dataesr/scientific-paragraphs-categorization huggingface dataset:
ru) which is very convenient to display the data but does not allow downloading data. Consequently, data have to be copied by hand or other solutions such as optical character recognition which are not fully satisfactory. Whatever the solution, it requests manual handling of numbers which may be another source of error. Getting data from the WRDC has some risk and burden. However, it is a much better situation than if one has to request data separately to each NMS. Several stations belong to the Global Atmosphere Watch (GAW) program of the WMO and exhibit good quality, including a quality flag. Data may be downloaded from the Web site (wrdc. mgo. rssi. ru) in HTML format. Other stations belong to the Baseline Solar Radiation Network (BSRN) and exhibit high to good quality, including quality flags.
Data may be downloaded from the Web site (wrdc. mgo. rssi. ru). In the present work, only ground stations that are known to the authors for the reliability of their measurements were kept. In addition, the authors have also collected data set from the PIRATA network of buoys in the Tropical Atlantic Ocean  [7]. These sites do not suffer any orographic effect and have been selected for the sake of the demonstration. Corrections are brought to measurements to take into account exposure of instruments to elements such as sea-spray, natural and anthropogenic aerosols. The corrected PIRATA data sets have been downloaded from the Pacific Marine Environmental Laboratory (PMEL) of the National Oceanic and Atmospheric Administration (NOAA) of the USA (www. pmel. noaa. gov/tao/data_deliv). Pyranometers installed on the buoys may experience accumulation of African dust, potentially leading to significant underestimation of the SSI  [13].
The results for HelioClim-1 have been added. For each climatic area, each table reports the mean of the observations for this area, the ranges of bias, relative bias, RMSE, relative RMSE and correlation coefficient observed for the set of stations located in the area. Figs. 5  and 6  exhibit the relative bias and relative RMSE for respectively the SSI and KT for the six areas and permit a visualisation of the differences between the data sets and the variability of performances within an area. Except in few occasions, MERRA overestimates the SSI and KT. The RMSE is large in all cases, except for desert areas, when clear sky conditions prevail and as a consequence, the influence of false prediction of clear sky conditions by MERRA is of lesser importance. The correlation coefficient for daily irradiation is usually large and greater than 0.
The number of reliable data from stations collected in the present study is very limited in the tropical humid climate affecting Central Africa, Equatorial Atlantic Ocean, and Northern South America and no clear explanation may be provided on why these areas exhibit greater uncertainty that the areas in dry climate or rainy climate with mild winters. To conclude, MERRA and ERA-Interim should only be used in solar energy with proper understanding of the limitations and uncertainties. In regions where clouds are rare, e. g. North Africa, MERRA or ERA-Interim may be used to provide a gross estimate of monthly or yearly irradiation. In all cases, a correction of MERRA or ERA-Interim by a function fitted on available ground measurements as shown in Figs. 1e4 will reduce the bias but not significantly the scattering of the estimates, i.
Acknowledgements The research leading to these results has received funding from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) under Grant Agreement no. 262892  ( ENDORSE  project). The authors thank all ground station operators of the WMO network, GAW, BSRN and PIRATA networks for their valuable measurements. They thank the  World Radiation Data Centre , the  National Renewable Energy Laboratory (USA) , the  Alfred-Wegener Institute  and the  PMEL  for hosting the websites for downloading data. The authors are indebted to  Francesca Di Giuseppe  and  Michael G. Bosilovich  for their help in understanding ERA-Interim and MERRA respectively and improving the content of the paper. The authors thank the company Transvalor which is taking care of the  SoDa Service  for the common good, therefore permitting an efficient access to the HelioClim databases.
The investigation of the ability to perceive, recognize, and judge upon social intentions, such as communicative intentions, on the basis of body motion is a growing research area. Cross-cultural differences in ability to perceive and interpret biological motion, however, have been poorly investigated so far. Progress in this domain strongly depends on the availability of suitable stimulus material. In the present method paper, we describe the multilingual CID-5, an extension of the CID-5 database, allowing for the investigation of how non-conventional communicative gestures are classified and identified by speakers of different languages. The CID-5 database contains 14 communicative interactions and 7 non-communicative actions performed by couples of agents and presented as point-light displays.
For each action, the database provides movie files with the point-light animation, text files with the 3-D spatial coordinates of the point-lights, and five different response alternatives. In the multilingual CID-5 the alternatives were translated into seven languages (Chinese, Dutch, English, French, German, Italian, and Polish). Preliminary data collected to assess the recognizability of the actions in the different languages suggest that, for most of the action stimuli, information presented in point-light displays is sufficient for the distinctive classification of the action as communicative vs. individual, as well as for identification of the specific communicative gesture performed by the actor in all the available languages.
In the following, we present first a brief background on the point-light technique used to create stimuli included in the multilingual CID-5, and in the original CID-5 database. Next, we provide a detailed description of multilingual CID-5 database, including all the materials available for download. Finally, we present normative data collected to assess the stimulus classification (communicative vs. individual) and intention identification by speakers of seven different languages, namely Chinese, Dutch, English, French, German, Italian, and Polish.
The CID-5 Database The CID-5 database (Communicative Interaction Database, Five Alternative Forced Choice format, 5AFC) contains 21 full-body point-light stimuli depicting two agents (A and B) engaged either in communicative interactions (N = 14) or noncommunicative individual actions (N = 7) as seen from four different viewpoints. Following  Dekeyser et al. (2002) , stimuli were constructed by combining motion capture techniques and 3-D animation software to provide precise control over the computer-generated actions and allow the actions of the two agents to be independently manipulated. For each action stimulus, the CID-5 provides (i) coordinate files for each actor; (ii) movie files depicting the action of the two agents as seen from four different perspectives; (iii) five action alternatives describing the action performed by the two agents.
The CID-5 database can be freely downloaded from http://bsb-lab. org/research/. Results collected on a sample of 113 Italian speaking participants using these stimuli confirmed that naive observers are able to distinguish communicative and individual gestures, and to identify the correct action description among the five alternatives  (Manera et al. , 2015). The multilingual CID-5 extends the CID-5 by providing a translation of the response alternatives into seven different languages: Chinese, Dutch, English, French, German, Italian, and Polish. Furthermore, it provides some normative data to validate the alternative action descriptions in the different languages.
The communicator (agent A) always initiated the interaction by performing a communicative gesture; the responder (agent B) perceived the communicative gesture and acted in response, based on a predefined interaction plot. To ensure that the responder's action matched the communicator's gesture in all respects (e. g. , timing, position, kinematics), interactions were captured in real time, with the actors facing each other, at a distance of approximately 2 m. Individual actions were performed by agent A acting in isolation. Objects (e. g. , table, chair, coins, fruits) were present during the production of actions to aid the actors in producing natural movements. After the capture session, the 2-D data from all the position units were processed offline to calculate the 3-D coordinates of the markers.
Stimuli and Procedure Twenty-one point-light actions taken from CID-5 database were employed  (Manera et al. , 2015) , including 14 communicative interactions in which the two agents (A and B) were engaged  the screen until a response was provided. Secondly, participants were asked to select the correct action description among five numbered response alternatives displayed simultaneously (intention identification). The order of the response alternatives was randomized across stimuli. The question was presented on the screen until response, with no time restriction. No feedback concerning response correctness was given to the participants. Depending on the sample, instructions, questions, and response alternatives were presented in Chinese, Dutch, English, French, German, Italian, or Polish.
Data analysis Global performance. To evaluate global performance, for each language we calculated the percentage of participants who correctly responded to the intention classification and the intention identification questions, and we extracted Signal Detection Theory (SDT) parameters. To evaluate how participants distinguished between communicative and individual action stimuli (intention classification), we calculated sensitivity (d ) and criterion (c ). For each participant, we calculated the proportion of hits (arbitrarily defined as "communicative" responses when the action stimulus was communicative) and false alarms ("communicative" responses when the action stimulus was individual). Proportions of 0 were replaced with 0. 5/N, and proportions of 1 were replaced with (N-0. 5)/N (where N is the number of communicative and individual stimuli).
DISCUSSION In the present paper we describe the multilingual CID-5, a database of 21 full-body point-light stimuli depicting two agents engaged in communicative interactions (N = 14) or performing non-communicative individual actions (N = 7) as seen from different viewpoints. For each stimulus, we provide five plausible response alternatives (only one being correct) translated into seven different languages (Chinese, Dutch, English, French, German, Italian, and Polish). Normative data collected from 140 naive participants (20 participants per language) confirmed that all the stimuli included in the multilingual CID-5 were classified as communicative vs. individual and recognized well above chance level from participants of all the seven language samples. Comparisons of global performance across different languages revealed no difference across samples in the ability to classify actions as communicative vs.
Furthermore, the error analysis showed that when the intention identification was incorrect, participants in the different language samples tended to select the same wrong alternative, suggesting that, for most of action stimuli, some response alternatives were more misleading than others in all languages. These results provide evidence of instrument validity of the multilingual CID-5 as a new tool for the investigation of nonconventional communicative gestures in different languages. It is important to note that our data collection was designed to validate the alternatives in the different languages, and not to explore systematically cultural differences. Thus, from the present results, we cannot conclude that classification and identification of communicative gestures does not vary across cultures.
As opposed to the former client-server approach to network communications, decentralized architectures rely on a network of peers that act both as clients and servers, depending on the circumstances. Every node in the network is equally important (although the model might allow for supernodes, which have priority over the other nodes) and they all contribute to managing access and routing traffic through the network of peers. This model is inspired from the advent of distributed applications designed to allocate tasks and workloads amongst a network of peers, first popularized with the deployment of P2P file sharing applications. Yet, the model inspired people to experiment with decentralized structures in many other areas of human interaction --from software development with the open source movement  (Healy & Schussman, 2003) , to artistic production with Creative Commons licenses  (Lessig, 2004; Benkler, 2006) , and, more recently, the implementation of decentralized monetary systems, such as Bitcoin and other derivative crypto-currencies  (Nakamoto, 2008).
Increasingly, citizens are being tracked by communicating smart-devices: computers, tablets, smart-phones or other interactive devices which constantly collect data (including personal data) from their surrounding environment, aggregate them into a central database and process them with a view to better understand the current state of affairs, or even anticipate potential problems and risks. Despite the significant costs it might entail in terms of privacy and data protection, such a massive collection and analysis of data can, however, hardly be avoided. Indeed, smart cities need to collect information about their citizens, in order to better understand their characteristics, behaviours, and needs, so as to provide them with a more customized service that is likely to increase the city's standard quality of life.
g. Golle & al. , 2001; Ranganathan & al. , 2003; Antoniadis & al. , 2004 ). An interesting application of CMN with reference to smart cities is illustrated by the Smart Citizen project, an initiative launched by Tomas Diez (director of Fab Lab Barcelona) aimed at empowering citizens to achieve a better quality of life by supporting and promoting more citizen's participation in better understanding and improving the city they live in. The Smart Citizen kit is an Arduino-operated device that comes with a set of low-cost modular open hardware sensors that can be used to capture, process and analyse real-time environmental data (such as air quality, temperature, sound or humidity). By creating a mesh network of such sensors, data collected by a variety of citizens can be shared on the Smart Citizen platform to be subsequently aggregated into a common database from which new knowledge or indicators can be extracted.
commotionwireless. net). Similar tools are also being developed by other communities -such as MeshNet (https://projectmeshnet. org), NodeWatcher (http://dev. wlan-si. net/wiki/Nodewatcher), or the Serval Project in Australia (http://www. servalproject. org) -whose goal is, ultimately, to allow anyone to deploy the necessary software infrastructure to enable direct communications between a variety of user's devices. Some communities even went one step further, by providing pre-installed and pre-configured hardware devicessuch as the Open-Mesh routers from MIT that only need to be plugged into an electrical outlet (and, ideally, to an Internet connection) to provide connectivity on-the-fly (see http://open-mesh. com for more details). But mesh networking technologies are also progressively being deployed on our everyday's devices.
ε = constant = f (h) Gentine et al. (2013a) Entraining plume model. ε = f (z) The Institut Pierre Simon Laplace (IPSL) climate model has been run with three different physics packages, corresponding to the two versions available in the CMIP5 archive plus a newer version. They have been run with 39 vertical levels and a 450 s time-step. The first physics package, LMDZ5A used in IPSL-CM5A  (Dufresne et al. , 2013) , is close to the version previously used in the CMIP3. The second physics package, LMDZ5B used in IPSL-CM5B, has been completely revisited, with modifications to the representation of the boundary-layer turbulence and how it is coupled to the convection scheme  (Hourdin et al. , 2013). The LMDZ5S (for LMDZ5 stochastic) uses the same physics package as the LMDZ5B, but with a modification of the triggering of deep convection that takes into account a spectrum of thermal sizes  (Rochetin et al.
Prediction of high-frequency EEG waveforms To predict somatosensory-evoked EEG bursts from neuronal spikes, we convolved single-neuron post-stimulus time histograms (PSTHs, bin width 0. 1 ms) with the respective single-neuron EEG-STAs. In most of the sessions, we recorded the activity of only a single neuron from the responding population; to approximate the mean response of the whole population, we averaged EEG contributions of neurons recorded across all the sessions. The statistical error in the predicted EEG burst may originate from noise in estimation of EEG-STA and from limited sampling of neurons used to estimate the population response. To derive confidence intervals that take into account both sources of variability, we used the bootstrap method. This method allows the estimation of the empirical distribution of EEG burst amplitudes based on a repeated sampling from the available dataset.
In the first step of the analysis, we randomly selected a single cell from the dataset. Next, we calculated the EEG-STA from spikes drawn randomly with repetition from all spikes of this cell, such that the total number of averaged segments was conserved, and we convolved it with the cell's PSTH. We repeated the procedure as many times as the total number of cells (by drawing 40 cells with repetition) and then averaged the results, which gave us a single estimate of an EEG burst. To obtain the amplitude distribution, we calculated 500 of such EEG-burst estimates. Time-dependent confidence intervals were then obtained by taking 5% and 95% percentiles of this distribution at each time point separately.
In 3 out of the 6 cells the EEG-STAs and spike amplitudes were positively correlated, so that cross-talk could not be ruled out (Inline Supplementary Fig. S2 ). Inline Supplementary Figs. S1, S2 and S3 can be found online at http://dx. doi. org/10. 1016/j. neuroimage. 2014. 12. 057. Since these cells might have biased the grand-average, we repeated the analysis on a reduced dataset (34 out of 40 cells) where all 6 cells with significant EEG-STA peaks were excluded. The shape of the resulting grand-average EEG-STA of the reduced dataset was similar to that of the full dataset (Fig. 2e ); the peaks were smaller but still significant (Table  1 , peak-to-peak amplitude: 26. 8 ± 6. 0 nV, mean ± s. e. m. ). In addition, the correlation analysis over subsets of spikes from these 34 cells did not reveal significant correlations between EEG-STA and spike amplitude (Inline Supplementary Fig.
Acknowledgments We wish to thank  Tommaso Fedele ,  Vadim Nikulin , and  Gunnar Waterstraat  for valuable discussions, and  Alain Destexhe  for constructive comments on earlier versions of the manuscript. We also thank  Gunnar Waterstraat  for sharing human EEG data and  Dmytro Bielievtsov  for his technical assistance. This study was supported by the  Deutsche Forschungsgemeinschaft  ( DFG: SFB 618 "Theoretical Biology "), the  Bundesministerium für Bildung und Forschung (BMBF: Bernstein Center for Computational Neuroscience Berlin , grant numbers  01GQ1001A  and  01GQ1001C ;  Bernstein Focus "Neuronal Basis of Learning ", grant number  01GQ0972 ;  Bernstein Focus Neurotechnology Berlin , grant number  01GQ0851 ); the  CNRS; European Community  ( BrainScales  project,  FP7-269921 ); and The  Wellcome Trust  ( 101002/ Z/13/Z ).
1  H chemical shifts were referenced using the resonance of the (CH 2 ) n of drosophila fatty acids at 1. 3 ppm  13  as an internal reference. Water suppression (WS) was achieved using a selective presaturation of the H 2 O resonance (low-power pulse duration of 1 s). Data were processed with zero filling of twice the number of real points. Exponential apodization with a 2 Hz line broadening was applied prior Fourier transform. Uncertainties on isotropic chemical shift measurements are 0. 02 ppm (i. e. 15 Hz). The 1 H HR-MAS spectra of living drosophila and of the dissected parts of the fly were recorded using a spin echo sequence (echo time TE 5 0. 19 ms). 512 transients were acquired with a recycle delay of 2 s, corresponding to an experimental time of , 20 min. Assignment of the 1 H resonances was performed using 2D through-bond 1 H homonuclear correlation HR-MAS spectra and data from the literature  13, 19, 21.
As a complement to the activity-based Enzyme Commission classification, the sequence-based Carbohydrate-Active Enzymes (CAZy; www. cazy. org) classification has, since its inception in 1991, significantly augmented the mapping of structure-function relationships among enzymes involved in glycosidic bond formation and cleavage  15. More recently, the CAZy classification has been extended to encompass a range of Auxiliary Activity (AA) families (currently 13), which comprise a diversity of redox enzymes acting on polyphenolics and carbohydrates  16. Among these families, Auxiliary Activity Family 5 (AA5) encompasses known (methyl)glyoxal oxidases in subfamily 1 (AA5_1) and galactose 6-oxidases in subfamily 2 (AA5_2)  16  (Fig. 2 ). The occurrence of AA5 family enzymes is wide: as of October 2015, AA5 members in the public CAZy database comprised B90 fungal sequences in subfamilies 1 and 2, as well as a large number of bacterial AA5_2 members (ca.
1 AA5_2 GalOx (EC 1. 1. 3. 9) AlcOx (EC 1. 1. 3. -) AA5_1 GlyoxOx (EC 1. 1. 3. -) Figure  2  | Phylogeny of AA5. Subfamilies 1 (AA5_1) and 2 (AA5_2) are indicated. GenBank identifiers (UniProt identifier Q01745 in the case of the F. graminarium GalOx) are given for all sequences available in the public release of the CAZy Database  16  as of June 2015; full genus and species names of source organisms can be obtained from the respective GenBank entries. AA5 members for which enzymological data have been conclusively linked to protein sequence are indicated as (methyl)glyoxal oxidases (GlyoxOx)  9, 13, 14, 17, 18   is given further weight from a small handful of early studies of AA5 enzymes, which hint at a limited number of aliphatic alkyl alcohols being substrates for AA5 enzymes, albeit at extremely low levels of activity  [21] [22] [23].
Phylogenetic relationships of CgrAlcOx and CglAlcOx in AA5. Sequence alignment of 75 AA5 members in isolation from all predicted signal peptides and other ancillary protein modules, constituting the complete cohort of fungal sequences in the public CAZy Database, resulted in phylogenetic tree revealing a clear distinction between AA5 Subfamily 1 (AA5_1) and Subfamily 2 (AA5_2; Fig. 2a , see also http://www. cazy. org/AA5_subfamilies. html). Mapping enzyme activity data on this phylogeny reveals that the three (methyl)glyoxal oxidases fall into AA5_1 and the five Fusarium spp. galactose 6-oxidases fall into a clade of AA5_2. In addition, within AA5_2, the gene products of C. graminicola M1. 001 locus GLRG_05590. 1 (UniProt E3QHV8; GenBank EFQ30446; hereafter 'CgrAlcOx') and C. gloeosporioides Nara gc5 locus CGGC5_13025 (GenBank ELA25906; hereafter 'CglAlcOx') were selected for enzyme characterization due to significant amino-acid sequence differences with the archetypal galactose 6-oxidase from F.