processes and exchanges with ambient air humidity. Based on repeated analyses of an internal laboratory standard over several months, the accuracy of the measurements is usually better than ±0. 05 ‰ for δ 18 O and ±0.
, 2016)  that have been amplified in 2014-2016. We compared the winter T and S anomalies from the binned analysis (B-AX02) with the deviations from the average seasonal cycle from the discrete water samples of the SURATLANT data set. The comparison is very encouraging, as illustrated by the time series at 60 • N (Fig. C2 ). Clearly the SURATLANT-reduced sampling is able to capture the largest signals in salinity (and in temperature) and thus in surface water masses. This holds also to a good extent in other seasons and less so on the Newfoundland shelf, where variability tends to be more high frequency. Notice though that there are interannual differences. For example, SURATLANT would describe (at this latitude) early 1997 as anomalously fresh, whereas B-AX02 shows near-normal salinity conditions.
C1 ) and in blue from the discrete salinity samples (for those, the analyzed seasonal cycle is presented on Fig. 3 ). conflict of interest. Easy access on the merchant vessels run or leased by EIMSKIP has been the core of this long effort to maintain surface sampling between Iceland and Newfoundland. The nearly 100 volunteer ship riders and their enthusiasm have been key to the success of this monitoring. The project was initiated when one of the authors, GR, was at LDEO, with initial support from this institution. NOAA/AOML and NOAA/CPO Ocean Observing and Monitoring Division have contributed by maintaining the thermosalinographs and providing XBTs on the different ships that have operated between Iceland and Newfoundland. The   511176 ) is also acknowledged. Some figures were plotted using Ocean Data View (ODV)  (Schlitzer, 2013).
2993-3C>T, exon 27 -5' splice junction variant, and from a sibling of a different proband carrying a c. 3020+2T>A, exon 27 -3' splice junction variant. These variants segregated with disease within each of these families. Interestingly, when we investigated the location of the six ALS associated variants present in KIF5A, all occurred within a 34 bp stretch of DNA and were predicted to effect splicing of exon 27 that encodes amino acids 998-1007 (Table  3 , Figure  4A ). Five of the six variants were located on sequential base pairs on the 3' end of the exon, whereas one was located 5' end of the exon. We used the application ASSEDA (Automated Splice Site and Exon Definition Analyses) to predict any mutant mRNA splice isoforms resulting from these variants  (Sheth et al. , 2006). This algorithm predicted a complete skipping of exon 27 for all variants, yielding a transcript with a frameshift at coding amino acid 998, the deletion of the normal C-terminal 34 amino acids of the cargobinding domain, and the extension of an aberrant 39 amino acids to the C-terminus (Table  3 , Figure  4B, 4C ).
Additional Screening of LOF Variants in KIF5A We next performed mutational screening of KIF5A in an additional cohort of 9,046 ALS cases that had not been included in our original RVB analysis. This revealed two additional carriers of C-terminal variants, namely a carrier of an exon 26 frameshift mutation (p. Asn997fs) and a carrier of an exon 27 splice altering mutation (c. 2993-1G>A, Table  3 ). Additionally, one patient was observed to carry a predicted splice altering variant proximal to exon 3 (c. 291+5G>A). However, this variant was not supported as creating an aberrant transcript by ASSEDA. The frequency of LOF variants in this cohort (2/9,046 cases, 0. 022%), which primarily comprised of sporadic ALS cases, is lower than that observed in our original FALS cohort (0. 703%), suggesting that KIF5A LOF variants display a high penetrance.
Study cohorts GWAS cohort I. We undertook a GWAS of patients diagnosed with ALS (case cohort) and neurologically normal control individuals (control cohort). DNA was extracted from either whole blood or frozen brain tissue samples using standard procedures. All 12,663 patients included in the case cohort had been diagnosed with ALS according to the El Escorial criteria  (Brooks, 1994 ) by a neurologist specializing in ALS, had onset of symptoms after age 18 years, and were of non-Hispanic white race/ethnicity. Both patients with familial ALS and patients with sporadic ALS were included in the analysis. ALS WXS/WGS replication cohort. Replication analyses included sequencing data for a further 9,046 ALS cases and 1,955 non-ALS controls that were not also represented in the FALS discovery set.
Data generation and pre-processing Generation of SNP array callset. The case cohort (n = 12,663 samples) and part of the control cohort (n = 2,112) were genotyped in the Laboratory of Neurogenetics, National Institute on Aging, using HumanOmniExpress BeadChips (version 1. 0, Illumina Inc. , San Diego, CA) according to the manufacturer's protocol. These SNP genotyping arrays assay 716,503 SNPs across the genome. Individual-level genotypes for these samples are available on the dbGaP web portal (www. ncbi. nlm. nih. gov/gap, accession number phs000101. v4. p1). The remainder of the control cohort had been previously genotyped on HumanOmni BeadChips (Illumina) as part of other GWAS efforts (see Table  S2 ). Analyses were confined to the 595,692 autosomal SNPs that were common across the SNP genotyping arrays.
All identified LOF variants were validated by Sanger sequencing. Generation of ALS case-control callset for KIF5A replication analysis. Data for the KIF5A locus was extracted from all independently generated sequencing datasets and remapped to GRCh37. Variant calling was performed using the GATK haplotype caller as described above. In addition to the KIF5A locus, data was also extracted for a panel of 240,715 common variant sites and used to perform a single unified sample QC as described below. All identified LOF variants were validated by Sanger sequencing.