dataesr/scientific-paragraphs-categorization huggingface dataset:
1. 2. 2. 2. Project no. 211250 ESCAPE; EU FP7-2008-ENV-1. 2. 1. 4 Envirogenomarkers; EU FP7-HEALTH-2009-single stage CHICOS; EU FP7 ENV. 2008. 1. 2. 1. 6. Proposal no. 226285 ENRIECO; EU-FP7-HEALTH-2012 Proposal no. 308333 HELIX); MeDALL (FP7 European Union project, no. 264357); and the Greek Ministry of Health (programme of prevention of obesity and neurodevelopmental disorders in preschool children, in Heraklion district, Crete, Greece, 2011-14; 'Rhea Plus': Primary Prevention Program of Environmental Risk Factors for Reproductive Health, and Child Health: 2012-15). The methylation assays were funded by the European Community's Seventh Framework Programme FP7/2007-2013 project EXPOsOMICS (grant no. 308610).
The FLEHS study was commissioned, financed and steered by the  Ministry of the Flemish Community (Department of Economics, Science and Innovation; Flemish Agency for Care and Health ; and  Department of Environment, Nature and Energy). The methylation work was funded by the  CEFIC LRI  award 2013 that was given to S. L. who is the beneficiary of the  Cefic-LRI Innovative Science Award 2013  and of a post-doctoral fellowship [12L5216N; http:// www. fwo. be/]provided by the  Research Foundation-Flanders (FWO)  and the  Flemish Institute for Technological Research (VITO). P. deB. is recipient of a  Bill & Melinda Gates Foundation Grand Challenges Exploration grant  ( OPP119403 ) in the field of saliva biomarker discovery. The  PREDO Study  has been funded by the  Academy of Finland , EraNet, EVO (a special state subsidy for health science research),  University of Helsinki Research Funds , the  Signe and Ane Gyllenberg foundation , the  Emil Aaltonen Foundation , the  Finnish Medical Foundation , the  Jane and Aatos Erkko Foundation , the  Novo Nordisk Foundation , the  Pa ¨ivikki and Sakari Sohlberg Foundation  and the  Sigrid Juselius Foundation  granted to members of the Predo study board.
Methylation assays were funded by the  Academy of Finland. J. L. has received funding from the  University of Helsinki  and  Academy of Finland. We would like to acknowledge the  University of Western Australia (UWA) ,  Curtin University , the  Raine Medical Research Foundation , the  UWA Faculty of Medicine, Dentistry and Health Sciences , the  Telethon Kids Institute , the  Women's and Infant's Research Foundation (KEMH)  and  Edith Cowan University  for providing funding for the core management of the Raine Study. The funding for the methylation assays was through the  National Health and Medical Research Council  grant  1059711. R. C. H. receives funding from the  National Health and Medical Research Council (NHMRC)  fellowship  1053384. P. M. is supported by funding from  Acknowledgements Avon Longitudinal Study of Parents And Children (ALSPAC) We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses.
Funding provided to J. L. W. by the  Center for Integrative Research on Childhood Leukemia  and the Environment (P01ES018172),  NIH  grants  P50ES018172  and  R01ES09137 ,  EPA RD83451101 ,  RD83615901 , and  NIH 5P30CA082103  (the  UCSF Comprehensive Cancer Center Support  grant). R. R. is supported by  P30 CA82103  (the  UCSF Comprehensive Cancer Center Support  grant). S. G. is supported by the  Swiss Science National Foundation  [grants number:  P2LAP3_158674 ] and the  Sutter-Sto ¨ttner Foundation.
Childhood Obesity Project (CHOP) The CHOP study and research reported herein were partially supported by: the  Commission of the European Community , specific  RTD Programme 'Quality of Life and Management of Living Resources ' within the  5th Framework Programme  (research grant nos. QLRT-2001-00389  and  QLK1-CT-2002-30582 ); the  6th Framework Programme  (contract no. 007036 ); the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ), project  EarlyNutrition  under grant agreement no. 289346 ; and the  European Research Council Advanced  grant  ERC-2012 -AdG -no. 322605META-GROWTH. This manuscript does not necessarily reflect the views of the Commission and in no way anticipates the future policy in this area. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
Funding Avon Longitudinal Study of Parents And Children (ALSPAC) The UK Medical Research Council and the Wellcome Trust (grant ref: 102215/2/13/2) and the University of Bristol provide core support for ALSPAC. The Accessible Resource for Integrated Epigenomics Studies (ARIES), which generated large-scale methylation data, was funded by the UK Biotechnology and Biological Sciences Research Council (BB/I025751/1 and BB/I025263/1). Additional epigenetic profiling on the ALSPAC cohort was supported by the UK Medical Research Council Integrative Epidemiology Unit and the University of Bristol (MC_UU_12013_1, MC_UU_12013_2, MC_UU_12013_5 and MC_UU_12013_8), the Genes-environments and Admixture in Latino Americans (GALA II) The GALA II study was supported in part by grants from the Sandler Family Foundation; the American Asthma Foundation; National Institutes of Health: the National Heart, Lung and Blood Institute (HL117004); the National Institute of Environmental Health Sciences (ES24844); the National Institute on Minority Health and Health Disparities (MD006902, MD009523); the National Institute of General Medical Sciences (GM007546); the Tobacco-Related Disease Research Program (24RT-0025).
1. 2. 2. 2. Project no. 211250 ESCAPE; EU FP7-2008-ENV-1. 2. 1. 4 Envirogenomarkers; EU FP7-HEALTH-2009-single stage CHICOS; EU FP7 ENV. 2008. 1. 2. 1. 6. Proposal no. 226285 ENRIECO; EU-FP7-HEALTH-2012 Proposal no. 308333 HELIX); MeDALL (FP7 European Union project, no. 264357); and the Greek Ministry of Health (programme of prevention of obesity and neurodevelopmental disorders in preschool children, in Heraklion district, Crete, Greece, 2011-14; 'Rhea Plus': Primary Prevention Program of Environmental Risk Factors for Reproductive Health, and Child Health: 2012-15). The methylation assays were funded by the European Community's Seventh Framework Programme FP7/2007-2013 project EXPOsOMICS (grant no. 308610). Early Autism Risk Longitudinal Investigation cohort (EARLI) Children's Allergy Environment Stockholm Epidemiology study (BAMSE) California Birth Cohort (CBC) ENVIRonmental influence ON early AGEing (ENVIRonAGE) Exploring Perinatal Outcomes in Children (EPOCH) EPOCH is funded by the following NIH grants: R01DK068001; R01 DK100340.
We are additionally including a halo occupation distribution (HOD) model  (MICE, Carretero et al. 2015)  in the comparison: as such models relate numerical data (for a given redshift) to observations in a statistical manner, they provide -by construction -an accurate reproduction of the galactic content of haloes. While they do not provide galaxy populations that self-consistently evolve in time, they nevertheless have great value when it comes to interpreting data from on-going and future galaxy redshift surveys (especially for clustering studies,  Pujol et al. 2017). In fact, the MICE model has been used to generate the flagship galaxy mock catalogue for the Euclid satellite mission (which is available at the COSMOHUB 1  database). We have made all the data for this project publicly available.
THE SIMULATION DATA The halo catalogues used for this paper are extracted from 125 snapshots 2  of a cosmological dark-matter-only simulation undertaken using the GADGET-3 N -body code  (Springel 2005)  with initial conditions drawn from the Planck cosmology  (Planck Collaboration et al. 2014 , Ωm = 0. 307, ΩΛ = 0. 693, Ω b = 0. 048, σ8 = 0. 829, h = 0. 677, ns = 0. 96). We use 512 3 particles in a box of co-moving width 125h -1 Mpc, with a darkmatter particle mass of 1. 24 × 10 9 h -1 M⊙. Haloes were identified with ROCKSTAR  (Behroozi et al. 2013a ) and merger trees generated with the CONSISTENTTREES code  (Behroozi et al. 2011). Even though both halo finder and tree builder have changed with respect to  Knebe et al. (2015) , the files passed to the modellers were in the same format. Essentially, these changes have improved the underlying simulation framework upon which this work is based: the box is bigger, there are more snapshots, the halo catalogues are more complete at early times and the tree construction includes patching when objects briefly disappear between snapshots.
• calibration #2 (c02, SMF at z = 0 & 2 + extra constraints): In addition to the constraint used for calibration #1, the black holebulge mass and cold gas mass at z = 0, the star formation rate function at z = 0. 15, and the stellar mass function at z = 2 have been used. We will from now on refer to this either as calibration '-c02' or simply the 'CARNage calibration'. This observational data set is motivated and described in detail in Section 3. 3. Note that the un-calibrated model will only serve as a connecting point to our previous comparison  (Knebe et al. 2015)  and will not enter the main part of this work. And even though results from the two calibration approaches are not mixed together in a single plot, we also chose to use different linestyles for these two catalogues: results from '-c01' will always be presented as dashed lines whereas the results from '-c02' are shown as solid lines.
Calibration steps and parameter changes The calibration #1 catalogue ('-c01') starts from the parameters used for '-uc' (i. e. the original parameters presented in the model's reference publication) and uses the observed SMF at redshift z = 0 as the only constraint. The calibration #2 catalogue ('-c02') starts from the parameter values found for '-c01', but now adds the provided SFRF, CGMF, and BHBM relations at redshift z ∼ 0 as well as the SMF at z = 2 to the constraints (see Section 3. 3 for further details). The intention here was to add the minimum number of additional calibration datasets which simultaneously constrained the key physical processes required for a model of galaxy formation. In order to limit the number of different calibrations for each model we decided to simultaneously apply all four of the constraints in '-c02'.
The first column after the model name is for the SMF at z = 0 in the calibration '-c01' data set; the following two columns are for the SMF at z = 0 and z = 2, respectively, in the calibration '-c02' data set. Note, only the range used during model calibration has entered into the χ 2 -calculation. as a common constraint. The most apparent deviations from the observations and amongst the models is at the high-mass end where there are only a few objects with little constraining power: the data in that range is dominated by systematic errors which is why this range has been excluded from the calibration set. model χ 2 z=0,c01 χ 2 z=0,c02 χ 2 z=2,c02 DLB07.
Beyond the calibration Even though the models have not been (actively) calibrated against anything else but the stellar mass function at redshift z = 0, we will now study the model data for the reminder of the observational 'CARNage calibration' set. This allows us to investigate how the residual scatter seen for the SMF in Fig. 1  propagates into other galaxy properties and their respective correlations. For most of the subsequent plots in this sub-section (except the BHBM relation) the 'CARNage set' will be represented as a shaded region indicating 1σ error bars. LGALAXIES-c01 MICE-c01 MORGANA-c01 SAG-c01 SAGE-c01 ySAM-c01.
Cold Gas Mass Fraction The cold gas fraction in galaxies as a function of stellar mass is shown in Fig. 3. In this figure, model galaxies are compared to the observational data from  Boselli et al. (2014). Almost all the models (bar YSAM) reproduce the declining trend seen in the observations. However, leaving this property unconstrained leads to a substantial model-to-model variation amplitude-wise. We can already deduce some interesting conclusions from this figure: for instance, if unconstrained by the CGMF both DLB07 and LGALAXIES would prefer higher cold gas mass fractions than observed.
CALIBRATION WITH THE CARNAGE DATA SET The 'CARNage calibration' data set has been introduced and motivated already in Section 3. 3 and its details (including a link to a public database) are given in Appendix A. All models have now either manually or automatically tuned their parameters with that particular set simultaneously. However, the modellers were given the freedom to assign weights to each observation individually: different models might be designed to perform better for some predictions/prescriptions and hence put more emphasis on reproducing, for instance, the black hole-bulge mass relation as opposed to the cold gas fraction. In passing we note that there is no difference between the '-c01' and '-c02' galaxy catalogues for GALICS 2. 0 and  In Fig. 6  we show the stellar mass function as given by the models for both redshift z = 0 (upper panel) and z = 2 (lower panel) in comparison to the observational data.
Beyond Calibration While all distribution functions and correlations (apart from the SMF at z = 0) studied for data set '-c01' in Section 4. 2 could be considered predictions, the '-c02' data set has been constrained by the local SFRF and SMF as well as the redshift z = 2 SMF. But will this be sufficient to 'predict', for instance, the so-called 'Madau-Lilly' plot  (Madau & Dickinson 2014; Madau et al. 1996; Lilly et al. 1996) , i. e. the evolution of the cosmic star formation rate density (cSFRD). We note that this plot is closely related to the the SMF as well as the SFRF: for instance, the integral over all masses of the SMF at a fixed redshift corresponds to the integral of the cSFRD up to that redshift; further, the integral over all SFR values in the SFRF gives the point in the cSFRD at the corresponding redshift.
When adding additional (orthogonal) constraints, the scatter generally tightens and models move towards closer agreement with each other. This is most prominent for the CGMF, i. e. the modelto-model variation substantially reduces when comparing Fig. 3 ('-c01 ' calibration that only uses the stellar mass function at redshift z = 0) to Fig. 8 ('-c02 ' calibration that now includes additional observational data at different redshifts as described in Section 3). Further, the SFRF scatter also decreases going from '-c01' to '-c02'. We can see that there is one model in particular, DLB07, which is a clear outlier in both the CGF and SFRF for '-c01', and shows dramatic improvement going to '-c02'. The other models that have a notable change in CGF have an accompanying change in SFRF -typically gas fractions drop and SFR goes up.
SHM relation We start with inspecting the SHM relation at redshift z = 0 for both the '-c01' and '-c02' data sets. The results (for central galaxies) can be viewed in Fig. 11  alongside the best-fit model of  Rodríguez-Puebla et al. (2017). 6 Even though that model encapsulates data for both central and satellite galaxies, we only compare against centrals -as suggested by Rodriguez-Puebla (private communication, but see also  Rodríguez-Puebla et al. 2012 ). To generate this plot the data has been binned logarithmically in halo mass for the x-axis and the y-axis shows the median M * /M halo of all central galaxies in that bin. For most of the models the additional constraints of the '-c02' calibration lead to no appreciable difference. The stellar-to-halo mass ratio is essentially determined purely by the SMF, with the difference that here it only applies to central galaxies.
We note that the stellar-to-halo mass ratio from the DLB07 model agrees significantly better with the observational results when the additional '-c02' constraints are used, while this relation remains more or less unaffected for the other models. We have also performed the test where we added the stellar mass of all satellites to the stellar mass of the central galaxy as the halo mass of the central is 'inclusive' (i. e. contains all the subhalo masses). This gives rise to a clear effect at the high-mass end of the SHM: the model-to-model variation is marginally reduced for halo masses M halo > 10 13 M⊙ and above the observational data by about a factor of two for M halo > 10 14 M⊙. It indicates the importance of how to count stellar and halo mass in theoretical models when comparing to observations.
This may particularly affect the 'prescriptions' and 'predictions' of the models. All the data used here are publicly available. 9 The nine models summarised in Table  1  have been calibrated in two ways: i) Just to the stellar mass function at z = 0 and ii) To the SMF, the star formation rate function, the cold gas mass, and the black hole-bulge mass relation at z ∼ 0 together with the SMF at higher redshift z = 2, i. e. the 'CARNage calibration' set (described in Section 3. 3). When calibrating all our models just to the SMF at z = 0as presented in Section 4 -the scatter is significantly reduced compared to models run with parameters fixed by other datasets. This indicates that the main conclusion of our earlier paper  (Knebe et al. 2015) , i. e. that the scatter was driven by the lack of re-calibration, was correct.
We re-confirm that galaxy formation models need to be recalibrated to the specific simulation, halo finder and merger tree being considered and, in general, cannot simply be re-run using parameters obtained from a different underlying simulation. At the high-mass end, where there are few objects, some models overproduce the number of massive galaxies. When calibrating simultaneously to all five constraints -as studied in Section 5 -the scatter in the SMF at z = 0 naturally grows. Even so, the scatter is still less than that obtained when using models with standard parameters tuned to other simulation datasets. The observed shape of the star formation rate function at z = 0 is reproduced by models calibrated just to the SMF at z = 0. When this star formation rate function is added as a constraint, the scatter is reduced.
ACKNOWLEDGMENTS The authors would like to express special thanks to the  Carnegie Observatories  for their support and hospitality during the workshop 'Cosmic CARNage' where all the calibration issues were discussed and the roadmap laid out for the work presented here. This work further builds up upon the results obtained during the three week workshop 'nIFTy Cosmology' hosted by the  Instituto de Fisica Teorica (IFT-UAM/CSIC in Madrid)  and we also like to thank it for its hospitality and support, via the  Centro de Excelencia Severo Ochoa Program  under Grant No. SEV-2012-0249. We further acknowledge the financial support of the 2014  University of Western Australia Research Collaboration Award for 'Fast Approximate Synthetic Universes  for the SKA', the  ARC Centre of Excellence for All Sky Astrophysics (CAASTRO)  grant number  CE110001020 , and the two ARC Discovery Projects  DP130100117  and  DP140100198.
This work used the DiRAC Data Centric system at Durham University, operated by the  Institute for Computational Cosmology  on behalf of the  STFC DiRAC HPC Facility  (www. dirac. ac. uk). This equipment was funded by  BIS National E-infrastructure capital  grant  ST/K00042X/1 ,  STFC  capital grant  ST/H008519/1 , and  STFC DiRAC Operations  grant  ST/K003267/1  and  Durham University. DiRAC is part of the  National E-Infrastructure. The work of BH was supported by  Advanced  Grant  246797 GALFOR-MOD  from the  European Research Council. MH acknowledges financial support from the  European Research Council  via an  Advanced Grant  under grant agreement no. 321323  NEOGAL. PM has been supported by a  FRA2012 grant  of the  University of Trieste ,  PRIN2010-2011  ( J91J12000450001 ) from  MIUR , and  Consorzio per la Fisica di Trieste.
NDP was supported by  BASAL   PFB-06   CATA , and Fondecyt  1150300. Part of the calculations presented here were run using the Geryon cluster at the  Center for Astro-Engineering at U. Catolica , which received funding from  QUIMAL 130008  and Fondequip AIC-57. CP acknowledges support of the  Australian Research Council (ARC)  through  Future Fellowship   FT130100041  and  Discovery  Project  DP140100198. WC and CP acknowledge support of  ARC   DP130100117. AP acknowledges support from beca  FI  and  2009-SGR-1398  from  Generalitat de Catalunya , project  AYA2012-39620  and  AYA2015-71825  from  MICINN , and from a  European Research Council   Starting Grant  ( LENA-678282 ). RA is funded by the  Science and Technology Funding Council (STFC)  through a studentship. RSS thanks the Downsbrough family for their generous support.
g. Ref. [43] ). Among the target elements in that case are C, O and H, which (to good precision) have N p /N n = 1 in their natural abundance, and would thus also be good candidates to contribute to the test of DM self-conjugacy studied here. To discriminate Dirac from Majorana DM, data from at least three different targets is required. In our analysis, we will examine 4 different ensembles of mock experiments: All ensembles include Xe and Ar because currently they are the most promising large scale targets for the detection of a DM signal. Let us emphasize that ensemble D corresponds to the combination of the XENONnT, DEAP-50T and EURECA phase 2 experiments 3  and is therefore closest to the current plans for future detectors.
BJK is supported by the European Research Council (Erc) under the EU Seventh Framework Programme (FP7/2007-2013)/Erc Starting Grant (agreement n. 278234 -'NewDark' project). WR is supported by the DFG with grant RO 2516/6-1 in the Heisenberg Programme. Access to the High Performance Computing facilities provided by the Institut des Sciences du Calcul et des Données (Sorbonne Universités) is gratefully acknowledged. Appendix A: Parameters scans.
ACKNOWLEDGMENTS BJK is supported by the European Research Council (Erc) under the EU Seventh Framework Programme (FP7/2007-2013)/Erc Starting Grant (agreement n. 278234 -'NewDark' project). WR is supported by the DFG with grant RO 2516/6-1 in the Heisenberg Programme. Access to the High Performance Computing facilities provided by the Institut des Sciences du Calcul et des Données (Sorbonne Universités) is gratefully acknowledged. Appendix A: Parameters scans.
Analysis of dependent and independent variables As the dependent variable's measurement scale is ordinal, we conducted an ordered logistic regression analysis for each vignette, in which the societal acceptance question is regressed on security concern, privacy importance, specific attitudes to privacy and data protection practices, social values and demographics as control variables. The independent variables were added as dummy variables in the analysis. We weighted the analysis to correct for population differences between the countries and the respondents in the dataset regarding age, gender and work status.
Young people and men have higher acceptance levels for smart meters collecting personal data and the DNA databases vignette than for ANPR use against speeding in neighbourhoods. Finally, political attitude showed to have an effect across all public/private vignettes: the higher the ' preference for left-wing parties, the higher the resistance. This effect, however, is weaker in relation to ANPR use against the speeding in neighbourhoods vignette. The vignettes in which public sector actors collect and process personal data elicit lower levels of resistance from the population than vignettes with private sector actors (see Table  1. 4). The foreign government monitoring vignette triggered the highest level of resistance, while monitoring terrorists' online behaviour was most accepted by respondents -though the practices are similar.