dataesr/scientific-paragraphs-categorization huggingface dataset:
Results Both accuracy and reaction time (RT) were measured. Raw RT data are available at https://zenodo. org/record/35563. A trial was considered incorrect if an incorrect key was pressed. RTs were measured from the onset of the directional cue and reflected the initiation of the movement away from the home position. The preprocessing of RTs involved multiple steps. First, incorrect trials were removed. Next, outlier RTs were removed. The outlier cutoff was adjusted so that less than 0. 5% of trials were excluded, based on the method described in  Masson et al. (2011). The cutoff was established at 900 ms in the egocentric condition (0. 491% of trials) and 990 ms in the exocentric condition (0. 465% of trials). After preprocessing, each participant's mean RT for each type of trial was calculated.
A compass containing tiltmeters and pressure sensor mounted on Vulcan also records the heading, pitch, roll and depth of the instrument. Accurate positions for DASI and the ROV were obtained from an ultrashort base-line (USBL) showing the continental slope survey area on the regional bathymetry map from international bathymetric chart of the Arctic Ocean (IBCAO) data  (Jakobsson et al. 2008 ). The regional velocity models obtained from seismic refraction data shown by the dotted red line -AWI-994000  (Ritzmann et al. 2004)    acoustic positioning system. The position of Vulcan was estimated during data analysis by assuming it followed the DASI track. The CSEM survey was designed with the objective of obtaining a resistivity image beneath the region where the BSR is observed within the lower slope sediments  (Sarkar et al.
CSEM data processing The Earth's frequency domain transfer function (TF) can be extracted from the CSEM data, which are sensitive to changes in conductivity, source-receiver offset and other geometric factors  (Constable 2010 ). The field data, which records the voltage difference across each receiver dipole were converted to the frequency domain using a fast Fourier transform over a 1-s window length. The amplitude and phase frequency response of the pre-amplifiers within each of the receivers were used to compute a calibrated voltage, which was then divided by the receiver dipole length to calculate the electric fields. The source response was then removed by normalizing the data with the source dipole moment (SDM) for each frequency to obtain the frequency domain Earth's TF. The current output by DASI was not reliably logged and only the voltage waveform was recorded for these CSEM lines.
Based on analysis of other CSEM lines from the same survey that recorded the current accurately, an ideal waveform (1 Hz, 81 A) was used to compute the frequency domain SDMs. The voltage record was used to calibrate the start time of the ideal waveform to ensure phase accuracy in the frequency domain Earth's TF. Extremely noisy data outliers were manually removed and the 1 s data were then stacked to obtain a data point every 60 s, that has an improved signal-to-noise (S/N) ratio. This process provided data with an along-track sample interval of approximately 46 m. Only the fundamental (1 Hz) and the first three harmonic frequencies (3, 5, 7 Hz) of OBE and Vulcan data were selected. Saturated OBE data and data below the noise floor were rejected. The OBEs saturate between 0 and 850 m offset and the maximum usable offset decreases from 2700 m at 1 Hz to 1700 m at 7 Hz.
CSEM inversion The MARE2DEM code  (Key & Ovall 2011)  was used to invert the CSEM data from the OBEs and Vulcan. OBE and the Vulcan data were inverted separately to obtain independent resistivity models. MARE2DEM uses a 2•5D finite element forward code  (Key & Ovall 2011)  and a fast implementation of Occam's inversion  (Constable et al. 1987)  to obtain the smoothest resistivity model from the family of models that can fit the data. It does so by automatically optimizing the values of Lagrange's multiplier and model roughness  (Constable et al. 1987; Key 2012)  for the specified misfit tolerance and data error. A simple starting model was specified containing a highly resistive air layer (10 12 m), seawater with three horizontal layers of constant resistivity (Fig. 3 ) and a starting resistivity of 1 m for the sediments beneath the seafloor.
C O N C L U S I O N S We analysed seafloor and towed receiver CSEM data along with high resolution seismic reflection data in a region of active methane seeps at the continental slope of west Svalbard margin. It has provided additional evidence and constraints for hydrate and free gas presence along the continental slope and at the continental shelf edge (Fig. 15 ). This study also provided a first look at the subsurface resistivity structure of the continental slope area. The new resistivity information should help future studies to design more targeted electromagnetic studies in the area. Based on our analysis, we conclude that: (1) High resistivities (4-12 m) within the predicted GHSZ and the presence of BSR in coincident seismic reflection data, suggest the presence of gas hydrate in the lower slope sediments.
Bioinformatics analyses for HPV genotyping HPV genotypes were identified by running a python script on the reads. At the beginning, all paired-end reads were trimmed, filtered according to their quality and merged at the end. All merged reads were mapped against a local HPV database built from all viral sequences found in GenBank (NCBI-GenBank Flat File Release 191. 0). To remove any risk of contamination among samples, all determined HPV genotypes represented by less than 2 % of the total number of reads in a sample were discarded. An HPV genotype was considered correctly identified if, among all reads related to the considered genotype, at least one read had a value of around 0 % of divergence. To validate a representative group of reads related to one specific HPV genotype, the distribution of the divergence percentage (DP) was determined based on the mean and the median of DPs of each HPV genotype as indicated in Additional file 1: Table  S2.
Sequence analysis of the E6 gene and LCR of HPV16 For HPV16-positive samples, the HPV E6 and LCR open reading frames were amplified by PCR as described in Yamada et al. [15]. Thermal cycling was based on the following program: 5 min at 95 °C, 40 cycles of 60 s at 95 °C, 60 s at 55 °C and 60 s (90 s for LCR) at 72 °C, with a final extension step at 72 °C for 5 min. The PCR mix contained 10 mM Tris (pH 8. 3), 200 μM of each dNTP, 4 mM MgCl 2 , 0. 125 μM of each sense-strand and anti-sense strand oligonucleotide primers, and 2. 5 U of Taq in a final volume of 100 μl. All PCR products were sequenced using the BigDye Terminator v3. 1 Cycle Sequencing kit (Applied Biosystems, ville?, pays?) according to the manufacturer's protocol. The full length E6 and LCR ORFs were aligned with the reference HPV16 sequence (GenBank accession number: K02718) using multiple sequence alignment of the MEGA 5.
Characterization of the E6/LCR sequence in HPV16 The E6 and LCR regions of the HPV16 genome were amplified and sequenced for 11 of the 22 detected HPV16 cases. Nucleotide (nt) changes were analyzed from nt 104 to 559 for the E6 gene and from nt 7384 to 7835 for the LCR sequence (Table  3 ). A comparison with the reference HPV16 sequence (GenBank accession number: K02718) showed that all sequences (E6 and LCR) contained at least one specific nucleotide variation. Fourteen nucleotide variations were found in the E6 region: nine non-synonymous mutations (G132C (R10T), G132T (R10I), C143G (Q14D), G145T (C51F), G255T (D64E), C256T (H78Y), T295G (L83V), C335T (H78Y) and T350G (L83V)) and five synonymous (silent) mutations (T109C, G196A, T286A, A289G and A403G). The non-synonymous mutations C143G, G145T, C335T and G132C/T that lead to Q14D and H78Y were the most frequent mutations (81.
Acknowledgements We acknowledge  Heïdi Lançon  and  Dr Carolyn Engel-Gautier  for her revision of English usage and grammar throughout the manuscript and  Dr. Samuel Alizon  for his help in this study. The authors wish to thank all the  Gabonese  women who participated in this study and the staff at the  ICL  and CHUL for their devotion and care to the patients included in this study. Funding ICL and CHUL are public hospitals supported by the government of Gabon. The CIRMF is supported by the government of Gabon,  Total-Fina-Elf Gabon , and the  Ministère de la Coopération Française. The funders had no role in study design, data analysis or preparation of the manuscript.
Comparison Dataset We analyzed the clinical isolates of S. sonnei alongside an existing global dataset compiled by KE Holt et al. (n = 118) (online Technical Appendix 1)  (16). In brief, this global context dataset comprises temporally (collected during 1943-2008, 70% collected after 1992) and geographically diverse (from 4 continents) S. sonnei isolates previously used to define the population structure of this pathogen. The dataset includes 1 sample collected in Israel in 2003.
Methods DNA was extracted at multiple sites by using the Wizard genomic DNA extraction kit (ProMega, Madison, WI, USA) according to manufacturer's instructions. DNA was sequenced by using the MiSeq and HiSeq 2000 platforms (Illumina, San Diego, CA, USA) at multiple institutes according to in-house protocols (online Technical Appendix 1)  (25) (26) (27). Sequencing data for all isolates described passed internal quality control and were assembled into <687 contiguous sequences with a total length of <5. 0 MB. Sequence data are available in the European Nucleotide Archive (http://www. ebi. ac. uk/ena; accession numbers in online Technical Appendix 2). Analysis of sequencing data was similar to that previously described  (28). Multiple sequence alignment for phylogenetic analysis was generated by mapping to reference isolate S.
sonnei Ss046 (GenBank accession no. CP000038), then masking mobile and repetitive elements  (16)  and stripping sites of recombination  (29). Analysis of remaining variable sites was performed by using maximum-likelihood analysis in RAxML version 7. 8. 6 to create phylogenetic trees  (30). For isolates in the OJC-associated lineage for which the sampling date was known (n = 333; online Technical Appendix 1), BEAST version 1. 8 software (http://beast. bio. ed. ac. uk/) was used to estimate the emergence date of the lineage  (31). Root-to-tip distances were generated by using Path-O-Gen version 1. 4  (32). BEAST results shown are from 4 chains of 100 million Markov chain Monte Carlo generations run according to a general time reversible plus gamma substitution model, with a relaxed normal clock and Bayesian Skyline Population model, previously used for this pathogen  (16, 17).
Chains were sampled every 1,000 generations with a 10% initial burn-in for root-height (time to most recent common ancestor) analysis. The maximum clade credibility tree was generated with a 10% burnin and sampling every 100,000 generations. These results were consistent with those generated similarly by using a constant population growth model (online Technical Appendix 2 Table ). De novo assembly, annotation, and antimicrobial resistance gene detection in the isolates was done as previously described (28) (accession numbers for annotated draft genome assemblies in online Technical Appendix 1). Contiguous sequences containing antimicrobial resistance genes were extracted from assemblies and the presence of plasmid incompatibility groups on these contiguous sequences was determined by using Plasmid-Finder  (33).
Results To determine the relationships among S. sonnei from OJCs inside and outside of Israel, we constructed a phylogenetic tree including whole-genome sequence data from 437 isolates of S. sonnei alongside the 118 isolates from the global context dataset (Table  1 ; Figure  2 ). This analysis showed the existence of a large, unique monophyletic sublineage (n = 396 isolates) of the global III lineage that was almost exclusively (388/396, 98%) composed of isolates from OJC-associated outbreaks and samples from persons in Israel (OJC-associated lineage; Figure  2 ; online Technical Appendix 2 Figure  2 ). This lineage contained nearly all isolates (217/221, 98%) sequenced from Israel and collected during1992-2014 (Figure  2 ); 170/171 (99%) of those identified from samples collected during the same time frame from OJCs in the United States, Canada, France, Belgium, and the United Kingdom; and the 1 isolate from the global context dataset that originated in Israel (Figure  2 ; online Technical Appendix 1).
Acknowledgments We thank  David Harris  and WTSI sequencing and informatics teams for coordinating sample management, sequencing, and automated analyses. K. S. B. is in receipt of a  Wellcome Trust Postdoctoral Training Fellowship for Clinicians  ( 106690/A/14/Z ) and all WTSI authors are supported by  Wellcome Trust  grant number  098051. This study was also aided by the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement  261472  'STOPENTERICS'. The French National Reference Center for E. coli,  Shigella and Salmonella  is co-funded by the  Institut de Veille Sanitaire. The  Unité des Bactéries Pathogènes Entériques belongs  to the  Integrative Biology of Emerging Infectious Diseases Laboratory of Excellence  funded by the  French Government Investissement d'Avenir programme  (grant no.
Evaluation of global carbon cycle models The historical simulations will be used for evaluation of the components of the carbon cycle (ocean and terrestrial carbon fluxes, anthropogenic carbon storage in the ocean, atmospheric CO 2 growth rate and variability). ESMs have increased rapidly in complexity but evaluation has not kept pace. Some evaluation of the carbon cycle was already performed in CMIP5 (e. g. Anav et al. , 2013; Bopp et al. , 2013; Hoffman et al. , 2014) , highlighting significant biases in key quantities in many ESMs. There is increasing need to develop evaluation techniques and activities, applied consistently and routinely across models, at both fine scales (process-level, "bottom-up" evaluation) and large scales (system-level, "topdown" evaluation"), as well as using complementary data streams relating to (bio)physical and biogeochemical processes to evaluate the ensemble of simulated processes (e.
, 2013)  and ocean databases with millions of in situ point measurements (e. g. with the Surface Ocean CO 2 Atlas (SOCAT) as in  Tjiputra et al. (2014)  for CMIP5 ESMs), or developing new techniques for model-data comparisons (e. g. water-mass framework;  Iudicone et al. , 2011). In the coming years, the increasing complexity of marine biogeochemical schemes used in ESMs will call for more advanced model-data comparison strategies. These will include the use of new data sets, such as biomass data for plankton functional types (MAREDAT;  Buitenhuis et al. , 2013)  or ocean distribution of the micro-nutrient iron  (Tagliabue et al. , 2012). Evaluations of land surface components of ESMs have often used gridded flux products (e. g. Bonan et al. , 2011; Anav et al. , 2013; Piao et al. , 2013)  obtained by extrapolating the FLUXNET measurement network of biosphere-atmosphere exchanges (e.
Links to and requirements from other MIPs The Ocean Model Intercomparison Project (OMIP;  Griffies et al. , 2016; Orr et al. , 2016)  will provide a baseline for assessment of ocean component model biogeochemical and historical carbon uptake fidelity. Ocean carbon cycle analysis has previously been conducted under the OCMIP (Ocean carbon-cycle model intercomparison project) intercomparison  (Orr et al. , 2001). In response to the WGCM (Working Group on Coupled Modelling) request, the OMIP and OCMIP have been merged under the OMIP umbrella. One main objective of OMIP is to coordinate CMIP6 ocean diagnostics including ocean physics, inert chemical tracers, and biogeochemistry for all CMIP6 simulations that include an ocean component. The second objective is to perform a global ocean-sea-ice simulation forced with common atmospheric data sets.
CO 2 concentrations and anthropogenic CO 2 emissions For concentration-driven simulations, atmospheric CO 2 should be prescribed as a globally well-mixed value provided by CMIP6. The CMIP6 paper  (Eyring et al. , 2016a ) and a range of papers in the GMD CMIP6 Special Issue will document the forcings in more detail. The data will be made available from the CMIP6 and PCMDI webpages (http://www. wcrp-climate. org/wgcm-cmip/wgcm-cmip6, https://pcmdi. llnl. gov/search/input4mips). For emissions-driven simulations, atmospheric CO 2 should be simulated prognostically by the model. External boundary conditions of anthropogenic CO 2 emissions will be provided and should be used as follows: -In esmPIcontrol, the emissions-driven control run, atmospheric CO 2 should be simulated by the model but no external emissions should be added during this simulation.
Land-use and land-use-induced land cover change LULCC affects climate via two aspects in CMIP6 simulations. In both concentration-driven and emission-driven simulations LULCC alters the distribution of vegetation covering the land surface, with consequences for the exchange of heat, water, and momentum with the atmosphere. Its effects on terrestrial carbon stocks allow us to infer LULCC emissions, more accurately labelled the "et LULCC flux"  (Brovkin et al. , 2013). In emission-driven simulations the net LULCC flux influences the atmospheric CO 2 concentration, contributing to subsequent carbon cycle feedbacks (e. g. ,  Strassmann et al. , 2008; Arora and Boer, 2010; Pongratz et al. , 2014). The LULCC forcing for the historical simulations will be based on the protocol and forcing data provided by CMIP6 for the DECK and the historical CMIP6 simulations.
Other forcings If the model requires any other external forcing not documented here, for example deposition of phosphorous, then it is at the model groups' discretion how to provide it. In the case of a model with an interactive phosphorous cycle, we recommend the forcing data are prepared in a way analogous to the nitrogen deposition described above. We recommend modelling groups to contact C4MIP for more details if this is applicable. Any additional forcings must be documented through the CMIP meta-data process or in the appropriate model description paper.
Output requirements It is vital for accurate analysis and model intercomparison that every model adheres to the definitions of each output variable in order for a like-for-like comparison to be made. In this section we describe in detail each requested output variable. The data request will be documented separately (by the WGCM Infrastructure Panel; https://www. earthsystemcog. org/projects/wip/) and will list the required variables output for each CMIP6 simulation along with their precise variable names, description, and required units. Here we aim to describe each variable so that its implementation and use are made consistent across all models and analyses.
Data availability As with all CMIP6-endorsed MIPs, the model output from the C4MIP simulations described in this paper will be distributed through the Earth System Grid Federation (ESGF). The natural and anthropogenic forcing data sets required for the simulations will be described in separate invited contributions to this special issue and made available through the ESGF with version control and digital object identifiers (DOIs) assigned. Links to all forcings data sets will be made available via the CMIP Panel website.
Acknowledgements. CRESCENDO project members (CDJ, PF, LB, VB, TI, SZ) acknowledge funding received from the  Horizon 2020 European Union 's  Framework Programme for Research and Innovation  under grant agreement no. 641816. CDJ was supported by the  Joint UK BEIS/Defra Met Office Hadley Centre Climate Programme  ( GA01101 ). HDG was supported by a  Marie Curie Career Integration Grant  from the  European Commission. JP is supported by the  German Research Foundation 's  Emmy Noether Program  ( PO 1751/1-1 ). Edited by: C. Sierra Reviewed by:  C. Huntingford  and two anonymous referees.
Recent Planck results The Planck satellite was launched on May 2009 and took data until October 2013. The 2015 results are based on data from the entire Planck mission, including temperature and polarization. The first cosmological results (2013) of the Planck Collaboration based on measurements of the temperature of the cosmic microwave background radiation and the power spectra of gravitational lensing potential were presented in  [6, 125]  (both revised in 2014). Constraints on inflation were discussed in  [7] , and have been examined again in 2015  [126]. Data on CMB anisotropy were futher analysed by Planck in January 2014, confirming  [22]  the observation of an asymmetry in power between two cosmic hemispheres defined by a preferred direction. No correction to this paper has been introduced in 2015, even it the question has been addressed  [127].
Subsequently, the UHE particle may not be allowed to automatically quit this vacuum region except possibly if a new interaction happens. These are just two examples of cosmological and astrophysical unconventional mechanisms that can act on the UHECR flux in the presence of a nonstandard vacuum. Deformed quantum mechanics can produce small energy and momentum uncertainties equally leading to a UHECR flux suppression. A similar role can be played by small violations of energy and momentum conservation. As a consequence of this kind of uncertainties, there is no real proof of the validity of models and algorithms for particle interactions at the highest energies used to analyse UHE data and study UHECR properties, even if interesting work is carried on  [185]. Another open question is whether cosmic rays can be sensitive to the privileged space direction generated by the SST as it can well be the case for the CMB.
Conclusion and comments Understanding the mathematical structure of space-time and the actual content of the physical vacuum must be a primordial goal for current and future Cosmology and Particle Physics. As already stressed in  [1] , the possible existence of primordial CMB B-modes of primordial origin, would in any case not be by itself an evidence cosmic inflation. Pre-Big Bang patterns would be able to provide alternative explanations of such a signature. Alternative particle physics and cosmologies should not be considered as marginal in data analysis. The design and realization of future experimental and observational projects should not ignore them. The 2016 Planck results will obviously be very important. But a new satellite mission devoted to the CMB, with new technology allowing by further data analyses, is in any case necessary.
However, a formal evaluation of the benefits of integrated monitoring systems had not, of yet, been performed. Using the wolverines in central Scandinavia as a study case, we assessed the benefits of data sharing for the monitoring of trans-boundary populations. We also assessed the performance of two demographic monitoring systems, one relying on a count of reproductive units, the other resulting from non-invasive genetic sampling and capture-recapture modeling. Sharing data across the border between Norway and Sweden allowed a strong increase in the precision of population size, population growth rate and vital rates estimates. It also allowed revealing that the probability to emigrate from Sweden to Norway was significantly higher than in the opposite direction, a required condition for the existence of a source-sink dynamics.