dataesr/scientific-paragraphs-categorization huggingface dataset:
The point related to the anthropogenic-induced signal can be solved having long temporal series of the observed quantity, where it is possible to assess that apparent trends in the data are not caused by long-time scale, but natural and system inherent variability. That is the case for long-term records of temperature  (Gillett et al. , 2012; Rowlands et al. , 2012)  but is a challenge for shorter datasets, as demonstrated by  (Henson et al. , 2010). Nevertheless, it is important to detect features in the data that highlight underlying trends, such as the sensitivity of processes/variables to particular drivers, that we know they will be impacted in the future, as for instance evidenced in ecosystem manipulation studies. This approach assumes that the ecosystem will have a response in the future, which is comparable to its present-day sensitivity to that driver.
Atmospheric CO 2 records from ice cores and monitoring stations (continuous and flask data), Table  4 , are one of the observational datasets most commonly used to evaluate and constrain the simulated carbon cycle in terms of climatology, but also trends and sensitivities  (Cadule et al. , 2010; Dalmonech and Zaehle, 2013; Dargaville et al. , 2002; Heimann et al. , 1998). change in atmospheric CO 2 as response to a change in global temperature can be used as a combined constraint on γ[D2] and β  (Cox and Jones, 2008; Gregory et al. , 2009; Scheffer et al. , 2006). Longer-time scale constraints require indirect measurements of changes in atmospheric CO 2 , such as derived from ice-cores. Using records of the temperature and CO 2 drop during the little ice age, 1500-1750 AD  Cox and Jones, (2008)   by  Cox and Jones (2008)  are assumed to be representative of the global sensitivity at centennial to multi-centennial scales, hence comparable to the carbon-climate projections for the next century.
Terrestrial dataset There is a large data-base of derived datasets, benefitting of extensive spatial and temporal coverage (Table  4 , see also  Luo et al. , 2012  for terrestrial data sets). Along to the already mentioned dataset, a CO 2 net land and ocean fluxes database based on inversions (Table  4 ), is available as a result of the TransCom3 project  (Gurney et al. , 2002)  and it has been recently used to evaluate the ESMs participating to the CMIP5  (Anav et al. , 2013a). Satellite-based dataset have been successfully used to evaluate marine and terrestrial ecosystem in several regions and over the globe  (Anav et al. , 2013b; Dalmonech and Zaehle, 2013; Friedrichs et al. , 2009; Guenet et al. , 2013; Kelley et al. , 2013; Randerson et al. , 2009)  and we can benefit of record of up to almost 30 yr of data.
Conclusions Figures Back Close Full carbon-process. While satellite-based datasets provide information only for the surface layer and for limited biological properties, they could be used in combination with other datasets such as vegetation height measurements  (Simard et al. , 2011). Global up-scaled records as the GPP-product by  (Jung et al. , 2011) , soil respiration (e. g. Bond-lamberty and Thomson, 2010), soil carbon (e. g. Nachtergaele et al. , 2012; FAO 2009)  and vegetation carbon stocks (e. g. Gibbs et al. , 2006)  are valuable datasets to evaluate the "climatology" of the carbon cycle. Nevertheless caution has to be used when these dataset are implemented for a quantitative constraint, due to the inherent uncertainties associated to the upscaled procedure and the lack of a temporal dimension with which to assess current trends.
Currently, tropical areas lack of extensive in situ "observational" records, including manipulative experiments  (Luo et al. , 2006)  and leaves traits  (Kattge et al. , 2011). This is fundamental in order to e. g. understand how fertilisation effect might counteract the direct effect of temperature on plant physiological response, and hence supporting plant resilience. Satellite based data are also affected by high uncertainties in tropical areas  (Asner and Alencar, 2010). As an example, Fig. 4  reports the standardised seasonal signal of selected satellite-based datasets of vegetation activity aggregated over the Amazon area (Appendix A). The figure shows the lack of agreement between different records in depicting the same underlying process and obstacles the selection of the most suitable dataset and hence the evaluation and constrain exercise.
Marine dataset Similarly as for land, TransCom 3 provides ocean air-sea CO 2 fluxes database (Table 4). The new Surface ocean CO 2 Atlas (SOCAT) aims to provide a comprehensive, publicly available, regularly updated global dataset of marine CO 2 which is independent from the Takahashi database  (Pfeil et al. , 2012)  (Table  4 ). Global scale estimates of variables such as chlorophyll a and diffuse attenuation coefficient are available from remotely sensed measurements from the Coastal Zone Color Scanner  (1978) (1979) (1980) (1981) (1982) (1983) (1984) (1985) (1986) ,  Sea-viewing Wide Field-of-view Sensor (1997 -2010)  and Aqua  Modis (2002 Modis ( -2012)). For marine ecosystems, the physical and the biological parts are tightly coupled and this makes the evaluation and constrain of the biological subcomponents difficult.
For example, in Fig. 5  the timing of the blooming of chlorophyll concentration has been computed for SeaWifs-based dataset. Although this is a robust observational feature linked to the phenology of marine PP, such information is linked not only to the marine ecosystem but also to the modelled physical ocean system via the vertical mixing. Therefore, this biogeochemical metric can be viewed as a way to support a joint constrain of circulation and biogeochemistry models. New marine dataset will allows to explore, along to stock data, information at community structural level and physiological level. The new MAREDAT (MARine Ecosystem DATa) database  (Buitenhuis et al. , 2013)  provides one of the first comprehensive biomass datasets to validate plankton in the ocean. The initiative provides global gridded data for 11 plankton functional types (PFTs) including 9 of the PFTs that have been proposed as essential in simulating important biogeochemical processes in the oceans (Le  Quéré et al.
Metrics Evaluation metrics can be used to synthesise the complexity of model-data comparison, and thereby to facilitate comparing and ranking the models, as well as formulating weights to provide probabilistic forecast in multi-model ensemble and perturbed ensemble (Sects. 5. 2. 1 and 5. 2. 2). Several evaluation analysis of the recent years differ in data used and metric proposed for marine e. g. (Doney et al. , 2009b; Gleckler et al. , 2008; Jolliff et al. , 2009)  and terrestrial ecosystems e. g. (Blyth et al. , 2011; Dalmonech and Zaehle, 2013; Kelley et al. , 2013). This evidences how the choice of dataset and metric carries a partial but inevitable degree of subjectivity and hence uncertainty. Although this demonstrates that we are still far from a common standard of evaluation,  Foley et al.
It is ascertain a robust interpretation of model-data differences based on only one metric, or expressing data-model difference in one specific field. Similarly, in our "data-rich" world, it is important to select datasets in a way such as to avoid potential for correlation between data of the same type. Non-independent datasets may provide metrics that contain redundant information, leading a biased assessment of the reliability of the models. All the mentioned evaluation studies are quite recent, hence there is not yet a formalisation of the metrics to constrain or how to for-Introduction.
2), in terms of model-data misfit functions (e. g. Booth et al. , 2012; Gregg et al. , 2009; Rayner et al. , 2011; Rowlands et al. , 2012)  -the most simplest being the root mean square error. These misfit-functions provide a means of including error estimates, when they are known e. g. (e. g. Friedrichs et al. , 2007; Kidston et al. , 2011; Raupach et al. , 2005) , but the specification of structural errors in observations is yet unresolved  (Raupach et al. , 2005). In these studies it has been also shown how it is possible extend the formulation of the misfit-function to more than one dataset (e. g. Model data fusion approach,  Keenan et al. , 2012) , and increase the potential to constrain the model parameters and the process of interest.
Back Close Full (e. g. Ricciuto et al. , 2008) , even though these different parameter combinations give rise to different trajectories in the future. A potential way around the equifinality problem is the use of multiple constraints (metrics), as recent work involving data-fusion methods to constrain a land ecosystem model and its future projections  (Ricciuto et al. , 2011; Keenan et al. , 2012)  have demonstrated the potential of a few years of multiple datasets to substantially reduce uncertainties over simulations of the ecosystem, in addition to identifying model structural errors. Perturbed-parameter ensembles have been shown to exhibit a lower compared to multi-model ensembles, at least as far as physical climate variables are concerned  (Yokohata et al. , 2013). One way to overcome this limitation is to include structural uncertainties in a single model, additionally to parameter uncertainties  (Watanabe et al.
Perturbed parameter ensembles of different terrestrial and marine ESM components would be helpful to separate parametric and structural uncertainty. In this context, systematic testing of a specific model sub-unit in a modelling framework could help to identify the impact of structural uncertainties on the model's future predictions and to diagnose the most reasonable model structure given presently available data. If the currently available data were not permitting to tell model structures apart despite diverging future predictions of the models, this approach may still yield insight into the data needed to reduce model structural uncertainty. Newly datasets are also reported (see also  Foley et al. , 2013; Luo et al. , 2012).
Participants and Procedure Participants (N = 3,470) were recruited from 26 nations. 2 After excluding 147 participants due to poor data quality (i. e. , incomplete or random responding), 3,323 participants were included in the present study. Table  1  reports sample sizes for each nation (median n/site = 100, range 49-283). The combined sample was 31% male with a mean age of 22. 3 years. Most participants were university students in their early 20s, but for three nations (Italy, South Korea, and the United States), subsamples included community-dwelling adults. 3 For a detailed description of recruitment procedures, data collection, and sample characteristics by country, refer to  Löckenhoff et al. (2009)  and  Chan et al. (2012). Participants were asked to rate the personality traits of the typical adolescent, adult, and old person in their nation on the National Character Survey (NCS;  Terracciano, Abdel-Khalek, et al.
Comparison Samples To estimate the accuracy of GSDs of personality, we drew on the prior literature to obtain sex difference scores based on self-ratings as well as observer-ratings of actual males and females provided by individuals who knew them well. Self-reports for college-aged and adult men and women were drawn from Costa and colleagues (2001), who compiled NEO-PI-R data from 11,690 adults and 10,952 college-aged respondents 5 from 26 nations. Fourteen of these nations overlap with the present sample. Observer-ratings for college-aged and adult targets were drawn from the Personality Profiles of Cultures Project (PPOC; McCrae, Terracciano, & 78 Members of the Personal Profiles of Cultures Project, 2005), which provides NEO-PI-R observer-ratings for 5,095 college-aged individuals (M = 19.
Nation-Level Indicators Socioeconomic development. Levels of socioeconomic development for each nation were assessed with the Human Development Index (HDI; n = 26), a composite of three aspects of development: life expectancy at birth, access to education (adult literacy and gross enrollment ratio across grade levels), and standard of living (gross domestic product per capita). Higher scores indicate higher development. Data for 2005 were retrieved from http://hdr. undp. org/en/media/HDR05_ complete. pdf Gender equality. Gender equality was assessed with two indicators: The Gender-Related Development Index (GDI; n = 25), which captures equalities in the HDI dimensions of life expectancy (male vs. female life expectancy at birth), education (male vs. female adult literacy and gross enrollment ratio), and standard of living (male vs.
female estimate earned income), and the Gender Empowerment Measure (GEM; n = 20), which captures women's involvement in political decision making (male vs. female parliamentary seats) and economic decision making (male vs. female shares in legislative, managerial, professional, and technical positions). Higher scores indicate greater equality and empowerment. Data for 2005 were retrieved from http://hdr. undp. org/en/media/HDR05_complete. pdf Value systems. Cultural differences in value systems were captured along the dimensions proposed by  Hofstede (1980 Hofstede ( , 2001 ; that is, power distance, uncertainty avoidance, individualism, and masculinity, n = 26),  Schwartz (2005;  that is, embeddedness, affective autonomy, intellectual autonomy, hierarchy, mastery, egalitarian commitment, and harmony, n = 26), and  Inglehart and Baker (2000;  that is, traditional values vs.
Alternatively, tighter nations may enforce sex-role differentiated behaviors and thus encourage both attribution and frame of reference effects. Although our correlational findings, of course, cannot directly speak to causal mechanism, these considerations suggest that our findings are more consistent with artifactual (as compared with substantive) interpretations of cross-national differences in GSDs. However, given that tightness-looseness data were only available for a small subset of the nations and effects remained at the trend level, these findings need to be corroborated by future research. Several additional limitations need to be considered when interpreting our results. First, respondents rated either men or women. As such, gender differences were examined at the group, not the individual level, and raters did not serve as their own controls, which likely increased random error and may have led to an underestimation of effect sizes.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported in part by the  Intramural Research Program  of the  National Institutes of Health, National Institute on Aging. Realo and  Allik  were supported by grants from the  Estonian Ministry of Education and Science  ( SF0180029s08  and  IUT2-13 ). Hřebíčková and Graf were supported by the  Czech Science Foundation  ( 13-25656S ) and by the  Institute of Psychology, Academy of Sciences of the Czech Republic  (RVO:  68081740 ).
, 2013). Because of the design of the IGAP study (a meta-analysis) and the low frequency of the TREM2 variant, it was not possible to determine whether the GWAS signal of this variant (rs9381040) was independent of the TREM2-p. R47H variant. In this study, we used exome-sequencing data to identify the most likely functional variant in TREML2 responsible for the GWAS signal and to determine whether this signal is independent of TREM2-p. R47H (rs75932628) variant.
Alzheimer's disease genetic consortium methods Data used in the preparation of this article were obtained from the Alzheimer's disease genetic consortium (ADGC). A description of the samples included in the study as well as the methods used can be found in  Naj et al. (2011). Imputed data from 10,067 AD cases and 9606 controls from the ADGC were used in this study  (Naj et al. , 2011). Genome-wide imputation was performed per cohort using MACH software with HapMap phase 2 (release 22) CEPH Utah pedigrees reference haplotypes and genotype data passing quality control as inference. Imputation quality was determined as r 2 and only SNPs imputed with r 2 ! 0. 50 were included in the analysis. A multivariate logistic regression was performed to evaluate the association between genetic markers and risk for late-onset AD (LOAD) adjusting for age, gender, population substructure, and study-specific effects.
) AD. All elderly controls were screened for dementia using the MMSE or ADAS-cog, were determined to be free from dementia at neuropathological examination or had a Braak score of 2. 5 or lower. Genotypes from all cases and control subjects were previously included in the AD GWAS by  Harold et al. (2009). Imputation of the dataset was performed using IMPUTE2 and the 1000 genomes (http://www. 1000genomes. org/) Dec2010 reference panel (NCBI build 37. 1). The imputed data was then analyzed using logistic regression including covariates for country of origin, gender, age, and 3 principal components were obtained with EIGENSTRAT (EIGENSOFT 4. 2)  (Patterson et al. , 2006)  software based on individual genotypes for the GERAD study participants.
After recruitment, 392 subjects withdrew from the study. Thus, 9294 subjects were finally included in the study  (2104 in Bordeaux, 4931 in Dijon, and 2259 in Montpellier). Genomic DNA samples 38 of 7200 individuals were transferred to the French Centre National de Génotypage. First stage samples that passed DNA quality control were genotyped with Illumina Human 610-Quad BeadChips (n ¼ 452). At the end, we removed 308 samples because they were found to be firstor seconddegree relatives of other study participants, or were assessed non-European descent based on genetic analysis using methods described in  Heath et al. (2008). In this final sample, at 7 years of follow-up, 459 individuals suffered from AD with 97 prevalent and 362 incident cases. These AD cases were included as cases in the European Alzheimer's disease initiative (EADI) discovery dataset.
CSF levels dataset A description of the CSF dataset used in this study can be found in  Cruchaga et al. (2013)  and data included 1269 unrelated individuals recruited through the Knight-ADRC at Washington University (n ¼ 501, 73% CDR ¼ 0), the Alzheimer's Disease Neuroimaging Initiative (n ¼ 394, 27% Clinical Dementia Rating [CDR] ¼ 0), a biomarker consortium of Alzheimer disease centers coordinated by the University of Washington (n ¼ 323, 61% CDR ¼ 0), and the University of Pennsylvania (UPenn) (n ¼ 51, 2% CDR ¼ 0). Briefly, CSF tau, phosphotau-181 (ptau), and amyloid beta (Ab 42 ) levels were from research participants enrolled in longitudinal studies at the Knight-ADRC, ADNI, University of Washington, and University of Pennsylvania. CSF collection and Ab 42 , tau, and ptau181 measurements were performed as described previously  (Fagan et al.
Meta-analysis was conducted using the META package (http://www. stats. ox. ac. uk/ wjsliu/meta. html) in R (version 3. 0. 1). Association of CSF ptau with the genetic variants was analyzed as described previously  (Cruchaga et al. , 2010 (Cruchaga et al. , , 2011;; Kauwe et al. , 2011). Briefly, CSF ptau values were log transformed to approximate a normal distribution. Because the CSF levels were measured using different platforms (Innotest plate ELISA vs. AlzBia3 beadbased ELISA, respectively), we were not able to combine the raw data. We extracted from the log-transformed value, the mean within each series for the log-transformation. No significant differences in the transformed CSF values of the different series were found. We used SAS (version 9. 2) to analyze the association of SNPs with CSF biomarker levels.
29e2. 85) (Fig. 1 , panel C). When rs3747742 or rs9381040 are included as covariates in a conditional analysis, rs75932628 remains highly significant (p ¼ 1. 27 Â 10 À4 and p ¼ 1. 19 Â 10 À4 , respectively) (Fig. 1 , panel D), suggesting that the TREML2 and TREM2 signals are independent from each other. We also performed a linear regression analysis for rs9381040 and rs3747742 with CSF levels of tau and ptau (n ¼ 1269 individuals)  (Cruchaga et al. , 2013). rs9381040 (p ¼ 4. 11 Â 10 À4 , beta ¼ À0. 02) and rs3747742 (p ¼ 1. 4 Â 10 À4 , beta ¼ À0. 02) both exhibit a strong association with CSF ptau levels. The respective associations with CSF ptau are no longer significant when either SNP is included as a covariate in the conditional analysis. These results confirm via 2 independent datasets that the associations of rs9381040 and rs3747742 with CSF biomarker levels and with AD risk represent the same signal.
Discussion In summary, these results demonstrate that the associations of missense variants in TREM2 and TREML2 with AD risk are independent. Moreover, our analyses suggest that the AD-associated GWAS signal is likely driven by the TREML2 coding missense variant p. S144G (rs3747742); it results in a similar odds ratio to rs9381040. We also validated 2 other coding variants p. V25A and p. S129T in TREML2 gene in moderate LD (r 2 ¼ 0. 05 and D 0 ¼ 1) with the GWAS SNP, which both exhibited a higher frequency among control subjects than in AD cases (Table  1 ). However, for both variants we only obtained data by whole-exome sequencing which  EADI1 was supported by the French National Foundation on Alzheimer's disease and related disorders. Data management involved the Centre National de Génotypage, the Institut Pasteur de Lille, Inserm, FRC (fondation pour la recherche sur le cerveau), and Rotary.
This work has been developed and supported by the LABEX (laboratory of excellence program investment for the future) DIS-TALZ grant (Development of Innovative Strategies for a Transdisciplinary approach to ALZheimer's disease). The Three-City Study was performed as part of collaboration between the Institut National de la Santé et de la Recherche Médicale (Inserm), the Victor Segalen Bordeaux II University, and Sanofi-Synthélabo. The Fondation pour la Recherche Médicale funded the preparation and initiation of the study. The 3C Study was also funded by the Caisse Nationale Maladie des Travailleurs Salariés, Direction Générale de la Santé, MGEN, Institut de la Longévité, Agence Française de Sécurité Sanitaire des Produits de Santé, the Aquitaine and Bourgogne Regional Councils, Fondation de France, and the joint French Ministry of Research and INSERM "Cohortes et collections de données biologiques" programme.
Acknowledgements This work was supported by grants from  National Institutes of Health  ( P30-NS069329e01 ,  R01-AG044546 ,  R01-AG035083 ,  R01-AG16208 ,  P50-AG05681 ,  P01-AG03991 ,  P01-AG026276 ,  AG05136 , and  PO1-AG05131 ,  U01-AG032984 ,  AG010124 ,  R01-AG042611 ), the  Alzheimer Association  ( NIRG-11e200110 ), and the  Barnes-Jewish Hospital Foundation. This research was conducted while  Carlos Cruchaga  was a recipient of a  New Investigator Award in Alzheimer's disease  from the  American Federation for Aging Research. CC is a recipient of a BrightFocus Foundation Alzheimer's  Disease Research Grant  ( A2013359S ). The authors thank the  Clinical and Genetics Cores of the Knight ADRC at Washington University  for clinical and cognitive assessments of the participants and for APOE genotypes and the  Biomarker Core of the Adult Children Study at Washington University  for the cerebrospinal fluid collection and ptau assays.
The  Charles Wolfson Charitable Trust  supported the  Oxford Project to Investigate Memory and Ageing group. Washington University was funded by  National Institutes of Health  grants,  Barnes Jewish Foundation , and the  Charles and Joanne Knight Alzheimer's Research Initiative. Patient recruitment for the  MRC Prion Unit  and  UCL Department of Neurodegenerative Disease collection  was supported by the  UCLH  and  UCL Biomedical Centre. London  and the South East Region AD project was funded by  Lundbeck SA. The Bonn group was supported by the  German Federal Ministry of Education and Research (BMBF) ,  Competence Network Dementia and Competence Network Degenerative Dementia , and by the  Alfried Krupp von Bohlen und Halbach-Stiftung. The  GERAD Consortium  also used samples ascertained by the  National Institute of Mental Health AD Genetics Initiative.