dataesr/scientific-paragraphs-categorization huggingface dataset:
These databases are generally more accurate and often contain manually curated sequence entries. Examples of wellcurated databases are CARD  [126]  for antibiotic resistance genes or BacMet  [127]  for antibacterial biocide and metal resistance genes. Since running all the tools mentioned above separately and integrating results for each gene from these different tools is not practical, integrated environments that group many of these methods, allowing automatic genome annotation, can be used. Among these, several online platforms support the submission of MAGs, such as MG-RAST v. 4. 0  [70] , Micro-Scope  [128] , or IMG/M v. 5. 0  [69]. More recently, other, more flexible, tools that allow the processing of metagenomic data from raw data or contigs have been developed. One example is MOCAT2  [129] , which allows quality trimming, decontamination, assembly, assembly revision and gene prediction.
This is not an issue when the aim is to obtain a general overview of the community functions, but it can result in highly inaccurate annotations when one is searching for specific functions. In this case, the best choice is to use specific curated databases, as mentioned above. Other options are standalone pipelines for the annotation of assembled contigs, scaffolds, or whole-genome sequences. Among these, we have the popular Prokka annotation tool  [131] , DFAST  [132] , which is especially useful to transfer annotations from other genomes, or the National Center for Biotechnology Information (NCBI) tool, PGAP  [133]. For eukaryotes, there are also specific tools for functional annotation, such as the previously mentioned MAKER2, or the Genome Sequence Annotation Server (GenSAS)  [134] , which has recently become available.
However, none of the previously mentioned tools has been developed specifically to cope with the typical problems associated with MAGs, such as poor quality assembly, possible contamination with foreign genes, or lack of close reference genomes. Recently developed tools such as the MetaErg  [135]  pipeline address these challenges, although at the cost of greater running time and increased computational resources. Going even further, the web application METAREP  [136]  allows one to analyse and compare annotated metagenomics datasets providing graphical summaries for top taxonomic and functional classifications, as well as a GO, NCBI Taxonomy and KEGG pathway browser. Finally, catalogues of reference genes from different microbiomes are emerging and are becoming crucial for functional metagenomic analyses.
However, these data also have the potential to reveal which organism encodes these functions, although this question is generally answered using marker gene profiling, an approach that will be described extensively later in this review. The advantage of using WGS metagenomics is that it bypasses the biases that may be introduced during the PCR amplification of the marker gene. For this purpose, software such as the previously mentioned MEGAN6 tool  [71] , some of the previously described binning methods, e. g. DIAMOND  [141] , or some of the above-cited annotation pipelines, such as MG-RAST v. 4  [142] , can be used. Another recently developed tool is the MAGpy pipeline  [143] , which can identify the likely taxonomy of hundreds or thousands of MAGs, draw a taxonomic tree and carry out genome annotation.
Thus, software that allows the analysis of raw metagenomic data directly for both taxonomic classification and functional assignments has been developed. The advantage of using WGS reads for taxonomic classification is that it allows the detection of organisms across all domains of life and alleviates biases due to primer choices for marker gene analyses  [93]. The above-mentioned binning methods can be used in addition to, for example, MG-RASTv. 4 [70] , MEGAN6  [71] , CARMA3  [64] , or Taxator-tk  [88] , which are well-established tools for reference-based classification. These methods are highly accurate but have the disadvantage of being slow. To speed up the process, alternative methods such as Kraken  [89]  or Clark  [149]  replace the direct alignment of a query against a reference database by a fast-lookup method of fixed-length k-mers extracted from the query.
Subsequent matching of the query k-mers to an index structure prebuilt from the references allows quick classification. The recent version of Kraken, Kraken2  [150] , achieved a major reduction in memory usage. Alternatively, there are web-based tools such as Taxonomer  [151]. Another fast tool is Centrifuge  [152] , an approach that reduces the high memory requirements of k-mer-indexing structures using a highly compressed Burrows-Wheeler-transformed Ferragina-Manzini (FM) index. This tool also implements a feature that combines shared sequences from closely related genomes, greatly reducing redundancy for species where dozens of strains have been sequenced. Another tool based on the FM index is Kaiju software  [153] , which uses a database of translated proteins, and the six-frame translations of reads are aligned against these protein databases.
When working directly with reads for annotation, traditional tools such as blast are often too slow because of the significant amount of data being processed. Thus, new methods employing optimized strategies that allow comparison of nucleotide sequences to protein databases have been developed to speed up the process. The first tools were Usearch  [159] , BLAT5  [160]  and the faster RAPSearch2  [161]. Since the quantity of data continues to increase, new tools such as DIAMOND  [141] , software that replaces blastx by reaching similar sensitivity levels but that is thousands of times faster by using double indexing, have been developed. Recently, new, even faster methods, such as PALADIN  [162] , have been developed to cope better with the analysis of the everincreasing quantities of sequences.
PALADIN provides results seven times faster than DIAMOND or GRASP2  [163]. Simultaneously, several pipelines were set up that integrate some of these and other previously mentioned tools, to allow direct annotation from raw sequencing data or contigs. For example, FUN4ME  [164]  integrates three tools: FragGeneScan for gene calling, RAPSearch2 for homology research and the MinPath  [165]  tool to allow biological pathway reconstruction. Classical, previously mentioned software such as MOCAT2  [129] , the web portal MG-RAST v. 4  [70] , or the IMG/M v. 5. 0  [69]  annotation server, also allow the comparison of metagenomic sequence reads to a reference database of functionally annotated protein families and use homology inference to annotate them. The more recent pipeline, MGS-Fast  [166] , allows both functional annotation and taxonomic profiling from reads and contigs.
More recently, advanced tools such as HUMAnN2  [167]  allow the inference of the functional and metabolic potential of a microbial metagenome directly from short sequence reads. The recently developed Carnelian  [168]  tool is recommended to perform comparative functional metagenomics. Further, more flexible tools enabling customizable annotation, such as MetaStorm  [169] , a web server that supports read or assembly annotation based on a reference dataset uploaded by the users, have been developed. This also provides enhanced interactive visualization and outperforms previous tools. The pipeline Shot-MAP  [170]  offers even more flexibility than MetaStorm. It was developed based on simulations to optimize metagenome annotation and allows users to select settings according to their data. For example, users can select from a variety of gene prediction and alignment algorithms, tune the specific thresholds used to classify reads into families or change the mapping parameters according to read length.
Therefore, recently, non-OTU-based methods such as DADA2  [8] , Deblur  [10] , or UNOISE3  [9]  have been developed to determine exact features named amplicon sequence variants (ASVs). These programs allow us to analyse the microbial diversity of various environments, such as the gut  [181] [182] [183] , oral  [184] , plant  [185] , or water microbiomes  [186]. DADA2 is an open-source R package that models and corrects errors produced during Illumina sequencing, identifies ASVs and resolves differences of as little as one nucleotide  [8]. The method is based on the Divisive Amplicon Denoising Algorithm (DADA), a model-based approach for correcting amplicon errors  [187]. DADA2 performs filtering, dereplication, removal of singletons, sample inference, chimera identification and merging of paired-end reads, providing data that are ready for further ecological and statistical analysis.
Taxonomic annotation and reference databases Assigning taxonomy to OTUs/ASVs is a critical step in microbial community analyses since it answers the question, 'who is there?'. Different classification methods and databases have been developed for the taxonomic assignment of the most common marker genes, the 16S rRNA and 18S RNA genes, and the ITS region. Typical classification software is based on different algorithms, such as RDP classifier  [190] , UCLUST  [159] , or blast.
BOX 1. Definitions and terms used in microbial ecology in the area of sequencing Alpha diversity: the diversity measured within a particular ecosystem or sample. It is commonly characterized by OTU/ASV richness, evenness and phylogenetic diversity. Beta diversity: diversity comparison between particular ecosystems or samples. It is commonly analysed through ecological and phylogenetic distances estimated from the sample composition. Rarefaction curves: plots representing the number of samples on the x-axis and the number of 'species' or diversity variants on the y-axis. The curves show how the richness increases with the increase of sequencing depth, and they are commonly used to select a threshold value (plateau) to perform diversity analyses. Metadata: all the variables and data relevant for the study providing information about the samples included.
For example, in a theoretical study of a water-associated microbial community, typical metadata could be measures of temperature, pH, salinity, or oxygen levels from the samples analysed. [191] , coupled to reference databases. RDP classifier, one of the most applied tools in taxonomic assignments, allows classification at the genus level with an accuracy of around 80 % by using trained naive Bayes models  [190]. On the other hand, UCLUST and blast are based on alignment methods  [159, 191]. UCLUST, a clustering method that employs the USEARCH algorithm to assign sequences to clusters shows high sensitivity and is faster than blast. As part of the QIIME 2 project, the q2-feature-classifier, a taxonomic classification method based on novel machine learning and alignmentbased methods, was developed  [192].
The q2-feature-classifier provides two alignment-based taxonomy classifiers based on blast+  [193]  and VSEARCH  [194] , and a multinomial naive Bayes machine learning classifier. Bokulich and colleagues performed a comparison of these novel methods with previous ones, including RDP classifier, UCLUST and blast. The new naive Bayes, VSEARCH and blast+ classifiers (included in the q2-feature-classifier plug-in) perform equally well or better than the previous generation of methods. Importantly, the optimization of parameter settings is as critical as choosing the correct method, because the accuracy of the classification and the ability to novel taxa may vary considerably depending on these choices  [192]. Furthermore, the output is, of course, influenced by the databases, and thus they also need to be chosen carefully.
The primary databases used for 16S rRNA gene analyses are Greengenes  [195] , the Ribosomal Database Project  [196]  and silva  [197]. silva also includes small and large subunits of the rRNA gene (16S/18S and 23S/28S). These curated databases are often integrated into the most common pipelines for marker gene analyses, such as QIIME 2  [188] , mothur  [198] , or RDP classifier  [190]. Micro-eukaryotic diversity has been characterized less than prokaryotic diversity, but it is essential to curate and unify all the available information into comprehensive databases. Besides silva, for the 18S rRNA gene, eukaryotic databases have been designed, such as EukRef  [199] , or a more group-specific database such as the Protist Ribosomal Reference database (PR 2 )  [200]  and the Planktonic foraminifera Ribosomal Reference database (PFR 2 )  [201].
The EukRef project aims to improve the taxonomic information for eukaryotes based on 18S rRNA data, to associate it with their environmental metadata and to create better reference databases for amplicon studies  [202]. Protists are a heterogenic group of organisms with a broad distribution and a high level of genetic and ecological diversity, making it difficult to infer their phylogeny and classification. To improve this, PR 2 , an 18S rRNA curated database focused on protists, was constructed. It includes other eukaryotes, such as metazoa, land plants, macrosporic fungi and eukaryotic organelles (mitochondrion, plastid)  [200]. In contrast, PFR 2 is a curated database of 18S rRNA from planktonic protists  [201]. The most up-to-date databases to analyse fungal diversity based on the ITS marker region are UNITE  [203]  and the Warcup ITS training set  [204].
Analysis of the ITS is not as straightforward as that for 16S rRNA or 18S rRNA, since the ITS region is highly variable in sequence and length, making it challenging to determine phylogenetic relationships. Warcup ITS is an ITS-derived training set adapted for use with the RDP classifier for the identification of fungi. The UNITE database can be handled from QIIME 2  [188] , mothur  [198]  and the RDP classifier  [190]. It is worth mentioning that databases are not perfect, since they contain sequencing and PCR errors, as well as incorrect sequence labels that may lead to wrong classifications. Further, there is a bias in databases towards human-associated pathogens, making the classification of other environments where micro-organisms are not associated with disease more difficult  [205].
Recently, with the update of PiCRUSt to PICRUSt2, the potential functions derived from 18S rRNA and ITS can also be inferred  [211]  from wholegenome sequencing metagenomics. The estimation of the functions present in a metagenome predicted from marker gene analyses depends on the availability of closely related reference genomes in databases or on the similarity of the 16S rRNA region. Since certain related micro-organisms have similar rRNA sequences but different genomic features, this can be a difficult task. In particular, incomplete functional profiles are obtained from samples enriched in novel species with no available genomes. Thus, these functional predictions are helpful, but they cannot be considered to be substitutes for whole genome-sequencing metagenomics.
From the abundance table proportions of reads are estimated (the number of reads corresponds to each OTU/ASV/taxon divided by the number of reads of the sample). Relative abundance tables that sum to 1 and are non-negative are considered 'compositional data' and cannot be analysed with standard statistical approaches  [213]. The variability in the number of sequences obtained per sample is due to sequencing factors such as effort, bias, or library preparation inaccuracies. A high abundance of 'zero' values, also known as sparsity, is usually due to (i) features that have a low abundance (such as rare species) and are not detected in samples where the sequencing effort is low; (ii) features that are unique for a sample or a group. Irregular sampling depth, sparsity and the compositional nature of the data are critical factors influencing the alpha and beta diversity results (Box 1).
Thus, before performing ecological and diversity estimations, and statistical comparisons, it is crucial to normalize the data to obtain comparable samples and meaningful results  [214]. The most used method in microbial community studies is rarefaction of the abundance table to the same depth. The depth can be determined by choosing the values where the rarefaction curves reach the plateau (Box 1). In this situation, all the samples are rarefied and set to the same number of reads. However, this method has some disadvantages, since using a threshold for the number of reads might lead to a bias in diversity estimations. It can also imply a loss of OTUs/ASVs/taxa and (or) samples from the dataset due to the differential sequencing effort between samples. The second group of methods is based on scaling the data.
Correcting by copy number In marker gene studies, one of the well-known biases is the variation in gene copy numbers between species  [222]. For example, there is considerable variability among bacteria, where the 16S rRNA copy number ranges from 1 to 15  [223]. The OTU/ASV counts are biased towards those species with higher copy numbers. Different software exists to correct the copy number, such as rrnDB  [224] , Copyrighter  [225]  and functions implemented in the picante R package  [226]  and pplacer  [227]. These approaches have so far only been developed to analyse 16S rRNA gene data, although the copy number of the 18S rRNA gene is also variable between species  [228, 229] , and thus this should be considered during analysis. Moreover, the ITS copy number is variable, since it depends on the copy number of ribosomal genes and thus should also be considered when analysing fungal diversity data.
Recently, the accuracy of these methods in microbiome analyses was assessed and compared independently  [230]. It was shown that copy number correction approaches are not accurate enough to be included in microbial community studies. The authors recommend excluding the copy number information in microbial community analyses except when the identified variants are sufficiently closely related to sequenced genomes or if there is a need for correct proportions of the OTUs/ ASVs. As copy number methods depend on sequenced and annotated genomes that are included in databases, it is still an unsolved issue in microbial ecology.
These non-parametric tests are more robust on marker gene data than traditional methods such as the Student's t-test, analysis of variance (ANOVA), or multivariate analysis of variance (MANOVA). A comparative study examined the effects of heterogeneity of multivariate dispersions of the data on PERMANOVA, ANOSIM and Mantel test  [255]. The authors showed that PERMANOVA performed better in detecting changes in community structure than the Mantel test, which performed better than ANOSIM. However, none of the tests was reliable when facing unbalanced designs  [255]. To identify OTUs/ASVs/taxa/genes with significantly differential relative abundance between two conditions (as part of a biomarker identification), differential abundance tests have been developed  [256, 257]. The linear discriminant analysis (LDA) effect size (LEfSe) focuses on biomarker identification from metagenomic data.
The methodology applies standard tests for statistical significance combined with methods related to biological consistency and effect size  [257]. The analysis of the composition of microbiomes (ANCOM) is a more recent test adjusted to the structure of microbial community structures, and it accounts for the compositional data  [256]. Other methods also considering the compositional data effect are Deseq2  [258] , edgeR  [219] , Voom  [259] , or metagenomeSeq  [212]. Weiss and collaborators performed a comparison between some of these differential abundance tests, including the Wilcoxon rank-sum test, Deseq2, edgeR, Voom, metagenomeSeq and ANOSIM  [214]. The authors proposed that tests based on general linear models using negative binomial or log-ratios would be useful. For example, Deseq2 works well on smaller datasets, but it shows a higher false discovery rate with a high number of samples, larger and/or uneven library sizes, and/or compositional effects.
SparCC allows the inference of correlations between genes or species from microbial data considering the compositionality of the data  [263]. SPIEC-EASI is a program that takes the compositional nature of marker gene data and a graphical model inference framework to infer possible microbial relations (tested with 16S rRNA data) into account. The output of both methods can be coupled with visualization programs such as the igraph package  [264]  implemented in R. This package can handle large graphs and can be embedded in other programming languages, such as Python, Perl, GNU and R, and can be used both interactively and non-interactively. To test the significance of associations between environmental variables from the metadata (e. g. temperature, PH, oxygen) and microbial composition, multivariate ANOVA based on dissimilarities (Adonis) is widely used  [249].
Recently, the microbial ecology research community has increasingly moved to machine learning techniques to make predictions and identify correlations and interactions between the microbial community composition and metadata variables  [265]. Machine-earning-based approaches have been applied extensively to human microbiome studies (reviewed and compared by Zhou and Gallins  [266] ). Some of the most used methods are the 'random forests' that classify based on decision trees and 'neural networks' that are based on an interconnected and weighted feed-forward network of nodes to map inputs and outcomes  [267] [268] [269]. Zhou and Gallins compared the performance of 17 datasets (microbiome) analysed with numerous prediction methods. The authors proposed that 'decision tree' methods and 'neural networks' performed well with the analysed datasets.
Funding information Work in the CB laboratory is financed by the  Institut Pasteur , the  Fondation pour la Recherche Médicale (FRM)  grant no. EQU201903007847  and grant no. ANR-10-LABX-62-IBEID. A. E. P. -C. was supported by a postdoctoral fellowship financed by  ANR-10-LABX-62-IBEID. The funders, other than the authors, did not play any role in the study or in the preparation of the article or decision to publish.
Data Access for Education We are providing access to a growing set of Jupyter Notebooks that have been developed for introductory 139  and upper-level 140  university astronomy laboratory courses. These Python-based activities are designed to be run on the SciServer platform 141  , which enables the analysis and visualization of the vast SDSS dataset from a web browser, without requiring any additional software or data downloads. Additionally, Voyages (http://voyages. sdss. org/) provides activities and resources to help younger audiences explore the SDSS data. Voyages has been specifically developed to be used in secondary schools, and contains pointers to K-12 US science standards. A Spanish language version of these resources is now available at http://voyages. sdss. org/es 4. APOGEE-2: FIRST RELEASE OF SOUTHERN HEMISPHERE DATA, AND MORE FROM THE NORTH APOGEE is performing a chemodynamical investigation across the entire Milky Way Galaxy with two similarly designed near-infrared, high-resolution multiplexed spectrographs.
The windows are chosen based on where our synthetic spectra are sensitive to a given element, and at the same time not sensitive to another element in the same abundance dimension. In addition to the elements measured for DR14, an attempt was made to measure the abundance of cerium using a single line from  Cunha et al. (2017) , but these results show significant scatter and may be of limited utility. In previous releases, we derived an internal calibration to the abundances to account for biases as a function of T eff , but for DR16 no such calibration is applied because, with the modification to the abundance pipeline, the trends with effective temperature for most elements have reduced amplitude as compared with previous data processing. The zero-point scale of the abundances was ad-143 https://github.