dataesr/scientific-paragraphs-categorization huggingface dataset:
Biological Data The dataset comprised 204 French lakes, 48 natural lakes, and 156 reservoirs, for which biological and environmental data were available and collected in a standardized manner. Fish samples were collected according to the Norden gillnet standardized protocol  [44]  during the period between July and mid-October. This protocol is based on a randomly stratified sampling design. Benthic gillnets, 30 m in length, 1. 5 m in height, composed of 12 panels with mesh sizes ranging from 5 to 55 mm knot-to-knot, were randomly distributed in the depth strata of the lakes. The gillnets were set before sunset and lifted after sunrise to cover peaks of maximal fish activity  [45, 46]. All fish caught were identified at the species level, then measured (total length in millimeters) and weighed (to the nearest gram).
Phytoplankton was collected using a standardized method  [47]  and processed in laboratory following the counting process of the European Standard NF15204  [48]. Four sampling campaigns a year are recommended for each lake: three during the warmer period (between May and October) and one in late winter. Phytoplankton was collected at the deepest point of the lakes, in the euphotic part of the water column. Taxa were determined at the species level in the laboratory and their abundances were weighted by taxa biovolume  [49]  using standard cell values defined in the software Phytobs  [50] , or measured directly from the sample if the values were lacking. Additionally, chlorophyll-a was collected from the euphotic zone during each sampling event and measured using the standard methods NF-T 90-117  [49, 51, 52].
Environmental Data The lakes were characterized by natural environmental variables potentially influencing the structure of biological assemblages  [67, 68] , i. e. , physical/morphological characteristic of their environment, and by stressors. The dataset comprised 204 French lakes, 48 natural lakes and 156 reservoirs. This corresponds to a diversity of lake throughout the French territory with morphological characteristics, ranging from the plain lake to the mountain lake, from the shallow lake to the deep lake, and very varied in terms of surface, area, the shape of the lake basin, and mean temperature or trophy level (details of the calculation of the index below) (Table  1 , see Supplementary Materials for more details). In order to limit the multi-collinearity between predictors (thus the redundancy between physical variables), we ran a principal component analysis.
Four variables emerged from this analysis and characterized lake morphology: the mean depth (Depth, m), the area (Area, km 2 ), the water volume (Volume, m 3 ), and the overall hill index (Ig, m/km). Ig was calculated from the maximum depth, perimeter, and area  [69, 70]  that characterizes the shape of the lake basin, according to the following Equation (1): 𝐼 , (1) with 𝐿𝑟 𝑃𝑙𝑎𝑘𝑒 , , 1 1 , , (2) and K 0,282 √ , ( 3 ) where Pmax is the maximum depth of the lake (m), Plake is its perimeter (km) and Alake its area (km 2 ). Two stressors were considered: temperature and eutrophication. Because water temperatures were not available for all the lakes but are strongly correlated with air temperatures  [71, 72]  (R 2 = 0. 82), we used the latter to characterize lake temperatures. To this end we used data from the SAFRAN reanalysis  [73, 74] , available at a spatial resolution of 8 km × 8 km.
To integrate the difference of altitude between grid cells and lakes, a correction of 6. 5 × 10 -3 C/m was applied. Eutrophication was described by three variables: total phosphorous concentration (TP, μg/L), nitrate concentration (NO3, μg/L) and importance of non-natural land cover in the catchment area (NNLC, percentage of the total catchment area). Phosphorus values range from 5 μg/L to 464 μg/L, corresponding to trophic states classified from oligotrophic to hyper-eutrophic  [75]. The nutrients were sampled in the euphotic zone during the four same annual campaigns as for phytoplankton, according to a standard sampling method. NNLC was defined as the percentage of non-natural areas in the catchment and derived from the Corine Land Cover database  [76]. This encompassed the CLC categories: (1) artificial territories and (2) agricultural territories (without 23 grasslands)  [77].
We summarized the information of these eutrophication measures in a synthetic index of eutrophication. First, TP and NO3 were log-transformed, NNLC was transformed by the arcsin of the square root, and then each variable was centered and reduced. The three transformed variables were averaged and these values were centered and reduced to produce the synthetic index. All the data used in this study were collected between 2005 and 2017 by research institutes or water agencies, and centralized in a database by our laboratory. To reduce the skewness of their distribution we log transformed maximum depth, lake area, lake volume, Ig, BPUEs, CPUEs, Chl-a, and the biovolumes of cyanobacteria and Chrysophyceae.
Environment and Pressures The two metrics describing the pressures were weakly correlated with each other and with the natural environmental variables (Table  2 ). Temperature varied between 2. 9 °C and 16. 3 °C and eutrophication between -2 (low level of eutrophication) and 3. 1 (high level of eutrophication) (Table  1 ). Not all possible combinations of pressure values were observed (white space in the right lower part of Figure  1 ). For instance, no lakes with an index value of eutrophication greater than -0. 5 had a temperature lower than 8 °C, or an eutrophication greater than 1 with a temperature lower than 8 °C. Finally, only a few were present at a temperature below 6 °C. These limitations of our dataset conditions will be taken into account in the following interaction analyze of pressure effects on biological metrics.
When all possible combinations of the pressures (Figure  2ac ) are used to visualize the estimated effect of each pressure (taking into account the second pressure due to the interaction between the two), some unexpected relationship may appear: for instance, a positive relationship between the ratio of perch/roach biomasses and nutrient enrichment for cold lakes (see Figure  2b ). This is probably due to deep extrapolations for non-observed pressure conditions. Unlike an experimental design, for which environmental conditions are controlled and a perfect crossover of pressures can be used, the cold lakes in our dataset were mainly oligotrophic. Eutrophic lakes were predominantly observed under cool and warm conditions (Figure  1 ). However, interactive effects of temperature and nutrients on community dynamics are very often studied and observed on phytoplankton  [37, 84, 85] , but poorly tested for fish.
It can also be noted that some α" bands can be observed on the Ti-12Mo EBSD maps (blue interfaces on Figure  5 ), but exhibiting a small Schmid factor with respect to external stress. It may be postulated that martensite in these grains is created to accommodate internal stress, as highlighted by  Danard et al. (2019). Comparison of Figures 5, 6 allows highlighting two different behaviors. The applied stress seems to be mainly accommodated by twin growth for Ti-12Mo. At the opposite, the nucleation mechanism appears to be favored in Ti-8. 5Cr-1. 5Sn, resulting in a high quantity of twins/matrix interfaces, at higher strain levels and despite an apparently smaller twinned area fraction. To quantify more precisely these evolutions, several parameters have been measured using image analysis. Thereby, three parameters have been measured to evaluate twin-size evolution: (1) the length of twin/matrix interfaces by surface unit (in µm -1 ), estimated from the length of CSL 11 lines (in µm) given by the OIM software and the size of grains (in µm 2 ) by image analysis using the ImageJ software (2) the average fraction of twinned surface per grain, measured by the ImageJ software (3) the average number of twins per grain at equal surface areas using the ImageJ software The results are reported in Figure  7 , in red for Ti-12Mo and in blue for Ti-8.
FUNDING This work was supported by the  French National Agency for Research (ANR)  in the  ANR-15-CE08-0013   TITWIP  project. YD and FP are grateful to SAFRAN company for its financial support on the YD Ph. D. grant. IF and MP declare that this study received funding from  SAFRAN TECH  through the  YD Ph. D. grant. The funder was not involved in the study design, collection, analysis, interpretation of the data, the writing of this article or the decision to submit it for publication.
| Trait data Much of the data relevant to this study was published by  Drury et al. (2018)  but we include data from an additional 30 species and add two morphological traits (one beak length measure and handwing index) described in the supplementary material. We collected morphological trait data for all 349 species of Thraupidae presented in the maximum clade credibility (MCC) tree from  Burns et al. (2014). Measurement methods are described in detail elsewhere  (Drury et al. , 2018; Trisos et al. , 2014)  and summarized briefly here. We aimed to measure at least four (average = 5. 7) museum specimens to compile data on eight continuous linear morphological measurements (given to the nearest tenth of a millimeter): culmen length, beak tip to anterior edge of nostrils, beak width and depth, tarsus length, wing length (from the bend of the wing to the tip of the longest primary), and tail length (from the tip of the longest rectrix to the point at which the two central rectrices protrude from the skin).
| Phylogenetic data and taxonomic diversification regimes The latest available MCC tree of Thraupidae was obtained from  Burns et al. (2014). It contains 349 (93%) of the 377 tanager species listed by  Clements et al. (Clements et al. , 2019). To quantify diversification rates across the tree, we following  Burns et al. (2014)  in using the Bayesian Analysis of Macroevolutionary Mixtures (BAMM) program  (Rabosky, 2014) , which is designed to detect and quantify heterogeneity in evolutionary rates using a reversible-jump Markov chain Monte Carlo (rjMCMC). Although recent criticisms have drawn attention to weaknesses in BAMM estimation of diversification rates and rate shifts  (Meyer et al. , 2018; Meyer & Wiens, 2017; Moore et al. , 2016) , the package matches or outperforms other methods when supplied with equivalent information  (Rabosky, 2018; Rabosky et al.
, 2017). We do not attempt to infer speciation rates, but rather the net result of speciation minus extinction, that is, diversification. We ran two MCMCs for 100,000,000 generations sampling every 1,000 generations, checking for convergence by plotting the log-likelihood trace of the MCMC output file and ensuring effective sample sizes exceeded 200 (after 30% burn-in) using the "coda" package  (Plummer et al. , 2006). A single expected shift, typical of trees with <500 tips, was computed following configuration of the control file according to BAMM specifications (http://bammproje ct. org/quick start. html). Using the "BAMMtools" package in R  (Rabosky, 2014) , we identified the maximum a posteriori probability (MAP) shift configuration (the distinct shift configuration with the highest posterior probability).
| Trait disparity among dietary guilds Since the beak morphology of species with identical or similar dietary compositions may be correlated, the independence assumptions made under traditional regression approaches or mixed models may be violated. Generalized estimation equation (GEE) addresses this problem by describing changes in a mean (in our case, beak measurements) given changes in covariates (in our case, diet) accounting for nonindependence  (Hubbard et al. 2010). We implemented a GEE model using the "ape" package  (Paradis & Claude, 2002)  to assess whether observed variation in beak morphology and overall morphology are associated with species diet. The model uses a correlation matrix specifying whether the dependence among observations are discrete or continuous  (Paradis & Claude, 2002).
We modeled the four linear beak dimensions against the primary diet category for all thraupid species under the approximation that the distribution of events in the analysis was binomial. Beak measurements were included as predictors and diet was treated as a binary response since species were assigned to a particular dietary category if it made up over 50% of their dietary composition (see trait data above). To obtain a Euclidean representation of the noncontinuous dietary information, a symmetric similarity/distance matrix was calculated from the original matrix of 353 species and 6 dietary items to conduct principal coordinates analysis (PCoA) in the "vegan" package  (Oksanen et al. 2019).
Motivation: In this work we present REINDEER, a novel computational method that performs indexing of sequences and records their abundances across a collection of datasets. To the best of our knowledge, other indexing methods have so far been unable to record abundances efficiently across large datasets. Results: We used REINDEER to index the abundances of sequences within 2585 human RNA-seq experiments in 45 h using only 56 GB of RAM. This makes REINDEER the first method able to record abundances at the scale of $4 billion distinct k-mers across 2585 datasets. REINDEER also supports exact presence/absence queries of k-mers. Briefly, REINDEER constructs the compacted de Bruijn graph of each dataset, then conceptually merges those de Bruijn graphs into a single global one. Then, REINDEER constructs and indexes monotigs, which in a nutshell are groups of k-mers of similar abundances.
Introduction An overwhelming amount of sequencing data is now publicly available in repositories such as the European Nucleotide Archive (ENA)  (Amid et al. , 2020)  and the Sequence Read Archive (SRA)  (Leinonen et al. , 2011). They contain a wealth of nucleic sequencing experiments on model and non-model species, tumor biopsies, cell lines, metagenomes. Searching within them, in a holistic setting, would open the tantalizing possibility of making scientific discoveries using world-scale biological data. However, the immense size of these repositories renders any large-scale investigation extremely difficult. SRA hosts around 30 petabases of experiments at the time of writing. Not only is searching within this mass of data impractical, but also merely downloading a copy of ENA or SRA would take in the order of years.
Thus, there is a pressing need for solutions that, possibly using a centralized server, would provide search functionality across these databases. We use the term dataset to refer to a set of reads resulting from sequencing an individual sample. Searching for a sequence within a single assembled genome [e. g. with BWA-MEM  (Li, 2013) ] or within a single dataset [e. g. with BEETL  (Janin et al. , 2014)  or SRA-BLAST  (Camacho et al. , 2009) ] is now routine and can be considered to be a well-managed computational problem. Since two decades, searching within collections of assembled genomes [e. g. with BLAST  (Altschul et al. , 1990)  or DIAMOND  (Buchfink et al. , 2015) ] is equally practical. However, searching for sequences within collections of (unassembled) datasets is still extremely challenging, and under very active computational investigation  (Marchet et al.
, 2019a). Examples of recent works are HowDeSBT  (Harris and Medvedev, 2018) , Mantis  (Pandey et al. , 2018) , SeqOthello  (Yu et al. , 2018)  and BIGSI  (Bradley et al. , 2019). They introduced novel k-mer (substrings of size k in biological sequences) data structures, showing that k-mer searches are a useful proxy for exact and approximate sequence search. In a nutshell, state of the art methods are able to determine the presence/absence of any k-mer within collections containing up to around 10 4 indexed datasets. Any longer sequence such as a gene or a transcript can be queried through its constituent k-mers. To the best of our knowledge, no method is currently able to record abundances of k-mers across collections at this scale. While nearly all recent works on indexing datasets have focused on recording the presence/absence of k-mers, in this work we focus on the more space-expensive matter of indexing their counts, i.
e. how many times is a k-mer present within each dataset. Foremost, large-scale efforts for indexing abundances in transcriptome collections are currently ongoing. For example the GTEx project  (Lonsdale et al. , 2013)  hosts a database of transcript-level abundances across 17k tissue samples (as of January 2020). Pan-cancer studies such as TCGA  (Tomczak et al. , 2015)  collect the expression of genes, exons, miRNAs, as well as genomic copy-numbers. Those signals are usually recorded at the level of genes or comparatively long genomic regions, yet they would further benefit from nucleotidelevel resolution, for example to investigate SNPs and splice junctions. Secondly, metagenomic sequencing is providing a widening window onto the diversity of life on earth. Comparing abundances of contigs, SNPs or species across different metagenomic datasets is a vital step in understanding how community composition varies with context.
Thirdly, sequencing now increasingly provides counters or sensors for events: this progression can be seen from CHIPseq, ATAC-seq, through to single-cell RNA sequencing-after which counts are modelled based on the precise experiment. We also highlight that merely adapting existing data structures by transforming the 1-bit presence/absence information into a (e. g. 16-bit) counter is unlikely to be a viable strategy. For instance, consider the HowDeSBT data structure  (Harris and Medvedev, 2018) , a recent technique for indexing the presence/absence of k-mers across dataset collections. It saves space by using a single memory location to encode the presence of a k-mer across multiple datasets. Yet this scheme cannot be adapted to record abundances, as a k-mer may be present in multiple datasets at different abundances, which cannot all be recorded by a single memory location.
Likewise, BIGSI  (Bradley et al. , 2019)  uses Bloom filters with 25% false positive rate to encode presence/absence of k-mers; extending Bloom filters to support abundance queries (e. g. using Count-Min sketches) at a comparable false positive rate would possibly introduce significant abundance estimation errors. Here, we introduce REINDEER (REad Index for abuNDancE quERy), a novel computational method that performs indexing of k-mers and records their counts across a collection of datasets. REINDEER uses a combination of several concepts. The first novelty is to associate k-mers to their counts within datasets, instead of only recording the presence/absence of k-mers as is nearly universally done in previous works. To achieve this, a second novelty is the introduction of monotigs, which allows space-efficient grouping of k-mers having similar count profiles across datasets.
Problem statement The questions addressed by REINDEER can be formally framed as follows. Let S be a nucleic sequence of arbitrary length (such as a gene, a transcript or a shorter sequence), and C a collection of datasets. The aims are, for each dataset in C, to either (a) determine the presence of S or (b) count the number of occurrences of S. In its simplest form, aim (a) amounts to returning the subset of datasets in which S appears. In a seminal work,  Solomon and Kingsford (2016)  slightly reformulated aim (a) to facilitate its resolution. Considering the set Q of all k-mers extracted from S, they propose to instead determine the subset of datasets in which at least t k-mers of Q appear, with 0 < t jQ. This is not strictly equivalent to computing the (exact or approximate) match of S within datasets.
In this new formulation k-mers from Q may also be found in different sequences across a dataset, e. g. if ACT and CTG are two k-mers seen in two different reads, sequence ACTG is assumed to be present in the dataset regardless of whether it is actually part of a read. Yet this reformulation can be considered as a reasonable approximation. Thus, in this work, we propose a low-footprint index, based on kmer counts, for efficiently supporting (a) and (b) queries and more concretely: 1. Assess S's count in datasets. Estimate the abundance of S in each dataset by reporting the mean count of all k-mers from Q that are (exactly) present in the dataset. 2. Assess S's presence in datasets. Output a list of datasets in which at least t k-mers from Q appear. Supporting queries of type 1 will be the main improvement proposed by this work compared to the state-of-the-art.
de Bruijn graphs and associated concepts In this section, we recall the concepts of de Bruijn graphs, unitigs and compacted de Bruijn graphs. We further describe two recent concepts: spectrum-preserving string sets  (Rahman and Medvedev, 2020)  and the BLight indexing scheme  (Marchet et al. , 2019b). A set of reads R (also called dataset) is a set of finite strings over the alphabet fA, C, G, Tg. We will consider all the strings of length k (called k-mers) present in R, for some fixed value of k > 0. For simplicity of presentation, strings are 1-indexed and reversecomplements are omitted from the definitions, but they are taken into account in the canonical way in the software: each k-mer is represented by the lexicographically smallest of the forward string and reverse-complemented string.
Definition 2 Unitig. The string corresponding to a unipath s ¼ u 0 ;. u n can be defined by concatenating u 0 with all the u i ½k (the kth letter of u i ), in order, for 0 < i n. The resulting string of length k þ n is called a unitig. Definition 3 Compacted de Bruijn graph. The directed graph where nodes are unitigs, and edges correspond to ðk À 1Þ-overlaps between two nodes sequences, is called a compacted de Bruijn graph. Contrary to a regular DBG, a compacted DBG has nodes of various lengths (! k). In the following, unless explicitly mentioned, the objects referred to as 'de Bruijn graphs' are compacted de Bruijn graphs. Even though compacted DBGs are technically not DBGs, they do equivalently represent the same k-mer set. Each k-mer has a count corresponding to the number of times it appears in the reads of a dataset, which can be obtained using a k-mer counter  (Kokot et al.
, 2017). Definition 4 Unitig abundance. We call the unitig abundance c(u): cðuÞ ¼ 1 n X n i¼1 countðk i Þ for k 1 ; ::; k n the k-mers of the unitig u of length n þ k À 1. Definition 5 Union de Bruijn graph. Given a collections of de Bruijn graphs fG 1 k ;. ; G c k g, we call union de Bruijn graph the de Bruijn graph which has a node set equal to the union of the nodes set of fG 1 k ;. ; G c k g, and edges correspond to ðk À 1Þ-overlaps between k-mers of the node set. It follows that a union DBG also represents a k-mer set (the union of c k-mer sets). We now recall the recent concept of spectrumpreserving string sets  (Rahman and Medvedev, 2020)  to draw new bridges between previous works, and also with our contribution. Definition 6 Spectrum-preserving string set (SPSS)  (Rahman and Medvedev, 2020).
Given a multiset of k-mers X, a set of strings S of length ! k is a SPSS representation of X if the multiset of all k-mers from S is exactly X. In particular, a SPSS can be used to represent the node set of a DBG. In such case, the SPSS represents a set (i. e. a multiset with no duplicates) as all k-mers are distinct. Unless explicitly noted, we will further consider in the rest of the article that all SPSSs represent sets and not multisets. Given a set of k-mers X, the trivial set of all k-mer strings of X is itself a SPSS of X, albeit not a very space-efficient one. The set of unitigs constructed from X is also of SPSS of X. We will discuss more types of SPSSs below.