dataesr/scientific-paragraphs-categorization huggingface dataset:
B. 3. Variables First, a general description of a variable with the main common attributes, followed by specific variables. B. 3. 1. Variable Name(dimension1, dimension2,. ) The name of the variable containing the following attributes. Here we give a short description of the attribute names generic to all variables. In the subsequent sections, we provide the actual values of these attributes. standard_name The standard name as mandated by CF-1. 6. This is a unique and precise version of the variable name. long_name The long name as mandated by CF-1. 6. This is the human-readable format. units The unit of the variable. This may depend on the dataset. valid_min, valid_max Minimum and maximum values allowed for this variable. missing_value A value that defines a missing value in the dataset.
B. 3. 3. wavelength(wavelength) This variable contains the second dimension of the data variable. For irradiance datasets this contains the wavelength in nanometers, otherwise it contains negative indices. While the variable name wavelength does not make sense in the latter case, we keep it for consistency, but use the correct values for the attributes. standard_name 'radiation_wavelength' or 'data_index'. long_name 'Wavelength' or 'Index'. units Wavelength are given in nanometers, 'nm', unless the dataset does not contain irradiances. This is the case for e. g. proxy data, in which case the unit is ''negative index'' and the variable wavelength contains -1, -2,. , -n. valid_min If the variable contains irradiance data, it is '0', otherwise it is not set. B. 3. 4. data(wavelength, time)  The actual data stored as a matrix of dimension time Â wavelength.
The values of most properties depend on the actual dataset. However, it does differ in the case of e. g. a proxy dataset. The simple wavelet noise estimator may overestimate the noise due to physical short-term variability and due to physical outliers. This can be compensated by removing physical signals using a multiple input single output autoregressor before estimating the noise via the wavelet noise estimator. We use radio flux data as the additional multiple input component. While it would be possible to also use the neighboring wavelength as additional input, this approach has two drawbacks, first it makes the noise estimate dependent on the number of measured wavelengths by the instrument (and the spectral resolution) and it will remove noise that influences several wavelengths at once.
A list of all reference spectra currently available in the SOLID database. Namesource The source of the data, this is usually a URL from where to retrieve the data. instrument_version The data version as provided by the instruments team. Conventions Convention used to name the variables and their properties. Currently this is the NetCDF Climate and Forecast (CF) Metadata Conventions, version 1. 6 (see Footnote 2), given as ''CF-1. 6''. history A history of the dataset, i. e. when was it converted to which level. SOLID_data_type The data type can be one of Instrument, Proxy, Reference, Composite, or Model. SOLID_creation_date Date of the file creation of the SOLID data file. SOLID_version SOLID data version. This specifies version of the used processing program. SOLID_system Name of the platform where the program was run, e.
Quantum mechanical calculations Quantum chemical calculations were performed in order to measure the variations in enthalpy in pairs of metal cyanide chains as a function of phase shift ∆θ. For each system, two periodic chains were considered, with their equilibrium geometry and a interchain distance fixed to the crystallographic values. The chains were then shifted by increments of 2 • , and for each value of the shift a single-point energy calculation was performed, resulting in ∆E(∆θ) profiles. In the case of the homometallic AgCN and AuCN chains, where there is no order in the orientation of the cyanide ions, we used for the chains a repeating unit equal to four times the basic -M-(CN)motif, in order to account for all four possible orientations of the two cyanide anions All calculations were performed in the density functional theory approach with localised basis sets, using the CRYSTAL14 software  (48).
Acknowledgements A. B. C. , M. J. C. , J. A. M. P. and A. L. G. gratefully acknowledge financial support from the  E. P. S. R. C. ( EP/G004528/2 ), S. T. F. C. and the  E. R. C. (Grant Ref:  279705 ). High-pressure synchrotron Xray powder diffraction measurements were carried out at the I15 beamline,  Diamond Light Source ,  U. K. Quantum  chemistry calculations made use of HPC resources from  GENCI  (Grant  x2015087069 ).
2010 ) and of the meso-scale activity  (Demirov and Pinardi 2007; Herrmann et al. 2008; Beuvier et al. 2012). Despite noticeable improvements in the last decades in the way to observe, model and understand the North-Western Mediterranean DWF, the long-term characterization of this phenomenon and its accurate modelling at climate scale still suffer from various deficiencies. Those deficiencies finally limit our understanding of its interannual variability and trends, as well as our capacity to foresee its possible evolution in a changing climate. For example, to our knowledge, since  Mertens and Schott (1998)  and  Béthoux et al. (2002) , no long-term reference dataset has been established to characterize the DWF interannual variability in terms of mixed layer depth, convective surface, DWF rate or water mass characteristics.
2010; Herrmann et al. 2011). Initial conditions for the ocean model have been chosen carefully in order to represent, as much as possible, the known state of the water masses before the 1980s. More precisely, we combine the 12-month climatology from MEDAR/MEDATLAS  Group (2002)  and the 10 year-filtered interannual dataset from  Rixen et al. (2005)  to reconstruct a typical month of July in the 1960s. In this initial condition file, the bottom layers (2300 m) of the GoL area defined above have the following mean characteristics: 12. 7 • C, 38. 41 and 29. 10 kg/m 3 with nearly no spatial variation over this area. Those values are in agreement with historical values reported in the literature (12. 7-12. 9 •  C, 38. 41-38. 46, 29. 09-29. 10 kg/m 3 ; Mertens and Schott 1998) for the period before the Western Mediterranean Transition starting during winter 2005  (Schroeder et al.
(2005)  interannual gridded product (10-year filtered dataset) over the NWMED domain, j the  Waldman et al. (2016)  estimates over their studied domain. For the LION bottom water characteristics, method k is based on historical data from  Houpert et al. (2015) , l on HYDROCHANGES LION deep mooring and m on MOOSE LION deep mooring. For the stratification index (LION point, December 1st of the year YYYY-1, 1000 m depth), method n uses LION surface buoy and deep moorings chlorophyll-a. In this transition area, the MLD estimated by the glider reached 1000 m or more. • when deep convection occurs in the Gulf of Lions, the warm and salty LIW layer is vertically mixed and cooled down. The mean temperature of the 400-600 m layer is hence a good indicator to discriminate whether the water column once underwent deep mixing or not.
Deep water characteristics The characteristics of the bottom water masses in the convection area allow to assess the trends in the WMDW formation zone. Three methods are explored to obtain data representative of the LION location. • we first assess the evolution of past bottom water masses in the GoL zone using a merged historical database presented in  Houpert et al. (2015)  by extracting all the measurements done at 2300 m and in a circle of 100 km around the LION mooring location. Values are reported as method k in Table  1. If the scarcity of the oceanographic cruises in the 1980s and the 1990s didn't allow to characterize the interannual variability of the θ-S characteristics of the deep water, this data- base is essential to describe the long-term evolution of the deep waters and the possible shifts (like for example after the very intense event of deep convection in 2005/2006) in the DW characteristics often described as following a linear trend  (Béthoux et al.
1990; Rixen et al. 2005). We are aware that a clean homogenization work is nearly impossible for this long-term time series due to the sparsity of the measurements and the variety of the data sources. Consequently results must be carefully interpreted. • data from one of the HYDROCHANGES deep moorings  (Schroeder et al. 2013). Initiated in 2003, the HYDROCHANGES programme aims at recording time series of the hydrological characteristics of the water masses and of their variability in key places (straits, DWF areas, trenches,. ) of the Mediterranean, with short and light moorings equipped with a high-precision and high-stability CTD probe (Seabird SBE37) located a few meters above the seafloor. The HYDRO-CHANGES LION mooring (HC-LION, see   1 ).
This index is very difficult to estimate from the observations for a given location as it requires long-term well sampled vertical profiles of temperature and salinity with high precision measurements. Recently, estimates of the index and of the related sources of error were obtained over the 2007-2013 period in interpolating data from the LION surface buoy and deep mooring  (Houpert et al. in revision). Before November 2011, the stratification due to the salinity in the first 200 m is unknown because of the absence of a conductivity sensor at the sea surface. To tackle this problem, we use a constant value corresponding to the shallowest salinity measurement of the mooring (at 170 m depth). To evaluate the error due to this approximation, we used independent hydrographic profiles collected in a 30 km radius from the mooring (from glider and R/V).
Acknowledgments This work is part of the  Med-CORDEX initiative  (www. medcordex. eu) and a contribution to the  MISTRALS/HyMeX  and  MISTRALS/MerMex programmes. Model data from the CNRM-RCSM4 simulation are available through the Med-CORDEX database. This research has received funding from the  French National 1 3 Research Agency (ANR)  projects  ASICS-MED  (contract  ANR-12-BS06-0003 ) and  REMEMBER  (contract  ANR-12-SENV-0001 ). We would like also to thank all the organizations and researchers that provided us with data used in this study, notably the MOOSE observatory and the  Météo-France/CMM  for the buoy data. The HYDROCHANGES LION mooring receives support from  HyMeX/ MISTRALS , and is a contribution to the  CIESM HYDROCHANGES  programme (http://www. ciesm. org/marine/programs/hydrochanges.
htm) and the data are avaiable on the SEDOO database ( Taupier-Letage  et al. 2016). We would like to thank both anonymous reviewers who helped to improve the manuscript as well as  Dr. Michel Crépon  who stimulated this work nearly 10 years ago. We would like to thank  Météo-France  for the financial support of the first author. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://crea tivecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
We monitored the color evolution of the nucleus from 3. 6 astronomical units (AU) to perihelion (1. 24 AU) and beyond by measuring changes in the spectral slope in the 535-to 882-nm range. Comparing the spectral slopes obtained in late August 2015, shortly after perihelion passage, with those obtained in early August 2014 after phase reddening correction (12) (Fig. 1 , figs. S1 to S3, and table  S1 ) clearly indicates that the nucleus has become relatively bluer-i. e. , the spectral slope has decreased as the comet approached perihelion. Even though only the equatorial regions such as Imhotep (  13 ) are common for both data sets (Figs. 1 and fig. S4 ), due to different seasonal insolation conditions, a decrease of about 30% in the mean spectral slope (12) from 3. 6 AU to the perihelion passage is measured in the common areas.
ACKNOWLEDGMENTS The authors thank the institutions and agencies that supported this work: the  Italian Space Agency (ASI) ,  Centre National d'Etudes Spatiales (CNES, France) ,  DLR (Germany) , and  NASA (USA). VIRTIS was built by a consortium from  Italy, France , and  Germany , under the scientific responsibility of  INAF-IAPS, Rome, Italy , which also led the scientific operations. The VIRTIS instrument development for ESA has been funded and managed by  ASI , with contributions from  Observatoire de Meudon  (financed by CNES) and  DLR. The VIRTIS instrument industrial prime contractor was formerly  Officine Galileo  and is now Leonardo SpA in  Campi Bisenzio ,  Florence, Italy. The authors thank the  Rosetta Liaison Scientists , the  Rosetta Science Ground Segment , and the  Rosetta Mission Operations Centre  for their support in planning the VIRTIS observations.
Crystallographic analysis The crystal structure of the synthesized platelets was studied by means of powder X-ray diffraction (XRD). Samples were inserted into a glass capillary (diameter: 0. 5 mm, glass type no 10; Hilgenberg GmbH, Germany) aligned and rotating on the goniometer axis in a Bruker D8 Advance diffractometer (Bruker AXS GmbH, Germany). XRD patterns were collected using digitally and Ni-filtered Cu K radiation (wavelength: 1. 540598 A ˚) in transmission geometry from 5 to 60 2 at a step size of 0. 012 and an acquisition time of 5. 75 s per step. The resulting patterns were analyzed by Rietveld refinement  (Rietveld, 1969)  using BGMN software, Version 4. 2. 22  (Bergmann et al. , 1998)  and Profex user interface, Version 3. 9. 2 (Doebelin & Kleeberg, 2015). Since varying fractions of monetite were present as a by-product of the -TCP synthesis, a monetite phase model  (Dickens et al.
Crystal structure model for Rietveld refinement The Ca4 and P1O 4 region of the stoichiometric -TCP crystal structure  (Dickens et al. , 1974)  is shown in Fig. 1(a). This structure was used as a starting model to fit XRD patterns of -TCP platelets produced in ethylene glycol. Applying this model confirmed space group R3c but, although refining unitcell dimensions, scale factor, crystallite size, microstrain and texture, resulted in substantial misfits of relative peak intensities between observed and calculated patterns (Fig. 1c ). Difference-Fourier maps were generated to visualize the misfits in direct space electron densities using the software Promap (Doebelin; unpublished addon for Profex;  Doebelin & Kleeberg, 2015). Most atoms showed slight displacements, which could be refined with stable convergence by defining the P2O 4 and P3O 4 tetrahedra as rigid bodies with the dimensions reported in the stoichiometric -TCP model  (Dickens et al.
, 1993). The Cadeficient -TCP platelets decomposed into -CPP (Ca/P = 1) and -TCP that exhibited no detectable Ca-deficiency when refined with the same structure model and no longer showed a band at 875 cm À1 in FTIR spectra (data not shown). Hence, the thermal decomposition during calcination can be described as -Ca 21Àf m ðHPO 4 Þ 2f m ðPO 4 Þ 14À2f m !ð7 À f m Þ-Ca 3 ðPO 4 Þ 2 þ f m -Ca 2 P 2 O 7 þ f m H 2 O: The as-determined overall Ca/P ratio of the biphasic sample coincided well with the refined Ca/P ratio of the as-synthesized -TCP sample, as well as with the elemental compositions determined by ICP-MS, which underlines the quantitative accuracy of the refinement model. Deprotonization and the precipitation of pyrophosphate was also observed when applying the refinement model to calcined synthetic Mgwhitlockite, which is in agreement with previous studies  (Adcock et al.
The representation of agro-food systems We use the generic representation of agro-food systems (GRAFS)  (Billen et al 2013 (Billen et al , 2014) )  which is based on functional relationships between crop farming, livestock breeding, and human nutrition expressed in terms of protein-N transfers. The system's driving variables are (1) the size of the human population; (2) the human apparent diet, including wastes generated at consumption; (3) the livestock population and grass consumption by grazing livestock; and (4) the intensity of fertilization of cropland by synthetic fertilizers, animal manure, symbiotic fixation and atmospheric deposition. The approach starts with the calculation of a yearly N budget over the period 1961-2009, based on production data from FAOSTAT (FAO 2012), for 12 world regions.
Acknowledgments This study was partly supported by the  SeasERA EMoSEM project  ( ANR-12-SEAS-0005-01 ) and the  RESET SeineAval  project ( SA5 ). We wish to thank the  FIRE (Fédération Ile de France de Recherche en Environnement ,  CNRS and UPMC). Nathaniel D Mueller  was supported by a postdoctoral fellowship from the  USDA Agriculture and Food Research Initiative. James S Gerber  received support from the  Belmont Forum/FACCE-JPI  funded  DEVIL  project ( NE/M02127/1 ). We thank  Javier Castrillo  and  Mizuki Ishikawa  for helpful suggestions and comments. We also thank to the  Scientific Network  on GHG mitigation from the agroforestry sector in Spain (REMEDIA) for providing a space for discussion and science development. We are grateful to two anonymous reviewers for constructive comments and suggestions.
Statistical Analysis. Statistical analysis was performed using BioStat 2009 software (AnalystSoft, Vancouver, Canada). The results are presented as means ± SD. Shapiro-Wilk  test was used to confirm normal distribution of the data. Homogeneity of the variance was studied by Levene's test using SPC for Excel Software (BPI Consulting, LLC, Cypress, TX, USA). Significant differences were determined using one-way ANOVA followed with a Tukey-Kremer post hoc analysis. Statistical differences between treated and untreated cells in experiments with neutralizing antibodies were determined using a paired Student's -test. Full-length as well as fragments of NS5A were expressed in Huh7 cells to comparable levels.
Clausen et al  [26]  described the results of depressurizing during CO 2 venting with an onshore 50 km long, 24 inch diameter buried pipeline from initially supercritical conditions. Pressure and temperature were measured at the two ends of the pipeline. The depressurization of this pipeline was also simulated with OLGA. According to both experimental data and simulations, the depressurization stayed well above the triple point of CO2, and there was no indication of dry ice formation upstream the two release points. Simulation results deviated from the experimental data after the inventory reached saturation conditions  [26]. DNV-GL carried out CO 2 depressurization experiments using a 30 m long, 2 inch diameter stainless steel tube, to study fast depressurization of high pressure liquid CO 2 inventories.
The installing angle of measurement points are shown in Fig. 4. Data was recorded using two independent measuring systems, an NI cRIO-9025 system which was used to simultaneously sample 4 low frequency pressure sensors and all the thermocouples and an NI cDAQ-9188 system which was used to sample 8 high frequency pressure sensors. The NI cRIO-9025 system consisted of one 9025, four 9144 chasses and twelve 9219 modules for temperature and pressure signal acquisition. The 5 chasses were connected using ordinary internet access cable. The communication protocol used EtherCAT at 110 ms/sample to ensure synchronised data gathering. All of the data acquired would be cached in the host 9025. The NI cDAQ-9188 system consisted of two 9188 of 4 channels with a high-speed of 500 kS/s. LabVIEW software was used to transfer the data from the 9025 or 9188 to a local computer by Ethernet.
Experiments Conducted In this paper, three groups of CO 2 release experiments were performed to investigate decompression behaviour and phase transition during the release of CO 2 from a pipeline. Each group used initially vapour and dense phase CO2. Three different orifice diameters were also used for each group of tests; 15 mm, 50 mm and Full Bore Rupture (FBR). Thus six experiments in total were conducted. The initial experimental conditions of the six tests are presented in Table  1. Table  2  reports the instruments from which data is available for the listed experiments, including instrument type, number and location.
Acknowledgments We would like to thank  Jyotirmoy Bhattacharya ,  Matthias Blau ,  Ori Ganor ,  Gianluca Grignani ,  Adolfo Guarino ,  Troels Harmark ,  Jelle Hartong ,  Gianluca Inverso ,  Cindy Keeler ,  Ricardo Monteiro  and  Marta Orselli  for useful discussions. The work of VN was supported in part by  European Unions Seventh Framework Programme  under grant agreements  FP7-REGPOT-2012-2013-1 no 316165 , and by  European Unions   Horizon 2020 Programme  under grant agreement  669288-SM-GRAV-ERC-2014-ADG. The work of JA was supported by the  ERC Starting  Grant  335146 HoloBHC. JA acknowledges two short term mission grants to NBI, one by the  Holograv network  and another by the  COST network. JG is supported by  VILLUM FONDEN , research grant  VKR023371. The work of NO is supported in part by the  Danish National Research Foun-dation  project "New horizons in particle and condensed matter physics from black holes".
Modified Delphi method A 3-round web-based Delphi approach was used in this consensus process  [21]. Surgical program directors, chairmen of liver surgery departments, academic surgeons and anesthesiologists with publications involving ERAS and/or liver surgery were identified using PubMed database. They were deemed as "experts". To ensure the international standpoint to this consensus, we aimed to recruit a panel of experts from America, Asia and Europe. A recruitment letter in English was sent via e-mail providing a brief outline of the project and its objectives. If invited experts did not respond to the invitation within 2 weeks, a reminder was sent out. Further experts were invited if no answer came in the next 2 weeks or the expert declined to participate in the study. A positive response to the recruitment letter served as informed consent.
Ethics statement Verbal or written consent was not necessary because the MRI/MRS protocol was performed in a clinical setting with the standard MRI/MRS sequences used in our institution (including diffusion-weighted MRI and 1 H-MRS) for the clinical examination of all neonates whether premature or not. The MRI/MRS data were anonymized by Pr Nadine Girard (head of the department of Neuroradiology at La Timone Hospital) after collection, before they were transferred to the research database at the Center for Magnetic Resonance in Medicine and Biology for analysis. This anonymization procedure and the retrospective analysis of the data were approved by our Institutional Ethics Committee (Comité d'Ethique d'Aix-Marseille Université, project number n°2014-09-30-07).
Localized 1 H-MRS was performed using a PRESS sequence (time of repetition = 1500 ms, TE = 30 and 135 ms, voxel size of 4. 5 cm 3 , 256 averages for TE = 30 ms and 278 averages for TE = 135 ms). 1 H-MRS spectra were acquired with water saturation with the Chemical Shift-Selective module at both TEs. An additional spectrum was acquired at a TE of 30 ms without water saturation. Proton-MRS data processing. MRS data were processed under CSIAPO, a home-made software  [32]  using a time-domain quantitation algorithm based on QUEST  [33]  and a simulated metabolic database created with VESPA and the GAVA package (http://scion. duhs. duke. edu/vespa/). The metabolite basis set included 15 metabolites (alanine, aspartate, β-glucose (resonances arising between 3. 2 and 3. 9 ppm), phosphocholine, creatine, glutamine, glutamate, glycine, glycerophosphocholine, lactate, myo-Ins, NAA, phosphocreatine, scylloinositol and taurine), and 12 different macromolecular resonances (MM_9, MM_12, MM_14, MM_17, MM_19, MM_20, MM_22, MM_27, MM_30, MM_32, MM_37, and MM_39).