dataesr/scientific-paragraphs-categorization huggingface dataset:
Detailed analysis has shown that most of the carbon particles are in the lower part of the carbon speciation zone, while some strongly absorbing particles, perhaps black carbon (with its fractal shape), are in the middle and upper part of the carbon speciation zone. For all solid particles, the global trend is a decrease of the speciation index with increasing size. On the contrary, the liquid droplets speciation index exhibits an increase with increasing diameter. The case presented in Fig. 6  has D 1 = 0. 35 µm and D 2 = 0. 51 µm, leading to a ratio of 1. 46, which is in the carbon speciation zone. The speciation indices obtained from LOAC observations in the atmosphere are compared to these reference charts obtained in the laboratory. The position of the data points in the various speciation zones provides the main typology of the particles.
In principle, this procedure can be conducted for each size class. In fact, due to the statistical dispersion of the results, it is better to consider several consecutive size classes to better conduct the identification. This is in particular necessary for the identification of droplets, whose speciation zone crosses all the speciation zones of the solid particles. It is obvious that the identification of the typology of the particles works well only in the case of a homogenous medium, when the speciation indices are not scattered through the various speciation zones. At present, the speciation zones are established for particles expected to be found in the troposphere and stratosphere, but the database is still evolving. Additional laboratory measurements can be conducted to retrieve the speciation zones for specific particles in the case of measurements in new specific environments.
Acknowledgements. The  LOAC  project was funded by the  French National Research Agency's ANR ECOTECH. The instrument and the gondola are built by Environnement-SA and MeteoModem companies. The balloon flights of the ChArMEx campaigns were funded and performed by the  French Space Agency CNES. The Icelandic flights were conducted by the  Iceland Meteorological Office. The various copies of LOAC used in the campaigns were funded with the support of  CNES ,  ADEME  and  INSU-CNRS  in the framework of the  MISTRALS Programme , and of the  French VOLTAIRE Labex (Laboratoire d'Excellence   ANR-10-LABX-100-01 ). The QAIDOMUS laboratory tests were funded by the  French Ministry of Industry. Some calibration tests were conducted at the Aerolab offices. We thank  Patrick Chazette  and  Julien Totems  ( CEA/DSM/LSCE ) for providing the WALI lidar data.
74%), ZIM (1. 85%), and BAR (1. 56%), with significant differences Fig. 1. Composition of the counted and captured communities at each site and in pooled data (TOTAL). The EpiFG combinations with ≥12 tested individuals are indicated with an asterix. The Spearman's rank correlations (ρ) between their proportions in the counted and captured communities are given with their associated P-value. res: resident species, afro: afrotropical migrants and nomadic species, pal: palearctic migrants, aqua: aquatic foragers, terr: terrestrial foragers, gr: ground roosters, mid: mid-story roosters, can: canopy roosters. between the sites (χ 2 = 62. 60, df = 3, P < 0. 001). Birds were significantly more infected by Haemoproteus in NGA than in BAR (χ 2 = 43. 35, df = 1, P < 0. 001), STR (χ 2 = 7. 08, df = 1, P = 0.
acknowledgMents We are grateful to more than 80 people who helped to collect the blood samples and undertake bird counts, and in particular to Mduduzi Ndlovu, Ngoni Chiweshe, Gregory Mutumi, and David Nkosi. Field data collection was funded by a USAID-sponsored Global Avian Influenza Network for Surveillance subcontract from the Wildlife Society to GSC, with additional contributions from the DST/NRF Centre of Excellence at the Percy FitzPatrick Institute. Additional funds for sample analysis were provided by the National Research Foundation of South Africa and the University of Cape Town. In Zimbabwe, we benefited from the "Mesures d'Urgence" and GRIPAVI projects, funded by the French Ministry of Foreign Affairs, and the scientific and logistical support of the Research Platform Production and Conservation in Partnership (RP-PCP).
Diatoms are micro-algal indicators of freshwater pollution. Current standardized methodologies are based on microscopic determinations, which is time consuming and prone to identification uncertainties. The use of DNA-barcoding has been proposed as a way to avoid these flaws. Combining barcoding with next-generation sequencing enables collection of a large quantity of barcodes from natural samples. These barcodes are identified as certain diatom taxa by comparing the sequences to a reference barcoding library using algorithms. Proof of concept was recently demonstrated for synthetic and natural communities and underlined the importance of the quality of this reference library. We present an open-access and curated reference barcoding database for diatoms, called R-Syst::diatom, developed in the framework of R-Syst, the network of systematic supported by INRA (French National Institute for Agricultural Research), see http://www.
rsyst. inra. fr/ en. R-Syst::diatom links DNA-barcodes to their taxonomical identifications, and is dedicated to identify barcodes from natural samples. The data come from two sources, a culture collection of freshwater algae maintained in INRA in which new strains are regularly deposited and barcoded and from the NCBI (National Center for Biotechnology Information) nucleotide database. Two kinds of barcodes were chosen to support the database: 18S (18S ribosomal RNA) and rbcL (Ribulose-1,5-bisphosphate carboxylase/oxygenase), because of their efficiency. Data are curated using innovative (Declic) and classical bioinformatic tools (Blast, classical phylogenies) and up-to-date taxonomy (Catalogues.
The molecular inventories closest to expected inventories were obtained with rbcL because it showed a higher polymorphism than 18S with an equivalent number of reference barcodes. These tests highlighted also that an Achilles heel of metabarcoding was the reference barcoding library. It must be as complete as possible and requires a regular expert curation to maintain its quality (i. e. taxonomic homogeneity of assignations, sequence quality and traceability of data and metadata). Indeed the value of a curated database is to enable other workers to use it practically without having to sort out the same taxonomic name problems each time one is working with it. Several curated databases already exist, such as PR2  (20)  or SILVA  (21). They cover all microbes. But, a reference library dedicated to diatoms with a fine tuned taxonomy and curation at genus and species level was lacking.
In this article, we describe an open-access reference library, called R-Syst::diatom, and its curation procedures. For most of the freshwater taxa, phenotypic information is given (morphology, life-forms and ecological requirements). This database was used in previous metabarcoding studies for river biomonitoring using diatoms  (18, 22, 23). R-Syst::diatom is included in the French barcoding network R-Syst and gathers data for two barcodes (18S and rbcL). It is freely accessible through a website (http://www. rsyst. inra. fr/) and a supercomputing platform adapted for NGS analyses (Y. C. Laizet et al. 2014, in preparation) (https://galaxy-pgtp. pierroton. inra. fr/). Data sources, metadata associated with the barcodes, data curation procedures, data storage and accessibility are presented in the methodology.
rsyst. inra. fr/). The strains are identified using updated literature such as the entire collection of Diatoms of Europe, Iconographia Diatomologica, Bibliotheca Diatomologica and peer reviewed papers. The TCC is regularly enriched with new isolated strains, which are sequenced for at least the two barcodes (18S, rbcL). Their entry in R-Syst::diatom is submitted to the curation process described in the section 'Data curation' here below. Nucleotide database of NCBI NCBI maintains a webserver that collects and provides molecular data and software. In particular, NCBI allows access to all public DNA sequence data via the GenBank database (24) (http://www. ncbi. nlm. nih. gov/genbank). We recovered all the nucleotide sequences of diatoms (freshwater and marine) available on GenBank main collection (CoreNucleotide) for the 18s (including V4 region) and rbcL whatever their length and their quality.
We limited ourselves to these markers because they generally discriminate well between species and are therefore useful for species identification  (25) (26) (27) (28) , they provide access to the largest taxonomic diversity and showed the best results for metabarcoding  (18, 22). Sequences for other genes suggested as diatom barcodes -28S and ITS rDNA and cox1-are not added to the database. These sequences are retrieved regularly (every 6 months) using the following keywords on the Nucleotide Advanced Search Builder selecting 'All fields' in the drop-down menu: '(18s OR rbcL) and (diatom OR Bacillariophyta)'. In addition to these keywords, a publication interval in NCBI is indicated in the Advanced Search Builder selecting 'Publication date' in the drop-down menu: the oldest is corresponding to the last R-Syst::diatom update and the most recent to the current date.
Phenotypic data For most species, three kinds of phenotypic data are given: (i) morphological, (ii) life-form and (iii) ecological. (i) Morphological data are gathering information about chloroplast and cell sizes. For the chloroplast, their shapes and number per cell are given for each taxon with the corresponding bibliographical references; most of the time, the publication of Cox  (29)  was used. When possible, photos of the strains were look at to get such information. Cell-dimensions (length, width, thickness), biovolume and size-class are given. Most of this information is derived from Rimet and Bouchez  (30)  which is a database gathering morphological and ecological information about freshwater diatoms. Omnidia  (31)  database which is gathering information about cell-biovolumes and sizes was also used.
Data curation The identifications and sequencing of diatoms included in R-Syst::diatom were carried out by different people and may not be equally reliable. There are three important drawbacks to take into account when gathering new sequences in R-Syst::diatom: -First, in NCBI, data were deposited by different authors at different times: the first data were deposited in 1998. From this date to the present, taxonomy has evolved. -Second, the identifications and taxonomic skills of the different authors who deposited their data in NCBI can be heterogeneous. The same problem is also visible for TCC. -Third, the length or the quality of the sequences cannot be adapted for correct taxonomic affiliation. These three drawbacks underline the necessity to curate the taxonomic names of the strains and their corresponding sequences in order to have homogeneous taxonomic names in R-Syst::diatom.
As diatom taxonomy is under active development, the aim is to achieve for similar sequences a similarity in their taxonomic names, and to ensure that these names are as taxonomically correct as possible according to the most recent taxonomy literature. However, as diatom taxonomy is under active development, there will be cases where only a consensus for practical use can be made and solutions regarding the correct name will have to await further scientific studies. In any case, if the original taxonomic name given by the authors of the sequence is changed during the curation procedure, the traceability of the original name is kept in the database and is visible on R-Syst web portal. This data curation is carried out in three steps (the first two steps are mandatory): • The first step is pre-curation.
The objective of this first step is to check if each newly retrieved sequence from NCBI or the TCC has a comparable taxonomic name to similar sequences formerly deposited in NCBI, and to check if the quality and length of these sequences is correct. For this purpose, the newly retrieved sequences are compared to the entire NCBI database using Blast. • The second step is detailed curation. The objective of this second step is to compare the new sequences meeting the criteria of the first step with the sequences already included in R-Syst::diatom, based on a local alignment methodology, called 'Declic algorithm' (for detail see 'Second curation step' section of this 'Data curation' part). If these sequences have taxonomic names similar to comparable sequences then their taxonomic names and the sequences are kept for the third curation step.
If the taxonomic names from comparable sequences are different, then the taxonomic names are checked through a taxonomic curation procedure. • The third step is an optional curation (for reasons see 'Third curation step' of the 'Data curation' part). The objective is to compare the new sequences meeting the criteria of the second step with those already included in R-Syst::diatom database, based on a global alignment and phylogenetic analyses. If the taxonomic names from comparable sequences are different, then the taxonomic names are checked through a taxonomic curation procedure. The second and third curation step are based on different algorithms which both have different advantages and so are complementary. The Declic analysis is based on local alignment and is run on all sequences whatever their length.
The phylogenetic analysis is based on global alignment and is run on a sub-set of sequences which have a long common covering (1000 bp at least). Declic has the advantage to compare all sequences whereas the phylogenetic analysis has the advantage to compare with better precision a sub-set of sequences. Figure  1  gives an overview of the general workflow of the curation procedures. Figure  2  gives details on the taxonomic curation procedure which is used several times in the general workflow of Figure  1. First curation step: pre-curation using NCBI and Blastn of each sequence For each sequence (whatever its quality or length), newly gathered from NCBI or coming from new strains of the TCC, a Blastn is run on the entire NCBI database. The 20 sequences showing the best pairwise identity matching to this new sequence are consulted.
asp) or Omnidia software  (31). If this enables the homogenization of its taxonomic name, then the new sequence and its new taxonomic name are kept for the second curation step. If it is not the case, then point (iii) of the taxonomic curation procedure is considered. iii. If no peer-reviewed publication exists, and if the taxonomic synonymies check was not successful, we check if some photos or slides associated to the sequence are available (e. g. in TCC or Algaterra databases  (38)  or websites of culture collections such as http://www. ccap. ac. uk/). If the re-examination of this material (photos/slides) shows that the strain was wrongly identified then a correct taxonomic name is given. If this new taxonomic name is similar to those of the 20 most similar NCBI sequences, this sequence is kept for the second curation step.
If it is not the case, no photos/ slides are available or the new taxonomic name still differs from those of the 20 most similar sequences, then this new sequence is not accepted in the database. After gathering all the new sequences from the first curation step, additional curation steps are done by comparing them to the sequences already in the R-Syst::diatom database. Two different and complementary tools are used. The first tool is Declic analyses (second curation step). This analysis is based on local alignments which are useful when sequences of dissimilar sizes have to be compared. If this is the case of the data usually gathered: depending on the authors, only parts of 18s/rbcl are sequenced. The second tool is phylogenetic trees (third optional curation step) based on global alignments.
Second curation step: use of Declic on the entire database In the second curation step, the newly retrieved sequences and the sequences of the R-Syst::diatom database are compared two by two by mean of Declic (for Delimitation of species with cliques) software written in python  (19)  which enable (i) to define OTU (Operational Taxonomic Units) through an unsupervised clustering algorithm and (ii) to represent these OTU in a two dimensional space. This software can be run with an R-package  (39)  or under a galaxy platform (https:// galaxy-pgtp. pierroton. inra. fr/) within the Virtual BiodiversityL@b folder (Y. C. Laizet et al. 2014, in preparation). Briefly, Declic analysis is run after computing pairwise local alignment scores  (40)  which are then transformed into distances. We then have a full pairwise distance matrix.
In the case that there is no publication available for the new sequence, the synonymies of the taxonomic names of the sequences inside this clique are checked (using i. e. Algaebase, Catalogue of diatom names, Omnidia). If this enables us to homogenize the taxonomic names, the sequences and their new taxonomic names are kept for the third curation step. If this is not the case, point (iii) is considered. iii. If no publication is associated to this new sequence and if the synonymies check did not enable us to with one Craticula cuspidata sequence (KM084917)-white circle. TCC460 strain identification was changed into G. bourbonense after checking photos. KM084917 was rejected since there is an obvious mistake of identification. homogenize taxonomic names, we check if photos/ slides associated to it (collections as TCC, Algaterra, Bold) are available.
The same verifications as those carried out in the second curation step are done: if taxonomic names in a given clade are heterogeneous, the taxonomic curation procedure is applied (Figure  2 ). These phylogenetic analyses are done to confirm the curation completed with Declic analyses. Nevertheless, phylogenetic analyses are carried out on a sub-set of the database only, since short sequences are not integrated in this analysis. The shortest sequences, which were not integrated in the phylogenetic analyses, are only curated with the Declic analyses and if they meet all the criteria of taxonomic homogeneity in the second curation step they are directly integrated in R-Syst::diatom.
Data storage and open access All the curated data are stored in a PostgresSQL database built in the frame of the R-Syst network. R-Syst is a collaborative network supported by INRA for studies in systematics. It comprises several tens of research teams including technicians, researchers and engineers in the fields of molecular biology, genetics and bioinformatics who are involved in the molecular and morphological characterization of organisms. Among those, micro-algae are represented and a dedicated web interface is available from the R-Syst web portal (http://www. rsyst. inra. fr/en) to browse the stored data of the diatom barcoding database. On this website, the algae section of the database gathers information about diatom strains (but also about Chlorophyta and Cyanophyta strains of the TCC) which were characterized for three kinds of criteria: taxonomic, phenotypic and genetic.
For each strain, the following information is given when available: (i) sampling site (name and location on Google map), (ii) type of habitat, (iii) strain code given by the laboratory, (iv) name of the project which funded the field sampling, sequencing, (v) laboratory responsible for field sampling, (vi) DNA extraction, (vii) PCR, sequencing and (viii) the dates of the different steps. A species name is given to each strain, except in a few cases where only genus level is given. Moreover the taxonomic affiliation is given until the regnum  (11, 37, 44). For molecular criteria, the database gives the type of marker (18S or rbcL), the primers used for sequencing and PCR. Protocols for DNA extraction and PCR are also given. The laboratory responsible of the sequence is given. For phenotypic information, photos (living material and empty frustules) of the TCC strains are given.
Examples of curation Results of the curation process of R-Syst::diatom in January 2015 are given in Table  1  and supplementary files give the list of sequences whose taxonomical name was changed after curation (Supplement data 1) and those which were not integrated in R-Syst::diatom (Supplement data 2). This curation was carried out on new sequences downloaded from NCBI and coming from the TCC between 29 July 2014 and 16 January 2015. First curation step: In the curation process carried out in January 2015 (Figure  1 ), several sets of sequences deposited in NCBI were not kept because determinations were clearly erroneous or insufficient. This was especially the case for 18S where 162 sequences were rejected because of an insufficient taxonomic identification (class level identifications).
Number and length of sequences The number of sequences available for 18S and rbcL is given in Table  2. Sequences from the TCC represent 21% of the total number of sequences for 18S, and 19% for the rbcL. Only 16. 7% of the 18S sequences of the TCC have been deposited on NCBI and have an accession number. Similarly, 21. 7% of the rbcL sequences of the TCC were deposited on NCBI. These sequences were deposited in the framework of several peer-reviewed publications, as phylogenetic studies  (45, 46) , metabarcoding analyses using NGS  (18, 22) , diversity studies  (47) , ecotoxicological studies  (48). The objective is to submit all accepted sequences from the TCC on NCBI. The lengths of the barcodes in R-Syst::diatom is given in Figure  5. For 18S, a large majority of the sequences have lengths of 1600-1800 bp.
Taxonomic coverage of diatom taxa To show the distribution of barcodes in the diatom taxonomy, we followed the classification given by Medlin  (52)  with the three classes Mediophyceae, Coscinodiscophyceae, Bacillariophyceae, and we kept the Fragilariophyceae as described in Round et al. (11) , which were recently shown to be non-monophyletic (except Bacillariophyceae) but were grades which evolved from radials (Coscinodiscophyceae) to polar (Mediophyceae) to araphids (Fragilariophyceae) and then to raphids (Bacillariophyceae)  (53). Figure  6  gives an overview of the number of barcodes and taxa for each class in the R-Syst::diatom database. The Bacillariophyceae is the most barcoded class with the Bacillariales and the Naviculales orders. In all diatom classes the number of barcodes is higher for 18S than for rbcL: this can be explained by the longer legacy of 18S in diatom phylogeny  (54 and 55) , than rbcL which started to be used in diatom barcoding and phylogeny more recently  (25).
In the Coscinodiscophyceae (Figure  7 ), the most sequenced genus is Aulacoseira (respectively, 31 and 23 for 18S and rbcL sequences). Aulacoseira strains are mostly sequenced by Shcherbakova (56), Edgar and Theriot  (57)  and  Medlin and Kaczmarska (58). In the Mediophyceae (Figure  8 ), the most sequenced genera are the Thalassiosira (respectively, 151 and 61 for 18S and rbcL sequences), Skeletonema (respectively, 107 and 16 for 18S and rbcL sequences) and the Cyclotella (respectively, 58 and 41 for 18S and rbcL sequences). In the Fragilariophyceae (Figure  9 ), the most sequenced genus is Fragilaria (respectively, 77 and 46 for 18S and rbcL sequences), the other genera are much less represented in the database: Asterionellopsis (respectively, 17 and 22 for 18S and rbcL sequences) and Diatoma (respectively, 18 and 5 for 18S and rbcL sequences).