dataesr/scientific-paragraphs-categorization huggingface dataset:
When looking at the background noise for the 180 Hz waveform, one sees some thin interference lines, the frequency of which varies from time to time and from one spacecraft to another. This may limit the sensitivity of the measurements in the higher frequency range. The increase of the noise level at, and above, 10 Hz (180 Hz) comes from the cut-off frequencies of the filters. Due to the effect of the spin signal (see below), the noise level is higher on Bx than on Bz (parallel to the spin axis) at low frequency. Figure  9  intends to show the possible evolution of the noise level with time. The spectra are obtained up to 9 Hz by 10 Hz low pass filter wave form data and above come from STAFF-SA. Data are averaged over one hour, taken in quiet periods in the same region for the four periods, in the Earth lobes.
The Geocentric Solar Ecliptic System (GSE) The GSE system is a well known system, with the Z axis perpendicular to the Ecliptic plane, and the X axis toward the Sun. To do the transformation of the SSS to the GSE, the direction of the spin axis in the GSE system is required. Due to the gyroscopic effect of a spinning spacecraft, the spin axis is ∼ constant in an inertial system, and so has a yearly variation in the GSE system, excepted during spacecraft operations. SR2 to GSE transformation is done using module "tsr2gse" routine of ROCOTLIB software (see  Robert, 1993 Robert, , 2003 Robert, , 2004)). The Cartesian GSE coordinates of the direction of spin axis is required, as the corresponding time measurement. To transform spin right ascension and spin declination angle, given in STAFF-SC CAA data in Geocentric Equatorial Inertial system (GEI), routine "tgeigse" can be used.
Simplification of the cumulative matrix products Cumulative matrix product requested to transform original data given in SCS coordinate to a fixed coordinate system such as SR2 can be strongly simplified if we neglect all small misalignment angles mentioned above. By the way, the first mass processing on the STAFF-SC data was to produce a data base for the level 1 data (telemetry data) in the DSS system, which is delivered to the CAA. The only difference between the DSS with the SCS sensor coordinate is a circular permutation of the components to get the Z axis close to the spin axis, since we assume that the SCS is orthogonal and equal to the OSS (see Sect. 5. 3). So to transform data expressed in DSS into the "fixed" SR2 we have to apply the cumulative matrix product:   x y z   SR2 = [SRS to SR2][BBS to SRS][DSS to BBS]   x y y   DSS Introduction.
Back Close Full We find again in this method the concept of Nkern and Nshift, with the same meaning, as the weighting function, and the need of "cleaning" the waveform by removing the spin tone before any other processing. Comparison of the two methods has been done by applying the two different softwares on the same data set, and concludes to a good agreement. Details of these comparisons will be done in another paper. Note that the calibration software used for CLUSTER is written in Fortran90, while the calibration software used for THEMIS is written in IDL. In this last case the convolution operation has been done by the build-in IDL function "CONVOL" which is very efficient. So, the good agreement of the two results is a proof of the validity of these two programs, which is very important for validate archive data bases.
Acknowledgements. The authors deeply thank  A. Balogh , first PI of FGM experiment, and the following ones,  E. A. Lucek  and  C. Carr , and the whole FGM team who has contributed to deliver to the CAA an invaluable set of data available to the scientific community. All engineers who have been working on the STAFF data since the beginning of the mission, too numerous to be mentioned, are fully thanked. The authors are indebted to  Christophe Coillot  for establishment of the corrective transfer function formula, and  Bertrand de la Porte  for the test on the transfer function. Many thanks to the  CAA itself  for its action, and for the organization of a long series of cross-calibration meetings from which a large part of this work is issued. STAFF instrumentation and part of data analysis have got support from  ESA  and  CNES.
ACPD  ACPD ACPD ACPD ACPD ACPD ACPD ACPD Acknowledgements. This work was supported by the  Research Committee of the Academy of Athens  and the  Mariolopoulos-Kanaginis Foundation  for the Environmental Sciences. The composite weather maps were provided by the  NOAA/ESRL Physical Sciences Division ,  Boulder Colorado  from their Web site at http://www. cdc. noaa. gov/. MOZAIC was funded by the  European Commission. The authors acknowledge for the strong support of the  European Commission, Airbus , and the  Airlines (Lufthansa, Austrian, Air France)  who carry free of charge the MOZAIC equipment and perform the maintenance since 1994. MOZAIC is presently funded by  INSU-CNRS (France) ,  Meteo-France , and  Forschungszentrum (FZJ, Julich, Germany ). The MOZAIC data base is supported by  ETHER  ( CNES  and  INSU-CNRS).
Samples were packed on standard 4 mm rotors, and spun at 5 kHz at the magic angle. ( 1 H π/2 pulse =5. 8 µs, CP mixing time = 2 ms). The pure amino acids used for the OL characterisation of NMR peaks were identical to those detected by the proteomic analysis of the OL: Glycine, L-Isoleucine, L-Leucine and L-Valine. ß-Chitin from shrimp shells and amino acids were purchased from Sigma-Aldrich Chimie (St Quentin Fallavier, France). The spectra were processed using Dmfit software  [24]. Proteomic approach. The OL membranes of snails collected aseptically were treated with 20 µg of trypsin (Proteomics grade, Sigma-Aldrich, St-Quentin Fallavier, france) overnight. The trypsin-digested material was further treated with 0. 6% TFA  [25]  (CarloErba, Val de Reuil, France) in water containing 0. 1 mM of Ditiothreitol (DTT).
The suspension was incubated for 7 h at 65 °C, before the recovery of the supernatant. A second digestion of the TFA-insoluble layers was performed overnight in 6% TFA in water with 0. 1 mM DTT at 65 °C. The assay for the peptide content in the TFA generated supernatant was completed using the bicinchoninic acid method. The peptides were separated on a C 18 column (150 x 0. 5 mm, Interchim, Montluçon, France) coupled to an ESI-QqTOF hybrid mass spectrometer (Pulsar, Applied Biosystems) using information dependent acquisition (IDA). After a 2 sec MS scan, the two most intense multiply charged precursor ions (+2 to +4) were selected for subsequent 2 sec MS/MS spectral acquisitions. The data were acquired and analysed with the Analyst QS software (v. 1. 1). The minimum threshold intensity of the ion was set to 10 counts.
Statistical analyses were performed with two R packages, the RDA analysis was carried out with Ade4  [30]  and the Vegan package was used to evaluate the fraction of the variation that was accounted for by the environmental variables  [31]. If environmental variation promoted different phenotypes with specific shell micro-scale structure traits in response to living conditions, then it would be necessary to investigate whether the mixing of two different age cohorts could artificially maximize microstructural differences, due to shell size increment during adult growth. Therefore, by employing analysis of covariance (ANCOVA), we looked for the relationship between shell size and shell thickness according to soil mineralogy, because shell growth is dependent on calcium dietary intake  [32].
Furthermore, the causal relationship between shell thickness and growth rate in molluscs seems to be well supported, experimentally  [33]. The best-fit model was determined according to the Akaike information criterion (AIC) value. To assess the strength of correlations in soils with Casmectite (Ca + ) and soils without clay or Ca-smectite (Ca -), linear regressions from the ANCOVA summary were calculated between shell thickness and shell size (here, Ca-smectite containing soils are referred to as "calcic soils"). The entire data set, including the response table (ad12. txt) and the table of predictors (Environnt. txt), is provided in the SIF, Text S6. The scripts used with R packages, related to RDA and MANOVA, are provided in the SIF, Text S7. The data set used for ANCOVA (Corr Size_Thick.
Organic layer of the Notodiscus shell Solid-state NMR spectroscopy of the OL confirmed the presence of glycine, leucine, isoleucine and valine in the organic layer, and revealed the absence of chitin (Figure  3 ). Solid-state NMR replicates of the OL layer of snails from both MAL800 and BRA produced the same profiles (See SIF, Text S3 and Figure  S5 ). The proteomic approach identified glycinerich proteins (GRPs) from the organic shell layer. The GRPs matched those found in insects (Bombyx mori, Apis mellifera,   S1 ). Many of the peptides that matched with the proteins in the database contained either GG or GGG as the repeating motifs in their primary sequence, and were interrupted by isoleucine or leucine.
Acknowledgements The authors would like to dedicate this work in memory of  P. Guénot  ( Prof. , University of Rennes 1, deceased in 2007) for his assistance in the early stages of the study, and for his helpful advice on RMN interpretations of the snail shell. We are grateful to  Chantal Aineto ,  Valérie Briand ,  Lionel Dubost ,  Antoine Gravot  and  Michel Thibaut  for technical assistance and data acquisition. The three anonymous referees are acknowledged for their useful comments and discussions on this manuscript.
A focus will be made here on the daily chemical composition of fine aerosol determined during one-year at one urban, one suburban and three rural sites of the region of Paris. This paper aims at presenting this chemical dataset including a description and evaluation of measurement methods, and a first analysis of its spatiotemporal variability. More precisely, Sect. 2 will describe (i) sampling sites, (ii) analytical techniques, (iii) chemical mass closure methodology and (iv) organic carbon to organic matter conversion factor's methodology. Section 3 will present (i) the representativeness of the studied period, (ii) first results regarding the chemical composition of PM 2. 5 , (iii) an evaluation of measurement methods and (iv) the adequate OC to OM conversion factor to use. Finally, Sect.
, 2011)  despite their spatial and temporal dependencies. We decided to estimate f OC-OM from our dataset by using a method adapted from  Guinot et al. (2007)  who used the chemical mass closure technique as a tool to assess f OC-OM from OC measurements. To infer f OC-OM , we assumed that CMC is achieved (PM grav =PM chem ), and used Eq. (1) to write the following equation: f OC-OM = 1/[OC] • ([PM grav ] -([Sea Salt] + [Dust] (6) +[SIA] + [EC] )) This will allow us to find the "best guess" f OC-OM value from our dataset. To simplify Eq. (  6 ), a chemical fraction named "Remaining mass" (RM) has been defined in Eq. (  7 ): [Remaining Mass] = [PM grav ] -([SeaSalt] + [Dust] (7a) +[SIA] + [EC]) thus leading to: [Remaining Mass] = f OC-OM • [OC] (7b) Two different methods were then used to estimate f OC-OM.
In the first method, RM concentrations are plotted against OC concentrations and f OC-OM is the slope of the linear regression (Eq. 7). We decided to use a linear function without y-intercept because of the analytical form of this equation. In the second method, we numerically calculated f OC-OM dayby-day from Eq. (  6 ). Days with f OC-OM higher than 3 and lower than 1 were considered as physically meaningless and were therefore excluded from the dataset. These days represent 6 to 14 % of the samples according to sites. Results will be discussed in Sect. 3. 5.
2  for both datasets for the whole duration of the campaign. TEOM-FDMS data plotted against gravimetric mass concentrations show a very good correlation (r 2 = 0. 94, n = 318, Fig. 3 ). However, filter measurements exhibit mass concentrations about 6 % higher than the on-line method (slope ± 1 standard error = 0. 938 ± 0. 007). This can be related to temperature differences during filter sampling (ambient temperature) and TEOM-FDMS measurements (30 • C), leading in the latter case to a partial volatilization of semi-volatile materials. Absorption of volatile organic compounds (VOC)  (Turpin et al. , 1994 (Turpin et al. , , 2000) )  and/or water onto filters  (Quinn and Coffman, 1998; Speer et al. , 2003; Hueglin et al. , 2005)  could also enhance filter masses. In addition, taking into account semi-volatile materials in TEOM-FDMS measurements (i.
005 using the Welch's t test;  Welch, 1947)  conversion factors at SUB and URB than at the rural sites (1. 96, 1. 92, 2. 08, 2. 05 and 2. 09 for SUB, URB, NER, NWR and SOR, respectively). High relative standard deviations are observed at every site, ranging from 15 to 20 %. This suggests that the organic matter chemical composition is strongly daily dependant, which can be related to the daily changing meteorological conditions (as air mass origins and air temperatures; see Sect. 4. 1). No clear seasonal pattern (Fig. S3 ) is observed with this second method as well. Comparable f OC-OM values are found with both methods in every environment, showing relative differences below 3 % for each site. The first method is however preferred as no days are excluded from the dataset and because of its more advanced mathematical approach.
5 mass concentrations are compared in Fig. 2. Very good correlations are found between the three datasets, with r 2 of 0. 98 and 0. 94, and slopes of 1. 00 and 1. 05 for chemically reconstructed against gravimetric, and against TEOM-FDMS determined mass concentrations, respectively. This confirms the consistency of our measurements and the conversion factors chosen to estimate organic matter.
, 2004; Putaud et al. , 2004a). In fact, annual PM grav mean concentrations are 15. 2, 14. 8, 12. 6, 11. 7 and 10. 8 µg m -3 for SUB, URB, NER, NWR and SOR sites, respectively (Table  1 ). Discrepancies between URB and SUB mean concentrations are not statistically significant (p>0. 25) and are due to differences between the sampling days discarded in each station (Table  S1 ), whereas discrepancies between NER, NWR and SOR sites cannot be explained by the former argument. The same conclusions can be drawn when comparing datasets reconstructed by excluding at every site each day missing at one site minimum. In a general way, differences observed between PM 2. 5 site's concentrations can mainly be attributed to local source emissions and chemical processes as it will be discussed later  (Sects.
9 µg m -3 at NER, NWR and SOR sites, respectively). The interpretation of the mineral dust dataset is complex because of the substantial uncertainties associated with the methodology chosen for its estimation, and because of the low contribution of this source in fine aerosol  (Maenhaut and Cafmeyer, 1998; Gehrig et al. , 2001; Putaud et al. , 2004a Putaud et al. , , 2010)). For these reasons, its spatial variability will not be discussed. Note however that the very high levels of dust estimated at the URB site during specific days (e. g. 7. 2 µg m -3 on 17 May 2010) are due to renovation of building facades located nearby. Sea salt concentrations are, as expected, similar at the five sites most of the time  (Figs. 10 and 11). The highest concentration is reached on 23 November 2009 (e. g.
Conclusion and perspectives This study allowed the construction of a one-year database describing the daily chemical composition of fine aerosol (PM 2. 5 ) in the region of Paris. It comprises EC, OC, Cl -, NO - 3 , SO 2- 4 , Na + , NH + 4 , K + , Mg 2+ , and Ca 2+ aerosol compositions at five sites being characteristic of urban, suburban and rural backgrounds. The chemical mass closure methodology was applied for the determination of the OC to OM conversion factor (f OC-OM ). Comparable f OC-OM values were determined for the URB and SUB sites (1. 95) and for the rural sites (2. 05), which is consistent with the strong homogeneity of OC concentrations observed in the region and its aged (i. e. oxidised) nature. Although clear daily variations are observed, this factor does not show a significant seasonal dependence despite the expected temporality of organic aerosol sources (biogenic, wood burning, etc.
EC is primarily locally emitted and associated with fossil fuel sources (predominantly traffic). The very large quality controlled aerosol database presented here within the LSCE-AIRPARIF "Particles" project will allow the identification of the major sources of PM as well as their geographical origins, which are prerequisites to implement effective abatement policies. In conjunction with the use of specific tracers, this aerosol database will also help in better documenting primary and secondary sources as well as atmospheric processing of carbonaceous aerosols. Furthermore, this chemical database should help modellers to better understand the transport and transformation of aerosols in the region, or toxicologists to better assess the toxicity of individual chemical compounds, to mention only a few.
Acknowledgements. The first author would like to acknowledge the  Agence de l'Environnement et de la Maîtrise de l'Energie (ADEME)  and the  Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA)  for funding his PhD. Région Ile-de-France ,  Ville de Paris ,  Centre National de la Recherche Scientifique  and  Université de Versailles Saint-Quentin-en-Yvelines  are also thanked for their support. The  French institute of meteorology "Meteo-France " is acknowledged for providing meteorological data. M. Reynaud and  M. Artufel  are gratefully acknowledged for their technical and analytical help. The Scientific Committee associated with this project is thanked for his fruitful comments. Finally, we are grateful to the anonymous referees and the editor for their valuable comments.
Maghreb countries are highly vulnerable to extreme hydrological events, such as floods and droughts, driven by the strong variability of precipitation. While several studies have analyzed the presence of trends in precipitation records for the Euro-Mediterranean basin, this study provides a regional assessment of trends on its southernmost shores. A database of 22 stations located in Algeria, Morocco and Tunisia with between 33 and 59 yr of daily precipitation records is considered. The change points and trends are analyzed for eleven climate indices, describing several features of the precipitation regime. The issue of conducting multiple hypothesis tests is addressed through the implementation of a false discovery rate procedure. The spatial and interannual variability of the precipitation indices at the different stations are analyzed and compared with largescale atmospheric circulation patterns, including the North Atlantic Oscillation (NAO), western Mediterranean Oscillation (WEMO), Mediterranean Oscillation (MO) and El Niño-Southern Oscillation (ENSO).
The daily data for the Melilla station (Spain) located in northern Morocco was downloaded from ECA&D. Data and metadata are available at http://eca. knmi. nl. The raw data record underwent a data quality control procedure, to check for missing data records and measurements errors such as the reporting of unrealistic precipitation values. Each station data has been carefully scrutinized, in particular to look for obvious breaks, absurd values and missing data by visual inspection. The stations that were subsequently selected did not have more than 5 % missing daily data between September and May. The years with more than 5 % missing daily data during this period have been removed. In the whole area, there is a strong seasonality signal with most of the precipitation occurring during late fall and winter.
Pettitt test for change points There is no homogeneity correction method specifically designed for daily precipitation time series and no consensus about the best method to be used  (Beaulieu et al. , 2007; Toreti et al. , 2010). However, homogeneity is a crucial aspect when dealing with trend detection or time series analysis. If the monotonic trends are likely caused by long-term climate change, step changes in precipitation series may be considered doubtful and possibly caused by station relocation or changes in the station instrumentation. The  Pettitt (1979)  test is able to detect potential change points in the mean of time series; it has been widely used with precipitation data  (Servat et al. , 1999; Klein Tank et al. , 2002; Wijngaard et al. , 2003; Beaulieu et al. , 2007; Villarini et al.
Acknowledgements. This research project has been supported by the  Institut de Recherche pour le Developpement (IRD, France) ; the financial support provided is gratefully acknowledged. The data sets have been provided by the  Agence Nationale des Ressources Hydrauliques (Algeria) ,  Direction de la Recherche et de la Planification de l'Eau (Morocco)  and  Direction Générale des Ressources en Eau (Tunisia). Special thanks are due to  H. Ben-Mansour ,  R. Bouaicha ,  L. Behlouli ,  K. Benhattab ,  R. Taibi ,  K. Yaalaoui , for their helpful contribution in the database collection and to  S. Beguería  who provided the NAO, WEMO and MO indices data. The authors wish to thank the associated editor,  A. Mugnai ,  M. C. Llasat ,  M. Donat  and the three anonymous reviewers for their useful comments.
Molecular characterization of host and associated bacteria DNA was extracted from gill tissue of three specimens using DNA Blood and Tissue Kit (QIAGEM, CA). Host gene encoding 28S rRNA was amplified using the primer combination LSU-900f/LSU-1600r  (Williams et al. 2004). Bacterial genes encoding 16S rRNA and adenosine 5'-phosphosulfate (APS) reductase were amplified using the primer combinations 27F/1492R  (Lane 1991 ) and APRA_1FW/APRA_5RV  (Meyer and Kuever 2007) , respectively. PCR products were sequenced directly at GATC Biotech (Constanz, Germany); as sequence chromatograms displayed no ambiguity such as double peaks, no additional cloning step was added for 16S rRNA and APS Sequences were compared with sequences available in Genbank using BLAST (http://www. ncbi. nlm. nih. gov) (Altschul et al.
ACKNOWLEDGMENTS The molecular research was supported by the  French ANR DeepOases, GDRE-DIWOOD. Clara Rodrigues  was also supported by a postdoctoral fellowship ( SFRH/BPD/64154/2009 ) from the  Fundação para a Ciência e a Tecnologia (FCT, Portugal). The construction and deployment of sulfide biogenerators was supported by the  United States National Oceanic and Atmospheric Administration Office of Ocean Exploration and Research. The crews of the  R/V Seaward Johnson  and the submersible  Johnson Sealink  provided critical technical support. V. Bertics  and  C-E Chow  carried out initial at-sea processing of devices. Paul Dando  ( Marine Biological Association, Plymouth ) is thanked for useful comments and discussion.
Oliver n. sp. Comparative material examined. As under S. emicatus above. Type material. Holotype, 1 sp. , RV Melville/ROV Jason cruise TUIM06MV, White Lady Vent Field, Fiji Back Arc Basin, 16. 99°S 173. 914°E, 1977 m, 31 May 2005. Hydrothermal seep with patches of Alviniconcha, SMNH -78311. Other material. 1 fragment with tissue as holotype, cross refer- ence to Genbank AM392452. 1 and AM392436. 1, SMNH -78311. 1 minute sp,1 mm, as holotype, SMNH -78558. 2 sh. , RV Melville/ ROV Jason cruise TUIM06MV, Hine Hina, Lau Back Arc Basin, 22. 534°S 176. 71001°E, 1847 m, Hydrothermal seep, in gravel with bacterial mat, SMNH -78277.