dataesr/scientific-paragraphs-categorization huggingface dataset:
CMB low multipoles anomalies It is usually stated that the six parameters of the ΛCDM model  1, 26, 27  are enough to describe the large scale Universe. However, some features are not well captured, and anomalies occur for instance at the largest CMB angular scales (see e. g. Ref. [28]), although they are often regarded as mere curiosities. We focus here on the lack of correlation  [29] [30] [31] [32] [33] [34] [35] [36] [37]  and explain why it deserves attention. The low variance anomaly  38  is a closely related observation, 36 so that the terms "lack of power" and "lack of correlation" are used as synonymous. There is a lack of power, with respect to ΛCDM, in the two-point correlation function of CMB temperature anisotropies for angles > ∼ 60 • , as originally noted with COBE c data 29 and then confirmed by the WMAP team already in their first year release.
30 In Ref. [31] this feature was associated to missing power in the quadrupole. WMAP-3yrs and WMAP-5yrs data were then used to show  32, 33  that a lack of correlation occurs only in 0. 03% of the ΛCDM realizations. A subsequent analysis 34 confirmed the anomaly using WMAP-5yrs data, and, at the same time, found, with a Bayesian approach, that the ΛCDM model cannot be excluded. From Ref. [25]. WMAP-7yrs data were taken into account in Ref. [35], while WMAP-9yrs data were considered in Ref. [36], where the lack of correlation was studied against the Galactic masking. Planck 2013 and WMAP-9yrs data were analyzed in Ref. [37], which confirmed for this anomaly a significance at the level of 99. 97%. Similar results were obtained in Ref. [39] where Planck 2015 data were taken into account. One intriguing feature of this anomaly is that it is more significant at high Galactic latitude.
Features in the primordial fluctuations The most recent CMB data by the Planck satellite 1 are in excellent agreement with the assumption of adiabatic primordial scalar perturbation with nearly scaleinvariant PS, described by a simple power law with spectral index n s very close to (albeit different from) unity. 46 It would be produced in the simplest inflationary scenario, in which a single minimally-coupled scalar field slowly rolls down a smooth potential. In spite of this, models that account for localized 'features' in the PPS could provide a better fit to data with respect to a smooth power-law spectrum. These features could be produced in inflationary models with departures from the near-scale-invariant-power-law spectrum of the standard simplest scenario, and observable signatures would be in the CMB anisotropy temperature APS and in the matter PS from galaxy surveys.
52 It is found to be essentially a power-law with superimposed oscillations localized only in a limited range of wavenumbers. It is noteworthy that this kind of models is able to produce oscillation at very high-, and it is very interesting looking to the CMB temperature anisotropies glitches in correspondence of = 22 and = 40, first observed by the WMAP experiment and later confirmed by the Planck satellite. We used here the Planck 2013 data release and a combined CMB and SDSS (DR-11) 53 dataset. Our results show that using the CMB data alone we have noevidence of improving the concordance with data and agree with the more recent results of the Planck Collaboration. 46 Instead, using the combined dataset of CMB SDSS-DR11 data, we can see a positive Bayesian evidence for the inflationary logspaced oscillation and step oscillation models.
g. , in single-field models of inflation with a non-canonical kinetic term, or with general higher-derivative interactions. Notably, the folded shape described above can be obtained as a linear combination of equilateral and orthogonal shapes. In light of this, actual measurements of PNG generally focus on local, equilateral and orthogonal templates. It must be noted that many but not all models are included in the previous classification. For example, models with a temporary breaking of slow-roll conditions generate strongly scale-dependent, oscillatory shapes that cannot be approximated by a combination of local, equilateral and orthogonal templates. Given the limited scope of this review, the focus here will be however only on the above main shapes. Having computed the CMB angular bispectrum templates arising from the various primordial shapes, non-Gaussianity estimation consists then essentially in fitting such templates to the 3-point function of the data in order to measure the Cosmology and fundamental physics with microwave surveys 11 best-fit amplitude parameters f NL.
This apparently straightforward approach actually presents many statistical and numerical complications. These arise mainly from the huge number of modes (triangles) which contribute to the signal for high resolution experiments like Planck and WMAP, and from spurious mode couplings from sky-cut and anisotropic instrumental noise. Several numerical techniques have been successfully tested in the literature and implemented by the Planck team in order to address these issues. ] [64] [65] [66]  We refer the reader to Planck papers on PNG,  67, 68  for all details of the analysis, including validation tests carried on data and simulations, descriptions of the various pipelines, and constraints on a much larger set of shapes than the three discussed here. The final Planck results for the local, equilateral and orthogonal shapes, from the 2015 combined analysis of temperature and polarization data, are as follows: f local NL = +0.
We perform a Bayesian analysis based on a MCMC method, using cosmoMC 76 as a generic sampler. We consider the following vector of base parameters: M ν , ∆m  2  21 , ∆m 2 31 , sin 2 θ 12 , sin 2 θ 13 , φ 2 , φ 3 , ξ where φ 2 ≡ α 21 , φ 3 ≡ α 31 -2δ and ξ is a "nuisance" parameter related to the nuclear modeling uncertainty. We assume uniform prior distributions for all parameters and neglect the mixing angle θ 23 , irrelevant for mass parameters. Our baseline dataset is the global fit of the updated neutrino oscillation parameters. 77 We model the likelihood as a the product of individual Gaussians (correlations can be neglected  77, 78  ). d KATRIN  79  and HOLMES 80 represent our forthcoming and next-generation direct measurement datasets, respectively. We take the likelihood for kinematic measurements to be a Gaussian in m 2 β > 0, with a width given by the expected sensitivity of the experiment, i.
e. σ(m 2 β ) = 0. 025, 0. 006 eV 2 for KATRIN and HOLMES, respectively. For 0ν2β searches, we consider the current Cosmology and fundamental physics with microwave surveys 13 data from the GERDA experiment  81  as the present dataset, its upgrade (GERDA-II) for the near-future, and the nEXO experiment e as a next-generation dataset. 0ν2β experiments are sensitive to the half-life of 0ν2β decay T 0ν 1/2. If neutrinos are Majorana particles, T 0ν 1/2 is related to the Majorana effective mass through T 0ν 1/2 = 1 G 0ν |M 0ν | 2 m 2 e m 2 ββ , where m e is the electron mass, G 0ν is a phase space factor and M 0ν is the nuclear matrix element. We model the likelihood of 0ν2β experiments as a Poisson distribution in the number of observed events, with an expected value λ = λ S + λ B given by the sum of signal (S) and background (B) contributions.
7 events ton -1 yr -1 in the region of interest and an exposure of 25 ton yr,  83  and the same efficiency as EXO. 84 n order to account for the uncertainty related to nuclear modeling,  85  we compute T 0ν 1/2 for a given m ββ using fiducial values of nuclear matrix elements (NME) and axial coupling constant, and then rescale it by a factor ξ 2 (see Ref. [86] for a similar approach). For what concerns the cosmological dataset, we use the posterior distribution of M ν from the combination of Planck temperature and polarization data with baryon acoustic oscillations (BAO), 1 as both our current and forthcoming reference dataset. Finally, we consider the Euclid mission (weak lensing tomography, galaxy clustering and ISW) in combination with data from Planck 21 as our reference next-generation experiment.
Results We present our results for M ν , m β and m ββ in Fig. 5 , both in the case where ξ is fixed to 1 and when ξ is marginalized over, in order to show the impact of uncertainties in nuclear modeling. Notice that the low mass region is excluded by the oscillation data, with the only exception of m ββ in the case of NH; the reason is that in this case the phases can arrange in order to yield m ββ = 0 even for finite values of the mass differences. Similar limits are provided by the "present" dataset independently of whether nuclear uncertainties are marginalized over: present cone https://www-project. slac. stanford. edu/exo/ 0. 00 0. 06 0. 12 0. 18 0. 24 0. 30 straints are dominated by the cosmological limit on M ν , that translates directly to bounds on m β and m ββ once oscillation data are taken into account.
Forthcoming datasets yield similar constraints for the mass parameters: the upgraded sensitivity of GERDA-II and the inclusion of KATRIN provide a marginal improvement to the Planck +BAO plus oscillations data combination. Substantial differences arise for next-generation experiments. In this case, cosmological observations and 0ν2β searches have comparable constraining power, and the nuclear uncertainties have a dramatic impact in deriving parameter constraints. Marginal evidence for nonminimal mass parameters can be highlighted in the case of normal hierarchy, even when nuclear uncertainties are taken into account.
We adopted a function, the PCHIP 97 in the same modified version 88 as in Sect. 6. 3, to interpolate the PPS values in a series of nodes at fixed position. We discuss here the ΛCDM model, extended with the axions hot thermal relics, together with the PPS approach (see Ref. [94] for a similar analysis with two coexisting hot dark matter species, thermal axion and massive neutrinos). We consider various CMB measurements: the temperature data from the Planck satellite,  98, 99  the WMAP-9yrs polarization measurements, 100 the SPT  101  and ACT 2 datasets. Concerning CMB datasets only, the bounds on the thermal axion mass are unconstrained in the case in which the PPS is not described by a simple power-law (see Tab. III in Ref. [94]), while in this last case m a < 1. 83 eV (see Tab. IV in Ref.
[94]). Including the Hubble Space Telescope (HST) prior on the Hubble constant,  90  H 0 = 70. 6 ± 3. 3 km/s/Mpc, provides a 95% CL upper limit on the thermal axion mass of 1. 31 eV. The further addition of the BAO measurements  91, 93, [102] [103] [104] [105]  brings this constraint down to 0. 91 eV, being these last data sets directly sensitive to the thermal axion free-streaming nature. These upper bounds are very similar to the ones obtained considering the standard power-law PPS (see Tab. IV in Ref. [94]). Adding the CFHTLenS 106 bounds on the σ 8 -Ω m relationship, the thermal axion mass bounds become weaker m a < 1. 29 eV, since this dataset prefers a lower σ 8 value. Finally, considering the Planck Sunyaev-Zeldovich (PSZ) 2013 catalogue 107 dataset with fixed cluster mass bias, σ 8 (Ω m /0.
We start discussing constraints on τ n from current cosmological data. Assuming Standard Big Bang Nucleosynthesis it is possible to evaluate primordial abundances of light elements from CMB as functions of few parameters: 110 the baryonic abundance, the relativistic degrees of freedom, the chemical potential of electron neutrinos and the neutron lifetime. Neglecting the chemical potential, considering the high precision achieved in the determination of baryonic abundance and fixing N eff to its standard value of 3. 046, from primordial abundances (in particular Helium abundance) we can infer the value of the neutron lifetime. We start analyzing Planck 2015 results as CMB dataset with the publicly available MCMC package cosmoMC. Table  1  reports the most interesting results (for complete analysis see Ref.
[111]). The next step is to combine CMB observations with direct astrophysical measurements of Helium. We consider eight primordial Helium measurements collected in the last ten years and combine them with Planck data and select two possible independent combinations of these astrophysical datasets (referred to as M12-P for Refs. [112, 113] and M12-I14 for Refs. [112, 114]). As shown in Table  1 , combining the constraining power of CMB data, sensitive to the baryon density, with the Helium astrophysical measurements we obtain more stringent limits on the neutron lifetime, with respect to cosmological data only. We extend the analysis performing some forecasts on future cosmological experiments. Considering that CMB sensitivity on τ n is encoded in the small-scale region, we expect tighter constraints from next CMB projects planned to measure the high range.
Acknowledgements -The authors that are members Planck Collaboration warmly thank the  Planck Collaboration  for numberless and constructive conversations on the subjects discussed here. Some of the results in this paper have been derived using the HEALPix 123 package. We acknowledge the use of the  NASA Legacy Archive for Microwave Background Data Analysis (LAMBDA)  and of the  ESA Planck Legacy Archive. CB, AG, ML and TT acknowledge partial support by  ASI/INAF Agreement 2014-024-R. 0  for the  Planck LFI Activity of Phase E2.
To streamline enrollment in AD clinical trials and speed up recruitment, registries of healthy aged and symptomatic individuals are vital. As well as providing a repository for information about people with a specific condition, registries provide a connection between people who are willing to participate in research and those recruiting for studies. More advanced registries with standardized demographic, genetic, biologic, cognitive, and environmental information on potential participants could reduce the length of clinical trials further after the initial eligibility testing has been established. The availability of serial assessment information within a database could permit selection of trial participants based on disease trajectory. The availability of a trialready cohort in which both amyloid and cognitive status are known could potentially provide the greatest time saving in treatment development; however, the cost of establishing such cohorts is significant.
org) [22] ; -The Alzheimer's Disease Cooperative Study (ADCS)  [23] , a federal-university collaboration and also part of the Alzheimer Prevention Initiative; and -The Join Dementia Research initiative  [24]  in the UK. Linking individual registries will further increase the potential participant pool, and that is beginning to occur. The Brain Health Registry has recently partnered with GAP to dramatically increase the registry's database size to tens of thousands of new registry members; under the joint effort, supporters hope to have 40,000 people registered before the end of 2016. Other channels of recruitment, including community screenings, advocacy forums, educational programs, social media campaigns, and use of conventional media (television, radio, newspapers), can function to refer potential participants to registries or directly to trial sites.
Collaborations Measures to speed up clinical trial execution and regulatory review will rely heavily on stakeholder collaborations and data sharing. There are numerous and increasing collaborations among relevant parties (pharma, government, academia, advocacy groups) and established collaborations are joining together. In Europe, the Innovative Medicines Initiative (IMI)  [54]  aims to accelerate drug development by facilitating collaboration among key players involved in healthcare research, including universities, industry, patient organizations, and medicines regulators. IMI projects include: the Prediction of cognitive properties of new drug candidates for neurodegenerative diseases in early clinical development (PharmaCog)  [55] , to increase the ability to predict new medicines from laboratory studies and clinical models; the European Medical Information Framework Platform (EMIF)  [56] , to develop a framework for evaluating, enhancing and providing access to AD data (including CSF data, MRI scans, PET scans, plasma samples, DNA samples and RNA samples) from across Europe using Electronic Healthcare Record databases, and to identify biomarkers of AD onset in the preclinical and prodromal phase as well as for disease progression and to identify high-risk individuals for prevention trial participation; and the European Prevention of Alzheimer's Dementia (EPAD) Initiative  [57] , to provide an environment for testing interventions targeted at delaying the onset of clinical symptoms with the aim of establishing a European-wide register of 24,000 participants.
Acknowledgements We thank  Bethany Fox  for her role in writing the initial outline of this manuscript. JR and SAD report being full-time employees and minor stockholders of  Eli Lilly and Company. PS reports having acquired grant support (for the institution) from  GE Healthcare , Danone Research, Piramal and MERCK; in the past 2 years, having received consultancy/speaker fees (paid to the institution) from  Lilly ,  GE Healthcare, Novartis, Sanofi, Nutricia, Probiodrug, Biogen, Roche, Avraham  and  EIP Pharma. He is editor-in-chief of Alzheimer' s Research & Therapy, but had no role in any part of the editorial process of this manuscript. One of the authors, Philip Scheltens, is an Editor-in-Chief of Alzheimer's Research & Therapy, but was not involved in peer review of this manuscript.
The term big data is currently a buzzword in social science, however its precise meaning is ambiguous. In this paper we focus on administrative data which is a distinctive form of big data. Exciting new opportunities for social science research will be afforded by new administrative data resources, but these are currently under appreciated by the research community. The central aim of this paper is to discuss the challenges associated with administrative data. We emphasise that it is critical for researchers to carefully consider how administrative data has been produced. We conclude that administrative datasets have the potential to contribute to the development of high-quality and impactful social science research, and should not be overlooked in the emerging field of big data.
What is big data? There is no single clear definition of big data. de  Goes (2013)  has gone as far as to suggest that the term big data is too vague and wide-ranging to be meaningful. In this section we summarise some of the definitions of big data in an attempt to bring more clarity to what big data constitutes. Taylor et al. (2014)  conducted a series of interviews with high profile economists working in this field in an attempt to better understand big data and its uses. These researchers identified the size and complexity of datasets as a key component of big data. Centrally, they emphasised that the increased number of observations and variables available in datasets were the result of a shift in the sources of data which were available to them (especially from the internet and social media).
Generally the size and coverage of datasets are a central element of the definition of big data. Einav and Levin (2013)  also emphasise that data is now available faster, and has a far greater coverage than the data resources which were previously available to social researchers. Much of the literature discussing big data focuses on data which results from online activities and the use of social media (see for example  Tinati et al. , 2014). This type of data may be produced through online searches, internet viewing histories, blogs, social media such as Twitter and Facebook posts, and the sharing of videos and pictures. 1 The growth of the internet and electronic social networking has resulted in the unprecedented collection of vast amounts of data. The use of internet and social media data have resulted in numerous research studies investigating a wide range of topics such as individual's moods (e.
Made data Made data collected through experimental methods are designed and collected to address well defined hypotheses (see first panel of Fig. 1 ). These data may be large and complex, but they are usually smaller than administrative social science data sources. These data are highly systematic (i. e. structured and clearly organised). The researcher also has clear information on the sample that these data come from. We can therefore be clear about the representativeness of these data and the possibilities for wider inferences. As these data are collected specifically for the analysis of a small number of hypotheses, the reuse value of these data by other researchers may be relatively limited. The second panel of Fig. 1  describes observational data. This is perhaps the most widely used type of data in social science research and includes the large multipurpose social survey datasets that are used extensively in quantitative social research (e.
g. the European Social Attitudes Survey, The German Socio-Economic Panel, The Panel Study of Income Dynamics, and the UK Millennium Cohort Study). In contrast to experimental data these resources are not collected with the aim of answering a single clearly defined research question. These infrastructural data resources are designed by researchers to provide the information required to answer a wide range of research questions. Because of the general nature of these data resources they have high re-use potential in social science research. As large multipurpose social survey datasets are designed for the purpose of research, a great deal of thought is placed into how information is collected to ensure that the data produced is of the highest quality, is suitable for research purposes, and that the measures included are valid and reliable.
Administrative social science data As we have described, administrative social science data differs from made data as it is not originally collected for the purpose of research (see panel 3 of Fig. 1 ). Researchers generally have no input into the design, structure and content of administrative social science data. These datasets can be large, however they are often not as large as the types of big data collected through for example, social media, GPS tracking, or supermarket transactions. These data are also likely to be more complex than the well curated social survey data resources which researchers may be accustomed to. The data may be messy and the use of administrative social science data resources is likely to involve substantial data management (or enabling) to clean and organise the data into the format required for analysis.
Many administrative social science datasets will be multidimensional in nature (i. e. the data will come in the form of fragments from different administrative systems). This multidimensional character is typically achieved through data linkage to join together datasets to gain all the pieces of information required to answer a social science research question (see  Elias, 2014). Administrative data will generally be drawn from a known population, and will often retrieve information from an entire population rather than a sample (e. g. all 16 year olds completing school examinations in a given year). Often the administrative data available will represent specific populations, for example welfare benefits records will only include information on those individuals who claimed benefits, and this may restrict the focus of some research questions.