dataesr/scientific-paragraphs-categorization huggingface dataset:
(2012) ; these data were thinned at a 15 km horizontal resolution to avoid horizontal error correlation problems in the data assimilation process. The associated observation errors were deduced by monitoring the standard deviation of differences between background simulations for new observation data types and observations, and they are displayed in Fig. 5. Some differences are observed on the plot for lidar data. The observation errors for Leandre II data are smaller than the other ones, and WALI assigned observation errors are slightly larger than BASIL and TEMP ones. Concerning temperature and wind the assigned observation errors are the same for dropsondes, radiosondes, and profilers; the aircraft data errors are larger. The amount of assimilated data per observation type for each AROME-WMED analysis version is given in Fig.
6. The number of assimilated data in REANA1 (red bars) is slightly increased with respect to the SOP1 version (black bars). This can be explained by the fact that all available observations, not only those present in real time in the Météo-France database, were assimilated; +9 % additional data were thus assimilated in REANA1 compared to SOP1. Concerning the REANA2 (blue bars), +24 % additional data with respect to SOP1 and +13 % with respect to REANA1 were assimilated. The higher amount of observations mainly comes from radiosondes (higher resolution and additional sites), profilers, satellite radiances, scatterometer wind estimates, surface parameters, and ground-based GNSS data. However, although five Spanish Doppler radars were included in REANA2, fewer data from radars were assimilated as a consequence of a revised statistic tuning.
Analysis and first guess As a first validation step, the performance of the data assimilation systems from the three AROME-WMED sets was evaluated based on the analysis (AN) and first-guess (FG is the 3 h forecast) departures from the assimilated observations. These departures provide information on the analysis increment for AN and on very-short-range forecast quality for FG. Some of these statistics (mean and root mean square, RMS) are plotted in Fig. 8  for observations related to humidity and in Figs. 9 and 10 for wind. These datasets differ with respect to the AROME-WMED version, as the quality check based on the difference between the observation and the simulation can discard (or not) some observations due to a different background value. In addition, some observation types such, as lidar observations and Spanish radars, are specifically assimilated in REANA2.
Surface parameter analysis and forecast The surface observations used for the evaluation were extracted from the HyMeX database, which gathers surface synoptic observations over the HyMeX area, additional hourly observations of temperature and humidity at 2 m from Météo-France, AEMET, and MeteoCat and the 10 m wind from some surface stations. The area selected for the evalua-  tion is similar to the HyMeX domain, i. e. , 36-47. 5 • N, 9 • W-17 • E. The various forecasts were compared with observations up to the 54 h forecast range (REANA1 and REANA2), except for SOP1, which was only run up to the 48 h forecast range (Fig. 11 ). The model underestimates the 2 m temperature diurnal cycle (forecast minus observations; Fig. 11 ), with a maximum absolute value of 0. 5 • C. The REANA2 simulation has a noticeable reduced bias for each forecast range, which is a positive impact due to modifications in the orography in RE-ANA2.
IWV The AROME-WMED model was also assessed using integrated water vapor (IWV) obtained from version 1 data of GNSS ground-based stations. IWV was indeed found to be linked to heavy precipitation, with a maximum observed before heavy precipitation events and a drop in its value occurring during the maximum of precipitation  (Bock et al. , 2016). Results are presented in Fig. 13. With these data assimilated in REANA2 (and not in SOP1 and REANA1), the highest correlation (0. 99) is found for each slot of the eight times at which a REANA2 analysis was performed. More than 32 000 colocations were available to perform these computations. As expected, REANA1 and SOP1 correlations are lower (around 0. 97); the maximum is observed for the 00:00 UTC analysis slot and the minimum is noticed in the afternoon at 15:00 UTC.
15 ), derived from a GNSS sensor onboard the Marfret Niolon (Fig. 14 ). These data, which were not assimilated, represent an interesting independent source of validation. This dataset is made up of 418 measurements collected during the period from 9 September 2012 00:00 UTC to 1 November 2012 21:00 UTC and mainly in the western Mediterranean part of the AROME-WMED domain. Due to the small amount of data available, results are noisy. Nevertheless, it is noteworthy that the correlation between forecasts and observations is higher up to the 24 h forecast range; the standard deviation is lower up to the 24 h forecast range for REANA2 compared to SOP1 and REANA1. For the three simulations, there is a diurnal cycle of the ZTD bias. A stronger positive (moist) bias can be seen for the early forecast ranges of REANA2.
Surface precipitation The evaluation is carried out with 24 h accumulated precipitation (from 5 September to 5 November 2012) from the HyMeX database available in July 2017 (version 4). These data were checked before computing scores. Only surface stations with daily precipitation for the full period (i. e. , with an uninterrupted series) were taken into account. Good coverage is obtained over France, Italy, and Spain (Fig. 16 ). RE-ANA2 seems to yield more precipitation compared to the other versions, especially over elevated terrain. This is confirmed with the frequency bias computed against rain gauges over the whole AROME-WMED domain (Fig. 17 ). This bias is improved for small thresholds (< 1 mm (24 h) -1 ) in REANA2 and these results are statistically significant. The degradation for thresholds exceeding 1 mm (24 h) -1 in RE-ANA2 is not significant according to a bootstrap test due to the lower number of observations.
Discussion and conclusion The AROME-WMED model was initially developed to study and forecast heavy-precipitating events over the western Mediterranean basin in the framework of the HyMeX program. This model ran in real time during both SOPs of HyMeX in autumn 2012 and winter 2013. Two reanalyses were run after the HyMeX autumn campaign. The first one was carried out just after the campaign to provide the same model configuration over the whole period because an upgrade of the AROME-WMED version occurred during the period. In addition, a second reanalysis was performed a few years after and took into account as many data as possible from the experimental campaign (i. e. , lidar and dropsonde humidity profiles) or from reprocessed datasets (such as GNSS ground station ZTD, wind profilers, high-verticalresolution radiosondes, and Spanish doppler radars).
It also benefited from a more recent version of the AROME code, including an orography change, and from improved background error statistics computed over a 15 d period of the first HyMeX observing period. The analysis and forecast fields of these three AROME-WMED versions are available in the HyMeX database (http://mistrals. sedoo. fr/HyMeX/, last access: 3 July 2019). The characteristics and the quality of the three AROME-WMED versions are discussed in this paper. More observations are assimilated in both reanalyses. The first reanalysis included 9 % additional data, and the second reanalysis assimilated 24 % more data. These data in the case of REANA2 mainly came from the GNSS ground stations, radiosondes, and satellite radiances. The use of background error statistics more representative of the studied period allows for better use of the observations in the second reanalysis.
Larger values of accumulated precipitation during the 2month period were obtained with the second reanalysis, and the comparison with observations suggests an overestimation of the large precipitation amount, mainly over relief. However, the frequency bias is decreased for smaller thresholds over the AROME-WMED domain. Concerning the 24 h precipitation evaluation, this positive impact is less noticeable, but at least some improvement is diagnosed for the Iberian Peninsula and France for thresholds lower than 10 mm (24 h) -1. The gain brought by the second reanalysis is smaller over Italy. Finally, the positive impact of the second AROME-WMED reanalysis was detailed for the IOP8 high-precipitating event that occurred over Spain and southern France at the end of September 2012. Preliminary studies with data assimilation experiments with only the code version changes, including the new background statistics, have shown that the gain in forecast score brought by REANA2 is due to the new observations assimilated and the new code version.
Data availability. The source code of AROME-WMED, derived from the operational AROME code, cannot be obtained, but the analyses and forecast fields are available in the HyMeX database (http://mistrals. sedoo. fr/HyMeX/, last access: 3 July 2019). SOP1 is available at https://doi. org/10. 6096/HYMEX. AROME_WMED. 2012. 02. 20 (Fourrié and Nuret, 2011), REANA1 at https://doi. org/10. 6096/HYMEX. REANALYSIS_AROME_ WMED_V1. 2014. 02. 10 (Fourrié and Nuret, 2014), and REANA2 at https://doi. org/10. 14768/MISTRALS-HYMEX. 1492 (Fourrié and Nuret, 2017).
However, for Trcompared to their control, the examined slides reveal the presence of a small, completely atrophied thyroid mass associated with vesicular fibrosis and with detachment of the colloid. For the renal organ, disturbances are observed: inflammation of the presence of tissue fibrosis and glomerular necrosis. For the liver, there is an appearance of inflammatory focus in different degrees around the portals. However, the results of the testes of both models compared to their controls revealed no histological abnormalities. The observed histological effects are correlated with the corresponding absorbed dose received by each organ and calculated using the RODES software [3, 4].
Pediatric Quality of Life Inventory The PedsQL is a modular questionnaire instrument designed to measure HRQoL in children and adolescents aged between 2 and 18 years. The 23-item PedsQL 4. 0 Generic Core Scale implemented in this study assesses the domains physical functioning (8 items), Emotional Functioning (5 items), Social Functioning (5 items), and School Functioning (5 items). In addition, a Psychosocial Health Summary Score can be derived from the questionnaire  [23]. A 5-point response scale is utilized across child self-reports for ages 8 to 18 years and parent proxy reports (0=never a problem to 4=almost always a problem). The aggregated reference data of international cohorts (eg, the United States and Great Britain) are available according to the age groups expected in this study  [23, 27, 28].
Assessment Timepoints QoL was measured prospectively at 4 timepoints reflecting important therapy milestones. The initial assessment at timepoint 1 (E1) was planned at week 5 after the start of preoperative chemotherapy (±1 week) but before surgical resection. E1 was debated considerably as investigators were interested in pretreatment QoL assessments, but it was recognized that it is difficult to obtain these data before initiation of treatment, and this would result in missing QoL forms for that timepoint. Timepoint 2 (E2) was planned 10 weeks after definitive surgery for a primary tumor (± 2 weeks) as a short-term assessment following surgery. Timepoints 3 (E3) and 4 (E4) were planned 18 and 36 months after the start of therapy (± 1 month), respectively, and were in place to assess long-term outcomes following therapy.
Design, Organization, and Study Structure The 4 study groups (COG, COSS, EOI, and SSG) established an infrastructure to ensure successful implementation of the EURAMOS-1 trial  [19]. Ethical approval for the QoL substudy was obtained in 2005 from the ethical authority of the University Düsseldorf and subsequently in all of the participating study groups. Common data elements were agreed to standardize data collection  [19]. The Quality of Life Coordinating Center (QLCC) in Germany was responsible for the QoL data storage and management. German patients returned questionnaires directly to QLCC. For other patients, the institutions sent the completed questionnaires to the national study groups, which transferred them to QLCC by post (SSG and EOI) or electronically (COG; Figure  2 ). Table  1  shows the questionnaires by age and source of report.
Patients' and parents' data were assessed independently and in parallel. QLCC was responsible for data entry, quality control, data base maintenance, and periodic reporting of the status of the QoL substudy. A comprehensive system of error checking was used to detect out-of-range or inconsistent values. If available, errors were compared with any paper records to determine the correct data values if discrepancies were found. The study was open from 2005 to 2011.
COSS, EOI, and SSG also allowed participants to be defined as having possible metastases (in addition to yes and no), COG did not do so; therefore, all possible metastases were classified as no metastases. In addition, the logistic regression was calculated for each study group separately. Odds ratios with 95% CI and Wald test P values were reported from the full model. Statistical analyses were performed using SAS software, version 9. 4 for Windows (SAS Institute). All P values and CIs are exploratory without adjustment for multiplicity.
Acknowledgments The authors thank all the patients and parents for their contribution and all data managers, especially  Ms Eva-Mari Olofsson  from the SSG office in Lund, for their support to collect the data from all the patients in timely order. The study sponsor was the  UK Medical Research Council in Europe  and the  US National Cancer Institute in North America and Australia. Each trial group organized local coordination elements; central coordination and analysis was led from  Medical Research Council Clinical Trials Unit at University College of London. Neither the sponsors nor the funders of the trial had a role in trial design, data analysis, or.
3  [41]. The data are collected with a two-level trigger system. The first level of the CMS trigger system, composed of custom hardware processors, uses information from the calorimeters and muon detectors to select the most interesting events  [42]. The high-level trigger (HLT) processor farm runs a version of the full event reconstruction software optimized for fast processing. A more detailed description of the CMS detector, together with a definition of the coordinate system used and the relevant kinematic variables, can be found in Ref. [43].
Data sample and Monte Carlo simulation The data set used in this analysis corresponds to 32. 6 nb -1 of integrated luminosity collected in pPb collisions by the CMS experiment in 2013, with beam energies of 4 TeV for the protons and 1. 58 TeV per nucleon for the lead nuclei, resulting in a nucleon-nucleon centre-of-mass energy of √ s NN = 5. 02 TeV. The data are the sum of the collected pPb and Pbp collision samples, with the incoming Pb ion going in the +z and -z beam directions, corresponding to integrated luminosities of 18. 8 and 13. 8 nb -1 , respectively. The photon-proton centre-of-mass energy, W γp , is related to the rapidity y of the Υ meson in the laboratory frame by W 2 γp = 2E p m Υ exp(±y), where E p is the proton energy, and the +(-) sign corresponds to the pPb (Pbp) beam configuration.
This formula, derived neglecting the transverse momenta involved in the interaction, approximates the true value of W γp to better than 1 per mille in the W γp range of this measurement. The data span the range 91 < W γp < 826 GeV, with the limits given by the maximum and minimum rapidities, over |y| < 2. 2, of the Υ mesons. Because the CMS detector is symmetric along z, the pPb and Pbp data samples are merged in this analysis after changing the sign of p z of the final state particles in the Pbp sample. The STARLIGHT (v3. 07)  [44, 45]  Monte Carlo (MC) event generator is used to simulate exclusive Υ(nS) photoproduction events (Fig. 1a ) and the exclusive QED background (Fig. 1c ). The STARLIGHT MC assumes that the photon flux from the incoming hadron(s) is described by the Weizsäcker-Williams equivalent photon approximation  [46, 47] , and uses an empirical fit of the exclusive vector meson photoproduction cross sections to the existing HERA γp data.
15 GeV, and away from the Υ resonances, 8 < m µ + µ -< 9. 1 GeV and 10. 6 < m µ + µ -< 12 GeV, where the QED process is dominant. The ratio of the measured yields in the data to those from the STARLIGHT MC in the control region is measured to be 1. 03 ± 0. 10, confirming that this event generator reproduces the QED background well, as observed previously in pPb and PbPb collisions at the LHC  [29] [30] [31] [32]. The QED contribution, estimated from the STARLIGHT MC in the signal region, amounts to 40% (64 and 8% in the lowest and highest dimuon p 2 T bins of the corresponding differential cross section, respectively). Backgrounds to the exclusive Υ → µ + µ -signal also originate from semiexclusive and inclusive Υ meson and Drell-Yan (DY) continuum production, where any additional hadronic activity falls outside the detector acceptance or below the detection thresholds.
Extraction of cross sections The dimuon events selected as described above are used to determine the differential Υ photoproduction cross sections in four bins of p 2 T over p 2 T = 0. 01-1 GeV 2 , and in four bins of y over |y| < 2. 2. Because of the limited size of the data sample, we first extract the differential cross sections for all Υ(nS) resonances combined. Then, the total cross section as a function of W γp is extracted for the Υ(1S) state alone, as described below, and is compared with previous experimental measurements and theoretical predictions. The background-subtracted p 2 T and y distributions are first unfolded over the region 0. 01 < p 2 T < 1 GeV 2 , |y| < 2. 2, and muon p µ T > 3. 3 GeV, by using the Bayesian iterative unfolding technique  [54] , as implemented in the ROOUNFOLD package  [55] , with four iterations.
The fIPsat calculations are consistent with the CMS measurement, but predict a somewhat higher cross section than that measured by LHCb. The IIM and bCGC predictions satisfactorily describe the rise of the cross section with γp centre-of-mass energy. As discussed in Ref. [10] , the gluon PDF associated with the JMRT NLO prediction, which is consistent with the CMS+LHCb data presented here, has a somewhat different shape at low-x than that predicted by standard pQCD collinear fits used at the LHC such as CT14  [66] , NNPDF3. 0  [67] , and MMHT  [68]. However, given the currently large statistical uncertainty of the results presented here, an improved understanding of the low-x gluon density, and its evolution with energy scale, will require more precise measurements with larger integrated luminosities and/or at higher centre-of-mass energies.
Individuals have received support from the  Marie-Curie  programme and the  European Research Council and Horizon 2020  Grant, contract No. 675440  ( European Union ); the  Leventis Foundation ; the A. P. Sloan Foundation; the  Alexander von Humboldt Foundation ; the  Belgian Federal Science Policy Office ; the  Fonds pour la Formation à la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium) ; the  Agentschap voor Innovatie door Wetenschap en Tech-nologie  ( IWT-Belgium ); the  F. R. S. -FNRS  and  FWO (Belgium)  under the  "Excellence of Science -EOS" -be. h project n. 30820817; the  Ministry of Education, Youth and Sports (MEYS)  of the Czech Republic; the  Lend ület ("Momentum") Programme  and the  János Bolyai Research Scholarship of the Hungarian Academy of Sciences , the  New National Excellence Program ÚNKP , the  NKFIA  research grants  123842 ,  123959 ,  124845 ,  124850  and  125105  (Hungary); the  Council of Science and Industrial Research, India ; the  HOMING PLUS programme of the Foundation for Polish Science , cofinanced from  European Union, Regional Development Fund , the  Mobility Plus programme of the Ministry of Science and Higher Education , the  National Science Center (Poland) , contracts Harmonia  2014/14/M/ST2/00428 ,  Opus 2014/13/B/ST2/02543 ,  2014/15/B/ST2/03998 , and  2015/19/B/ST2/02861 , Sonata-bis  2012/07/E/ST2/01406 ; the  National Priorities Research Program  by  Qatar National Research Fund ; the  Programa Estatal de Fomento de la Investigaci ón Científica y Técnica de Excelencia María de Maeztu , grant  MDM-2015-0509  and the  Programa Severo Ochoa del Principado de Asturias ; the Thalis and Aristeia programmes cofinanced by  EU-ESF  and the  Greek NSRF ; the  Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University  and the  Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand) ; the  Welch Foundation , contract  C-1845 ; and the  Weston Havens Foundation (USA).
As always with this kind of projects, one needs to gain a lot of interest (the website has received nearly 100,000 visits since its beginning) to be able to recruit only a handful of very active participants. These are highly motivated people, generally producing high quality work since more than 94% of the transcribed texts have been added to the database after being checked and corrected (which means less than 6% of the transcribed texts are rejected, mainly because they have been only partially transcribed). The transcription effort is however far from complete and several years will still be necessary to complete the whole task, hopefully within two decades from now, if the work continues at the current speed. The corpus is nonetheless still largely unpublished and, even if available, hard to access given the number of files and the amount of text to read.
Corpus Preprocessing Regarding text preprocessing performed before feeding text to the NLP tools, recall that the transcripts are encoded in TEI, providing information like added or deleted text, marginal notes etc. The NLP tools we applied take unannotated text as input, rather than TEI-encoded text. The TEI-information encoded was not essential for our text mining work, even if exploiting this information could be useful, e. g. for a special treatment of deleted items, added items, or of the small proportion of material not authored by Bentham, like correspondence he received, or material that he collected from other sources. A TEI document's two outermost elements are a teiHeader tag, for metadata, and a text tag, for the document content. Our preprocessing ignored the teiHeader tag, as it only encodes information relevant internally at Transcribe Bentham-the metadata mentioned above like dates or document types are available in a database and had not been rendered in our source TEI.
The potential of integrating handwritten text recognition technology in the transcription process is also analyzed [Causer and Terras 2014b; Toselli and  Vidal 2015]. Besides these accounts of how the corpus was created, including cataloguing (metadata), digitization (photographs of the manuscripts), and transcription, there are two platforms that offer some corpus navigation functions for those outputs. The Bentham Papers Database allows searching in metadata fields, returning matching records, and providing a link to the record's transcript when available. 11 UCL Libraries' Digital Collections created a platform where, besides metadata, the full text of available transcripts can be searched. It returns links to the document's image and its TEI transcript if available. 12 garding Bentham studies work based on the transcriptions themselves, a major output the transcripts are contributing to is the Bentham Project's edition of Bentham's Collected Works [Bentham, 1968 -ongoing].