dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2015)  and Weissflujoch in the Plessur Alps of Switzerland  (WSL, 2015). All of these are highelevation, mid-latitude sites; there has been a lack of comparable datasets that could be used for evaluating snow models at high latitudes. Snow models operating on energy balance principles form components of land surface models that are used to provide energy and moisture flux boundary conditions for the atmosphere in numerical weather prediction and climate models, but they can also be driven with measured meteorological data. The typical input data required are downwelling shortwave and longwave radiation fluxes, precipitation rate, air temperature, humidity, wind speed and atmospheric pressure. All of these variables can be measured with low-power instruments, but all are challenging to measure in cold and snowy environments where instruments can be covered by snow or ice and access for maintenance may be difficult.
Model-driving data have to be continuous, so gap filling is required if instrument or power failures occur. Data time steps Published by Copernicus Publications on behalf of the European Geosciences Union. have to be somewhat shorter than a day (often 30 min or 1 h) if situations in which snow melts during the day and refreezes at night are to be explicitly represented. This paper presents model-driving and evaluation datasets collated from measurements made at the Finnish Meteorological Institute's Arctic Research Centre (FMI-ARC) over the 7-year period starting on 1 October 2007. Descriptions are given of the site, instrumentation, gap filling used to construct a continuous driving dataset and adjustments of abovecanopy measurements to allow for influences of shading by trees in below-canopy conditions.
In addition to regular measurement programmes, the Sodankylä area has been used in many remote-sensing missions and field campaigns, including the Nordic Snow Radar Experiment (NoSREx), the Snow Reflectance Transition Experiment (SnoRTEx) and the Solid Precipitation Intercomparison Experiment (SPICE). Figure  1b  is an aerial orthophotograph of the site. The area around FMI-ARC is level and forested, predominantly with pine trees about 15 m tall, but many measurements are made in clearings or in a large wetland area to the east of the site. Driving data for this paper are taken from an automatic weather station (Fig. 1c ) and a radiometer tower (Fig. 1d ) 30 m apart, with instruments that are calibrated annually. Evaluation data are taken from an intensive observation area (IOA) (Fig. 1e ) that was established 590 m to the south of the weather station for NoSREx  (Lemmetyinen et al.
Driving data and gap filling All of the meteorological variables necessary for model driving are measured by the AWS and the radiometer tower at FMI-ARC with the instruments and at the heights listed in Table  1 ; note that radiation and wind measurements are made at heights above the forest canopy. The radiometers are ventilated, the anemometer is heated to reduce problems with freezing or snow accumulation and instruments are cleaned after every snowfall or at least three times a week. Temperature and humidity sensors are naturally ventilated inside a Stevenson screen. Precipitation is measured using an optical sensor and two weighing gauges which give similar total amounts; data from the optical sensor are used here. There is no nearby wind speed measurement that could be used for gauge correction, but wind speeds are generally low, and measured snowfall has been adjusted to match snow accumulation on the ground as described below.
FMI-ARC is staffed 5 days a week, and automatic error checking can identify instrument problems immediately. For the 7-year period collated here, fewer than 1 % of hourly data (visible as red points in Fig. 2 ) are missing for any variable with the exception of longwave radiation; the longest period of missing data is a 52-day gap in the longwave radiation measurements from 10 September to 31 October 2011 because of a faulty power supply. The archived driving data files include a flag that records which data were missing and had to be filled for each hour. Measurements from the AWS and the radiometer tower are used for driving data whenever they are available, but gaps have to be filled to form a complete driving dataset. Gaps of 4 h or shorter are filled by linear interpolation. For shortwave radiation, air temperature, humidity and wind speed, longer gaps are filled with data from nearby instruments.
Direct measurements of longwave radiation are rarely available for cold regions, and snow models are known to be sensitive to longwave driving data  (Raleigh et al. , 2016) ; having near-continuous longwave measurements is therefore a distinct advantage of the FMI-ARC site. Seven-year series of gap-filled hourly data are shown in Fig. 2  for all of the driving variables apart from precipitation. Measuring solid precipitation is particularly challenging, and uncertainties in snowfall inputs are a major source of uncertainty in snow model outputs  (Raleigh et al. , 2015). Total precipitation is usually measured but has to be partitioned into snow and rain for mass balance calculations, either in the driving data or by the model. This is usually done by selecting a threshold or function of air temperature or wet-bulb temperature discriminating between rain and snow  (Auer, 1974; Sims and Liu, 2015).
Snowfall data are therefore scaled by the factors required to match the maximum measured SWE each winter (Table  2 ); cumulated snowfall then also matches the rate of accumulation on the ground quite well, as shown in Fig. 3b. The snow measurement points in the IOA (Fig. 2e ) are not directly beneath trees, so snow accumulation there will not be greatly affected by canopy interception, but they are shaded from direct solar radiation by nearby trees. The presence of the trees will also increase the incoming longwave radiation and decrease the wind speed near the ground relative to more open locations. Measurements above the forest canopy height do not take these influences into account. To allow the use of snow models without representations of forest canopies, radiation fluxes and wind speed are adjusted in a modified driving dataset.
The resulting decreases in solar radiation and increases in longwave radiation are shown in Fig. 4b. Solar and longwave radiation will both be underestimated by these modifications close to tree trunks at the sun-lit northern edge of the IOA clearing where the snow is observed to melt first. An anemometer installed temporarily at 2 m height above the ground close to the IOA for 7 days in March 2012 recorded an average wind speed that was 35 % of the wind speed at 22 m height (equal to the ratio given by a logarithmic wind profile with a roughness length of 0. 55 m). This ratio is used to scale the wind speed in the modified driving dataset. There is no permanently installed anemometer below the canopy height at the IOA, so the wind adjustment is highly uncertain. Because the wind is rarely strong enough to move snow in the IOA and snowmelt is dominated by radiation in spring, however, it is expected that models will not be highly sensitive to the wind adjustment.
Model results Preliminary versions of the driving and evaluation datasets were used in a study with the Joint UK Land Environment Simulator (JULES) land surface model by  Ménard et al. (2015). The above-canopy and modified driving datasets are used here to drive Crocus  (Vionnet et al. , 2012) , which is a detailed multi-layer snowpack model originally developed for avalanche forecasting in the French mountains. Although physically based, some of the processes in Crocus have been parametrized using experimental results from the midlatitude site at Col de Porte (45. 3 • N, 5. 8 • E, 1325 m a. s. l. ), which is much warmer than Sodankylä in winter and has heavier snowfall. Figure  8  compares Crocus simulations driven by the above-canopy and below-canopy datasets with measurements of snow depth, SWE and soil temperature.
Data from the FMI Arctic Research Centre at Sodankylä have been used to construct datasets that will allow driving of snow models for multiple years and evaluation of model outputs against multiple types of observations. There are some gaps in the data, but the availability of additional instruments and high-quality atmospheric reanalyses give confidence in the filling of gaps to provide continuous driving data. The utility of the datasets has been demonstrated by driving the Crocus snow model and evaluating its outputs against snow depth, SWE, snow density, snow temperature and soil temperature measurements. The physical basis of the model allows it to perform well in an Arctic environment very different to the mid-latitude mountain environments for which it was first developed. It is intended that Sodankylä will be used as a reference site in an upcoming evaluation of snow simulations in Earth System models (http://www.
climate-cryosphere. org/ activities/targeted/esm-snowmip). Under the open-data policy of the Finnish Ministry of Transport and Communications, FMI is committed to the long-term upkeep and public distribution of its data; the datasets used in this paper can be downloaded from the FMI litdb archive at http://litdb. fmi. fi/ ESMSnowMIP. php. The Supplement related to this article is available online at doi:10. 5194/gi-5-219-2016-supplement.
Acknowledgements. The staff at  FMI-ARC  are thanked for data collection and maintenance of instruments. Collection of evaluation data in the IOA was supported by  ESA ESTEC  contract  22671/09/NL/JA/ef. Visits to FMI-ARC by the first author were supported by  NERC  grant  NE/H008187/1  and  ESA ESTEC  contract  23103/09/NL/JC. Samuel Morin  and  Matthieu Lafaysse  assisted with the Crocus simulations. The orthophotograph in Fig. 1b was supplied by the  Finnish Geospatial Research Institute. We are grateful to  Charles Fierz  and  Mark Raleigh  for their helpful comments in reviewing this paper. Edited by:  J. Pulliainen.
Strain identification and phylogenetic classification The isolate was cultured in blood culture bottles supplemented with sheep blood, (BioMérieux, Marcy l'Etoile, France) during a 7-day pre-incubation period at 37°C in aerobic atmosphere. The identification of the strain was carried out by MALDI-TOF mass spectrometric analysis with a Microflex (Bruker Daltonics, Leipzig, Germany), as previously described  [19]. Obtained spectra were then compared with the Bruker database and our database, which is continuously updated. If no identification is possible at the genus or species level, sequencing of the 16S rRNA gene is used to achieve a correct identification  [20]. DNA extraction was performed using EZ1 DNA Tissue Kit (Qiagen, Courtaboeuf, France). The DNA extract was amplified using PCR technology and universal primers FD1 and RP2  [21]  (Eurogentec, Angers, France).
The amplifications and sequencing of the amplified products were performed as previously described  [22]. Then, 16S rRNA gene sequences were compared with those which are available in GenBank (http://www. ncbi. nlm. nih. gov/ genbank/). Identification at the species level was defined by a 16S rRNA gene sequence similarity 99% with the sequence of the prototype strain in GenBank. When the percentage of identity is <98. 7%, the studied strain is considered as a new species  [11]. Phylogenetic analysis based on 16S rRNA of our isolate was performed to identify its phylogenetic affiliations with other near isolates, including other members of the genus Tessaracoccus. The MEGA 6 (Molecular Evolutionary Genetics Analysis) software allowed us to construct a phylogenetic tree  [23]. Sequence alignment of the different species was performed using CLUSTAL W  [24, 25]  and the evolutionary distance was calculated with the Kimura two-parameter model  [26].
Genome annotation and comparison Open reading frames (ORFs) were predicted using PRODIGAL  [27]  with default parameters but the predicted ORFs were excluded if they spanned a sequencing gap region. The predicted bacterial protein sequences were searched against the GenBank database  [28]  and the Clusters of Orthologous Groups (COG) database using BLASTP (E-value 1e -03 , coverage 0. 7 and identity percent 30%). If no hit was found, a search against the N R database using BLASTP was performed, with an E-value of 1e -03 , coverage 0. 7 and identity 30%. If the sequence length was <80 amino acids, we used an E-value of 1e -05. The tRNASCANSE tool  [29]  was used to find tRNA genes, whereas ribosomal RNAs were found using RNAMMER  [30]  and BLASTN against the GenBank database. Lipoprotein signal peptides and the number of transmembrane helices were predicted using PHOBIUS  [31].
ORFans were identified if their BLASTP E-value was lower than 1e -03 for alignment length >80 amino acids. If alignment lengths were <80 amino acids, we used an E-value of 1e -05. Such parameter thresholds have already been used in previous works to define ORFans. ARTEMIS  [32]  was used for data management and DNA PLOTTER  [33]  for visualization of genomic features. The MAUVE alignment tool (version 2. 3. 1) was used for multiple genomic sequence alignment  [34]. To estimate the mean level of nucleotide sequence similarity at the genome level, we used the MAGI home-made software to calculate the average genomic identity of gene sequences among compared genomes. Briefly, this software combines the PROTEINORTHO software  [35]  for detecting orthologous proteins in pairwise genomic comparisons, then retrieves the corresponding genes and determines the mean percentage of nucleotide sequence identity among orthologous ORFs using the Needleman-Wunsch global alignment algorithm.
(KE384018) Tessaracoccus oleiagri CGMCC 1. 9159 (PRJNA303532). The genomic similarity was evaluated among closely studied species from T. massiliensis by the DNA-DNA hybridization. Tessaracoccus massiliensis was locally aligned 2-by-2 using BLAT algorithm  [38, 39]  against each selected genome previously cited and DNA-DNA hybridization values were estimated from a generalized model  [40].
Genome properties The genome is 3 212 234 bp long with 67. 81% G+C content (Table  3  and Fig. 5 ). It is composed of 17 scaffolds (composed of 20 contigs). On the 3107 predicted genes, 3058 were protein-coding genes, and 49 were RNAs (one gene is 5S rRNA, one gene is 16S rRNA, one gene is 23S rRNA, 46 genes are tRNA genes). A total of 2042 genes (66. 78%) were assigned as putative function (by COGs or by NR BLAST). In all, 405 genes were identified as ORFans (13. 24%). The remaining genes (499 genes; 16. 32%) were annotated as hypothetical proteins. The details of statistics of the genome are presented in Table  3. Table  4  and Fig. 6  shows the proportion of genes divided in the different COGs functional categories. The genome sequence has been deposited in EMBL-EBI under accession number CCYJ00000000.
Genome comparison The draft genome sequence of T. massiliensis is smaller than those of P. propionicum F0230a (CP002734), (3. 21 and 3. 45 Mb respectively), but larger than P. avidum 44067 (CP005287), P. acnes KPA171202 (AE017283), P. freudenreichii subsp. shermanii CIRM-BIA1 (FN806773), P. thoenii DSM 20276 (KE384018), A. kwangyangense DSM 21549 (ATXE01000000), P. jensenii DSM 20535 (AUDD00000000) and T. oleiagri CGMCC 1. 9159 (PRJNA303532) (2. 52; 2. 56; 2. 61; 2. 93, 2. 99; 3. 02; 3. 15 MB, respectively). The G+C content of T. massiliensis is smaller than those of P. thoenii, A. kwangyangense, P. jensenii, T. oleiagri (68%, 68. 5%, 68. 7%, 69. 7%, respectively), but larger than those of P. freudenreichii subsp. shermanii, P. avidum, P. propionicum and P. acnes (67. 27%, 63. 48%, 66. 06% and 60.
5). Cells measure 4-5 μm in length and 0. 6 μm in diameter. Using API 50CH and ZYM strip, positive reactions were observed for D-mannose, D-arabinose, D-galactose, D-ribose, D- sucrose, D-fructose and D-glucose, esterase(C4), leucine arylamidase, α-chymotrypsin, alkaline phosphatase, cystine arylamidase, α-glucosidase, β-glucosidase, N-acetyl-β-glucosaminidase, α-mannosidase and β-galactosidase. API 20NE strip showed that T. massiliensis assimilates D-mannose, D-glucose and L-arabinose. Potassium nitrate is reduced. SIT-7 T was susceptible to doxycycline, rifampicin, vancomycin, nitrofurantoin, amoxicillin, erythromycin, ampicillin, ceftriaxone, ciprofloxacin, gentamicin, penicillin, trimethoprim/sulfamethoxazole and imipenem. The G+C content of the genome is 67. 81%. Accession numbers of the sequences of 16S rRNA and genome deposited in EMBL-EBI are LK985394 and CCYJ00000000, respectively.
05) following correction for multiple hypotheses testing. Whole-metagenome shotgun sequencing (WGS)  37  of three An. coluzzii reproductive tissues confirmed our results and identified Acinetobacter lwoffii as the species in the Acinetobacter genus previously detected by 16S rRNA sequencing (Fig. 3A, B ). Our A. lwoffii reads provided almost complete genome coverage and placed the Anopheles strain close to the previously published NIPH 478 (Fig. 3B ). Escherichia coli was the prevalent microorganism in the Enterobacteriaceae family, and Propionibacterium acnes were identified in all three samples. We observed lower intra-sample diversity than in the 16S rRNA dataset likely due to the reduced sensitivity achievable by WGS, especially given the high fraction of host DNA contamination. This may explain the absence of Acinetobacter in one of three samples sequenced (Fig.
Contrary to higher estimates previously obtained using targeted PCR investigations  39  , we detected Asaia only in 5% of samples. This apparent inconsistency could be explained by a potential positive bias of the PCR primers used in the 16S rRNA sequencing for the entire Acetobacteraceae family that comprises Asaia. Many OTUs could only be assigned at the family level, which suggests either the presence of unknown genera in this family or the existence of organisms belonging to subclades of known genera (including Asaia) that are still uncharacterized in the 16S rRNA databases. We hypothesize that bacteria from Asaia and very closely related organisms are indeed common colonizers of the reproductive tracts of An. gambiae and An. coluzzii, and that the current characterization and sampling of Asaia is not capturing the overall in-field diversity of this genus.
16S rRNA and whole-genome shotgun sequencing and analysis. MiSeq sequencing of the V4 variable region of the 16S rRNA gene generated a total of 2. 32 M reads (average 22,560, s. d. 14,471 reads per sample) from 102 high-quality samples (18 samples were removed because of technical failures). This dataset was pre-processed and analyzed with the Qiime pipeline  32  for taxonomic composition, alpha diversity, and beta diversity analysis  32, 33  , as previously described  20  , to characterize the microbiome structure, composition, and variability. Three samples were also subject to deep shotgun metagenomic sequencing (Illumina HiSeq 2000 with NexteraXT Library preparation) generating a total of 1,29 M reads (avg 431 M, s. d. 22 M, Supplementary Table  S3 ), that were processed for host DNA removal with BowTie2 60 against the Anopheles genomes, species-level taxonomic profiling with MetaPhlAn  33, 61  , coverage analysis with SAMTools 62 , and biomarker discovery with LEfSe  38.
Acknowledgements We thank  S. Ilboudo ,  H. Guel ,  D. D. Soma  for help with sample collection and dissection,  V. De Sanctis  and  R. Bertorelli  ( NGS Facility at the Centre for Integrative Biology, University of Trento ) for WGS sequencing, and  D. E. Neafsey  for helpful discussions. We thank the  Broad Institute  sequencing platform for 16S sequencing.
The 440 monitored glaciers represent only 0. 25% of the nearly 200 000 glaciers inventoried worldwide and included in the Randolph Glacier Inventory and GLIMS databases  (Pfeffer and others, 2014 and www. glims. org). The small number of available datasets is due to the financial and human investment, and the time required for field measurements. Although few in number, field studies on the monitored glaciers have made it possible to highlight the key climate variables controlling the glacier mass-balance processes and the links with regional or global patterns of atmospheric circulation (e. g. Vincent, 2002; Vincent and others, 2004; Huss and Bauder, 2009; Thibert and others, 2013; Rabatel and others, 2013a). They have also helped to better characterize the variables and processes that affect ice dynamics including ice thickness, flow velocities at the surface and thermal diffusion (e.
1 ), Glacier d'Argentière, monitored since 1975 (# 2 in Table  1  and Fig. 1 ), and Glacier de Gébroulaz monitored since 1983 (# 16 in Table  1  and Fig. 1 ). Available data on the glacier-wide mass balance and topography of these three glaciers were used to assess the representativeness of the glacier-wide mass balance computed from the satellite images. It should be noted that the glacier-wide mass balances of these three glaciers were computed using the glaciological method and adjusted with the geodetic method (e. g. Vincent, 2002; Vincent and others, 2009). It should also be noted that the DEMs used to compute the geodetic balance in the case of GLACIOCLIM are not the ones used in the current study. Indeed, the DEMs used for GLACIOCLIM glaciers come from photogrammetric restitutions of aerial photographs only taken on GLACIOCLIM glaciers.
Reconstructing the glacier-wide mass balance from optical remote-sensing data For temperate (outer tropical) glaciers, the end-of-summer (dry season) snow line is an accurate proxy of the equilibrium line  (Lliboutry, 1965; Braithwaite, 1984; Rabatel and others, 2005 Rabatel and others, , 2008 Rabatel and others, , 2012a)). Moreover, the snow line is easy to discern on optical remote-sensing images. After geometrical and radiometric corrections of the remote-sensing images, the altitude of the end-of-summer snow line (hereafter referred to as SLA) was calculated for each year of the study period (written SLA i , in m) using DEMs. The glacier-wide mass-balance series can then be reconstructed in two steps. First, for each year, we calculated the difference between SLA i and ELA eq , where ELA eq is the theoretical altitude of the equilibrium line if the glacier had a balanced budget (glacierwide mass balance = 0) for the study period.
In addition, snow falls frequently at the elevation of the SLA in the late summer season, which significantly affects the ablation rates and which would also need to be taken into account. Finally, one advantage of the method is that it can be used for glaciers for which no glaciological or meteorological data are available. For these reasons, we preferred to use the SLA data available from the image with no adjustment, and to evaluate the performance of the method even if, in a few cases, the images did not strictly match the end of the ablation season. Three DEMs were used in this study. The first one is a photogrammetric DEM from the French National Geographic Institute (Institut Géographique National, French acronym IGN) with a horizontal resolution of 25 m. This DEM, named '1979', covers the entire RoI and was extracted from aerial photographs taken close to the year 1979.