dataesr/scientific-paragraphs-categorization huggingface dataset:
We notice clearly the strong signature of the floods caused by cyclone Charlotte, since the regional solutions reveal important positive water heights of ≈ 600 mm of EWH, higher than the levels described by the solutions of the previous months. For our regional solutions, this corresponding TWS anomaly is localized in the Carpentaria drainage basins area (see Fig. 1 ). This sudden meteorological event agrees quite well with the BoM rainfall database (Fig. 13d ) and GRGS RL02 estimates. However, ICA 400 km-filtered solutions show a very low amplitudes of this event, as these combined solutions remain smooth. Leblanc et al. (2009)  have shown the effects of drought over Murray-Darling Basin from 2002 to 2009, and they compared GRGS-RL02 global solutions to the sum of in situ data for surface and ground waters plus model output for soil moisture.
Acknowledgements. We would like to thank  Laurent Longuevergne  and anonymous reviewers for their fruitful comments during the preparation of the manuscript,  Adam Fakes  for assisting with the processing of in situ groundwater data and  Albert Van Dijk  for providing Soil Moisture outputs of AWRA model. Lucia Seoane 's work was funded by  ARC  discovery projet at  James Cook University (Australia). Edited by:  Y. Fan The publication of this article is financed by  CNRS-INSU.
We introduce an automated method for the bottom-up reconstruction of the cognitive evolution of science, based on bigdata issued from digital libraries, and modeled as lineage relationships between scientific fields. We refer to these dynamic structures as phylomemetic networks or phylomemies, by analogy with biological evolution; and we show that they exhibit strong regularities, with clearly identifiable phylomemetic patterns. Some structural properties of the scientific fields -in particular their density -, which are defined independently of the phylomemy reconstruction, are clearly correlated with their status and their fate in the phylomemy (like their age or their short term survival). Within the framework of a quantitative epistemology, this approach raises the question of predictibility for science evolution, and sketches a prototypical life cycle of the scientific fields: an increase of their cohesion after their emergence, the renewal of their conceptual background through branching or merging events, before decaying when their density is getting too low.
However, whatever the driving forces behind science evolution, there is still a lack of empirical instruments allowing researchers to fully understand the nature of these transformations. Meanwhile, the recent ICT revolution has, at an ever-growing pace, opened up new digital spaces, offering new opportunities to track the dynamics of knowledge through the examination of its digital trails. Scientific research pioneered this trend towards a stigmergic society when the ''Science citation Index'' was first published in 1961  [8]. The current massive availability of online data related to science production provides a unique opportunity to map the ever-fluctuating scientific landscape and to unveil regular dynamical patterns from large-scale longitudinal observations. Nevertheless, we still need specific data-mining methods to extract patterns from these large databases.
These structures allow us to focus on the dynamics of science, whereas most empirical studies usually focus on the structure of science and think of datasets as crystallized snapshots of science production at a given period. These science phylomemies describe how scientific fields evolve, and provide a convenient model to answer questions such as: what are the most striking regularities in science evolution? What are the emerging fields at a given time? From which scientific fields does a new field inherit its intellectual background? Which ''paradigmatic'' shifts have been the most dramatic? etc. In this article, we show that, from this perspective, co-word analysis is a suitable approach. We firstly describe the general methodology which has been devised to enable a bottom-up reconstruction of science phylomemy, and then demonstrate that this approach provides evidence of strong regularities in science evolution.
Key-phrase Extraction In order to propose methods which can be scaled to various data sources, we chose to work with aggregated co-occurrences data relevant to terms in documents, which can easily be extracted from digital corpora using classical indexing technique. Our main dataset is based on a scientific publications corpus related to research in embryology. It comprises approximately 200,000 articles, extracted from the ''Thomson Web of Science'' (WoS), in which the stem ''embryo'' is used in their title or abstract. Co-word analysis critically depends on the initial set of terms chosen for the study and can be biased by the ''indexer effect''  [11] [12] [13] : library managers may choose keywords which are too general, place incorrect emphasis on some terms, etc. For this reason, we performed an automatic lexical extraction of the set of terms to be considered based on titles and abstracts in our datasets.
The Natural Language Processing (NLP) tools we apply for the purposes of lexical extraction allows not only simple terms, but also multi-terms (up to 4, also called n-grams) to be identified. Although the automatic extraction of multi-terms is a classical task in NLP, the existing tools are not always well suited when one wishes to extract only domain-specific noun phrases, that is groups of relevant terms featuring both high unithood and high termhood, as defined in  [14]. For that reason, we designed our own workflow for processing of the textual data, which is described in Supporting Information S1. An improved version of this workflow is available online in the open access tool CorText Manager (http://manager. cortext. net). This linguistic processing chain produced a list L of 2000 ngrams which should constitute the most salient terms in embryology science.
This list was checked manually by experts in the field, who identified a little more than ten considered to be irrelevant. In view of this this high rate of relevance, we chose to keep the full list, in order to minimize the influence of human intervention in the process. These terms were then indexed in the 200,000 retrieved titles and abstracts dating from 1990 to 2010, to build up the set of co-occurrence matrix fM t ,t[½1990,2010g based on this terms list. The co-occurrence n T ij between two terms i and j over a period T is simply defined as the number of articles published during this period which mentioned both of the terms i and j at least once in their abstract or title. In the case of our dataset, T is taken to be one year, which is the finest resolution which can be achieved for this parameter.
In order to test the robustness of our results with respect to dataset variations and the quality of the terms list, we performed a similar analysis on a second dataset, but choose to remove irrelevant terms. This second dataset, called ''bio-networks'', is more trans-disciplinary. It comprises a biomedical corpus related to the research which recourses to the concept of networks. It includes approximately 140,000 articles extracted from Medline (http://www. ncbi. nlm. nih. gov) using the stem ''netw'' in their title or abstract. A list of 834 salient terms have been extracted from this corpus. As presented in the Supporting Information S2, all our results were reproduced on this second dataset. All terms lists and co-occurrence data used in this paper are available online at http://hdl. handle.
Measuring Proximities The use of co-occurrence data in scientometrics assumes that the greater the probability of two elements co-occuring in the same article, the more strongly they are related. This co-occurrence data can be of various types and may connect authors -co-authorship networks  [15] , references -co-citation networks  [16] , or terms -coword networks  [17, 18]. We focus on the latter type of data, in the framework our of co-word analysis. In this approach, cooccurrences of terms are computed over large corpora. A graph is then generated, on which the nodes represent the terms, and the edge strength represents their alleged similarity, which is a function of the co-occurrence data. Higher level structures reflecting various domains of science are then derived using clustering methods, by analyzing patterns within this graph.
Related Work To our knowledge, four other papers address the modeling of scientific field transformations based on bibliometric data  [28] [29] [30] [31]. [28, 29]  study journal citation data, which provide a different level of analysis, yet complementary to co-occurrence analysis, and leads to a lesser resolution than term co-occurrence analysis for the reconstruction of science evolution. There are also significant differences between  [30, 31]  and our approach regarding the initial methodological step. [31]  directly mines raw textual content but does not involve advanced semantic pre-processing: every monograms is taken into account as primary material. This strategy may imperfectly reflect the richness of scientific landscapes. As for  [30] , publications are described through PACS numbers which offer a general top-down scheme for categorizing Physics literature.
Results We performed a phylomemy reconstruction on the WoS database focusing on studies of embryology between 1991 and 2010 (see Fig. 4  for an example and the appendix for the delineation of the corpus and terms list). After the removal of interlocked clusters, the phylomemetic network of all fields of more than four elements features 122,416 non-isolated nodes. It comprises different disconnected components, corresponding to large domains of embryology clustered in a time-consistent manner.
Another possible mechanism is based on the hypothesis of a change in the fundamental constants of nature, specifically the fine structure constant, α ([8]-  [11] ). In this framework, an interesting discrepancy with the expectations of the standard model has recently been discovered in the small CMB scale measurements of the ACT  [2]  and SPT  [4]  experiments. The effective number of relativistic degrees of freedom N ef f (for a rewiev on N ef f see  [12] ) has been reported as higher (at more than two standard deviations) than the expected standard value of N ef f = 3. 046 (the little deviation from N ef f = 3 takes into account effects from the non-instantaneous neutrino decoupling from the primordial photon-baryon plasma, see for more deatails  [13] ). This discrepancy has been confirmed by several recent analyses of the ACT and SPT datasets (see e.
Method and Analysis The analysis method we adopted is based on the publicly available Monte Carlo Markov Chain package cosmomc  [21]  with a convergence diagnostic done through the Gelman and Rubin statistic. A COSMOMC analysis  [21]  was performed combining the following CMB datasets: WMAP7  [1] , ACT  [2] , ACBAR  [3] , SPT  [4] : we analyzed datasets out to l max = 3000, and we include also information on dark matter clustering by using the SDSS-DR7 luminous red galaxy sample  [5]. We impose a prior on the Hubble parameter based on the last Hubble Space Telescope observations  [22]. We sample the following six-dimensional standard cosmological parameters, adopting flat priors on them: the baryon and cold dark matter densities Ω b h 2 and Ω c h 2 , the Hubble constant H 0 , the optical depth to reionization τ , the scalar spectral index n S , and the overall normalization of the spectrum A S  [23].
In order to perform this analysis, we consider purely adiabatic initial conditions and we impose spatial flatness. The variations in the fine structure constant α/α 0 where α 0 is the current, local, value are implemented in the code by modifying the RECFAST recombination subroutine ([24] -  [27] ) following the procedure described in  [28]. Table  1. In the table are reported the results for the three analyses described in the text by using a MCMC estimation of the cosmological parameters from the dataset described in the Analysis section. Upper bounds at 95% c. l. are reported for foregrounds parameters  [23]. primordial Helium abundance Y p , otherwise fixed at the values N ef f = 3. 046 and Y p = 0. 24, are included. Since we are varying also the Helium abundance, we considered variations in α also in the process of Helium recombination.
A ∼ 5% change of α for Helium recombination changes the CMB angular spectra by less than 0. 5% up to l = 1500. We focus on constraing the variations of the fine structure constant at the epoch of Recombination and we fixed the fine structure constant to the local standard value α = α 0 in the epoch of reionization  [23]. P arameter α/α 0 α/α 0 +N ef f α/α 0 +N ef f +Y p Ω b h 2 0. The dataset considered prefers a value of α/α 0 smaller than unity at more than two standard deviations when both the N ef f and Y p are kept fixed at their standard values as shown in the table 1. This result was to be expected since is clearly driven by data preference for larger values of N ef f. Allowing for variations in N ef f significantly shifts the best fit value for α/α 0 , causing it to be now consistent with the standard value.
Conclusions We presented new constraints on variation of the fine structure constant using the recent CMB anisotropy measurements from the ACT and SPT experiments, combined with other cosmological datasets. Assuming the standard three active neutrino framework and a primordial Helium abundance of Y p = 0. 24 the current data favours a lower value for the fine structure constant at more than two standard deviations with α/α 0 = 0. 984 ± 0. 005. When the number of relativistic degrees of freedom is let to vary freely, the standard value is consistent with the data considered, while varying the primordial Helium abundance further enlarges the error bars. The recent Planck data delivered on March 2013 shows an improvement on the constraints on the variations of the fine structure constant by a factor of about five if compared with those from WMAP-9 data.
The scaling between X-ray observables and mass for galaxy clusters and groups is instrumental for cluster based cosmology and an important probe for the thermodynamics of the intracluster gas. We calibrate a scaling relation between the weak lensing mass and X-ray spectroscopic temperature for 10 galaxy groups in the COSMOS field, combined with 55 higher mass clusters from the literature. The COSMOS data includes HST imaging and redshift measurements of 46 source galaxies per arcmin 2 , enabling us to perform unique weak lensing measurements of low mass systems. Our sample extends the mass range of the lensing calibrated M-T relation an order of magnitude lower than any previous study, resulting in a power-law slope of 1. 48 +0. 13  -0. 09. The slope is consistent with the self-similar model, predictions from simulations, and observations of clusters.
However, this type of studies have only been performed in the cluster mass regime by  Smith et al. (2005) ; Hoekstra (2007);  Okabe et al. (2010) ;  Jee et al. (2011); Hoekstra et al. (2012) ;  Mahdavi et al. (2013). The aim of this work is to calibrate the scaling between weak lensing masses and Xray temperatures of the hot intracluster gas for a sample of galaxy groups in the COSMOS survey field. This work is an extension to  Leauthaud et al. (2010) , who investigated the scaling between weak lensing mass and X-ray luminosity in the same field. This paper is organized as follows. We present the data and galaxy group sample used for our analysis in Sections 2 and 3, and give details on the X-ray and weak lensing analysis in Sections 4 and 5. We present the resulting M -T relation in Section 6, discuss our findings in Section 7, conclude and summarize our findings in Section 8.
2007 , XFLAG = 1). As our data allows us to extend our lensing analysis out to large radii, possible substructure in the central parts visible in X-rays is not relevant for our mass estimates. Instead, infalling subgroups at cluster outskirts are more important. Based on our X-ray group catalog, we can rule out this kind of substructure at > 20-30 % level in mass. We adopt the coordinates of the X-ray peaks as the locations of the group centers, but we also tested the effect of using the MMGG as a center in performing the lensing analysis (Section 5. 3). The properties of the clusters in our sample are presented in Table  1. The deep X-ray coverage and high density of background galaxies with determined shear in the COSMOS field allows us to treat each system individually in our analysis.
X-RAY REDUCTION AND ANALYSIS For the X-ray analysis we used EPIC-pn data from the XMM-Newton wide field survey of the COSMOS field  (Hasinger et al. 2007)  with the latest calibration information available in October 2012 and XMM Scientific Analysis System (SAS) release xmmsas 20120621 1321-12. 0. 1. We produced event files with the epchain tool and merged the event files of pointings which were within 10 arcmin of the adopted group center for each system. The merged event files were filtered, excluding bad pixels and CCD gaps and periods contaminated by flares, and including only events with patterns 0 -4. We generated out-of-time event files, which we subsequently used to subtract events registered during pn readout. We extracted spectra from an annulus corresponding to 0. 1 -0. 5 R 500 (see Table  2 ).
The lensing signal might be diluted, if a significant number of group galaxies are scattered into the source sample. For instance,  Hoekstra (2007)  showed in Fig. 3  that the effect is modest for high mass clusters using ground based data (∼ 20 % at R 2500 ). As our space based data is deeper, giving a larger number of sources, and we analyse low mass systems with a smaller number of member galaxies, the effect on our sample is significantly smaller. The effect is mainly limited to the central parts of the groups, which we cut out from our analysis. Furthermore, as our photometric redshifts are based on 30+ bands and we exclude source galaxies with secondary redshift peaks, our lensing masses are unaffected by contamination by group members. We calculated the surface mass density contrast ∆Σ i,j for each lens -source pair using Equations 1 and 2.
The lensing masses, based on deep CFHT imaging data, are presented in  Hoekstra et al. (2012) , whereas the Xray temperatures are taken from  Mahdavi et al. (2013). The X-ray temperatures in  Mahdavi et al. (2013)  are obtained with both Chandra and XMM-Newton, but Chandra temperatures are adjusted to match XMM-Newton calibration. This gives us a total sample of 65 systems with masses and temperatures spanning the range of a few times 10 13 to a few times 10 15 M ⊙ and 1-12 keV. Fitting the M 500 -T X relation to the whole extended sample we obtained the best-fit parameters α = 1. 48 +0. 13  -0. 09 and log 10 N = 0. 34 +0. 02 -0. 04 with χ 2 = 112. 57 for 63 degrees of freedom (see Table  4 , Fig. 7  and 8 ). We evaluated intrinsic scatter of the relation by making a distribution of the ratio of data to best-fit model for each point and computing the dispersion.
1  (right). The 2011 Pb-Pb data sample, collected with the combination of a minimum bias trigger and a centrality trigger based on the VZERO scintillators response, allowed to roughly collect a factor of 10 more statistics in the 0-7. 5% centrality class than in the 2010 data sample, and triplicate the statistics it in the 10-50% centrality class. In addition, the EMCAL triggers allowed to enhance the electron sample. Prompt D meson R AA (p T ) was measured in the 0-7. 5% centrality class with the 2011 data sample up to 36 GeV/c, see Fig. 1  (left)  [19, 20]. D 0 , D + and D * + meson R AA (p T ) agree within their uncertainties and show a suppression of up to a factor of 5 at p T ∼ 10 GeV/c. The first measurement of D + s R AA in heavy-ion collisions is also shown. In the 8-12 GeV/c p T bin, the D + s R AA is compatible with that of non-strange charmed mesons.
Selecting the science targets for the programme The sample contains supergiants and long-period variables (LPVs), bright enough (m K < 2) to be measured by the VLTI subarray (1. 80 m auxiliary telescopes) with high SNR. In Table  1 , we compile their relevant observational parameters, including possible multiplicity and variability. On one hand, the scientific targets must be resolved well enough, which results in visibilities clearly smaller than unity. On the other hand, visibilities higher than ∼ 0. 1 1 acronym of SPectro-Interferometric Data Analysis Software Tool (H band) are necessary to allow the fringe-tracker FINITO 2  to work under optimal conditions  (Gai et al. 2004 ). These two contradictory constraints impose the usable range of the spatial frequencies f = B/λ, where B is the baseline length and λ the observation wavelength, to be that associated with the second lobe of the Uniform-Disc (UD) visibility function.
DERIVING THE ANGULAR DIAMETERS The true (calibrated) observables, defined hereafter, are derived from the AMBER output measurements, using the spidast modular software suite we have developed since 2006  (Cruzalèbes et al. 2008 (Cruzalèbes et al. , 2010 (Cruzalèbes et al. , 2013)). Recently made available to the community 3 , spidast performs the following automatised operations: weighting of non-aberrant visibility and triple product data, fine spectral calibration at sub-pixel level, accurate and robust determinations of stellar diameters for calibrator sources, and of their uncertainties as well, correction for the degradations of the interferometer response in visibility and triple product, fit of parametric chromatic models on SPI observables, extraction of model parameters. We measure the angular diameter for each scientific target, by fitting synthetic limb-darkened brightness profiles on the visibility and the triple product.
ACKNOWLEDGMENTS The authors thank the  ESO-Paranal VLTI team  for supporting their AMBER observations, especially the night astronomers  A. Mérand ,  G. Montagnier ,  F. Patru ,  J. -B. Le Bouquin ,  S. Rengaswamy , and  W. J. de Wit , the VLTI group coordinator S. Brillant, and the telescope and instrument operators A. and  J. Cortes ,  A. Pino ,  C. Herrera ,  D. Castex ,  S. Cerda , and  C. Cid. A. J. is grateful to  T. Masseron  for his ongoing support on the use of the marcs code. The authors also thank the  Programme National de Physique Stellaire (PNPS)  for supporting part of this collaborative research. S. S. was partly supported by the  Austrian Science Fund  through  FWF  project  P19503-N16 ; A. C. was supported by  F. R. S. -FNRS (Belgium ; grant  2. 4513. 11 );E. P. is supported by PRODEX; K.
Another interesting problem to solve is to include higher twist operators, at least at the leading g 0 order. It would be also interesting to compare our results to the strong coupling calculations of similar correlators (corresponding to the GKP states on the string side) performed in  [19]. The details of this comparison contain subtle questions of normalization of operators which have to be elucidated prior to such comparison. manuscript. Our work was also partly supported by the ANR grants StrongInt (BLANC-SIMI-4-2011) and by the ESF grants HOLOGRAV-09-RNP-092 and ITGP. We are thankful to the Israel Institute for Advanced Studies in Jerusalem and to Institut für Mathematik und Institut für Physik, Humboldt-Universitat zu Berlin for hospitality during the initial stage of this work. V. K.
Acknowledgements We thank  N. Gromov ,  I. Kostov ,  R. Janik ,  D. Serban ,  A. Sever ,  P. Vieira , and  K. Zarembo  for interesting discussions. The comments of N. Gromov,  D. Serban ,  P. Vieira , and  K. Zarembo  to the manuscript was very useful. We are especially grateful to  G. Korchemsky  who taught us a lot of very useful facts at various stages of this project and carefully read the.