dataesr/scientific-paragraphs-categorization huggingface dataset:
e. a few months per year or maximally one whole year is missing). Scoresbysund and Canadian stations have reported background currents to the archives since 2000. Due to the limitations of the Lagrangian match technique presented here (cf. Sects. 2. 3 and 3), the influence of the background current on the sonde performance can be thoroughly investigated only at De Bilt, Legionowo, Payerne and Uccle. The latter three, however, report no change in treatment nor large variations of the background current in the 1994-2009 period. Ozonesonde data can be downloaded from several archives: ftp servers at the WOUDC (World Ozone and Ultraviolet Radiation Data Centre), NDACC (Network for the Detection of Atmospheric Composition Change), SHADOZ (Southern Hemisphere ADditional OZonesondes), NILU (Norwegian Institute for Air Research), and NOAA (National Oceanic and Atmospheric Administration).
Records from most stations can be found either on the WOUDC or NDACC homepages, or on both. Most tropical stations are now part of the SHADOZ network  (Thompson et al. , 2003a, b). NILU offers campaign data, for example, measurements from the VINTERSOL campaign (European field campaign studying stratospheric ozone), and high latitude station data. The Intensive Ozonesonde Network Study (IONS) experiments  (Tarasick et al. , 2010; Thompson et al. , 2011 ) over North America have operated in 2004 , 2006 , 2008 , and 2013. This has augmented regular US launches at Boulder (Colorado), Huntsville (Alabama), and Wallops Island (Virginia) as well as most of the Canadian stations listed in Table  1. The data are archived at NASA/Langley and WOUDC. For some stations we needed to switch between the archives to obtain the highest number of soundings.
We found that the archives do not necessarily contain the same number of soundings for the same period. Some years are missing at one archive but available at another. Some years are also missing in all archives. Data from Boulder, De Bilt, Izaña (Canary Islands), MOHp, OHP, and Sodankylä were obtained from the NDACC archive in March and April 2010. Sodankylä data from 2004 onwards were obtained from NILU's NADIR database in May 2010. Data from the Canadian sites (Alert, Churchill, Edmonton, Eureka, Goose Bay, Resolute), as well as for Huntsville, Legionowo, Lerwick, Lindenberg, Madrid, Naha, Nairobi, Payerne, Uccle, and Sapporo and Tsukuba (both in Japan) were also obtained from the WOUDC in March and April 2010. Uccle data from     aircraft program (1994-2009) , summed up over a 3   (Brazil)  and Paramaribo (Suriname) data were obtained from the SHADOZ database in April 2010, with the exception of data from the Natal site for 1997, which was obtained from the WOUDC.
Wallops Island data for  1994, 1995, 2008, and 2009  were obtained from the WOUDC in March and April 2010, while all remaining data were obtained from the NDACC website in April 2010. MOHp, Payerne, and Uccle typically launch two to three ozonesondes per week, whereas most other sites typically launch one sonde per week. It is important to note that not all sites flew ozonesondes for the entire MOZAIC period, with some stations starting later, particularly tropical ones. Consequently, the total number of launches for the entire MOZAIC period  (August 1994 -March 2009 ) is quite different from station to station, ranging from 300-400 (e. g. Paramaribo, Irene) to more than 2000 launches (e. g. MOHp, Payerne, Uccle, cf. Table  1 ).
MOZAIC ozone observations The MOZAIC program and data from it are described and analysed in detail by  Thouret et al. (1998). Here, only the main characteristics are summarised. Dual-beam UV absorption models from Thermo Environment were installed on several commercial aircraft participating in the MOZAIC project. These UV photometers have a response time of 4 s, a detection limit of 2 ppbv, and an uncertainty of ±[2 ppbv+ 2 %]. For example, for O 3 = 100 ppbv this results in an uncertainty of ±4 ppbv. The quality assurance and control procedures have not changed since the project started in 1994. MOZAIC analysers are inspected annually and periodically calibrated (about every 12 months) with a reference analyser at the French National Institute of Standards and Technology. Additionally, the analysers are checked in-flight with a builtin ozone generator to detect any drift in instrument efficiency.
MOZAIC is considered as a standard reference (for example in  Thouret et al. , 1998 Thouret et al. , , 2009;; Schnadt Poberaj et al. , 2009)  due to its regular inspections and checks as well as the unchanged quality assurance procedures. However, the recent analyses of both  Logan et al. (2012)  and  Staufer et al. (2013)  cast some doubts on the long-term stability of the MOZAIC ozone data. MOZAIC's main flight route is the North Atlantic flight corridor, but aircraft also fly to airports in South America, East Asia, and Southern Africa. The flight distribution of the aircraft is shown in Fig. 1. The sounding sites investigated in this work are chosen according to these flight routes. In total, 31 494 flights were available when we downloaded the data (March 2010), covering the period from August 1994 to March 2009.
Comparison methodology For the comparison between routinely flown ozonesondes and ozone measurements from any MOZAIC aircraft we use trajectories to ensure both instrument platforms observe the same air mass. In a companion paper  (Staufer et al. , 2013) , we test and apply this method of comparisons between aircraft measurements from both MOZAIC and NOXAR aircraft and ozonesonde data from Payerne, Switzerland. Here, we summarise just the main points of this method, which is similar to the trajectory match technique used by  Rex et al. (1998)  or the trajectory hunting technique described by  Danilin et al. (2002). After reconstructing the sonde's flight path using wind data (speed and direction) from the radiosonde, the trajectory tool LAGRANTO  (Wernli and Davies, 1997 ) is used to calculate 6 day forward and backward trajectories for each sounding.
woudc. org, the Network for the Detection of Atmospheric Composition Change (NDACC; data publicly available at http://www. ndsc. ncep. noaa. gov), SHADOZ (http://croc. gsfc. nasa. gov/shadoz/), a NOAA ftp server (ftp://ftp. cmdl. noaa. gov/ozwv/ozone/), and from NILU's NADIR database. We would also like to thank Bryan J. Johnson and Alberto Redondas for providing information regarding the sonde configuration used at the NOAA sites and Izaña, respectively. Thanks are also due to Ankie Piters and Marc Allaart from KNMI (Royal Netherlands Meteorological Institute) for providing the information on data processing and sonde preparation changes at De Bilt and Paramaribo. Finally, we thank the anonymous reviewers for their constructive and helpful suggestions and comments that helped to improve the manuscript.
Acknowledgements. J. Staufer 's research was funded by a  Me-teoSwiss GAW-CH grant. The authors would like to thank the  European Commission, Airbus ,  CNRS-France , and  FZJ-Germany  for their strong support of the  MOZAIC  project, as well as the airlines (Lufthansa, AirFrance,  Austrian ,  Air Namibia , and former Sabena) involved in the project who have carried the MOZAIC instrumentation free of charge since 1994. MOZAIC is presently funded by  INSU-CNRS (France) ,  Météo-France ,  CNES ,  Université Paul Sabatier (Toulouse, France)  and  Research Center Jülich (FZJ, Jülich, Germany ). IAGOS is additionally funded by the  EU projects IAGOS-DS, IAGOS-ERI , and IGAS. MOZAIC-IAGOS.
Artificial seconds, in some cases including tenths or hundreds of a second, were generated for these valuable data, such that they can remain in SOCAT version 2. Every effort will be made to retrieve a more adequate time stamp for these data sets for future versions. Unlike version 1, which has version 1. 4 and 1. 5 data products, version 2 data products have a single version number (Table  1 ). Expocode for moorings and drifters: SOCAT uses twelve character expocodes  (Swift, 2008)  as stable and unique data set identifiers. For example, 49P120101218 indicates a cruise on the Japanese (49) ship of opportunity Pyxis (P1) with the first day of the cruise on 18 December 2010. In contrast to version 1, expocodes have been assigned for moorings and drifters in version 2, by registering a "vessel code" at International Council for the Exploration of the Sea (ICES) in collaboration with the National Oceanographic Data Center (NODC) and the British Oceanographic Data Centre (BODC) (Table  1 ).
Individual data set files Individual data set files provide surface water f CO 2 , the parameters used to (re-)calculate f CO 2 and the original CO 2 parameter(s) reported by the data contributor for data sets with a flag of A, B, C or D (Table  2 ). The files include all surface water f CO 2 values with WOCE flags of 2, 3 and 4. Individual data set files are archived at PANGAEA ® (doi:10. 1594/PANGAEA. 811776). Each data set has a digital object identifier (doi) (Table  3 ). Metadata provided by the data contributor accompany the data set files. As in version 1, the individual data set and synthesis files include the climatological values of salinity and atmospheric pressure from reanalysis. The files also contain values for the water depth, the distance to a major land mass and the atmospheric CO 2 mole fraction interpolated from GLOBALVIEW-CO2 (2012).
Global synthesis product The global synthesis data set consists of individual data sets with flags of A, B, C and D and contains f CO 2 values with a WOCE flag of 2 (Table  2 ). The synthesis files do not contain the original CO 2 values (Table  3 ). Each line in the files lists the doi-number of the corresponding individual data set file at PANGAEA ® (Sect. 2. 4. 3). The synthesis data set is available as global and regional files for the SOCAT regions (Fig. 5 ). The regional files only contain data from within that region, so that data from most cruises are split between several regional files. The global and regional files are publicly available as compressed zip text files via CDIAC (http://cdiac. ornl. gov/ftp/oceans/SOCATv2/). Matlab code is available for reading these synthesis files.
pmel. noaa. gov/SOCAT_ gridded_viewer/) (Table  2 ). Matlab code is available for reading the NetCDF files. The capabilities of the Gridded Data Viewer have been expanded in version 2. The number of different years is a new variable in the monthly climatological gridded data set. The Gridded Data Viewer now shows the 400 km continental margin mask at 1 min resolution used for defining the Coastal Region (Table  5 ) and the distance to the nearest major land mass from 0 to 1000 km at 20 min resolution. The Gridded Data Viewer has an option for animation of gridded products. The interface has a new comparison capability for up to four gridded data sets. This enables the user to visualise, for example, gridded data products in SOCAT versions 1 and 2 in a multiple-plot view.
Acknowledgements. The  International Ocean Carbon Coordination Project (IOCCP) , the  Surface Ocean Lower Atmosphere Study (SOLAS) , and the  Integrated Marine Biogeochemistry and Ecosystem Research program  (IMBER) promote SOCAT. We thank both referees, anonymous referee 1 and  D. Woolf , for their constructive and detailed comments, which have considerably improved the Cruise Data Viewer and this manuscript. Support for SOCAT has been received from the  University of East Anglia (UK) , the  Bjerknes Centre for Climate Research , the  Geophysical Institute at the University of Bergen (Norway) , the  Climate Observation Division of the Climate Program Office  of the  US National Oceanic and Atmospheric Administration , the  University of Washington ,  Oak Ridge National Laboratory (USA) , PANGAEA ® Data Publisher for Earth and  Environmental Data (Germany) , the  Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (Germany) , the  National Institute for Environmental Studies (Japan) , the  Korean Institute of Ocean Science and Technology , the  European Union projects CarboChange  ( FP7 264879 ) and  GEO-CARBON  ( FP7 283080 ), the  UK Ocean Acidification Research Programme  ( NE/H017046/1 ; funded by the  Natural Environment Research Council , the  Department for Energy and Climate Change  and the  Department for Environment, Food  and Rural Affairs), the  Research Council of Norway (CARBON-HEAT) , the  Scientific Committee on Oceanic Research (SCOR, US ,  OCE-0938349 ), the  US National Science Foundation  ( OCE-1068958 ),  Swedish Research Council  projects ( 2009-2994 ;  2008-6228 ;  2009-2994 ) and the  Swedish National Space Board  ( RESCUE  project;  REmote Sensing ocean Carbon UptakE ).
f Piperacillin-tazobactam is frequently used for empirical and targeted therapy of infections in critically ill patients. Considerable pharmacokinetic (PK) variability is observed in critically ill patients. By estimating an individual's PK, dosage optimization Bayesian estimation techniques can be used to calculate the appropriate piperacillin regimen to achieve desired drug exposure targets. The aim of this study was to establish a population PK model for piperacillin in critically ill patients and then analyze the performance of the model in the dose optimization software program BestDose. Linear, with estimated creatinine clearance and weight as covariates, Michaelis-Menten (MM) and parallel linear/MM structural models were fitted to the data from 146 critically ill patients with nosocomial infection.
MATERIALS AND METHODS An overview of the development of the population pharmacokinetic model and the building, testing, and demonstration of the dosage optimization software is shown in Fig. 1. Pharmacokinetic studies of critically ill patients. Pharmacokinetic data from 146 patients from three previously published studies were obtained  (6, 20, 21). A total of 803 piperacillin concentrations were available with each patient contributing 2 to 10 observations. Patients were administered between 2 and 4 g of piperacillin by 30-min infusion every 8 h, 4-h infusion every 8 h, or continuous infusion. Lodise et al. provided data from 76 patients undergoing abdominal or thoracic surgery who received 2 g of piperacillin over 30 min  (20). Plasma drug concentrations were measured in the first dosing interval.
We evaluated several structural PK models. The details of these structural models are shown in Fig. 2. The models differed in the way in which piperacillin was cleared from the central compartment and by the covariates that were included. The models were parameterized in the following ways: (i) elimination by a first-order process (Fig. 2 , equation 1a), (ii) elimination by a Michaelis-Menten process (Fig. 2 , equation 1b), (iii) elimination by parallel first-order and Michaelis-Menten processes (Fig. 2 , equation 1c), and (iv) elimination by a first-order process with creatinine clearance as a covariate and body mass as a covariate for the volume of the central compartment (Fig. 2 , equation 1d). Piperacillin elimination, by a Michaelis-Menten process, is biologically plausible and has previ- ously been identified to best describe the observed data in a population pharmacokinetic analysis  (6).
e. , Bayesian prior) and individual patients (i. e. , Bayesian posterior) were examined. The fit of each of the four structural models to the data was assessed using a combination of the following: (i) the log likelihood value, (ii) the Akaike information criterion (AIC), and (iii) the coefficients of determination (r 2 ) from a linear regression of the observed-predicted plots before and after the Bayesian step. Differences between the various models were also assessed statistically by calculating the difference in log likelihood values and comparing this value to a 2 distribution with the degrees of freedom equal to the difference in the number of parameters between each model. Building the piperacillin dose optimization software. We used the dose optimization software program BestDose to estimate each individual patient's pharmacokinetics and the optimal individual dosages for each patient.
BestDose is based on software originally developed nearly 20 years ago by the Laboratory of Applied Pharmacokinetics, University of Southern California, Los Angeles  (24). This dose optimization software has previously been used to individualize therapy with vancomycin, voriconazole, and antiretroviral therapy  (25, 26). The BestDose software requires four specific components: (i) a structural mathematical model that best describes the pharmacokinetics (we used the fourth structural pharmacokinetic model above; see Results); (ii) the "density" file (one of the outputs of the Pmetrics analysis), which serves as the Bayesian prior; (iii) a patient "past" file that contains the observed drug concentrations and details of the administered regimen; and (iv) a patient "future" file which contains the target drug concentrations deemed to be appropriate for that patient and initial estimates of the required drug dosages and frequency of administration.
Second, the dose optimization software was tested by comparing the estimated delivered piperacillin dosage predicted from the observed piperacillin concentrations after 24 h of therapy. Assessment was made by comparison of actual and predicted piperacillin dosages after 24 h of treatment. Linear regression was performed using GraphPad Prism version 5 for Windows (GraphPad Software, San Diego, CA, USA). Estimation of the mean weighted prediction error and the bias-adjusted squared prediction error was performed in R 3. 0. 1  (29).
ACKNOWLEDGMENTS T. W. Felton  is an  MRC Clinical Training Fellow  supported by the  North West England Medical Research Council Fellowship Scheme in Clinical Pharmacology and Therapeutics , which is funded by the  Medical Research Council  (grant  G1000417/94909 ),  ICON ,  GlaxoSmithKline ,  AstraZeneca , and the  Medical Evaluation Unit. J. A. Roberts  is funded by a  Career Development Fellowship  from the  National Health and Medical Research Council of Australia  ( APP1048652 ). W. W. Hope is supported by a  Clinician Scientist Fellowship  from the  National Institute of Health Research.
Over the past few years, several collaborative tools have been proposed, such as Teamlab focused on project management, Mendeley on research papers, GitHub on software source code, ShareLaTeX on LaTeX documents and Google Drive on office documents. Unfortunately, none of these tools applies when phylogenies are the documents to be shared and jointly edited. Recently, a collaborative tool devoted to tree-building, Phylografter  [1] , was developed. It aims to facilitate the process of collaborative tree-assembly while providing links to published phylogenetic hypotheses and represents a web-based content management system for phylogenetic information that provides node-by-node provenance of grafted trees. However, in this software, the collaborative aspect corresponds to the sharing of data (trees and annotations) and tree manipulation (edition, visualization,.
) is made in an asynchronous way. The bioinformatic resource proposed here, named CompPhy, is a good step forward. In a few words, CompPhy is a web platform devoted to collaborative work on phylogenies, providing facilities to jointly visualize, manipulate and mainly compare trees. Note that it allows a user to actually see on the screen changes performed by distant collaborators. This is very useful to support a phone or video conversation about a collection of trees tp overcome the need for repeated exchanges of tree files on a shared folder together with round trips with the users' separate -hence unsynchronized -tree visualization software. Visualizing trees is a recurrent activity in evolutionary biology and systematics due to the central role of trees in these fields. This has led to the development of more than a dozen standalone software programs to visualize trees, such as TreeView  [2] , TreeDyn  [3] , Dendroscope  [4] , Archaeopteryx  [5] , FigTree  [6]  and most recently some web tools, such as ScripTree  [7] , iTOL  [8] , jsPhyloSVG  [9]  and EvolView  [10].
Some of these tools also allow for tree annotation. This process consists of highlighting parts of trees (tips, nodes, branches, subtrees) or whole trees with various kinds of information (e. g. , geographical and molecular data, references). Depending on the tool, complex annotations, such as histograms or pie charts can also be associated with subtrees to indicate the proportion of their taxa having specific properties. These are powerful tools to visualize trees individually but most of them lack features to compare trees. Tree visualization in systematic and evolutionary biology is often done with tree comparison in mind, e. g. , how does the inferred tree change when tuning this or that parameter in the inference? What is the difference between trees obtained by this or that method, or from this or that dataset? How much does a gene tree differ from the species tree? Starting from this observation, CompPhy is built with an approach symmetrical to that of the above-mentioned software: yes, it offers tree visualization features, but with tree comparison as a main focus; its name stems from "Comparing Phylogenies".
But, Mega only allows users to compute a consensus tree in the context of a bootstrap analysis. Dendroscope can compute several kinds of consensus but it does not highlight agreeing or disagreeing parts of the initial trees, which is however often required to identify interesting parts in the source trees. In contrast, TreeJuxtaposer  [21]  enables simultaneous comparison of several trees, notably by highlighting structural differences and similarities between trees displayed side by side. Unfortunately this software does not run on current computers. We also refer readers to DensiTree  [22] , which provides a graphical summary of large collections of trees. It is however not suited to a detailed comparison of trees. Finally, for those familiar with the command line, the PAUP*  [23]  and PHYLIP  [24]  packages can be used to compute consensus trees.
g. ,  [23, 26, 27] ) are used for the supertree inference, and the newick form of the obtained supertree is subsequently cut and pasted into another tool to display it. The analysis is usually repeated several times to generate other supertrees by changing parameters in the supertree computation or by selecting a slightly different subset of source trees. Overall, it is quite easy to get mislead in this juggling act involving different files and tools. Unfortunately, there is a real lack of tools for managing a collection of trees and building consensus and supertrees from selected trees: RadCon software  [28]  could once be used to perform some of the above mentioned tasks, but it does not run on modern computers. Morover, there is a shortage of web tools that offer to compute supertrees, and those available do not allow tree collection management and visualization  [29].
When two or more trees containing a few dozen taxa are compared, several graphical operations are needed, such as a consistent rooting of trees, a common ordering of their tips (if the potential topological disagreement allows it), differential coloration of taxa depending on the molecular, taxonomic or geographical information. Maximum comfort is achieved when these tasks can be performed in an automated way, but this can only be achieved in a tool that manages trees collectively. This excludes a good deal of the software cited above where trees have to be opened independently from one another (usually in different windows), except when scripting facilities are provided, such as in Dendroscope and ScripTree. This outline of tree comparison functions spread out in different tree display tools, or simply missing, and the absence of tools for collaborative exploration and management of trees prompted us to develop the CompPhy web-based platform.