dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, this study shows that IAGOS can be seen as a complement of surface stations to monitor the air quality in/around the agglomeration. Although sporadic in time, IAGOS data provide useful information on the vertical distribution of pollution in the boundary layer. The spatial representativeness of the IAGOS observations in the lower troposphere appears large enough to offer a new opportunity to validate models and satellites in the PBL, in complement to surface stations and ozonesondes. The importance of improving models on the vertical dimension has been already underlined in several studies  (Solazzo et al. , 2013; and references therein). Besides, this has recently justified the use of IAGOS to validate operationally the CAMS regional chemistry-transport models in Europe (see http://www.
iagos. fr/cams for daily comparisons). This study confirms that such comparisons are entirely relevant. In addition, IAGOS offers the major advantage of providing observations in many large cities with the same instrumental system, thus ensuring a consistency between all the measurements. This is particularly interesting for comparing the air quality in cities from different countries that may not use comparable instruments and standard scales at the surface stations. It is worth remembering that these results are based on a limited set of airports. In particular, the most in-depth analysis was restricted to Frankfurt airport where the IAGOS data are the most numerous. Considering the potentially large differences of environment from one airport to the other (e. g. intensity of the local air traffic, distance from the agglomeration, size of the agglomeration and intensity of local emissions, orography, local meteorology), they may not be valid for all airports visited by IAGOS aircraft since 1994 ( ~290).
Background: To assess the quality of life of people with dementia, measures are required for self-rating by the person with dementia, and for proxy rating by others. The Quality of Life in Alzheimer's Disease scale (QoL-AD) is available in two versions, QoL-AD-SR (self-rating) and QoL-AD-PR (proxy rating). The aim of our study was to analyse the inter-rater agreement between self-and proxy ratings, in terms of both the total score and the items, including an analysis specific to care setting, and to identify factors associated with this agreement. Methods: Cross-sectional QoL-AD data from the 7th Framework European RightTimePlaceCare study were analysed. A total of 1330 cases were included: n = 854 receiving home care and n = 476 receiving institutional longterm nursing care. The proxy raters were informal carers (home care) and best-informed professional carers (institutional long-term nursing care).
Interpreting correlation analyses as agreement assessment must therefore be questioned and should generally be avoided. Only two studies  [25, 28]  used the Bland-Altman plots as recommended for metric variables  [44, 45] ; the presented data indicate an unacceptable range of agreement. Factors associated with the level of agreement between self-and proxy ratings were reported in eleven studies  [16-22, 25, 27, 28, 36]. The most frequently mentioned factors for different ratings were behavioural and psychological symptoms of dementia (BPSD) and severity of cognitive impairment of those with dementia, as well as caregiver burden (see Additional file 1). We found no studies investigating the level of agreement between QoL-AD-SR and QoL-AD-PR or agreement at the item level in a large European sample.
The RTPC study The RTPC study comprised a prospective, multi-centre cohort study in eight European countries: Estonia, Finland, France, Germany, the Netherlands, Spain, Sweden and the UK. The survey (start in 2010) generated primary data for the development of best-practice recommendations on the transition from home care to institutional nursing care for European citizens with dementia. Two types of dyads of people with dementia and their informal carers were investigated  [46] : 1) People with dementia (and their informal carers) living in an institutional long-term nursing care (ILTC) facility, admitted one to 3 months ago; 2) People with dementia (and their informal carers) living at home receiving professional long-term home nursing care (HC), who were assessed as being at risk of institutionalisation within the next 6 months.
Variables from the RTPC data set for the secondary analysis QoL-AD measure To analyse the inter-rater agreement, baseline data of the QoL-AD self-and proxy ratings are important. Therefore, only cases with total scores available for both the QoL-AD-SR and the QoL-AD-PR were included in our secondary data analysis. In total, n = 1330 cases were selected (see Additional file 2). The QoL of people with dementia was assessed by self-and proxy ratings in all eight countries  [46]  using the 13-item version of the QoL-AD  [14]. The self-rating was assessed by those with dementia if they had an S-MMSE score of three or higher. The QoL-AD proxy rating is assessed from the proxy's own perspective (proxy-proxy perspective), in contrast to answering as the patient would (person-proxy perspective). The proxy report was obtained from the best informed proxy, i.
e. QoL in HC was assessed by informal carers and by professional carers in ILTC (i. e. , nursing staff ). Data were collected between November 2010 and April 2012. To standardise the data collection, an instruction manual  [48]  was provided and implemented through training. This included instructions for the face-to-face interviews of the QoL-AD according to the detailed instructions for interviewers in the original questionnaire. The countries' main investigators were responsible for the training of the interviewers; all interviewers received training regarding the content and completion of questionnaires  [46]. The total scores of the self-and proxy ratings were calculated according to the specifications of the original authors  [8, 14] : Based on the four response categories (1 = poor, 2 = fair, 3 = good, 4 = excellent) of the 13 items, the total score ranges from 13 to 52, with higher values indicating a higher QoL.
Variables for the analysis of associated factors on the agreement To analyse associated factors on the agreement of the self-and proxy ratings relevant clinical variables were selected from the RTPC data set. Our selection criteria were guided by the findings from the systematic literature search concerning the factors associated with the level of agreement between self-and proxy ratings. The corresponding variables from the RTPC data set are presented in detail in Additional file 1, including description, value ranges, and interpretation of the measures. Variables of people with dementia were BPSD measured by the Neuropsychiatric Inventory Questionnaire (NPI-Q)  [49, 50] , cognitive function/severity of dementia measured by the Standardized Mini Mental-State Examination (S-MMSE)  [47, 51]  (sometimes used as a surrogate method for staging dementia  [52] ), depression measured by the Cornell Scale for Depression in Dementia (CSDD)  [53] , functional status measured by the Katz Index of Independence in Activities of Daily Living (KATZ ADL)  [54] , educational background, and care setting.
Variables of informal carers were caregiver burden measured by the Zarit Burden Inventory (ZBI)  [55] , the subscale 'Lack of family support' of the Caregiver Reaction Assessment (CRA)  [56] , and the Neuropsychiatric Inventory Questionnaire-Caregiver Distress (NPI-Q-D)  [49, 50] , QoL measured by the European Quality of Life Scale (EQ-5D-3 L, EQ-5D VAS)  [57, 58] , the General Health Questionnaire 12-item version (GHQ-12)  [59] , and the subscale 'Impact on health' of the Caregiver Reaction Assessment (CRA)  [56] , kind of relationship to the person with dementia, and gender. Sociodemographic or clinical variables of the professional carers relevant to the secondary data analysis were not collected in the RTPC study.
Statistical analysis Descriptive data from people with dementia, informal carers and professional carers were analysed using the statistical software SPSS Version 24. Frequencies, proportions, means and standard deviations were calculated. With reference to the descriptive methods recommended for metric variables  [44, 60] , we took an exploratory approach for the assessment of the agreement between self-and proxy ratings of the QoL-AD total score and used Bland-Altman plots  [44, 61]  created with the statistical software R Version 3. 2. Bland-Altman plots usually consist of a line representing the mean of all differences of the compared methods and the Limits of Agreement (LoA). We initially decided that an acceptable difference between the self-and proxy rating of the QoL-AD would be within a range of -3 to + 3 points in the total score.
Using the given data from Zhao et al. [28] , LoA ranges from -7. 3 to 13. 9, indicating an unacceptable range of agreement. Therefore, the QoL-AD proxy rating is not directly interchangeable with and cannot replace the QoL-AD self-rating of a person with dementia. At the item level, we did not find any agreement better than poor except for the item 'Marriage' , which achieved a fair agreement. The agreement on item level is comparable to the results of the studies by Conde-Sala et al. [33] , Hoe et al. [40]  and León-Salas et al. [31] , which demonstrate mostly poor agreement on the item level. Chan et al. [29]  and Wolak et al. [37]  had a more positive conclusion, stating that agreement on the item level was largely good. Both studies inappropriately used correlation analysis for agreement rating.
Strengths and limitations The RTPC data for secondary data analysis provided us with access to a large European sample comprising QoL-AD self-ratings and proxy ratings. However, setting-specific results should be interpreted with caution since ILTC settings might differ across countries. To ensure a widely representative sample 256 ILTC locations were included in the RTPC study; for the secondary analysis 476 professional carers rated the QoL. Cross-cultural comparisons could not be conducted since the national sample sizes varied. However, we tested the assessment of the agreement between selfand proxy ratings of the QoL-AD total score on the German subsample (ILTC: n = 64; HC: n = 67). Again, no clear pattern of difference of the paired observations could be identified in the Bland-Altman plot.
Conclusion Our analysis showed that professional and informal carers appear to generate lower proxy ratings of QoL than those with dementia themselves. The assessment of the inter-rater agreement of the two measurement methods QoL-AD-SR and QoL-AD-PR revealed pronounced differences. Nevertheless, the QoL-AD benefits from good psychometric properties and the applicability to people with a wide range of dementia severity to rate themselves. Our data indicate that the QoL-AD self-and proxy ratings are not directly interchangeable due to the inter-rater gap. Thus, QoL-AD-PR provides a complementary perspective rather than a substitute for self-rating. In particular, a mix of self-rating and proxy rating may be biased. From a clinical point of view, our study suggests that either only one rating method should be performed or both rating methods parallel with separate analyses grouped in self-and proxy ratings.
Acknowledgments Thanks to our anonymous reviewers, whose comments improved a previous version of the manuscript. We thank  C. Campbell ,  B. Kurtoglu ,  M. Mueller  and  M. Wood  for their help in the field,  F. Goin  and  J. -Y. Crochet  for discussions regarding early Cenozoic metatherian evolution,  O. Sanisidro  for graphics and artwork,  D. Burnham  for specimen preparation, molding and casting,  B. Khameiss  and  M. Sims  for picking and sorting through screen-washed residue,  C. Sagebiel  for access to comparative specimens, and  M. C. Maas  for providing valuable data regarding her prior work in the  Uzuncarşıdere Formation.
(2016) ,  Knist et al. (2016)  and  Frei et al. (2018). The relatively large ensemble of EURO-CORDEX highresolution RCM climate change scenarios constitutes a valuable data set for impact studies. Some of these simulations and from the earlier ENSEMBLES project have been used for considerations of climate change at different warming levels (e. g. Vautard et al. , 2014; Maule et al. , 2017)  and in impact studies (e. g. Alfieri et al. , 2015; Donnelly et al. , 2017). However, previous studies have either been based on earlier RCM ensembles or only on smaller subsets of the full EURO-CORDEX set of RCM simulations. In this study we therefore focus on how the European climate may change at the 1. 5 or 2 • C of global warming levels in the larger set of EURO-CORDEX simulations at 12. 5 km grid spacing.
Climate model simulations We use RCM data from 18 EURO-CORDEX simulations for the European area; see Table  1  and Fig. 1. Specifically, we analyse seasonal mean, 2 m temperature, precipitation, wind speed and mean sea level pressure (MSLP) for all RCMs with the exception of WRF, for which MSLP data are missing. All RCM simulations have been performed with forcing following RCP8. 5 (Representative Concentration Pathway; see  Moss et al. , 2010). The chosen simulations allow us to address the impact of different driving GCMs on the resulting climate change signal. In addition, the impact of the choice of different RCMs can be investigated for both the three-member RCM ensembles downscaling MPI-ESM-LR-r1, EC-Earth-r12, HadGEM2-ES, and CNRM-CM5 and for the two-member ensemble downscaling IPSL-CM5A-MR.
This choice is made as (i) the starting point  (1971)  is the first possible as not all RCMs have data for earlier years and (ii) the end point (  2000 ) is before the Table  2. CMIP5 GCMs assessed here. Columns SWL1. 5 and SWL2 show the central year in a 30-year period when GCMs reach the 1. 5 and 2 • C of warming levels (i. e. 2030 represents 2016-2045) under RCP8. 5. GCMs are listed in order of when they reach SWL2. Only ensemble member r1 has been used unless otherwise noted in brackets after the GCM name. GCMs in italics have been downscaled by RCMs (see Table  1 ). For more information see  Taylor et al. (2012)   first year in any of the 30-year SWL1. 5 time periods downscaled here (the IPSL model, number 7 in Table  2 ). From observations we note that the global warming between 1861-1890 (pre-industrial) and 1971-2000 (reference) is 0.
Simulated changes in MSLP Figures  2  and 3  show the changes in MSLP in each ensemble member at SWL2 for winter and summer respectively (apart from WRF for which MSLP data are missing. We have, however, included a blank panel for WRF for consistency with later figures). It is clear that there are considerable differences among the different simulations and that these differences are closely connected to both the choice of GCMs (e. g. comparing CNRM-CM5-driven simulations with those driven by HadGEM2-ES) but also to the choice of ensemble member (as illustrated by the two realizations of MPI-ESM-LR or the three EC-EARTH members). Further, we note that there are some but weaker differences also due to the RCM. The latter can be seen from the six panels showing the RACMO, CCLM and RCA4 simulations downscaling EC-EARTH-r12 and HadGEM2-ES.
This similarity has implications for how to assess differences between the periods in a statistically rigorous way as data in the two samples are not independent. All 31 GCMs in Table  2  have also been run for RCP4. 5. For that scenario SWL1. 5 is reached between 2008 and 2061 in the different models (not shown). SWL2, however, is Table 4. Summary statistics showing temperature and precipitation changes at SWL1. 5 for the eight regions in Fig. 1. For each region there are three sets of data for each season and variable representing the full CMIP5 ensemble (top), the nine-member GCM ensemble downscaled by the RCMs (middle) and the 18-member RCM ensemble (lower). The numbers represent the minimum (left), maximum (right) and mean plus or minus 1 SD (middle) of the ensemble members' individual area mean climate changes.
5 in the subregions defined in Fig. 1. Figure  8  and Table  4  show that simulated temperature changes in Scandinavia are larger in winter (1. 7 • C) than in summer (1. 5 • C). For comparison with pre-industrial conditions we remind ourselves that this change is to be added to the 0. 41 • C increase in global mean temperature between  1861-1890 and 1971-2000. For the Scandinavian region past changes are larger; data representing Sweden show that warming over this period is almost 1 • C (data taken   2 ), (ii) the nine-member GCM ensemble that has been downscaled and (iii) the 18-member RCM ensemble (Table  1 ). Unfilled circles are CMIP5 GCMs listed in Table  2  that have not been downscaled. Filled circles represent GCMs that have been downscaled and these are connected by a line to the RCM(s) that have been used for downscaling.
Acknowledgements. Part of the work was carried out in the European Union Seventh  Framework Programme in projects IMPACT2C  under grant agreement  282746  and  HELIX  under grant agreement  603864. Part of this work was performed within the  Swedish research program MSB-HazardSupport. We acknowledge the  World Climate Research Programme 's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups (listed in Table 2 of this paper) for producing and making their model output available. For CMIP the  US Department of Energy's Program for Climate Model Diagnosis and Intercomparison  provide coordinating support and led development of software infrastructure in partnership with the  Global Organization for Earth System Science Portals. Edited by: Zhenghui Xie Reviewed by: two anonymous referees.
Data availability. The main web page documenting data availability for EURO-CORDEX data can be found at http://euro-cordex. net/060378/index. php. en (EURO-CORDEX  community, 2018). In general all CMIP5 and EURO-CORDEX simulations that have been analysed here are accessible via the international Earth System Grid Federation (ESGF). HadCRUT4 data analysed for global mean temperature changes were downloaded at https://www. metoffice. gov. uk/hadobs/hadcrut4/data/current/time_ series/HadCRUT. 4. 6. 0. 0. annual_ns_avg. txt (Met Office Hadley  Centre, 2018). The Supplement related to this article is available online at https://doi. org/10. 5194/esd-9-459-2018-supplement.
Surface ocean metabarcoding confirms limited diversity in planktonic foraminifera but reveals unknown hyper-abundant lineages Raphaël Since the advent of DNA metabarcoding surveys, the planktonic realm is considered a treasure trove of diversity, inhabited by a small number of abundant taxa, and a hugely diverse and taxonomically uncharacterized consortium of rare species. Here we assess if the apparent underestimation of plankton diversity applies universally. We target planktonic foraminifera, a group of protists whose known morphological diversity is limited, taxonomically resolved and linked to ribosomal DNA barcodes. We generated a pyrosequencing dataset of ~100,000 partial 18S rRNA foraminiferal sequences from 32 size fractioned photic-zone plankton samples collected at 8 stations in the Indian and Atlantic Oceans during the Tara Oceans expedition (2009-2012).
We identified 69 genetic types belonging to 41 morphotaxa in our metabarcoding dataset. The diversity saturated at local and regional scale as well as in the three size fractions and the two depths sampled indicating that the diversity of foraminifera is modest and finite. The large majority of the newly discovered lineages occur in the small size fraction, neglected by classical taxonomy. These unknown lineages dominate the bulk [>0. 8 µm] size fraction, implying that a considerable part of the planktonic foraminifera community biomass has its origin in unknown lineages. After ~250 years of Linnean taxonomic work, >90% of the ocean's biodiversity still appears to be undescribed  1. The quest to obtain an inventory of marine species is hampered by the difficulty to exhaustively sample the vast three-dimensional oceanic habitat.
No time-consuming sorting of single specimen and expert taxonomic assessment is involved in the process, but the actual organisms are never directly observed. Instead, the link with existing taxonomic knowledge is made by the comparison of the MOTUs with curated reference databases  5, 6  , mostly derived from barcoding efforts  7. The taxonomic interpretation of environmental MOTUs relies on the completeness of the existing reference databases and the taxonomic resolution of the chosen barcodes. So far, metagenomic surveys of marine biodiversity depict the oceanic realm as a treasure trove of diversity  8. Microbial marine communities seem to be composed of a small number of abundant taxa co-occurring with hugely diverse, undescribed and rare consortia. This "rare biosphere" 8 is universally observed amongst planktonic  9, 10  and benthic  11  communities.
Indeed, the recent metabarcoding survey of sunlit ocean by the Tara Oceans project revealed that cosmopolitan MOTUs, representing 0. 35% of the diversity, accounted for 68% of the volume of the dataset  10. Less than 1% of the detected MOTUs had a perfect match with a reference sequence. This suggests that a large portion of the marine biosphere is still uncharacterized by DNA barcoding. However, metabarcode diversity can be inflated by the presence of chimeric sequences generated during PCR-based amplification  12  , or by sequencing errors  13. Stringent quality control and detection algorithm such as UCHIME  14  are commonly used to remove potential chimeras from the dataset. But this practice bears the risk of removing genuinely rare sequences from the dataset and thus "throw the baby out with the bathwater"  15.
Parsing the ecological signal from the noise in metagenomic datasets is challenging  16  and mostly study-dependent, which impedes effort to compare different datasets that could reveal informative fine-scales community structures  17. Modeling approaches  18  , mock communities  16  , and application of ecological metrics 15 can help to assess or reduce the impact of false and low abundance MOTUs, but ultimately the genuine species richness of an ecosystem investigated with metabarcoding approach is debatable  19. In summary, the availability of novel sequencing technologies brings new challenges in the assessment of biodiversity. Such assessment requires the simultaneous availability of curated reference databases, well-resolved barcodes, stringent but flexible bioinformatics pipelines and sufficient background taxonomic knowledge.
Their global geographic distribution, seasonal dynamics and trophic behavior have been studied by sampling in the plankton  28  , sediment traps  29  and surface sediments  30. After two centuries of taxonomic investigations, extant planktonic foraminifera diversity seems to have settled with ~50 morphologically defined species  31  , much less than the 600-800 extant species of radiolaria  32  , the sister group of foraminifera within the Rhizaria. This traditional view of the diversity in planktonic foraminifera has been challenged by the discovery of cryptic diversity manifested in rRNA sequences  33. It revealed that most modern morpho-species of planktonic foraminifera are aggregates of cryptic biological species  34. This unique single-cell sampling and rRNA sequencing effort has resulted in the creation of a comprehensive reference database, which includes 3,322 single-cell sequences of major morphospecies producing >90% of the recent fossil assemblages  35  with curated taxonomy at the level of morphological and cryptic (biological) species.