dataesr/scientific-paragraphs-categorization huggingface dataset:
orientations, and surface deformations of all the sensors are aligned relative to these high-level alignables. During Run 1, the high-level alignables usually corresponded to the high-level structures. During Run 2 this was still true for the strip detector, but smaller mechanical structures were chosen in the pixel detector to absorb the effects of radiation damage accumulating over time. This strategy was applied for the first time in 2016, where ladders and blades, rather than the highlevel structures, were treated as the high-level alignables. In 2018, with the increased level of radiation, a better performance was obtained by considering ladders in the BPIX and the modules in the FPIX as the high-level parameters. The IOVs for each set of alignment constants are determined from several sources: • magnet cycles and known changes of temperature; • changes in the hit reconstruction (including changes of the local calibration, changes of voltage, and ageing due to the irradiation); • changes to the distributions of the impact parameters in the transverse plane d xy and on the longitudinal axis d z , as a function of the track angular variables η and φ (as introduced in Section 7.
For 2017 and 2018 (Phase 1), different high-level alignables were chosen for the pixel detector: ladders were used in BPIX and modules in FPIX. In addition, different IOV boundaries were chosen in the pixel and strip detectors. Additional IOVs were introduced in the pixel detector to absorb the bias from the hit reconstruction in the alignment constants. Adapting the strategy based on the results of the studies described in Section 5, systematic deformations were avoided or reduced, except for the 2018 data-taking year. There, in addition to performing the alignment fit with MILLEPEDE-II, as the obtained alignment fit seemed to contain a residual but significant twist deformation, an inverted twist transformation was also applied to the geometry to compensate for this systematic distortion.
Tracking performance Figure  24  shows DMRs (introduced in Section 7. 1. 1) along the local x coordinate of the modules belonging either to the BPIX or to the TID. These distributions are produced with the singlemuon data set instead of the L1 inclusive sample that is usually used for DMRs. This is done to compare the performance in the data and simulated samples with similar event topologies, which is achieved by requiring exactly 100 hits per module in the simulation and in each IOV. Pixel detector (strip detector) modules with fewer than 100 hits per IOV over a period of data taking corresponding to an integrated luminosity of more than 2 (7) fb -1 were excluded from this study. The distributions are then averaged over all IOVs weighted by the corresponding delivered integrated luminosity.
They correspond to the first and last set of alignment parameters of the 5th pixel IOV in 2017, where the ∆µ from Fig. 25  indicates large radiation effects. The legacy reprocessing includes more updates during this period, which means the effects of accumulating radiation damage can be absorbed in the alignment. This results in an alternating pattern in the ladders, since the modules of neighbouring ladders have opposite orientations. This pattern is especially visible in layer 1 of the BPIX. It is less visible in the next layers, where the effect is reduced due to the distance and is folded in with other effects. Such other effects include movements of the high-level structures, e. g. the half barrels of the BPIX. The understanding of the interplay of the local reconstruction and of the alignment procedure is a great improvement in the physics performance in the legacy reprocessing.
Finally, the change in the estimation of the alignment accuracy is also visible in Fig. 37 , where the distribution of the RMS of the normalized track-hit residuals produced with the singlemuon data set is shown for the BPIX and the TID. In this figure, the same high-level structures, and the same condition on the number of hits per module, as in Fig. 24  are used. The distributions in data are averaged over all IOVs, weighted by the corresponding delivered integrated luminosity. These distributions are compared with the realistic and ideal single-muon MC scenarios that will be introduced in Section 9. The improvement from the legacy reprocessing related to the finer granularity can also be seen, because the means of the distributions shift closer towards unity. The distributions are not expected to be centred exactly around unity, since an incorrect estimation of the track or hit uncertainty causes this centre to deviate from unity.
Low-pileup runs and runs at 5. 02 TeV Several low-pileup runs were taken during Run 2. These runs had an average of three interactions per bunch crossing, whereas under normal operating conditions the average number of interactions per bunch crossing was greater than 20. In 2017 (2018) a data set corresponding to an integrated luminosity of 0. 22 (0. 03) fb -1 was collected under these low-pileup conditions. The low-pileup runs in 2017 were included in the global legacy alignment procedure described high-level structures of the pixel detector were observed by the PCL alignment procedure at the beginning of HI data taking. The automated alignment procedure triggered by these movements improved the tracking and vertexing performance. To further improve the performance, a module-level alignment fit was performed in the BPIX and FPIX using the HIPPY algorithm with 30 iterations.
This leads to an underestimation of the simulation, compared with data, for lower values of the χ 2 probability. Overall, good agreement between the data and simulation is seen in both the 2016 data-taking period with the old APV settings and in 2017. From the comparison of the data and the simulation, we conclude that the updated alignment conditions for the legacy reprocessing in data lead to consistent good agreement with the MC simulation. It also confirms a consistent alignment of the tracker during Run 2. The MILLEPEDE-II software is provided by DESY in the framework of the Helmholtz Terascale Alliance.
Figure3: Normalized track p T (left) and η (right) distributions for the inclusive L1 trigger (top), Z → µµ (middle), and interfill cosmic ray muon (bottom) data sets in arbitrary units. Data collected with the CMS detector in 2018 and used for the final alignment in that year (solid black circles) are compared with the simulation (solid coloured lines). Distributions in data are obtained from a sample of 11 × 10 6 , 55 × 10 6 , and 3. 4 × 10 6 tracks for the inclusive L1 trigger, Z → µµ, and cosmic ray muon data sets, respectively. For the inclusive L1 trigger data set, data are compared with two sets of simulated QCD events with different ranges of transverse momentum transfers pT. The green line corresponds to pT between 15 and 30 GeV, whereas the magenta line corresponds to pT between 1000 and 1400 GeV.
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid and other centres for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC, the  CMS detector , and the supporting computing infrastructure provided by the following funding agencies: the  Austrian Federal Ministry of Education, Science and Research and the Austrian Science Fund ; the  Belgian Fonds de la Recherche Scientifique , and  Fonds voor Weten-.
Metagenomics studies have revealed tremendous viral diversity in aquatic environments. Yet, while the genomic data they have provided is extensive, it is unannotated. For example, most phage sequences lack accurate information about their bacterial host, which prevents reliable phage identification and the investigation of phage-host interactions. This study aimed to take this knowledge further, using a viral metagenomic framework to decipher the composition and diversity of phage communities and to predict their bacterial hosts. To this end, we used water and sediment samples collected from seven sites with varying contamination levels in the Ebrié Lagoon in Abidjan, Ivory Coast. The bacterial communities were characterized using the 16S rRNA metabarcoding approach, and a framework was developed to investigate the virome datasets that: (1) identified phage contigs with VirSorter and VIBRANT; (2) classified these contigs with MetaPhinder using the phage database (taxonomic annotation); and (3) predicted the phages' bacterial hosts with a machine learning-based tool: the Prokaryotic Virus-Host Predictor.
The identification of the viral host is essential for the characterization of phages, as they depend on the host for survival  [3]. Currently, the most common method used to determine the host of a phage is through cultures, but this can be inefficient, time consuming and expensive  [6]. In recent decades, shotgun metagenomic sequencing has been proposed to study genomes of uncultured viral populations in the environment, an approach known as viral metagenomics  [7]. This has allowed virome analysis (of all viral assemblies in a given environment) to uncover many new phage genomes never previously reported, enriching viral sequence databases  [7]. However, in contrast to the conventional culture-based approach, which provides direct host information, viral metagenomics does not reveal the relationships between phages and their hosts  [8].
Alignment-free methods predict the host of a virus based on the co-occurring k-mers (the oligomers of length k) of phages with known hosts  [13]  or the similarity of sequence signatures between viruses and their hosts  [8]. To determine the most likely host, these methods calculate the similarity between the phage sequence and the genome of each candidate host using oligonucleotide frequency, a Markov chain model, or a Gaussian model  [10]. Of the latter approaches, VirHostMatcher  [12]  and WIsH  [14]  have shown the highest accuracy in predicting hosts. For this study, we chose the Prokaryotic Host Predictor (PHP) (https://github. com/ congyulu-bioinfo/PHP) as it has greater prediction accuracy than VirHostMatcher and WIsH (28-34%, genus level)  [11]. This software tool uses a Gaussian model to predict the hosts of prokaryotic viruses by looking for differences in k-mer frequencies between viral and host genomic sequences  [11].
16S rRNA Gene Sequence Analysis Bioinformatic analyses were performed using RStudio (v2021. 9. 0) and R version 4. 1. 2 (Figure  S1 ). Raw reads were preprocessed using DADA2 v1. 22. 0, a model-based approach for correcting sequencing errors  [17]. The 16 rRNA paired-end reads were quality checked, trimmed, dereplicated, denoised, assembled and the chimeras were discarded following the DADA2 pipeline  [17]. The high-quality sequences obtained were considered amplicon sequence variants (ASVs), in which each ASV differs from the others by at least one nucleotide. The taxonomic assignment of ASVs was performed using the SILVA database, version 132  [18] , with 100% of sequence identity required for species ranking. The final ASV abundance table was normalized by subsampling for downstream analysis.
The quality control of reads was carried out using Trimmomatic [21], AfterQC  [22]  and FASTQc  [23]. The resulting reads were assembled with MetaSpades  [24]. Contig assemblies were aligned with DIAMOND  [25]  using BLASTx against the non-redundant (nr) NCBI GenBank protein database with an e-value of 10 -3. Taxonomic annotation was performed with MEGAN-CE (MEtaGenome Analyzer; v. 6. 3) [26]  using the lowest common ancestor (LCA) algorithm with a min-score of 50, a top-percent filter of 0. 001 and a min-support filter of 1. Contigs annotated as phages were exported and processed for taxa relative abundance analysis. The visualization and comparison of the taxonomic profiles of the phage communities in the water and sediment samples from the seven stations were performed by hierarchical clustering using the Bray-Curtis dissimilarity  [20].
Identification and Classification of Phage Contigs To predict phage contigs from virome datasets, we used the CyVerse Discovery Environment platform (https://de. cyverse. org, accessed on 7 January 2020) to run VirSorter v1. 0. 3  [27]  and VIBRANT v1. 2. 0  [28]. The used contigs represent genome fragments with a minimum length of 1000 base pairs (bp) and a maximum length of 58,536 bp. The minimum contig length was chosen to obtain reliable phage contigs (partial genomes) for estimating the associated host, as viral genomes vary considerably in length. No maximum size requirement was imposed to increase the possibility of having complete phage genomes. VirSorter annotates contigs using MetaGeneAnnotator  [29] , and then uses hmmsearch  [30]  to predict PFAM domains  [31]  and viral domains on the annotated genes.
VirSorter was run in decontamination mode with the virome database. Predicted phages were assigned to categories (from 1 to 6). We retained phages in categories 1 to 3, which corresponded to the "most reliable" (1), "likely" (2), and "possible" (3) predictions. As a second complementary approach, we used VIBRANT, a hybrid machine-learning and protein-similarity tool that allows the automated recovery of both free and integrated phage genomes from metagenome assemblies. Only phage sequences predicted and classified (category 1, 2 and 3) by the two aforementioned tools (VirSorter and VIBRANT) were used for host prediction and were examined with MetaPhinder v. 2. 1  [32] , which compares contigs to a database of the whole genome sequences of phages.
Statistical Analysis Statistical analyses of viromes, 16S rRNA gene sequence, and phage-host predictions were performed using the R packages FSA v0. 9. 3 and Stat v4. 1. 2. The significance threshold for the p-value was 0. 05. The adjusted p-value for correcting multiple tests was based on the Benjamini-Hochberg procedure  [33]. All plots were created using the R package ggplot2 v. 3. 3. 5. To test for significant differences between the taxonomic profiles of bacterial communities in sediment and water samples, permutational multivariate analysis of variance (PerMANOVA) was performed based on the Bray-Curtis distance using the adonis function in the R package vegan. To test the homogeneity of multivariate dispersions (i. e. , deviations from centroids) between sample types, a permutation multivariate analysis of dispersion (PERMDISP) was performed using the betadisper and permutest function from the R package vegan.
Discussion This study is the first to our knowledge to focus on the phage-host relationships of lagoon viromes. Investigating a viromic dataset with two metagenomic approaches, it resulted in a comprehensive map of phage identification that revealed Siphoviridae, Myoviridae, Podoviridae and Microviridae as the most dominant phage families, and their main putative bacterial hosts the phyla Proteobacteria, Firmicutes and Bacteroidetes. Using both methods (HP or 16S), distinct bacterial communities were found in water and sediment samples, and the same trend was found for phage communities. Of key interest, a taxonomic group of phages could be associated with a specific group of bacterial hosts. In sediment samples, a phage belonging to the Microviridae family was associated with Bacteroidetes and Firmicutes hosts, while in water samples, phages belonging to the order Caudovirales (families Siphoviridae, Myoviridae and Podoviridae) were associated with Actinobacteria, Firmicutes and Proteobacteria hosts.
These findings suggest specific viral-bacterial community profiles depending on habitat type  [34]  and a close association between phages and their bacterial hosts. As phages are dependent on their hosts, their frequency and distribution are likely linked to that of their host  [3]. They have also evolved with their host and often exhibit similar oligonucleotide frequency patterns with host genomes  [3, 10, 11]. The "predicted" relative abundance of bacterial communities (at phylum level) showed a positive and significant correlation with the "real" relative abundance obtained in 16S metabarcoding, demonstrating the reliability of the predictive approach based on viral datasets generated by shotgun sequencing. A challenge of the approach is that host prediction based on the genomic signature could not distinguish which phage infects which bacteria at species level, so we characterized the bacterial hosts of phages at the phylum level to avoid potential misclassification.
It is essential to consider database dependency and the limited number of characterized viral species when analyzing viromic datasets. Notably, there were distinct variations in the bacterial community composition identified by the two approaches at phylum level. For example, the phylum Epsilonbacteraeota was found exclusively in the bacterial communities identified by 16S, and Canditatus was found exclusively in bacterial communities identified by HP. A handful of previous studies  [34, 37, 38]  have compared predicted phage hosts based on viromes versus bacterial taxonomic profiles obtained by 16S, and have also demonstrated that the relative abundance of bacterial communities from the two approaches is not always similar. The nature and limitations of the approaches used (viral metagenomics/computational approach versus 16S metabarcoding) may explain some of the observed differences.
A variety of factors may contribute to the discrepancies between these approaches, including limited availability of host genomes, misannotated or incomplete annotation of bacterial genomes used for host prediction (this was the case in our study), primer specificity/sensitivity, and taxonomical annotation bias due to uneven representation of bacterial genes in databases for 16S-based methods  [39, 40]. The phyla absent in bacterial communities obtained by 16S but present in those predicted by HP may have 16S rRNA gene sequences that do not perfectly match the primers used during the amplification step. Although 16S rRNA PCR primers are commonly referred to as "universal", there is considerable sequence diversity in the 16S rRNA gene, even in the most well-conserved regions and among bacteria of the same species  [41, 42].
Conclusions The immense diversity of viruses, especially bacteriophages, in different aquatic ecosystems is only beginning to be explored. Using a pioneering predictive approach combining a computational method and dedicated phage bioinformatics tools, this study shows that it is possible, to some extent, to improve our ability to identify a phage host without the need to culture each pair, a development that should contribute to a better understanding of viral ecology. Using a single viromic dataset, we were able to characterize phage communities and their putative hosts. The results found that phage-host prediction is reliable and allows the rapid identification of viral hosts, based on a comparison of the taxonomic profiles of the bacterial hosts obtained by the conventional metabarcoding approach targeting the 16S rRNA gene.
Replicate station data were defined as multiple measurements of the same variable, with the same date, time, latitude, and longitude. For the latter case, a search window of 5 min in time and 200 m in distance was given to account for station drift. A small number of observations that were identified as replicates had a different "subdataset" identifiers (i. e. different cruise names). These observations were considered suspicious if the values were different and discarded. If the values were the same, one of the observations was retained. https://doi. org/10. 5194/essd-14-5737-2022 Earth Syst. Sci. Data, 14, 5737-5770, 2022  This possibly originated from the same group of data being contributed to an archive by two different data contributors. Once a set of data was homogenized, its data were integrated into a unique table.
This final merging focused on the removal of duplicates between the sets of data. Although some duplicates are known (e. g. MOBY, BOUS-SOLE, AERONET-OC, and NOMAD data are found in SeaBASS and MERMAID), others are unknown (e. g. how many of GeP&CO, ICES, AMT, and HOT are within NO-MAD, SeaBASS, and MERMAID). Therefore, duplicates were identified using the metadata ("dataset" and "subdataset") when possible, and temporal-spatial matches, as an additional precaution. For temporal-spatial matches, several thresholds were used, but typically 5 min and 200 m were taken to be sufficient to identify most duplicated data, which reflected small differences in time, latitude, and longitude, between the different sets of data. Larger thresholds were used in some cases as a cautionary procedure. This was the case when searching for NOMAD data in other data sets, because NOMAD includes a few cases where merging of radiometric and pigment data was done with large spatial-temporal thresholds  (Werdell and Bailey, 2005).
Global database of in situ bio-optical data matched with concurrent MERIS Level 2 satellite ocean colour products The "extract matchup" tool to acquire data was used. Data were compiled between 2002-2012. Access has been granted through a signed Service Level Agreement. Compiled standard variables: "rrs", "chla_hplc", "chl_fluor", "aph", "adg", "bbp", "kd", and "tsm". Multidisciplinary programme that makes biological, chemical, and physical oceanographic measurements during an annual voyage between the United Kingdom and destinations in the South Atlantic. It has compiled observations of chlorophyll-a concentration between 1997 (AMT5) and 2018 (AMT28). Data were provided by the British Oceanographic Data Centre (BODC) and directly from data contributors. Compiled standard variables: "chla_hplc", "chl_fluor".
International Council for the Exploration of the Sea (ICES) ICES is a network of more than 4000 scientists from almost 300 institutes, with 1600 scientists participating in activities annually. The ICES Data Centre manages a number of large data set collections related to the marine environment covering the Northeast Atlantic, Baltic Sea, Greenland Sea, and Norwegian Sea. Most of data originate from national institutes that are part of the ICES network of member countries. Data were provided (on 28 April 2014) from the ICES database on the marine environment (Copenhagen, Denmark) following a specific request. The ICES data were made available under the ICES data policy, and if there is any conflict between this and the policy adopted by the users, then the ICES policy applies. The compiled variables were "chla_hplc" and "chla_fluor".
The Fisheries and Oceans Canada database for biological and chemical data (BIOCHEM) BioChem is an archive of marine biological and chemical data maintained by Fisheries and Oceans Canada  (DFO, 2018; Devine et al. , 2014)  BODC is the designated marine science data centre for the United Kingdom. The data used in this work derive from a specific request for discrete observations of chlorophylla concentration since 1997. Initially, this request was used to compile AMT data (see Sect. 2. 2. 9). The remaining data comprising observations of chlorophyll-a concentration from fluorometric and HPLC methods, mostly sampled in the North Atlantic, were analysed and added (the "dataset" string for this data source is "bodc"). Data not flagged with highest quality or without method of measurement were discarded.