dataesr/scientific-paragraphs-categorization huggingface dataset:
The air quality monitoring data, including 1 h PM 2. 5 , 8 h CO, and NO 2 , from the website of the National Environment Agency (https://www. haze. gov. sg/, last access: 15 November 2018) were also used in the analy-Table  1. Average concentrations with 1 standard deviation (SD) of chemical species measured during the October 2015 haze episode in Singapore using ToF-ACSM (online) and integrated filter samples (offline) measurements. Wind speed and precipitation data were retrieved from the website of the Meteorological Service Singapore, and other meteorological parameters were from measurement at the sampling location. sis. Meteorological data, including wind speed, precipitation, relative humidity (RH), and temperature, were obtained from the website of the Meteorological Service Singapore (http://www.
S2 ). The four OA factors from the 2015 haze data sets include both primary and secondary factors. The hydrocarbon-like organic aerosol (HOA) from fossil fuel combustion, biomass burning organic aerosol (BBOA), and peat burning organic aerosol (PBOA) from non-fossil burning processes are surrogates of POA. The OOA can be considered as a surrogate of both SOA and oxidized POA (OPOA)  (Donahue et al. , 2009; Jimenez et al. , 2009; Zhang et al. , 2005a Zhang et al. , , 2007b)). Figure  4a  and b show the mass spectra and time series of the OA factors, respectively. To support the identification of the OA factors, we compare the OA factors with chemical species identified by offline analyses (i. e. , LC-MS, GC-MS, Sunset OC/EC, and IC-MS). Using the offline analyses, we characterized ambient particle tracers, such as levoglucosan and BrC constituents  (Budisulistiorini et al.
Acknowledgements. We acknowledge  Haris Gunawan  for supporting our research in Indonesia. We thank  Gisella B. Lebron  and  Web-Chien Lee  for assisting in the particle sampling and data collection, and  Kyle Niezgoda  and  Shaoneng R. He  for the meteorological data. We gratefully acknowledge the  NOAA Air Resources Laboratory (ARL)  for the provision of the HYS-PLIT transport and dispersion model and/or READY website (https://www. ready. noaa. gov,last access: 17 September 2018) used in this publication. We acknowledge the use of fire data from LANCE-FIRMS operated by the  NASA/GSFC/Earth Science Data and Information System (ESDIS)  with funding provided by  NASA/HQ. The research is funded by the  National Research Foundation Singapore (NRF)  under its  Singapore National Research Fellowship scheme (National Research Fellow Award ,  NRF2012NRF-NRFF001-031 ),  NRF Campus for Research Excellence and Technological Enterprise  ( CREATE) program  ( NRF2016-ITC001-021 ), the  Earth Observatory of Singapore (EOS) , and  Nanyang Technological University.
Reads assignment to taxonomic units is a key step in microbiome analysis pipelines. To date, accurate taxonomy annotation, particularly at species rank, is still challenging due to the short size of read sequences and differently curated classification databases. However, the close phylogenetic relationship between species encountered in dairy products requires accurate species annotation to achieve sufficient phylogenetic resolution for further downstream ecological studies or for food diagnostics. Taxonomy annotation in universal 16S databases with environmental sequences like Silva, RDP or Greengenes is based on predictions rather than on studies of type strains or isolates. We provide a manually curated database composed of 10'290 full-length 16S rRNA gene sequences from prokaryotes tailored for dairy products analysis (https://github.
com/marcomeola/DAIRYdb). The performance of the DAIRYdb was compared with the universal databases Silva, LTP, RDP and Greengenes. The DAIRYdb significantly outperformed all other databases independently of the classification algorithm by enabling higher accurate taxonomy annotation down to the species rank. The DAIRYdb accurately annotates over 90% of the sequences of either single or paired hypervariable regions automatically. The manually curated DAIRYdb strongly improves taxonomic classification accuracy for microbiome studies in dairy environments. The DAIRYdb is a practical solution that enables automatization of this key step, thus facilitating the routine application of NGS microbiome analyses for microbial ecology studies and diagnostics in dairy products.
Classification tools. Taxonomic classification of the 16S is not trivial and requires both familiarity with prokaryotic phylogeny and often manual intervention due to poor annotation of the operational taxonomic units (OTUs) by the available 16S databases  (21). On the one hand, NGS has triggered the acquisition of enormous amounts of sequencing data, offering the possibility to overcome the limitations of Sanger sequencing. On the other hand it has brought huge computational challenges, such as the risk of taxonomic missannotations or ambiguous results during taxonomic classification steps. Despite the steadily increasing read length obtained by NGS, the need for trustworthy classification of very short 16S sequences covering only one to three HVR remains a crucial step to obtain robust and accurate taxonomic classification in modern microbiology  (22).
Numerous classification predictors algorithms have been developed and optimized in recent years with the aim to ac-Fig. 1. Development of the DAIRYdb consisted in three main steps: construction, curation and validation. For construction, dairy products specific 16S rRNA gene sequences were retrieved from Silva, RDP and Greengenes using Genbank NCBI, EMBL, Agroscope and INRA sequences. Curation was performed based on the cross-validation results from the leave-one-out test of SATIVA and highly iterated RAxML tree, followed by manual curation of taxonomic assignment and consistency throughout all taxonomic ranks, with a particular focus on singleton taxons with no reference sequence. Validation was performed comparing identification accuracy of single and HVR pairs by the five databases (Greengenes 13.
org) and FROGS  (38). Here we used three taxonomy predictors based on different algorithms and programming languages  (39) , in which dedicated databases can be integrated and used for classification. One of the three classification tools used in this study to test the performance of the DAIRYdb was the updated version of Blast, Blast+  (40). It is based on an heuristic method for identification of database sequences that resemble the query sequence above a certain threshold. It searches for short sequence matches, which are locally aligned after the first match  (40). Another classification tool used in this study was Metaxa2, an HMM based software tool for automated detection and classification of short NGS fragments, such as ribosomal small and large subunits, SSU and LSU, respectively, or any gene of interest useful for classification of any organism  (27, 41).
16S repositories. Although classification prediction algorithms have strongly improved, manually curated databases containing only authoritative full-length 16S sequences from type strains and cultivated reference strains can potentially compensate the limitations of short read sequences annotations by means of sophisticated algorithms. To date, three main independent universal repositories dedicated to universal 16S sequences from prokaryotes are widely used: Silva, The Ribosomal Database Project (RDP), and Greengenes  (42). Silva is the universal 16S repository with the highest number of sequences. The latest release of Silva SSU/LSU 132 (www. arb-silva. de) contained 6'073'181 16S sequences of at least 300 bp, with 2'090'668 good quality sequences with at least 900 bp length  (43) (44) (45).
Taxonomic rank information of Silva and Living Tree Project (LTP) are based on the Bergey's Taxonomic Outlines and the List of Prokaryotic Names with Standing Nomenclature (LPSN)  (46). Minimal training sets, such as the SSU Ref NR 99 or the LTP  (47) , offer a reduced number of sequences for faster classification but still cover-D R A F T Fig. 2. Origin of sequence in the DAIRYdb. A) Five-factors Venn diagram comparing the origins of the sequences (9'948) retrieved from the public repositories Genbank NCBI and EMBL associated to the keywords "cheese", "dairy", "milk", "teat" and "whey/starter". About 12. 7% (1'263) sequences were only detected in cheese and 15. 1%  (1'507)  were detected in all three cheese, milk and dairy environments. B) Total number of sequences associated to a particular keyword.
C) Number of sequences shared by 1 to 5 keywords. About 19. 4% (1'933) sequences were detected in all 5 keywords, while 17. 1% (1'700) sequences were unique to one keyword. ing the broadest currently known biodiversity. The second biggest repository, the Ribosomal Database Project (RDP Release 11, Update 5; http://rdp. cme. msu. edu) (48) , contained at the time of writing 3'356'809 16S sequences from the International Nucleotide Sequence Database Collaboration (INSDC)  (49). The nomenclature is based on the Bacterial Nomenclature Up-to-Date and the taxonomic rank information on the Bergey's Manual. Greengenes v13_5 (50) contains 1'800'000 quality filtered 16S sequences. Classification nomenclature is based on automatic de novo tree construction and rank mapping with the NCBI Taxonomy database  (51).
Although frequently used in community studies together with Qiime (37), the last update dates back to 2013 with no indication for an imminent update. Taxonomy annotation in microbiology. Phylogenetic classification has tailored taxa by means of phylogenetic, phenotype and genomic coherence that make taxonomic units unique within the classification schema  (52). Phylogenetic coherence is determined by the 16S for which the previously mentioned databases provide a valuable tool for tax-Fig. 3. Complete microbial diversity present in the DAIRYdb. Prokaryotic biodiversity in the DAIRYdb is represented by 2 kingdoms, 47 phyla, 136 classes, 249 orders, 463 families, 1'757 genera and 4'030 unique species-like groups. The most represented phyla is Firmicutes (37% of all sequences), followed by the Proteobacteria (22%), Bacteroidetes (14%), Actinobacteria (9%), Chloroflexi (2%), Acidobacteria (2%), Archaea (1%) and 34 other minor phyla.
onomic classification  (52). However, the exponential increase of 16S sequences from previously unknown and uncultured bacteria led to an explosion of exotic labels at any taxonomic rank with often contrasting taxonomic classifications between databases based on different taxonomic catalogues (e. g. , Bergey's Taxonomic Outlines, List of Prokaryotic Names with Standing Nomenclature (LPSN), International Sequence Database Collaboration (INSDC), Bacterial Nomenclature Up-to-Date  (53, 54) ). The lack of consensus on a widely accepted taxonomy, as well as the lack of taxonomic characterisation of yet uncultured bacteria, are severely limiting communication among scientists and may lead to incorrect annotations and thus "poison every experiment that makes use of them"  (52, 55). In worst cases, incorrectly annotated bacteria are included in databases further used to classify new sequences.
In fact, it has been shown that there are numerous unambiguous disagreements between the nomenclature hierarchies, where many taxon names are placed into different parent taxa in the databases Silva and Greengenes or the taxonomy is not consistent with the tree  (54). In addition to hierarchy disagreements, about 34% of identical sequences in Silva and Greengenes databases presented annotation conflicts, 24% of which were blanks (unclassified) in either of both database  (54). While the blanks imply a false negative by one database or a false positive by the other, differently annotated sequences with the same Accession number or identical sequence string must be due to annotation error in one or both databases  (54). While NGS has allowed researchers to obtain deep insights into the microbial community structures inhabiting various environments, the complexity of the analytical process and taxonomy annotation on short read sequences is still challenging and prevents researchers to deploy microbial community analysis for diagnostic purposes in an accurate and reproducible way  (1, 67).
Previous studies have highlighted the importance of high-quality data for improving the classifications of the obtained OTUs  (22, 68, 69). Although universal 16S databases cover vast prokaryotic biodiversity, they often fail to guarantee accurate classification to the species rank for sequences obtained from a highly studied environment, such as dairy products. In fact, classification accuracy at lower taxonomic ranks increases with a gold standard training set encompassing only full-length and good quality representative sequences innate to the investigated environment  (22, 54, 69, 70).
ibm. com/press/us/en/pressrelease/45938. wss and www-03. ibm. com/press/us/en /pressrelease/52690. wss). However, fast and accurate, thus automatized classification of the OTUs is not yet possible at the biologically most significant species rank due to the short sequence fragments and the absence of food-dedicated, thoroughly curated 16S databases, particularly. Therefore,  Here we present a comprehensive gold standard database, DAIRYdb (Database, Agroscope, Inra, Ribosomal, accu-racY), for 16S OTUs classification from NGS data of dairy products. The main goal was to develop a dedicated database that allow researchers to accurately and automatically annotate short reads of 16S down to the species level. Manual curation of the database and its restriction to the biodiversity expected in dairy products strongly improves accuracy and reproducibility of phylogenetic classification to all taxonomic ranks.
Results Construction. The 16S sequence database of dairy products DAIRYdb was constructed using a set of over 390'000 sequences associated to the selected keywords (cheese, milk, teat, dairy, starter, whey) deposited in NCBI GenBank and ENA/EMBL, as well as sequences with 97% ANI from Silva, RDP and Greengenes (Figure  1 ). About 10'000 best quality reference sequences were retained after filtering based on sequence length (>1300 bp), quality (pintail >75) and potential chimeras. Finally, 16S sequences of important species from cheese and dairy environments  (73, 74) , whose sequences were lost during the clustering, were added, resulting to the final number of 10'290 16S sequences. The observed distribution among the different key words might reflect the unequal distribution of microbiome studies predominantly performed on cheese, dairy and milk samples, as compared to teats and whey.
Curation. During the first step of data curation, the sequences were taxonomically annotated with Silva by means of SINA  (75). The resulted annotation at all taxonomic ranks underwent a first manual check and cleaning for taxonomic inconsistencies through cross-comparison with the other members of the same taxonomic rank in a phylogenetic tree. No taxonomic overlaps comparable to other databases are present in the DAIRYdb, where different species of the same genus fall under different taxonomic lineages  (54). A maximum of three closest neighbour type strains (CN) with authoritative taxonomy (CN) from Silva sharing 99% global sequence similarity to each sequence in the DAIRYdb were added to the 10'290 sequences in the DAIRYdb as reference during the curation process and removed at the end of the curation process.
For ranks at which no taxonomic annotation was possible with certainty due to the lack of authoritative type strains within the same clade (i. e. , commonly labelled unknown, uncultured etc. in universal databases), the lowest common rank (LCR)  (70)  was used down to the species rank with the addition of the unclassified rank. Although not all OTUs in a microbiome study will be classified to the species rank, at least they will not all be merged to the same unclassified species rank, but taking over the LCR to differentiate between all unclassified OTUs. As an example, a sequence assigned to the LCR, the genus Sporichthya, was named at species rank Sporichthya_Species. This approach avoids the merging of abundance values from different unknown species to biological uninformative groups, thus improving communication among scientists  (53).
The cheese microbiome is often dominated by few phylogenetically closely related species, of lactic acid bacteria (LAB) belonging to a few genera (e. g. , Lactobacillus, Lactococcus, Leuconostoc and Streptococcus)  (9). Therefore, special attention was put into the manual curation of the DAIRYdb sequences at species rank. Despite the genotypic and phenotypic characteristics of the most common LAB in cheese are extensively studied and described, several unresolved controversies regarding the nomenclature of some keystone species still remain unsolved, such as for the species L. helveticus and L. gallinarum, S. thermophilus and S. salivarius, L. casei and L. paracasei and L. zeae, L. plantarum and L. paraplantarum (77-79). The DAIRYdb is composed of sequences retrieved from the Silva database along with their respective taxonomy, which was manually inspected for nomenclature hierarchy conflicts based on the phylogenetic position within the tree.
However, some conflicting annotations of the same sequence were detected between the Silva taxonomy and the Bacterial Diversity Metadatabase, such as the species assignment of the type strain sequence Accession AB008205, which is labelled as L. casei in Silva and L. paracasei in BacDive  (80). For the reference sequences of the most crucial species, it was tried to use bacterial names listed in the actual "List of prokaryotic names" according to BacDive, however, further disagreements between Silva and BacDive cannot be completely excluded. Moreover, it is also possible that some crucial genera in dairy products may undergo a radical genomebased relabelling in order to have more homogeneous clusters  (79). Different approaches were applied on inpure taxa, i. e. taxa that overlap in the tree despite being assigned to different nomenclature  (54) , by the universal databases.
For instances, for the genera Escherichia and Shigella, Silva, LTP and RDP use the combined genus name Escherichia-Shigella but retain well-established species names, such as Escherichia coli. Differently, Greengenes leaves their sequences unclassified at ranks below the family Enterobacteriaceae (54). The different taxonomic nomenclature references used by the three databases have an impact on revisions to resolve conflicts with sequence-based phylogenies and the labelling of new candidate groups identified in environmental sequences. However, discussion on the taxonomic inconsistencies and limitations of the universal databases (Silva, LTP, RDP and Greengenes), which the DAIRYdb was compared with, goes beyond the scope of this study and was extensively discussed elsewhere  (54, 70). The DAIRYdb will undergo regular updates in accordance to update on bacterial nomenclature (79), integrating the novelties or correcting the changes.
The largest biodiversity coverage by the DAIRYdb was achieved by the single HVR V4 (92%), followed by the HVR pairs V5V6 and V3V4 (89%). The taxonomy annotation accuracy of the DAIRYdb was compared with other universal databases, such as Silva128, RDP trainset v16, LTP and Greengenes analysing fragments of single HVRs or HVR pairs extracted from the sequences in the DAIRYdb with V-Xtractor and in silico PCR. The synthetic HVR fragments were extracted from 1000 subsamples of each 100 randomly selected sequences from the DAIRYdb by either V-Xtractor or in silico PCR and assigned to all taxonomic ranks by the means of three different classification predictors (Blast+, Metaxa2 and SINTAX) using the aforementioned databases. Taxonomic annotation accuracy of the single HVR extracted with V-Xtractor with the DAIRYdb using SINTAX was above 75% at all taxonomic ranks (Figure  5 ).
Accuracy was highest for the even HVRs (V2, V4, V6 and V8) as compared to the odd HVRs (V1, V3, V5, V7 and V9). The region V2 presented the greatest classification accuracy, which is in line with other findings showing that the regions V1 and V2 resulted in a more accurate OTU clustering at 97%, 98% and 99%  (32). Overall, the universal databases were less accurate with decreasing taxonomic rank (Figure  5B-D ). Only the RDP trainset v16 achieved about 25% of correct species annotations, while the other databases only classified to genus rank. Although the RDP trainset v16 performed best among all universal databases, annotation accuracy was below the accuracy values assessed in previous studies  (54). Different to the DAIRYdb, the HVR V4 performed best with the universal databases with exception to Silva, where V2 achieved a higher accuracy (Figure  5 ).
Generally, the difference in classification accuracy was stable through all HVRs with exception to the Silva database, where bigger oscillations were observed between the HVRs showing a clear drop for V6 and V7 (Figure  5E ). All HVRs taken together, the DAIRYdb achieved a significantly better taxonomy annotation accuracy of average 88. 9% ± 5. 5 as compared to the universal databases tested, particularly at order to species ranks (Figure  5F ). The annotation accuracy results with Blast+ and Metaxa2 of single HVR extracted with in silico PCR (Additional File 3, Figures  1, 3  and 7 ), V-Xtractor (Additional File 3, Figures  5  and 9 ), and HVR pairs with in silico PCR (Additional File 3, Figures  2, 4  and 8 ), V-Xtractor (Additional File 3, Figures  6  and 10  The results with the HVR pairs was similar to the single HVRs (Figure  6 ).
8 (Figure  6F ). Only species annotation with the RDP trainset v16 achieved 25% of correct annotations. The BLAST16S database was shown to obtain genus accuracies ∼50% for V4, which improves with increasing length to ∼60% with V3-V5 and ∼70% with full-length 16S  (70). As expected, the increasing number of HVR increases the confidence in taxonomy annotation. Taxonomy annotation accuracy varied only little between different taxonomy predictors with the DAIRYdb and not significantly with either both, single HVR (Additional File 3, Figure  11 ) or HVR pairs and (Additional File 3, Figure  12 ). In fact, classification annotation accuracy performance varied more dependent on the database rather than the classification predictor. These results indicate that annotation of the members of the bacterial community is primarily influenced by the selection of the database, by the HVR, and only then by the taxonomy predictor (Additional File 3, Figures  1 2 3 4 5 6 7 8 9 10 ).
We therefore recommend to use both, Metaxa2 with integrated Blast+ and SINTAX to obtain taxonomy annotations closest to the ground truth. Although a lower SINTAX cutoff of 0. 6 increases the risk of over-classification, it is justified by the better quality of the DAIRYdb and the comparison with Metaxa2 for definitive taxonomy annotation (more details on the recommended usage on real samples are described on https://github. com/marcomeola/DAIRYdb).
For dairy products, the DAIRYdb achieves both, covering all the biodiversity expected in these environments, while achieving specificity in taxonomic annotation. The main scope of the DAIRYdb is to improve accurate species classification in dairy products. Beyond this, it covers a considerable diversity in agreement with the diversity detected in dairy products so far. However, the DAIRYdb does not necessarily perform better than universal databases on a set of sequences from another environments, such as the human gut. Classification accuracy performed on sequences from type strains included in the Human Intestinal Tract database (HITdb) showed that the DAIRYdb performed comparably well to the RDP trainset v16 and significantly better than Silva and Greengenes (Figure  8 ) (  69 ). Yet, the way and ability to recognize the basic unit for taxonomy of prokaryotes depends on the resolution power of the observational methods actually available  (52).