dataesr/scientific-paragraphs-categorization huggingface dataset:
The instrumental setup and the data independent acquisition (DIA) method were used according to the method of Michalik et al. (82)  with slight modifications (Table  S4 ). The ion library for DIA data analysis was constructed based on a Comet database search using an S. aureus protein fasta file from AureoWiki (http://aureowiki. med. uni-greifswald. de/Main_Page) comprising 2,852 protein entries in a target-decoy approach using trypsin/P digest rule with number of tolerable termini (NTT) 2, Ϯ30 ppm (MS1), and 0. 01-Da fragment mass tolerance as well as static (sample preparation-dependent carbamidomethylation; 57. 021464) and variable (methionine oxidation; 15. 9949) modifications. For library construction, previously published  (82)  and newly generated (Table  S5 ) data sets were acquired on a Q Exactive mass spectrometer (Thermo Fisher Scientific).
The results were processed using the Trans-Proteomic Pipeline (TPP; version 4. 8. 0 philae) (83) including PeptideProphet and iProphet (84, 85) (minimal peptide length, 7 amino acids). A global protein false-discovery rate (FDR) of 1% was set using MAYU  (86). The filtered peptide spectrum matches were used to generate the ion library in Spectronaut (v11. 0. 15038. 14. 27660 [Asimov]; Biognosys AG, Schlieren, Switzerland) with the following settings: m/z mass range of 300 to 2,000, 6 to 10 fragments per peptide, removal of fragments smaller than 3 amino acids, no segmented regression, and a minimum root mean square error of 0. 5. The constructed library consists of 2,154 proteins with 38,570 tryptic peptides. The analysis of the DIA data (82) was carried out using Spectronaut (v11. 0. 15038.
2 ). Significant difference in the survival of the three groups of mice was determined with the Gehan-Breslow-Wilcoxon test (software: GraphPad Prism 5. 0). Transcriptome analysis. RNA was prepared by acid-phenol extraction after mechanical cell disruption as described previously  (49). The quality of the RNA was assessed by means of an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA) according to the manufacturer's instructions. For Northern blot analysis, 7 g of total RNA per sample was separated under denaturing conditions in a 1% agarose gel and transferred onto a positively charged nylon membrane (Roche Pharma AG, Grenzach-Wyhlen, Germany) by vacuum blotting. After cross-linking (120 mJ/cm 2 ; Stratalinker UV Crosslinker 1800; Stratagene, CA, USA), hybridization was performed as described previously  (87).
The samples were labeled with Cy5, and a reference pool containing equal amounts of RNA from each sample was labeled with Cy3. Seventy-five nanograms of the two cDNAs were hybridized together to the microarray according to Agilent's hybridization, washing, and scanning protocol (two-color microarray-based gene expression analysis, version 5. 5). Data were extracted and processed using Feature Extraction software (version 11. 5. 1. 1). For each gene, the median of the individual probe ratios was calculated. For data analysis, Genedata Analyst software (Genedata AG, Switzerland) was used.
00 to 93. 00% sequence identity with the type strains of previously identified and/or newly introduced Clavibacter species/subspecies. These ANI values are far below the accepted threshold (95 to 96%) for the definition of prokaryotic species  (14) , suggesting that the strains CFBP 8615 and CFBP 8616 could be defined as a new species. A comprehensive multiphasic taxonomic study using the genome sequences provided in this study to reevaluate the taxonomy of Clavibacter spp. and clarify the position of the strains CFBP 8615 and CFBP 8616 within the genus is ongoing. Data availability. These whole-genome shotgun projects have been deposited at DDBJ/EMBL/GenBank under the accession numbers shown in Table  1. For all sequences, the first versions of the accession numbers are described in this paper.
The raw reads of the sequences of all strains were submitted to the Sequence Read Archive (SRA) database  (15) , and the corresponding accession numbers are shown in Table  1. All of the strains listed in Table  1  are available at CIRM-CFBP, the French Collection of Plant-Associated Bacteria (http://www6. inra. fr/cirm_eng/CFBP-Plant-Associated-Bacteria).
ACKNOWLEDGMENTS We thank  Cécile Dutrieux  and  Audrey Lathus  for strain preservation and supply, the  French Network on Xanthomonads (FNX)  (https://www. reseau-xantho. org/)for recurrent scientific exchanges, and  CATI BBRIC  for bioinformatics facilities. We benefited from interactions promoted by  COST Action CA16107 EuroXanth  (https://euroxanth. eu/).
Nomenclature CIRM-CFBP a code Host of isolation Yr of isolation Country of isolation Genome information Accession no. Sequence Read Archive Reference DDBJ/ENA/ or source GenBank Run Expt QWDZ01000000 SRR7977544 SRX4810326 16 QWEA01000000 SRR7977605 SRX4810381 2 QWEB01000000 SRR7977581 SRX4810357 This study QWEC01000000 SRR7977687 SRX4810463 This study QWED01000000 SRR7977688 SRX4810464 2 QWEE01000000 SRR7983517 SRX4814786 3 QWGS01000000 SRR7983516 SRX4814785 3 QWGT01000000 SRR7983538 SRX4814807 6 QWGU01000000 SRR7983541 SRX4814809 6 QWGV01000000 SRR7983540 SRX4814808 4 No. of pseudogenes 199 329 122 91 373 105 114 78 151 205 No. of RNA genes 52 52 51 51 53 51 54 50 50 53 No. of protein- coding genes 3,333 3,890 3,560 3,487 3,291 3,367 3,356 3,236 3,342 3,289 Sequencing coverage (؋) 450 435 475 570 510 515 450 580 555 460 No.
In this paper, we introduce and provide access to a daily  and hourly  dataset of snow and meteorological data measured at the Col de Porte site, 1325 m a. s. l, Charteuse, France. Site metadata and ancillary measurements such as soil properties and masks of the incident solar radiation are also provided. Weekly snow profiles are made available from September 1993 to April 2015. A detailed study of the uncertainties originating from both measurements errors and spatial variability within the measurement site is provided for several variables. We show that the estimates of the ratio of diffuse to total shortwave broadband irradiance is affected by an uncertainty of ± 0. 21. The estimated root mean squared deviation, that can be mainly attributed to spatial variability, is ± 10 cm for snow depth, ± 25 kg m -2 for snow water and ± 1 K for soil temperature (± 0.
4 K during the snow season). The daily dataset can be used to quantify the effect of climate change at this site with a reduction of the mean snow depth (Dec. 1 st to April 30 th ) of 39 cm from 1960-1990 to 1990-2017  and an increase in temperature of + 0. 90 K for the same periods. Finally, we show that the daily and hourly datasets are useful and appropriate for driving and evaluating a snowpack model over such a long period. The data are placed on the repository of the Observatoire.
Gaillardet et al. , in review). It is also labeled as a member of the World Meteorological Observation Global Cryospheric Watch Cryonet network and of the INARCH network. CDP snow and meteorological observations have been selected as an indicator of climate change effect at medium elevation by the national climate change observatory (ONERC). CDP is also an ideal place for specific snow related measurements campaigns, e. g. intercomparison of measurement methods for solid precipitation (SPICE), measurement of the spectral reflectance of snow  (Dumont et al. , 2017; Tuzet et al. , 2017) , snow surface roughness  (Picard et al. , 2016) , snow under forest  (Sicart et al. , 2017). The objectives of the present paper are (i) extending the hourly dataset published in  Morin et al. (2012 Morin et al.
( ) from 1993 Morin et al. ( -2011 Morin et al. ( to 1993 Morin et al. ( -2017, (ii) , (ii)  providing a daily dataset over the 1960-2017 period and (iii) providing estimates of the uncertainties of several variables due to both spatial variability within the observation site and measurements uncertainties. The paper first describes the site and the dataset. The second section is dedicated to provide estimates of measurements uncertainties and spatial variability within the site and the last section describes some examples of the use of this dataset. Text similar to  Morin et al. (2012)  is underlined.
Data description The Col de Porte site (Fig. 1 ) is a grassy meadow surrounded by mainly coniferous (spruces) and some lobed-leave trees. All the instruments are located within an area of 40×50 m 2 (Fig. 2 , Tabs. 2, 3, 4). The height of the trees ranges from 10 to 40 m. Note that all datasets are provided in universal time coordinate (UTC).
3. 1. The discrepancies between the two masks are most likely due to changes of the vegetation (growing and major trees cut in 1999, see  Morin et al. , 2012). On 29 September 2008, the soil properties were measured over the first meter as illustrated by Fig. 4. The layering of the soil was estimated visually and is provided in Tab. 1. The soil properties (particles size analysis, organic matter, nitrogen and carbon total content) were also analyzed down to 87 cm depth. The dataset is provided as a. csv file (soil_properties_2008. csv).
Soil and vegetation properties On October 2nd 2012, the same analysis was conducted over the first 30 cm of soil at location 24 along with measurements of the soil dry density. The dataset is provided as a. csv file (soil_properties_2012. csv). The two. csv files are available as doi:10. 17178/CRYOBSCLIM. CDP. 2018. Soil. m -2 for dry mass. The height of the grass during the time of measurements can be considered as typical for late fall. Note that the grass is frequently cut during summer. These measured soil and vegetation properties can be useful to constrain soil and vegetation schemes which are often coupled with snowpack models  (Decharme et al. , 2013).
Meteorological hourly data, 1993-2017 The meteorological hourly dataset over 1993-2017 is an extension of the meteorological dataset provided in  Morin et al. (2012). An extensive description of the dataset is available in this study. Below are reported only changes that happened after 2011 and additional details not provided in  Morin et al. (2012). The dataset is provided as a continuous hourly dataset since 1993, so that it can be easily used to drive snowpack models. The partitioning of the dataset between in-situ data and the output of the meteorological analysis and downscaling tool SAFRAN  (Durand et al. , 1999)  is the same as in figure  4  of  Morin et al. (2012). For years 2011 to 2015, in-situ data are restricted to the period 20th October of one year to 10th June of the next year.
Starting on 10th June 2015, all data are in-situ year-round except for very short periods with observation issues. An in situ flag is provided together with the meteorological data (value = 1 for in situ data). Table  2  provides an udpate of the type of sensors used for meteorological measurements with respect to Tab. 1 in  Morin et al. (2012). The dataset is provided in netCDF format (doi:10. 17178/CRYOBSCLIM. CDP. 2018. MetInsitu) in the standard format for SURFEX surface model meteorological inputs  (Vionnet et al. , 2012; Masson et al. , 2013). The atmopsheric pressure value corresponds to the mean climatological value at CDP.
Shortwave incoming radiation The meterological dataset provides both total and diffuse incoming broadband radiations at location 31. The diffuse shortwave radiation is not measured but calculated from total shortwave and longwave incident radiation and air temperature as described in the following. The first step of the procedure is to compute a cloudiness value, η (no unit, between 0 for clear sky and 1 for fully overcast) from measured air temperature T air (K), longwave radiation LW down (W m -2 ) and specific humidity using Equations 1 and 2 from  Berliand (1952). LW down = εσT 4 air (1) ε = 0. 58 + 0. 9(0. 09 + 0. 2η)η 2 + 0. 06e air -0. 05 * (1 -(0. 09 + 0. 2η)η 2 ) ( 2 ) where σ is the Stefan-Boltzman constant, and e air is the water vapour partial pressure calculated from measured T air and relative humidity, expressed in hPa.
Longwave incident radiation The sensor for incident longwave radiation was replaced in October 2015 by a Kipp&Zonen CGR4 sensor (location 30 in  2. 4 Snow and soil data, 1993-2017 The evaluation hourly dataset over 1993-2017 is an extension of the evaluation dataset provided in  Morin et al. (2012). An extensive description of the dataset is available in the latter study. Below are reported only changes that happened after 2011 and additional details not provided in  Morin et al. (2012). The hourly dataset is provided as a netCDF file (doi:10. 17178/CRYOBSCLIM. CDP. 2018. HourlySnow). Within this dataset, the soil temperature, soil humidity and settling disk temperature are raw measurements (uncorrected). Table  3  provides an udpate of the type of sensors used for evaluation measurements with respect to Tab.
2 in  Morin et al. (2012). Starting on October 2010, the snow depth at location 32 has been measured with a Dimetix Laser ranger. The field-of-view is a few mm diameter spot and the accuracy provided by the manufacturer is ± 1. 5 mm. Since October 2010, the snow depth measurements provided in the dataset (reference snow depth) is the measurements of the Dimetix Laser ranger. Data from the other sensors are used to evaluate and correct the Laser data. The surface temperature reference values contained in the dataset mainly originates from the Kipp&Zonen updward pyrgeometer (location 25, same sensor as location 30 in Tab. 2). Since september 2010, these data are complemented by the other surface temperature sensors with a conical field of view shown in Tab. 3. The reference surface temperature is bounded to 273.
They can be visualized using the niViz software. An example is displayed in Fig. 6  for 13 January 2001. These profiles are available on a weekly basis from September 1993 to April 2015 (doi:10. 17178/CRYOBSCLIM. CDP. 2018. SnowProfile). Table  3. Overview of the sensors used to gather the hourly and daily snow and soil data, between 1993 and 2017 at Col de Porte, France. Note that outgoing shortwave and longwave radiation is measured using instruments similar to the corresponding incoming radiation, described in Table  2. Note also that snow surface temperature can be derived from the outgoing longwave radiation sensor, in addition to the sensors presented here. The index refers to the location of the sensor represented in Fig. 2. a Sensor including a shielding for ground-originating neutrons (reduced data scatter).
b Height adjusted manually above snow surface (≈ weekly). c Progressive migration from mercury to solid state electric contact. The sensors have been installed for the WMO SPICE project and are used in this study only to complement the dataset if a problem exists for the reference sensor. (cf Tab. 4). The total precipitation dataset is measured with the PG2000 sensor thus mitigating the impact of the undercatch correction. In addition, the total precipitation time serie may be qualified as inhomogeneous in time due to the various changes in precipitation gauges. The daily SWE automatic measurements (loc. 16) are discarded for snow season 2015/2016 due to a disfunction of the sensor. Note also that the daily albedo data are uncorrected for local snow surface slope. The hourly meteorological dataset that contains the whole SAFRAN reanalysis  (Durand et al.
, 2009)  at Col de Porte for the period 1960-2017 is provided in order to drive snowpack simulation over the whole period. The dataset is provided in netCDF format (doi:10. 17178/CRYOBSCLIM. CDP. 2018. MetSafran) in the standard format for SURFEX meteorological inputs  (Vionnet et al. , 2012; Masson et al. , 2013). The solar mask measured in 1998 (Fig. 3 ) is accounted for in this dataset. Note that rain and snow variables are provided only when in situ measurements are available (i. e in situ flag of Tab. 2 -see also figure  4  in  Morin et al. , 2012). The dataset presented in this study is, like any observation dataset, affected by different sources of uncertainties. Regardless whether these data should be used for model evaluation or process study, characterizing their associated uncertainties is essential for a proper use of the data.
The uncertainties of the dataset may come from measurements uncertainties (including instrumental and environmental uncertainties) but also from the spatial variability of the variables within the measurement plot. A lower bound of the uncertainty for each variable can be estimated from the information provided by the sensor manufacturer. Some variables are measured at different locations within the field sites and by different sensors. This provides a better insight of the uncertainty associated to both sources for each variable. Lafaysse et al. (  2017 ) already provided a first estimate of the uncertainty associated to snow depth, snow water equivalent, bulk density, broadband albedo, soil temperature and snow surface temperature. In this section, we extend the period and the number of points used for the uncertainties evaluation for snow depth, snow water equivalent and soil temperature for which several measurements are available over a sufficiently long period.
6 show that the mean difference between the automatic and the manual measurements in the main snow pit field reaches -17 kg m -2 with RMSD of almost 25 kg m -2. The comparison between the three locations of manual measurements displays RMSD reaching 25 kg m -2. This value is consistent with the spatial variability of snow depth and can probably be used as an estimate of the uncertainty associated to the SWE dataset both due to measurements errors and spatial variability. Figure  10  and Tab. 7 compare the different soil temperature measurements at 10 and 20 cm depths for locations 23 and 24 (Fig. 2 ). The first column display the statistics of the different temperature probes located index 23 and spaces by roughly 10 cm. It indicates that the RMSD between the probes is lower than 0. 25 K. The second column compares location 24 (old sensors) and 23 (new sensors) for two periods : summer (20 June to 10 October) and snow season (11 October to 19 June).
Acknowledgements. Many thanks are expressed to people of CNRM/CEN who have assisted in the collection, collation and archive of this unique dataset. We thank in particular  É. Pougatch ,  Y. Danielou ,  J. -L. Dumas  for their crucial work on the database. Generating and distributing this dataset directly benefitted from the  LabEX OSUG@2020  ( ANR10 LABX56 ). The authors are also thankful to EDF/DTG,  ONF, SOERE CryObsClim, GCW  and the  IDEX Univ. Grenoble Alpes Cross Disciplinary Project Trajectories. The authors also thanks  Arnaud Foulquier  ( LECA ) for his help with soil and vegetation properties measurements and  Laurent Bourges (OSUG)  for the establishment of the data repository and the allocation of dedicated dois.
1960-2017 Data Table  4  describes the daily dataset that combines snow and meteorological measurements. The dataset is provided in netCDF format (doi:10. 17178/CRYOBSCLIM. CDP. 2018. MetSnowDaily). Variable names correspond to the names listed in Tab. 4. Within this daily dataset, the total precipitation dataset is not corrected for undercatch contrary to rain and snow datasets (starting in Sept. 1993). The total precipitation dataset is also not measured by the same sensor as the rain and snow datasets Data Discussions Manuscript under review for journal Earth Syst. Sci. Data Discussion started: 15 August 2018 c Author(s) 2018. CC BY 4. 0 License. Data Discussions Manuscript under review for journal Earth Syst. Sci. Data Discussion started: 15 August 2018 c Author(s) 2018. CC BY 4. 0 License.
Snow model evaluation This dataset has been widely used to drive and evaluate snow models (e. g. Essery et al. , 2013; Magnusson et al. , 2015;      Decharme et al. , 2016; Lafaysse et al. , 2017; Piazzi et al. , 2018). A list of the studies using CDP dataset is available at http://www. umr-cnrm. fr/spip. php?article533. It has also been included in the Earth System Model -Snow Model Intercomparison Project (ESM-SnowMIP).
Data availability The database (doi:10. 17178/CRYOBSCLIM. CDP. 2018) presented and described in this article is available for download at http://dx. doi. org/10. 17178/CRYOBSCLIM. CDP. 2018. Tab. 8 provides the links to the different datasets. Table 8. Link to the dataset repository Dataset Period Format Repository Solar Mask July 1998 and June 2018 csv http://dx. doi. org/10. 17178/CRYOBSCLIM. CDP. 2018. SolarMask Soil properties 29 September 2008 and October 2nd 2012 csv http://dx. doi. org/10. 17178/CRYOBSCLIM. CDP. 2018. Soil Hourly in situ meteorological data August 1st 1993 to 31 July 2017 netCDF http://dx. doi. org/10. 17178/CRYOBSCLIM. CDP. 2018. MetInsitu Hourly SAFRAN meteorological data August 1st 1960 to 31 July 2017 netCDF http://dx. doi. org/10. 17178/CRYOBSCLIM. CDP. 2018. MetSafran Daily snow and meteorological data August 1st 1960 to 31 July 2017 netCDF http://dx.
Acknowledgements 169 The later European developments are primarily based on the recent surveys of  John A. Lynn , 'The  Evolution of Army Style  in the Modern West, 800-2000' , The International History Review 18, no. 3 (1996)  505-545 , and the edited volume that takes Lynn's periodization as a starting point:  Erik-Jan Zürcher , Fighting for a Living: A Comparative History of Military Labour 1500-2000 (Amsterdam, 2013), in particular the contribution by  Frank Tallett , 'Soldiers in Western Europe, c. 1500-1790' ,  135-169. For the continued role of the aristocracy in early modern armies, see  Christopher Storrs  and  H. M. Scott , 'The Military Revolution and the European Aristocracy, c. 1600-1800' , War in History 3, no. 1 (1996) 1-41; for France:  Guy Rowlands , 'Louis xiv, Aristocratic Power and the  Elite Units of the French Army ' ,  French History  13, no.
The research sample In the Slovakia exist several databases which may be sources of information on public procurement -Public Procurement Notice, Electronic Contracts System, the Uvostat and the database tender. sme. sk. In agriculture and forestry sector, between 1 January 2014 and May 2017, 1,330 contracts were available. Taking into account the specifics of the individual databases and the objectives of our research, we have selected the database tender. sme. sk. From the contracts, we needed to obtain the following information: estimated cost and the final price of the contract, type of procurement procedure, quality criterion, and EU funding, electronic auction, SME (small and medium enterprise) participation in public procurement, subcontractor participation and number of bids. Based on the required input information requirements, 291 were selected from the total group of contracts, and we were subjected to a follow-up investigation using an appropriate methodology.
Conclusions The presented study in the field of agriculture and forestry represents the results on a selected sample of contracts. Nevertheless, it points to the problems of public procurement inefficiency in a low bidding competition. At present, such analyzes are relatively demanding as there are no databases that offer more detailed information on public procurement. Available databases offer the opportunity to analyze the savings achieved or the number of tenders, but not the determinants themselves that have such a significant impact on the outcome of the procurement. Knowledge of these determinants can make a significant contribution to increasing the efficiency of public procurement and may also identify some reasons for a slow consolidation of public spending. Public procurement studies confirm the positive impact of competition on the supply side of savings in public procurement.