dataesr/scientific-paragraphs-categorization huggingface dataset:
New types of distortions During the design phase of TID2013, we had to decide how many new types of distortions have to be exemplified Certainly, it was necessary to add such types of distortions that are valuable from both theoretical and practical points of view. We created a list of possible types of distortions that had more than ten positions. This list was discussed in teams of five countries the authors of this paper are from. Several factors were taken into account as does a new type of distortions considerably differ from the ones already existing in TID2008, how often customers and industry deal with a given type of distortion, has a given new technology perspectives in future, etc. One could ask why not to add more types of distortions? The answer is the following. Creating the database, we had to take into account some limitations.
5 ) may come as a result of different factors at the stages of image acquisition and processing. In particular, it can arise due to a large quantization of colour components in JPEG-based compression of images and video  [28]. Such a distortion might also take place in colour image printing. All simulations have been carried out in Matlab. Modelling of these distortions has been performed after image transformation from RGB to YCbCr colour space using function rgb2ycbcr. The component Y (intensity) remained untouched and the components Cb  and Cr were transformed as Cb¼128 þ(Cb À 128) Â K and Cr¼128 þ (CrÀ 128) Â K where K is a variable parameter. After such a transformation, the obtained image has been converted to the original color space using function ycbcr2rgb. K equals to 1 relates to the absence of distortion, the use of smaller values of K leads to making image less 'colorful'.
K equals to 0 makes a color image to look as a grayscale one. K values have been adjusted to provide a desired PSNR. In some cases, to provide low PSNRs (21 or 24 dB) we needed to use negative values of K resulted in the specific (inverse) color distortions. Multiplicative Gaussian noise (distortion type # 19, see example in Fig. 6 ) represents a wide class of signaldependent noise. As far as we know, such type of distortions is absent in other databases. Meanwhile, signal-dependent noise occurs in images in many applications where visual quality of images is of a prime importance  [29]  including single-and multichannel radar imaging  [30] , multispectral remote sensing, medical imaging  [31] , etc. A multiplicative Gaussian noise has been simulated separately (independently) for each colour (RGB) component with equal variance of multiplicative noise σ 2 μ in all components.
Besides, it has been stated by many researchers that usually there are several types of distortions simultaneously present in images and video whilst databases commonly contain images with "pure" distortions. To make an impact of noise and lossy compression comparable, the distortions have been modelled as follows (see Fig. 9 ). Additive white Gaussian noise with variance σ 2 has been added to each colour component where noise was independent for colour components. After this, lossy compression has been performed using DCT-based coder ADCT  [35]  with the quantization step set equal to 1. 73σ. Such quantization step is chosen in order to provide a visibility of both distortions from compression noise and residual noise. Noise standard deviation has been individually adjusted for each test image to provide a desired value of PSNR.
Examples of images with the considered type of distortions are presented in Fig. 10. As it can be seen, the distortions can be quite specific. Image colour quantization with dither (# 22) is typical in image printing. It is one more popular application which is paid particular attention nowadays  [36]. Distortions of this type have been modelled using the Matlab function rgb2ind. It converts an RGB image to the indexed image using dither. To provide a desired PSNR, the number of quantization levels was adjusted individually for each test    Besides, further slight blurring of shifted components has been performed. Shifting and blurring parameters have been adjusted to provide a desired PSNR. An example is shown in Fig. 13. Finally, the last distortion type (#24) relates to compressive sensing (sparse sampling and reconstruction) that has become a hot research topic  [37, 38].
As far as we know, HVSmetrics have not been exploited in this area yet although their usefulness is expected. An example of distortions for this application is presented in Fig. 14  though they can depend upon a method of compressive sensing used. For us, it was convenient to use the method  [38]  and available software for obtaining reconstructed images with distortions. As earlier, modelling is carried out separately for components Y, Cb, and Cr. Some details for Y component are explained by Fig. 15. The Y component image is subject to the 2D discrete cosine transform (DCT) applied to the entire image getting   As it follows from the description presented above, the new types of distortions introduced in the database TID2013 are quite different. It took a lot of time to carry out extensive computations in order to provide desired levels of distortions.
MOS obtaining Having a database, MOS is to be provided for each distorted image in it. There are several methodologies used to assess the visual quality of an image  [39] [40] [41]. For example, the observers might be asked to assess the absolute quality of an image or its similarity to a reference one. Then, the subject judgment is expressed with a grading scale that can be of a different type. Five gradations have been used in  [39]  with the corresponding five categories as "Bad", "Poor", "Fair", "Good", and "Excellent". A drawback of this methodology is that it might be difficult for an observer to assign gradations to the distorted images, especially at the beginning of experiments when an observer has a little experience. This leads to the observer's willingness to change the previously assigned grade.
17 ). Moreover, there were no MOS values larger than 7. 5. This shows that conditions of comparisons were quite difficult especially for distorted images with rather high visual quality. Experiments for TID2013 were conducted in five countries (Finland, France, Italy, Ukraine, USA). Three persons from other countries participated in experiments as well. This is because it was possible to carry out experiments both in laboratory conditions (under control of tutors) and distantly via Internet. The results obtained in different countries were in good agreement (see details later). Note that approximately equal number (over 30) of experiments was performed for each reference image. This allows stating that MOS is of approximately the same accuracy for all reference images. Some other data describing experiments and accuracy of the estimated MOS are presented in Table  2.
MOS property analysis Let us consider some other properties of MOS. Its values for all 3000 distorted images in the database are presented as scatterplot in Fig. 18  where first (leftmost) 120 points correspond to the reference image #1, next 120 points relate to the distorted images that have the same reference image #2 and so on. This scatterplot shows that MOS values are most dense within the interval from 3 to 6 and, thus, the task of comparing image visual quality was not trivial. MOS values averaged for all observers that carried out experiments are presented for each distortion type and level in Fig. 19. Each 5 level points for a given type of distortions (24 totally) are connected to see a tendency if it exists. For most types of distortions, the tendency is clear and obvious-average MOS decreases if distortion level becomes larger.
Mean RMSE values are almost the same for all levels for distortion types #14, #15, #16, and #18. All these distortion types can be referred to the class (subset) called Exotic  [42]. Note that the database TID2013 contains not only the file "mos. txt" of MOS values but also the file "mos_std. txt" of MOS standard deviations for each distorted image). The obtained RMSEs of MOS allow analyzing what is the agreement between the results obtained in different countries and conditions-in laboratory and via Internet. For this purpose, we have calculated Spearman rank order correlation coefficient (SROCC) between mean opinion scores averaged for Ukrainian participants (602 experiments, all done in laboratory) and participants from other countries (369 totally, mostly carried out via Internet). The calculated SROCC value is equal to 0.
Comparative analysis for quality metrics Practice of analysis for visual quality metric using TID2008 has demonstrated that it is reasonable to study MOS estimated for all types of distortions as well as for particular subsets  [11, 16, 43]. That is why we have used this approach for analyzing data for TID2013. A subset is usually formed by researchers depending upon an application and it may include one or several types of distortions. Table  3  shows subsets used below for verification of quality metrics (distortions that belong to a given subset are marked by þ). The subset "Noise" contains different types of noise and distortions in conventional image processing; the subset "Actual" relates to types of distortions most common in practice of image/video processing including compression, the sunset "Simple" includes only three standard types of distortions; the subset "Exotic" corresponds to distortions that happen not often but are among the "most difficult" for visual quality metrics.
Many of these metrics have been calculated using Metrix MUX Visual Quality Assessment Package  [60]. Table  4  presents the values of Spearman rank order correlation coefficients (SROCC) for the considered metrics and the aforementioned subsets. Similarly, Table  5  contains the corresponding values of Kendall rank order correlation coefficients (KROCC)  [61]. SROCC and KROCC 1 Additive Gaussian noise þ þ þ À À À þ 2 Noise in color comp. þ À À À À þ þ 3 Spatially correl. Noise þ þ À À À À þ 4 Masked noise þ þ À À À À þ 5 High frequency noise þ þ À À À À þ 6 Impulse noise þ þ À À À À þ 7 Quantization noise þ À À À À þ þ 8 Gaussian blur þ þ þ À À À þ 9 Image denoising þ þ À À À À þ 10 JPEG compression À þ þ À À þ þ 11 JPEG2000 compression À þ À À À À þ 12 JPEG transm. Errors À À À þ À À þ 13 JPEG2000 transm.
errors À À À þ À À þ 14 Non ecc. patt. Noise À À À þ À À þ 15 Local block-wise dist. À À À þ À À þ 16 Mean shift À À À þ À À þ 17 Contrast change À À - þ À À þ 18 Change of color saturation À À À À þ þ þ 19 Multipl. Gauss. Noise þ þ À À þ À þ 20 Comfort noise À À À þ þ À þ 21 Lossy compr. of noisy images þ þ À À þ À þ 22 Image color quant. w. dither À À À À þ þ þ 23 Chromatic aberrations À À À þ þ þ þ 24 Sparse sampl. and reconstr. À À À þ þ À þ values for the full set of the database TID2008 are given in the rightmost column of Tables  4  and 5 , respectively, for comparison purposes. We prefer to analyze rank order correlation coefficients between MOS and quality metrics because this allows avoiding fitting procedures that can be not unique. In fact, The first conclusion that follows from analysis of data presented in Table  4  is that even the best metrics (SFF and FSIMc) provide SROCC about 0.
85 for all types of distortions (see data in column Full) and it is worse than the best metrics for the set Full of TID2008 (over 0. 9, see data in the rightmost column of Table  4 ). This shows that the database TID2013 is really challenging for HVS-metrics and we have gained one of our intentions. Consider now particular subsets. For the subset "Noise", the situation is rather good since there are several metrics (BMMF, PSNR-HA, PSNR-HVS, PSNR-HMA, PSNR-HVS-M, FSIMc, SR_SIM) for which SROCC is larger than 0. 9, i. e. appropriate adequateness is provided. For the subset "actual", the situation is similar. There are quite many metrics that provide SROCC over 0. 9 and reaching almost 0. 95 for the best metrics. The situation is even better for the subset "Simple" where even the standard PSNR possesses SROCC over 0.
Access to TID2013, conclusions and acknowledgements The archive TID2013 is available for free downloading from http://ponomarenko. info/tid2013. htm. The archive includes image files, the file containing the MOS values, the file containing the RMSE of MOS, the programs for calculation of Spearman and Kendall correlations, the Readme file that explains how to exploit the database. Also, archive contains the values of most known quality metrics calculated for TID2013. Note that TID2013 occupies about 1. 7 GB on a hard disk and about 900 MB in the archive. We would like to underline the following advantages of TID2013. It contains many different types of distortion that deal with various peculiarities of HVS. Seven new types of distortions and one new level of distortions have been added to TID2013 compared to TID2008.
2  The Meningitis Research Foundation Meningococcus Genome Library (MGL) is an exemplar of this concept, containing genomes for all English and Welsh and Northern Irish invasive isolates received by the Public Health England Meningococcal Reference Unit (MRU) from July 2010 to June 2013 (inclusive). These highresolution data facilitate surveillance and the identification of outbreaks. The power of the MGL is enhanced by its capacity to compare current data to historical and international cases within the broader PubMLST Neisseria database e an open access repository of Neisserial isolate data including provenance, pheno/genotyping data and, increasingly, whole genome sequences, submitted by researchers and reference laboratories around the world (http://pubmlst. org/ neisseria/). The elevated levels of disease caused by cc11 and the historical significance of these hyperinvasive meningococci make them a priority for such an analysis.
Isolates and genomes All genomes are available open access in the PubMLST Neisseria database (accessed 29 October 2014). Unique PubMLST IDs and typing data are listed in Supplementary Table  1. Genomes of 448 of the isolates were sequenced, assembled and annotated as part of the present study, as described previously. 18 The isolates covered in the study comprised a mixture of MGL isolates, isolates from noteworthy episodes of cc11 disease (mainly sequenced for the purpose of the study) and other available cc11 genomes on the PubMLST Neisseria database (Supplementary methods). Briefly, the study included genomes for 750 cc11 isolates comprising 246 serogroup C isolates, 470 serogroup W isolates and 34 serogroup B isolates. These included 663 invasive disease isolates, 40 upper respiratory tract carriage isolates and 47 isolates from unspecified or unknown sites.
Phylogenetic analyses Genomes were compared using the PubMLST Genome Comparator tool,  21  which employs a 'gene by gene' approach comparing arbitrary, sequentially assigned, preindexed allele identifiers at each locus. 18 A set of 1546 indexed core genome (cg) loci were used for comparisons as described previously. 12 SplitsTree4 (version 4. 12. 8) was used to visualise the resulting distance matrices as Neighbour-net networks. 18, 22 le of the funding sources The funding sources for the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.
Information from Chilean isolates would provide useful additional information, as this is the most recent South American country to report the expansion of MenW:cc11 disease. One of the advantages of the PubMLST Neisseria database is that comparable data are available to reference laboratories worldwide and will facilitate the monitoring of global epidemiology. Owing to the high case fatality rates and persistence of MenW:cc11 disease in South America, the relatedness of the currently expanding UK strain is of concern. The English/Welsh strain has already exhibited similar behaviour by expanding first among the elderly and subsequently all age groups. Results of the current study show that the strain was being carried in at least five of the six institutions partaking in the UK carriage study from 2010 to 2012.
Considering such parameters, the detection limit for Th, U and Pb is 150 ppm  (Cocherie and Albarede, 2001). X-ray element maps were also recorded with an accelerating voltage of 20 kV and beam current of 100 nA, using the wavelength-dispersive spectrometers. Details on the analytical procedure are the same as those used in  Cocherie et al. (2005)  and  Faure et al. (2008 Faure et al. ( , 2010)). Age calculations were done using the Isoplot/Ex (2. 49) program of  Ludwig (2001)  and a Microsoft Excel add-in program for determining U-Th-Pb ages from EPMA measurements  (Pommier et al. 2002). The 2 errors given on individual ages depend on U, Th, and Pb contents, and are calculated by propagating the uncertainties of these elements (at 95% confidence level). Because the mean age of an homogeneous age population is directly drived from the isochron plot Th/Pb vs U/Pb, only the uncertainty on these two parameters (Th/Pb and U/Pb) are required to calculate the uncertainty on the mean age.
Kyanite-garnet granulitic paragneiss (SO15) of Saugères. Monazite in sample SO15 exhibits a core-rim zonation of the grain, and in consequence, EPMA data were processed separately for the different domains in order to calculate two isochron ages. 47 and 21 analyses were performed in the core and rim of a single grain, respectively. The 47 analyses from the core on the Th/Pb vs. U/Pb diagram are clustered, leading to a large error envelope. The intercept ages are similar within errors, since the intercept U-Pb age and the intercept Th-Pb age are at 517+281/-322 Ma, and 390+81/-57 Ma, respectively. The calculated MSWD of 1. 15 indicates that the population of the 47 analyses is statistically homogeneous within the analytical errors  (Wendt and Carl 1991). As a consequence, it is possible to calculate a mean age at the centroid.
Measured compositions were corrected for common Pb using non-radiogenic 204 Pb, and an average of crustal composition  (Stacey and Kramers 1975)  was used for the common Pb. Data reduction was carried out using the Isoplot/Ex v. 2. 49 program  (Ludwig 2001). In order to monitor the external uncertainties of SIMS U-Pb zircon measurements, an in-house standard zircon Qinghu was alternately analyzed as an unknown together with other unknowns. Twenty measurements on Qinghu zircon (Table  3 ) yield a Concordia age of 160 ± 1 Ma (2SE, MSWD of concordance = 0. 92), which is in good agreement within errors with the recommended U-Pb age of 159. 5 ± 0. 2 Ma  (Li et al. 2009). Zircon U-Pb age data are presented in Table  3.
Acknowledgements Field, and analytical expenses for this study have been supported by the  French national program  " Carte géologique de la France au 1/50 000 " led in BRGM, and by a  National Natural Science Foundation of China (NNSFC)  grant  41273070. Ida di Carlo  and  Olivier Rouer  are acknowledged for their help for the acquisition of SEM and EPMA analyses. Jean-Marc Lardeaux  and  Emilien Oliot  are thanked for their constructive reviews.
DATA ANALYSES Resulting binary ". sff " files were extracted using Mothur  (Schloss et al. , 2009). Sequences shorter than 250 bp, longer than 350 bp and containing Ns were eliminated from further analysis. Filtered sequences were sorted by their MID sequences into separate Fasta files. Typical 454 sequencing errors and PCR single base errors were screened using the PyroNoise and SeqNoise modules of the AmpliconNoise software  (Quince et al. , 2011)  with default parameters. Sequences with MIDs and primers removed were used to generate a Needleman-Wunsch distance matrix (function NDist) and clustered into operational taxonomic units (OTUs, function Fcluster) using the same software. The matrix of sequence abundances per OTU was generated using Python scripts using 97% identity threshold for OTU definition (Dataset_1 in Table  S1 ).
ANALYSES OF OTU ABUNDANCES AND SAMPLES COMPARISON The microbial α-diversity indices (Shannon and Invsimpson), community microbial richness indices (Chao1 and ACE) and rarefaction data were computed with Mothur on Dataset_1  (Schloss et al. , 2009). The second dataset was prepared by eliminating all sequences present in less than 5 copies in the total number of reads per sample (rare OTUs eliminated) and was screened for symbiont-related sequences (Dataset_2 in Table  S1 ). For further community abundance and statistical analyses, OTUs containing in total less than 40 sequences, which represents ∼1% of the total number of sequences in the smallest sample (4543) were eliminated, thus generating a reduced data matrix with abundant sequences (Dataset_3 in Table  S1 ). All statistical analyses were performed using R (R Development Core Team, 2013).
Non-Metric Multidimensional Scaling (NMDS) was performed using "metaMDS" function in the MASS package. Average agglomerative clustering dendrograms were generated using Unweighted Pair-Group Method with arithmetic Average (UPGMA) on a Bray-Curtis distance matrix. Abundance data were transformed using the Hellinger method  (Legendre and Gallagher, 2001)  and then used for transformation-based redundancy analyses (tb-RDA). Substrate type and region were used as factors and water temperature, depth, and deployment time were used as variables into the constrained RDA, in order to estimate their contribution to the global variance. Significance was assessed using ANOVA permutation tests.
IDENTIFICATION OF OTUs RELATED TO METAZOAN-ASSOCIATED BACTERIAL SYMBIONTS The Dataset_2 was used for screening for symbiont-related sequences. Sequences from this dataset were blasted (with 97% identity threshold) against a local database (Supplementary File), containing sequences of bacteria living in symbiosis with deep-sea fauna (Potential_symbionts in Table  S1 ). These sequences were then blasted against public database and their blast hits definitions were then filtered by scanning for the "symbiont" key word (Confirmed_symbionts in Table  S1 ). In parallel, a phylogenetic analysis was computed in order to evaluate the phylogenetic relationship of sequences to known symbionts. For this, 16S rRNA sequences of documented symbiotic bacteria from vent and seep metazoans and several best blast hits from our OTUs were aligned with SINA Web Aligner  (Pruesse et al.
, 2012)  and truncated to the V5-V6 region. Symbiont-related OTUs from this study were added to this dataset, aligned with ClustalX  (Larkin et al. , 2007) , and alignments were manually checked. Phylogenetic relationships among sequences were estimated from a 250-bp alignment with MEGA6  (Tamura et al. , 2013)  using distance methods and neighbor-joining. Bootstrap values were computed on 1000 replicates.
PREPARATION OF DATASETS Bacterial diversity was investigated on 25 individual CHEMECOLIs filled with either pine wood cubes or alfalfa and deployed for periods of 10-1112 days in four areas in the North East Atlantic and Mediterranean. A total of 564925 V5-V6 reads was obtained. After length filtering, elimination of PCR and random sequencing errors, a raw dataset containing 364633 sequences distributed in 22721 OTUs (97% cut-off) was obtained. Screening for symbiont-related sequences was made on a reduced dataset including 332621 sequences representing 2641 OTUs. Community composition and comparisons were performed on a dataset containing only abundant sequences, overall 306716 sequences in 658 OTUs. On average, dataset sizes were reduced by 35, 41, and 46% of total read numbers and contained 7012, 6397, and 5898 sequences per sample (2 replicate samples per CHEMECOLI).