dataesr/scientific-paragraphs-categorization huggingface dataset:
Short-read data sets We analyzed Illumina short-read data sets from 446 individuals of 116 species (1-11 individuals per species; Additional file 1: Table  S1 ). Read length was 100 in 358 individuals (92 species), 75 in 12 individuals (three species), and 50 in 76 individuals (21 species). Single-end sequencing was ordered in all cases. Occasionally, sequencing centers still returned paired-end reads, which were treated as single-end reads in our analyses for the sake of homogeneitymeaning that both reads were treated as independent events. The total number of reads varied from 2. 44 to 76 millions among samples. Samples sequenced later in the project typically received more reads than early-sequenced samples. Most of the generated data sets have been submitted to the National Center for Biotechnology Information (NCBI) Sequence Reads Archive (SRA) under bioprojects PRJNA230239, PRJNA249058, PRJNA268920, PRJNA278516, PRJNA3 22119, and PRJNA326910.
cox1 reference database We created a reference database of cox1 sequences for subsequent sequence similarity searches. This database had three components: a target species component, a companion species component, and a model species component. The target species component corresponds to cox1 sequences from the species that have been subjected to DNA barcoding and/or RNA extraction in our laboratory between 2009 and 2014. We automatically downloaded target species cox1 sequences from the Barcode Of Life Database  [32]  (May 6, 2016), accounting for taxonomic synonymywe equated Myodes with Clethrionomys, Cervus with Rucervus, Parus with Cyanistes, Mellicta with Melitaea, Physa with Physella, Galba with Lymnaea, Lineus with Ramphogordius, and Abatus agassizi with Abatus agassizii. Sequences in the Barcode of Life Data System (bold) database are binned based on similarity.
We kept a single cox1 sequence per bin per target species, maximizing sequence length and number of annotationsgeographic origin, collector, sampling date, lifestyle, tissue, and existence of voucher. Sequences not assigned to a bin were excluded. A similarity search was performed by BLAST to the NCBI non-redundant (NR) database. Thirteen sequences not hitting any cox1 sequences from NR were removed or manually replaced. Some of our target species were not represented in the bold database. For these we performed a manual search in GenBank and retrieved additional cox1 sequences. The companion species component of our reference cox1 database corresponds to species that never entered our laboratory, but that are phylogenetically related to target species. For each genus of our target species sample, we identified a companion genus from the same family or same order in which cox1 sequences were available in a roughly equivalent number of species (Additional file 1: Table  S1 ).
The same automatic and cleaning procedure as described above for target species was applied to companion species, with the exception that we did not manually search GenBank for companion species. The companion species component was added as a negative control, that is, a measure of the prevalence of seemingly foreign cox1 sequences in our samples due to experimental noise, in the absence of contamination. The model species component of our reference cox1 database corresponds to species of animals that are frequently subjected to NGS projects, with which our samples might have been in contact at some point during the experimental protocolincluding Homo sapiens. We selected the 20 species of animals with the largest number of entries in the NCBI-SRA database (March 15, 2016), and retrieved the complete cox1 sequence of each of these.
Detection of between-species contamination Short-read Illumina data sets were cleaned for lowquality reads or read portions as described previously  [35]. Reads were mapped to the reference cox1 database using Burrows-Wheeler Aligner (BWA) software  [36]. Mapping tolerance was one mismatch for data sets in which read length was 50 and two mismatches for data sets in which read length was 75 or 100. The best scoring hit of each read, if any, was recordednote that in case of equal mapping scores, BWA will randomly output one of the highest scoring hits. For each individual, the number of hits for each sequence of the reference database was recorded and normalized by total number of reads for the considered individual. The results were then summed up by species in order to calculate, for every pair of species (sp1, sp2), the total number of cox1 reads from sp1 mapping to a reference sequence from sp2.
Species from the reference database between which cox1 divergence was <5% were considered as nondiagnostic, meaning that a hit of a sp1 read to a sp2 read was only considered to reflect contamination if cox1 divergence between sp1 and sp2 was >5%. When more than one reference sequence per species was available, we required that the minimal divergence between any two cox1 sequences from the two species be >5%. Sixty such closely related species pairs were identified. These analyses were performed using homemade programs in C++ and R.
1  Detection of within-species contamination through homo-quartet analysis. Each multicolored square represents a quartet, that is, read counts for states A (green), C (yellow), G (blue), and T (orange) at a specific position in a specific individual, zeros being omitted. A fictive dataset of four individuals (Ind 1 to Ind 4 ) and five positions (Pos q a ab ½ ¼ 1-γ ð Þ 1=2-ε ð Þ þ γ f a 0 1-3ε ð Þþ 1-f a 0 ð Þε ð Þ ð 12Þ q c ab ½ ¼ 1-γ ð Þε þ γ f c 0 1-3ε ð Þþ 1-f c 0 ð Þε ð Þ ð 13Þ where f x ' is the frequency of reads of state x at the considered position excluding the focal individual. Equations 10, 11, 12, and 13 assume that with probability (1 -γ) a read state is determined by genotype and error rate, as in the original method, whereas with probability γ a read state is obtained by randomly sampling one state at the considered position, excluding reads from the focal individual.
cox1 reference database We created a reference database of 624 aligned, partial, 351-bp-long cox1 sequences. The database included a mixture of sequences from our target species (378 sequences from 149 species), companion species (226 sequences from 139 species), and model species (20 sequences from 20 species). Target species were intended to trace cross-contamination among samples. Companion species were introduced as negative controls. Model species were introduced to search for contamination by standard laboratory organisms. In our reference databases, 31 of our target species were not represented at all, 98 were represented by a single cox1 sequence, and six were represented by more than ten cox1 sequences, implying that our ability to detect the occurrence of a given species in a given sample varied among species.
Patterns of between-species contamination Short sequence reads from each of 446 samples (individuals) from 116 species were aligned to our reference cox1 database using BWA. The number of hits to each reference sequence was recorded and divided by the number of millions of reads of the considered sample. For each sample, we calculated the prevalence of cox1 hits to a reference sequence from the expected species, and the prevalence of cox1 hits to a reference sequence from an unexpected speciesthat is, a species differing from the expected species by >5% of cox1 divergence. Hits to a species different but <5% divergent from the expected one were not counted. Figure  2  shows an overview of the contamination pattern in this large-scale data set. Figure  2a  shows the across-samples distribution of the prevalence of expected (gray) vs.
unexpected (red) cox1 reads, while Fig. 2b  plots these two variables. The across-samples median prevalence of expected cox1 reads was 674 cox1 reads per million. The prevalence of expected cox1 reads was sometimes low: it was <10 per million in 86 samples, and zero in 52 samples, of which 13 were from a species that was represented in our reference cox1 database. This is quite surprising, given that cox1 is considered a generally highexpressed gene. This result might be explained by insufficient/inappropriate species representation in the reference database for these particular samples. It might also be that in some taxa mitochondrial transcripts lack a polyA tail (or use it as a degradation signal, as in plants  [39] ) and were therefore excluded at the retrotranscription stage in our protocol.
Summing contaminant species across samples, we found that the data set had been affected by at least 782 distinct events of between-species contamination. This is an underestimate, due to the incompleteness of our reference database, our inability to detect contamination between closely related species, and the possibility of multiple events of contaminations of a given sample by a given species. The number of expected cox1 reads, unexpected cox1 reads, and contaminant species per sample are available in Additional file 2: Table  S2. Reversely, 94 of the 180 species we processed in this project did contaminate at least one sample from another species. Among these, four species contaminated more than 15 distinct samples, and one, king penguin Aptenodytes patagonicus, contaminated samples from as many as 11 distinct species (Additional file 4: Figure  S1 ).
cinxia reference, but a b Fig. 2  Overall pattern of between-species contamination. a Among-sample distribution of the prevalence of reads mapping to a cox1 reference from the expected (gray) or an unexpected (red) species. Prevalence is defined as the number of cox1 reads per million reads. b Relationship between the prevalence of cox1 reads mapping to the expected (x-axis) vs. an unexpected (y-axis) species, again per million reads. Each dot represents a sample. Plain line: ratio of unexpected to expected cox1 reads is one. Dotted lines: ratio of unexpected to expected cox1 reads is 0. 1 (respectively, 0. 01). Samples from species not represented in our cox1 reference database are not shown >26,000 cox1 reads that mapped to a reference from the Spanish fritillary Euphydryas desfontainii.
Analysis of laboratory metadata We created a between-species contamination matrix M in which cell m ij contained zero in the absence of evidence for contamination of species j by species i, one in case of the detected contamination of species j by species i, and missing data if species i and j were <5% divergent cox1-wise, such that contamination detection was assumed to be unreliable. Here, a single read from any individual of species i hitting a reference sequence from species j was considered sufficient to attest for an event of contamination of i by j. Requiring at least ten unexpected reads, instead of just one, yielded qualitatively similar results. The 39 samples from species not represented in our reference cox1 database were here disregarded, so that sample size was 407 in this analysis.
Similarly, the same_shipment, same_flowcell, and same_lane variables indicated whether at least one sample of each of the two considered species had been shipped on the same day to the same sequencing center, or sequenced on the same flowcell/same lane, respectively. We calculated the average value of these variables across all pairs of species for which an event of contamination was attested (Fig. 3 , red vertical bars), and compared these to null distributions obtained by shuffling zeros and ones in the contamination matrix (Fig. 3 , white histograms, 1000 replicates). More precisely, each cell of a randomized matrix was assigned one with probability p, or zero with probability (1p), with missing data being left unchanged, where p = 0. 0133 was the overall probability of contamination (see above).
Laboratory contamination: detailed analysis Eight events of contamination were detected between species that were not shipped on the same date. Of these, four involved Glanville fritillary M. cinxia. This is the one species in our data set that included samples for which data on shipment date are missing (GA08B to GA08F, Additional file 2: Table  S2 ). The three species that contaminated or were contaminated by M. cinxia but lacked an attested shared shipment date with M. cinxia -Iberian hare Lepus granatensis, mountain hare L. timidus and ascidian Ciona intestinalis Awere shipped the same day, May 26, 2010. It seems therefore possible, not to say probable, that samples GA08B to GA08F were actually sent out for sequencing on May 26, 2010, and that contamination occurred in the sequencing center in this case, too.
Besides these four cases, one detected event of contamination between species not shipped on the same date involved gorgonian Eunicella cavolini and European blue mussel M. galloprovincialis. E. cavolini, however, shares a shipment date (January 23, 2013) with M. edulis, the other species of European mussel, which hybridizes with M. galloprovincialisthe two species have very similar haplotypes in our reference cox1 database. A closer inspection of the data revealed that the single E. cavolini sample, GA31L, affected by contamination from M. galloprovincialis is the single E. cavolini sample that was shipped on January 23, 2013. Eight cox1 reads from this sample mapped to a M. edulis reference and two mapped to a M. galloprovincialis reference. In conclusion, only three events of between-species contamination out of 782 can be unambiguously assigned to our laboratory: contamination of European pond turtle Emys orbicularis by ascidian Ciona intestinalis A and of seahorses Hippocampus hippocampus and H.
The index of allele leakage, λ, varied from 0. 19 to 8. 5, when λ = 0 would be expected in the absence of contamination. This analysis therefore indicates that within-species contamination is widespread in our dataset and probably affects all the sequenced species. We investigated the influence of laboratory metadata, and particularly the date of shipment to sequencing centers, on the prevalence of within-species contamination. To this end, we focused on the 12 species of our data set in which not all samples were shipped the same day that is, most often at two distinct dates, and up to four dates in the blue tit Parus caeruleus. In these species, we measured λ', the index of allele leakage between samples sent on different dates. This was achieved by only considering homo-quartets occurring at positions that were biallelic across the whole sample of individuals, but monoallelic in the subsample of individuals shipped the same day as the focal individual (Additional file 7: Figure  S3 ).
Contamination-aware SNP calling To assess the robustness of our published results to the problem of within-species contamination, we re-called SNPs and genotypes using a modified method accounting for allele leakage between individuals. Compared to our original SNP-calling method, a parameter γ was added, which represents the probability that a read originates from another individual of the sample. Three arbitrary values of γ were used: 0. 05, 0. 1, and 0. 2. Contamination-aware SNP calling was applied to the 39 species of our sample in which at least four individuals were available. Classical population genomic statistics were calculated from this data set using the same pipeline as in  [18]. To save computational time, SNP calling was applied to reduced data sets consisting of exactly one million positions per species, instead of the 1.
2. We compiled a worldwide fine-root trait dataset, featuring 1115 species from contrasting climatic areas, phylogeny and growth forms to test a series of hypotheses pertaining to the influence of plant functional types, soil and climate variables, and the degree of manipulation of plant growing conditions on species fine-root trait variation. Most particularly, we tested the competing hypotheses that fine-root traits typical of faster return on investment would be most strongly associated with conditions of limiting versus favourable soil resource availability. We accounted for both data source and species phylogenetic relatedness.
Accepted Article This article is protected by copyright. All rights reserved. or herbaceous species, and limit their scope to few drivers of trait variation (e. g. Kerkhoff et al. 2006; Salpagarova et al. 2014; Iversen et al. 2015). These studies nonetheless provide useful insights and hypotheses that could be tested globally on a more comprehensive dataset. For this purpose, we performed a global compilation of fine-root trait data including 1115 species from 55 published and unpublished studies that we refer to as the Rhizopolis database and explored the global variation in the four most commonly measured traits, fine-root nitrogen (N) concentration, mean diameter, tissue density and specific root length (SRL). The role of these four traits in plant functioning and resource economics (sensu  Wright et al.
DATA COLLECTION Data for the four fine-root traits used in this study were extracted from the Rhizopolis database. This database was compiled by the authors of this study from both published and unpublished datasets resulting from their own work and an independent literature survey (Table  S1 ). This database is available as part of the Fine-Root Ecology Database (FRED; http://roots. ornl. gov; Iversen et al. , in press) , released in March 2017. A dataset was considered suitable only when it contained at least two of the focal traits for at least three species grown in greenhouse, common garden, plantation or natural conditions. Root trait data obtained on seedlings (subjective cut-off of < 2 months for herbaceous and < 6 months for woody species) or in hydroponic conditions were excluded.
Within each site, trait values from all replicates of a given species were averaged. When datasets included data from the same species in different natural sites, all observations were retained. This resulted in 55 datasets with 8714 root trait observations for a total of 1115 species from 597 genera in 135 families. Taxonomic nomenclature was standardised using accepted names from The Plant List (http://www. theplantlist. org/). Taxon records for which the name was not fully resolved (e. g. Aster sp. ) were given unique identifiers. With respect to plant functional types, species were sorted into four main growth forms (Graminoids, Forbs, Shrubs and Trees) and N 2 -fixation ability (able/not able to fix atmospheric N 2 ), which were established based on the information.
Accepted Article This article is protected by copyright. All rights reserved. provided in the TRY database (https://www. try-db. org/TryWeb/Home. php). In the case of species not referenced in TRY, information was retrieved from alternative sources (Tela Botanica 2015; USDA NRCS 2015). Plant growth conditions were categorized as in 'pot' (indoors or outdoors), in 'common garden' (outdoors plantations), or in 'field' conditions (natural conditions), in order to differentiate between different degrees of climate and soil manipulation. In addition, we recorded how the fine-root sampling pool was defined in each study. Four root sampling categories were most commonly employed: roots ≤ 2 mm in diameter, roots ≤ 1 mm in diameter, first-order roots and the first-three-order roots (i. e. , functional approach;  McCormack et al.
It is only slightly biased by the use of a ≤ 2 mm cut-off limit because within this category fine-roots close to 2mm in diameter represent only a small proportion of the total length of fine-roots over which fine-root diameter is averaged. Owing to gaps in locally-measured climate data, we used climate data available from the Worldclim database  (Hijmans et al. 2005)  to represent common-garden and field observations. Climate variables retrieved were mean annual temperature (MAT) and mean annual precipitation (MAP) averaged over the period.
Accepted Article This article is protected by copyright. All rights reserved. 1 km 2 , and discrepancies between local climate data and the Worldclim database were observed for high elevation sites where abrupt variation in altitude may not be accurately accounted for by the 1 km 2 grid. To limit this bias, whenever high discrepancies were recorded, local climate data rather than global data were retained. Across all studies, little common information was available regarding edaphic characterization. Common-garden and field observations were therefore linked to soil properties as found in the Harmonized World Soil Database (FAO/IIASA/ISRIC/ISS-CAS/JRC 2012) based on their geographic coordinates. We assumed that soils of commongarden studies were representative of that of the local area, if not otherwise stated in the published articles.
The main topsoil (0-30 cm depth) properties retrieved included soil pH, cation exchange capacity, organic carbon content, clay fraction and bulk density. Only soil pH, cation exchange capacity and bulk density were kept in subsequent analyses because of strong correlations among variables in the retrieved soil datacation exchange capacity was strongly correlated to soil organic carbon content (R 2 = 0. 72) and bulk density strongly correlated with soil clay content (R 2 = 0. 67)in order to avoid issues of collinearity. Soil data were obtained from gridded datasets of 1 km 2 , which we acknowledge can lead to biased representation of these variables in locations with wide ranges in climate and soil conditions, such as mountainous areas.
ROOT DATA COVERAGE The database covers most of the range of mean annual temperature-rainfall space in which higher plants are found, as well as the major climatic areas of the world. However, some large geographic regions are not represented, such as Siberia, the Middle East, North and Central Africa, and Central America (Fig. 1 ). The majority of species originate from the temperate area (48% of observations), followed by cold (21%), arid (16%) and tropical areas.
Accepted Article This article is protected by copyright. All rights reserved. (15%). With respect to species counts across broad phylogenetic clades, the representativeness of the dataset is reasonably good. Eudicots (56% of this dataset, compared to ~66% of the world's species) and monocots (37% vs ~26%) account for the largest number of species of vascular plants, followed by magnoliids (3. 6% vs ~3. 4%) and gymnosperms (2. 6% vs ~0. 3%), whereas pteridophytes are under-represented (1. 1% vs ~4. 3%). The four main growth forms are all well represented in the dataset, with forbs (37% of observations in this dataset, compared to ~34% of species in the TRY database) and graminoids (35% vs ~24%) being the two groups most represented, followed by trees (15% vs ~24%) and shrubs (12% vs ~18%). The group of N 2 -fixers is also well represented with over 8% of observations.
PHYLOGENY CONSTRUCTION A phylogenetic tree was constructed (Fig. S1 ) using the comprehensive Angiosperm specieslevel phylogeny from Zanne et al. (2014) , as updated by  Qian & Yin (2015). This timecalibrated tree includes nearly all families of extant seed plants: species placement relied on seven gene regions, with orders and families constrained by the APG III (2009). We used S. PhyloMaker  (Qian & Jin 2015)  to generate a phylogeny containing species from the Rhizopolis database from this megaphylogeny. More than 98% of species represented by at least one of the four focal traits (N, diameter, tissue density, SRL) could be included in the resulting phylogenetic tree. Where species or genera were not represented in the larger tree, they were added as uninformative (e. g. basal) polytomies.