dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2007)  with the 1. 5 order TKE closure  (Lilly, 1967). The WRF-333m-LES and WRF-111m-LES simulations have a time step of 0. 3 s and 0. 1 s, respectively. An additional 333-m simulation is made running the microscale domain D5 with the PBL YSU scheme (WRF-333m-YSU). The real nested Large-eddy simulations are performed in two steps. First, a two-way four-level downscaling (D1, D2, D3 and D4) allows to obtain 1-km meteorological fields over the Guadeloupe archipelago with a history interval of 10 min (WRF-1km-YSU). In this mesoscale nesting, domain D1 is 6-hourly initialized with meteorological data from the 110 km NCEP FNL operational model global tropospheric analyses (continuing from July 1999, available online at http://rda. ucar. edu/datasets/ds083. 2/). Then, a one-way two-level downscaling (D5 and D6) is used to obtain 333-m and 111-m meterorological fields over the focused urban area with a history interval of 10 min (i.
Comparison of terrain data for the three resolutions The relatively flat topography of the focused area includes the highest terrain elevation of 131 m on the windward slopes of Basse-Terre island (Fig. 6d ). The low topography around the source PWP and the station AQS1 suggests weak shear inhibiting turbulent motions and particle dispersion. The 1-km interpolated topography smoothes this basin area between Basse-Terre island and Grande-Terre island (Fig. 6a ). While the 333-m interpolated topography renders the main relief patterns, the 111-m grid defines most details of the original 25-m topography (Fig. 6 ). The main land-use categories in the region of interest include: marine surfaces, urban and built-up land and crops/wood mozaic (Fig. 7d ). These land-use categories determine albedo and roughness length values which are critical parameters affecting skin surface temperature and friction velocities.
8a3  and a4 ). During the night, simulation outputs show occurrences of westerly flows. Unfortunately, these nocturnal circulations linked with wind speed lower than 1 m s À1 cannot be recorded by WS2 wind sensor. For the three resolutions, the location of WS1 coastal station corresponds to the mangrove land. At this land-sea interface point, the four variables seem better simulated in the 111-m grid (red line, Fig. 8b1eb4 ). For the 2-m temperature, WRF-1km-YSU, WRF-333m-YSU and WRF-333m-LES outputs show similar values with large underestimations of the nocturnal radiative cooling (Fig. 8b1 ). The maximum difference between observational data and these outputs reaches 3. 7 C at 0530 LT. For all simulations, the model inhibits the diurnal variation of air temperature and overestimates the water vapor mixing ratio (Fig.
Summary and conclusions The main goal of this study was to analyze how microscale resolutions affect air pollution modelling in a small tropical island during a real case of weak trade winds. Three resolutions of domain grid have been selected: 1 km, 333 m and 111 m. The WRF-LES model was used to produce real nested microscale meteorological fields starting from 6-h global analysis data with a resolution of 1. In order to dissociate the resolution effects from the turbulence scheme effects in the "turbulence grey zone", an additional 333-m grid simulation was made with the ensemble-mean YSU scheme. Then the WRF-1km-YSU, WRF-333m-YSU, WRF-333m-LES and WRF-111m-LES weather outputs initialized the Lagrangian Particle Dispersion Model FLEXPART with an interval time of 10 min. The present study was focused on NO x air pollution in the densely populated area of the Guadeloupe archipelago, during a typical day of severe pollution.
Acknowledgments This study was supported by the  Research Laboratory in Geosciences and Energy (LaRGE, University of the French West Indies, Guadeloupe)  and the  Direction of Environment, Development and Housing (DEAL Guadeloupe ). The author is thankful to the  Air Quality Agency of Guadeloupe (Gwadair)  for providing observational data recorded from AQS1 and  AQS2. The weather observational data from WS2 were obtained from the  French Met Office (M et eo France). The authors gratefully acknowledge  Dr. C. d'Alexis  for providing the 1-Hz weather observational data recorded from WS1. The WRF simulations were computed on the Wahoo cluster [Intensive Computing Center ( C3I ), University of the French West Indies] thanks to engineer Rapha€ el Pasquier. The authors wish to thank  Dani ele Frison  who helped with the translation.
2016; Schindler et al. 2016a ). The choice process will be made transparent through pre-established protocols that lay out the circumstances under which each the methods is recommended, as well as their strengths and limitations, and required type of information, expert involvement and resources. Despite recent improvements (e. g. by GBIF, LifeWatch, LTER and EUBON/GEO-BON), answering questions and producing knowledge that require interpretation of biodiversity data is still hampered by lack of harmonized, reliable and publicly-accessible databases. The lack of agreement in relatively simple matters, such as the use of standardized protocols, can result in multiple experts disagreeing with each other already at the data integration level  (Bendix et al. 2012; Enke et al. 2012). This is a serious constraint to transparent and easy-to-understand communication with requesters at a later stage of knowledge compilation, and may weaken the credibility of the information provided by the scientific community.
The reluctance of many researchers to openly share data often arises from complex issues like confidentiality, ownership (data owners often do not agree to publish their data due to legal issues), or data sensitivity (red list data for instance), and may severely hinder the timely and constant integration of new data into shared databases  (Moritz et al. 2011; Enke et al. 2012). This underlying challenge cannot be tackled directly by the NoK, but rather by specialized processes in science like GBIF, LifeWatch, LTER and GEOBON, or through specific agreements between science, environmental agencies (or other continuously working institutions), and sometimes society (e. g. in the context of citizen science). It will, however, remain an obstacle for better informed policy-making in both science as well as in policy, and preclude the use of data-demanding methods to analyze existing knowledge  (Wetzel et al.
Acknowledgments The  KNEU  project was funded by the 7th  Framework Programme  of the  European Commission  (Contract No. 265299 ). We thank the numerous experts and stakeholders that participated in the project's activities, its workshop, conferences, survey, questionnaires and test cases. The authors also thank all project partners in the KNEU partners from the following institutions:  UFZ (Germany) ,  NERC-CEH (UK) ,  RBINS (Belgium) ,  CIIMAR (Portugal) ,  NIOZ (Netherlands) ,  FRB (France) ,  ALTERRA (Netherlands) ,  University of Vienna (Austria) ,  NINA (Norway) ,  CSIC (Spain) ,  MTA (Hungary) ,  ECNC (Netherlands) ,  Bangor University (UK) ,  INBO (Belgium) ,  EAA (Austria) ,  SYKE (Finland) ,  BEC (Ireland)  and  VLIZ (Belgium). I. R. G. was partly funded by the  EU   BON  project ( EU FP 7 Grant No.
308454 ) and contributes to the  Labex OT-Med  (No. ANR-11-LABX-0061 ) funded by the  French Government  through the A* MIDEX  Project (No. ANR-11-IDEX-0001-02 ). Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4. 0/),which permits unrestricted 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 terms of oncological outcomes, because this is a new procedure, only short-term follow up data are available for analysis. The OS at 1 and 2 years was 59% and 41%, and the diseasefree survival at 1 and 2 years was 88% and 74%, respectively, from the ALPPS registry. These findings compare favorably with those provided in the few existing international series of TSH, in which patients who did not reach the second stage were excluded from the survival analysis in most cases  [1, 4, 22, 29, 30]. When comparing resectability between ALPPS and TSH, even though no randomized studies have been performed, there seems to be evidence in favor of ALPPS. A multicenter study compared the rate of complete resection between ALPPS and TSH in patients with oncologically unfavorable factors. This study showed that the rate of complete resection in ALPPS was greater than that in TSH, with 83% in ALPPS vs 63% in TSH (p<0.
(ii) Indicators, weights, and thresholds Of the two service coverage domains, prevention is the easier to operationalize given data availability. We use four indicators to capture the prevention domain: four or more antenatal care (ANC) visits; full immunization of a child; breast cancer screening; and cervical cancer screening. 15 In all four cases, there is an objectively defined sub-population in need. We do not attempt any quality adjustment, so our indicators are of coverage rather than 'effective coverage'; in the literature to date, as far as we are aware, quality adjustments have been attempted only in the case of ANC  (Lozano et al. , 2006; Hodgins and D'Agostino, 2014). The treatment domain is harder to operationalize and the data harder to come by. We use three indicators, all relating to young children: whether a baby was delivered by a skilled birth attendant (SBA); whether a child with diarrhoea received given oral rehydration salts (ORS) or a home-made solution; and whether a child with acute respiratory infection (ARI) received medical treatment.
Chile went a long way down this road in the 1950s with the creation of a national health service that merged the provider networks of the social security system and those of the MOH; it continued in this path in the late-1970s with the establishment of a Fondo Nacional de Salud (FONASA); but, by allowing better-off families to put their SHI contribution towards the cost of a private insurance premium, the government ended up institutionalizing a two-tiered system. More frequently the reduction in inequality comes about by narrowing coverage gaps between different sub-populations-between and within sub-systems. Here we see two approaches. 18 The OECD figure was calculated by the authors using the OECD Health Statistics online database (consulted in November 2014). In Asia, the share is around 5 per cent, except in Bangladesh and Vietnam where it is over 10 per cent.
(i) Data Our household surveys are drawn from the data initiatives mentioned above. We also use some of the results in a study of UHC in Latin America  (Wagstaff et al. , 2015) , notably the results produced by researchers from the individual countries who analysed household expenditure surveys that are not available through one of the aforementioned data harmonization exercises. We ended up excluding some data on the grounds the results were implausible, given the numbers we obtained using other surveys, what other researchers have concluded about the data, and what we suspect about how the dataset was prepared before it came to us-see annex for further details.
There are, of course, plenty of opportunities for further work in this area. Work is already under way applying the index to other countries (see, for example,  Wagstaff et al. , 2015). In that work, we have also broken apart the index, showing, for example, in one chart trends in the gap in indicators between the poorest and richest quintiles; such complementary charts are useful since changes in the index could be due to changes in the population mean, or to changes in inequality, or both. Adding countries and years to the database is likely to be of value to countries wanting to benchmark their levels and progress against those of other countries. It will also likely help donors to identify lagging countries that may benefit from additional technical and/or financial assistance, as well as to help countries themselves identify areas within the UHC index where their performance is relatively weak.
21  Arguably, the biggest challenge in the future is extending the indicator set to get a more complete picture of service coverage. Boerma et al. (2014a)  suggest several candidates for inclusion in a broader service coverage indicator set (cf. their Table  1 ), and propose 10 effective coverage indicators, five for each of the prevention and treatment domains (cf. their Table  2 ). We considered, in fact, several of these, but decided to exclude them. In some cases, we felt that the proposed indicator was not actually a health 21 There are issues, however, that we have been unable to resolve satisfactorily on the data front. The 40-49 age range was chosen for the breast cancer screening indicator in part because of data availability. The WHS uses the 40-69 age range; however, the DHS uses the 15-49 or 40-49 age range.
We have therefore worked with the lowest common denominator-the 40-49 age range. In the US, there is divergence of view over whether screening should start at 40 or 50, with the American Cancer Society and the Mayo Clinic continuing to recommend starting at age 40, and the US Preventive Services Task Force in 2009 recommending starting only at age 50 instead of at age 40 as it had recommended in its 2002 recommendation. The 18-49 age range was chosen for the cervical cancer screening indicator also in part because of data availability. The WHS uses the 18-69 age range; however, the DHS uses the 15-49 age range. We have therefore worked with the lowest common denominator-the 18-49 age range. The WHS age range is more in line with national screening guidelines-currently screening is recommended (every 3 years) for the 21-65 age group in the US.
One would, of course, like to move beyond correlations; it may be, for example, that the countries of Latin America with the more integrated health systems also have relatively high per capita incomes. In principle, one could assemble a country-level panel dataset, and use it to tease out the effects of reforms, controlling for (changes in) other determinants of UHC such as per capita income. 23 Such an analysis would require many more data points than we currently have, and would also be challenged by the heterogeneity of UHC-inspired reforms around the world. But this, like the other avenues for future research suggested above, is nonetheless likely to be one worth exploring.
Annex: Notes on data We decided not to include data on maternal and child health (MCH) indicators from the World Health Survey (WHS): the rates are typically very different from those obtained from the Demographic and Health Survey (DHS) and the Multiple Indicator Cluster Survey (MICS) surveys, which are the gold standard for MCH indicators, even after we made adjustments to the three surveys to ensure consistency in recall period, the number of children covered by the question, etc. We also decided not to include data on financial protection from both the WHS and the Multi-Country Survey Study (MCSS) surveys: rates of catastrophic and impoverishing spending were significantly different from those obtained from other surveys, likely due to the fact that data on household consumption were collected through just a handful of questions, with a focus on expenditure rather than the broader concept of consumption.
Lu et al. (2009)  find that use of the single-item question (Q0804) leads to a smaller estimate of out-of-pocket spending than the multi-item question (Q0807-Q0814). They also find that using the inpatient question with a 4-week recall period (Q0807) leads to a larger estimate of out-of-pocket spending than the inpatient question using the 11-month recall period (Q0816). using other surveys, and our suspicion is that the data for these two countries have been subject to a substantial amount of trimming, reducing the incidence of large out-ofpocket payments; by contrast, the LIS data for Mexico and South Africa were in line with estimates from other datasets. 25 Finally, where we had multiple sources over time in a country, we analysed their consistency with one another, and where inconsistencies emerged we selected one or two sources that looked the most reliable.
Status of ACT quality Establishing the scale of the problem of poor quality ACT remains a challenge. The WWARN Antimalarial Quality Surveyor Database (AQSD) was created to collate and present a comprehensive overview of the quality of antimalarials, incorporating reports from both the scientific and grey literature  [15]. It includes studies on anti-malarial drug quality spanning the past 67 years and indicates that around 30 % of anti-malarials tested have not met the criteria for good quality drugs, containing the acceptable amount of SAPI  [16]. However, comparison of drug quality findings across time and place is hampered by methodological differences in the sampling strategies and laboratory techniques used in different studies. In some cases, it is not clear whether the samples examined were representative of all anti-malarial drugs on the market.
Findings from the ACT Consortium drug quality programme Reports of fake drugs formed the rationale for the ACTc-DQP to investigate the quality of ACT at a larger scale than in previous studies with the aim of providing Ministries of Health with evidence upon which local regulators could take action. Harparkash Kaur, the lead investigator of the ACTc-DQP presented the findings on behalf of the drug quality teams  [11]. The ACTc-DQP collected 10,079 samples from ACT tablets from six countries; Equatorial Guinea (Bioko Island), Cambodia, Ghana, Nigeria, Rwanda and Tanzania, primarily using random sampling and both overt and mystery client approaches, Table  2. The samples were processed in the three corroborating laboratories as shown in Fig. 1. All sample information was logged onto the country specific databases.
The samples from each study were analysed by HPLC at LSHTM, UK and a duplicate set was sent to Michael Green's team at the Centre for Disease Control, Georgia, USA, who randomly selected ten percent of samples out of the total sent and analysed them using HPLC to corroborate the results from LSHTM. Additional validation was provided by Facundo Fernandez's team, who analysed a duplicate set of samples using MS at the Georgia Institute of Technology, USA to confirm the presence of the stated SAPI or the detection of unstated compound instead. Laboratory analysis results for each sample were added to the database of sample information for each country. Results from the three laboratories enabled robust estimates of quality and cross-validation demonstrating that if the sampling strategy is representative and the analytical methods are of the highest standard, then findings are repeatable and reliable.
The surveillance and monitoring system was established to better gauge the scope, scale and harm caused by SSFFC medical products and provide technical support and alerts where required. In addition, a well-functioning surveillance system produces a validated evidence base for policy makers. Since 2013, the programme has engaged 113 member states with over 920 suspect products reported from 83 countries. Falsified drugs remain a concern with WHO receiving regular reports i. e. 126 of suspected falsified artemether/lumefantrine (innovator and generic versions), from 14 sub-Saharan African countries have been filed since July 2013. On investigation by the WHO, they were found to have less than 10 genuine medicines. A database logging reports of SSFFC and substandard drugs has been created and with relevance to falsified anti-malarials, 57 % of those reported so far have been artemisinin-based.
Acknowledgements The research presented by  Harparkash Kaur  as lead investigator of the drug quality programme was funded by the  Bill and Melinda Gates Foundation  as part of an award to the  ACT Consortium at the London School of Hygiene and Tropical Medicine. The surveyors are thanked for their discretion and participation in purchasing the samples in each of the studies. The laboratory teams are thanked for their patience, dedication and hard work throughout the programme. Bianca Dsouza ,  Philippe Verstraete ,  Amina Sugimoto ,  Alice Perry  and  Ann Fazakerley  helped organise the dissemination meeting in Geneva, Switzerland.
Context. Studying the coma environment of comet 67P/Churyumov-Gerasimenko (67P) is one of the primary scientific goals of the VIRTIS experiment on the ESA Rosetta mission. Aims. The distribution and variability of water vapour and carbon dioxide in the comet's coma are needed to estimate their production rate, abundances in the nucleus, and the spatial distribution of the active regions. Methods. Infrared emission lines from vibrational bands of water and carbon dioxide at 2. 67 and 4. 27 µm, respectively, were observed by the VIRTIS-M imaging channel and mapped from close to the nucleus up to ∼10 km altitude with a resolution of ∼40 m/px. A dataset consisting of 74 observations in the 1-5 µm spectral range acquired from 8 to 14 April 2015 when 67P was at a heliocentric distance of 1. 9 AU is analysed in this work.
(2015a) , for VIRTIS-M data acquired in December 2014. The asymmetric distribution of H 2 O and CO 2 , detected in the analysed spectra, was evident, but no quantitative estimation of column densities was reported. In the present paper we discuss data acquired by the mapping channel of VIRTIS onboard Rosetta from 8 to 14 April 2015, when the comet was at a heliocentric distance of 1. 9 AU. In Sect. 2 data selection and processing are described. Results are presented in Sect. 3 for the identified species; they are discussed in terms of spatial distribution, in relation to the comet's surface (Sect. 4). Conclusions and perspectives are provided in Sect. 5.
Selected dataset In Table  A. 1 we list the 74 observations acquired by the IR channel (1-5 µm) of VIRTIS-M between 8 and 14 April 2015, which are analysed in this work. During this period, 67P was approaching the Sun at a heliocentric distance of 1. 9 AU and the coma activity was well developed and observable around the nucleus. The sequence of VIRTIS-M observations includes two types of coma scans: a) observations across the subsolar limb in which only the coma region located above the illuminated part of the nucleus is acquired, up to about 5 km altitude; b) observations Fig. 1. Viewing and illumination geometry during coma observations. Lines correspond to solar direction (in yellow), nadir direction from the spacecraft (in green), and line of sight (LOS) through the coma (in white). The white dash line taken perpendicular to the LOS and passing through the comet nucleus centre defines the tangent altitude of a given point of the coma above the surface.
The spectral range selected for the band integration is chosen to agree with the values adopted for analysing VIRTIS-H data, where the higher spectral resolution allows the ro-vibrational structure of the H 2 O ν 3 and possibly the ν 1 bands to be partly resolved  (Bockelée-Morvan et al. 2015). In the case of CO 2 , the considered wavelength region encompasses the ν 3 band. Considering the high variability of the signal-to-noise ratio in the processed dataset, the H 2 O with CO 2 average band area errors are 15% and 10%, respectively, calculated by propagating the formal errors. Band area (I) is converted to column density (N), adopting the g-factors (g f ) at 1 AU equal to 3. 349 × 10 -4 s -1  (Villanueva et al. 2012 ) and 2. 86 × 10 -3 s -1  (Crovisier & Encrenaz 1983)  for H 2 O and CO 2 , respectively.