dataesr/scientific-paragraphs-categorization huggingface dataset:
Results Global taxonomic coverage and taxonomic precision. 24 classes of organisms recorded in the GBIF database had more than 1 million occurrences, with widely variable numbers of occurrence recordings (Table  1  was obtained before the use of any filter. The number of species (p >1 ) corresponds to the number of unique scientific names having at least one occurrence. In bold are the eight classes selected to study the taxonomic bias at the ordinal level. med /sp is the median number of occurrences per species and mad is the associated median deviation. Taxonomic precision is the proportion of taxa identified at least at the species level. data), even though birds represent only 1% of the total number of species catalogued in GBIF. Aves was also the class with the highest median number of occurrences per species (med /sp = 371).
Taxonomic bias. Of the 2. 2 million of species referenced in the GBIF taxonomic backbone, 1. 2 million species can be found in the GBIF published datasets and 1. 01 million belong to the 24 classes selected here. The number of recorded species per class was not proportional to their known species richness, highlighting a strong taxonomic bias. Aves and Insecta were, by far, the most over-and under-represented classes, respectively. Mammalia, Liliopsida, Actinopterygii, Amphibia and Magnoliopsida were also over-represented, whereas Arachnida, Gastropoda, Agaricomycetes, Malacostraca and Bacillariophyceae were under-represented (Fig. 1  and Supplementary Fig. 1 ). This taxonomic bias was already apparent more than 50 years ago, meaning that classes that were over-or under-represented in the 1950′s are still over-or under-represented today (Fig.
2 ). Nonetheless, we found an increase in taxonomic bias over time, mostly due to the faster accumulation of data for birds compared to other classes (Fig. 3  top; 283 million bird occurrences recorded between 2000 and 2016). Recently, data has accumulated faster than ever before for most classes (Fig. 3  top, middle and Supplementary Fig. 2 ) however, Figure  1. Taxonomic bias in biodiversity occurrence data. The vertical line at x = 0 depicts the 'ideal' number of occurrences per class, where each class is sampled proportionally to its number of known species. Green and red bars show the classes that are over-and under-represented in the GBIF mediated database compared to this 'ideal' sampling, respectively. Insects lack >200 millions occurrences and birds have an excess of >200 millions occurrences compared to an unbiased taxonomic sampling.
e. with 20 spatially distinct occurrences). This contrasted strikingly with the Arthropod classes, where, at best, 9% of species were "decently" sampled, even though Malacostraca had 68% of its species recorded in the GBIF. This taxonomic bias recurs at a lower taxonomic scales. We selected eight classes and showed that, for all of them, some orders were better represented in the GBIF-mediated database than others (Table  2  and Supplementary Table  S1 ). For instance, the median number of occurrences varied largely within each class, some orders having medians that were more than 50 times higher than those of other orders of the same class (e. g. m Phaethontiformes = 5504 vs m Sphenisciformes = 2; m Chiroptera = 107 vs m Cetacea = 2). The smallest difference in medians was found within poorly represented classes, in which all orders have medians less than 20.
Some studies highlighted the over-representation of birds in diverse disciplines ranging from behavioural ecology to evolution and conservation  9, 40. The ever-growing number of observations that bird enthusiasts report undoubtedly amplify bias. Other vertebrate classes (Actinopterygii and Mammalia, and to a lesser extent Reptilia and Amphibia) are relatively well represented in the GBIF-mediated database, as are most Plantae classes, especially Liliopsida and Magnoliopsida. On the other hand, Arthropods (Insecta, Arachnida, Malacostraca and Maxillopoda) and Mollusca (Gastropoda and Bivalvia) are under-represented, with insects being particularly mis-represented. Birds and insects are obvious outliers but, beyond these two classes, the taxonomic bias in biodiversity data remains blatant. Taxonomic bias is even more apparent when considering "decently" sampled species, namely species sampled in at least 20 different points on the globe.
9  , focusing on vertebrates, showed there had been no changes in taxonomic chauvinism in ecology and behavioural research. Similarly, Stahlschmidt  21  reported a static taxonomic bias from 2001 to 2010 in parental care research. He noted, however, that the absolute number of publications on parental care in birds increased significantly over this period. Along the same lines, Di Marco et al. 8  emphasized that, in conservation science, some historically under-studied taxa were receiving more attention today, but underlined that a taxonomic bias toward taxa that are threatened or less rich in biodiversity still exists. Our results confirm this status quo situation at a larger taxonomic scale: most classes that were under-or over-represented in the GBIF mediated database in 1950 are still under-or over-represented today.
Even though most classes are better recorded today than before, the gap between birds and the rest of biodiversity (i. e. ∼99% of known biodiversity) increases with time because bird occurrences accumulate much faster than other class occurrences. Thus, while most of biodiversity remains to be described  25  , the same taxa are preferentially studied and recorded over and over again. The large taxonomic scale approach we used here comes with a few limitations. First, it must be emphasized that big datasets, like all sampling, are biased so that conclusions must be drawn accordingly  29. Second, this large-scale approach implies that each species is equivalent and directly comparable, which is obviously arguable. Third, it neglects scale effects: species richness in insects is so large that whatever the means used, this class is always at risk of being understudied.
Our results provide further evidence of this trend, highlighting the active role of the general public in biodiversity data collecting, given that, for instance, the biggest dataset was provided by eBird (211 million occurrences), a collective enterprise devoted to birds and partly relying on citizen science  45. For multiple reasons (e. g. the difficulty of obtaining permits, more and more endangered species, citizen science programmes, population decline, etc. ), less specimen-based occurrences are now reported. Amphibia, Gastropoda and Reptilia, the three classes with the highest proportion of specimen-based occurrences, are also the classes with a decreasing or stabilizing trend in data accumulation. We thus anticipate an increasing bias between taxa mostly known from observation-based occurrences and taxa mostly known from specimen-based occurrences.
Methods Dataset. We downloaded all available occurrence records from the GBIF data portal in June 2016 (http://doi. org/10. 15468/dl. hqesx6). 649 million occurrences were saved as a Darwin Core archive. Occurrences from this archive were extracted and imported into a SQL database, where data were indexed to reduce the computation time of subsequent queries. We focused on 24 taxonomic classes out of the 297 referenced in GBIF, excluding classes with less than 1 million occurrences (9. 4 million occurrences from 19,000 species, had no class affiliation). We ended up with 626 million occurrences (NB occ ) and 1. 01 million species, representing more than 96% of the total number of occurrences and 84% of the total number of species in GBIF. All statistics were computed from this dataset. Taxonomic errors: imprecision and bias.
This method excluded synonyms. Furthermore, we computed p >20d , the proportion of species with at least 20 spatially distinct occurrences. Two occurrences were considered spatially distinct when, using a global grid of 10*10 km cells based on the pseudocylindrical equal-area map projection Eckert IV, they fell in two different cells. We chose a threshold of 20 spatially distinct occurrences because it is a common threshold in niche modelling analyses  61. Occurrences without spatial coordinates were excluded when computing the number of spatially distinct occurrences. We calculated how each class deviates from an 'ideal' sampling I, where each class is sampled proportionally to its number of known species (N). I = NB occ *(N/N tot ) where N tot is the total number of known species. To investigate the evolution of taxonomic bias over time, we excluded i) occurrences without a collection year and ii) occurrences recorded during the last 10 years because of the lag between recording and integration in the GBIF database (S.
Gaiji, pers. comm. ). The 'ideal' sampling I was calculated every ten years between 1956-2006 and deviations from these 'ideal' samplings were plotted for each class. Statistics were computed at the ordinal level for Agaricomycetes, Amphibia, Aves, Insecta, Lecanoromycetes, Magnoliopsida, Mammalia and Reptilia using the same methods. These classes were chosen due to their relatively high number of occurrences and/or species, and because of the diversity of patterns they exhibited in our preliminary results. We also tried to cover a large taxonomic range (Tetrapods, Arthropoda, Plantae, Fungi) to include as much biodiversity as possible. Explanatory variables computed from the GBIF dataset. Data origin. In GBIF, the origin of an occurrence can be specified using a controlled vocabulary in the 'basisOfRecord' field.
Statistical analyses. We favored medians (m) over means because of their robustness to outliers. For the same reason, we used the median absolute deviation (mad), which represents the median of the absolute deviation from the median, as a measure of statistical dispersion. In all analyses needing spatial or temporal information, O -space and O -time occurrences were ignored, respectively. The relationship between data origin, completeness and year of record was investigated using multiple correspondence analyses (MCA). Analyses were done on three samples of five million random occurrences from our dataset. The variables were: class (24 categories), year of the record (categories: '<1900' , '1900-1949' , '1950-1974' ,  '1975-1999' , '2000-2004' , '2005-2009' , '2010-2014' , '> = 2015'), data origin (categories: specimen, observation, unknown), data completeness (categories: no problem, missing temporal information, missing spatial information, missing both).
Because results can be hard to interpret when categories with very few observations are used  59  , each analysis was performed a second time ventilating the categories represented in less than 0. 5% of the dataset. To explore the relative impact of public interest and taxonomic research quantity on taxonomic bias, we used generalized linear models  62, 63  (GLM). For each of the 24 classes, we looked at the effect of these two variables and their interaction on the number of occurrences per species in GBIF. We used an identical model for all classes, which was fitted using a negative binomial distribution to take into account overdispersion. Half of the GLMs were computed using the 1,000 best-represented species in GBIF (Best), while the other half used 1,000 random species referenced in GBIF (Random).
Acknowledgements This study was developed as part of a Ph. D. project and was funded as a grant by the  Ministère de la Recherche  to JT. It was in part presented at the  IBS Special Meeting in Beijing, China. We thank the organizers for giving us the opportunity to present these results and the attendees for their useful questions and suggestions. We thank  Anne-Sophie Archambeau ,  Michel Baylac ,  Samy Gaiji ,  Marie-Elise Lecoq ,  Sophie Pamerlon ,  Roseli Pellens ,  Tim Robertson ,  Dmitri Schigel ,  Jérôme Sueur  and  Wilfried Thuillier  for fruitful discussions. We thank  Hélène Citerne  for revising the English. We thank two anonymous reviewers who helped improving the manuscript.
Three technical replicates of each PCR were performed and the mean of these was used in subsequent analyses. All the PCR efficiencies were between 97 and 103%. Statistical analysis of survival data and viral titers R version 3. 2. 1 (R Development Core Team 2011) was used for statistical analyses. In the experiments using flies overexpressing pst and/or flies transformed with a modified BAC clone, we recorded the life span of individual flies. These data were analyzed with a Cox proportional hazard mixed model, fitted using the R package "coxme. " The genotype of the fly line was treated as a fixed effect. The random effects were the vial in which a fly was kept, which was nested in the replicate fly line (where the same fly genotype had been generated twice by independent transformation events).
Identifying structural variants of pst We identified structural variants by looking at the sequence data of the DGRP genomes  (Mackay et al. 2012). Structural variants were detected when two halves of the same sequence read or read pair map to different positions or orientations within the reference genome. We analyzed 205 Freeze 2 BAM files of the DGRP lines  (Mackay et al. 2012 ) using Pindel_0. 2. 0  (Ye et al. 2009)  to identify the breakpoints of structural variants among the lines (deletions, tandem duplications, and large and small insertions). In 178 of the 205 lines (from which fly DNA was available), we confirmed the structural variants by carrying out PCR using primers either overlapping breakpoints or flanking them (Table  S2 in File S1 ). We repeated this twice for the small number of lines that showed conflicting results with the Pindel analysis.
Identifying cis-regulatory polymorphisms in pst To look for cis-regulatory polymorphisms that cause variation in pst expression, we used a set of microarray data of female head tissue in the DSPR  (King et al. 2014). The mean normalized expression of three pst probes that did not contain any SNPs segregating in the panel (FBtr0273398P00800, FBtr0273398P01433, and FBtr0273398P01911) were used. The QTL analysis was performed using the R package DSPRqtl (http://FlyRILs. org/Tools/Tutorial) (King et al. 2012b ) following  Cogni et al. (2016).
Association between pst expression and DCV resistance To test whether the structural variants were associated with pst expression or susceptibility to DCV, we genotyped 178 DGRP lines for structural variants by PCR (primers listed in Table  S2  in File S1). These variants were then combined with sequence data from the DGRP lines (Freeze 2). We have previously measured the survival of these fly lines after DCV infection  (Magwire et al. 2012). The mean pst expression level was measured in 196 DGRP lines (see above). We then tested for associations between the SNPs in the region of  3L: 7, 311, 381, 508 (BDGP5)  and the mean of pst expression of each DGRP line using a linear model. To estimate the genetic correlation between pst expression and survival after DCV infection in the DGRP lines, we used a bivariate general linear mixed model.
Test for natural selection on pst To investigate the frequency of resistance allele of A2469G in populations worldwide, we looked at publically available genome resequencing data sets of the Global Diversity Lines  (Grenier et al. 2015) , North American population (DGRP)  (Mackay et al. 2012) , and Zambian population [Drosophila Population Genomics Project (DPGP)]  (Pool et al. 2012). We also collected 341 iso-female D. melanogaster from Accra, Ghana and genotyped a pool of flies from these lines for SNP A2469G by PCR as described above. To test for a signature of natural selection on pst, we analyzed the sequence around pst from publically available genome sequences of Drosophila. These sequences were either from inbred lines or haploid genomes, so the data were phased as haplotypes. We analyzed data from two populations of D.
melanogaster with large sample sizes: a North American population (DGRP, 205 lines) and a Zambian population (DPGP3, 197 lines). The variant calls from these lines in VCF file format of Freeze 2 DGRP was downloaded from the Baylor College of Medicine, Human Genome Sequencing Center Web site  (Mackay et al. 2012). Because duplication and rearrangement of pst is very common in D. melanogaster, in the DGRP lines we Sanger sequenced pst from 35 lines of variant 3 and 28 lines of variant 4 so that we only analyzed data from the complete copy of the gene. These sequences were combined with 105 DGRP lines without rearrangement, resulting in a total of 165 DGRP lines with pst sequences. This was not possible for the data from Zambia as the original lines are not available. Here, consensus sequences of 197 D.
melanogaster samples were downloaded from http://www. dpgp. org/. About 20-kb sequence around pst (3L: 7,340,375-7,363,363) were pulled out from all lines using the scripts "breaker. pl" and "dataslice. pl" written by the authors, returning FastA files. Then FastA file was converted into a VCF file by PGDSpider  (Lischer and Excoffier 2012). To examine how allele frequencies differ between populations, F ST was calculated on a per-site basis for a North American population (DGRP) and a Zambian population (DPGP3) by VCFtools  (Danecek et al. 2011). To detect LD around SNP A2469G, we estimated LD between all pairs of SNPs in a 20-kb region around it. The R packages "genetics" and "LDheatmap"  (Shin et al. 2006)  were used to calculate and plot LD in a heat map. We then applied the long-range haplotype test  (Sabeti et al.
05, ** P , 0. 01, *** P , 0. 001). In total, the survival of 13,919 flies was analyzed. Error bars are SEs. The locations of the SNPs in chromosome 3L of the Drosophila genome are C398A: 7352966; G484A: 7352880; A1870G: 7351494; A2469G: 7350895; TA2911(2)CC: 7350452-3. The structural variants are named according to whether they are tandem duplications (TD) or deletions (D). expression affects susceptibility to the virus. We investigated using published microarray data from F individuals from crosses between two panels of recombinant fly lines derived from 15 founder lines from around the world (crosses between DSPR panel A females and panel B males)  (King et al. 2014). To map regions of the genome affecting pst expression, we used the mean normalized expression of three pst probes (FBtr0273398P00800, FBtr0273398P01433, and FBtr0273398P01911) that did not contain any SNPs.
Without having an estimate of the harm flies suffer due to DCV infection in nature it becomes impossible to reject the hypothesis that the benefits of resistance are balanced by pleiotropic costs. Second, costs of resistance are typically only expressed in certain environments and may affect many different traits   1 ). Red • 's represent lines that contain resistant allele G for SNP A2469G and blue • 's represent lines contain "T" for SNP A1455T. Gene expression was measured by qRT-PCR on 654 biological replicates of 196 fly lines. Survival after DCV infection was estimated from 730 vials of flies, with the data from  Magwire et al. (2012). (Kraaijeveld and Godfray 1997; McKean et al. 2008). It is possible that costs may detected if they are measured in the "wrong" environment or the trait affected is not measured-for example, it may increase susceptibility to other pathogen genotypes.
Figure5Cis-regulatory variation in pst expression. (A) Map of QTL associated with pst expression in female head of DSPR crosses. A single peak at position 3L: 7,350,000 was found (LOD = 35). The horizontal line is the genome-wide significance threshold obtained by permutation (P , 0. 05, LOD = 7. 12). Expression data are from published microarray analysis (King et al. 2014). (B) Association between pst expression and its SNPs and structural variants in DGRP lines. Gene expression was measured by qRT-PCR on 654 biological replicates of 196 fly lines. 2Log 10 (P-value) for the association between SNPs and expression in the region of3L: 7,311,381,508 (BDGP 5) is plotted against genome positions of the SNPs. SNPs are showed as s; SNP A2469G in red. Structural variants of pst are showed in orange :'s.
Acknowledgments Thanks to  Professor Jean-Luc Imler  for helpful and  Yuk-Sang Chan  for embryo micro-injection. work was supported by the  European Research Council  ( 281668  to F. M. J. ) and  Natural Environment Research Council  ( NE/L004232/1  to F. M. J. ). C. C. 's PhD was funded by the  Cambridge Commonwealth, European and International Trust and China Scholarship Council. R. C. is founded by the  São Paulo Research Foundation (Fundação de Amparo à Pesquisa do Estado de São Paulo) , grant no. 2015/08307-3  and  2013/25991-0.
Background: Independent Component Analysis (ICA) is a method that models gene expression data as an action of a set of statistically independent hidden factors. The output of ICA depends on a fundamental parameter: the number of components (factors) to compute. The optimal choice of this parameter, related to determining the effective data dimension, remains an open question in the application of blind source separation techniques to transcriptomic data. Results: Here we address the question of optimizing the number of statistically independent components in the analysis of transcriptomic data for reproducibility of the components in multiple runs of ICA (within the same or within varying effective dimensions) and in multiple independent datasets. To this end, we introduce ranking of independent components based on their stability in multiple ICA computation runs and define a distinguished number of components (Most Stable Transcriptome Dimension, MSTD) corresponding to the point of the qualitative change of the stability profile.
Based on a large body of data, we demonstrate that a sufficient number of dimensions is required for biological interpretability of the ICA decomposition and that the most stable components with ranks below MSTD have more chances to be reproduced in independent studies compared to the less stable ones. At the same time, we show that a transcriptomics dataset can be reduced to a relatively high number of dimensions without losing the interpretability of ICA, even though higher dimensions give rise to components driven by small gene sets.
Standard statistical tests applied to these vectors can then relate a component to a reference gene set (e. g. , cell cycle genes), or to clinical annotations accompanying the transcriptomic study (e. g. , tumor grade). The application of ICA to multiple expression datasets has been shown to uncover insightful knowledge about cancer biology  [3, 8]. In  [3]  a large multi-cancer ICA-based metaanalysis of transcriptomic data defined a set of metagenes associated with factors that are universal for many cancer types. Metagenes associated with cell cycle, inflammation, mitochondria function, GC-content, gender, basal-like cancer types reflected the intrinsic cancer cell properties. ICA was also able to unravel the organization of tumor microenvironment such as the presence of lymphocytes B and T, myofibroblasts, adipose tissue, smooth muscle cells and interferon signaling.
For example, ICA appeared to be a powerful method for analyzing the fMRI (functional magnetic resonance) data  [9] [10] [11] [12]. In this field, it was shown that choosing a too small effective data dimension might generate "fused components," not reflecting the heterogeneity of the data, leading to a loss of interesting sources (under-decomposition). At the same time, choosing the effective dimension too high might lead to signal-to-noise ratio deterioration, overfitting and splitting of the meaningful components (over-decomposition)  [10] [11] [12]. The influence of the effective dimension choice on the ICA performance has not been well studied in the context of transcriptomic data analysis. For example, in  [3]  each dataset was decomposed into a number of components in an ad hoc manner (n = 20).