dataesr/scientific-paragraphs-categorization huggingface dataset:
This result shows the importance of the training data set, which was very poorly represented for bad quality classes in this study. Indeed, if during the cross-validation process some species with high abundance on poor sites are not represented in the training data set, a lower abundance of this species on other sites will force the optima of the species to better values than if the species was also represented in poor sites. Interestingly, when the two genetic markers are compared, a general slightly better correlation with morphological reference is observed with rbcL marker. It is possible that this is due to the fact that the rbcL marker is more taxonomically resolutive and the distinction of diatom and other species is more accurate as shown previously by  Kermarrec et al. (2013). As shown by our BLAST results (Figure  S1 ) it does not seem that the rbcL primers are more specific than the 18S V4 primers.
However, it is important to note that most of unassigned hits in rbcL BLAST data sets could possibly also belong to diatoms but are not identified as such. This could be due to lack of higher-level taxonomic signal in rbcL barcode as well as to the high level of unassigned environmental sequences in the GenBank database. The hypothesis that rbcL primers are very specific to diatoms is supported by the assignment with the curated Diat. barcode database for which the amount of sequences assigned to diatom (not necessarily to species level) range between 37% and 99% with a median value of 93%. An important advantage of using rbcL is its comprehensive reference database, which is much more developed than the one for the 18S marker  (Rimet et al. , 2019). Both the completeness of the rbcL database, and its high level of curation certainly explain the higher correlation of the taxonomy-based approach using this marker.
It shows that the taxonomy-free approaches perform as well or better than those based on taxonomic assignment of metabarcoding data when compared to morphological analyses used as reference. Our results also emphasize the importance of well-curated reference sequences database. Upon the sustained efforts to complete such reference databases, as in the case of rbcL, the taxonomy-based analytical approach can provide results similar to those of the taxonomy-free approaches. Finally, the performance of taxonomy-free approaches in recovering the reference values is highly depending on the balanced coverage of sites of contrasting ecological status. Taken together, our results highlight the need to sustain ongoing efforts to build comprehensive reference databases. Such databases would be either composed of curated reference sequences for taxonomy-based approaches or composed of data sets containing both metabarcoding data and independently established ecological quality status for taxonomy-free approaches.
Participants were also presented with neutral trials in which the arrow was replaced by a neutral cue conveying no information regarding the location of the upcoming dot motion. During the experiment, we recorded participants' EEG activity and eye position using an eye-tracker. Eye tracking data allowed us to check that participants maintained central fixation throughout a trial and to assess the presence of possible oculomotor artifacts that could have contaminated EEG signals (i. e. , microsaccades  Engbert and Kliegl, 2003). Using multivariate pattern classification analysis we assessed whether both alpha power and raw channel activity recorded during the cue-response interval could predict the orientation and temporal dynamics of covert attention. Our results corroborate the notion that alpha power and raw activity traces reflect different aspects of spatial attention, with the first associated with the orientation of covert attention in space (cf.
Apparatus Stimuli were presented on a CRT Sony GDM-C520 (100 Hz refresh rate) with a resolution of 1024 × 768 pixels. Stimulus presentation and data collection were performed using MATLAB with the Psychophysics Toolbox  (Pelli, 1997)  and Eyelink Toolbox  (Cornelissen et al. , 2002)  extensions. EEG data were recorded with 64 Ag/AgC1 electrodes mounted on an elastic cap and amplified by an ActiCHamp amplifier (Brain Product). The sampling rate of the EEG recording was set to 500 Hz. Electrodes were arranged according to the international 10-20 systems. Two of the electrodes (FT9 and FT10) were used to record horizontal eye movements (left and right HEOG, respectively), and two other electrodes (TP9 and TP10) were placed on the mastoids (M1 and M2, respectively). The right mastoid (M2) was used as online reference.
Our classification procedure implemented a Monte Carlo cross-validation method  (Dubitzky et al. , 2007). Notably, each classifier was trained on 90% of the available dataset and tested on each of the remaining trials. This procedure was repeated 200 times. Each time a random 90% of trials was used as training set and the rest as test set. Classification accuracy was estimated with a Receiver Operating Characteristic (ROC) curve analysis and was summarized by the Area Under the Curve (AUC). The ROC curve presents the hit rate (the proportion of trials A classified as A) as a function of the false alarm rate (the proportion of trials B classified as A). It provides a less biased measure of classification accuracy when compared to the proportion of correct classification. Indeed, biased measures of classification accuracy can emerge, for instance, when two classes have unequal numbers of observations.
Eye Movement Analysis Eye-movement data were recorded and analyzed to control for potential oculomotor artifact that might have contaminated EEG data. For offline analyses, eye movement samples were smoothed with SR Research's proprietary algorithms. As for the EEG data, we segmented eye-gaze data into 1500 ms long segments (i. e. , from -1500 to 0 ms) time locked to the onset of participants' response. Firstly, we removed from the eye-movement data the corresponding trials marked as containing artifacts in the EEG data. This was performed because we were interested in eye-movement data principally to control for any possible oculomotor artifacts in the EEG data. From the remaining segments we identified microsaccades using the algorithm described in Engbert and Kliegl  (Engbert and Kliegl, 2003).
Behavioral Data We measured average reaction times defined as the response latency from the beginning of the coherent motion, and proportion of correct responses (i. e. , correct identification of the coherent motion direction) for each participant and condition (attention and neutral). Their comparison allowed us to assess whether our cues successfully manipulated covert spatial attention. In particular, we expected faster reaction times (RTs) in the attention compared to the neutral trials. Furthermore, we also investigated whether reaction times, number of responses and proportion of correct responses varied between left and right target trials both in the attention and the neutral condition. These analyses were performed in order to control for any potential lateralized confound that could contaminate EEG signals.
Second, we assessed the correlation between eye position and brain activity recorded by fronto-lateral electrodes. We subtracted the brain activity recorded by the right frontal electrodes (rHEOG, Fp2, AF8, F8) from that recorded by the left frontal electrodes (lHEOG, Fp1, AF7, F7). We then calculated a time point-by-time point linear regression of average brain activity difference on the average horizontal component of eye position for each participant. Given that eye position was recorded at 1000 Hz and EEG at 500 Hz, we subsampled eye position data to the time points that were available in the EEG data. If participants performed eye-movements in the direction of the target and these movements drove the brain activity, we expected that the more leftward the horizontal component of eye-position, the more positive the left fronto-lateral EEG activity would be relative to right fronto-lateral.
Our statistics were performed with MATLAB in combination with the circular statistics toolbox  (Berens, 2009). We then calculated the mean, median and standard deviation of these distributions of directions and we performed a series of Wilcoxon signed rank tests to evaluate whether the mean/median/sd directions observe in the left and the right target trials differed in the attention and the neutral condition. No difference was observed between left and right target trials in the attention (p = 0. 54) and the neutral condition (p = 0. 45). Similarly, no difference was observed between the attention and neutral condition (p = 0. 30). Second, we pooled together observers' distribution of microsaccade directions for left and right target trials in the neutral and attention condition. We then compared these distributions using a non-parametric test for circular data (i.
However, our failure to replicate previous findings linking attention and microsaccades may simply be a result of our lack of statistical power. Microsaccades are infrequent (i. e. , 1 or 2 microsaccades are observed every 1/2 s). Studies investigating microsaccades typically collect a very large number of trials  (White and Rolfs, 2016). Given the length of our recording epochs of 1500 ms and the number of trials we analyzed (on average 110 trials per condition), we do not have the data density required to examine the link between microsaccades and attention. And, indeed, our study did not aim at investigating these differences. Eye-movements were recorded in order to control for any contamination of oculomotor artifacts on the EEG signals. In this respect, we believe that our results unambiguously show that the EEG decoding was not due to eye-movements.
FUNDING This research was funded by the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013) / ERC  grant agreement No. AG324070  awarded to PC and by the  Agence Nationale de la Recherche , research funding  ANR JCJC , project number  ANR-18-CE10-0001 , awarded to AD. HH was also supported by the  Australian Government  through the  Australian Research Council's Discovery Projects funding scheme  (project  DP180102268 ).
, 2017b). For the first time, in this work these methods are applied to multi-annual data sets of under-ice hyperspectral measurements conducted over hundreds to thousands of meters, providing a unique large-scale study of the spatial variability of Arctic and Antarctic sea-ice algae biomass estimates. In the present study, we present data collected with a Surface and Under Ice Trawl (SUIT) and used these data to characterize ice-associated environments in the Arctic Ocean and the Southern Ocean. We aim to provide a regional, and seasonal comparison of sea-ice properties on large scales in both Polar regions with a focus on: (1) under-ice water properties, (2) ice thickness, (3) light transmittance through sea ice and under-ice light, and (4) in-ice chl a estimates. Moreover, the spatial scale covered by the present data sets also allow for an (5) unprecedented investigation of the scales of variability for all the variables measured.
4 (see also Table  S1 ). Snow depth was recorded by visual observation of the ice under which the SUIT was traveling: A marked stick extending from the starboard side of the ship helps in quantifying the thickness of snow on top of the ice floes that are tilted during the ship passage. Part of these data have been used in previous works to study the relationship of environmental properties of seaice habitats with the community structure of sympagic fauna in the Arctic Ocean (PS80 and PS92,  David et al. , 2015 David et al. , , 2016;; Schaafsma, 2018; Flores et al. , 2019; Ehrlich et al. , 2020) , and in the Southern Ocean (PS81,  Schaafsma et al. , 2016 Schaafsma et al. , , 2017;; David et al. , 2017) , to develop and test algorithms for estimating inice chl a (PS80,  Lange et al. , 2016; Lange, 2017) , to retrieve Arctic primary production on large scales in the Arctic summer (PS80  Lange, 2017; Lange et al.
, 2017b) , and to estimate Arctic under-ice primary production based on under-ice irradiance measurements (PS92,  Massicotte et al. , 2019). All data collected during PS89 and PS106 are so far unpublished. Although data were collected during different years, this data set allows the investigation of differences between seasons in both the Arctic and the Antarctic, assuming that the sampling is representative of the season. The profile lengths used for calculation of mean quantities (e. g. , ice and snow thickness, water temperature and salinity, chl a in water and sea ice) correspond to the distance between the start and end trawl points, excluding parts of the profiles where the sensors data were unreliable due, for example, to sensors failure or damage. Thus, the profile length does not always correspond to the trawled distance, i.
(2) This is equivalent of assuming that the SUIT is towed perfectly parallel to the ice and, even if only an approximation, it has been proven reliable (R 2 = 0. 78) in determining draft when the entire set of information was not available  (Lange, 2017; Lange et al. , 2017b). Handling of data gaps due to sensors malfunctioning or failure is explained in the Supplementary Material. Ice concentration for each profile was computed as the percentage of data points where ice thickness H i was larger than zero. Ice thickness was computed by using a fixed density value (ρ = 0. 917 g cm -3 ) for the sea ice. By doing this, final ice thickness values include both sea ice and snow, and we will refer to it as total thickness in the following  (Lange et al. , 2019). Mean and median thicknesses were computed for H i > 0 and they include ridges.
Under-Ice Light Irradiance and radiance values were integrated over the Photosynthetically Active Radiation wavelength range (PAR; 400-700 nm). Since the SUIT does not always travel parallel to the ice but can encounter obstacles that make it swing, we included in the analysis only data with an inclination angle lower than 15 •  (Lange et al. , 2017b). In order to reduce the effect of underice water between the actual position of the sensors mounted in the SUIT frame and the underneath side of the ice, we excluded all measurements associated with an altimeter value larger than 1. 5 m following works by  Katlein et al. (2015 Katlein et al. ( , 2016 Katlein et al. ( , 2017)) ,  Lange et al. (2016 Lange et al. ( , 2017b)). Transmittance was calculated as the ratio between underice and incoming radiation, the latter measured with a Ramses sensor mounted on the ship's crows nest.
Due to a sensor failure during summer 2012 (PS80) we used incoming global radiation data, which are obtained with a pyanometer, also placed in the same region as the above described sensor. For comparison, a regression between the two sensors was calculated for the simultaneous measuring period and gave excellent agreement. Thus, data from this were used to calculate transmittance values in September 2012. A detailed method used to obtain radiation values to cover this period can be found in the Supplementary Material. The quantity of light reaching the underside of the ice layer is important for sea-ice algae and phytoplankton, as well as for sympagic fauna. In order to compare the amount of light between expeditions and sampling stations independently from the time of the day when we sampled, we calculated the insolation parameter as: S i = 24 0 I h TR i dh , (3) where S i is the insolation value (mol photons m -2 d -1 ) at position i along each profile, TR i is the transmittance value at position i, and I h is the modeled hourly incoming solar radiation (µmol photons m -2 s -1 ) above the surface (1 • x1 • spatial resolution, daily temporal resolution, interpolated hourly) based on the radiative transfer model SBDART  (Ricchiazzi et al.
The algorithms applied to the different data sets are listed in Table  2. For Arctic summer (PS80) data we used the EOF algorithm developed by  Lange et al. (2016)  based, amongst others, on the PS80 data set. Lange et al. (2016)  recommended the EOF algorithm, instead of NDI, because it proved to be most reliable in catching the variability of in-ice chl a in the PS80 data set. For the Southern Ocean (PS81 and PS89) we used the NDI algorithm derived by  Melbourne-Thomas et al. (2015)  for the Weddell Sea. This algorithm was found to provide the most robust predictions of integrated chl a in comparison to others tested by  Melbourne-Thomas et al. (2015). The NDI algorithm was developed based on summer data, and it is thus applicable to Antarctic summer (PS89). Meiners et al. (2017)  applied the same algorithm to under-ice hyperspectral data collected with an ROV deployed during the Antarctic winter (PS81).
Results showed that the algorithm provides a good fit with ice-core data collected during ice stations, we thus applied the same algorithm to the SUIT winter data (PS81). The NDI algorithm for Arctic spring (PS92 and PS106) was developed by comparison between coincident under-ice hyperspectral and in-ice chl a measurements of in total 8 ice stations carried out during the same campaigns. The accordance in sampling time (May-June-July), sampling region (Figure  1 ), and similarity of sea-ice properties (see section 3. 2. 1) justifies merging the data from these two spring expeditions. We used a total of 19 coincident measurements of under-ice irradiance and integrated chl a retrieved by measuring autotrophic pigments with High-Performance Liquid Chromatography (HPLC, for more details, see  Tran et al.
Spatial Autocorrelation Analysis Spatial autocorrelation was used to investigate the horizontal patchiness of sea-ice draft for 55 stations. Spatial autocorrelation analyses were also conducted for transmittance and in-ice chl a biomass. However, the transect nature of the surveys, the large interval spacing and small sample sizes, in comparison to draft, limited the spatial analyses of in-ice chl a to only 7 stations and of transmittance to only 10 stations. Autocorrelation was estimated using Moran's I  (Moran, 1950; Legendre and Fortin, 1989; Legendre and Legendre, 1998) , which was calculated for each SUIT survey at equally spaced (25 m) distance classes. Individual autocorrelation coefficients (e. g. , Moran's I estimates) were plotted for each distance class as a spatial correlogram  (Legendre and Fortin, 1989; Legendre and Legendre, 1998)  using the R software function correlog from the pgirmess package  (Giraudoux, 2018).
In light gray shades are the stations located in the Marginal Ice Zone in December (referred to as Dec-MIZ), in white are the stations in the pack-ice area, whereas in dark gray are the stations located in the Marginal Ice Zone in January (referred to as Jan-MIZ). *Values for in-ice chl a at stations 70_2 and 71_1 were retrieved even if the altimeter values were missing (so it was not possible to filter the data) and are given to help the data interpretation in section 4. stn Profile length Ā Hs Hi chl a w T S ĀSat In-ice chl a TR S i P d (m) (%) (cm) (m) (mg m -2 ) ( • C) (mg m -2 ) mol photons m -2 d -1 (m) 24_2 (PS81) than in summer 2014/15 (33. 63 ± 0. 24 psu). Between December (PS89, Dec-MIZ) and January (PS89, Jan-MIZ), both under-ice water temperature and chl a decreased, whereas salinity did not show any pattern (Table  7 ).
CONCLUSIONS In this study, we presented and analyzed a large data set collected during five expeditions covering different years, seasons, and regions in both the Arctic Ocean and the Southern Ocean. Data were collected with a Surface and Under-Ice Trawl (SUIT), which allows the characterization of the sympagic environment on the kilometer scale. The data proved to be representative in characterizing the physical environment, and highlighted regional and seasonal differences in both hemispheres. Analysis of spatial variability showed a significant difference in the spatial scale of the variability of sea-ice properties between the Arctic and Antarctic, and showed that the total ice thickness was a driver for variability in sea-ice transmittance. The introduction of a new parameter, the insolation (i.
ACKNOWLEDGMENTS We thank  Captain Uwe Pahl ,  Captain Stefan Schwarze ,  Captain Thomas Wunderlich , and the crews of the RV Polarstern. We thank the scientific cruise leaders  Antje Boetius  ( PS80 ),  Bettina Meyer  ( PS81 ),  Olaf Boebel  ( PS89 ), IP ( PS92 ), and  Andreas Macke  and  HF (PS106 ) for their excellent support and guidance with work at sea. We thank  Jan Andries van Franeker  ( WUR ) for kindly providing the  Surface and Under-Ice Trawl (SUIT)  and  Michiel van Dorssen  for technical support. The sampling with the SUIT, and on the ice, would have not been possible without the help of many volunteers during each expedition.
The most realistic way to compensate the practical intractability of standardized designs to survey rare organisms, such as reptile assemblages, remains to rely on volunteer-based databases, which offer a sampling of species occurrence over unequalled spatial coverage and at limited cost  (Dickinson et al. , 2012; Griffiths et al. , 2015). These opportunistic records have already been used convincingly to investigate the imprints of land-use gradients and ecological traits on the distributions of reptiles  (De Solan et al. , 2019; Todd et al. , 2016 Todd et al. , , 2017) )  and other vertebrate taxa  (Callaghan & Gawlik, 2015; Van Strien et al. , 2013). However, due to the lack of standardization, opportunistic data from volunteers suffer from well-identified limitations, in particular heterogeneous reliability, unequal sampling effort K E Y W O R D S community assembly, functional diversity, land use, Mediterranean basin, opportunistic data, regional scale, reptiles, RLQ analysis and lack of absence data  (Burgess et al.
| Reptile data We made use of 49,821 reptile records extracted from the Malpolon data base, a long-term repository of amphibians and reptile occurrence data covering the western French Mediterranean since the 1970s  (Geniez & Cheylan, 2012). Seventy per cent of the records were opportunistically reported by volunteer naturalists without any standardized protocol; the remaining 30% were gathered from standardized surveys and literature records. All records (one record = one species at a given date and location) were expert-validated prior to inclusion in the database; we retained only those with the highest level of certainty with respect to species and geographic location. We restricted the present analysis to records gathered between 1996 and 2015, anterior data lacking precise geolocation (final sample size = 27,858 records; spatial variations in survey effort in Appendix S1, Figure  S1.
| Community-level analysis Analyses of species and trait diversity are affected by unequal sampling effort, in particular when analysed through non-standardized, opportunistic data  (Higa et al. , 2015; Isaac et al. , 2014). We therefore investigated the relationship between sampling effort and species richness with rarefaction curves from 1 to the number of individual records in each cell. All curves started to bend around 20 records, suggesting this sample size as the best compromise between a representative assessment of the richness of reptile assemblages, and sufficient spatial coverage (Appendix S3). We therefore computed 100 rarefied cell × species matrices at 20 records per cell, which we converted into presence-absence data and used for all subsequent assemblage-level analyses. From rarefaction curves, we extracted rarefied species richness, the estimated number of species per cell for an equal sampling effort of 20 records, as an assemblage-level measure of species diversity.