dataesr/scientific-paragraphs-categorization huggingface dataset:
Ultimately, "connected objects" will become a standard part of agriculture. Connected weather stations, linked to agricultural machinery and sensor networks on the farm, are a first example of connected objects. Smartphones can implement dedicated applications that capture operational data, provide advice to the farmer, recognize weeds, carry out specific measurements (of colour, shape, density, etc. ) and access reference databases. When no sensor is available to measure a given variable (such as disease or attacks by pests), the relevant information can be entered manually on a terminal that transmits the data in a formatted and usable form. Smartphones are increasingly used for this purpose, bringing together various functions (such as measurement, geolocalisation, connection and display) useful for professional applications.
Topical Issue D307, Page 3 of 7 V. E. Bellon  Maurel and C. Huyghe: OCL 2017, 24 (3), D307 Data is then transferred via high-speed systems provided by mobile or, if collected at low frequency, by low-throughput operators (SiFox, LoRa, etc. ). For storage and exchange, data clouds, although not without security issues, are often portrayed as revolutionary. Database interoperability is a further concern since it is dependent on the normalisation and standardisation of exchange protocols and data repositories. 2. 1. 2 Reaping the benefits of data processing: smart data and big data Data generated by sensors or operators are converted into indicators, advice, recommendations and automated instructions using either conventional statistical processing methods or methods derived from artificial intelligence, also called "deep learning", thereby becoming "smart data".
Acknowledgement. The authors acknowledge the contribution of colleagues from  INRA  (from  H. Guyomard ,  J. L. Peyraud ,  J. F Soussana ,  C. Gascuel ,  G. Richard ,  M. Voltz ,  M. Cerf ,  X. Reboud ,  T. Caquet ),  Irstea  (  R. Lenain ,  J. P. Chanet ,  M. Berducat ,  O. Naud) & Montpellier Supagro  (B. Tisseyre).
, 2012; Bertin et al. , 2013; Dodet et al. , 2010; Atan et al. , 2016; Santo et al. , 2016a). Le  Cozannet et al. (2011)  carried out a regional study using the ERA-40 dataset  (Uppala et al. , 2005)  to drive the WAve Model (WAM) for the Bay/Gulf of Biscay (west of France) and also found a strong influence of the NAO/EA on the wave climatology in that region. The East Atlantic Western Russian (EAWR) pattern and the Scandinavian pattern are other modes of Northern Hemisphere atmospheric variability, and along with the EA have a weaker influence on the North Atlantic than the NAO  (Santo et al. , 2016b). For the purpose of the study presented in this paper, we focus on the NAO which is the most important North-Published by Copernicus Publications. E. Gleeson et al. :  Extreme waves in the North Atlantic Ocean ern Hemisphere mode of variability  (Greatbatch, 2000; van Loon and Rogers, 1978; Hurrell, 1996).
Stykkisholmur, Reykavik or Akureyri (all in Iceland) are commonly used for the specific locations in the Icelandic Low and Ponta Delgada (Azores), Lisbon (Portugal) or Gibraltar for the Azores High  (Hurrell, 1996; Pokorná and Huth, 2015). The stationbased NAO index can thus be calculated using station observations or gridded reanalysis datasets (using the nearest grid point to the location of interest or some weighted average). In the case of the latter, 500 hPa geopotential height is also often used in place of MSLP. As well as basing the index on fixed locations, it can also be defined using the location of the centres of both pressure regions (Icelandic Low and Azores High), which change with time  (Hameed and Piontkovski, 2004 ). An alternative approach for the calculation of the index involves principle component analysis (PCA).
Gridded, rather than station, data are required where either MSLP or 500 hPa geopotential height may be employed. A PCA of the leading Empirical Orthogonal Function (EOF) of MSLP/500 hPa height anomalies over the North Atlantic region spanning 20-80 • N and 90 • W-40 • E is used to calculate the NAO time-series. Either correlation or covariance matrices can be used with this method and the EOFs may or may not be rotated. The PCA method is particularly sensitive to the spatial domain and time period used. Dommenget and Latif (2002)  showed that patterns derived from EOFs may be misleading at times and not associated with climate physics. Gallagher et al. (2014)  identified a strong link between the station-derived NAO and significant wave height (H s ), wave period and peak direction for winter and spring off the west coast of Ireland where WAVEWATCH III  (Tolman, 2014) , driven by ERA-Interim data, was used to derive the wave climatology.
Models and datasets The EC-Earth global climate model and WAVEWATCH III wave model were used to generate the atmospheric and wave datasets used in this study. A third datatset, the National Centre for Atmospheric Research (NCAR) NAO station-based time-series was also used. Details regarding the models and datasets are provided in this section. The EC-Earth global climate model (version 2. 3) used in this study consisted of an atmosphere-land surface module coupled to an ocean-sea ice module  (Hazeleger et al. , 2010 (Hazeleger et al. , , 2012)). The atmospheric component of the model was based on the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System cycle 31r1 with T159L62 spectral resolution (corresponding to 1. 125 • or approximately 125 km) and 62 vertical layers up to 5 hPa.
The North and Northeast Atlantic grids were twoway nested, whereas the innermost grid was run separately using the wave spectra output from the second grid to force the innermost grid 3 at its boundaries. Grid 3 was constructed using an unstructured grid formulation  (Roland, 2008). Further details on this set-up can be found in  Gallagher et al. (2016a). The EC-Earth historical simulations span from 1850 to 2009 and include observed greenhouse gas and aerosol concentrations, including volcanic eruptions. The future simulations ran from 2006 to 2100 where the RCP4. 5 and RCP8. 5 climate scenarios developed for CMIP5, the Coupled Model Intercomparison Project 5  (Taylor et al. , 2012) , were applied. To generate the wave datasets, 3 of the 14 EC-Earth ensemble members were used; these were representative of the spread of the EC-Earth ensemble.
This is because the EC-Earth ensemble does not have a very large spread in terms of mean annual wind speeds and the three chosen ensemble members cover the range of interannual variability of the winds. Each member consists of an historical simulation and 2 future simulations (RCP4. 5 and RCP8. 5) and are denoted meiX, me4X and me8X where X = 1, 2, 3 denotes the ensemble member. WAVEWATCH III simulations were run for the following 30-year periods: 1980-2009 and 2070-2099 for each available EC-Earth ensemble member. The comparisons referred to hereafter are between the future period 2070-2099 and the historical period 1980-2009. The final dataset used in this study is the monthly observation station-based NAO index by NCAR which was computed using MSLP data recorded in Reykjavik (Iceland) and Ponta Delgada (Azores) and is based on  Hurrell (1996).
EC-Earth and WAVEWATCH III validation and projection summary A full validation of means and extremes of EC-Earth surface winds and WAVEWATCH III H s is presented in  Gallagher et al. (2016a). The EC-Earth and WAVEWATCH III data capture wind/wave extremes well for the period 1981-2009 compared to ERA-Interim and an ERA-Interim driven wave hindcast, which was independently validated using wave data from 11 buoys located around the coast of Ireland. The EC-Earth RCP4. 5 and RCP8. 5 projections show an average decrease in mean 10 m wind speeds over the North Atlantic Ocean for each season, greater under RCP8. 5 than RCP4. 5  (Gallagher et al. , 2016a, b). They also suggest decreases in wind extremes (e. g. a 14 % decrease in the 95th percentile of 10 m wind speed in winter under RCP8. 5, see Fig.
North Atlantic Oscillation (NAO) There are many ways to describe the temporal evolution of the NAO index. The most popular and simplest method involves calculating a station-based NAO index, as discussed in Sect. 2. This method was also applied to the EC-Earth meiX historical simulation data and me4X and me8X RCP4. 5 and RCP8. 5 projection data. As for the observation data monthly indices were computed for each of the three EC-Earth ensembles. Note that the future projection data were normalised using the 1864-1983 data from the corresponding historical simulation. MSLP values at Reykjavik and Ponta Delgado were extracted from the EC-Earth MSLP gridded fields using the nearest neighbour remapping algorithm (remapnn) available in the CDO (Climate Data Operators:  Schulzweida et al. , 2006)  package.
NAO versus extreme waves In this section we examine the effect of the NAO on the most extreme winter sea states, by fitting the Generalised Extreme Value (GEV) distribution to the simulated H s data on the second grid shown in Fig. 1b  covering part of the Northeast Atlantic. The GEV models the maxima of blocks of data; for a full introduction, see  Coles (2001). Here we consider monthly maxima of H s for the DJFM months and fit the GEV using maximum likelihood (ML) inference with the R package ismev (https://CRAN. R-project. org/package=ismev). The GEV distribution function contains three parameters and is given by G(z) = exp -1 + ξ z -µ σ -1/ξ (1) where -∞ < ξ < ∞ is known as the shape, -∞ < µ < ∞ is the location and σ > 0 is the scale. Once the parameters have been fitted to the data, we may invert the distribution to find N-year return levels; that is, the value of H s which we expect to be exceeded once every N years.
5 scenarios), the strong influence of the NAO on the wave climate of Ireland persists to the end of the century. In order to examine extreme sea states, we fitted a nonstationary Generalised Extreme Value distribution to the datasets. The resulting 20-year return levels of H s show an important influence from the NAO covariate. During periods of neutral NAO index, we may expect future extremes to be reduced when compared with the historical period. However, there may be a strong enhancement of extremes by the NAO, with high positive values of the index leading to higher winter return levels. This amplification shows a significant spa-tial variation, with a much stronger effect seen, for example, to the north-west of Scotland. This suggests a possible future extension of this work with the use of a more sophisticated spatial model of extremes (e.
practices, where information from secondary care and hospitalizations is also aggregated. Data were collected over one baseline (pre-initiation) and one outcome year. Results: Of 32,235 patients with asthma (aged 12-80 years), 54% initiated ICS with extrafine-particle ICS and 46% with fine-particle ICS. Overall, mean age (SD) was 41  (17)  years; 60% female; 24% current and 20% ex-smokers.
As particle size decreases, the fraction reaching the peripheral areas of the lung increases  [11]. Drug formulations that have a small particle size are called extrafine, and RCTs have shown that, when prescribed as a single therapy, extrafine ICS provide additional clinical benefits compared with the same fine-particle therapy or placebo  [12, 13]. Extra-fine-particle dose-response may continue beyond that of fine-particle ICS therapy owing to greater peripheral lung deposition  [14]. This hypothesis is supported by real-life research reporting similar, or in some instances significantly better, asthma control outcomes with extrafine drug formulations compared with fine-particle ones, and at a lower dose  [15, 16]. Using nationwide databases to conduct real-life studies allows the generation of large and heterogeneous populations of patients with asthma.
This permits us to examine longer-term outcomes, providing information to complement the results of RCTs through the inclusion of unselected representative patients managed in real-life clinical practice  [17] [18] [19]. Thus, exploring the dose-response relationship of extrafine versus fine-particle ICS may have clinical implications but may also provide insights on the clinical relevance of the ''distal airways inflammation'' concept. The aim of this study was to investigate the hypothesis that the dose-response effect on the change in the rate of severe asthma exacerbations is steeper for extrafine particle ICS than for fine-particle ICS in a real-life dataset.
Data Source The study utilized two large UK primary care databases, the Optimum Patient Care Research Database (OPCRD)  [20]  and the Clinical Practice Research Datalink (CPRD)  [21]. At the time of the study, the OPCRD contained anonymous, longitudinal data for over 84,534 patients with asthma. The OPCRD is approved by the Health Research Authority of the UK NHS for clinical research use (REC reference: 15/EM/0150). The CPRD is the world's largest validated computerized database of anonymized longitudinal medical records for primary care  [22]. At the time of the study, data were available for approximately 13 million patient records from over 450 primary care practices throughout the UK. Records contain complete prescribing and coded diagnostic and clinical information as well as information on tests requested, laboratory results and referrals made at, or following on from, each consultation  [23].
Patients Eligible patients were aged 12 to 80 years with mild to moderate asthma, received C2 asthma prescriptions during the outcome year (i. e. , C1 in addition to the ICS prescription at the index date), initiated ICS as a single therapy, and had 2 years of continuous practice data (comprising C1 year of baseline data and 1 year of outcome data). Patients were excluded if they had a diagnosis for any chronic respiratory disease other than asthma at any time in their records, received maintenance oral corticosteroids during the baseline year, received a co-prescription for a long-acting beta-agonist (LABA), leukotriene receptor antagonist (LTRA), or theophylline, and were a smoker or ex-smoker over the age of 60 (to minimize the risk of including patients with COPD).
Furthermore, the analysis was based on the prescribed dose at ICS initiation, as opposed to ICS doses during the outcome year. In this observational study the response to consumed dose would not be a meaningful analysis as the results would therefore be confounded by severity. We relied on prescription information from the primary care databases we used. This was an intention-to-treat analysis, thus the data allowed us to identify instances of when prescriptions were written by GPs. It did not include information on whether the prescription was filled, and thus we are unable to guarantee consumption of the medications in this study. However, this does not rule out that the relationship between initial severity and dose response efficacy is attributable to decreased responsiveness at lower doses among patients with more severe asthma  [35] , rather than to increased responsiveness to medication.
Study findings are dependent on the availability and quality of the data in the OPCRD and CPRD, which contain limited information on hospitalizations. At the time of this study both ciclesonide and EF HFA-BDP were prescribed for treatment of asthma in the UK. Although we did not specifically look at how many of the patients examined were on different EF ICS preparations, most patients in the UK, at the time of this study, prescribed extrafine ICS were given beclomethasone, not ciclesonide. A recent publication has shown that ciclesonide and EF HFA-BDP have similar potency and that oncedaily dosing of beclomethasone and ciclesonide is similar in efficacy  [36]. It is therefore unlikely for individual drug type to have a significant effect on the outcomes. A measure of exhaled nitric oxide would allow a more concrete evaluation of the effect of treatment on the peripheral airways, as this has previously been reported to decrease upon use of EF-BDP compared with the non-extrafine formulation  [37].
Nonetheless, the OPCRD is accepted as a high-quality database with the advantages of large size and inclusion of a representative sample of the UK population. The use of patient-centered severe exacerbations as the primary outcome further strengthens this study. Evaluation over one full baseline year and one full outcome year was done to ensure minimal effect of seasonal variations as well as ensure recording of any infrequent outcomes, such as exacerbations. The inclusion and exclusion criteria were selected to minimize potential confounding factors such as other asthma pharmacotherapies, and to identify patients from a large clinical population receiving initial ICS therapy.
Compliance with Ethics Guidelines. This was a retrospective study utilizing anonymized patient data, for which formal consent is not required. Data Availability. The datasets analyzed during the current study are available from the corresponding author on reasonable request. Funding. Teva Pharmaceuticals Europe B. V. Open Access. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4. 0 International License (http://creativecommons. org/licenses/ by-nc/4. 0/), which permits any noncommercial use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. , in asthma. J Allergy Clin Immunol. 2000;106(6):1209-26.
ACKNOWLEDGEMENTS This study was conducted in collaboration with  Teva Pharmaceuticals Europe B. V. , who also provided full funding support for the study and article processing charges. All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this manuscript, take responsibility for the integrity of the work and have given final approval to the version to be published. All authors had full access to all of the data in this study and take complete responsibility for the integrity of the data and accuracy of the data analysis. The authors would like to thank  Martina Stagno  d'Alcontres, a current employee of.
Adams N, Bestall JM, Jones PW. Inhaled beclomethasone versus budesonide for chronic 2002;1:CD003530. stock/stock inhaled asthma. Cochrane Database Syst Rev. member of Medical Advisory Board of Astra-Zeneca. Nicolas Roche has received over the past 3 years (i) fees for speaking, organizing education, participation in advisory boards or consulting from 3 M, Aerocrine, Almirall, AstraZeneca, Boehringer Ingelheim, Chiesi, Cipla, GlaxoSmithKline, MSD-Chibret, Mundi-pharma, Novartis, Pfizer, Sanofi, Sandoz, Teva; (ii) research grants from Novartis, Boehringer Ingelheim and Pfizer. Anne Burden was an employee of OPRI, which conducted this study and which has conducted paid research in res-piratory disease on behalf of the following other organizations in the past 5 years: Aerocrine, AKL Research and Development Ltd, Almirall, AstraZeneca, Boehringer Ingelheim, Chiesi, GlaxoSmithKline, Meda, Mundipharma, Napp, Novartis, Orion, Takeda, Teva, and Zentiva.
9995%) helium carrier gas. External reproducibility was better than 0. 05 and 0. 10‰ for C and O, respectively. For the Hendy test samples, about 0. 05 to 0. 10 mg of calcite powder were weighed and placed in glass vials and analysed on a Nu Instruments Perspective dual-inlet isotope-ratio mass spectrometer. The samples were acidified with 105% H 3 PO 4 at 70°C in a Nu Instruments NuCarb sample preparation unit and the sample gas CO 2 admitted into the ion source under vacuum. External reproducibility was 0. 03 and 0. 06‰ for C and O, respectively. Sample data from both instruments were normalised to the V-PDB scale using two inhouse calcite standards (a Carrara Marble standard, 'NEW1', and a powdered homogeneous calcite prism of unknown origin, 'NEW12'), both previously calibrated against the international reference standards NBS-18 and NBS-19.
However, calcite pattern variations can be subtle, and are not always readily resolved; sub-type classifications may have to be defined for specific samples. Comparison with other speleothem proxies is always necessary in order to refine the interpretations. Finally, as petrography becomes more widely used in speleothem research, it will be necessary to ensure consistency in nomenclature, and to avoid the multiplication and redundancies of sub-classification and terminologies. Accordingly, the development of a working group would be highly desirable, facilitated via workshops and an online discussion platform, including a petrographic database.
ACKNOWLEDGMENTS We are grateful to the " Gruppo Speleologico Bolognese/Unione Speleologica Bolognese " for the technical help during sampling campaigns in Bosnia, to the " Federazione Speleologica dell'Emilia Romagna" and the "Gruppo Speleologico Faentino " for funding provided and to the  Federal Hydrometeorological Institute of Sarajevo  for providing historical meteorological data. A special thanks to  Emilie Chalmin  ( Université Savoie Mont-Blanc, France ) and  Diego Ercole Angelucci  ( University of Trento, Italy ) for the support provided during microscopic observations;  Roland Maas  ( University of Melbourne, Australia ) is thanked for the support provided for U-Th dating;  Fausto Desalvo  ( Department of Mathematics, University of Bologna, Italy ) for the help provided in the statistical analyses.
Brand drug A brand drug is a drug molecule developed a pharmaceutical firm after research and is patented for a number of years depending on the country's drug regulations. [5, 7] neric/me-too drugs Generic or me-too drugs are copies of the brand drug molecule and contain same active ingredient. They are available after the expiry of the patent period and are less expensive thus affordable for the poorer patients. [5]  Fast-moving and slow-moving drugs Fast-moving drugs were those which according to the pharmacy's sale data had a relatively higher inventory turnover rate (ITOR) as compared to the other drugs and vice versa. ITOR is the number of times a particular drug stock is completely sold in the pharmacy. [14]  Participants and eligibility criteria Community and hospital pharmacies were included in the study.
Research instrument and piloting The study employed a checklist termed as FQL checklist for gathering the data which was exclusively developed by a team of experts comprising of a health economist and pharmacists with clinical, pharmaceutical, and regulatory knowledge. It was subjected to physical piloting and reliability analysis. The research instrument was piloted among 12 pharmacies and was validated. The reliability analysis reported Cronbach alpha value of 0. 7. The FQL checklist contained 6 items out of which 3 items, namely, location in districts, name of drug, price of drug were open-ended, and other 3 items i. e. , type of pharmacy, type of drug, category of drug were close ended. The variables identified were demographics of the pharmacy such as the location in the city which was identified by city districts.
Data analysis and presentation The data gathered was entered in Statistical Package for Social Sciences IBM SPSS version 22 (Statistical Package for Social Sciences) SPSS Inc. , Chicago USA. software and was analyzed using frequency descriptive statistics, Chi-square ( 2 ) test, and cross tabulation. A significance level alpha (α) was noted at 0. 05. The results were expressed as sample counts (N), percentages (%), and mean values (X). Statistical significance was reported in terms of significant P < 0. 05.