dataesr/scientific-paragraphs-categorization huggingface dataset:
aeronomie. be) that centralises the data (near real time and archive), globally and for a set of predefined regions, in the form of maps (images). This service is also linked to other European initiatives such as the Support to Aviation for Volcanic Ash Avoidance (SAVAA) ESA project (http://savaa. nilu. no), the Volcanic Ash Strategic initiative Team (VAST) ESA project (http://vast. nilu. no) and the European Volcano Observatory Space Services (EVOSS) project of the European Union Framework Programmes EU FP7 (http://www. evoss. eu), whose general aims are to define and demonstrate an optimal system for volcanic ash plume monitoring and prediction. SACS delivers global data sets for sulfur dioxide (SO 2 ) and aerosol (ash) related to volcanic eruptions from measurements by space-based instruments.
At the time of writing, SACS is based on polar sun-synchronous satellite instruments widely used to sound atmospheric composition and for meteorological and scientific applications: AIRS/Aqua  (Aumann et al. , 2000 (Aumann et al. , , 2003;; Hofstadter et al. , 2000) , OMI/Aura  (Levelt et al. , 2006) , GOME-2A&B  (Munro et al. , 2006)  and IASI-A&B  (Clerbaux et al. , 2009; Hilton et al. , 2012). All SACS data retrievals are available in NRT (which means as soon as the data process allows retrieving SO 2 and ash observations). Figure  1  shows the different sounders that SACS currently uses and their local overpass time. It shows the advantage of combining multiple sensors which have different overpass times in a single system as it allows the reliable and timely monitoring of volcanic plumes on a global scale.
Temporal and spatial sampling It is important to know precisely the time and space sampling of the SACS monitoring system. Because of the use of polar-orbiting satellites with different overpass times, some geographical regions are observed more frequently than others. To evaluate the revisiting frequency of SACS, we have defined seven geographical latitude zones (see Fig. 6a ) and, based on data of October 2012, we have estimated the percentage of the areas that have been observed at least once by one of the satellite instruments (OMI, GOME-2A, IASI-A and AIRS). The calculation was done for several time intervals (see, e. g. , the coverage of the Earth by SACS after 8 h of monitoring in Fig. 6b ). Table  2  presents the results for the different zones and time durations. A set of two parameters has been evaluated: Pixel is the percentage of the region of interest sampled by the SACS system, and max is the maximum number of overpasses.
Criteria for SO 2 and ash notifications The criteria for the detection of exceptional SO 2 concentrations from the seven instruments has been derived from the analysis of historical results (see Table  1  for the data periods considered). Each instrument and retrieval method has different characteristics; therefore, for each data source different notification threshold values have been established. Note that, even though it no longer provides data, SCIAMACHY is included here because it has been used in the past and notifications based on its data are in the SACS archive. The detection of exceptional ash concentrations is not applicable (N/A) for the UV-visible instruments. For the three thermal infrared sensors, the criteria of this detection are generally straightforward using the three-step approach specified in Sect.
SO 2 notification from UV-visible sensors The instrumental noise on the UV-visible data is a limitation and can lead to false notifications. For this reason, a first selection criterion is based on the maximum observed value for the SO 2 column which must exceed the relevant threshold. An additional criterion is based on the neighbouring pixels (inside a threshold radius; see Table  3  and Fig. 7 ), for which the observed SO 2 column for more than half of the observations must also exceed the threshold value. A notification is generated only when both criteria are fulfilled. An illustration is given in Fig. 7  for GOME-2 for an SO 2 plume from the Copahue Volcano (23 December 2012). Another limitation of UV-visible sensors comes from the South Atlantic Anomaly (see Sect. 3. 5. 3). To avoid false notifications in this region, a stricter threshold value is considered (see Table  3 ).
SO 2 and ash notifications for a dedicated region As soon as SACS harvests new SO 2 and ash observations (from one of the instruments), the first step is the analysis of the data using the criteria defined in Sect. 4. 2. 1. After a volcanic eruption, there are potentially multiple warnings generated by the system. For this reason, it has been decided to consider a set of world regions of 30 • by 30 • plus two polar regions polewards of 75 • in latitude. Figure  9  shows the locations (and associated name/number) of the 62 regions of the SACS system. Note that the same regions are used in the near-real-time monitoring and archive services to display the SO 2 and ash images. As soon as a notification is issued, the related region is flagged "ON".
Dedicated SO 2 /ash notifications web page An example of a web page generated by SACS using GOME-2A data for a specific SO 2 notification is shown in Fig. 11. This notification was sent to the users 1 h 01 min after MetOp-A passed over this area. The page shows a box with the information given by the email notification (time of measurement, location of the warning, SZA, maximum SO 2 value and the use of cloud data in calculations) and the images of SO 2 and aerosols/ash side by side. Below the SO 2 image, one can find two links to the same image but with different mapping tools (interpolated image using the Generic Mapping Tools from the University from Hawaii, see http://gmt. soest. hawaii. edu, and a Google Earth file, see http://www. google. com/earth). For the UV-visible instruments, there is also a link to the corresponding cloudcover fraction (CCF) image.
In case of an IASI notification, in addition to the SO 2 brightness temperature index image, there is also a link to the SO 2 VCD image. As a complement, a map showing the volcanoes in the region (with a link to a listing including more information about each of them) is also provided on this web page. Furthermore, a direct link to the near-real-time monitoring web page is given with all corresponding SACS images (for all instruments) and a link to the data (files or link to data providers) for the region considered.
10 years of data and notifications archive Although the multi-sensor notification system has only been operational since April 2012, 10 years of historical data have been reanalysed for the development of the warning service. This section presents an overview of all the SO 2 and ash notifications identified by SACS using the seven sensors (AIRS, OMI, SCIAMACHY, GOME-2A, IASI-A, GOME-2B and IASI-B) on-board Aqua, Aura, Envisat, MetOp-A and MetOp-B. For each year (from 2004 to 2013), an image of all the notifications is shown with a highlight of the major events. This tool, available on the SACS portal, allows any interested person to investigate historical eruptive emissions in a quick and user-friendly way. In addition to our archive of exceptional volcanic emissions, note that our system also allows for the monitoring of small volcanic eruptions which might not always lead to a notification.
Notifications from 2004 to 2006 (SCIAMACHY, OMI and AIRS) Data from the SCIAMACHY, OMI and AIRS instruments are available for this period (note that AIRS data are recorded from 2002 onwards). The main volcanic activity detected by SACS in 2004 was in the Independent State of Papua New Guinea, the archipelago of the Republic of Vanuatu and the Democratic Republic of Congo (DRC), as shown in Fig. 13a. The borderland between Rwanda, the DRC and Uganda is one of the most active regions, containing eight volcanoes, including Mount Nyamuragira and Mount Nyiragongo which were both erupting on 25 May 2004. Details of the satellite monitoring of SO 2 emissions from Nyamuragira from 1979 to 2005 can be found in  Bluth and Carn (2008). Figure  13a  also presents several SO 2 and ash notifications over Europe in 2004.
Focussing on instruments on-board polar-orbiting platforms, this study presents the Support to Aviation Control Service. This free service provides images of SO 2 and ash in near real time on a global scale via a web interface (http://sacs. aeronomie. be). We have set up a notification system and demonstrated that it is able to detect major volcanic events that occurred during the last decade and posed a threat to aviation. The current system uses in-synergy data products from UV-visible sensors (OMI, GOME-2/MetOp-A and GOME-2/MetOp-B) and thermal IR sensors (AIRS, IASI/MetOp-A and IASI/MetOp-B) with a revisiting time of (at least) 6 h anywhere on Earth. We have assessed the limitations of our system and estimated the success rate of the notifications to be larger than 95 % Finally, the next objectives for our service are: -to incorporate more sensors in the system: in particular OMPS (Ozone Mapping Profiler Suite), CrIS (Crosstrack Infrared Sounder) and VIIRS (Visible Infrared Imaging Radiometer Suite) on the Suomi National Polar-orbiting Partnership mission (Suomi NPP) and TROPOMI (TROPOspheric Monitoring Instrument) on-board Sentinel 5 Precursor; -to improve satellite data products for ash (as an outcome of the on-going SACS2 project); -to provide information in NRT on volcanic SO 2 plume height from satellite observations (e.
Acknowledgements. Many thanks to ESA for the funding (SACS, SACS+ and SACS2 projects) and the support. We are grateful to  NASA  and  FMI  for the very fast delivery of  OMI ,  CNES  and  EU-METSAT  for data of IASI and GOME-2. We thank also  JPL/NASA  for the free access to AIRS data on EOSDIS. We would like also to acknowledge the team from the  Toulouse VAAC (Météo-France) , the  London VAAC (UK Met Office) , the  Darwin VAAC (Bureau of Meteorology) , the  Anchorage VAAC (NOAA) , the  Wellington VAAC (MetService) , the  Montréal VAAC (Government of Canada ) and all the users for their very useful feedback, which brings us energy and motivation and helps us to continue and to improve our service. The authors thank the reviewers for their comments and suggestions. This work was also undertaken under the auspices of the  O3M   SAF  project of the EUMETSAT.
P. F. Coheur and L. Clarisse are respectively Research Associate and Postdoctoral Researcher with F. R. S. -FNRS. The research in Belgium was funded by the  F. R. S. -FNRS , the  Belgian State Federal Office for Scientific, Technical and Cultural Affairs  and the  European Space Agency  ( ESA-Prodex arrangements C-4000103226 ). Financial support by the  "Actions de Recherche Concertées" (Communauté Française de Belgique)  is also acknowledged. Edited by:  A. Mugnai  Reviewed by:  F. Cairo  and one anonymous referee.
The Support to Aviation Control Service is a project funded by the European Space Agency (ESA) developed within the Data User Element of the Tropospheric Emission Monitoring Internet Service (DUE-TEMIS, http://www. temis. nl) and the PROtocol MOniToring service for the Global Monitoring for Environment and Security (GMES) Service Element (GSE- PROMOTE, http://www. gse-promote. org) programmes (Van Geffen et al. , 2007). SACS is a collaborative project that currently involves the following partners: the Belgian Insti- tute for Space Aeronomy (BIRA-IASB; leader), the Royal Netherlands Meteorological Institute (KNMI), the University of Brussels (ULB) and the German Aerospace Center (DLR). The SACS project is also indirectly supported by the fol- lowing agencies/institutions: EUMETSAT (via the Satellite Application Facility on Ozone Monitoring (O3M SAF) pro- gram), Centre National d'Etudes Spatiales (CNES), Finnish Meteorological Institute (FMI, Ilmatieteen Laitos), Norsk In- stitutt for Luftforskning (NILU), Jet Propulsion Laboratory (JPL) and the National Aeronautics and Space Administra- tion (NASA.
Aphids constitute a diverse group of plant-feeding insects and are among the most important crop pests in temperate regions. Their morphological identification is time-consuming and requires specific knowledge, training and skills that may take years to acquire. We assessed the advantages and limits of DNA barcoding with the standard COI barcode fragment for the identification of European aphids. We constructed a large reference dataset of barcodes from 1020 specimens belonging to 274 species and 87 genera sampled throughout Europe and set up a database-driven website allowing species identification from query sequences.
Most DNA barcoding-based studies in aphids have involved comparisons of small numbers of economically important species  [21] ,  [22] ,  [23] ,  [24]. Only a few recent studies have included relatively large numbers of aphid species. Wang et al. [25]  focused on subtribe Aphidina, a difficult group, and two studies have demonstrated the utility of DNA barcoding for the identification of specimens from the large regional aphid fauna of North America  [1] ,  [26]  and Korea  [27]. However, the accuracy of DNA barcoding for the identification of European aphids has never before been assessed. Here (i) we present the first European aphid barcode database including a large number of species (274), (ii) we discuss the usefulness, accuracy and limitations of this database for identifying European aphids and (iii) we introduce a database-driven website including taxonomic and biological data and images and allowing the identification of species through BLAST sequence comparisons with a query sequence.
Samples were subjected to initial denaturation at 94uC for 3 minutes, followed by 30 cycles of 30 s at 94uC, 1 minute at 48uC and 1 minute at 72uC, before a final elongation for 10 minutes at 72uC. PCR products were purified by treatment with exonuclease I and phosphatase and sequenced directly with the Big Dye Terminator V3. 1 kit (Applied Biosystems) and an ABI3730XL sequencer at Genoscope, Evry, France. Contigs were assembled from forward and reverse reads and corrected with GENEIOUS V3. 7 sequence editing software  [35]. The same software was used to align the sequences, and the alignment was translated into an amino-acid sequence with MEGA ve. 5 software  [36] , which was used to detect frameshift mutations and premature stop codons potentially indicative of the presence of pseudogenes.
We tried to obtain complete sequences (658 bp) with no ambiguous nucleotides for any specimen, to establish a valuable reference database. We therefore repeated PCR and sequencing for all sequences that were incomplete or contained ambiguous base pairs. All sequences were deposited in GenBank (KF638720 to KF639739) and are also available from the PhylAphidB@se website (http://aphiddb. supagro. inra. fr) and from BOLD (http:// www. barcodinglife. org).
Data Analyses We first evaluated the extent to which our database was representative of the known European fauna  [37]. We then compared the number of haplotypes obtained with the number of specimens sequenced per species. We also constructed frequency histograms of pairwise genetic distance values at three levels: between specimens from the same species (intraspecific), between species from the same genus (congeneric) and between species from different genera (intergeneric). The distribution of pairwise distances and associated statistical values may be biased by the uneven sampling of different taxa (e. g. intensive sampling of a few species and the overrepresentation of a few haplotypes), so we repeated our analyses, taking into account a maximum of two specimens per haplotype for each species, which is equivalent to considering haplotype diversity instead of haplotype frequency.
Database and Website A database was constructed with BioloMICS Software (www. Bio-Aware. com) [42] , to manage all arthropod specimens, including aphids, hosted by the Center for the Biology and Management of Populations (CBGP, France). This database includes taxonomic and collection information, the DNA sequences available for each specimen, photographs and host-plant associations, when relevant. The BioloMICS Net Module was used to create the PhylAphid database (PhylAphid@base, available from http://aphiddb. supagro. inra. fr) dedicated to aphid specimens. The pairwise sequence alignment function embedded in BioloMICS Software is implemented in PhylAphid@base as an identification tool. The Fauna Europaea  [37]  and Aphid Species Files V. 5. 0  [31]  were used as references for aphid species names.
Representativeness of the Dataset Our complete dataset included 1020 samples, from 274 species (20% of all European species), 87 genera (38. 5% of the genera present in Europe) and 11 subfamilies (Table  1 ). All European subfamilies with more than five species were represented, with the exception of Saltusaphidinae. The number of species sampled per genus was significantly correlated with the number of species from the aphid genus concerned known to be present in Europe (Figure  1 ; R 2 = 0. 9562, t = 48. 9198, df = 224, p-value,0. 001). Our dataset may therefore be considered to correspond to an unbiased sample of the taxonomic diversity of European aphids. Aphis, the genus with the largest number of species in Europe, was slightly oversampled, but only five genera containing more than 10 European species were not represented in our dataset: Microsiphum (10 European species), Xerobion  (12) , Eulachnus (13), Coloradoa  (21)  and Schizaphis  (27).
Most specimens were sampled in France (730), Greece (148) and Italy (112). A few were collected in the United Kingdom  (24)  and Serbia (6) (Table  S1 ). After several rounds of PCR amplification and sequencing of COI, only four of the 1020 barcodes still contained ambiguous bases at either the 59-(specimens ACOE1772, ACOE1982, ACOE1007) or 39-(specimen ACOE1586) end. As these specimens were singletons, their incompleteness had little impact on the analysis and we left them in the dataset. Alignment was straightforward, due to a lack of sequence length variation and an absence of stop codons and frameshifts, suggesting that our dataset contained no NUMts. We obtained a mean of 3. 7 barcode sequences per species, with 58% of the species represented by at least two barcodes and 40% represented by at least three barcodes (Figure  2  A, Table  S3 ).
Species Identification through the Exploration of Genetic Variation within and between Species Species assignment was correct for 85% of the 274 species included in our dataset. Species represented by a single specimen (n = 119) had distinct haplotypes, and those represented by more than one specimen (155) had a Max-WSD value that was smaller than min-BSD (Figure  5 , Table  S3 ). NJ analysis generated a tree (Figure  S4 ) in which most species formed distinct genetic units; 77. 8% of the species represented by several specimens were recovered as monophyletic units, 95% of which were supported by a bootstrap value (BP). 80%. A misleading barcode-based assignment to a particular species could occurs when the maximum sequence divergence among individuals belonging to one species (max-WSD) equals or exceeds the minimum sequence divergence with another species (min-BSD)  (Hajibabaei et al.
, 2006). In our dataset, this situation was encountered for 41 species (dots below the diagonal on Figure  5 , species shown in bold in Table  S3 ). Two of these species, Brachycaudus helichrysi and Myzocallis coryli (green dots in Figure  5 ) were previously identified as species with exceptionally high levels of intraspecific divergence. In the NJ tree (Figure  6. 4), specimens of B. helichrysi were segregated into two well supported clades (BP = 100) (containing 16 and 26 specimens, respectively). B. helichrysi was rendered paraphyletic by one specimen of B. spiraeae (Figure  6. 4) branching with a high BP value (88) as a sister group to one of the clades. The high degree of intraspecific divergence observed for Myzocallis coryli (Figure  6. 1) was due to a single specimen, which diverged strongly from the other representatives of the species.
prunicola (Clade K) from one another. More than half (6/11) the Dysaphis species included in our dataset were grouped into a single clade displaying little differentiation (Clade L Table  2 , Figure  6. 3). Three of these species -D. apiifolia, D. crataegi and D. crithmi -were represented by specimens sharing one haplotype. The single specimen of D. angelicae was nested within these polyphyletic species, like D. tulipae and D. lauberti specimens, although these two species formed monophyletic groups that were either highly (BP = 87 for D. lauberti) or poorly (BP = 63 for D. tulipae) supported. Macrosiphum cerinthiacum and M. stellariae could not be distinguished by barcoding, because they share a common haplotype (Clade M, Figure  7. 6). Finally, the single specimen of Macrosiphum cholodkovskyi was nested within the clade including all haplotypes of M.
Database and Website Use Data for the specimens included in this study have been uploaded to the PhylAphidB@se database and can be accessed online via the following website: http://aphiddb. supagro. inra. fr/. PhylAphidB@ase queries can be carried out easily with basic or advanced search tools. Detailed information about the records (species, specimens, geography, pictures, taxonomy, molecular data, etc. ) are automatically displayed. The locations at which specimens were collected can be visualized with Google Earth maps. The PhylAphidB@se pairwise sequence alignment tool allows users to run an algorithm similar to Blastn, to align unknown COI DNA sequences with the reference sequence in the database. Several pairwise alignment parameters can be modified by the user (e. g. minimum similarity, minimum overlap).
Discussion DNA barcoding aims to identify species, as accurately as, and faster than a taxonomist. It requires the use of an appropriate DNA marker with an adequate rate of evolution, and the availability of a reference dataset representative of the taxonomic diversity of the group studied. We present here the first large barcoding dataset for European aphids, providing records for 1020 individuals from 274 species. We show that this dataset corresponds to an unbiased sample of the taxonomic diversity of European aphids and provides a useful tool for species identification, at least as useful as an aphid taxonomist, who would not conduct thorough and time-consuming comprehensive studies on each problematic taxon.
COI Variation and its Use for Barcoding in Aphids The intraspecific and interspecific COI divergences obtained for our dataset were of a similar order of magnitude to those for the North American and Korean aphid fauna  [1] ,  [27]. However, the mean intraspecific divergence (0. 29%, range: 0-3. 9%) was slightly higher than the values obtained for the North American (mean: 0. 201%, SE 0. 004) and Korean (mean: 0. 05%; range: 0. 00-1. 00%) fauna, possibly reflecting differences in the magnitude of sampling efforts rather than differences between the fauna. Indeed, the number of specimens per species was higher in our dataset (1020 specimens/274 species; ratio: 3. 72) than for the North American (690/335; ratio: 2. 06) and Korean (249/154; ratio: 1. 61) datasets. This increase in intraspecific divergence with the number of specimens sampled per species has already been highlighted by several studies  [44] ,  [45] , although exceptions have been reported  [46].
Even for highly sampled species (96 specimens of Aphis fabae), haplotype accumulation curves never reached an asymptote. This is consistent with the results of Zhang et al. [47]  for neotropical butterflies, showing that a sample size of 32 to 618 specimens per species was required to unravel most of the genetic diversity (80%) in simulated cases, and that a sample size of 9. 5-216. 6 was required for the actual species they were studying. However, increasing levels of genetic diversity does not necessarily affect deeply intraspecific divergence values if haplotypes differ at only a few autapomorphic positions, as appears to be the case in our dataset. If we increased our intraspecific sampling effort, the mean intraspecific divergence would probably increase a little, but would probably remain low.