dataesr/scientific-paragraphs-categorization huggingface dataset:
8 and Novel 9 ( 9 Fig A in S1 File). Genbank searches against the Gallus gallus genome revealed that most of the novel exons (Novel 7-9) are located upstream of ASIP on chromosome 20, as expected, whereas quail novel 6 showed no hits against the chicken genome. Novel exons 7 and 8 are 40kb upstream of the ASIP promoter site 1 (Fig 2, Fig A in S1 File). Novel exon 9, is situated 30kb upstream of the first ASIP coding exon (e6), between E1L and E2 [4].
, 2012; Meister and Franz, 2014). When conducting the same exercise with OC-CCI data (Fig. 2 ), several observations can be made. Differences between SeaWiFS and OC-CCI Chla are the lowest (in modulus) with small oscillations. This agreement is favored by two factors (see Section 2. 2): first, by construction the OC-CCI series should mostly reproduce the SeaWiFS record over the years when only SeaWiFS data are available (up to 2002); then, for subsequent years, the bias correction scheme should bring the products from the other missions largely in line with the SeaWiFS record. In that respect, the bias correction appears to fulfill its task since the temporal stability of the x series is remarkable even when MERIS and MODIS-A data are added in the data stream (see also  Couto et al. , 2016).
This being said, a residual effect can still be noticed, the average x changing from -3. 4 % over the period 1998-2001 (when only SeaWiFS was available) to -2. 6 % over the period  2003 -2007. Between 2002 and 2009, ,  x between OC-CCI and MODIS-A Chla (Fig. 2 ) is generally below 5% (in modulus) and negative (with exceptions in 2002 and from 2007), but larger oscillations (with x increasingly negative) are noticed in the later part of the record, which again might be partly explained by calibration issues. On average OC-CCI Chla appears higher than MERIS values, which is coherent with the comparison between SeaWiFS and MERIS data (Fig. 1 ); the signature of the OCL deactivation period is also clearly seen. The difference between OC-CCI and VIIRS Chla shows seasonal variations between +4% and -6 %.
Of course these results would show significant regional variations (e. g. Djavidnia et al. , 2010; Mélin, 2010) , the analysis of which is beyond the scope of this study and would deserve additional dedicated studies. The existence of regional variations implies that the methods developed to assess the trends associated with the multi-mission data set need to accommodate a spatial dimension. However, from the global average of x, some general characteristics of the time series can be observed that are relevant for the rest of the study. First, if considering SeaWiFS, MERIS and MODIS-A data, the x series display a relative temporal stability, notwithstanding some oscillations (and the OCL issue for MERIS). This further supports the use of single-mission products as series of reference. But this might not be fully applicable to recent years considering the issue of aging associated with MODIS-A.
The trends obtained by MODIS-A and MODIS-T were compared over the same periods. The contingency matrices indicate that the part of the ocean with differing diagnostics remains constant and fairly low, between 16. 1% and 17. 4% (j between 0. 62 and 0. 68). From the statistical comparison of trends, only 0. 7-0. 9 % of the domain have P lower than 0. 05 (from 29. 8% to 32. 7% for P < 0. 5). These results are consistent with the fact that the calibration of MODIS-T is MODIS-T and OC-CCI data over the period January 2012 to December 2015 (four years). The part of the ocean with differing trend diagnostics is approximately 18% when comparing MODIS (-A or -T) with VIIRS series (with j of approximately 0. 65), while P is found lower than 0. 05 for 1. 7-2. 0 % of the ocean. When comparing OC-CCI and VIIRS data, these statistics are 14.
9% (associated with j as high as 0. 72) and 1. 3%, respectively, which means that the trends found from both series are actually very consistent. The median difference between trend slopes is only -0. 3% year -1 , which is very low considering that trend slopes tend to be higher over a short period. It is worth recalling that over the considered time interval, the OC-CCI data are mostly the result of merging VIIRS and MODIS-A data (the MERIS mission ending in April 2012). The agreement is here illustrated with the trend maps associated with both products (Fig. 11 ). It is underlined that the scale is extended with respect to other maps to accommodate higher values of trend slopes associated with only four years of data. There is a strong signature of a decreasing Chla in the central and eastern tropical Pacific that is reminiscent of El Niño events (e.
Particularly, trends of linear regression can easily be compared through statistical tests at the same resolution as ocean color products. It is stressed here that presenting, interpreting and understanding the observed trends were not among the objectives of the present study. presenting the time series through their globally averaged differences (Figs. 1 and 2, Section 3), the general approach was based on comparisons between the trends obtained with OC-CCI data and those associated with single-mission series. Considering the activities carried out by space agencies on the calibration of instruments and their stability in time  (Xiong et al. , 2010; Eplee et al. , 2012; Cao et al. , 2013; Eplee et al. , 2015; IOCCG, 2013) , single-mission series were assumed free of spurious temporal artifacts so that multi-mission series should reproduce the trends observed by single-mission products over common periods.
In other words, the trends observed from single-mission series were assumed to be sufficiently faithful to reality to serve as benchmarks for multi-mission data (this point is further discussed below). In that context, the use of an independent series (i. e. , not included in the multi-mission data set) would be ideal. This was here attempted using the MODIS-T data (not included in the OC-CCI data stream) but as mentioned above (Section 3) these do not constitute a fully independent data record. More generally, the number of ocean color missions is fairly limited for any given period so that excluding one mission from merging is a serious limitation. Moreover, as far as trends are concerned, the multi-mission series should in any case agree with all available single-mission series over the full time interval it covers.
In that context, the ocean color community has the chance to have almost a decade of coincident ocean color data from two well-performing missions (MODIS-A and MERIS). Processed with the same strategy and algorithms, they may represent the best possible agreement to be expected from two products. Additional overlapping data sets are available from SeaWiFS, MODIS-T, and VIIRS. Different criteria of comparison were presented that covered several characteristics of trend distributions: • contingency matrices, easily summarized by metrics such Cohen's j, were useful to reveal how much of the global ocean shared the same diagnostic about the existence, significance and signs of trends; • the distributions of differences in trend slopes gave a quantitative measure of the discrepancies between trends; • maps of the level of significance P of a t-test quantifying the degree to which two trend estimates differed provided a statistical, spatially-resolved, evaluation.
Interpretation of the results The application of these criteria to the OC-CCI data showed that the agreement with single-mission products was similar to the consistency shown by these products. The agreement of OC-CCI with the SeaWiFS series (1998-2007) was remarkable with only 1. 2% of the domain of analysis characterized by P lower than 0. 05 and a j index of 0. 66, while the differences in trend slopes were nicely centered on zero (Fig. 5 ). When compared with MERIS or MODIS-A data over August 2002-July 2011 or 2012, P was lower than 0. 05 for approximately 2% of the ocean, and j was ∼0. 64. However, an area of lower P was observed in the south central equatorial Pacific, associated with different magnitudes of the trends but not with differences in patterns (Figs. 8  and 9 ). As baseline comparison, the agreement between MODIS-A and MERIS over the period 2002-2011 could be summarized by a proportion of the ocean with P < 0.
05 of 1. 9% and a j of 0. 60. These results suggest that the OC-CCI record can be considered fit to conduct temporal analyses over the period 1997 to 2012 (with some caution in the south central Pacific). Considering that the OC-CCI data set relies on the SeaWiFS, MERIS and MODIS-A data for its construction, this conclusion stating a good agreement might have been expected, but this reasoning is in large part misleading. In particular, trend detection based on multi-mission (merged or concatenated) data appears extremely sensitive to inter-mission biases  (Mélin, 2016) , so that the agreement observed between OC-CCI data and single-mission products by no means should be taken for granted. In fact the results have evolved with the OC-CCI data set versions: for instance the comparison between OC-CCI version 2 and MERIS or MODIS-A data did not show the pattern of low P values in the south central Pacific, whereas the agreement with SeaWiFS series was significantly degraded from version 1 to version 2 (not shown) and improved again with version 3.
, 2008)  limits its use for independent assessments. In fact, the trend distributions associated with MODIS-A and MODIS-T appeared very consistent, for instance with a percentage of the ocean with P < 0. 05 lower than 1% regardless of the considered period. In the case of OC-CCI, extending the series after 2012 meant a degraded agreement with respect to MODIS-A, the part of the ocean characterized by P < 0. 05 increasing from 1. 6% to 5. 1% as four more years were included (2012 to 2015). It is recalled that the OC-CCI data relied exclusively on MODIS-A and VIIRS over the period from April 2012 (demise of MERIS) to 2015. Part of this degradation in trend consistency might be due to the aging of the MODIS-A sensor  (Meister et al. , 2012; Meister and Franz, 2014) , even though it can not be excluded that residual inter-mission differences had an impact on the construction of the OC-CCI record.
It is also worth noting that this degradation happened largely in relation with the pattern of low P values observed in Fig. 9  in the central Pacific south of the Equator: as more years were added, P further decreased and the pattern spread. The rest of the ocean appeared mostly unaffected. In conclusion it seems that the OC-CCI data are suitable for temporal analysis, with more caution due (particularly in the equatorial Pacific) as the time interval of interest exceeds 2012. As a positive note, and considering a history of regular reprocessing and progress, future improvements in the characterization of the MODIS-A calibration including the final part of the data record are possible. Unfortunately, the period covered by VIIRS is still rather short and with a small overlap with MERIS. The overlap with MODIS-A is more substantial but it is a period when that instrument showed signs of radiometric degradation.
Nevertheless the comparison of trends over four years of data gave an excellent agreement between products, particularly when OC-CCI and VIIRS data were considered (the proportion of the ocean with P < 0. 05 being 1. 3% and j equal to 0. 72). The only noticeable pattern with low P was again seen in the south central Pacific. So, with some caution, the OC-CCI data record appears appropriate to study inter-annual variability over the last few years. These considerations highlight the challenges inherent to building a multi-mission data record suitable for climate studies, challenges that are compounded when using aging satellites or successive missions with little overlap. In particular properly bridging the period from 2012 (arbitrarily taken as a date when MODIS data start losing quality) to 2016 (when the overlap between VIIRS and OLCI is initiated) seems critical.
Overall, the study shows encouraging results about our capacity to create Chla climate data records. But creating climate-quality single-mission series and ensuring a cross-mission consistency allowing climate research is still a major task  (Mélin and Franz, 2014)  that requires sustained efforts if we want to make best use of the first twenty years of continuous ocean color data. Environment Research Council of UK. Acknowledgments are due to ESA and NASA for the distribution of satellite data.
(ii) Procedure The procedure was similar to the MEG experiment described in  [19]. Subjects were engaged in an incidental visual task rstb. royalsocietypublishing. org Phil. Trans. R. Soc. B 372: 20160105 and were naive about the nature of the auditory stimuli. Auditory stimuli were presented binaurally with the Psychophysics Toolbox extension in MATLAB  [55]. In total, subjects heard 700 unique stimuli (100 for each condition). The inter-stimulus interval (ISI) was jittered between 1100 and 1500 ms. The visual task was displayed on a separate computer using Cogent 2000 in MATLAB (www. vislab. ucl. ac. uk/ cogent. php). The timing was not correlated with that of the auditory stimuli. For each trial, three colour photographs of landscapes were shown for 5 s each, and images faded gradually from one image to the next to minimize visual transients.
Subjects were instructed to press a keyboard button if the first and third image within a trial were identical (10% of trials), and to withhold a response otherwise. Inter-trial interval was jittered between 2 and 5 s. The session was split into four consecutive blocks. Feedback (number of hits, misses and false alarms) for the visual task was provided at the end of each block. (iii) Recording and data preprocessing EEG signals were recorded using a Biosemi system (Biosemi Active Two AD-box ADC-17, Biosemi, Netherlands) with 64 electrodes; at a sampling rate of 2048 Hz. Recording was restarted at each block. Data were analysed with SPM12 (Statistical Parametric Mapping; http://www. fil. ion. ucl. ac. uk/ spm/) and Fieldtrip (http://www. fieldtriptoolbox. org/; [56] ) toolboxes for MATLAB (2015a, MathWorks).
(iv) Data analysis For each participant and condition, the root-mean-square (RMS) over channels was calculated at each time sample in the epoch. This was used as a measure of brain activation over time. The distribution of RMS (mean, s. e. ) was then estimated for each condition using bootstrap resampling across subjects (1000 iterations;  [59] ). This was used to calculate the group-level t-statistic of the difference between pairs of conditions at each time-point. T-tests (two-tail) were performed using t-statistics computed on clusters in time, and controlled for a family-wise error rate of 0. 05  [60]. Additionally, a repeated-measures ANOVA with factors of regularity and alphabet size was performed on the mean RMS power between 1000 and 3000 ms, including all conditions except RAND20 to give a balanced design.
Spurious precipitation in the Pluvio 2 measurements in windy conditions, leading to phantom accumulations, is observed and partly removed through synergistic use of MRR data. The yearly accumulated precipitation of snow (300 m above ground), obtained by means of a local conversion relation of MRR data, trained on the Pluvio 2 measurement of the summer period, is estimated to be 815 mm of water equivalent, with a confidence interval ranging between 739. 5 and 989 mm. Data obtained in previous research from satellite-borne radars, and the ERA-Interim reanalysis of the European Centre for Medium-Range Weather Forecasts (ECMWF) provide lower yearly totals: 655 mm for ERA-Interim and 679 mm for the climatological data over DDU. ERA-Interim overestimates the occurrence of low-intensity precipitation events especially in summer, but it compensates for them by underestimating the snowfall amounts carried by the most intense events.
The data were collected in the framework of the APRES3 project (Antarctic Precipitation, Remote Sensing from Surface and Space, see http://apres3. osug. fr). We provide statistics of precipitation quantity and occurrence, and we compare them with model reanalyses and with the visual observations collected by the French meteorological office (Météo France) throughout the year. The main scientific objectives of this work are to contribute to a better quantification of precipitation in Antarctica (also by evaluating the products of numerical weather models) and to underline the innovative and promising aspects of the data collected until now, which may serve as an example for long-term monitoring of precipitation in other Antarctic regions. The paper is structured as follows: Sect. 2 describes the precipitation measurements, Sect.
Methods Here we present data collected at a coastal location of Antarctica: the station Dumont d'Urville (DDU). The base is situated in Adélie Land, -66. 6628 • S, 140. 0014 • E, (41 m a. s. l. ), on a coastal location highlighted in Fig. 1a. This region is located at the transition between the Antarctic continent and the Southern Ocean, where the terrain, which slopes downward from the inner continent to the coast, meets the ocean.
The raw K-band reflectivity measurements collected by the MRR were first processed with the method proposed by  Maahn and Kollias (2012) , then converted to X-band reflectivities and in a third step to snowfall intensities. Additional information about the processing of the MRR data is provided in Sect. 2. 2. 1. A second radar, named MXPol (Mobile X-band dual-Polarization) collected measurements in the months of December 2015 and January 2016. This system, described in  Schneebeli et al. (2013)  and in  Scipion et al. (2013) , is a scanning dual-polarization Doppler radar. During its operation period at DDU, it was mainly collecting data at 75 m radial resolution and a maximum radial distance of 30 km, mostly conducting different types of scans within a repeating scanning sequence of 5 min: (i) plan position indicator (PPI) scans, i.
e. , quasi-horizontal slices of the atmosphere, (ii) range height indicator (RHI) scans, i. e. , vertical slices of the atmosphere, and (iii) static vertical profiles, such as the ones performed by the MRR. A depolarization lidar (e. g. , Del Guasta et al. , 1993), deployed at a distance of about 200 m from MXPol, collected data in December 2015 and January 2016, as a test-bed for future long-term installation of a similar device. Lidar measurements allow for the discrimination of the phase of the tropospheric clouds and detection of the occurrence of supercooled liquid water, and they complement the observations of ground-based radars, which are often not sensitive to these particles. An example is given in Fig. 3 , where the time   series of MRR reflectivity, lidar signal and depolarization ratio are shown for the 15 December 2015.
Supercooled liquid water appears in the lidar data as a layer of enhanced signal and low depolarization ratio (e. g. , Del  Guasta et al. , 1993; Hogan et al. , 2003) , often when no MRR signal is visible. On the contrary, when precipitation occurs, (around 04:00 UTC, and from 14:00 to 24:00 UTC) the lidar signal gets fully attenuated in the lowest 500 m while the MRR is still able to sample the vertical precipitation column. A weighing precipitation gauge (Pluvio 2 , manufactured by OTT) was deployed from November 2015 to January 2016. This instrument provides the liquid water equivalent of snowfall falling within its measurement area at a time resolution of 1 min. To avoid excessive contamination of precipitation signals by blowing snow, the Pluvio 2 was installed at a height of about 3 m above ground and its inlet was protected by a standard wind fence designed by the same manufacturer as the instrument.
The riming degree is textural information obtained by supervised classification originating from a manually labeled training set including almost 3400 images, as detailed in  Praz et al. (2017). The choice of the available classes is based on the widely used scheme of  Magono and Lee (1966). Because the instruments are deployed at a height lower than 3 m a. g. l. , both precipitation and blowing snow particles are recorded and classified. A second classification method is obtained from the polarimetric data of MXPol, which can be converted into hydrometeor measurements with an hydrometeor classification algorithm  (Grazioli et al. , 2015). This algorithm was developed by partitioning a large number of radar observations into spatially coherent clusters by means of data mining techniques and then assigning to each cluster a dominant hydrometeor type by means of scattering simulations, interpretation of po-Figure  6.
One year of MRR precipitation data The MRR instrument collected precipitation data uninterruptedly, covering the evolution of precipitation over the entire year. It therefore offers an interesting ground-based (but remotely sensed) set of data to compare with modelbased data and with available human observations. Figure  9  shows the estimates coming from the MRR and other available sources of information over a year of measurements. As expected, the agreement of the local MRR relation with the Pluvio 2 is good over the summer period (December-January), during which the relation was obtained. Also in this period the estimate of ERA-Interim provides a total cumulated precipitation within the envelope of values of the optimized Z-S relation, even though the curves show some differences in precipitation occurrence.
(1993) , and it could be observed also in the test data collected with the depolarization lidar (see Table  1 , and Fig. 3 ) during the summer period. At the ground level the large majority of hydrometeors recorded by the MASC were small particles of nondiscernible habit and nondefinable riming degree (given the MASC resolution), with an occurrence three times higher than similar measurements conducted in Alpine locations. This is probably the signature of the significant contribution of blowing snow to the near-ground snow flux, which is particularly effective in recirculating small and light particles (e. g. ,  Mann et al. , 2000; Gordon et al. , 2009) , but it could partly be the result of the fragmentation of aggregates in the lower levels of the atmosphere, where strong katabatic winds blow.
, 2010; Palm et al. , 2011) , which rarely exceeds 200 m of vertical development. This combination of ground-based and in situ instruments should be proposed again, and maintained for at least a full year to cover all the seasons. The main limitation of the filter of Pluvio 2 data is that they cannot detect cases when blowing snow occurs together with precipitation. In the case of our measurements, the total accumulated precipitation of the Pluvio 2 in the summer period drops from 66 to 52 mm, after the censoring is performed. The removed portion is then 21 % of the total raw accumulated precipitation. Regarding blowing snow, the comparison between the occurrence of signal at the lowest MRR gate and the visual weather reports conducted by Météo France, summarized in Table  4 , confirms that the current MRR configuration does not capture the occurrence of blowing snow events.
Both from radar-based hydrometeor classification and from MASC measurements, it appeared that riming is a significantly active process. About 10 % of the radar measurements at low-level were classified as containing rimed hydrometeors, 11 % of the hydrometeors were classified as fully developed graupel (23 % if small particles are not considered), and most of the other hydrometeors classified with the MASC showed riming degrees even larger than 0. 5. The presence of supercooled liquid water, a necessary ingredient for riming, has been reported at DDU by previous studies and was evident in the lidar measurements collected in 2015. One year of MRR data allowed for the estimation of the total yearly precipitation, from October 2015 to October 2016, giving values ranging between 740 and 989 mm, at least 10 % larger than that provided by ERA-Interim reanalysis (655 mm).