dataesr/scientific-paragraphs-categorization huggingface dataset:
We additionally used the occurrence datasets of two real species (Figure  2 ). We compiled a set of 1825 occurrences for the painted turtle (Chrysemys picta) downloaded from the World Turtle Database (http://emys. geo. orst. edu; accessed on May 2011). The second species was the white-spotted slimy salamander (Plethodon cylindraceus) for which we collected a set of 208 observations. A part of the dataset was provided by J. Milanovich and additional records were obtained from the Global Biodiversity Information Facility (http://www. gbif. org, accessed on May 2011). According to our knowledge of the distribution of these species, these original datasets seem relatively unbiased, i. e. the distribution of records over space reflects the known spatial distribution of the species.
Environmental predictors We used distinct sets of environmental predictors depending on the modeled species. Climatic and topographic grids were downloaded from the WorldClim database  [32]  (http://www. worldclim. org) at a resolution of 2. 5 arc-min (4. 63 km at the equator). The global map of land cover provided by the European Spatial Agency was downloaded in its 2009 version (Globcover 2009  [56] , http://due. esrin. esa. int/globcover) and rescaled to fit the 2. 5 arc-min resolution of the other variables. Finally, we compiled 5 years of 10-day periods of NDVI (2007-2011), a measure of vegetation productivity derived from multispectral remote-sensing images, downloaded from the SPOT-VEGETA-TION project  [57]  (http://free. vgt. vito. be). We averaged across these 5 years three layers of mean, minimum and maximum annual NDVI.
Generation of sampling bias The three original datasets were altered to generate four types of bias that might occur when collecting observations (Figure  3 ). The original datasets were thus subsampled so that the remaining records were biased in the geographical space. We also created three levels of bias intensity, hereafter referred as ''low'', ''medium'' and ''high'' to assess the effect of this parameter on model outputs (Figure  3 ). For each species, each combination of bias type (4) and bias intensity (3) was replicated 10 times resulting in a total of 360 biased datasets used to model distribution. The four types of sampling bias were generated as follows: (1) TWO AREAS -The original dataset was biased such that its northern part exhibited a high density of records and the southern part a low density.
Species distribution modeling We used for modeling the software MAXENT  [30] , a machine learning algorithm that applies the principle of maximum entropy to predict the potential distribution of species from presence-only data and environmental variables  [26]. Currently, this widely used method is particularly efficient to handle complex interactions between response and predictor variables  [15, 28] , and is little sensitive to small sample sizes  [29]. All models were computed using the version 3. 3. 3k of MAXENT (http://www. cs. princeton. edu/,schapire/maxent/). Runs were conducted with the default variable responses settings, and a logistic output format which results in a map of habitat suitability of the species ranging from 0 to 1 per grid cell, wherein the average observation should be close to 0.
Methods of sampling bias correction We applied on all our biased datasets five methods of bias correction that have been already published. In order to evaluate their usefulness in real conditions, we used these methods as if the source, shape or strength of the sampling bias was unknown. Therefore, we did not select a correction method according to our knowledge of the bias, as this information is unknown in most empirical studies. (1) Systematic Sampling. A subsample of records regularly distributed in the geographical space was selected  [46, 54, 55, 62]. MAXENT already discards redundant records that occur in a single cell. We removed neighboring occurrences at a coarser resolution than MAXENT does. We created a grid of a defined cell size and randomly sampled one occurrence per grid cell.
This subsampling reduces the spatial aggregation of records but does not correct the lack of data due to low sampling effort in some areas. This method could also underestimate the contribution of suitable areas where the high density or records reflects the true ecological value for the species. The resolution of the reference grid was 2 degrees for Chrysemys picta and the virtual species, and 0. 2 degree for Plethodon cylindraceus. (2) Bias File. This option is implemented in MAXENT. The software can be fed with a bias grid  [49, 63]  that is a sampling probability surface. The cell values reflect sampling effort and give a weight to random background data used for modeling. An ideal way of creating biasfiles would be to represent the actual sampling intensity across the study area. Although it can be roughly estimated by the aggregation of occurrences from closely related species  [48] , in most real modeling situations, this information is lacking.
The reliability of predictions should be improved when the model is transferred to the rest of the area. Following this recommendation, we randomly sampled 10000 pseudo-absences in buffer areas around occurrences and used them as background samples in MAXENT. Buffer size was a radius of 500 km for the virtual species and Chrysemys picta, and 100 km for Plethodon cylindraceus. (4) Cluster. Biased datasets typically lead to spatial autocorrelation of records and artificial spatial clusters of observations thus violating the assumption of independence  [67]. This bias can be circumvented by sampling one point per cluster in environmental space  [53, 68, 69]. We first performed a principal component analysis (PCA) on the environmental descriptors of occurrences using the ''ade4'' R package  [70]  in order to define independent axes in the environmental space.
Then, we ran a cluster analysis based on Euclidean distance on PCA axes space. The resulting dendrogram was used to define a number of classes corresponding to half of the occurrences. One record was randomly sampled per class and the models were run on these subsampled datasets. (5) Split. When occurrence frequency greatly differs between two areas because of unequal sampling effort, the area can be split in two strata within which coverage probability is more homogeneous, and one model be computed for each stratum. This method has been used for species occurring over a large distribution range and extended environmental gradients  [44, 51, 71]. We split our biased datasets in a northern and a southern stratum. We combined the model outputs to produce a composite model for the entire range, keeping the highest value in pixels where strata overlapped.
Evaluation of correction methods To estimate the ability of each correction method to recover the information contained in the original unbiased data set, we used 4 criteria that correspond to the interests of different end users of SDMs. We compared the models obtained after applying a bias in the dataset to the original model, and after applying a sampling bias correction method. The original dataset of the virtual species was created to be unbiased. The model computed from it will be thus referred as the unbiased model. Although we do not formally know the actual bias in the Plethodon and Chrysemys original datasets, we will also refer to the models computed from their original datasets as unbiased models. We should keep in mind that we compare the change in the resulting model, whatever the original bias.
The models referred as biased were computed after applying a sampling bias and the corrected models after applying a correction method to the biased dataset. (1) AUC. The area under the receiver operating curve (ROC), known as the AUC is one of the most common statistics to assess model performance. AUC can be interpreted as the probability that a presence cell have a higher predicted value than a absence cell (or pseudo-absence), both of them being chosen randomly  [28]. Although the use of AUC for the evaluation of ecological models has been criticized, especially when calculating against background points rather than true-absences  [72, 73] , it should be reliable to compare models generated for a single species in the same area and the same predictors. The calculation of AUC was performed using the R package ''PresenceAbsence''  [74] , using as test points the fraction of the original dataset which was excluded to create the biased dataset.
The metrics was calculated by the comparison between these test points and either (i) 10000 true absence points sampled outside the range, which corresponds here to the true occupancy for the virtual species, or (ii) 10000 background points randomly sampled in all the study area for the real species. For original models, in the absence of true test points, a mean AUC value was computed The selected layers are indicated for each species, as well as the extent of modeling (in decimal degrees). doi:10. 1371/journal. pone. 0097122. t001 using 5 random splits of the dataset, each subsample being used in turn to evaluate the model. (2) D GEO overlap in the geographical space. Several metrics of niche overlap are available (e. g. D  [75] , modified Hellinger distance I  [76]  or BC  [77] ). We used the Schoener's D index  [75]  that has been suggested to be best suited for SDM outputs  [78].
This statistics considers the probability distributions across space of the difference in the probability of presence of two species, based on their respective distribution models. D geo index is ranged from 0 (no overlap) to 1 (complete overlap, identical models). (3) D ENV overlap in the environmental space. We estimated niche overlap in the environmental space between the unbiased, biased, and corrected models. We used the PCA-env approach described by Broennimann  [79]  to calculate Schoener's D index based on the environmental characteristics of two sets of occurrences. This approach defines the environmental space by the two first axes of a principal component analysis of all the pixels of the study area. The niche overlap is calculated from the smoothed density of occurrences in the environmental space following a kernel density function applied on each dataset.
Depending on the correction method used, some models can be based on the same input dataset (e. g. the biasfile correction uses the same records as the biased model). In order to compare our SDMs, we applied the PCA-env method on 500 points sampled from the SDM outputs instead of on the input records. We selected these points in the output map using the SDM probability of presence as a sampling probability. (4) G OVER overlap between binary maps. SDM outputs are often converted to binary maps that are more tractable for conservationists who for instance need to delineate protected areas. In such maps, a pixel is considered as either suitable to the species or not. We used the non-fixed 10 th percentile training presence threshold value to generate binary maps as proposed by Liu et al. [80].
07%). All bias types had also globally similar effects in terms of deviation from the unbiased model (Figure  4 ). For Chrysemys picta, all evaluation measures were strongly affected by the ''center'' bias. AUC decreased more than 5%, and overlaps with the unbiased model ranged from 0. 26 to 0. 49. In contrast, the P. cylindraceus dataset was overall weakly affected by the biases so that the biased models did not lead to noticeable differences with the unbiased model. The strong effect of the ''center'' bias was visible in Plethodon cylindraceus only for the overlap of binary maps (G over ). This bias in which only the central zone is sampled may exclude a large part of the original environmental space and lead to very inaccurate SDM outputs. Interestingly, the decrease in AUC performance for all bias types was more pronounced in C.
For the virtual species, and considering DD geo , 57% of corrected models (34 out of 60 combinations of bias type, bias intensity and correction method) were more similar to the model generated with the unbiased dataset than the biased model (Table  2 ). Most cases for which no method was able to provide bias correction were ''center'' and ''travel time'' biases, with medium to high intensities. Conversely, only 7% of P. cylindraceus models were corrected (4 cases out of 60), while 25% of C. picta models were corrected (15 cases out of 60) and offered a better result than the biased model. Regardless of the species, the bias type, and the metrics considered, the restricted background method failed to improve the biased models in almost all tested cases. The other methods performed better but were ranked differently depending on bias type.
Because we have a reference model, we will mainly focus on the overlap indices with the unbiased model as a measure of predictive accuracy performance. Contrary to previous studies investigating sampling bias correction in SDM that focused on a few methods and simple biases  [41, [52] [53] [54] , we reviewed here five different ways to deal with sampling bias and used both real and virtual datasets under various bias scenarios. We also considered bias intensity that has been to our knowledge never assessed and proved to be of as a high concern as the type of bias. Moreover, instead of relying only on classical measures of SDM performance such as AUC (as used in Syfert et al. [41] , Varela et al. [53]  and Boria et al;  [54] ) or omission/commission error (as used in Kramer-Schadt et al. [52] ), we evaluated the correction performance by directly comparing the SDM outputs.
Finally, the five methods tested here are not necessarily exclusive. For instance, it is possible to split a large dataset and apply systematic sampling to each dataset in species with broad distributions  [44]  or to apply the biasfile method after using first another correction method Nevertheless, we found that only systematic sampling constantly performed well irrespective of species and bias type. Interestingly enough, it happened to be the simplest and most obvious way to solve the geographic bias. Beside, this method can be quickly applied to any dataset, even if the nature of the bias is unknown. Kramer-Schadt et al. [52]  and Boria et al. [54]  also identified this technique, which they named spatial filtering, as the most effective (but note that Varela et al. [53]  found that environmental filtering, equivalent to our cluster method, may provide better results).
We might keep in mind that this method may have a few drawbacks though. Subsampling the complete dataset may alter the distribution of occurrences in the environmental space and exclude some portion of environmental space from the input records. This problem may be circumvented by adjusting the grid size but this may not be possible when the sample size of occurrences (presences) is too low. On the contrary, smoothing the distribution of occurrences may lead to overestimating the probability presence in marginal areas. The grid cell size used to sample the occurrences may also be a source of problem; it must be large enough to resolve the bias but not too large to result in a strong loss in resolution. A fine adjustment of the parameters of the method (e. g. the resolution of systematic sampling that provides the optimal trade-off between sampling bias correction and information reduction) is thus necessary to maximize its performance.
Our results suggest that the difference in performance between the systematic sampling method and the optimal method for the dataset under investigation is slight. Since systematic sampling seems to be robust enough to differentiate sampling bias and species, we suggest that this could be the method selected first if no further attempts to correct sampling bias are to be made. This method provides a quick and efficient way to improve SDM from a biased dataset and is likely to be relevant most of the time. Even if the elaboration of a step-by-step framework would be ideal to assist in the definition of a reliable strategy of bias correction, we highlight that most SDMs studies, at least those that use MAXENT as modeling algorithm, would highly benefit from this simple tweaking of input occurrences.
Figure1. Workflow used in analyses. Original datasets of a virtual and 2 real species were altered to create 12 bias combining 4 bias types and 3 bias intensities. Five methods of sampling bias correction were employed to assess the improvement in the modeled distribution relative to the original distribution using MAXENT. Correction performance was assessed using AUC and 3 measures of overlap between the corrected the original unbiased model. doi:10. 1371/journal. pone. 0097122. g001.
In aquaculture, several studies have described the implementation of 454 pyrosequencing for de novo transcriptome sequencing of some Mediterranean species such as seabream  [21, 22] , common sole  [20]  or seabass  [23]. Most of these studies have focused on the characterization of transcriptomes under specific developmental stages, pathogen challenge or tissue-specific profiles. Nevertheless, the design of a dedicated species-specific database would be useful for easier management of genomic information (detailed annotations, tissue-specific and whole transcriptomes) and development of complementary techniques such as microarrays or RNA-seq. The main aim of this work was the generation of a representative transcriptome for S. senegalensis and S. solea after processing an important large set of transcriptomic information produced by Roche/454 and Illumina paired-end NGS technologies covering a large number of developmental stages associated to physiological challenges.
Finally, that S. senegalensis v4 had a higher number of orthologous IDs and a lower number of different complete ORFs than S. solea v1, which may be due to a greater transcript fragmentation in S. senegalensis because Roche/454 data only contributed to increase the orthology in the final assembly. The extremely large number of reads used to assemble the two sole transcriptomes could favour the accumulation A, strategy for a set of only long reads. B, strategy combining long and short reads. The numbers with "#" serve as a reference for Table  1  description. The values were calculated using FullLengtherNext software. The minimum number of transcripts that can be considered as a reference transcriptome is shown in bold. 1 Percentages for subclassifications of this category were calculated using this line as 100% reference.
Artifacts refer mainly to misassemblies and chimeric contigs. nc: Non-calculated. of assembly errors  [28, 31]. Evaluation of transcript accuracy was initially based on mapping useful reads of two randomly selected libraries of each sole transcriptome using Bowtie2 (not shown). Since 96. 7-98. 7% of the reads mapped onto assembled transcripts, the assembly errors can be considered negligible. Interestingly, the longest transcript in S. senegalensis v4 and S. solea v1 transcriptomes (Additional file 2, "Longest transcript") is clearly not an artifact: in both species, it corresponded to a titin-like protein highly similar to a long mRNA (94,446 bp) previously assembled in tilapia (Acc No XM_005460929). Titin is a giant filamentous protein highly abundant in muscle that forms a separate myofilament system in both skeletal and cardiac muscle  [32].
SoleaDB, a database for browsing Solea transcriptomes Genomic databases are extremely useful for target sequence retrieval, Blast comparisons, sequence management and compilation of all information that can help to a better understanding of the function and roles of genes. A preliminary database devoted to host Sanger EST information, microarray data and ISH pictures was developed for S. senegalensis  [19]. Nevertheless, this database was not suitable to manage the important volume of information generated using NGS. Hence, a new database, SoleaDB, was built to host all the information for S. senegalensis and S. solea transcriptomes, following the same architecture as reported for EuroPineDB  [33]  and SustainPine  [34]. SoleaDB was structured and designed in a user-friendly manner showing all information regarding experimental conditions, NGS libraries characteristics and processing pipelines to clean, assemble and annotate the transcriptomes.
Navigation is very intuitive with information structured by assemblies including global assemblies (with history versions) and by experimental conditions. A search tool to find specific transcript information by different fields and a Blast tool was also incorporated. In the "Assemblies" tab, different transcriptome versions can be browsed (Figure  3A ). For each assembly (i. e. , transcriptome version), there is a brief description ("Assembly info" tab) of experimental conditions as well as assembly, markers and annotation statistics. Raw sequencing reads, pre-processing reports, Fasta and ACE files, as well as annotations for functional analysis can be downloaded from the same tab (Figure  3B ). In the "Unigenes" section, users can search for specific transcripts and browse their specific information.
Information available for each transcript includes the consensus sequence, the corresponding contig, functional annotations, fulllength status, ORF prediction, and putative markers. By means of KEGG and EC annotations, genes in specific pathways can also be retrieved. All annotations incorporated in SoleaDB are freely downloadable for the scientific community. Therefore, SoleaDB can be extremely useful for data comparison across experiments allowing for the identification of paralogues, alternative spliced transcripts and novel genes. It represents a new, easy-to-use, valuable tool to host NGS data and for sharing genomic information between users applying these techniques. Moreover, whole and reference transcriptomes will be a useful tool for downstream applications such as RNA-seq. Examples of usefulness of SoleaDB can be found in the next sections.
Among the 7. 30% and 8. 37% of transcripts without ortholog in S. senegalensis v4 and S. solea v1, respectively, some showed a testcode index >0. 94 (Table  2 , "Putative new transcripts") and, therefore, are likely coding transcripts. Those transcripts referred to as non-redundant new transcripts (14,451 and 15,503 transcripts in S. senegalensis v4 and S. solea v1, respectively; Table  2 ), based on the absence of orthologs in UniProtKB database, may represent "new" proteins (or fragments) in sole.
1) consisted of 59,514 transcripts and the S. solea (named S. solea v1. 1) reference transcriptome contained 54,005 transcripts (Table  2 , last row). When useful reads (Additional file 1 and Table  1 ) were mapped onto these reference transcriptomes, 82. 3-87. 5% of reads were mapped onto transcripts, while 76. 5-93. 3% of transcripts received more than one read, suggesting that they represent adequately the transcriptome. Additional verification of the reference transcriptomes (v4. 1 and v1. 1) was based on an orthology analysis using zebrafish (43,132 entries available in RefSeq and 42,555 in ENSEMBL). In S. senegalensis v4. 1, 39,851 reference transcripts were found to be orthologs to 21,542 RefSeq and to 20,753 ENSEMBL zebrafish entries, and in S. solea v1. 1 34,949 reference transcripts were found to be orthologs to 20,594 RefSeq and to 19,632 ENSEMBL zebrafish entries (Additional file 3).