dataesr/scientific-paragraphs-categorization huggingface dataset:
Data collection We retrospectively searched for all relevant sources of data documenting the CALIBAN intervention and the pneumonia guidelines. We used contact with key informants, the IPC and CALIBAN archives, the National Institute of Public Health and MoH websites. Data collected included project protocols, reports, published literature, and meeting minutes from CALIBAN. It also included the successive drafts of guidelines, email correspondence between researchers, the CALIBAN network and the Task force, as well as notes taken during interviews with a key informant from the NGO who assisted the guidelines update. The review of these documents enabled us to describe and model the KT intervention.
UML modeling Secondly, we modeled and analyzed the CALIBAN KT process within the 'guidelines updating' system, using the Unified Modeling Language (UML) graphical tools. The purpose was to describe how this intervention worked and to provide a framework for assessing its effectiveness. UML is an object-oriented modeling tool developed in 1997 for documenting, designing, and evaluating complex systems  [26]. It is an effective medium of communication and development for both theorists and practitioners  [27]. It has been used to model different aspects of healthcare systems  [28, 29]  including the analysis of clinical trials  [30]. We initially developed a series of UML 'use cases. ' A 'use case' represents a series of steps defining interactions between 'actors' and a 'system, ' to successfully achieve a goal.
Exp. label Coupling Snow acc. Liq. prc. acc. Climatology CLIMICE × SNOW-SO × PRECIP-SO × PRECIP-SO × SNOW × × PRECIP × × over continents  (New et al. , 1999)  and the Global precipitation climatology project (GPCP) data set over oceans  (Adler et al. , 2003). In addition, precipitation data for the arctic area comes from  Serreze and Hurst (2001). This experiment is referred to as CLIMICE in the following. Furthermore, since we also want to understand the effect the ice-sheet feedback has on the atmospheric model, we performed two semicoupled climate-ice-sheet simulations where the feedback of the ice sheet on climate is removed. That is, the atmosphere model is permantly forced by the observed ice-sheet thickness and extent. There are two simulations since we need one each for the PRECIP and SNOW method.
The Model of Emissions of Gases and Aerosols from Nature (MEGANv2. 1) together with the Modern-Era Retrospective Analysis for Research and Applications (MERRA) meteorological fields were used to create a global emission data set of biogenic volatile organic compounds (BVOC) available on a monthly basis for the time period of 1980-2010. This data set, developed under the Monitoring Atmospheric Composition and Climate project (MACC), is called MEGAN-MACC. The model estimated mean annual total BVOC emission of 760 Tg (C) yr -1 consisting of isoprene (70 %), monoterpenes (11 %), methanol (6 %), acetone (3 %), sesquiterpenes (2. 5 %) and other BVOC species each contributing less than 2 %. Several sensitivity model runs were performed to study the impact of different model input and model settings on isoprene estimates and resulted in differences of up to ±17 % of the reference isoprene total.
Reflecting the measurements of isoprene emissions under drought conditions, MEGAN also introduced a parameterization of isoprene emission reduction due to soil moisture deficit. Sakulyanontvittaya et al. (2008)  developed a US regional emission factor database for monoterpenes and sesquiterpenes and extended the MEGAN model to simulate emissions of these chemical species (MEGANv2. 02). Previous studies have shown that these compounds are to a large extent emitted in a manner similar to that of isoprene, i. e. depending on both temperature and light. Sakulyanontvittaya et al. therefore introduced a light dependence factor assigned to each modelled species in order to simulate different levels of light-emission dependence. The current version of the MEGAN model (MEGANv2. 1) described in  Guenther et al.
, 2011). During the past few years, the MEGAN model has been widely used within the scientific community for the estimation of BVOC emissions and has been incorporated into various earth system and chemistry transport models (e. g. Guenther et al. , 2006; Heald et al. , 2008; Pfister et al. , 2008; Stavrakou et al. , 2009; Emmons et al. , 2010; Millet et al. , 2010; Guenther et al. , 2012). The long-term data set presented in this paper has been developed in support of the MACC (Monitoring Atmospheric Composition and Climate project: http://www. gmes-atmosphere. eu) project and of the CCMI (Chemistry-Climate Model Initiative: http://www. met. reading. ac. uk/ccmi/). The MEGAN model setting and selection of input data are addressed in Sect. 2, description of the emission data set (MEGAN-MACC) and a set of model sensitivity studies focusing on isoprene is given in Sect.
Meteorological fields The MEGAN model was driven by meteorological fields of Modern-Era Retrospective Analysis for Research and Applications (MERRA) provided by the NASA's Goddard Space Flight Center  (Rienecker et al. , 2011). MERRA data are produced by the Goddard Earth Observing System (GEOS) atmospheric model version 5. 2. 0 with a data assimilation system (DAS). The full data set covers the modern satellite era For the reference MEGAN model simulation (MEGAN-MACC) we have used 6 hourly instantaneous analysis fields of surface temperature, air pressure, humidity and wind speed given on the model grid with horizontal spatial resolution of 0. 5 • latitude and 0. 666 • longitude. Since the MEGAN model requires hourly input data, the 6 hourly fields were interpolated. Spatial resolution of the meteorological fields was shown to have a negligible impact on global isoprene emissions, however, it might cause a significant isoprene variations at local and regional scales  (Pugh et al.
, 2013). Calculating hourly meteorological data from 6 hourly inputs can lower the global isoprene emissions by up to 3 % and using monthly averages of hourly daily profile might lead to additional decrease of 7 % globally. Temporal averaging of meteorological inputs can have more substantial impacts at regional level  (Ashworth et al. , 2010). The MERRA data set does not directly provide the photosynthetic photon flux density (PPFD) needed for calculation of the γ P factor in Eq. (  2 ). Values of PPFD can be obtained from photosynthetically active radiation (PAR, W m -2 ) provided on hourly basis by the MERRA Land model. As discussed in  Guenther et al. (2012) , selection of a conversion factor between PAR and PPFD is an important issue. In MEGAN, conversion factors of 4. 6 and 4. 0 µmol photons J -1 are used for diffuse and direct radiation, respectively.
Vegetation and emission factors Vegetation composition inside the model domain is the principal information needed to estimate BVOC emissions. A distribution of 16 plant functional types (PFT) consistent with the vegetation categories used in the Community Land Model version 4 (CLM4)  (Lawrence and Chase, 2007)  was used to define the vegetation spatial distribution in our model setup. The data set provides a percentage of PFT coverage in each model grid cell. Another important input parameter is the emission potential of individual modelled species. In MEGANv2. 1, there are two possibilities for assigning a grid cell with an emission potential value. One option is to use the global gridded highresolution emission potential maps ( MAP ) provided together with the MEGAN model code for the predominant BVOC species.
5 % and 77 %, respectively. For isoprene, the second largest source corresponds to temperate shrubs (10 %) followed by cool and warm grass (4 %) and finally needleleaf forest in boreal regions with 1. 5 %. Shrubs and needleleaf forest contribute equally to monoterpene emissions with about 10 %. Grass is only a minor monoterpene source with 0. 5 % of total monoterpene emission. The MEGAN-MACC emission inventory is available for download at the Emission of atmospheric Compounds and Compilation of Ancillary Data database (EC-CAD, http://www. pole-ether. fr/eccad). ECCAD is an online database of global and regional emission inventories. It includes anthropogenic, biogenic and biomass burning emission data together with a list of ancillary data such as population density, land cover maps and fire emission proxies.
Leaf area index In order to evaluate the influence of different sources of LAI data used in the MEGAN model, MODIS values were replaced by leaf area index from the MERRA reanalysis data set in the sensitivity run S1. MERRA-LAI is calculated by the Land module of the MERRA model system. When compared to LAI from MODIS (MODIS-LAI), MERRA-LAI is generally higher. The largest differences, i. e. about 1. 2 m 2 m -2 are found in the Amazon, the western coast of the US, Canada, northern Europe and Australia.
Input emission potential of isoprene Isoprene emission potential values PFT (described in Sect. 2. 2. 2 and calculated following Eq. (  5 )) were used in sensitivity run S2 instead of emission potentials from gridded maps ( MAP ). On the global scale isoprene emission potentials PFT are about 14 % lower than emission potentials MAP. The largest difference is again over the region of Australia with a relative decrease of 47 % when compared to MAP. A notable decrease of 28 % can be observed also in southern Africa, further in sub-Saharan Africa and in the southeast US. On the contrary, PFT is higher than MAP in South America, especially in northern Amazon (+10 %), and in Europe except for Spain and Portugal (+18 %). The differences between the two emission potential databases lie in the different land cover types and emission factors serving as a basis for their calculation.
However, γ CO 2 will be an important factor for future simulations when CO 2 concentration is predicted to greatly exceed 400 ppmv (e. g. Arneth et al. , 2007b; Heald et al. , 2009; Young et al. , 2009). The S4 simulation led to large changes in isoprene emissions, when compared to the reference, as indicated in Fig. 9a. The inclusion of the soil moisture algorithm led to a general decrease of isoprene emissions (50 % of the reference run totals), especially in arid and semi-arid regions such as central Africa, southern part of South-American tropical region and Australia where the emission reduction reached up to 70 % with respect to the reference run. This decrease in isoprene emissions is much higher than that presented by  Guenther et al. (2006) , who applied the same algorithm using NCEP (National Centers for Environmental Prediction) soil moisture values, the 2-D gridded wilting point values from the database of Chen and Dudhia (  2001 ) and PFT dependent determination of the depth of the root zone  (Zeng, 2001).
Spatio-temporal comparison of MEGAN-MACC isoprene with other data sets MEGAN-MACC isoprene estimates were compared to the results of four other studies. Table  4  summarizes general information about isoprene inventories used in the comparison. Data availability indicates the original temporal extent of each data set. However, in our study we focused on the period of 2000-2009 only. BISA (Belgian Institute for Space Aeronomy) data sets were provided by the Belgian Institute for Space Aeronomy, and are both available on the institute's website (http://tropo. aeronomie. be/models/isoprene. htm). BISAbottom-up is an updated version of the emission inventory described in  Müller et al. (2008)  who applied MEGANv2. 02 model driven by ERA-Interim reanalyzed meteorological fields (ECMWF)  (Dee et al.
, 2011). In their calculations, Müller et al. replaced the built-in MEGAN canopy environment model for determination of leaf temperature and radiation fluxes by the canopy environment model MOHYCAN (MOdel for HYdrocarbon emissions by the CANopy)  (Wallens, 2004). The most recent BISA isoprene inventory, here titled as BISA-top-down, was constrained by global formaldehyde retrievals from the GOME-2 satellite instrument using the IMAGESv2 (Intermediate Model of Global Evolution of Species) chemistry transport model  (Bauwens et al. , 2013 ;  MEGANv2 is available for the year 2003 only and is repeated for all the other years. List of emission data sets is given in the enclosed legend. Stavrakou et al. , 2014). A-priori isoprene emissions for the IMAGESv2 model were calculated by MEGANv2. 1 and ERA-Interim meteorology.
Data set GUESS-ES was calculated by a coupled system of dynamic global vegetation model LPJ-GUESS  (Smith et al. , 2001; Sitch et al. , 2003)  and physiological isoprene emission algorithm  (Arneth et al. , 2007b). The algorithm is based on the approach of  Niinemets et al. (1999)  which simulates physiological processes inside the plant leading to isoprene production through electron transport rate required for isoprene synthesis. The model was driven by the meteorological fields of Climatic Research Unit (CRU) The discrepancy in annual cycles is caused by different intensities of emitting regions on the globe over the course of the year. As can be seen in Fig. 12 , for all data sets the predominant source of isoprene is the southern tropics. Its contribution to annual global total is, however, significantly lower for GUESS-ES (42 %) when compared to MEGANbased data sets (50-54 %).
These lower GUESS-ES emissions in the tropics are compensated in the northern temperate region, which contributes 18 %, while for the rest of the data sets this part contributes only 8-10 %. Figure  12  shows also a spatial distribution of regional emission activity during the year 2007 represented in zonal means of monthly isoprene emissions. The tropical region is clearly an important isoprene source for all inventories. It is pronounced in the warmest half of the year of Southern Hemisphere, es-   especially in Australia, south and equatorial Africa and South America. On the other hand, GUESS-ES and MEGANv2 are significantly higher than MEGAN-MACC in Europe and Russia, and together with BISA-bottom-up in North America. MEGANv2 reaches considerably higher isoprene values (+71 %) in Southeast Asia when compared to MEGAN-MACC.
The difference is even higher when compared to the rest of the inventories. The graph shows that the updating of BISA emissions from bottom-up to top-down led to a substantial decrease of emissions in Southeast Asia. Several reasons for discrepancies between MEGAN-MACC values and estimates of other data sets can be identified. Differences in spatial isoprene emission patterns likely originate in unequal distribution of vegetation types in different models represented by PFT maps, and/or their assignment with isoprene emission capacity values (emission potentials for MEGAN, fractions of electrons in GUESS-ES). Application of different PFT and LAI inputs can result in a wide range of isoprene estimates (e. g. sensitivity run S2 in this study;  Pfister et al. , 2008; Guenther et al. , 2012). All five presented data sets were driven by different meteorological input fields.
However, previous studies have shown inconsistencies in emission estimates suggesting that use of satellite data for these purposes is still connected to uncertainties. In order to increase accuracy of these methods, more investigation is needed in the validation of data from different satellite instruments, in evaluation of dependence of the method on a priori emissions and on chemical scheme applied. at PNNL. TS and JFM were supported by projects PRODEX-A3C funded by the Belgian Science Policy Office and GlobEmission funded by ESA. MERRA data were provided by the Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center through the NASA GES DISC online archive. The satellite derived PAR data were provided by the University of Maryland, Department of Atmospheric and Oceanic Science, Radiation Budget team (R.
The upper few meters of the ocean form a critical layer for air-sea interaction, but because of observational challenges this region is undersampled. However, the physical processes controlling momentum transfer, gas exchange, and heat transfer are all concentrated in the uppermost region of the ocean. To study this region, the Air-Sea Interaction Profiler (ASIP) was developed. This is an autonomous microstructure vertical profiling instrument that provides data from a maximum depth of 100 m to the ocean surface and allows measurements to be performed in an undisturbed environment. The core sensor package on ASIP includes shear probes, microstructure and CTD-quality temperature and conductivity sensors, a photosynthetically active radiation (PAR) sensor, and an oxygen optode providing a repeated high-resolution dataset immediately below the air-sea interface.
Deep atmospheric convection is very sensitive to small temperature variations above the 28. 58C threshold (e. g. ,  Gadgil et al. 1984; Graham and Barnett 1987). In tropical regions, the diurnal cycle of SST can strongly interact with convection and hence with largescale phenomena like the Madden-Julian oscillation (e. g. ,  Bellenger et al. 2010). Some other studies find that resolving the diurnal cycle of SST can induce a more consistent restitution of air-sea feedbacks and hence lead to an improvement of El Niño simulation  (Masson et al. 2012). Increasing the comprehensiveness of oceanographic observational datasets under a variety of environmental conditions will provide the information necessary to test existing hypotheses concerning the dynamics of the ocean boundary layer. This has the potential to lead to the development of new theories for understanding fluxes across the ocean surface; the interactions between currents, the SOBL, and the surface wave field, including wave breaking and the injection of turbulence; interactions between breaking waves and Langmuir cells; and interactions between SOBL dynamics, waves, and horizontal ocean fronts in regions where both turbulence and submesoscale dynamics are important.
Such datasets would provide a stringent metric for verification and validation of emerging numerical models for the next decade. These efforts would lead to improved modeling of the upper-ocean mixed layer incorporating waves, and the lower atmospheric boundary layer. Turbulence in the upper ocean, induced by wind shear, breaking waves, and buoyancy flux, periodically erodes the hierarchy of molecular sublayers immediately below the air-sea interface. The viscous, thermal, haline, and diffusive sublayers control the air-sea fluxes of momentum, heat, water, and gas  (Lorke and Peeters 2006) , but turbulence in the upper ocean, induced by wind and Stokes shear, breaking waves, and convection, periodically erodes the hierarchy of molecular sublayers immediately below the air-sea interface. The greater the amount of turbulence, the more enhanced are the fluxes between the ocean and atmosphere.
An overview of ASIP and the data processing, which addresses issues of converting the data to quantitative measurements of key properties of the upper ocean, is provided in section 2. Section 3 presents examples of the data acquired during several of its field measurement campaigns. A summary of the paper is provided in section 4. The appendixes to the paper provide the descriptions of the mechanical, electrical, and software of ASIP, and details of data recording and the onboard software for mission control and data storage.
The instrument is positively buoyant, and for each profile ASIP dives using three thrusters (SeaBotix BTD150) to a maximum depth of 100 m. Once the desired depth has been reached, the thrusters cease operation, and the instrument then ascends through the water column under its own buoyancy. Data are acquired throughout the ascent until the sensor package breaks through the free surface. ASIP transmits a short message upon completion of the profile using the Iridium shortburst data (SBD) messaging service (alternatively, this may occur after a prescribed number of rapid profiles, which will be described in more detail in appendix B). Two-way communication allows the mission settings to be reprogrammed in real time. The battery lifetime allows for a total of roughly 6000 m of cumulative profiling per deployment, for example, approximately 60 profiles from the maximum depth of 100 m.
ASIP has a core suite of microstructure sensors to provide estimates of the rate of dissipation of turbulent kinetic energy and small-scale temperature and salinity (in combination with the CTD-quality data for in situ calibration). These are fundamental processes for airsea exchange, and much of the effort in developing the data processing has been dedicated to utilizing the best techniques for processing the data. The extended suite of sensors mounted on ASIP provides a rich dataset with which to study a variety of scientific questions from a single platform. To date, data from ASIP has been used to investigate ocean surface processes, such as diurnal warming at the ocean surface, internal breaking waves, surface scaling of dissipation at the ocean surface, the time response of the upper ocean to atmospheric forcing, and salinity gradients.
8 ). This would be valuable for studying the air-sea freshwater budget in the open ocean  (Hu et al. 2004) , and would help improve estimates of salinity from space with the National Aeronautics and Space Administration's (NASA) Aquarius satellite and the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite. As new sensor technology becomes available, the design of ASIP lends itself to the ease of integration of these new sensors, which will undoubtedly provide new insights into upper-ocean processes. Council co-funded by Marie Curie Actions under FP7. We gratefully appreciate the support from the captain and crew of the R/V Suroît, CCGS Hudson, R/V Knorr, and R/V Thalassa. Paula Fratantoni and Jon Hare provided helpful comments for Fig. 8.
Mission Software and Control The mission controller on ASIP is a Persistor CF2 microcomputer runs a C language custom application. Tables C1-C5 refer to aspects of the ASIP application software, as described within this subsection. There is user access to the CF2 RS232 user port via a Teledyne Impulse connector located toward the top of ASIP. This serial connection to a PC allows new versions of the software to be uploaded to the CF2, as well as sending commands. The available commands are presented in Table  C1. An ASIP mission is programmed with the mission command, with the parameters outlined in Table  C2. The CF2 then goes into sleep mode, and ASIP is loaded into the small boat and deployed. Once in the water and with the sensor cover removed, the CF2 is woken up by placing an external magnet over a reed switch located in the upper section and marked by one of the SAE fittings.
The data and control lines between the Science PC and the concentrator are accomplished with a digital input/output (I/O) card. A dedicated program written in Fortran controls the data flow and storage onto the onboard solid-state hard drive; this program starts automatically when the Science PC is booted. The Science PC gets powered by the mission controller and runs the data handling program-its only task-to acquire and store the data. There is a limited set of commands that may be transmitted to the Science PC from the mission controller over a serial link. These commands are shown in Table  C3. The communication system with ASIP is illustrated in Fig. C1. When at the surface, after a normal profile, ASIP extends its antenna and attempts to obtain a GPS fix, which will time out after 180 s if a good fix is not obtained.