dataesr/scientific-paragraphs-categorization huggingface dataset:
Applying such an sPV criterion to screen the result did not change the mean difference much. This is consistent with the result from  Holl et al. (2016) , who showed differences in CH 4 values observed in the northern high latitudes, and the sPV criterion with a value of 0. 2 × 10 -4 s -1 has little effect below 25 km in altitude. We also examined results from 56 hPa in Fig. 7b. Similar to the results from 18 hPa, larger O 3 differences are found with larger sPV differences. Applying certain sPV criterion to these data, the mean difference changes only slightly, but the rms difference decreases, similar to the results from   18 hPa. The results for other pressure levels are summarized in Sect. 4. 4. Since the MERRA-2 dataset also provides the O 3 value  (Wargan et al. , 2017) , we examined those data instead of the sPV value.
1 ppmv (±3 %) from 6 to 56 hPa, showing good agreement regardless of the large sPV variability between each matching pair. The DIAL data are also compared with outputs from the MIROC-CTM model simulation. The mean differences between DIAL and MIROC-CTM are within ±0. 3 ppmv (±8 %) from 10 to 56 hPa. Above 8 hPa, the mean differences increase to -0. 6 ppmv (-8 %). To measure variability in the comparison, rms differences between DIAL and MLS or MIROC-CTM are also evaluated. For both DIAL-MLS and DIAL-MIROC-CTM comparisons, the rms differences are nearly 0. 5 ppmv for pressure levels between 30 and 100 hPa and increase with increasing altitudes up to 6 hPa, reaching 1. 1-1. 2 ppmv. From the DIAL-MLS comparison, the O 3 differences depend on sPV differences at 18 hPa. Therefore, another criterion for comparison is proposed: pairs with absolute sPV differences that exceed 0.
5 ppmv (116 %) and 0. 9 ppmv (292 %) at 100 hPa, respectively. One possible cause may be a low bias in the DIAL O 3 measurement, but this hypothesis was not confirmed in this study. Nevertheless, finding good agreement between DIAL and MLS O 3 measurements between 6 and 56 hPa is a necessary step for studies evaluating the bias and longterm stability of satellite sensors in the future. Because of very sparse observations from SH ground-based stations, the continuation of long-term measurements there for NDACC is highly recommended. This study provides an outlook for continuing measurements at the OAPA site. The DIAL measurements at the OAPA site are available for all years since 2005, except 2016 when no measurements were collected. The result of the DIAL-MLS comparison using these longterm data will be published elsewhere.
Acknowledgements. This research was supported by the  Science and Technology Research Partnership for Sustainable Development (SATREPS) ,  Japan Science and Technology Agency (JST) , and  Japan International Cooperation Agency (JICA). The DIAL construction and maintenance from 2005 until the present were supported by projects lead by  Eduardo Quel  (CEILAP), type PICT from the  Science and Technology Ministry of Argentina , and in collaboration with  CONICET , as well as the late  Gérard Mégie  and  Sophie Godin-Beekmann  ( CNRS, France ) and  Hideaki Nakane  ( Kochi University of Technology, Japan ). The Aura MLS data were produced at the  Jet Propulsion Laboratory (JPL), California Institute of Technology , under contract with the  National Aeronautics and Space Administration (NASA). CTM computations were conducted on NEC-SX9/A(ECO) computers at CGER,  NIES.
Data availability. DIAL data can be obtained from http://ndacc-lidar. org/index. php?id=70/Data. htm (Steinbrecht  and Leblanc, 2017), and MLS data can be obtained from https://doi. org/10. 5067/AURA/MLS/DATA2017 (Schwartz et al. , 2015). The simulation data are stored at the CCMI site of CEDA at http://data. ceda. ac. uk/badc/wcrp-ccmi/data/CCMI-1/output/ (Hegglin and Lamarque, 2015). The MERRA-2 reanalysis can be obtained from https://doi. org/10. 5067/QBZ6MG944HW0 (GMAO,  2015). The Supplement related to this article is available online at https://doi. org/10. 5194/amt-10-4947-2017-supplement.
The Arctic Ocean is undersampled compared to much of the world ocean  [Abrahamsen, 2014]. Estimating trends and understanding mechanisms in an extreme seasonal environment is a challenge with few comprehensive observational campaigns outside the spring and summer periods  [Grotefendt et al. , 1998 ]. The Norwegian young sea ICE expedition (N-ICE2015) took place north of Svalbard in 2015 to investigate the new thinner Arctic sea ice regime  [Renner et al. , 2013]  and associated interactions between the ice, ocean, and atmosphere, and the feedbacks between physical and biogeochemical processes  [Granskog et al. , 2016]. We present hydrographic and ocean current observations collected from January to June 2015 during N-ICE2015 in the Nansen Basin and over the Yermak Plateau. These 6 months of observations, spanning the winter and spring is a unique data set and provides the oceanographic context for topical studies presented in N-ICE2015 companion papers.
Ship-Board CTD Data The ship-board CTD was a Sea-Bird Electronics SBE911 plus with two sets of sensors attached to a multibottle Sea-Bird carousel water sampler holding 11 Niskin bottles of 8 L each. Separate water samples for salinity analyses were drawn from the Niskin bottles immediately after the CTD package was secured in a heated area. These salinity samples were analyzed at sea using a Guildline Portasal salinometer with accuracy ca. 60. 003. Prior to and after the N-ICE2015 expedition, the CTD sensors were calibrated at Sea-Bird. The temperature sensors drifts were negligible. The conductivity sensors had drifted and conductivity slope corrections were calculated using bottle salinity measurements. The data accuracy for conductivity estimates was 60. 0003 S m 21 and for temperature of 6 0.
IAOOS Profilers Data Two IAOOS buoys (http://iaoos. ipev. fr/index. php?lang5en)  [Provost et al. , 2015; Koenig et al. , 2016]  that carried ice-tethered profilers manufactured by NKE (PROVOR SPI) were deployed during Floe 1. The profilers were equipped with a Seabird 41CP CTD and an Aanderaa 4330 optode for dissolved oxygen. The profilers were set to collect two profiles per day down to 500 m depth, and gathered a total of 112 profiles. In addition, during Floes 1 and 2, tests were carried out involving a profiler on a 800 m long instrumented line in a tent-covered testing-hole. A total of 42 profiles used in this study were obtained with this set up (26 during Floes 1 and 16 during Floe 2). The vertical resolution of the processed CTD data is 1 dbar in the upper 400 dbars, 5 dbars from 400 to 550 dbars and 10 dbars from 550 to 850 dbars.
Microstructure Profiler Data A total of 588 microstructure profiles were collected in 173 sets with two loosely tethered free-fall MSS-90 microstructure profilers  [Prandke and Stips, 1998 ] developed by ISW Wassermesstechnik. A set, which corresponds here to consecutively sampled profiles, was usually composed of three profiles during the N-ICE2015 expedition. The profilers had precision conductivity, temperature, and depth sensors as well as turbulence sensors including two airfoil shear probes, a fast response thermistor, and a microconductivity sensor. Here we use the CTD profiles while details of the microstructure data processing and a description of the vertical mixing and turbulence characteristics can be found in  Meyer et al. [2017]. The microstructure profilers were deployed through a hole in the sea ice from a heated tent a few hundred meters away from the ship.
The profiles (only the downcasts are used) started immediately below the ice and reached on average 150 m during Floe 1 and 300 m during Floes 2, 3, and 4. Data processing followed  Fer [2006]. High-resolution profiles, sampled at 1024 Hz, were vertically averaged to 20 cm. The CTD data from the microstructure profilers were compared with the ship CTD data for validation, and salinity drift corrections of 0. 021 g kg 21 for one and 0. 065 g kg 21 for the second profiler were applied. 2. 2. 4. Dissolved Oxygen Data (Water Samples) A total of 175 samples of dissolved oxygen were collected directly from Niskin bottles on either the ship CTD rosette (68 samples) or from bottles on a Hydro-Bios water sampler deployed from the sea ice (107 samples). This small water sampler (SlimLine 6) with integrated CT-set, hereinafter ''on-ice CTD,'' was operated from the same tent and hole as the microstructure profiler, several hundred meters away from R.
During drift 3, the instrument averaged 40 velocity profiles (pings) at 10 min intervals in 8 m thick cells, with the first cell centered at 21 m, while during drift 4, 55 velocity profiles were averaged at 20 min intervals, with the first cell centered at 19 m. Ensembles with excessive tilt (pitch and roll more than 6208), and bins with weak average echo intensity (less than 40 counts) or less than 50% good three-beam and fourbeam solutions were discarded. Absolute current velocity was obtained by adding the ice velocity as measured by the ship's Global Positioning System (GPS) to the relative velocity profile. The final vertical range of the Long Ranger ADCP data was 480 m which is typical for Arctic waters where acoustic scatterer concentrations are low.
Tidal Current Observations: Complex Demodulation Our current observations were collected from a drifting platform. The time series are therefore affected by both temporal and spatial variability, and standard tidal harmonic analysis cannot be used. Using complex demodulation, we attempted to isolate the tidal signals in the ADCP current data time series. The algorithm is described in  Emery and Thomson [2001, chap. 5, pp. 402-403]. Rotary component amplitude and phase of the diurnal and semidiurnal tides were estimated at, respectively, 24 and 12 h frequencies using 48 h long segments. Tidal estimates are not sensitive to the exact chosen time segment; the latest is determined by a compromise between too little data (short-time segment) and decreasing precision (large time segment). We cannot distinguish between the different constituents in the diurnal band or in the semidiurnal band.
Climatologies and Reanalysis Products 2. 4. 1. Ocean Climatology: MIMOC The global monthly isopycnal mixed-layer ocean climatology (MIMOC) covers the 0-1950 dbar range with 0. 58 3 0. 58 spatial resolution, uses objective mapping routines and emphasizes data from the last decade Journal of Geophysical Research: Oceans 10. 1002/2016JC012391  [Schmidtko et al. , 2013]. For each location of observations along the N-ICE2015 drift tracks, we linearly interpolate MIMOC in space and time to derive the best guess climatological values from MIMOC for comparison with our observations. Thus, each interpolated MIMOC profile is based on the eight MIMOC profiles closest in space and time.
Reanalysis Product: ERA-Interim The ERA-Interim reanalysis data set is a global atmospheric product that is updated in near real time. It is based on the ECMWF integrated Forecast System  [Dee et al. , 2011]. It uses a fixed version of a numerical weather prediction system to produce reanalysed data. The spatial resolution is approximately 80 km with 60 vertical levels, while the temporal resolution is 6 hourly.
Water Masses During the drifts, six different water masses were identified using Rudels et al. [2000] classification (Figure  5 ). The water mass analysis was based on data from the ship CTD, the microstructure profilers and the IAOOS buoys profilers (Table  1 ). At the surface, we found a layer of Polar Surface Water (PSW, r 0 < 27:70 and h < 08C) throughout the N-ICE2015 expedition, on average 93 m thick in winter and 78 m thick in spring (Figures  5  and 6 ). Its temperature was near freezing in winter (mean 21. 768C) and higher in the spring (mean 21. 628C). Patches of warm Polar Surface Water (PSWw, r 0 < 27:70 and h < 08C), a signature of ice melt water, were observed in the upper 50 m during spring on the Yermak Plateau, at the end of Floes 3 and 4 (Figure  6 ). PSWw average temperature was 0.
Discussion The new observations from the N-ICE2015 expedition have long lasting potential to improve our understanding of the processes governing the Arctic Ocean during winter. N-ICE2015 was able to collect more than 250 winter CTD casts from ship CTD, microstructure profiles, and buoys, in a region with extremely sparse winter data coverage (Figure  9a ). N-ICE2015 creates an unprecedented basis for studies on Arctic ocean winter processes and thus is crucial to reduce bias for modelling future Arctic conditions. This region is in a transition period, with basin wide sea ice thickness reduced from 3. 60 to 1. 25 m between 1975 and 2012  [Lindsay and Schweiger, 2015]. The general trend of thinning sea ice in the Fram Strait area is similar to that in the wider Arctic Ocean  [Renner et al.
Conclusions The N-ICE2015 expedition data set spanning January to June 2015 provides an updated picture of the hydrography and circulation in the Arctic Ocean north of Svalbard. In addition, these rare winter data are valuable to the community with the potential to reduce bias when modelling the new Arctic. The new observations show a surprisingly deep mixed layer for the first five months of the N-ICE2015 expedition likely due to high sea ice growth rates in numerous leads, a characteristic of the area  [Willmes and Heinemann, 2016]. Because few earlier observations are available, it is not possible to conclude whether this is unusual and a result from the ''new Arctic'' with a thinner sea ice cover, or if this has been the typical state for this region in past decades. Late spring conditions, closer to the ice edge are dominated by a strong pycnocline and shallow mixed layer, the result of large sea ice melt events.
Although verified extinctions are such rare events that it is difficult to generalize, aerial predation by skuas (Catharacta spp. ) can significantly decrease reproductive success in very small populations  45  (a component Allee effect). Unfortunately, we were unable to model the probability of a population going extinct as a function of population size (Eq. (12)  in Supplementary Data 1). This is partially a statistical artifact of having few extinction events in the database but also reflects the species-agnostic nature of predator-driven Allee effects in this system. Small populations of Adélie penguins may be able to persist indefinitely if they breed in mixed colonies with other Pygoscelis spp. penguins, as often occurs where their ranges overlap on the Antarctic Peninsula. A multi-species dynamical model that estimates the total abundance of all Pygoscelis penguins at a breeding colony is required to accurately estimate the strength of the Allee effects in population colonization and local extirpation.
Methods Population dynamics model. Using a hierarchical Bayesian population dynamics model, parameterized by all the publicly available data on Adélie penguin abundance and distribution since the 1982/1983 austral summer and accompanying sea ice data extracted from satellite imagery over the same period, we estimate a sitespecific mean population growth rate and an abundance in each year since 1982 for each of Antarctica's 267 known Adélie breeding populations (see Supplementary Data 1-2 for a comprehensive explanation of the Adélie model, and details regarding modeling fitting, checking, and validation). Population growth rate was modeled as a linear function of peak sea ice across the last five winters (y-4 to y), which we included as a surrogate for krill recruitment; summer sea ice in year y-4, which relates to colony access in the birth year for new recruits to the breeding population; and a random effect for year shared by all the colonies.
Random site and CCAMLR subarea effects to capture site or management unit differences not otherwise captured by spatial covariates were not supported by the data and were not, therefore, included in the final model. Likewise, covariates related to krill catch (reported by CCAMLR Secretariat's Data Centre  52  ) were not found to be strongly correlated with population growth rates and are also excluded from the final model. We modeled reproductive success (chicks per occupied nest) hierarchically, drawing breeding productivity values for each site by year combination from a truncated normal distribution that spans the possible range of penguin productivity. We relied on prior information regarding reproductive success  53  in order for the mean and variance of this distribution to be identifiable.
Acknowledgements H. J. L. , C. C. -C. , G. H. , C. Y. , and K. T. S. gratefully acknowledge funding provided by  US National Aeronautics and Space Administration  Award No. NNX14AC32G  and  U. S. National Science Foundation Office of Polar Programs  Award No. NSF/OPP-1255058. S. J. , L. L. , M. M. H. , Y. L. , and  R. J. gratefully acknowledge funding provided by  US National Aeronautics and Space Administration  Award No. NNX14AH74G. H. J. L. , C. Y. , S. J. , Y. L. , and  R. J. gratefully acknowledge funding provided by  U. S. National Science Foundation Office of Polar Programs  Award No. NSF/PLR-1341548. S. J. gratefully acknowledges support from the  Dalio Explore Fund. We would like to thank  Julienne Stroeve  and  Garrett Campbell  from the  National Snow and Ice Data Center (Boulder, CO, USA)  for their guidance and discussions about sea ice dynamics.
Code availability. All model code and associated documentation are available in Supplementary Data 1-3. Data availability. Additional analyses associated with this manuscript are provided in Supplementary Data 4-10. The Adélie abundance and sea ice concentration data that support the findings of this study are publicly available through the Mapping Application for Penguin Populations and Projected Dynamics website (MAPPPD)  54  , http://www. penguinmap. com. Supplementary Data 3 provides code to query MAPPPD's underlying PostgreSQL database, which is publicly available on Amazon RDS, and format the query results as model input.
Patient data The patient is a right-handed 41-year-old female initially diagnosed with bitemporal epilepsy. The patient underwent comprehensive presurgical evaluation, including clinical history, neurological examination, neuropsychological testing, structural and diffusion MRI scanning, EEG and stereotactic EEG (SEEG) recordings along with video monitoring. Nine SEEG electrodes were placed in critical regions (see Table  1 ) based on the presurgical evaluation. SEEG electrodes comprise 10 to 15 contacts. Each contact is 2 mm of length, 0. 8 mm in diameter and is 1. 5 mm apart from other contacts. Brain signals were recorded using a 128-channel Deltamed™ system (sampling rate: 512 Hz, hardware band-pass filtering: between 0. 16 and 97 Hz). Structural and diffusion MRI were acquired with a Siemens Magnetom Verio 3 T MR-Scanner Siemens, Erlangen, Germany).
Structural reconstruction The large-scale connectivity and the cortical surface of the patient were reconstructed using SCRIPTS (available on GitHub: https:// github. com/timpx/scripts), a processing pipeline tailored for TVB  (Proix et al. , 2016). The pipeline uses FreeSurfer  (Fischl, 2012) , FSL  (Jenkinson et al. , 2012) , remesher  (Fuhrmann et al. , 2010) , and MRtrix  (Smith et al. , 2013 (Smith et al. , , 2012;; Tournier et al. , 2007)  to process T1 and dMRI scans. The brain is divided in several regions according to a parcellation template, which is used for whole brain tractography to develop the connectivity (number of streamlines) and delay (length of streamlines) matrices. Cortical and subcortical surfaces are reconstructed and downsampled, along with a mapping of vertices to corresponding region labels.
Hidden states in Bayesian modeling represent states of the generative model (variables, parameters) that are not directly observable. Uninformative priors are placed on the hidden states' initial conditions, while their evolution follows a Euler-Maruyama discretization of the corresponding stochastic differential equations with linear additive normally distributed noise. Uninformative priors are also placed on the excitability parameter per node x 0 observation baseline power, scale and noise. Finally the length of the seizure is also allowed to freely vary to match that of a given recorded seizure. Structural connectivity specifies a gamma prior on the connectivity used in the generative method. This model is implemented using Stan, a software for Bayesian inference, which implements both Hamiltonian Monte-Carlo and automatic variational inference algorithms for generic differential probability models  (Hoffman and Gelman, 2011; The Stan Development Team, 2015).
Empirical patient data The patient was diagnosed with bitemporal epilepsy and experienced simple and secondary generalized seizures, which were accompanied by déjà-vu hallucinations, associated with palpitations, horripilation, and frisson sensations. Fixed gaze, chewing up and pallor were observed during the seizure. In the post-critic period the patient showed temporal disorientation, repetition of the same questions and retrograde amnesia during one week. The MRI examination revealed a hypothalamic hamartoma. Surface EEG recordings revealed interictal spikes and indicated a bias towards the left hemisphere. Based on the presurgical evaluation, seven SEEG electrodes were implanted in the left hemisphere, and two in the right hemisphere. One electrode was implanted in the hypothalamic hamartoma.
, 2012; Jeha et al. , 2007; Lopez-Gonzalez et al. , 2012). In patients with non-lesional MRI, localization and surgical success in seizure control are even more challenging  (Jeha et al. , 2007)  Despite the advances in imaging technologies, a significant number of surgical patients with focal epilepsy (~25%) continues to have non-lesional MRIs  (Jeha et al. , 2007; Widdess-Walsh et al. , 2007) , which is a problem calling for novel approaches. We propose such a novel approach to brain interventions based on large-scale brain network based models that are derived from noninvasive structural data of individual patients. Here we have shown step by step, along the example of a patient with bitemporal epilepsy, how to create a virtual patient brain network model. The Virtual Epileptic Patient (VEP) model provides the perfect test bed for clinical hypothesis testing of questions linked to network-based mechanisms.
Acknowledgements The research reported herein was supported by the  Brain Network Recovery Group  ( 220020255 ) through the  James S. McDonnell Foundation  and funding from the  European Union Seventh Framework Programme Human Brain Project  (grant no. 60402 ),  Agence National de la Recherche " Vibrations " ( ANR 13 PRTS 0011 01 ) and within the  FHU EPINEXT  by the A* MIDEX  project ( ANR-11-IDEX-0001-02 ) funded by the " Investissements d'Avenir" French Governement program  managed by the  French National Research Agency (ANR).
NP-SLB in situ interaction in 55% FBS In the second set of experiments we analysed the protein effect on the structure of the SLB dispersing PS-COOH20 NPs in a 55% FBS solution (in situ NPs). When the NPs are in contact with the FBS both hard and soft protein coronas are adsorbed on their surface, which modulate the interaction with the SLB. To disentangle the effect due to free proteins and NP-associated proteins, we also performed a control measurement in the presence of FBS without NPs. The QCM-D data obtained for SLBs treated with 55% FBS and in situ NPs dispersed in 55% FBS at the various stages of the protocol are shown in Fig. 4. In the presence of in situ NPs, we observed a shift of the frequency toward more negative values and an increase of the dissipation that persisted after washing.