dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions The present study is part of the Chemical and Aerosol Mediterranean Experiment (ChArMEx) programme. The aim is to investigate the tropospheric CH 4 time and space variations above the MB and to attribute the variability to differing synoptic and global scales depending on the season and the altitude layer considered. Since the analysis of the mid-to-upper tropospheric CH 4 distribution from spaceborne sensors and model outputs is challenging, we have used a wide variety of data sets. (1) First are the spaceborne measurements from thermal infrared (TIR) instruments: the Thermal And Near infrared Sensor for carbon Observations -Fourier Transform Spectrometer (TANSO-FTS) instrument on the Greenhouse gases Observing SATellite (GOSAT) satellite, the Atmospheric InfraRed Spectrometer (AIRS) on the Aura platform and the Infrared Atmospheric Sounder Interferometer (IASI) instrument aboard the MetOp-A platform.
Conversely, in the lower troposphere, the CH 4 variability is driven by the local sources of emission in the vicinity of the MB. Other constituents can also be affected by this summer mechanism, e. g. O 3 and CO (not shown). In a forthcoming paper, the time evolution of the CH 4 , O 3 and CO fields above the MB and at the Asian scale is studied by considering the outputs from different CCMs in the contemporary period  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010)  in order to study the future evolution of the chemical climate over the MB by 2100. Finally, despite the fact that IASI CH 4 data as delivered by EUMETSAT are not operational, the seasonal variation of the east-west difference in CH 4 total columns is nevertheless consistent with theoretical results and measurements from AIRS and IASI.
Acknowledgements. We would like to thank the following institutes and programme for funding our study:  Centre National de la Recherche Scientifique-Institut National des Sciences de l'Univers/CNRS-INSU, Centre National des Etudes Spatiales/CNES, Agence de l'Environnement et de la Maîtrise del'Energie/ADEME  through the programme the  Mediterranean Integrated STudies at Regional And Local Scales/MISTRALS Chemistry-Aerosol Mediterranean Experiment/ChArMEx. We also thank the following databases for allowing access to their data: the  French database Ether , the  European Organisation for the Exploitation of Meteorological Satellites , the  GOSAT User Interface Gateway , the  National Oceanic and Atmospheric Administration , the  British Atmospheric Data Centre  to access the  LMDz-OR-INCA model data through the international Atmospheric Chemistry and Climate Model Intercomparison Project initiative , and the  Global Atmosphere Watch  from the  World Meteorological Organization.
Nevertheless, for practical reasons (access to the glacier is impossible after mid-October) and in view of the fact that both October and November are usually characterized by a non-significant mass balance, starting the hydrological year at the beginning of October does not change the results. Two meteorological stations (AWS1 and AWS2) have been operated on Chhota Shigri Glacier (Fig. 1 ). AWS1 was operated between 12 August 2012 and 4 October 2013, in the middle of ablation zone (4670 m a. s. l. ) on an almost horizontal and homogeneous surface while AWS2 is located off-glacier on a western lateral moraine (4863 m a. s. l. ), functioning continuously since 18 August 2009. At AWS1 and AWS2, meteorological variables were recorded as halfhourly means with a 30 s time step, except for wind direction (half-hourly instantaneous values), and stored in a Campbell CR1000 data logger.
AWS1 was equipped with a tripod standing freely on the glacier with wooden plates at the base of its legs and sinks with the melting surface. AWS2 provides pluri-annual meteorological data (from 2009 to 2013) allowing the characterization of the seasons as well as the analysis of the local climatic conditions on Chhota Shigri Glacier. Both AWS1 and AWS2 were checked and maintained every month during the summers (accessibility in winter was not possible). At the glacier base camp (3850 m a. s. l. ), an all-weather precipitation gauge with a hanging weighing transducer (Geonor T-200B) has been operating continuously since 7 July 2012 (Fig. 1 ). The Geonor sensor is suitable for both solid and liquid precipitation measurements. Table  1  gives the list of meteorological variables used in this study, with the sensor specifications.
Meteorological data and corrections Only AWS1 data were used for SEB calculations. During winter, the lower sensors (T air , RH, u) were buried under heavy snowfalls on 18 January 2013, and AWS1 stopped operating completely on 11 February until 7 July 2013 when the glacier was again accessible and AWS1 could be repaired. To ensure good data quality, the period between 4 and 11 February 2013 was eliminated as this period was supposed to be influenced by near surface snow. Thus, complete data sets of 263 days in two separate periods (13 August 2012 to 3 February 2013 and 8 July to 3 October 2013) are available for analysis, except for sensor SR50A, for which data are also missing from 8 September to 9 October 2012. The records from AWS2 have very few data gaps (0. 003, 0. 29, and 0. 07 % data gaps over the 4-year period for T air , u and WD, respectively).
These gaps were filled by linear interpolation using the adjacent data. Only one long gap exists for LWI data between 18 August 2009 and 22 May 2010. Radiation fluxes are directly measured in the field (Table 1), however several corrections were applied to these data before using in the SEB model. Night values of SWI and SWO were set to zero. At high elevation sites, such as the Himalaya, measured SWO can be higher than SWI (2. 6 % of total data here) during the morning and evening time when the solar angle is low because of poor cosine response of the upward-looking radiation (SWI) sensor  (Nicholson et al. , 2013). Besides, as AWS1 was installed on the middle of the ablation area, the unstable glacier surface during the ablation season conceivably gave rise to a phase shift by mast tilt  (Giesen et al.
Accumulation and ablation data The SR50A sensor records the accumulation of snow (decreasing the sensor distance to the surface) or the melting of ice and melting or packing of snow (increasing the sensor distance from the surface) at 4670 m a. s. l. close to AWS1 (Fig. 3 ). This sensor does not involve an internal temperature sensor to correct for the variations in speed of sound as a function of T air. Without this correction the measured distance may reduce during the evening, which could be misunderstood as a snowfall event  (Maussion et al. , 2011). Therefore, temperature corrections for the speed of sound were applied to the sensor output using T air recorded at the higher level. Also, to reduce the noise, a 3 h moving mean is applied to smooth the SR50A data. During the summer-monsoon on Chhota Shigri Glacier, sporadic snowfall events and followup melting may occur within hours.
Therefore, the surface height variations from the 3 h smoothed SR50A data should be calculated over a time interval long enough to detect the true height changes during the snowfalls and short enough to detect a snowfall before melting begins. Given that SR50A measurements have an uncertainty of ±1 cm, an agreement was achieved with a 6 h time step between smoothed SR50 data to extract surface changes greater than 1 cm. Point mass balance was measured from ablation stake no. VI located at the same elevation and around 20 m south of AWS1. Frequent measurements, with intervals of some days to a couple of weeks, were made at stake no. VI during summer expeditions. In summer 2012, three stake measurements with intervals of 10 to 15 days were performed from 8 August to 21 September 2012, while in summer 2013, six measurements with intervals of 7 to 30 days were carried out from 8 July to 3 October 2013.
Representativeness of 2012/2013 hydrological year Given that long-term meteorological data at the glacier are unavailable, the representativeness of the meteorological conditions prevailing during the 2012/2013 hydrological year is assessed at Bhuntar using T air and precipitation data from the Bhuntar meteorological station. Figure  6a  shows the comparison of 2012/2013 T air with the long-term mean between 1969 and 2013 at seasonal as well as annual scales. T air in 2012/2013 hydrological year was systematically higher for all seasons (0. 5, 0. 5 and 0. 6 • C in winter, pre-monsoon and summer-monsoon, respectively) except for post-monsoon when it was lower (0. 4 • C) than the mean seasonal T air over the 1969-2013 period. At the annual scale, the 2012/2013 hydrological year was 0. 4 • C warmer with T air close to the 75th percentile of the annual mean T air between 1969 and 2013.
SEB equation The meteorological data from AWS1 were used to derive the SEB at point scale. The incoming energy at the glacier surface (F surface ) is computed following  Favier et al. (2011) : SWI -SWO + LWI -(1 -ε)LWI + εσ T 4 s_mod + H + LE = F surface , (2) where SWI, SWO and LWI are the incident shortwave, outgoing shortwave and incoming longwave radiations, respectively and the term in square brackets is the modelled outgoing longwave radiation (LWO mod hereafter) that was deduced from the Stefan-Boltzmann equation (ε is surface emissivity, assumed to be equal to 1 for snow and ice surfaces and σ = 5. 67 × 10 -8 W m -2 K -4 is the Stefan-Boltzmann constant) using modelled surface temperature T s_mod. H and LE are the sensible and latent turbulent heat fluxes, respectively. The heat supplied by precipitation on glaciers is insignificant compared to the other fluxes  (Oerlemans, 2001)  and is therefore neglected here.
T air and u are taken from the upper level (2. 5 m) that provides a longer period for investigation. The sensor heights were extracted from SR50A records except during a data gap between 8 September and 9 October 2012. Over this period sensor heights were assumed to be constant and set as 2. 5 m, this being AWS1 in free-standing position. g is the acceleration of gravity (g = 9. 81 m s -2 ). z 0m and z 0T are the surface roughness parameters (in metres) for momentum and temperature, respectively. Assuming that local gradients of mean horizontal u, mean T air and mean specific humidity q are equal to the finite differences between the measurement level and the surface, it is possible to give analytical expressions for the turbulent fluxes (e. g. Oke, 1987) : H = ρ C P k 2 u T air -T s_mod I n z z 0m ln z z 0T ( m h ) -1 (7) LE = ρ L s k 2 u (q -q s ) ln z z 0m I n z z 0q ( m v ) -1 , ( 8 ) where ρ is the air density (in kg m -3 ) at 4670 m a.
005 < Ri b < 0. 005 (11 % of our total data set, at halfhourly time step), it was assumed that conditions are neutral, and half-hourly values for z 0m were calculated using Eq. (  11 ). Half-hourly values of z 0m were assessed separately for ice and snow surfaces, based on field observations (snowcovered surface between 16 September 2012 and 17 January 2013 and ice-covered surface the rest of the time). The z 0m was calculated as 0. 016 m (with STD of 0. 026 m) and 0. 001 m (0. 003 m) for ice and snow surfaces, respectively. During the summer-monsoon, the surface is covered with hummocks and gullies and z 0m is large whereas in winter, snow covers all surface irregularities and fills up the gullies (Fig. 3 ) providing small values of z 0m. The ratio between roughness lengths (z 0m /z 0q and z 0m /z 0T ) depends on the Reynolds number of the flow according to  Andreas (1987)  polynomials.
Analysis of the meteorological conditions at AWS1 In order to understand the seasonal evolution of the physical processes controlling the mass balance of the glacier, different representative periods for various seasons of 60 days duration were selected for inter-seasonal comparisons, based on the meteorological conditions observed in Sect. 2. 4 and the available data set at AWS1. The selected representative periods are post-monsoon (1 October to 29 November 2012), winter (1 December 2012 to 29 January 2013) and the summer-monsoon (8 July to 5 September 2013). The same length of 60 days of each representative period was chosen for justified comparison among different seasons. Unfortunately, data were not available for pre-monsoon. Measurements (T air , RH, u and WD) recorded at the upper-level sensors were used for the analysis, since the records from the lower-level sensors have longer data gap because of early burial of sensors.
7 (0. 7) 4. 9 (1. 1) 3. 6 (0. 5) STOA (W m -2 ) 276 (  39 ) 216 (  11 ) 458 (  25 ) SWI (W m -2 ) 175 (  46 ) 130 (  44 ) 248 (  67 ) SWO (W m -2 ) 127 (  31   44 ) H (W m -2 ) 10 (  13 ) 28 (  23 ) 31 (  10 ) LE (W m -2 ) -45 (9) -27 (11) 11 (  13 ) G (W m -2 ) 1 (1) 0 (1) 4 (5) SW sub (W m -2 ) -10 (3) -3 (2) -40 (11) H + LE (W m -2 ) -36 (11) 1 (  11 ) 42 (  21 ) F surface (W m -2 ) -56 (16) -24 (28) 233 (  59 ) Precipitation (mm w. e. d -1 ) 0. 6 (1. 0) 5. 0 (8. 9) 0. 5 (0. 9) Snow (mm w. e. d  AWS1 is surrounded by steep N-S valley walls. In order to analyse the impact of synoptic-scale circulation at AWS1 site, we compared the wind directions at AWS1 with those at 450 hPa pressure level obtained from High Asia Reanalysis data (HAR,  Maussion et al. , 2014)  at hourly scale. HAR wind data are available at 10 km resolution for different pressure levels for the 2001-2012 period.
13 ). Given that the SR50A at AWS1 site has a data gap between 8 September and 9 October 2012 and that this sensor cannot record rain events, daily precipitations, collected at glacier base camp (3850 m a. s. l. ), are used in this analysis. These precipitation values are extrapolated at AWS1 assuming a zero-precipitation gradient and are considered as rain (snow) at AWS1 site when T air at AWS1 is above (below) 1 • C (e. g. Wagnon et al. , 2009). In the summer-monsoon 2012, Chhota Shigri Glacier received one important snowfall of 25 mm w. e. (equivalent to 125 mm of fresh snow applying a density of 200 kg m -3 ) during the period 17-19 September. This snowfall abruptly changed the surface conditions by varying the surface albedo from 0. 19 to 0. 73 (Fig. 13a ). Therefore, the energy F surface available at the glacier surface suddenly dropped from 123 W m -2 on 16 September to 14 W m -2 on 17 September as shown by the sharp change in the melting rate (slope of the melting curve on Fig.
Conclusions In the Indian Himalaya where meteorological observations are short and scarce, the meteorological data set recorded since August 2009 at 4863 m a. s. l. on a lateral moraine of Chhota Shigri Glacier (AWS2) is one of the longest ever recorded data sets at high elevation. Mean monthly meteorological conditions at AWS2 show large month-to-month variability. A warm and calm summer-monsoon with high relative humidity from June to September and a cold and windy winter season with comparatively less humidity from December to March were identified. A pre-monsoon from April to May and a post-monsoon from October to November with intermediate conditions were also defined. Precipitation records at glacier base camp suggest that Chhota Shigri Glacier is a winter-accumulation type glacier receiving around 80 % of its annual precipitation from MLW in winter and 20 % from ISM; but longer precipitation records at glacier site are still needed to confirm this feature.
A physically based energy balance experiment, using a model computing surface and subsurface heat fluxes, was carried out to understand the melting processes on Chhota Shigri Glacier based on the forcing data over two separate periods from 13 August 2012 to 3 February 2013 and from 8 July to 3 October 2013 recorded at an in situ meteorological station (AWS1, 4670 m a. s. l. ) in the ablation zone. The roughness length for momentum was calculated separately for ice and snow surfaces as 0. 016 and 0. 001 m, respectively whereas roughness lengths for temperature and humidity were derived from the Reynolds number and the roughness length for momentum. Net shortwave radiation was highly variable with the lowest mean value (29 W m -2 ) in winter to the highest (202 W m -2 ) in the summer-monsoon period, while net longwave radiation exerted lower seasonality with minimum values in post-monsoon and winter periods (-69 and -54 W m -2 , respectively) and maximum in the summer-monsoon period (-14 W m -2 ).
Acknowledgements. This work has been supported by  IRD  as a part of M. F. Azam's PhD. Authors are also grateful to  DST-IFCPAR / CEFIPRA  project no. 3900-W1 , the  French Service d'Observation GLACIOCLIM as well as the Department of Science and Technology (DST), Government of India. We thank the  Indian Meteorological Department (IMD) ,  New Delhi  for providing the data from Bhuntar meteorological station. We thank  Jawaharlal Nehru University, New Delhi  for providing all the facilities to carry out this work. M. F. Azam is grateful to  Rajdeep Singh  for improving the content of the paper,  Romain Biron  for sharing his knowledge and experience about AWS assembling and  Fabien Maussion  for providing High Asia Reanalysis data. We are also grateful to the two anonymous referees, whose thorough comments significantly improved the paper as well as  Mauri Pelto ,  Thomas Mölg ,  Koji Fujita  and one anonymous reviewer for providing constructive comments and suggestions.
Because of these intrinsic characteristics and the potential uses of this model, numerous efforts have been combined to develop and adapt techniques and protocols to facilitate the study of B. schlosseri. The anatomy and histology of the blastozooid has been described  [40, [43] [44] [45] [46] [47] [48] [49] [50] ; and basic molecular tools have been developed, including development of genetically defined lines  [51, 52] ; morpholino and siRNA knockdown  [9, 13, 18, 19, 53, 54] , transplantation and in vivo tracking of cells  [23, 27, 30, 41]. In addition, vast transcriptome datasets have been generated  [3, [55] [56] [57] , and genome has been assembled  [3]. From these databases, the initial gene ontology, annotation and assignment to chromosomes were presented, exponentially enriching the accessibility to molecular markers.
Discussion Using the rules defined by the Open Biomedical Ontologies (OBO) Foundry  [65]  we have built the BODA with the final goal of gathering and formalizing the existent data about the anatomy and blastogenesis of B. schlosseri. The BODA features will allow the users: 1) to easily search and identify anatomical structures in colony and zooid, 2) to define the correct developmental stage and 3) to follow the morphogenetic events of a tissue and/or organ of interest throughout asexual development. Why an Ontology for Botryllus schlosseri Asexual Reproduction? B. schlosseri reproduces both sexually and asexually. During sexual reproduction, the fertilization and the development are internal  [46]  and only a small amount of eggs/embryos per zooid reach maturation. Embryogenesis lasts five days at 18-19uC and late developmental stages are mainly identified by means of tail length of embryos that grows below the vitelline coat.
Structure of BODA, Its Implementation and Integration BODA can be accessed freely from the Ontology Bioportal (http://bioportal. bioontology. org/ontologies/BODA) [66]  and/ or NISEED platform (Fig. 4 ), or downloaded from OBO Foundry portal  [65] , from the Ontology Bioportal  [66]  or from the Tunicate Portal (http://www. tunicate-portal. org/wordpress/ ?page_id = 145). BODA is integrated on the NISEED platform  [63]  i. e. the reference portal in the tunicate community, which has already assembled data from several ascidian species such as Ciona intestinalis, Halocynthia roretzi and Phallusia mammillata. The integration on this common platform facilitates comparative analysis across species. In this respect, the BODA is the first ascidian ontology to be enriched with definitions of anatomical entities.
intestinalis Ontology is based on embryonic cell lineage. As a consequence, the anatomical entities for which the exact embryonic derivation is not known were not included in the Ontology. The structure of BODA allows the system to be easily implemented with new heterogeneous data and metadata. For example, sexual development definition, gene expression profiles, transcriptomic data, phenotypes generated by knock-down experiments, drug treatment tests and all the related literature could be easily introduced once available. This will facilitate the ability to find integrated information. For example, by editing keywords or searching for sequences, a specific gene can be easily related to the anatomical entities and developmental stages in which it is expressed, representing its spatio-temporal expression profile.
Acknowledgments The authors dedicate this paper to  Professors Armando Sabbadin  and  Paolo Burighel  of the  University of Padova Italy ), both significantly advancing the study of this organism. A. Sabbadin  introduced B. schlosseri as a laboratory animal and perfected the staging method;  P. Burighel  contributed his vast knowledge of the anatomy and development of this species. This manuscript is based on the discussions during the 2 nd international workshop on Tunicate Information System (Kyoto, Nov  26-27 2012 ) and 7 th International Tunicate Meeting (Naples, July  21-26 2013 ), where preliminary versions of BODA were proposed to the tunicate scientific community. We thank the organizers of these meetings for providing us with this opportunity. We thank  Prof. Paolo Burighel  and  Dr.
To avoid the controversy between models I and II, here we propose to revisit the geometric mean regression model by taking into account the relative uncertainties of the measurements of X var and Y var , i. e. the measurement error, and their intrinsic relationship, i. e. the equation error. A generalised consensus least square regression model is used to fulfil the discontinuity between models I and II. The formulation is given in Appendix A1 and the estimation of weight factor in Appendix A2. Readyto-use functions to install in MATLAB or Excel are also provided as supplementary online material and explained in Appendix A3.
To study the ratios between inorganic nutrients and AOU, we have proposed a consensus least squares linear regression model which takes into account the relative error on both variables, X var and Y var , to fulfil the discontinuity between the widely used models I and II. Ready-to-use functions to install in MATLAB and Excel are provided as supplementary material. If N/P ratios were only affected by mixing of OMP type nutrient concentrations, i. e. the (N/P) T ratios, they would vary between 16-18 in the Central Water realms, decrease to 15-16 in the AAIW, further decrease to 14 in the UCDW and keep constant at deeper levels. This pattern was altered by local mineralization processes, accounted for by the (N/P) ∆ ratios. Differences between (N/P) T and (N/P) ∆ ratios were significantly higher within the western basin of the South Atlantic.
Acknowledgements The authors wish to thank the captain and the crew of  R/V Maurice Ewing  and  R/V L'Atalante  for their help during the cruises. We are very grateful to  C. G. Castro  and  M. J. Pazó  for their participation in nutrient measurements. Financial support from this work came from the  Spanish CICYT  contracts  ANT93-1156-E  and  ANT94-1168-E  and, and from the  IFREMER  contract No 94  1430 087. X. A. A. S. and M. A. were supported by the  Spanish Ministry of Science and Innovation (MALASPINA expedition , grant number  CSD2008-00077 ). M. A. was funded by the grant ORCASEX ( RYC-2006-001836 ), program Ramón y Cajal from the  Spanish Ministry of Science and Technology.