dataesr/scientific-paragraphs-categorization huggingface dataset:
The Decadal Climate Prediction Project (DCPP) is a coordinated multi-model investigation into decadal climate prediction, predictability, and variability. The DCPP makes use of past experience in simulating and predicting decadal variability and forced climate change gained from CMIP5 and elsewhere. It builds on recent improvements in models, in the reanalysis of climate data, in methods of initialization and ensemble generation, and in data treatment and analysis to propose an extended comprehensive decadal prediction investigation as part of CMIP6. The DCPP consists of three Components. Component A comprises the production and analysis of an extensive archive of retrospective forecasts to be used to assess and understand historical decadal prediction skill, as a basis for improvements in all aspects of end-toend decadal prediction, and as a basis for forecasting on annual to decadal timescales.
, 2015). The DCPP website (http://www. wcrp-climate. org/dcp-overview) contains up-to-date information on the DCPP and related issues. The CMIP6 website (http://www. wcrp-climate. org/wgcm-cmip/wgcm-cmip6) contains information on CMIP6, including links to forcing information and data treatment. Predictability is a feature of a physical and/or mathematical system which characterizes "its ability to be predicted" as indicated, for instance, by the rate at which the trajectories of initially close states separate. Predictability may be estimated from models although with the proviso that such indications depend on the model on which they are based and do not necessarily fully represent the behaviour of the physical climate system. Predictability studies, used with care, can give an indication as to where, under what circumstances, and the level of confidence with which it may be possible to predict various climate parameters on timescales from seasons to decades.
DCPP Component A: A multi-year multi-model decadal hindcast experiment The decadal hindcast component of CMIP follows the example of other coordinated experiments as a protocol-driven multimodel multi-national project with data production and data sharing as integral components.  a system view (data; analyses; initial conditions; ensemble generation; models and forecast production; post processing and assessment) of decadal prediction  the investigation of broad questions (e. g. sources and limits of predictability, current abilities with respect to decadal prediction, potential applications, …)  the provision of benchmarks against which to compare improvements in forecast system components and their contribution to prediction quality  information on processes and mechanisms of interest (e.
DCPP Component B: Experimental real-time multi-model decadal predictions The real-time decadal prediction component of the DCPP will also follow the example of other coordinated experiments as a protocol-driven multi-model multi-national project with data production and data sharing as integral components. The WMO structure already in place for seasonal forecasts is an example. Forecasts and verification statistics will be made available on the ESGF as part of CMIP6. Current efforts in quasi-real time annual and multi-annual predictions are being undertaken by individual groups, are collected at the UK Met Office (http://www. metoffice. gov. uk/research/climate/seasonal-todecadal/long-range/decadal-multimodel), and provide the basis of a multi-model prediction effort  (Smith et al. 2013 ).
Concluding comments The DCPP is unique in bringing together researchers from communities with expertise in seasonal to interannual prediction (as represented by WGSIP), climate simulation (as represented by WGCM), and decadal variability and predictability in general (as represented by CLIVAR). The models used and approaches taken represent to varying degrees the interests and abilities of these communities. For climate models, control and sensitivity experiments are a necessary backdrop to climate change simulations. Most models used in the DCPP will also participate in other aspects of CMIP6 and will have performed climate integrations as well as other simulations and MIP experiments. The data retained for these studies provides information on forced responses and the statistics of internal variability which are important for DCPP-related studies of many different aspects of decadal variability and prediction.
The forecasting aspect of DCPP encourages emphasis on methods of initializing models, generating ensembles of forecasts and, especially, on assessing results against observations. The two approaches represent complementary views for the understanding and prediction of forced and internally generated climate variations. The tiered set of retained data for the DCPP is intended to assist in the evaluation and analysis of DCPP results, but groups are encouraged to retain additional data relevant to other MIPs if possible. We believe that the Decadal Climate Prediction Project represents an important evolutionary advance from the CMIP5 decadal prediction component and addresses an integrated range of scientific issues broadly characterized as the ability of the system to be predicted on decadal timescales, the currently available skill, the mechanisms that control long timescale variability, and the ongoing production of forecasts of potential benefit for both science and societal applications.
Data Availability The model output from DCPP hindcasts, forecasts, and targeted experiments described in this paper will be distributed through the Earth System Grid Federation (ESGF) with digital object identifiers (DOIs) assigned. The list of requested variables, including frequencies and priorities, is given in Appendix D and has been submitted as part of the "CMIP6 Data Request Compilation". As in CMIP5, the model output will be freely accessible through data portals after a simple registration process that is unique to all CMIP6 components. In order to document CMIP6's scientific impact and enable ongoing support of CMIP, users are requested to acknowledge CMIP6, the participating modelling groups, and the ESGF centres (see details on the CMIP website). Further information about the infrastructure supporting CMIP6, the metadata describing the model output, and the terms governing its use are provided by the WGCM Infrastructure Panel (WIP).
Links to this information may be found on the CMIP6 website and is discussed in the WIP contribution to this Special Issue. Along with the data itself, the provenance of the data will be recorded, and DOI's will be assigned to collections of output so that they can be appropriately cited. This information will be made readily available so that research results can be compared and the modelling groups providing the data can be credited. The WIP is coordinating and encouraging the development of the infrastructure needed to archive and deliver the large amount of information generated by CMIP6. Datasets of natural and anthropogenic forcing information are required for the DCPP hindcasts, forecasts, and simulations. These datasets are described in separate contributions to this Special Issue and will be made available through the ESGF with version control and DOIs assigned.
The DCPP strongly encourages participants to perform the DECK simulations but recognizes that this may not be feasible for all groups (those proposing to use high resolution models for prediction for instance). It is not intended that the DECK requirements should bar DCPP participation in these special cases. Data retention. See the CMIP6 website for links to the CMIP6 Data Request Compilation and CMIP6 Forcing Data Sets. The DCPP input to the CMIP6 Data Request appears in Appendix D and applies to all experiment tiers. Data are to be served via the ESGF and to parallel CMIP5 although with changes to protocols as specified by the WIP. At this time, 6hourly decadal prediction data for dynamical downscaling are not considered a priority. The hope is that, in conjunction with the WIP, a coordinated set of "basic" or "common" tiered data tables can be developed across MIPs together with "MIP specific" tables associated with individual MIPs.
Explanatory comment Component B real-time decadal forecasts are currently being produced based on CMIP5 and using other models and hindcast data sets. The intent is that the forecasts produced by these models will be augmented by Component A results as they become available. protocol for experiments C1. 9-1. 10 but using idealised fixed SST patterns (Figures  6  and 7 , and available from 10 http://rda. ucar. edu/). These patterns are derived from the difference between observations and the ensemble mean of coupled model historical simulations  (Ting et al. 2009)  and are an estimate of unforced internal variability. Although this estimate is not perfect because the modelled response to external factors such as anthropogenic aerosols may not be entirely correct, the experiments nevertheless provide information on the climate response to North Atlantic and Pacific SST variations.
The experiments are based on model control integrations rather than historical simulations and therefore may be performed before the updated CMIP6 forcings become available. Experiments C1. 7 and C1. 8 follow experiments C1. 2 and C1. 3 but apply the SST anomalies in the extratropical and tropical regions separately (Figure  6 , data available from http://rda. ucar. edu/) in order to assess their relative importance. Experiment C1. 9 follows the design of  Kosaka and Xie (2013)  in which observed SST anomalies are imposed in the tropical Pacific region in coupled model simulations. The results will be compared to the standard historical simulations to infer the impact of the tropical Pacific SSTs. Experiment C1. 10 follows C1. 9 but imposing observed time series of the SST anomalies in the North Atlantic.
Only in the radio band have follow up observations of GRBs occasionally extended further. In Sect. 2, we present the observations, the data reduction, and the results of our analyses. In Sect. 3 we model the X-ray afterglow of GRB 130427A, while in Sect. 4 we present our conclusions. We adopt the cosmological parameters determined by the Planck mission, i. e. H0 = 67. 8 km s -1 Mpc Ωm = 0. 31, ΩΛ = 0. 69  (Ade et al. 2014). The afterglow emission is described by Fν ∝ t -α ν -β , where t is the time from trigger, ν the frequency, α and β are the decay and spectral indices, respectively. Errors are reported at 68% confidence level (C. L. ) unless otherwise specified.
Swift-XRT observations The Swift X-ray Telescope (XRT;  Burrows et al. 2005)  began observing GRB 130427A (M14) less than 200 s after the start of the prompt emission (indicated as T0), and continued to monitor the source for ≃ 15. 8 Ms (≃ 180 days). The XRT count-rate light-curve was obtained from the UK Swift Science Data Centre on-line light-curve repository  (Evans et al. 2007 (Evans et al. , 2009)) , and binned to a uniform ∆T /T = 0. 05. The XMM-Newton data were reduced using the science analysis system (sas) version 14. 0. Periods of high background were identified in full-field light-curves of events with energy > 5 keV, and excluded from the analysis. Table  1  gives the exposure times for each observation after periods of high background have been excluded. At each epoch spectra were extracted from the MOS and pn data using a circular aperture centred on the source.
The radius of the source aperture was chosen according to the brightness of the source: radii of 40 and 15 arcsec were chosen for the 2013 May 13 and 2013 June 20 data respectively. For subsequent observations an aperture of radius 7 arcsec was chosen, the small aperture was necessitated by the presence of a nearby source which would otherwise contaminate the extracted spectrum of GRB 130427A. None of the spectra are affected by photon pile-up. The two observations taken in November 2013 are separated by only ∆T /T ≃ 0. 01, so these data were combined to form a single point in the light-curve. Similarly, the two observations taken in December 2015 are separated by ∆T /T ≃ 0. 01, and were combined to form one point in the light-curve.
Building the X-ray light-curve In the subsections above we summarized the observations of the X-ray instruments and data reduction. The resulting flux of each XMM-Newton and Chandra observation is shown in Table  2. We now describe how we combined these different datasets into a homogeneous flux light-curve. We assumed the XMM-Newton derived spectral parameters (see Sect. 2. 5) to translate the measurements from Swift XRT, Chandra and XMM-Newton to 0. 3-10 keV flux units in a consistent fashion. For XMM-Newton the fluxes were derived directly from the spectra using xspec. For Swift XRT the conversion factor from count rate to flux was obtained with the portable multi-mission interactive simulator (pimms 1  , Mukai 1993); in particular, the difference between our conversion factor and that found at UK Swift Science Data Centre at the University of Leicester is only 0.
35%. We also obtained a good match between the XMM-Newton and Swift measurements when observations were simultaneous. For Chandra the conversion factors from count rates to flux were derived using xspec from response files generated for the two observations using the ciao script specextract. A 10 per cent uncertainty was added to the errors on the Chandra and XMM-Newton fluxes to account for systematic calibration differences between these instruments and Swift XRT  (Tsujimoto et al. 2011 ).
However, in our analysis we considered data from 47 ks (that is, after the 3rd Swift orbit). We decided to exclude prior data because we are interested in the late X-ray afterglow; our discussion focuses on the consistency between models and late X-ray data. When fitting this X-ray light-curve with a simple powerlaw model, we obtain α = 1. 309 ± 0. 007. This fit model yields χ 2 = 75. 8 with 66 degrees of freedom (d. o. f. ). The decay slope is similar to the previous measurements obtained over a smaller timescale. For example, M14, L13, P14 and K13 determined α = 1. 35 ± 0. 01, α ≃ 1. 35, α = 1. 35, and α ≃ 1. 281 ± 0. 004 respectively, using data up to ≃ 100 days after the trigger. To test for the presence of any break after 47 ks, we fit our light-curve with a smoothly broken powerlaw model  (Beuermann et al.
DISCUSSION We will now explore the durability of several models proposed in the literature for GRB 130427A, which were built on the basis of data up to ≃ 100 days after the trigger. We will check whether these models can still hold when we include new data gathered over much longer timescales. We will not examine models that do not assume the emission mechanisms typically invoked for afterglows (for example  Dado & Dar 2016)  or heavily modify them (e. g. Vurm et al. 2001). The reader is referred to the aforementioned articles for a different approach. The standard forward shock (FS; for a review, see  Gao et al. 2013 ) model for GRBs predicts several possible phenomena that leave their signatures on the late X-ray afterglow. The very long temporal baseline of GRB 130427A observations provides us with an ideal opportunity to look for these signatures.
5  of  Nava et al. 2013 ). To achieve a loss of 65% over t/T dec ≃ 6, one would need ǫe ≃ 0. 8 -1, regardless of ISM or wind environment. This value of ǫe is extremely high and difficult to explain; furthermore, it is at odds with the modeling of GRB 130427A by L13. One has therefore to assume that the afterglow ejecta had a kinetic energy of EK ≃ 7 × 10 52 erg from the deceleration time. The efficiency of converting the initial energy into prompt emission is η = Eγ /(Eγ + EK). For Eγ = 8. 5 × 10 53 erg, and EK = 7 × 10 52 erg determined by L13, the emission mechanisms must have had an efficiency of η = 92%, which poses a very serious difficulty for any dissipation and emission models. We also note that using a wider dataset, which includes early GeV emission (not studied by L13), other authors (e.
CONCLUSIONS In this article, we presented XMM-Newton and Chandra X-ray observations of the exceptionally bright Swift GRB 130427A, which have been carried out up to 83 Ms after the burst trigger. Such a timescale is unparalleled for X-ray afterglows of GRBs. We reconstructed the X-ray light-curve to very late epochs, and we find that the simple power-law decay with a slope of α = 1. 309 ± 0. 007, which was found from as early as 30 ks after the trigger, extends up to the end of our observations. No jet break or flattening of the light-curve are visible. We discussed the consequences of this result with respect to the results of modelling presented in the literature, which considered data up to ∼ 10 Ms. We have treated the models in which the external medium is a stellar wind (P14, L13, VA14, K13) separately to that in which the medium has a constant density profile (M14).
The XMM-Newton and Chandra X-ray observations of GRB 130427A, presented in this paper, constitute an important legacy for the study of this once in a lifetime event. These data have enabled us to explore the physics of GRBs in unexplored epochs, and highlighted problems with the standard models which will need addressing in the future. We showed that late time observations of bright GRBs are a powerful test of the models, and the sensitivity of facilities such as Athena  (Nandra et al. 2013 ) will enable us to push the limits even further. In fact, even if the break in the light-curve should occur now, the X-ray flux expected at the time of the launch of Athena (2028) is around 10 -16 erg cm -2 s -1 , thus being detectable by Athena itself -allowing to extend the time coverage by almost one order of magnitude more.
ACKNOWLEDGMENTS MDP thanks  Lara Nava  and  Daisuke Kawata  for helpful discussions and references,  Alice Breeveld  for her helping hand, and his friends  Marco D'Alessandro ,  John Moore ,  Ernesto Amato ,  Peter Rockhill ,  Peter Včasný ,  Alexander J. Zech , and  Pierluigi Cox  for their encouragement. AR acknowledge support from  PRIN-INAF 2012/13  and from Premiale LBT 2013. BG acknowledges financial support of the  NASA  through the  NASA  Award  NNX13AD28A  and the  NASA  Award  NNX15AP95A. Part of this work is under the auspice of the  FIGARONet collaborative network , supported by the  Agence Nationale de la Recherche , program  ANR-14-CE33. DAK acknowledges support by  TLS Tautenburg  in form of a research stipend. DM acknowledges support from Ida as well as financial support from  Instrument Center for Danish Astrophysics (IDA).
Comments and discussions Firstly, we note that the regime change observed in the section appears as the threshold region of the two schemes of fractional relaxation. We remark also that the exponential model used to fit the whole range of data does not match with this latter around this region. In the other hand, similar behavior is found for both fractional relaxation laws. These results suggest that two separate schemes may exist at two different time scales. Such conclusion has to be confirmed by the analysis of other datasets. If the result holds, we believe that an unified model is essential to depict the dynamic evolution of the charge loss over time. This may be reached by use of more complex random physical models such as anomalous diffusion process which takes account of possible long time trapping of particles inside the medium.
For a clinical study in the European research network on better diagnosis for neglected infectious diseases (NIDIAG) project (Better Diagnosis of Neglected Infectious Diseases: www. nidiag. org), we developed Standard Operating Procedures (SOPs), which we implemented in a basically equipped laboratory in a 380-bed rural hospital ("Hôpital Général de Référence Mosango") in the Kwilu province in the Democratic Republic of the Congo (DRC). The study aimed to improve the early diagnosis of severe and treatable infections among patients with neurological disorders and took place over a 20-month period (14/09/2012-24/05/2014) (Clin-icalTrials. gov Identifier: NCT01589289). The set of 50 SOPs (S1 Appendix), all in French, include procedures related to the inclusion and clinical management of patients with neurological disorders (n = 4), diagnostic testing (n = 33), data collection and management (n = 5), and quality assurance (n = 8).
Language and terminology Most laboratory staff in low-resource settings are not native English (or French) speakers and are often not expert in the particular domain of care or research; therefore, their literacy level may be lower than anticipated. This language barrier is often not overcome by simple translation into the local language, as words in different languages are not always identical in meaning and function  [20]. In addition, producing a high-quality translation is labor intensive  [21]. Web-based translation machines may be inaccurate: as an example, Google Translate only had an accuracy of 57. 7% for the translation of common medical statements to 26 languages  [22]. Apart from interlanguage differences, there is also the issue of terminology. For instance, the "buffer" used for RDTs is also called "blood lysis buffer, " "clearing buffer, " "assay (or sample) diluent, " or "reagent, " and the "specimen transfer device" may be named "tube, " "straw, " or "pipette"  [23] [24].
In this paper, we report preliminary experiments performed (i) with a H 2 vapor bubble in liquid H 2 under magnetic compensation of gravity near its critical point and (ii) in liquid-vapor water at saturation very near its critical point under weightlessness. The data are analyzed in the framework of a 1D model that ignores hydrodynamic effects, but captures the main characteristics of the problem, including phase change, release of latent heat and compressibility (piston effect). A realistic temperature distribution in the fluid is calculated. The bubble displacement caused by the evaporation/condensation process is evaluated, in particular at short times after temperature has been changed at the boundary, and late times when a steady gradient has taken place.
In this respect, the comprehensive long-term ecological and socialecological data available from the ILTER sites provide an excellent platform for analysis, as well as for site-scale application of the different mapping and modeling tools. Several ILTER-based studies have already been conducted along these lines (e. g. ,  Vadineanu et al. 2003 , Maass et al. 2005 , Vadineanu 2007 , Ohtsuka et al. 2009 , Forsius et al. 2013 , Dick et al. 2014 , Fu and Forsius 2015 , Holmberg et al. 2015). With this accumulated and expanding experience, our goal is to contribute novel conceptual and methodological approaches, simple (not simplistic) enough to be implemented in highly heterogeneous and contrasting social-ecological settings. By emphasizing the transdisciplinary approach to assessing and valuing ecosystem services (incorporating stakeholders and expert knowledge and using multiple disciplinary approaches), we propose both a capacity building effort, as well as a data gathering one.
Site comparison and cross-scale interactions Cross-site research is a key component of ILTER work because it allows case study comparisons, the integrative testing of ideas/ hypotheses using data from a cluster of sites, and long-term or longitudinal assessments. Integration is not simply produced by assembling a data set, but also by ensuring that the focus and design of cross-site activities are jointly defined by both social and natural scientists. As we have been arguing here, the network of ILTER sites offers an ideal platform for assessing cross-site comparison including investigating cross-scale interactions among services, stakeholders, and components of well-being closely paralleling the PECS approach. We intend to assess the relative influence of human and biophysical drivers of change across a wide spectrum of social-ecological conditions, to generate and test new theories and approaches to study and deal with human-environment linkages, such as the Integrative Science for Society and Environmental (ISSE) conceptual framework  (Collins et al.