dataesr/scientific-paragraphs-categorization huggingface dataset:
More detailed measurements are currently limited by the size of the pp reference dataset at √ s = 2. 76 TeV. This is especially the case for the measurement of the possible ψ(2S) enhancement in the kinematic range 3 < p T < 30 GeV/c and 1. 6 < |y| < 2. 4, which is opposite to the expected behavior in the sequential melting picture. For open heavy-flavour, the most important result that the 2011 PbPb run helped achieve is the heavy-flavour separation measurement. This was done with high precision and in fine bins by CMS, which measured the remnants of the b-quark fragmentation (via non-promt J/ψ), and by ALICE  [18] , which measured those from the c-quark fragmentation (via D mesons). The two results combined in Figure  7  show, for the most central PbPb collisions, less suppression   6 : Minimum bias R AA for all quarkonia states measured by CMS.
The MODIS data and preprocessing The data used in this study are the MODIS Collection 5 LAI products (MOD15A2) derived from TERRA and AQUA platforms. The products were downloaded from the land processes distributed active archive center (https://lpdaac. usgs. gov) for the 2000-2008 9 yr period. They correspond to 1 km spatial sampling interval using a sinusoidal projection system. The temporal sampling is 8 days based on a daily composition: all observations available in the 8-day compositing window are accumulated, and the one getting the maximum FaPAR (Fraction of Absorbed Photosynthetically Active Radiation) value is selected. The main MODIS LAI retrieval algorithm relies on the inversion of a 3-D radiative transfer model using the red and near-infrared bidirectional reflectance factor values, associated uncertainties, the viewillumination geometry and biome type (within eight types based on MOD12Q1 land-cover map) as inputs  (Myneni et al.
Evaluation approach The approach proposed to evaluate the methods is based on two steps as sketched in Fig. 1. The first one is dedicated to the preparation of reference time series over a limited number of representative sites. As a result of this step, two time series will be output: (i) a time series with no gaps and small uncertainties considered as the reference (LAI ref ), and (ii) a time series with no gaps with realistic uncertainties (LAI comp ). In the second step, time series with variable occurrence of missing data will be simulated (LAI sim ) from the previously LAI comp time series. Each method will be applied on this LAI sim data to get the corresponding reconstructed time series (LAI rec ). Finally, the LAI rec obtained with each of the 8 methods will be evaluated based on a range of metrics describing the fidelity, the roughness of time series and the accuracy of phenological stages that can be derived from the time series.
4 ). Forests represent about 1/3 of the global land area with relatively high fraction of missing data. However, sites with less than 9 observations for the whole 9 yr period (i. e. less than one observation per year in average) were not considered since none of the methods will be able to provide a fair reconstruction of the LAI time course. 2 ) and the 384 sites used to simulate gaps in the data (×). SB: shrub savannah bare, GC: crop grassland, DB: deciduous broadleaf forests, EB: evergreen broadleaf forests, NF: needleleaf forests. A total of 384 sites were used to simulate the gaps in the data (Table  3 ). Because each vegetation class is represented by 5 sites used for the LAI ref and LAI comp values, a total of 1920 cases (384 × 5) with realistic LAI uncertainties and gap structure were finally available.
Fidelity to LAI ref Fidelity is quantified by RMSE. To better highlight the reconstruction capacity of the methods, RMSE were computed by comparing LAI rec with LAI ref either over actual dates with observations or over dates with missing data in LAI sim. Results show that, except for Clim and SGF, all the other methods show generally good fidelity with LAI ref for the dates where observations are available (Fig. 7 ). These good performances are observed almost independently from the occurrence of missing data (Fig. 7 ). The higher RMSE values observed for SGF are due to a positive bias induced by the fitting of the upper envelope of the observations. Clim shows a RMSE value close to that of SGF (Fig. 7 ) that mostly refers to the inter-annual variability of LAI seasonality. Over dates of missing observations, the reconstruction capacity degrades rapidly as a function of the length of gaps for all methods except Clim that keeps a RMSE value around 0.
Performances for describing the phenology The capacity of the several methods to identify the main phenological stages (SoS, MoS and EoS) was evaluated using the dates derived from LAI ref as a reference. The performances (RMSE in days) were analyzed as a function of the occurrence of missing data (% gap). Results show a general degradation of RMSE when % gap increases for the three stages considered. Closer inspection of performances in terms of RMSE for SoS estimates shows large differences between methods (Fig. 10a ). For complete time series (% gap = 0), RMSE values are between 3 days (LPF) and 15 days (AGF), with the exception of the climatology with a RMSE around 25 days, indicating a significant inter-annual variability in the timing of SoS. EMD, TSGF, Whit and SGF have RMSE around 10 days.
The behavior of each method as a function of the occurrence of missing data is well sketched in the (performances, slopes) feature plane (Fig. 12 ). For low amounts of missing data, all the methods provide good performances except SGF and Clim for the reasons exposed previously. When the fraction of missing data increases, each method follows a particular pattern (the black arrow in Fig. 12 ) with a degradation of the performances and an increase in the slope indicating more emphasis on the smoothness of the temporal profiles through a reduction of the roughness. For medium and high occurrence of missing data, TSGF provides clearly the best overall performances although restricted to gaps smaller than 128 days, followed by Whit. SGF and ICSSA show poor performances. The consequences of the application of the several time series processing methods on their capacity to describe phenology characteristics were finally evaluated.
Acknowledgements. This research has received funding from the  Geoland2 European Community 's  Seventh Framework Program  ( FP7/2007-2013)  under grant agreement no. 218795. A. Verger  was funded by the  VALi+d and Juan de la Cierva postdoctoral programs. Many thanks also to the MODIS teams who made available to the wide community the LAI data used in this study. Edited by:  P. Stoy.
Acknowledgements The authors thank the  European Union  ( ERC-2008-AdG-233130-HEPCENT  and  Interreg IV FEDER-Hepato-Regio -Net 2009, T. F. B. ), the  Agence Nationale de Recherches sur le SIDA (ANRS)  ( 2010-307/2011-415 ; C. S. ), the  European Association for Study of the Liver (EASL)  (fellowship award to D. J. F. ), and the  French National -Investissement d'Avenir‖ LabEx HEPSYS  (T. F. B. , C. S. ).
ACKNOWLEDGMENTS We acknowledge useful conversations with  Andy Cohen ,  Christophe Grojean ,  Tom Schwartz ,  Pekka Sinervo ,  Christian Spethmann , and  Jessie Thaler. We also acknowledge the  CERN ''TH-LPCC Summer Institute on LHC Physics ,'' during which this project was initiated. M. S. was supported in part by  DOE  Grant No. DE-FG02-01ER-40676.
a b s t r a c t A dataset consisting of AC-S measurements of (hyper-) spectral particulate absorption, scattering and attenuation coefficients were obtained from measurements performed on the flow-through system of the R/V Tara during its 2. 5-year long expedition. The AC-S instruments were robust, working continuously with weekly maintenance for about 3 months at a time, and provided absorption (attenuation) data for 454 (375) days, or 90% (75%) of total possible days during the expedition. This dataset has been mapped to 1 km × 1 km bins to avoid over emphasizing redundant data, and to match the spatial scale of typical ocean color satellite sensors. It consists of nearly 70,000 particulate absorption spectra and about 60,000 particulate scattering and attenuation spectra. These data are found to be consistent with chlorophyll extraction and with the published average shapes of particulate absorption and scattering spectra and bio-optical relationships.
While this method has been evaluated previously (e. g. ,  Dall'Olmo et al. , 2009; Slade et al. , 2010; Westberry et al. , 2010) , it has never been used to this extent. In this manuscript we analyze the robustness of the AC-S, present the salient features of the absorption and attenuation data set obtained, and highlight some of the biogeochemical parameters that can be derived from it. In particular, we compare our hyper-spectral data with the multi-spectral data (measured with WET Labs AC-9 instruments) presented in  Barnard et al. (1998)  and  Babin et al. (2003) , the most extensive datasets of particulate IOPs published to date.
Note that due to the subtraction of the dissolved spectra and the residual temperature correction, the SBE45 temperature and salinity measurements were not necessary for the processing of the flow-through AC-S measurements. Data were first averaged to 1-min time bins (to average high frequency system noise, e. g. due to particles which are observed differently by the a-and c-sides of the instrument). These 1 min data were submitted to databases (SeaBASS and PANGAEA) as merged files (using time as the common Open access under CC BY-NC-ND license. reference) of the time (synced with GPS), position (GPS), temperature, salinity (TSG) and particulate absorption and attenuation (AC-S). Second, we further spatially median binned the data into 1 km×1 km bins to provide similar scales to those associated with ocean color satellites.
Spatial binning avoided over-weighing relatively large datasets collected over nearly two days during Tara Oceans stations conducted near one location while the in-line system was on. The 1 km × 1 km global grid was generated using the SeaWiFS equal area gridding scheme  (Campbell et al. , 2013). In the analysis requiring uncertainties we only use 1 km 2 bins where at least three 1 min spectra were located, and their standard deviation is used as the measure of uncertainty. Seawater was collected near the surface (depth < 5. 1 m) for HPLC pigment analysis at 85 locations where we had AC-S measurements along the cruise track of the R/V Tara. For each sample, we vacuumfiltered 2 L of water through 25 mm (in some cases, 47 mm) diameter Whatman GF/F glass filters with 0. 7 µm capacity. Filters were frozen in liquid nitrogen and shipped back at the end of up to three legs for extraction and HPLC analysis at the Laboratoire d'Oceanographie de Villefranche.
The filters were extracted in 100% methanol (3 mL for 25-mm filters, 6 mL for 47-mm filters) for 2 h, disrupted by sonication, and then clarified by filtration (Whatman GF/F). HPLC analysis was carried out within 24 h after extraction, according to the method described by  Ras et al. (2008)  adapted from  Van Heukelem and Thomas (2001). Here we use only the Total Chlorophyll a value ( = ''true'' Chlorophyll a + Divinyl-Chlorophyll a + Chlorophyllide a) in order to calibrate the chlorophyll a pigment product derived from the AC-S. To compute averaged spectra for different trophic regions, we divided the dataset into those with chlorophyll a (as determined by AC-S absorption, see below) concentration below 0. 1 mg m -3 (oligotrophic), those with chlorophyll a concentration between 0. 1 and 1 mg m -3 (mesotrophic) and those with chlorophyll a concentration greater than 1 mg m -3 (eutrophic).
3 months per sensor, approximately 3 cruise legs, with typical break of ∼1 week between legs). After each deployment the AC-S lamps were replaced at WET Labs and the sensor was recalibrated. Given that the tables for temperature corrections were created for the S/N 007 sensor  (Sullivan et al. , 2006) , it was not evident that they would apply for other sensors. However, the lack of obvious spectral features in the range of 700-750 nm (see below) suggests using those tables for other sensors (or the same sensor after it had been rebuilt) is sensible. We obtained a little more than 310,000 1 min averaged particulate absorption spectra and nearly 70,000 1 km 2 averaged spectra. For comparison, the 'global' data set in  Barnard et al. (1998)  had a total over 1000 profiles (unique locations), which were reduced to 89 'representative' vertical profiles that included 1914 depth-binned spectra.
Distributions of values The distributions of IOPs and biogeochemical variables obtained are within the range reported in the literature (Fig. 3 , Tables  1  and 2 ). C a, AC-S spans three orders of magnitude while POC spans nearly two. The distributions are skewed and do not conform to a parametric log-normal distribution, which is often assumed for bio-optical variables (e. g. Campbell, 1995). This may reflect the fact that these distributions are the sum of different particles (each kind possibly log-normally distributed) of different composition and optical properties. This data set is particularly rich in oligotrophic data, compared to databases designed for remote sensing applications  (Werdell and Bailey, 2005). The value of the spectral exponent of c p is relatively constant and has an average spectral shape proportional to λ -1 (Fig.
4 ). Note, however, that the exponent of the power-law fit to particulate scattering (which is the same as that of backscattering if the particulate ratio is constant spectrally, e. g. Whitmire et al. (2007) ) is significantly lower (due to the Fig. 4. Averaged particulate absorption (left), scattering (middle) and attenuation (right) spectra for C a,AC-S < 0. 1 (blue), 0. 1 < C a,AC-S < 1 (green) and 1 < C a,AC-S (red) (top panel) and their normalized shapes (bottom panels). Black solid lines are the normalized spectra for the ''global'' dataset of  Barnard et al. (1998) , and the yellow line is from  Babin et al. (2003). Black dashed line is the normalized λ -1 curve while the red dashed line is the normalized λ -0. 75 curve, where λ denotes the wavelength in vacuum. N represents the number of spectra that were uncertainty-weight-averaged to provide the one displayed.
Comparison to published bio-optical relationships and databases The derived relationship between absorption line height and chlorophyll (a φ (676) = 0. 016 (+/-0. 003) C 0. 82+/-0. 08 a-Tot , see Section 2) is similar to  Bricaud et al. (1995) , who found a φ (676) = 0. 02C 0. 841 a-Tot , and  Bricaud et al. (1998)  who found a φ (676) = 0. 018C 0. 816 a-Tot , obtained with a bench-top Table  3  Number of available 1 km 2 estimates of measurements of particulate absorption, scattering and attenuation at specific wavelengths used to derive a relationship with AC-S estimated total chlorophyll a compared with the relationship provided in  Morel (2009). Correlation coefficient (ρ) and root-mean-square relative error apply to the equation derived for the Tara dataset. 95% confidence intervals of regression parameters are smaller or equal to the uncertainties implied in the rounding of these parameters.
This dataset differs significantly from previous ones  (Barnard et al. , 1998; Babin et al. , 2003)  in that it contains significantly more data in oligotrophic waters (e. g. Fig. 4 ) and agrees well with the more globally representative dataset used in the previous Case 1 studies  (Morel, 2009). As with the older datasets, we expect this new dataset to be useful for parameterization of IOPs in radiative transfer modeling of the ocean. In addition, it provides a wide range of conditions to be used for the validation of remote sensing algorithms (see companion paper by  Werdell et al. (2013) ). Frequency distributions of particulate IOPs and derived variables are not consistent with a log-normal distribution, in contrast to often-used parameterizations (e. g. Campbell (1995)  but see  Whitmire et al.
21 (RMSE of +/-0. 06). This suggests that the particulate backscattering spectra, if retrievable from the Ocean Color, could, indeed, provide information about size tendencies (yet validation of this concept on oceanic scales is lacking). The consistency between the bio-optical relationship within the Tara dataset and those obtained previously  (Morel, 2009)  using widely different instrumentation and processing, suggests that the Tara dataset can be used for remote-sensing validation activities. Indeed,  Werdell et al. (2013)  find them to be consistent with the previous validation data.
Acknowledgments The AC-S instrument, used here, would not exist without  Ron Zaneveld 's vision and energy. Thanks to  J. Loftin  for taking care of logistics associated with the AC-S, to  S. Searson  for taking great care of the AC-S on board Tara, to  H. Le Goff  for assistance with the installation of the system on board Tara, to  S. Kandels  for logistical support throughout the Tara Oceans adventure and to  World Courier  for diligently shipping sensors across the world. Additional thanks to  A. Bricaud  for discussions of this work and to  M. Babin  for providing data from his 2003 manuscript. Comments by  J. Sullivan ,  S. Ackleson  and an anonymous reviewer have helped to improve this manuscript. We are keen to thank the commitment of the people and the following institutions and sponsors who made this singular expedition possible:  CNRS, EMBL, Genoscope/CEA ,  UPMC VIB ,  Stazione Zoologica Anton Dohrn ,  UNIMIB ,  ANR ,  FWO ,  BIO5 , Biosphere 2, agnès b.
, the  Veolia Environment Foundation ,  Region Bretagne ,  World Courier ,  Cap L'Orient , the  Foundation EDF Diversiterre, FRB , the  Prince Albert II de Monaco Foundation ,  Etienne Bourgois , the  Tara Foundation  teams and crew. Tara Oceans  would not exist without the continuous support of the participating institutes (see http://dx. doi. org/10. 1371/journal. pbio. 1001177). This is contribution no. 11  of the Tara Oceans Expedition 2009-2012. Funding for the collection and processing of the AC-S data set was provided by  NASA   Ocean Biology and Biogeochemistry program  under grants  NNX11AQ14G  and  NNX09AU43G  to the  University of Maine.
The calibration scheme is as follows. 1. Before the launch, the instrument will be characterized with accuracy better than 5%. The characteristics will be stored into a data base. 2. Just after the launch, the end-to-end performance will be measured with a Xe flasher lamp on a NASA aircraft flying under the ISS. 3. The degradation of the instrument with time will be monitored with the following for methods. -Degradation of the focal surface detector and the lenses will be measured with on-board light sources. Since the on-board resource is limited, the measurement of the efficiency will be limited to the relative change in principle, not the absolute scale. However, all gain changes will be measured absolutely. -Absolute efficiency will be measured with Xe flasher on aircraft about once a month.
Therefore, the efficiency can be obtained better than a few percent. The data will be taken with the photon-counting method in general, and several items like linearity check will be taken with more intense light. Another idea is to use the spot of an old analog oscilloscope. Next, to check the trigger efficiency of PDM, fluorescence image from a cosmic ray air shower will be emulated with an array of UV LEDs. UV beam reflected by a rotating mirror is another candidate to emulate moving EAS tracks. The rotating speed will be adjusted to reproduce the light spot speed of EAS on the focal surface.
Overview As described in Sect. 3. 1, 12 units of ground based light system will be placed around the world. All of them have Xe flashers and 6 of them have also steerable lasers described in the following sections. The locations will be chosen for their low background and the altitude to avoid the effect of Mie scattering by dust in the boundary layer as much as possible. Atmospheric condition will be monitored with flashers and reconstruction quality of the EAS events will be evaluated with lasers in various atmospheric conditions. Balloon flights are planned to demonstrate a prototype of the JEM-EUSO telescope  [10]. The balloon experiment targets the proof of the end-to-end JEM-EUSO techniques, measurement of background and the acquisition of real cosmic ray data above 10 18 eV. A prototype of the airborne GLS will be deployed to proof the GLS concept.
A new landscape format has easy to use tables comparing related targets. It is a condensed version of material contemporary to late 2013, which is presented in greater detail and constantly updated on the website www. guidetopharmacology. org, superseding data presented in previous Guides to Receptors and Channels. It is produced in conjunction with NC-IUPHAR and provides the official IUPHAR classification and nomenclature for human drug targets, where appropriate. It consolidates information previously curated and displayed separately in IUPHAR-DB and the Guide to Receptors and Channels, providing a permanent, citable, point-in-time record that will survive database updates.
P. H. Alexander et al. The Concise Guide to PHARMACOLOGY 2013/14: Ion Channels. British Journal of Pharmacology (2013) 170, Searchable database: http://www. guidetopharmacology. org/index. jsp Ion channels 1607 Full Contents of Concise Guide: http://onlinelibrary. wiley. com/doi/10. 1111/bph. 12444/full.
17x10 -5 M) [326] 2-APB, 6-tert-butyl-m-cresol, camphor, carvacrol, diphenylboronic anhydride, eugenol, incensole acetate, (-)-menthol, thymol 4α-PDD, 4α-PDH, bisandrographolide, phorbol 12-myristate 13-acetate, GSK1016790A (2. 1x10 -9 M) [336. H. Alexander et al. The Concise Guide to PHARMACOLOGY 2013/14: Ion Channels. British Journal of Pharmacology (2013) 170, 1607-1651 Searchable database: http://www. guidetopharmacology. org/index. jsp Transient receptor potential channels 1638 Full Contents of Concise Guide: http://onlinelibrary. wiley. com/doi/10. 1111/bph. 12444/full.
Acknowledgements We wish to acknowledge the tremendous help provided by the Consultants to the Guides past and present (see list in the Overview, p. 1452). We are also extremely grateful for the financial contributions from the  British Pharmacological Society , the  International Union of Basic and Clinical Pharmacology , the  Wellcome Trust  ( 099156/Z/12/Z ]), which support the website and the  University of Edinburgh , who host the guidetopharmacology. org website.
SN. P. H. Alexander et al. The Concise Guide to PHARMACOLOGY 2013/14: Ion Channels. British Journal of Pharmacology (2013) 170, Searchable database: http://www. guidetopharmacology. org/index. jsp Ion channels 1607 Full Contents of Concise Guide: http://onlinelibrary. wiley. com/doi/10. 1111/bph. 12444/full.
1651 Searchable database: http://www. guidetopharmacology. org/index. jsp Chloride channels 1614 Full Contents of Concise Guide: http://onlinelibrary. wiley. com/doi/10. 1111/bph. 12444/full S. P. H. Alexander et al. The Concise Guide to PHARMACOLOGY 2013/14: Ion Channels. British Journal of Pharmacology (2013) 170, 1607-.
6); monovalent cation with a preference for divalents (PCa/PNa = 4. 5-5. 0); with a preference for divalents (PCa/ PCs = 5. 9); current suppressed by extracellular Ca 2+ ; dual monovalent cation current suppressed by modest outward rectification (monovalent cation (inward and outward) rectification extracellular Ca 2+ and Mg 2+ , dual rectification (inward current recorded in the absence of extracellular and outward), or inward rectification divalents); monovalent cation current suppressed by extracellular Ca 2+ and Mg 2+ Nomenclature TRPM1 TRPM3 HGNC, UniProt TRPM1, Q7Z4N2 TRPM3, Q9HCF6 Physical activators - heat (Q10 = 7. 2 between 15 -25°C; Vriens et al. , 2011), hypotonic cell swelling [346] Searchable database: http://www. guidetopharmacology. org/index.