dataesr/scientific-paragraphs-categorization huggingface dataset:
Additional software can assist you in the detection of the artifacts seen on ECG signals, like ArtIifact  (Kaufmann et al. , 2011). Finally, we can refer the reader to a useful guide on how to prepare the HRV recording prior to the analysis  (Shaffer and Combatalade, 2013). As a last remark, in case the experiment involved different conditions it would be advantageous that the person handling the HRV data, particularly when performing the artifact analysis, is unaware of the experimental conditions. This reduces experimenter bias and reduces the possibility of investigating for particular or suggestive results.
HRV Data Reporting Recent guidelines regarding the reporting of HRV studies were introduced by  Quintana et al. (2016)  and insist on the need to consistently report key experimental elements in order to advance of the field. The guidelines include four main elements: participant selection, IBI collection, IBI analysis and cleaning, and HRV calculation. They came up with an easyto-follow 13-items checklist that will prove to be very useful for psychophysiological researchers, even though the guidelines focused on psychiatry. Given the extensive description of those guidelines concerning the reporting of HRV studies, we won't elaborate in details here on this topic, and instead refer the reader to this very informative paper. However, we would just like to stress a crucial point regarding the specific reporting of HRV data in scientific papers: currently, readers may find themselves frustrated when reading the result sections of HRV psychophysiological experiments because often the variables used to display vagal tone were not the same.
This makes it extremely In the paper present one main variable illustrating vagal tone for comprehension purposes (e. g. , RMSSD, peak-valley, HF AR, and HF FFT); then submit as Supplementary Material all raw data as well as the analysis ran with the other HRV parameters to contribute to the development of HRV metrics and guidelines as well as the analyses ran with the other HRV parameters hard to compare results across studies as well as complicating the integration of findings into a comprehensive review or a meta-analysis, which subsequently hinders the development of the field. We understand that scientific journals have space restrictions and that every single HRV variable can't be displayed in every table. However, as more and more journals allow now attaching files as supplementary online material, whenever possible we recommend researchers to update their HRV raw data files as well as the statistical analyses realized with the other HRV variables, ensuring previously that ethics committee and participants explicitly agreed to public data sharing, even in an anonymous form.
CONCLUSION The aim of this paper was to provide the field of psychophysiology with practical recommendations concerning research conducted with HRV, specifically highlighting its ability to index cardiac vagal tone, which is relevant for many psychophysiological phenomena, such as self-regulation mechanisms linked to cognitive, affective, social, and health  (Porges, 2007; Thayer et al. , 2009; McCraty and Shaffer, 2015). These recommendations aimed to cover experiment planning, measurement standards, recommendations regarding data analysis and data reporting. Our endeavor here was not to strive to establish standards surrounding HRV, but rather to offer the reader with a comprehensive overview of the different issues to consider. Again, we could not be exhaustive and address in-depth all issues we talked about in this paper, but we always refer the reader to useful contributions that will help them make educated choices.
However, only a small number of systems perform well on languages other than English, and in particular, as in our case, on French contents  (Azpeitia et al. , 2014). Previous initiatives targeted specifically old French newspapers corpora  (Mosallam et al. , 2014) ; however, fictional novels are challenging in a different way since their referents are typically not listed in databases commonly used by many NER systems, like DBPedia, 1 Wikidata, 2 or YAGO. 3  One additional difficulty with non-English languages is the limited amount of readily available sets to train machine learning recognizers. Therefore, solutions that can achieve good results without the need of training beforehand are highly relevant. Our solution 4 combines a set of different standalone unsupervised recognition strategies feeding a meta-recognition system.
Test procedure We built a test corpus made out of 35 classic works (5M tokens) in French, stemming from digitized books part of Project Gutenberg. 5 The works were handpicked in a way to get a mix of different genres and narration types. They include 3 sagas, 6 classical books that were translated from other languages and 25 French classical novels. For each piece of work, we produced a corresponding reference file by extracting the proper nouns occurring more than five times and manually labeling each of them with the category they fall in (character, place, or other). The resulting database references about 3,700 categorized named entities, along with the work they come from and their number of occurrences. Given our further needs, we considered names referring to pets, companies, and groups of persons as regular characters whenever they serve the same narrative purpose (people mostly interacting with corporations or groups of people).
Conversely, they were considered places in the case of names used to qualify where actions take place (as with corporations considered as locations), or ethnical groups used to name their originating place (as in "le pays des Oreillons"). This differs a lot from most knowledge databases, as the very bounds between characters and places might be fuzzy and context dependent. For instance, if the word "France" is part of the expression "Roi de France, " we do not want it to be labeled as a place, but the whole entity as a character instead. For the actual test procedure, we ran each classifier on our corpus and computed the averagely yield precision (positive predictive value), recall (true positive rate) and F1 score of the classification task. Section 2 shortly discusses state of the art of existing tools under the same constraints, and sections 3.
Many traditional NER methods do not exploit this characteristic as they on the contrary rely on external databases to identify and classify named entities  (Jovanovic et al. , 2014). • Each novel tends to be characterized by specific stylistic features whose purpose precisely is to give the narration a unique taste. On the one hand, a novelist's writing diverges in nature from journalistic, academic, or other forms of non-fiction writings, and on the other hand, it is usually purposely meant to introduce noticeable differences compared to the style of other authors. This phenomenon is obvious enough that it was shown to allow to confidently attribute texts to their authors in many contexts  (Stamatatos, 2009). This intrinsic diversity makes a one-size-fits-all approach to NER difficult and is problematic for pattern-inducing machine learning algorithms.
classifier #4: grammatical Structure As characters and places serve different narrative purposes, one may expect the grammatical constructs surrounding them to differ in a significant way. For instance, place names are often preceded with prepositions or determiners, whereas it is expected for character names to be more often directly followed by verbs. We thus introduced a script classifying names based on its knowledge of the full text, grammatically tagged using TreeTagger, 11 and tokenized in sentences. To guess the nature of the names, it then matches all sentences containing them against a set of rules that are typical constructions one uses when writing about a person or a place. We tried out a set of seven manually established rules covering the most straightforward grammatical constructs (described in details in Table  1 ), plus two that help filter out tokenization errors at a sentence level by flagging words that are preceded by a punctuation mark or that are alone in their sentence.
classifier #6: Quotes-Based Several works already showed the relevance of locating direct and indirect speech parts to identify characters in novels  (Glass and Bangay, 2007; Goh et al. , 2012; Karsdorp et al. , 2012). Most of these approaches rely heavily on the lexical database WordNet 12  to find out speech-related verbs and refine their accuracy, but for performance reasons and since we wanted the classifiers to remain efficient even on very long texts we implemented a simpler version that simply checks the proximity of detected proper nouns to quotation marks. For each proper noun w appearing mw times, the system would essentially count the number qw of mentions that appear near quotations. It then computes: r m q w w w = (1) the quotation ratios for each noun, and:  t r r n r r w n n n = = + ( + median for an odd number of words { } 2 2 1 2 1 2 ) )      for an even number of words n (2) the median value of all ratios, to be considered the differentiation threshold for this book.
AcKnoWleDgMenTS This research was made possible by a very precious and close collaboration with Daniel de Roulet, Swiss architect, computer scientist, and author. It was supported by the Fondation Jan Michalski pour l'Ecriture et la Littérature. perform on average better than that, and we can see an encouraging trend that the various strategies we tried out tended to get increasingly better results and to close the standard deviation gap. That being said, as far as SD is concerned and taken into account, this improvement was not statistically significative. Yet, the meta-recognition method managed a F1 score no worse than 0. 674 and as high as 0. 99 on one of the books, on which it basically found all entities and only misclassified one of them.
The first attempts were using only one model as in  Vautard et al. (2006) ,  Jonson et al. (2006), and Wilson et al. (2012). A first ensemble was proposed through the European Project CityZen which relied on six models  (Colette et al. , 2011). While these studies were limited to about 10-year time periods, a 20-year hindcast study was presented in  Banzhaf et al. (2015) , relying again, however, on a single model. It is therefore timely to engage in a multi-model hindcast of air quality over 2 decades. The purpose of the present paper is to define the science and policy questions that are addressed by the EDT exercise, and introduce the experimental setup designed to answer these questions. The models participating in the experiment will also be presented, as well as the project database of model results.
Participating models Eight European modelling teams submitted their calculation results to the EDT database for at least one tier of experiment (see the experiment design in Sect. 2) using state-of-the-art air quality models: CHIMERE  (Menut et al. , 2013; Mailler et al. , 2017) , CMAQ  (Byun and Schere, 2006) , EMEP/MSC-W  (Simpson et al. , 2012) , LOTOS-EUROS  (Schaap et al. , 2008; Manders et al. , 2017) , MATCH  (Robertson et al. , 1999) , MINNI  (Mircea et al. , 2016) , Polyphemus  (Mallet et al. , 2007) , and WRF-Chem  (Grell et al. , 2005; Mar et al. , 2016). The main specifications of the eight participating models are summarised in Table  1  (note that they can differ from the public release of the various models according to the elements provided in the table ). The representation of physical and chemical processes differs in the models.
The vertical distribution of model layers (including altitude of the top layer and derivation of surface concentrations at 3 m height in the case of EMEP, LOTOS-EUROS, and MATCH) is not prescribed either. However, as further explained in the article, the other features of the model setup are largely constrained by the experiment input data such as forcing meteorology, boundary conditions, emissions, and the experiment characteristics such as horizontal domain and resolution. Only one of the participat- ing models included online coupled chemistry-meteorology (WRF-Chem), while all the other models are offline CTMs.
, 2015)  was used as initial and lateral boundary conditions and for applying four-dimensional data assimilation (FDDA), with coefficients as described in  Mar et al. (2016). The CMAQ model, which runs on a Lambert conformal conic projection, could not use the meteorological data provided on the Euro-Cordex grid, so that WRF was re-run in a Lambert conformal projection at 25 km horizontal resolution using identical WRF setup and version (3. 3. 1). The CTMs LOTOS-EUROS and MATCH have been meteorologically forced by ERA-Interim series further downscaled with RACMO2 (van  Meijgaard, 2012)  and HIRLAM  (Dahlgren et al. , 2016) , respectively. RACMO2, used here, was part of the EuroCordex studies documented in  Jacob et al. (2013)  and  Kotlarski et al. (2014)  and excludes nudging towards Era-Interim.
The HIRLAM EURO4M reanalysis uses data assimilation in three dimensions for upper air and optimal interpolation for surface fields. An initial analysis is conducted every 6 h with subsequent forecasts saved on 3-hourly temporal resolution. ERA-Interim is forced to the lateral boundaries. The HIRLAM reanalysis was interpolated from the original 0. 2 horizontal resolution on a rotated latitude-longitude grid (ca. 22 km) to the EDT grid. The main features of the meso-scale meteorological models are synthesised in Table  4.
This emission dataset was completed in April 2014 and is referred to as ECLIPSE_V5; it is part of a global emission set established during the EU-funded FP7 project ECLIPSE. More detailed description of the data and applied emission calculation methodology is given in Amann et al. (  2012 ) and   Morrison DM  (Morrison et al. , 2009)  Large-scale condensation with Rasch-Kristjansson scheme  (Rasch and Kristjánsson, 1998)  Prognostic cloud scheme  (Tiedtke, 1993) , large-scale condensation  (Tompkins et al. , 2007) , boundary layer clouds  (Neggers, 2009)  LW, RW radiation RRTMG  (Iacono et al. , 2008)  RRTMG  (Iacono et al. , 2008)  RRTMG  (Iacono et al. , 2008 )  Savijärvi (1990)  Shortwave radiation  (Clough et al. , 2005; Morcrette et al. , 2008)  Longwave radiation  (Mlawer et al. , 1997; Morcrette et al.
Chemical boundary conditions Two sources of lateral and top chemical boundary conditions are used by the regional CTMs: a climatology of observational data and global model results. Both have pros and cons. Global models carry biases but include a wider array of chemical species. The trend in observations matches in situ data by nature, but only at one point over the domain. For the EDT experiment the consensus in the experiment design was in favour of observation-based boundary conditions for most experiments (tier 1A, 1B, 2A, 2C, 3A, 3B) but also includes a sensitivity study based on modelled boundary conditions (tier 2B). Note that a possible impact of changing chemistry composition on large-scale circulation was integrated in the forcing meteorological fields through the data assimilation of the ERA-Interim reanalysis.
Observation-based boundary conditions The boundary conditions are a simplified version of those used in the standard EMEP/MSC-W model  (Simpson et al. , 2012a). The values are based upon climatological data (except from those for natural particles). The most important gaseous boundary condition compounds are O 3 , CO, and CH 4. For ozone, the three-dimensional climatology based on observational vertical profiles constructed by  Logan (1998)  are used in conjunction with a temporal (monthly) variation over the past 20 years. These climatological values are modified each month to ensure that their variability matches the observed variability of concentrations in the clean westerly Atlantic air masses as measured at Mace Head on the coast of Ireland. The "Mace Head correction" has been derived for each year from ozone data from Mace Head, sorted using www.
Output format and database status The model simulations were delivered in a common NetCDF format, so that each of the files contains gridded fields of one pollutant for a whole year. The air pollutant concentrations from only the lowest model level (or corrected to 3 m height for EMEP, LOTOS-EUROS, and MATCH) are delivered to the project database, but the participants are encouraged to store three-dimensional data if their storage capacities allow such an archiving. The requested variables are as follows: -Hourly concentrations of O 3 (O3_HL) and NO 2 (NO 2 _HL); -Daily concentrations of aerosols (nitrate (NO - 3 ), sulfate (SO 2- 4 ) and ammonia (NH + 4 )), sea-salt, dust, total primary PM, anthropogenic and biogenic secondary organic aerosols, and total PM, for both the fraction below 2.
5 µm (PM 2. 5 ) and the fraction below 10 µm (PM 10 ); -Daily concentrations of reactive gases: NH 3 , SO 2 , an indicator of alpha-pinene that shall depend on the chemical mechanism of each model, isoprene, HNO 3 , H 2 O 2 , HCHO, PAN, total VOC, and biogenic VOC; -Daily emission rate of biogenic species: isoprene and an indicator of alpha-pinene that shall depend on the chemical mechanism of each model;  -Monthly dry and wet deposition of total oxidised sulfur (SO x ), oxidised nitrogen (NO x ), and reduced nitrogen (NH x ); -Hourly meteorological fields: temperature at 2 m, wind speed, PBL, and rain. Additional diagnostics were subsequently computed and delivered on the common database; the list of indicators and their definitions is available in Table  5. The status of models' delivery of results for each of the experiment tiers at the time of submission of the present article is summarised in obtained from the eight models which delivered output to tier 1A.
The infrastructure is funded by ENEA, the Italian National Agency for New Technologies, Energy and Sustainable Economic Development and by Italian and European research programmes (http://www. cresco. enea. it/english). MINNI participation to this project was supported by the "Cooperation Agreement for support to international Conventions, Protocols and related negotiations on air pollution issues", funded by the Italian Ministry for Environment and Territory and Sea. RACMO2 simulations at KNMI to provide meteorological forcings for LOTOS-EUROS were supported by the Dutch Ministry of Infrastructure and the Environment. The MATCH participation was partly funded by the Swedish Environmental Protection Agency through the research program Swedish Clean Air and Climate (SCAC) and NordForsk through the research programme Nordic WelfAir (grant no.
Mar, Noelia Otero, Narendra Ojha). After this date, users must inform the EURODELTA-Trends coordinator (augustin. colette@ineris. fr) about the expected use of the data. The coordinator will, in turn, inform a representative from each modelling team. Competing interests. The authors declare that they have no conflict of interest. Acknowledgements. The EMEP MSC-W work has been funded by the EMEP Trust fund and has received support from the Research Council of Norway (Programme for Supercomputing) through CPU time granted at the super computers at NTNU in Trondheim, the University of Tromsø, and the University of Bergen. Michael Schultz and Anna Maria Katarina Benedictow are also gratefully acknowledged for hosting of the EURODELTA-Trends database on the AEROCOM server. The GAINS emission trends were produced as part of the FP7 European Research Project ECLIPSE (Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants); grant no.
Data The MODIS GPP (MOD17)  (Zhao and Running 2010)  is the first satellite-based modeled dataset for monitoring vegetation productivity at the global scale. A light use efficiency model is the core of MODIS GPP algorithm  (Running et al 2004). The MODIS GPP data is available for 8 day, monthly and annual timescales at 1 km and 0. 05 • × 0. 05 • spatial resolution since 2000, and we analyzed the data for 2000-2010 period in this study. The MODIS GPP products were resampled to 0. 5 • × 0. 5 • resolution by area-weighted averaging of the values in each 10 × 10 pixel window from the original 0. 05 • × 0. 05 • product. The MTE-GPP product  (Jung et al 2009 (Jung et al , 2011 ) was upscaled based on the globally distributed FLUXNET eddy-covariance tower measurements with a 'Model Tree Ensembles' machine-learning algorithm.
The MTE-GPP is currently available as a monthly We used eight ISIMIP2a biome models: CARAIB, DLEM, JULES, LPJmL, LPJ-GUESS, ORICHIDEE, VEGAS, and VISIT from the ISIMIP Phase 2a project. A summary of underlying processes for these models is presented in table 1. We used the simulated GPP from these eight models for 1971-2010 period, all at 0. 5 • × 0. 5 • spatial resolution. We calculated the mean GPP from the eight models (hereafter 'ENSEMBLE'). The models' simulations were performed using the common ISIMIP2a protocol, including time variant CO 2 concentrations, climate forcing, and land use change data. All models used the same climate forcing data from Global Soil Wetness Project 3 (GSWP3). More details of ISIMIP2a models are in the supplementary material available at stacks. iop. org/ERL/12/105005/mmedia, and more information about the models' simulation protocol and input data are available at www.
We compared the IAV of ISIMIP2a model estimated GPP against MODIS and MTE by calculating the Pearson's correlation coefficient (R), with the detrended annual GPP. For each region and the entire globe, we calculated the mean monthly GPP to analyze the seasonal variations, which are also compared across ISIMIP2a models, MODIS and MTE with the Pearson's correlation coefficient. We also calculated each region's relative contribution to global GPP trend, seasonality and IAV based on the method from  Ahlström et al (2015)  (see supplementary material for details). The Matlab R2016b was used for all statistical analysis.
Mean seasonality and regional contributions Figure  6  illustrates the mean seasonal cycle of global and regional GPP. At the global scale, all datasets show similar seasonal cycles. For MODIS and ENSEMBLE, global GPP starts from as low as about 50 g C m -2 month -1 in the Northern Hemisphere (NH) winter months (i. e. December, January and February) to high values of about 100 g C m -2 month -1 in the NH summer months (i. e. June, July and August). The amplitude of MTE GPP is about 10 g C month -1 higher. The NH regions generally have similar seasonal patterns but higher amplitudes (as large as ∼150 g C m -2 month -1 ) than the global results, but the amplitudes of GPP seasonality in Semi-arid and Arid Asia-Europe-Africa are much lower than the other NH regions. There are no strong seasonal variation in tropical regions such as Tropical Latin America, Southern Africa and Tropical Asia.
Acknowledgments This study was funded primarily by a  Laboratory Directed Research and Development  project sponsored by the  Pacific Northwest National Laboratory  managed by  Battelle Memorial Institute  for the  US Department of Energy. The study was also partly supported by the  National Aeronautics and Space Administration Carbon Monitoring System (CMS)  and ACCESS programs under projects  NNH12AU35I  and  NNH13AW58I. The  ISIMIP  project is grateful for funding from the  German Federal Ministry of Education and Research (BMBF , grant no. 01LS1201A1 ). Catherine Morfopoulos ,  Richard Betts  and  Jinfeng Chang  received support from the  European Commission 's  7th Framework Programme  ( EU/FP7 ) under Grant Agreement 603864 (HELIX). Tian, Pan, and Yang acknowledge funding support from  US National Science Foundation  ( 1210360 ,  1243232 ),  National Key Research and Development Program of China  (no.