dataesr/scientific-paragraphs-categorization huggingface dataset:
Computing the CO 2 flux and the ''compatible emissions'' of CO 2 For the historical period and for each of the RCP scenarios, the land (ORCHIDEE) and ocean (PISCES) carbon cycle models generate spatially-explicit carbon fluxes in response to the atmospheric CO 2 concentrations and simulated climate. The simulated net land carbon flux includes a land-use component but the decomposition of this net flux into its land-use and natural parts has not yet been analyzed. Piao et al. (2009)  however did show that a similar version of ORCHIDEE was able to reproduce the estimated land use change related to carbon emissions when forced over the historical period by the Climate Research Unit temperatures and precipitations datasets  (Jones et al. 1999; Brohan et al. 2006; Doherty et al. 1999). Only the results of IPSL-CM5A-LR and CM5A-MR runs are presented here because the carbon pools have not reached an equilibrium state for IPSL-CM5B-LR (Sect.
Acknowledgments The development of the IPSL coupled model and of its various components has largely benefited from the work of numerous colleagues, post-doctoral scientists, or Ph. D. students. We gratefully acknowledgement their contribution to this community effort, and among them  Gillali Abdelaziz ,  Gae ¨lle Drouot ,  Alexandre Durand. The research leading to these results was supported by  CNRS ,  CEA , the  INSU-LEFE French Program  under the project  MissTerre , the  European Commission 's  7th Framework Programme , under the projects  COMBINE (Grant n°226520 ) and  IS-ENES  (grant  n°228203 ). This work was made possible thanks to the HPC resources of CCRT and IDRIS made available by  GENCI  ( Grand Equipement National de Calcul Intensif ),  CEA (Commissariat a l'Energie Atomique et aux Energies Alternatives)  and  CNRS  ( Centre National de la Recherche Scientifique)  (project  016178 ).
Free-living (FL) and particle-attached (PA) bacterial assemblages in the Northwest Mediterranean Sea were studied using pyrosequencing data of the 16S rRNA. We have described and compared the richness, the distribution of Operational Taxonomic Units (OTUs) within the two fractions, the spatial distribution, and the taxonomic composition of FL and PA bacterial assemblages. The number of OTUs in the present work was two orders of magnitude higher than in previous studies. Only 25% of the total OTUs were common to both fractions, whereas 49% OTUs were exclusive to the PA fraction and 26% to the FL fraction. The OTUs exclusively present in PA or FL assemblages were very low in abundance (6% of total abundance). Detection of the rare OTUs revealed the larger richness of PA bacteria that was hidden in previous studies.
Chimera checking and removal was performed using the Uchime option of USearch. This was done using a combination of reference set comparison (similar to Chimera Slayer) and de novo checking (directly comparing sequences within each PCR amplification). Taxonomy of V6 tags was assigned using the tag mapping methodology GAST (Global Alignment for Sequence Taxonomy) described in  Sogin et al. (2006)  and in the VAMPS webpage (http://vamps. mbl. edu/). This methodology uses reference databases of rRNA based on SILVA  (Pruesse et al. 2007 ) and MUSCLE to align the tag sequences to the reference tags corresponding to the top 100 BLAST hits  (Sogin et al. 2006 ). In addition, the tags were subjected to the preclustering method mentioned by  Huse et al. (2010). All sequences obtained for this study are available at http://vamps.
Tag clustering and normalization The bacteria collected on the 3-lm filter were considered to be the PA fraction and the bacteria collected on the 0. 2-lm Sterivex unit were the FL fraction. Normalization of sample size was achieved by randomly resampling the same number of reads for each sample based on the smallest sample size. This approach was carried out using the inbuilt com- mand sub. sample in MOTHUR v1. 22  (Schloss et al. 2009 ). The subsample dataset was then clustered in OTUs of decreasing genetic distance using MOTHUR v1. 22  (Schloss et al. 2009 ) according to the furthest neighbor-clustering algorithm. To build the clusters in MOTHUR, we used the aligned version (April 2010) of SILVA  (Pruesse et al. 2007). The output at each level for each distance level was then parsed to produce abundance tables of each OTU in each sample at different clustering levels.
Analysis of sequences and comparison of assemblages Rarefaction curves were calculated using Ecosim v 7. 72 (Gotelli and Entsminger 2011). Nonmetric multidimensional scaling (NMDS) was carried out with a Bray-Curtis (BC) distance matrix using the "MASS" package  (Venables and Ripley 2002)  from R software (R Development Core Team 2012). Two different nonparametric multivariate analyses of the data, Permutational Multivariate Analysis of Variance (PERMANOVA) and Analysis Of Similarities (ANOSIM), were carried out using the "vegan" package  (Oksanen et al. 2012 ) from R software (R Development Core Team 2012). ANOSIM and PERMANOVA were done using both BC distances matrices and Jaccard presence/absence similarity matrices and 9999 permutations. We used Metastats  (White et al. 2009)  for detection of significant differences between FL and PA assemblages in abundance of phyla and taxa.
OTUs richness A total of 174,665 tags were obtained, 107,348 tags from FL samples and 67,317 from PA samples. We normalized the tag sequences obtained to the smallest sample size (10,299 tags) and we clustered them at 0. 03 difference level obtaining 3588 OTUs. The average 293 singletons per sample in our dataset represented 44. 2% of the total of the OTUs (Table  1 ). We grouped OTUs in taxa and phyla, obtaining 513 taxonomic groups and 30 phyla, for further analyses. Rarefaction curves were computed for the samples (Fig. 2 ). These curves allow comparisons of the OTU richness of each sample if the same number of tags had been obtained from all of them. The curves did not reach an asymptote. Rarefaction curves for the PA fraction reached higher number of OTUs per tags than the curves for the FL fraction in all samples except for sample D65 (Fig.
6, 7; Table  2 ). We have not found significant differences in Bacteroidetes between FL and PA bacterial assemblages (Table  S3 ), probably due to masking of significance by the high abundance of this phylum in the PA fraction at coastal waters (Table  2 ). This high abundance, however, revealed the importance of Bacteroidetes in the PA fraction, at least at the coastal station as was also found by  DeLong et al. (1993). The low abundance of Actinobacteria and Verrucomicrobia may have also masked any significance of their higher abundance in the PA bacteria than in the FL bacteria. The Proteobacteria phylum was important in explaining differences between FL and PA bacterial assemblages, but once Proteobacteria were removed from the analysis, the less abundant groups were also important. The present data corroborate that FL PA bacteria are phylogenetically different  (DeLong et al.
Acknowledgments The Modivus cruise was supported by the  Spanish MIC-INN  project  MODIVUS  ( CTM2005-04795/MAR ). We thank the captain and the crew of the  RV Garc ıa del Cid. We thank  V. Balagu  e for help with sampling and  P. J. Wallhead  for statistical advice. We thank two anonymous reviewers for their valuable comments. Pyrosequencing was carried out at the  Josephine Bay Paul Center, Marine Biological Laboratory, Woods Hole, MA, U. S. A. , under the auspices of the  International Census of Marine Microbes (ICoMM)  via a grant from the  Keck Foundation. Initial analysis of the data was carried out at the VAMPS portal, supported by the  Josephine Bay Paul -Center  and the  Alfred.
This paper reports on the development of the 'Communicate to vaccinate' taxonomy. Methods: The taxonomy was developed in two stages. Stage 1 included: 1) forming an advisory group; 2) searching for descriptions of interventions in trials (CENTRAL database) and general health literature (Medline); 3) developing a sampling strategy; 4) screening the search results; 5) developing a data extraction form; and 6) extracting intervention data. Stage 2 included: 1) grouping the interventions according to purpose; 2) holding deliberative forums in English and French with key vaccination stakeholders to gather feedback; 3) conducting a targeted search of grey literature to supplement the taxonomy; 4) finalising the taxonomy based on the input provided.
Key task 2: search for interventions We obtained the interventions used to formulate the taxonomy from a variety of sources. We retrieved published trials and medical literature from two databases: The Cochrane Central Register of Controlled Trials (CENTRAL) and Medline. CENTRAL is a comprehensive databases of trials on the effects of health interventions and we therefore anticipated that it would yield a high proportion of relevant interventions. However, not all communication interventions have been evaluated in trials and new interventions are constantly being developed. We therefore also searched Medline without any study design filter so as to retrieve any kind of paper describing a communication intervention for vaccination. We developed search strategies for Medline and CEN-TRAL (Additional files 1 and 2) based on Cochrane Collaboration principles  [30].
Key task 5: develop a data extraction form We developed a data extraction form (see Additional file 3), building on the data extraction form recommended for authors of systematic reviews for the CC&CRG  [34]. We piloted the form within the project team to ensure ease of use and clarity. The final form contained the following data extraction cells: For trials, we extracted whatever information was available about the communication intervention being evaluated, along with outcomes features. Much of this information was intended to be utilised in later stages of the project, as the focus of the taxonomy was primarily on intervention type and purpose. For other types of articles, we extracted any information reported on all of the communication interventions described in the article, even if some interventions were only mentioned briefly.
Key task 1: group the interventions Once all the available data were extracted, we compiled a complete list of interventions in Microsoft Excel in which we described each intervention using simple, nontechnical terms. This 'raw' interventions list became the launching pad for our taxonomy. By discussing common themes and features in this list, NW, JK and SH grouped interventions conceptually according to their purpose. We then developed a coding system to organise and manage these data. Once the interventions were grouped, the major overarching categories in the taxonomy were formed. In establishing the purpose of interventions we were guided by the language used by the authors of the articles, rather than imposing our own interpretations. The overarching primary categories were then further divided to provide greater clarity and definition between the different intervention types.
Key task 3: search the grey literature We conducted a focussed search of the grey literature in order to complement database searching. Websites were selected following extensive discussions within the project team, with input from the advisory group. We searched the websites of the Communication Initiative Network, World Health Organization (WHO), United Nation Children's Funds (UNICEF), Global Alliance for Vaccines and Immunization (GAVI Alliance), Immunization Basics, Programme for Appropriate Technologies in Health (PATH), Centre for Global Health Communication and Marketing, US Centers for Disease Control and Prevention (CDC), and the Maternal and Child Health Integrated Programme (MCHIP). New or unique interventions described by these sources were added to existing taxonomy categories where appropriate, or used to refine new taxonomy categories.
Acknowledgements COMMVAC is a two-year project funded by the programme for  Global Health and Vaccination Research (GLOBVAC) of the Research Council of Norway. The project partners include the  Norwegian Knowledge Centre for the Health Services, Norway ;  La Trobe University, Australia ;  Swiss Tropical and Public Health Institute, Switzerland ;  University of Cape Town, South Africa ; and the  International Union for Health Promotion and Education, France. The  COMMVAC  project team acknowledges individuals who have assisted during various stages of the COMMVAC project. Leila H. Abdullahi  for her help with searching and selecting the grey literature,  Gregory Hussey  for his support with the project's inception and  Zainab Waggie  for participating in the selection of the grey literature.
The authors evaluate the ability of 10 regional climate models (RCMs) to simulate precipitation over Southern Africa within the Coordinated Regional Climate Downscaling Experiment (CORDEX) framework. An ensemble of 10 regional climate simulations and the ensemble average is analyzed to evaluate the models' ability to reproduce seasonal and interannual regional climatic features over regions of the subcontinent. All the RCMs use a similar domain, have a spatial resolution of ;50 km, and are driven by the Interim ECMWF Re-Analysis (ERA-Interim; 1989 -2008). Results are compared against a number of observational datasets. In general, the spatial and temporal nature of rainfall over the region is captured by all RCMs, although individual models exhibit wet or dry biases over particular regions of the domain.
Models generally produce lower seasonal variability of precipitation compared to observations and the magnitude of the variability varies in space and time. Model biases are related to model setup, simulated circulation anomalies, and moisture transport. The multimodel ensemble mean generally outperforms individual models, with bias magnitudes similar to differences across the observational datasets. In the northern parts of the domain, some of the RCMs and the ensemble average improve the precipitation climate compared to that of ERA-Interim. The models are generally able to capture the dry (wet) precipitation anomaly associated with El Niño (La Niña) events across the region. Based on this analysis, the authors suggest that the present set of RCMs can be used to provide useful information on climate projections of rainfall over Southern Africa.
An alternative but computationally intensive approach that does not suffer from this restraint and captures mesoscale nonlinear effects is dynamical downscaling using high-resolution RCMs or global models with variable spatial resolution  (Giorgi 1990; Giorgi and Mearns 1999). RCMs are a widely used tool for producing regional climate data that use boundary conditions from a forcing coarse-scale global dataset such as a GCM or reanalysis to simulate the climate of a particular region. Previous studies demonstrate that RCMs, while showing some improvements over the driving GCMs in simulating precipitation over Southern Africa, also show some persistent biases  (Arnell et al. 2003; Haensler et al. 2011 ) such as having unrealistic rain day frequency and rainfall intensity  (Tadross et al. 2006).
However, through this experience CORDEX has evolved very strict data standards and requirements as well as distribution nodes, which will facilitate direct access to CORDEX data in the future. The evaluation of precipitation results in the reference climate simulations is restricted by the availability of high quality observational datasets for the region of study that are at the appropriate spatial resolution and for the required time period. Several observational datasets are available for model evaluation and have been reviewed to some extent in  Nikulin et al. (2012)  and  Sylla et al. (2013). Nikulin et al. (2012)  show large differences between satellite-and gauge-based products and highlight the lack of reporting station gauges over large parts of Africa. Sylla et al. (2013)  state that the uncertainty in the observations is a key factor preventing a rigorous and unambiguous evaluation of climate models over Africa.
To obtain an estimate of the spread in the precipitation values between the individual observed fields and provide a sense of observational uncertainty, model results were compared against a number of different observational datasets. Three gauge-based gridded observational datasets are used, which are available at 0. 58 latitude-longitude grid and include the region of Southern Africa and the time period 1990-2006: the Global Precipitation Climatology Centre (GPCC), version 5  (Rudolf et al. 2010) ; the University of Delaware (UDEL), version 2. 01  (Legates and Willmott 1990) ; and the Climatic Research Unit (CRU) at the University of East Anglia, version 3. 0  (Mitchell and Jones 2005). For the limited time period of 1998-2006, the results from the gaugebased datasets are compared against the satellite-based dataset from the Tropical Rainfall Measuring Mission (TRMM) 3B42, version 6  (Huffman et al.
2007 ) with a 0. 258 spatial resolution and the satellite-gauge dataset from the Global Precipitation Climatology Project (GPCP), version 1. 1  (Huffman et al. 2001) , with a 18 spatial resolution. The different gridded products agree quite well in terms of large-scale precipitation patterns  (Gruber et al. 2000; Fekete et al. 2004) , though the scarcity of station data in certain regions of Southern Africa  (Nikulin et al. 2012 ), compared to other regions of the world, can lead to local deviations. In accordance to other studies, the TRMM dataset is found to have a dry precipitation bias in the entire north part of Southern Africa during the wet season (e. g. ,  Nikulin et al. 2012 ) and a spatially confined dry bias in the southeastern part of South Africa during the austral winter season.
The GPCC, UDEL, and GPCP data were compared against mean monthly precipitation data available for 428 stations in South Africa and Mozambique for the period 1990-2006 and all three datasets were found to compare well against point observations (appendix). Although GPCC has an overall slightly better performance than the UDEL and GPCP gridded datasets, local differences can occur (see appendix). The GPCC dataset was chosen as the reference observational dataset as it is of a sufficiently long time series that it covers the CORDEX downscaling period  (1989) (1990) (1991) (1992) (1993) (1994) (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) , it has same resolution as the RCM simulated data, and the regions used in the analysis were classified based on GPCC data.
Other datasets are also compared against GPCC, and areas where station data are scarce and larger deviations may be expected are highlighted. Nevertheless, the primary goal of the work presented in this paper, which is to assess the models' relative performance, is not found to be affected by the choice of observational dataset. We evaluate the downscaled atmospheric fields against ERA-Interim, as this was also the reanalysis dataset chosen by the CORDEX initiative to provide the lateral boundary conditions for the RCM reference climate simulations. The third-generation ERA-Interim includes a number of improvements over the 40-yr ECMWF Re-Analysis (ERA-40;  Dee et al. 2011). It has been found to agree well with gauge-based and combined gauge and satellite products  (Simmons et al. 2010)  and is thus considered an appropriate dataset for the assessment of model performance for a number of climate variables.
To facilitate comparison against the different observational datasets, the results presented focus on the period 1990-2006. Despite that each model used its individual setup (number of vertical levels, grid rotation, parameterization schemes, etc. ), a common horizontal resolution of 0. 448 was required. In the results presented, this is used as a reference grid and all datasets on other grids are interpolated to this common resolution. The TRMM dataset was aggregated from the native 0. 258 grid onto the reference grid by a first-order conservative remapping  (Jones 1999) , while all other datasets with similar or coarser resolution than 0. 44 were remapped through bilinear interpolation as in  Nikulin et al. (2012). The details of each model can be found in Table  1. In addition to the overall circulation patterns over Southern Africa, within the study area three specific subregions were also examined to provide an overview of model performance across regions with nominally homogeneous rainfall annual cycle patterns  (Liebmann et al.
To provide an overview of observational uncertainty, UDEL, CRU, GPCP, TRMM, and ERA-Interim are also compared to GPCC and plotted on the same diagram. Similar analysis (Taylor diagrams) conducted using UDEL and CRU datasets as reference (not shown) confirm that model performance is largely independent of the reference dataset. Where possible, ERA-Interim-derived rainfall is used to provide a measure of uncertainty between the reanalysis data and observations. The seasonal cycle results for each subregion area-averaged monthly mean precipitation values are shown in Fig. 5. For subregion 1 (Fig. 2 ) large differences are observed in model performance across all seasons. The models are able to capture the phase of interannual variability better during the wet (DJF) and dry (JJA) seasons, where more than half of the RCMs are found to have correlation coefficients greater than 0.
50 (statistical significance at the 5% level). The correlation is weaker for  MAM and SON (Figs. 2b, d). In agreement with previous studies (e. g. ,  Weigel et al. 2010; Nikulin et al. 2012) , the ensemble mean is generally found to outperform the individual models. It performs better in terms of correlation coefficient and RMSE, though it tends to systematically underestimate the magnitude of the interannual variability relative to GPCC, except in SON. Furthermore, the correlation and RMSE between the ensemble mean and GPCC is similar to or better than the correlation and RMSE of the other observed datasets. Notable is the poor correlation between the UDEL and CRU observational datasets with the GPCC during SON and JJA, which may be attributed to spatial differences within the chosen subregion across the gridded datasets, discussed further in section 3b.
Although the relative model performance, both in terms of area-averaged results and in terms of spatial precipitation patterns (discussed in section 3c) was found to be largely independent of the reference dataset; for these two seasons in subregion 1 a much weaker correlation coefficient between all model results and the UDEL and CRU datasets was found. For JJA a correlation below 0. 1 was found for all models compared to UDEL and below 0. 5 compared to CRU, whereas the correlation against GPCC is between 0. 4 and 0. 85. On an individual model basis, the ARPEGE5. 1 model consistently outperforms all models as it captures the interannual variation of seasonal precipitation very well for all seasons and has a similar (in one case better) correlation coefficient, RMSE and standard deviation compared to the ensemble mean.
6, the standard deviation is close to that of GPCC, and the RMSE is less than 0. 5 for all but two models. The seasonal cycle results for this subregion shown in Fig. 5  (area-averaged monthly mean precipitation values) confirm that the models have a realistic cessation of the rainfall season over this area. Similarly to subregion 1 results, the ARPEGE5. 1 model consistently outperforms all models for all seasons and captures the interannual variation of seasonal precipitation very well, with a correlation coefficient of above 0. 86 in all cases. It is found to have a standard deviation which is generally very close to or slightly less than that of GPCC, confirming that it is also able to capture the amplitude of the interannual variation. For all seasons the ARPEGE5. 1 model is found to perform better than the multimodel ensemble and also has similar statistics compared to the other observational datasets.